A NEW GENERATION OF DRONES SYSTEMS
Mario Arturo Ruiz EstradaThe present electronic monograph (E-monograph) is divided into eight chapters are followed by: The first chapter introduces the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box): the hardware, algorithm, and a new special military Drone or UAV. The first section presents the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box) to adapt to any drone to the main control system of any drone. Second section is the algorithm is using chaos theory and Econographicology. third section we present the groundbreaking prototype known as the "Black Nightmare V.7." The Black Nightmare V.7 drone bombardier boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box) to control the Multiple Ailerons System (MAS) and Multi-Missiles System (MM-System) connected to the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box). The second chapter introduces a groundbreaking prototype known as the " WATER DRONE PROPULSION SYSTEM MAR777." The Water Drone Propulsion System MAR777 boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)" in the water drone propulsion system MAR777 to detect survivors or missing people in rain floods and Tsunami. This involves strategically placing all cameras (under water and sea level) of the WATER DRONE PROPULSION SYSTEM MAR777 to observe the damage of floods and Tsunami in real-time. Additionally, the Water Drone Propulsion System MAR777 features an innovative autopilot design referred to as the "AI Searching System (AISS)." This system incorporates a system to scanning and locate possible areas with large damage by the rain floods or Tsunami, accompanied by a series of specialized detectors that operate in precise synchronization to searching people to rescue with a high accuracy of 99.5%. Furthermore, a cutting-edge concept called the "Searching and Rescue Emergency for Rain Floods and Tsunami System (SRERFT-System)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR777 is equipped with storage box to support survivors with suppliers and medicine. Notably, the Water Drone Propulsion System MAR777 is capable of carrying also any suppliers, be it water or life jackets, in the rain floods or Tsunami anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System MAR777 indispensable for a wide array of natural disasters emergency missions. The third chapter introduces a groundbreaking prototype known as the "Water Drone Propulsion System MAR37." The Water Drone Propulsion System MAR37 boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)" in the MAR37. This involves strategically placing all 2 large motors within the main body structure of the MAR37. Additionally, the Water Drone Propulsion System MAR37 features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates 2 potent motors within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Waves System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR37 is equipped with storage box to support armies with suppliers and military equipment simultaneously. Notably, the Water Drone Propulsion System MAR37 can supply any cargo anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System MAR37 indispensable for a wide array of natural disasters emergency missions. The fourth chapter introduces a groundbreaking prototype known as the " WATER DRONE PROPULSION SYSTEM MAR25." The Water Drone Propulsion System MAR25 boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)" in the water drone propulsion system. This involves strategically placing all ailerons within the main body structure of the WATER DRONE PROPULSION SYSTEM MAR25. Additionally, the Water Drone Propulsion System MAR25features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates a potent motor within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Waves System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR25 is equipped with storage box to support armies with suppliers and military equipment simultaneously. Notably, the Water Drone Propulsion System MAR25 is capable of carrying military suppliers, be it guns or munitions, in the field of action anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System MAR25 indispensable for a wide array of military and natural disasters emergency missions. The fifth chapter introduces a groundbreaking prototype known as the "Water Drone Propulsion System MAR31." The Water Drone Propulsion System MAR31 boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)" in the MAR31. This involves strategically placing all 3 large motors within the main body structure of the MAR31. Additionally, the Water Drone Propulsion System MAR31 features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates 3 potent motors within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Waves System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR31 is equipped with storage box to support armies with suppliers and military equipment simultaneously. Notably, the Water Drone Propulsion System MAR31 can supply any cargo anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System MAR31 indispensable for a wide array of natural disasters emergency missions. The sixth chapter introduces a groundbreaking prototype known as the "Black Nightmare V.7." The Black Nightmare V.7 drone bombardier boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Ailerons System (MAS) and Multi-Missiles System (MM-System)" in the Black Nightmare V.7 drone bombardier. This involves strategically placing all ailerons within the main body structure of the aircraft. Additionally, the Black Nightmare V.7 drone bombardier features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates a potent motor within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce departure and landing noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Winds System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Black Nightmare V.7 drone bombardier is equipped with solar panels, ensuring a continuous charge to support its four powerful motors simultaneously. Notably, the Black Nightmare V.7 drone bombardier is capable of carrying four heavy bombs and three missiles (air-air). Finally, its versatile capabilities render the Black Nightmare V.7 drone bombardier indispensable for a wide array of military and national emergency missions. The seventh chapter presents an innovative military sea security defense system never introduced before. We present the first aquatic smart platforms carrier system (Hydronescarrier) and different aquatic smart platforms that is a vital part of the military equipment for the Hydronescarrier. We aim to present all the features and functionalities of this new military defense system, which can play an important role in defense and attack operations more quickly and efficiently. The Hydronescarrier includes an artificial intelligence system, autonomous defense systems, energy systems, and smart control systems. These components form a complex and dynamic defense system that provides immediate high-level protection to strategic areas, even at a distance. Some specifications are withheld for national security reasons. Additionally, this basic manual introduces the NeuronDrone-Box and Mega-NeuronDrone-Box: the hardware, algorithm, and a new special military Hydrone. In a special section, we like to mention that all algorithms in this basic manual use chaos theory and Econographicology. Subsequently, we present the groundbreaking prototypes known as the MAR107X aquatic smart platform, The MAR107Y aquatic smart platform, and the MAR107Z aqua...
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A New Advanced Military Drones Systems: Prototypes Modeling
SSRN, 2024
The present electronic handbook (E-Handbook) is divided into eight chapters are followed by: The first chapter introduces the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box): the hardware, algorithm, and a new special military Drone or UAV. The first section presents the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box) to adapt to any drone to the main control system of any drone. Second section is the algorithm is using chaos theory and Econographicology. third section we present the groundbreaking prototype known as the "Black Nightmare V.7." The Black Nightmare V.7 drone bombardier boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box) to control the Multiple Ailerons System (MAS) and Multi-Missiles System (MM-System) connected to the full autonomous artificial intelligence in attack or defense decisions making in military drones’ system box (The NeuronDrone-Box). The second chapter introduces a groundbreaking prototype known as the " WATER DRONE PROPULSION SYSTEM MAR777." The Water Drone Propulsion System MAR777 boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)" in the water drone propulsion system MAR777 to detect survivors or missing people in rain floods and Tsunami. This involves strategically placing all cameras (under water and sea level) of the WATER DRONE PROPULSION SYSTEM MAR777 to observe the damage of floods and Tsunami in real-time. Additionally, the Water Drone Propulsion System MAR777 features an innovative autopilot design referred to as the "AI Searching System (AISS)." This system incorporates a system to scanning and locate possible areas with large damage by the rain floods or Tsunami, accompanied by a series of specialized detectors that operate in precise synchronization to searching people to rescue with a high accuracy of 99.5%. Furthermore, a cutting-edge concept called the "Searching and Rescue Emergency for Rain Floods and Tsunami System (SRERFT-System)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR777 is equipped with storage box to support survivors with suppliers and medicine. Notably, the Water Drone Propulsion System MAR777 is capable of carrying also any suppliers, be it water or life jackets, in the rain floods or Tsunami anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System MAR777 indispensable for a wide array of natural disasters emergency missions. The third chapter introduces a groundbreaking prototype known as the "Water Drone Propulsion System MAR37." The Water Drone Propulsion System MAR37 boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)" in the MAR37. This involves strategically placing all 2 large motors within the main body structure of the MAR37. Additionally, the Water Drone Propulsion System MAR37 features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates 2 potent motors within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Waves System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR37 is equipped with storage box to support armies with suppliers and military equipment simultaneously. Notably, the Water Drone Propulsion System MAR37 can supply any cargo anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System MAR37 indispensable for a wide array of natural disasters emergency missions. The fourth chapter introduces a groundbreaking prototype known as the " WATER DRONE PROPULSION SYSTEM MAR25." The Water Drone Propulsion System MAR25 boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)" in the water drone propulsion system. This involves strategically placing all ailerons within the main body structure of the WATER DRONE PROPULSION SYSTEM MAR25. Additionally, the Water Drone Propulsion System MAR25features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates a potent motor within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Waves System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR25 is equipped with storage box to support armies with suppliers and military equipment simultaneously. Notably, the Water Drone Propulsion System MAR25 is capable of carrying military suppliers, be it guns or munitions, in the field of action anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System MAR25 indispensable for a wide array of military and natural disasters emergency missions. The fifth chapter introduces a groundbreaking prototype known as the "Water Drone Propulsion System MAR31." The Water Drone Propulsion System MAR31 boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)" in the MAR31. This involves strategically placing all 3 large motors within the main body structure of the MAR31. Additionally, the Water Drone Propulsion System MAR31 features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates 3 potent motors within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Waves System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR31 is equipped with storage box to support armies with suppliers and military equipment simultaneously. Notably, the Water Drone Propulsion System MAR31 can supply any cargo anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System MAR31 indispensable for a wide array of natural disasters emergency missions. The sixth chapter introduces a groundbreaking prototype known as the "Black Nightmare V.7." The Black Nightmare V.7 drone bombardier boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Ailerons System (MAS) and Multi-Missiles System (MM-System)" in the Black Nightmare V.7 drone bombardier. This involves strategically placing all ailerons within the main body structure of the aircraft. Additionally, the Black Nightmare V.7 drone bombardier features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates a potent motor within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce departure and landing noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Winds System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Black Nightmare V.7 drone bombardier is equipped with solar panels, ensuring a continuous charge to support its four powerful motors simultaneously. Notably, the Black Nightmare V.7 drone bombardier is capable of carrying four heavy bombs and three missiles (air-air). Finally, its versatile capabilities render the Black Nightmare V.7 drone bombardier indispensable for a wide array of military and national emergency missions. The seventh chapter presents an innovative military sea security defense system never introduced before. We present the first aquatic smart platforms carrier system (Hydronescarrier) and different aquatic smart platforms that is a vital part of the military equipment for the Hydronescarrier. We aim to present all the features and functionalities of this new military defense system, which can play an important role in defense and attack operations more quickly and efficiently. The Hydronescarrier includes an artificial intelligence system, autonomous defense systems, energy systems, and smart control systems. These components form a complex and dynamic defense system that provides immediate high-level protection to strategic areas, even at a distance. Some specifications are withheld for national security reasons. Additionally, this basic manual introduces the NeuronDrone-Box and Mega-NeuronDrone-Box: the hardware, algorithm, and a new special military Hydrone. In a special section, we like to mention that all algorithms in this basic manual use chaos theory and Econographicology. Subsequently, we present the groundbreaking prototypes known as the MAR107X aquatic smart platform, The MAR107Y aquatic smart platform, and the MAR107Z aqua...
Water Drone Propulsion System MAR35
This research paper introduces a groundbreaking prototype known as the "Water Drone Propulsion System MAR35." The Water Drone Propulsion System MAR35 boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)" in the MAR35. This involves strategically placing all 3 small motors within the main body structure of the MAR35. Additionally, the Water Drone Propulsion System MAR35 features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates 3 potent motors within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Waves System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR35 is equipped with storage box to support armies with suppliers and military equipment simultaneously. Notably, the Water Drone Propulsion System MAR35 can supply any cargo anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System MAR35 indispensable for a wide array of natural disasters emergency missions.
Hybrid Drones for Military and Emergency Relief
This Electronic Mongraph (E-Monpgraph) is interested to present different hybrid drones prototypes and artificial intelligence (AI) algorithms using Econographicology (Ruiz Estrada, 2017) as the main analytical and theoretical framework to support the construction of all these prototypes and a set of AI algorithms subsequently. This E-Monograph is divided into seven chapters. The first chapter is the introduction of six chapters in analysis. The second chapter presents a new prototype is called “MR8-UAV.” The MR8-UAV has its unique and special features and applications are presented in this chapter. Firstly, we promote in this research the uses of “Internal Ailerons System (IAS).” We suggest to located all aleirons into the body of the main structure of MR8-UAV. At the same time, the MR8-UAV presents a new propeller design under the “Anti-noise Propeller System (APS).” The MR8-UAV keeps a powerful motor inside of its main structure with a serial of special propellers interconnected and working synchronized with high precision to avoid the noise in 97% in departure, flying, and landing respectively. Thirdly, a new concept of “Breaking Sensibility Winds System (BSWS)” under the uses of artificial intelligence. The use of MR8-UAV is unlimited for military, commercial, and national emergencies missions. This research presents an original UAV with unique features and military applications such as invisibility system under the uses of transparent fiber plastic together with a strong and light titanium structure) and a special and different types of propollers to generate full silence in missions of surveillance and spying military missions. third chapter shows a new UAV is called “Wind-MAR10”. Initially, the Wind-MAR10 has a large structure to facilitate to carry suffisticated camera systems and light weapons in case of defense or attak specific targets efficiently. The WIND-MAR10 can handle also to do it a deep evaluation of data of possible targets using artificial intelligence (AI). The Construction of the WIND-MAR10 is based on a high reception radio control system, modern GPS system, high reception antenna and radar systems, HD cameras (live), powerful batteries, high resistance ESC systems, and powerful electric motors to support large operations in case of any military conflict. The fourth chapter introduces the Multi-Disks Networks Autonomous Decisions Maker Artificial Intelligence Mapping (MNADMAI-Mapping) for Hydrones and Drones revolves around the efficacy of a multi-level mega-data analysis algorithm with the uses of a new mathematical and graphical approach as the most effective analytical control system to programming any UAV (Zenko, 2013) such as Hydrone (drones for water or under water) and Drone (air) missions. The main motivation behind the creation of the MNADMAI-Mapping for Hydrones and Drones is to offer a new artificial intelligence machine tool to evaluate and take auto-decisions in a possible war (Ashkenazi, 2016), warlike, domestic conflicts, or border problem militarily under a large list of possible military actions (defense or attack) efficiently. Thereby, the mission of the MNADMAI-Mapping for Hydrones and Drones is to offer to any army, navy, and air force an alternative artificial intelligence system (Leys, 2018). Fifth chapter presents a new hybrid propson system to generate a new way of impulse and ventilation, this prototype is supported by a main propeller (with a main motor) and sub-motors (2 motors or many motors) together in the same physical space to generate different ways of synergies to maimize a single propulsion simultaneously. This new prototype is called “The Multi-Motors Integrated Hybrid PropuIsion System - (MIHP-System)-.” The MIHP-System design revolves around the efficacy of a multi-level mega-data analysis algorithm is called MIHP-System evaluator with the uses of a new mathematical and graphical modeling as the most effective analytical tool to evaluate this specific propulsion system under a new experimental design efficiently. The sixth chapter presents two new smart hydrones in case of floods or Tsunami res-cue and relief operations anywhere and anytime. We have two models of smart hydrones such as the MAR21 smart hydrone and MAR17 smart hydrone. Initially, the MAR21 smart hydrone has a large structure to facilitate the carrying of food, medicines, and water to areas were affected severally by any flood or Tsunami. At the same time, the MAR21 smart hydrone can handle also to do it a deep cleaning service of any area was affected by a flood or Tsunami subsequently. Additionally, the MAR21 smart hydrone and MAR17 smart hydrone offers to government and humanitarian agencies an alter-native emergency maritime transportation system in case of floods or Tsunami disasters. The MAR21 smart hydrone and MAR17 smart hydrone construction is based on a high reception radio control system, a modern GPS system, high reception antenna and radar systems, HD cameras (live), powerful batteries, high resistance ESC systems, and pow erful electric motors to support large operation in case of a flood or Tsunami rescue and relief operations anytime. The design of the structure of the MAR21 smart hy-drone and MAR17 smart hydrone has special characteristics that can be adapted to any weather condition, temperature levels, and levels of water respective-ly. In fact, the cost of production of MAR21 smart hydrone and MAR17 Smart hydrone is relatively cheap and easy for his construction accord ing to this research. Finally, the seventh chapter shows the Multi-Level National Security Intelligence System (MNSI--System) revolves around the efficacy of multi-level mega-data analysis frameworks together with the uses of multidimensional graphs as the most effective analytical artificial intelligence learning machine tool to understand and solve any military and national security problems from a holistic approach. The main motivation behind the creation of the MNSI-System is to offer a new artificial intelligence learning machine tool to evaluate dynamic and complex possible war, warlike, domestic conflicts, or border problems militarily under a large list of possible strategies efficiently. Thereby, the mission of the MNSI-System is to offer army, navy, air force, intelligence, and governments an alternative artificial intelligence approach. Hence, the MNSI-System is offering a set of different types of multidimensional artificial intelligence learning machines tools are presented: the mega-dynamic disks coordinate space (vertical position and horizontal position) learning machine tool and the mega-disks networks mapping (MDN-Mapping) learning machine tool respectively.
<title>Land, sea, and air unmanned systems research and development at SPAWAR Systems Center Pacific</title>
Unmanned Systems Technology XI, 2009
New Fighter Unmanned Aerial Vehicles (FUAV's): Designing and Prototyping
This Electronic Monograph (E-Monpgraph) is interested to present different models of fighter UAVs for defense and offensive military missions using Artificial Intelligence (AI) and Econographicology (Ruiz Estrada, 2017) as the main analytical and theoretical framework to support the construction of all these prototypes. This E-Monograph is divided into seven chapters. The second chapter The Multi-Disks Networks Autonomous Decisions Maker Artificial Intelligence Mapping (MNADMAI-Mapping) for Drones is founded on an advanced multi-level mega-data analysis algorithm, coupled with a novel mathematical and graphical approach. This integrated analytical control system stands out as an exceptionally efficient programming tool for a diverse range of UAVs, including Drones (for aerial missions). The development of MNADMAI-Mapping for and Drones stems from the imperative need for a cutting-edge artificial intelligence framework capable of assessing and autonomously deciding courses of action in various scenarios, including possible conflicts, domestic disputes, and border-related military challenges, extending to a wide spectrum of potential military maneuvers, be they defensive or offensive in nature. In essence, MNADMAI-Mapping for Drones serves the critical purpose of providing armed forces, encompassing armies, navies, and air forces, with an alternative artificial intelligence system, further augmenting their strategic capabilities. The third chapter introduces a groundbreaking prototype known as the "MR12-UAV Bombardier." The MR12-UAV Bombardier boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Ailerons System (MAS)" in the MR12-UAV Bombardier. This involves strategically placing all ailerons within the main body structure of the aircraft. Additionally, the MR12-UAV Bombardier features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates a potent motor within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce departure and landing noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Winds System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the MR12-UAV Bombardier is equipped with solar panels, ensuring a continuous charge to support its four powerful motors simultaneously. Notably, the MR12-UAV Bombardier is capable of carrying two heavy payloads, be it bombs or missiles, for both air-land and air-sea operations. Finally, its versatile capabilities render the MR12-UAV Bombardier indispensable for a wide array of military and national emergency missions. The chapter fourth introduces a groundbreaking prototype named the "Auto-position Sensorial Surface Anti-Turbulence System (ASSA-System)" integrated into the construction of the MRNSP-V.8-UAV. This report comprehensively outlines the unique features and diverse applications of the MRNSP-V.8-UAV. First and foremost, we advocate for the implementation of the "Internal Ailerons System (IAS)" in this research. We propose the incorporation of all ailerons within the main structural body of the MRNSP-V.8-UAV. Concurrently, the MRNSP-V.8-UAV showcases a novel propeller design under the "Anti-noise Propeller System (APS)." The ASSA-System houses a potent motor within its primary structure, along with a series of specialized propellers intricately synchronized to achieve a remarkable noise reduction of 97% during departure, flight, and landing. Additionally, we introduce a pioneering concept: the "Breaking Sensibility Winds System (BSWS)," leveraging the capabilities of artificial intelligence. The versatility of the auto-positioning sensorial surface anti-turbulence system (ASSA-System) extends to military applications and national relief efforts in the event of natural disasters. The fifth chapter introduces the innovative prototype known as the "MR1-UAV," outlining its distinctive features and diverse range of applications. A primary focus of this study is the implementation of the "Quiet Propeller System (QPS)." The MR1-UAV integrates a robust motor within its core structure, coupled with a series of specialized propellers meticulously synchronized to significantly reduce noise levels by 97% during departure, flight, and landing phases. Additionally, a pioneering concept of the "Ultra-sensitivity Winds System (UWS)" is proposed, leveraging artificial intelligence for enhanced performance. The versatility of the MR1-UAV extends to encompass military, commercial, and critical national emergency missions. The chapter sixth shows the MULTICOPTER MR10-UAV concept, employing a strategically arranged set of propellers within a unified structure (Jones, 2017). This configuration maximizes energy efficiency during takeoff and minimizes impact forces during landing. By closely interconnecting ten propellers, a unique and harmonized propulsion system is achieved, enabling rapid and secure flight. The MULTICOPTER MR10-UAV incorporates an integral propeller system known as the "Closely Integrated Propeller System (CIPS)" (Refer to Figures 1, 2, 3, 4, and 11). This design integrates ten robust motors, each equipped with an independent battery, enhancing its flight endurance. These motors operate in synchronized harmony with high precision, resulting in a 99% reduction in departure and landing noise levels. Moreover, the MR10-UAV boasts the capacity to transport substantial payloads reliably and efficiently. Its versatility extends to a wide array of applications, including but not limited to commercial ventures, logistical operations, military endeavors, and responding to national emergencies. Finally, the chapter seventh introduces the Multi-Level Drones National Intelligence Security System (CORAZA-System). We evaluate the effectiveness of multi-level mega-data analysis frameworks, coupled with the utilization of multidimensional graphs as a potent analytical tool within artificial intelligence. This approach offers a holistic perspective in comprehending and addressing complex military and national security challenges. The primary impetus behind the development of the CORAZA-System is to provide a novel artificial intelligence tool capable of efficiently assessing dynamic and intricate scenarios, including potential wars, conflicts, and border issues. This tool aims to evaluate diverse strategies in a comprehensive manner. Consequently, the CORAZA-System endeavors to furnish armed forces, intelligence agencies, and governments with an alternative artificial intelligence approach.
The Drone Bombardier: Black Nightmare V.7
This research paper introduces a groundbreaking prototype known as the "Black Nightmare V.7." The Black Nightmare V.7 drone bombardier boasts a range of distinctive features and applications, which are detailed in this technical report. Firstly, we advocate for the implementation of the "Multiple Ailerons System (MAS) and Multi-Missiles System (MM-System)" in the Black Nightmare V.7 drone bombardier. This involves strategically placing all ailerons within the main body structure of the aircraft. Additionally, the Black Nightmare V.7 drone bombardier features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This system incorporates a potent motor within the main structure, accompanied by a series of specialized propellers that operate in precise synchronization to reduce departure and landing noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Winds System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Black Nightmare V.7 drone bombardier is equipped with solar panels, ensuring a continuous charge to support its four powerful motors simultaneously. Notably, the Black Nightmare V.7 drone bombardier is capable of carrying four heavy bombs and three missiles (air-air). Finally, its versatile capabilities render the Black Nightmare V.7 drone bombardier indispensable for a wide array of military and national emergency missions.
An Introduction to Smart Hydrones (Platforms): Design, Performance, and Applications
This electric monograph is divided into five chapters: The first presents a general description of each chapter in detail. The second chapter evaluates the crucial role of unmanned aerial vehicles –UAV’s- (or Drone) in the case of natural disasters response and relief humanitarian aid. The main objective of this chapter is to evaluate how unmanned aerial vehicles –UAV’s- (or Drone) in the present and near future can play a crucial role to help survivors in the case of a tsunami, earthquake, flooding, and any natural disaster. Basically, we need to assume that in any natural disaster always exist the high possibility of damage in the infrastructure, transportation systems, telecommunications systems access, and basic services immediately. We propose in three areas the uses of unmanned aerial vehicles –UAV’s- (or Drone) in the case of natural disasters response and relief humanitarian aid: (i) the aerial monitoring post-natural disaster damage evaluation; (ii) the natural disaster logistic and cargo delivery; (iii) the post-natural disaster aerial evaluation. The third chapter presents three new smart hydrones for surveillance, rescue, security, cleaning, searching, and logistic operations anywhere and any time. We have three new models of smart hydrones such as the MAR21 smart hydrone, MAR25 smart hydrone, and MAR27 smart hydrone. Initially, the MAR21 smart hydrone has a large structure to facilitate surveillance, rescue, security, cleaning, and logistic operations anywhere and anytime. At the same time, the MAR21 smart hydrone can handle also to do it a deep cleaning service of any area was affected by pollution or natural disasters damages subsequently. Additionally, the MAR21 smart hydrone, MAR25 smart hydrone, and MAR27 smart hydrone offers to private sector, government, and humanitarian agencies an alternative maritime transportation system. The MAR21 smart hydrone, MAR25 smart hydrone, and MAR27 smart hydrone construction is based on basic materials such as PVC pipes with PVC glue, high reception radio control systems, a modern GPS system, high reception antenna and radar systems, HD cameras (live), powerful lipo batteries, high resistance ESC systems, and powerful electric motors to support large operations in case of surveillance, rescue, security, cleaning, searching, and logistic operations. The design of the structure of the MAR21 smart hydrone, MAR25 smart hydrone, and MAR27 smart hydrone have different and special characteristics that can be adapted to any weather condition, temperature levels, and levels of wa-ter respectively. In fact, the cost of production of MAR21 smart platform, MAR25 smart hydrone, and MAR27 smart hydrone is relatively cheap and easy to be produced in developing countries and least developed countries according to this research. Subsequently, the chapter fourth introduces a new concept of unmanned aerial vehicles –UAV’s- are called Smart Flying Platforms (SFP). The main objective of this chapter is to evaluate how smart flying platforms in the present and near future can play a crucial role to help to fight terrorism. Basically, we need to assume that in any terrorist attack always exist the high possibility of damage in the people, infra-structure, transportation systems, telecommunications systems access, and basic services immediately. We propose in three areas the uses of smart flying platforms in the case to fight terrorism response: (i) the aerial monitoring pre-terrorist attack evaluation; (ii) the destruction of terrorist logistic and cargo delivery; (iii) the post-terrorist attack aerial evaluation. Finally, the chapter fifth presents two new smart hydrones in case of floods or Tsunami rescue and relief operations anywhere and anytime. We have two models of smart hydrones such as the MAR21 smart hydrone and MAR27 smart hydrone. Initially, the MAR21 smart hydrone has a large structure to facilitate the carrying of food, medi-cines, and water to areas were affected severally by any flood or Tsunami. At the same time, the MAR21 smart hydrone can handle also to do it a deep cleaning service of any area was affected by a flood or Tsunami subsequently. Addition ally, the MAR21 smart hydrone and MAR27 smart hydrone offers to government and humanitarian agencies an alternative emergency maritime transportation system in case of floods or Tsunami disasters. The MAR21 smart hydrone and MAR27 smart hydrone construction is based on a high reception radio control system, a modern GPS system, high reception antenna and radar systems, HD cameras (live), powerful batteries, high resistance ESC systems, and powerful electric motors to support large operation in case of a flood or Tsunami rescue and relief operations anytime. The design of the structure of the MAR21 smart hydrone and MAR27 smart hydrone has special characteristics that can be adapted to any weather condition, temperature levels, and levels of water respectively. In fact, the cost of production of MAR21 smart hydrone and MAR27 smart hydrone is relatively cheap and easy for his construction according to this research.
Unmanned Aircraft Systems in Maritime Operations: Challenges addressed in the scope of the SEAGULL project
Unmanned Aircraft Systems in Maritime Operations: Challenges addressed in the scope of the SEAGULL project, 2015
The SEAGULL project aims at the development of intelligent systems to support maritime situation awareness based on unmanned aerial vehicles. It proposes to create an intelligent maritime surveillance system by equipping unmanned aerial vehicles (UAVs) with different types of optical sensors. Optical sensors such as cameras (visible, infrared, multi and hyper spectral) can contribute significantly to the generation of situational awareness of maritime events such as (i) detection and georeferencing of oil spills or hazardous and noxious substances; (ii) tracking systems (e.g. vessels, shipwrecked, lifeboat, debris, etc.); (iii) recognizing behavioral patterns (e.g. vessels rendezvous, high-speed vessels, atypical patterns of navigation, etc.); and (iv) monitoring parameters and indicators of good environmental status. On-board transponders will be used for collision detection and avoidance mechanism (sense and avoid). This paper describes the core of the research and development work done during the first 2 years of the project with particular emphasis on the following topics: system architecture, automatic detection of sea vessels by vision sensors and custom designed computer vision algorithms; and a sense and avoid system developed in the theoretical framework of zero-sum differential games.
Güçlü ve Zayıf Düşünceler (Ontolojiler) Arasında Kutsal Kitap The Holy Book Between Strong and Weak Thought Burhanettin TATAR
Gümüşhane İlahiyat Fakültesi Dergisi, 2024
Bu makalede Eliade’ın arkaik (primitif) ontolojisini dikkate alarak, Meşşai filozofların metafiziksel (güçlü) düşünce sistemleri ve Vattimo ile Caputo’nun postmetafiziksel felsefeleri içinde kutsal kitabın nasıl konumlandırıldığını belirlemeye çalışmaktayız. Bu yazının merkezinde şu sorular bulunmaktadır: İnsan gerçekliğe şu veya bu ölçüde tanıklık etmeye çalışan bir varlık mıdır? Yoksa gerçek ile gerçek olmayanı saf haliyle birbirinden ayırabilecek bir konuma yükselebilen, dolayısıyla birbirinden farklı (zıt, çatışan) gerçeklik tasavvurları arasında sınırlar çizen evrensel hakem rolünü üstlenebilir mi? Bu sorular post-modern düşünce ile klasik ve modern metafiziksel sistemlerin birbirinden ayrıştığı noktayı gösterirler. Postmodern filozoflar en fazla gerçekliğe tanık olmaya çalışabileceğimizi, metafiziksel sistemlerin ürettiği üst anlatılarının sadece tarihsel bir yorum veya perspektifin evrensel bir üst-anlatı gibi takdiminden ibaret olduğunu ileri sürerler. Klasik ve modern metafiziksel yaklaşımlar ise insan aklının evrensel hakem konumuna yükselebileceğini kabul ederler. Bu iki yaklaşımı birbirinden ayıran en temel husus ise insanın gerçekliğe doğrudan nüfuz edip edemeyeceği sorusudur. Vattimo ve Caputo insanın gerçekliğe ancak dolaylı yani yorum (kültür, gelenek, dil vs) üzerinden erişebildiğini kabul ederken, metafiziksel felsefeler insanın gerçekliğe yorum mekânını aşarak doğrudan nüfuz edebildiğini ileri sürerler
DIREITO PROCESSUAL PENAL
PRINCÍPIOS CONSTITUCIONAIS PROCESSUAIS PENAIS À LUZ DAS DOUTRINAS DE NORBERTO AVENA E NESTOR TÁVORA.
Full Copyright under Mantarraya Negra UAV © 2024
A NEW GENERATION OF
DRONES SYSTEMS
E-Monograph
By
Mario Arturo Ruiz Estrada
Mantarraya Negra UAV
E-mail : marioarturoruiz@gmail.com
Website: https://www.instagram.com/mantarray_negra_smart_platform/
Tel: +6012-6850293
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Content
Introduction
3.
Chapter I
Full Autonomous Artificial Intelligence in Attack or Defense Decisions Making in
Military Drones Box: The NeuronDrone-Box
6.
Chapter II
How The Water Drone Propulsion System MAR777 can helps in Case of Rain Floods
or Tsunami?
19.
Chapter III
Water Drone Propulsion System MAR37
31.
Chapter IV
Water Drone Propulsion System MAR25
41.
Chapter V
Water Drone Propulsion System MAR31
48.
Chapter VI
The Drone Bombardier: Black Nightmare V.7
58.
Chapter VII
Hydronescarrier
72.
Chapter VIII
Mantarraya Negra Dronescarrier
94.
Chapter IX
The Black&White Non-linear Irregular Strokes Camouflage System
139.
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Introduction
The present electronic monograph (E-monograph) is divided into eight chapters are followed by: The
first chapter introduces the full autonomous artificial intelligence in attack or defense decisions making
in military drones’ system box (The NeuronDrone-Box): the hardware, algorithm, and a new special
military Drone or UAV. The first section presents the full autonomous artificial intelligence in attack
or defense decisions making in military drones’ system box (The NeuronDrone-Box) to adapt to any
drone to the main control system of any drone. Second section is the algorithm is using chaos theory
and Econographicology. third section we present the groundbreaking prototype known as the "Black
Nightmare V.7." The Black Nightmare V.7 drone bombardier boasts a range of distinctive features and
applications, which are detailed in this technical report. Firstly, we advocate for the implementation of
the full autonomous artificial intelligence in attack or defense decisions making in military drones’
system box (The NeuronDrone-Box) to control the Multiple Ailerons System (MAS) and MultiMissiles System (MM-System) connected to the full autonomous artificial intelligence in attack or
defense decisions making in military drones’ system box (The NeuronDrone-Box). The second chapter
introduces a groundbreaking prototype known as the " WATER DRONE PROPULSION SYSTEM
MAR777." The Water Drone Propulsion System MAR777 boasts a range of distinctive features and
applications, which are detailed in this technical report. Firstly, we advocate for the implementation of
the "Multiple Sensors System (MSS)" in the water drone propulsion system MAR777 to detect
survivors or missing people in rain floods and Tsunami. This involves strategically placing all cameras
(under water and sea level) of the WATER DRONE PROPULSION SYSTEM MAR777 to observe the
damage of floods and Tsunami in real-time. Additionally, the Water Drone Propulsion System MAR777
features an innovative autopilot design referred to as the "AI Searching System (AISS)." This system
incorporates a system to scanning and locate possible areas with large damage by the rain floods or
Tsunami, accompanied by a series of specialized detectors that operate in precise synchronization to
searching people to rescue with a high accuracy of 99.5%. Furthermore, a cutting-edge concept called
the "Searching and Rescue Emergency for Rain Floods and Tsunami System (SRERFT-System)" is
integrated, leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the
Water Drone Propulsion System MAR777 is equipped with storage box to support survivors with
suppliers and medicine. Notably, the Water Drone Propulsion System MAR777 is capable of carrying
also any suppliers, be it water or life jackets, in the rain floods or Tsunami anytime and everywhere.
Finally, its versatile capabilities render the Water Drone Propulsion System MAR777 indispensable for
a wide array of natural disasters emergency missions. The third chapter introduces a groundbreaking
prototype known as the "Water Drone Propulsion System MAR37." The Water Drone Propulsion
System MAR37 boasts a range of distinctive features and applications, which are detailed in this
technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)"
in the MAR37. This involves strategically placing all 2 large motors within the main body structure of
the MAR37. Additionally, the Water Drone Propulsion System MAR37 features an innovative propeller
design referred to as the "Silent Propeller System (SPS)." This system incorporates 2 potent motors
within the main structure, accompanied by a series of specialized propellers that operate in precise
synchronization to reduce noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept
called the "Sensibility Waves System (SWS)" is integrated, leveraging artificial intelligence for
enhanced performance. In a bid for sustainability, the Water Drone Propulsion System MAR37 is
equipped with storage box to support armies with suppliers and military equipment simultaneously.
Notably, the Water Drone Propulsion System MAR37 can supply any cargo anytime and everywhere.
Finally, its versatile capabilities render the Water Drone Propulsion System MAR37 indispensable for
a wide array of natural disasters emergency missions. The fourth chapter introduces a groundbreaking
prototype known as the " WATER DRONE PROPULSION SYSTEM MAR25." The Water Drone
Propulsion System MAR25 boasts a range of distinctive features and applications, which are detailed
in this technical report. Firstly, we advocate for the implementation of the "Multiple Sensors System
(MSS)" in the water drone propulsion system. This involves strategically placing all ailerons within the
3
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main body structure of the WATER DRONE PROPULSION SYSTEM MAR25. Additionally, the
Water Drone Propulsion System MAR25features an innovative propeller design referred to as the
"Silent Propeller System (SPS)." This system incorporates a potent motor within the main structure,
accompanied by a series of specialized propellers that operate in precise synchronization to reduce noise
levels by an impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Waves
System (SWS)" is integrated, leveraging artificial intelligence for enhanced performance. In a bid for
sustainability, the Water Drone Propulsion System MAR25 is equipped with storage box to support
armies with suppliers and military equipment simultaneously. Notably, the Water Drone Propulsion
System MAR25 is capable of carrying military suppliers, be it guns or munitions, in the field of action
anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System
MAR25 indispensable for a wide array of military and natural disasters emergency missions. The fifth
chapter introduces a groundbreaking prototype known as the "Water Drone Propulsion System
MAR31." The Water Drone Propulsion System MAR31 boasts a range of distinctive features and
applications, which are detailed in this technical report. Firstly, we advocate for the implementation of
the "Multiple Sensors System (MSS)" in the MAR31. This involves strategically placing all 3 large
motors within the main body structure of the MAR31. Additionally, the Water Drone Propulsion System
MAR31 features an innovative propeller design referred to as the "Silent Propeller System (SPS)." This
system incorporates 3 potent motors within the main structure, accompanied by a series of specialized
propellers that operate in precise synchronization to reduce noise levels by an impressive 99.5%.
Furthermore, a cutting-edge concept called the "Sensibility Waves System (SWS)" is integrated,
leveraging artificial intelligence for enhanced performance. In a bid for sustainability, the Water Drone
Propulsion System MAR31 is equipped with storage box to support armies with suppliers and military
equipment simultaneously. Notably, the Water Drone Propulsion System MAR31 can supply any cargo
anytime and everywhere. Finally, its versatile capabilities render the Water Drone Propulsion System
MAR31 indispensable for a wide array of natural disasters emergency missions. The sixth chapter
introduces a groundbreaking prototype known as the "Black Nightmare V.7." The Black Nightmare V.7
drone bombardier boasts a range of distinctive features and applications, which are detailed in this
technical report. Firstly, we advocate for the implementation of the "Multiple Ailerons System (MAS)
and Multi-Missiles System (MM-System)" in the Black Nightmare V.7 drone bombardier. This
involves strategically placing all ailerons within the main body structure of the aircraft. Additionally,
the Black Nightmare V.7 drone bombardier features an innovative propeller design referred to as the
"Silent Propeller System (SPS)." This system incorporates a potent motor within the main structure,
accompanied by a series of specialized propellers that operate in precise synchronization to reduce
departure and landing noise levels by an impressive 99.5%. Furthermore, a cutting-edge concept called
the "Sensibility Winds System (SWS)" is integrated, leveraging artificial intelligence for enhanced
performance. In a bid for sustainability, the Black Nightmare V.7 drone bombardier is equipped with
solar panels, ensuring a continuous charge to support its four powerful motors simultaneously. Notably,
the Black Nightmare V.7 drone bombardier is capable of carrying four heavy bombs and three missiles
(air-air). Finally, its versatile capabilities render the Black Nightmare V.7 drone bombardier
indispensable for a wide array of military and national emergency missions. The seventh chapter
presents an innovative military sea security defense system never introduced before. We present the
first aquatic smart platforms carrier system (Hydronescarrier) and different aquatic smart platforms that
is a vital part of the military equipment for the Hydronescarrier. We aim to present all the features and
functionalities of this new military defense system, which can play an important role in defense and
attack operations more quickly and efficiently. The Hydronescarrier includes an artificial intelligence
system, autonomous defense systems, energy systems, and smart control systems. These components
form a complex and dynamic defense system that provides immediate high-level protection to strategic
areas, even at a distance. Some specifications are withheld for national security reasons. Additionally,
this basic manual introduces the NeuronDrone-Box and Mega-NeuronDrone-Box: the hardware,
algorithm, and a new special military Hydrone. In a special section, we like to mention that all
algorithms in this basic manual use chaos theory and Econographicology. Subsequently, we present the
groundbreaking prototypes known as the MAR107X aquatic smart platform, The MAR107Y aquatic
smart platform, and the MAR107Z aquatic smart platform, The three aquatic smart platforms boast a
range of distinctive features and applications, which are detailed in this technical report. In fact, we
advocate for the implementation of fully autonomous artificial intelligence in attack or defense
4
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decision-making in the military drones’ system box (The NeuronDrone-Box) to control the
Hydronescarrier connected to the fully autonomous artificial intelligence in attack or defense decisionmaking in the military drones’ system box: The NeuronDrone-Box. The eight chapter presents an
innovative military air security defense system never introduced before. We present the first drones air
carrier system (Mantarraya Negra Dronescarrier) and a new UAV that is a vital part of the military
equipment for the Mantarraya Negra Dronescarrier. We aim to present all the features and
functionalities of this new military defense system, which can play an important role in defense and
attack operations more quickly and efficiently. The Mantarraya Negra Dronescarrier includes an
artificial intelligence system, autonomous defense systems, energy systems, and smart control systems.
These components form a complex and dynamic defense system that provides immediate high-level
protection to strategic areas, even at a distance. Some specifications are withheld for national security
reasons. Additionally, this basic manual introduces the NeuronDrone-Box and Mega-NeuronDroneBox: the hardware, algorithm, and a new special military Drone or UAV. In a special section, we like
to mention that all algorithms in this basic manual use chaos theory and Econographicology.
Subsequently, we present the groundbreaking prototype known as the "Black Nightmare Drone
Bombardier V.7." The Black Nightmare Drone Bombardier V.7 boasts a range of distinctive features
and applications, which are detailed in this technical report. In fact, we advocate for the implementation
of fully autonomous artificial intelligence in attack or defense decision-making in the military drones’
system box (The NeuronDrone-Box) to control the Multiple Ailerons System (MAS) and MultiMissiles System (MM-System) connected to the fully autonomous artificial intelligence in attack or
defense decision-making in the military drones’ system box: The NeuronDrone-Box. The last chapter
(eight) aims to present an alternative camouflage system for military missions: the Black & White Nonlinear Irregular Strokes Camouflage System. The first part of our research provides a basic historical
description of camouflage System. The second section presents the theoretical framework of the black
& white non-linear Irregular strokes camouflage system, both technically and mathematically. This
camouflage System was applied to a basic cubic structure and the Black Nightmare Drone Bomber V.7
drone bombardier. The third section presents the conclusions and suggestions for using the black &
white non-linear irregular strokes camouflage system. This camouflage system presents an innovative
military design for security defense systems never before introduced. We aim to present all the features
and functionalities of this new camouflage system, which can play an important role in defense and
attack operations more quickly and efficiently. The black & white Non-linear irregular strokes
camouflage System includes an artificial intelligence system, autonomous color adaptation systems,
and smart control systems using the NeuronDrone-Box. It has an autofocus system to change its colors
according to the weather and temperatures. We are using a sophisticated algorithm based on the
butterfly chaos theory and Econographicology simultaneously. Subsequently, we present the
groundbreaking prototypes known as the Black & White Non-linear irregular strokes camouflage
system for air, sea, and land military missions.
5
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Chapter I
Full Autonomous Artificial Intelligence in Attack or
Defense Decisions Making in Military Drones Box:
The NeuronDrone-Box
By
Mario Arturo Ruiz Estrada
1.1. Introduction
2024). This technical report presents an alternative hardware (the Full Autonomous Artificial
Intelligence in Attack or Defense Decisions Making in Military Drones’ System Box (The
NeuronDrone Box), software (algorithm), and special drone called Black Nightmare V.7.
Drone Bombardier. Hence, this research chapter introduces a full autonomous artificial
intelligence in attack or defence decisions making in military drones’ system box (The
NeuronDrone-Box) is presenting the hardware, algorithm, and a new special military Drone or
UAV. The first section presents the full autonomous artificial intelligence in attack or defence
decisions making in military drones’ system box (The NeuronDrone-Box) to adapt to any drone
to the main control system of any drone (3 different control systems sources such as the Cube
Orange box (Cubepilot Ecosystem, 2024), the George UAV Autopilot (VERONTE
Autopilots, 2024), and the VPX3U-A4500E-VO (WOLF-1448) from Wolf-AdvancedTechnology (2024) wants to be centralized in a single box using these 3 systems
simultaneously. Second section is the algorithm is using chaos theory and Econographicology
(Ruiz Estrada, 2017) later we will adapt to the software configuration for each control systems
source. In the third section we present the groundbreaking prototype known as the "Black
Nightmare V.7." The Black Nightmare V.7 drone bombardier boasts a range of distinctive
features and applications, which are detailed in this technical report. Firstly, we advocate for
the implementation of the full autonomous artificial intelligence in attack or defense decisions
making in military drones’ system box (The NeuronDrone-Box) to control the Multiple
Ailerons System (MAS) and Multi-Missiles System (MM-System) in the Black Nightmare V.7
drone bombardier. This involves strategically placing all ailerons within the main body
structure of the aircraft. Additionally, the Black Nightmare V.7 drone bombardier features an
innovative propeller design referred to as the Silent Propeller System (SPS). This system
incorporates a potent motor within the main structure, accompanied by a series of specialized
propellers that operate in precise synchronization to reduce departure and landing noise levels
by an impressive 99.5%. Furthermore, a cutting-edge concept called the Sensibility Winds
System (SWS) is integrated, leveraging artificial intelligence for enhanced performance. In a
bid for sustainability, the Black Nightmare V.7 drone bombardier is equipped with solar panels,
ensuring a continuous charge to support its four powerful motors simultaneously. Notably, the
Black Nightmare V.7 drone bombardier is capable of carrying four heavy bombs and three
missiles (air-air). Finally, its versatile capabilities render the Black Nightmare V.7 drone
bombardier indispensable for a wide array of military and national emergency missions.
6
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1.2. The Full Autonomous Artificial Intelligence in Attack or Defense Decisions
Making in Military Drones’ System Box (The NeuronDrone-Box)
The first section presents the full autonomous artificial intelligence in attack or defence
decisions making in military drones’ system box (The NeuronDrone-Box) (See Figure 1) to
adapt to any drone to the main control system of any drone (3 different control systems sources
such as the Cubepilot Ecosystem, VERONTE Autopilots, and Wolf-Advanced-Technology
centralized in a single box using backups systems among all these three control systems
according to different environments and circumstances.
Fig. 1. The Full Autonomous Artificial Intelligence in Attack or Defense Decisions
Making in Military Drones’ System Box (The NeuronDrone-Box)
Source: Author
The Cube Orange from Cubepilot Ecosystem is (See Video 1) shows a high flexibility of its
uses and adaptation in any drone, we are using as the first control system in the full autonomous
artificial intelligence in attack or defense decisions making in military drones’ system box (The
NeuronDrone-Box).
Video 1: The Cube Orange
Source: The Cubepilot Ecosystem (2024)
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The second control system in the full autonomous artificial intelligence in attack or defense
decisions making in military drones’ system box (The NeuronDrone-Box) is the George UAV
Autopilot from VERONTE Autopilot (VERONTE, 2024) (See Video 2).
Video 2: The George UAV Autopilot
Source: VERONTE (2024)
The third control system in the full autonomous artificial intelligence in attack or defense
decisions making in military drones’ system box (The NeuronDrone-Box) is the VPX3UA4500E-VO (Wolf-1448) from Wolf Advanced Technology (Wolf Advanced Technology),
2024) give a full support to the full autonomous artificial intelligence in attack or defense
decisions making in military drones’ system box (The NeuronDrone-Box) (see Video 3)
Video 3: VPX3U-A4500E-VO (WOLF-1448)
Source: (Wolf Advanced Technology, 2024)
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Finally, the full autonomous artificial intelligence in attack or defense decisions making in
military drones’ system box (The NeuronDrone-Box) tries to allocate three control systems to
generate more precision in any attack or defense systems with a high precision.
1.3. The Attacks or Defense Decision System (ADD-System) Algorithm: Theoretical
Framework
The Attacks & Defense Making Decisions System (A&DMD-System) follows five
fundamental phases: First Phase: Input and Storage in the possible attacks and Defense plots
In this initial phase, two sections are defined: Input and Storage (Refer to Stage 1). The Input
section entails a variety of inputs (IXi) derived from different information resources (espionage
information), including quantitative data such as weak targets and high population density
places. These inputs are further classified as positive (+) or negative (-) based on the type of
information they represent (See Expression 1).
Xi = ƒ (+/-IX1, +/-IX2, …, +/-IX∞…) ≡ IXi = ƒ (+/-IXi) where i =1,2,…,∞ (1)
In the Storage of the Mega-Database section, the information inputs (IXi) are recorded in
distinct databases (DBXi) corresponding to i = 1,2,… ∞ (See Expression 2).
DBXi = ƒ (DBX1<+/-IX1>, DBX2<+/-IX2>,…, DBx∞<+/-IX∞>…) (2)
Second Phase: Visualization of possible targets
This phase involves real-time visualization of the possible attacks and timing enemies can
arrive it (Refer to Stage 2). It is based on continuous inputs of information (Ixi) along each
respective axis (Xi) from various Mega-Databases (DBXi) sources. The interconnected
relationship between each input of information (Ixi) and its corresponding axis (Xi) ensures
that all multi-dimensional graphs within the possible targets are continually updated in realtime (See Expression 3).
MD = X1: [+/-Ix1], X2:[+/-Ix2] , …, X∞:[+/-Ix∞]… (3)
Third Phase: Alert of Possible Attacks and Defense Failures
In this phase, the system provides alerts for potential failures (Refer to Stage 3). The alerts are
contingent on the position of the multi-dimensional graph within its physical coordinate
system. If the information falls within the negative quadrant, denoted as -Xi = [-IXi], the SWS
issues possible allocations (See Expression 4 and 5).
If W1: [-IX1], -W2:[-IX2] , …….…, -W∞:[-IX∞]… (4)
then W1: [»Possible Attack«], -W2:[»Possible Attack«] , …, -W∞:[»Possible Attack«] (5)
Inputs in the negative quadrant, denoted as -Wi:[-IXi], are labeled as "Weak Locations." When
the targets and defense identify an allocation (Ai), it initiates a search (☼) to determine its
potential position within the attack speed (Wi) domain, as outlined in Expression 6.
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Ai = -IX1 ☼ A1 : -IX2 ☼ A2 : … : -IX∞ ☼ A∞ (6)
Fourth Phase: Set of Directions of attacks and defense
This phase includes two sections related to the Mega-Database of final Directions (Refer to
Stage 4). The Mega-Database of Final Directions is an amalgamation (╦) of numerous
databases (DBXi), each containing a range of possible attacks and defense points directions
and speeds (Wi) (See Expression 7).
DMi = A1:[ Σ DBX1 <W1>] ╦ A2:[ Σ DBx2 <W2>] ╦ …╦ A∞ [ Σ DBX∞ <W∞>] (7)
Final Phase: Final Output of the targets and Defense
In this phase, the final output or attack and defense position by places (PO) is determined (Refer
to Phase 5). This is derived from the last partial differentiation (ƒi) of the extensive list of attack
and defense directions (Sxi). The objective is to refine the strategy for optimum stability,
minimizing risk and vulnerability of attack. (See Expression 9).
ƒ(A1) = A1 ◊ A2 ◊… ◊ A∞
ƒ (A2)’ = A1 ◊ A2 ◊… ◊ A∞
ƒ (A3)’’ = A1◊ A2 ◊… ◊ A∞
ƒ(Ai)i = 0 thus i = 1,2…∞ (9)
Initially, the mega-data disks coordinate space (Ruiz Estrada, 2017) is crucial part of the
Attacks or Defense Decision System (ADD-System) Algorithm. We have a powerful an
analytical graphical modeling to visualize and analyze a large amount of data. Firstly, this
specific coordinate space shows one single vertical straight axis that is pending among all
endogenous variables (the final decision of attack or defense: Shotting). Hence, we are
available to plotting our endogenous variable on this single vertical straight axis that is
represented by αV+/- (Different factors are taking in consideration to attack or defend: Shotting).
Secondly, each exogenous variable in analysis is represented by its specific coordinate system
to attack or defend: Shotting such as βΦi:ζj. Where “Φi” represents the sub-space level in
analysis, in this case either from sub-space level zero (SS0°) to sub-space level infinite (SS360°);
“ζj” represents the disk level in analysis at the same quadrant of exogenous variables (in our
case, from disk level j=1, disk level j=2, disk level j=3,…, to disk level j=∞…). In fact, we
assume that all exogenous variables are using only real positive numbers -rational factors of to
decide or not shotting- (R+). In order to plot different exogenous variables in the mega-data
disks coordinate space, each value need to be plotted directly on its radial subspace in analysis
(Φi) and disk level in analysis (ζj) respectively. Each “i” is a radius that emanates from the
origin and in defined by the angle which can range from 0 to just before 360°, a theoretical
infinite range of shotting. Each disk is a concentric circle that starts from the origin outwards
towards a theoretical infinite value. At the same time, all these values plotted in different axis
levels in analysis (Φi) and disk levels in analysis (ζj) need to be joined with its endogenous
variable “αV+/-” until we build a series of coordinates to attack or defend (Shotting). All these
coordinates need to be joined by straight lines until yields an asymmetric spiral-shaped
geometrical figure with n-faces (see Figure 2) and disk levels in analysis (ζj) need to be joined
together by straight lines directly to the endogenous variable αV+/- (final target to shotting) until
10
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a cone-shaped figure with n-faces is built. It is important to mention at this juncture that the
endogenous variables “αV+/-” is fixed according to any change associated with its
corresponding exogenous variables (precision levels) in βΦi:ζj, where i = {0°, 1°, 2°,…,360°}
and j = {0, 1, 2,…,∞…} , αV+/-. Hence, we can imagine a large number of exogenous variables
moving all the time in different positions within its radius in real time continuously (decision
of attack and defense (shotting). At the same time, we can visualize how all these exogenous
variables directly affect on the behavior the endogenous variable (αV+/-) (Final Target to
shotting) simultaneously. αV+/- is fixed according to any change can be occurred among the
infinite exogenous variables in βΦi:ζj, where i = {0°, 1°, 2°,…,360°} and j = {0, 1, 2,…,∞…},
YV+/-. Hence, we can imagine a large number of infinite exogenous variables moving all the
time in different positions within its radius in real time continuously. At the same time, we can
visualize how all these exogenous variables (rational or irrational decisions evaluation) are
affecting directly on the behavior of the endogenous variable (αV+/-) (final decision to attack or
defense: Shooting) simultaneously. Moreover, the endogenous variable (αV+/-) can fluctuate
freely (see Figure 2). In our case, the endogenous variables (αV+/-) can show positive/negative
final decisions of attack or according to our multidimensional coordinate space. In the case of
exogenous variables, these can only experience non-negative properties. The mega-data disks
multivariable random coordinate space in vertical position is represented by:
(βΦi:ζj, αV+/-) where βΦi:ζj ≥ 0; i = θ° ; j =R+ ≥ 0; αV+/-= R+/αV+/- = ƒ (βΦi:ζj)
(10)
(11)
Fig. 2:
The Mega-Data Disks Coordinate Space
Source: Ruiz Estrada (2017)
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Hence, this algorithm apply a specific trigonometry function such as the tangent
/tan(βφi:Ψj/αh+/-)/ or /tan(βΦi:ζj/αv+/-)/ and inverse tangent tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj
/αv+/-)-1. Initially, the calculation of the tangent /tan(βφi:Ψj/αh+/-)/ or /tan(βΦi:ζj/αh+/-)/ is
equal to β (adjacent) divided by α (opposite). Our graphical modeling applies absolute value to
eliminate negative values in the construction of our new coordinate space (Final attack:
Shooting). The main objective to calculate the inverse tangent tan(βφi:Ψj/αh+/-)-1 or
tan(βΦi:ζj/αv+/-)-1 is to find each angle that is located into the mega-data disks coordinate space
in vertical and horizontal position. Therefore, the tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj/αv+/-)-1can help
us to study easily the relationship between αh+/- or αv+/- (opposite) and βφi:Ψj or βΦi:ζj (adjacent)
in different periods of analysis. In fact, we are establishing three different parameters are
followed by (i) the representative area to attack or defense that keep angles between 50° and
40°; (ii) the acceptable area to attack or defense is located between 65°/51° and 41°/25°; (iii)
non-representative area to attack or defense that is fixed between 65°/90° and 26°/0° (see
Figure 6). We like to mention that each tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj/αv+/-)-1 is located
between 0° and 90° (See Figure 3).
Fig. 3: tan(β/α)-1
Source: Ruiz Estrada (2017)
Finally, all tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj/αv+/-)-1 results are organized in descendent order from
the smallest angle to the largest angle. Finally, we transfer all these results to the mega-data
disks coordinate space in vertical and horizontal position to visualize the behavior of all angles
that help us to appreciate clearly the behavior of multi-data analysis before to attack or defense
any target.
1.4. An Introduction to the Black Nightmare V.7. Drone Bombardier
The new prototype, Black Nightmare V.7 drone bombardier (refer to Figures 4 and 5),
represents a significant leap in aviation technology (Evans, 2018), integrating robotics (Lee,
12
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2020) with advanced artificial intelligence (Martinez, 2017). This chapter aims to highlight its
distinct features and specifications, showcasing its exceptional capabilities (refer to Figures 4).
The Black Nightmare V.7 drone bombardier underwent rigorous evaluations, testing its speed
and rotation resistance across various levels (Johnson, 2018). These assessments confirm its
potential as a groundbreaking air transportation system, offering superior propulsion and
stability compared to conventional propeller and aileron systems.
The precision of the Black Nightmare V.7 drone bombardier (see Video 1) stems from its
unique features and applications, as detailed in this technical report. Firstly, we advocate for
the implementation of the "Multiple Ailerons System (MAS)" and Multi-Missiles System
(MM-System) (see Figure 4). Our proposal involves situating all ailerons within the main body
structure of the Black Nightmare V.7 drone bombardier. Simultaneously, the aircraft introduces
an innovative propeller design known as the "Silent Propeller System (SPS)" (see Figure 3).
Housed within the main structure, the Black Nightmare V.7 drone bombardier boasts a robust
motor, complemented by a series of specialized propellers working in precise synchronization,
resulting in a remarkable 99.05% reduction in noise during departure, flight, and landing.
Furthermore, a novel concept of "Sensibility Winds System (SWS)" employs artificial
intelligence to enhance operational efficiency (see Figure 4 and Video 4). The Black Nightmare
V.7 drone bombardier is equipped to carry four booms or three missiles for air-air attacks,
further extending its versatility (see Figure 6 with Video 5). On top of the Black Nightmare
V.7 drone bombardier, there is a missile designed to intercept and attack any aircraft or object
that attempts to approach from above (see Figure 5).
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Fig. 4: Black Nightmare V.7 drone bombardier
Source: author
Fig. 5: Black Nightmare V.7 drone bombardier
Source: author
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Fig. 6: Black Nightmare V.7 drone bombardier
Source: author
Video 4: Black Nightmare V.7 drone bombardier
Source: Author
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Video 5: Black Nightmare V.7 drone bombardier
Source: Author
The strategic placement of propellers and ailerons within the Black Nightmare V.7 Black
Nightmare V.7 main structure has been executed with remarkable precision, as depicted in
Figure 3. This meticulous placement serves the dual purpose of optimizing propulsion and
enhancing aerial stability within short timeframes. Consequently, the synergy between the
positioning of ailerons and the noise-reduction propellers is carefully orchestrated to ensure
superior performance. This harmonization has been validated through a series of experiments
conducted across diverse environments and under varying weather conditions (Smith, 2017
and 2019).
The Black Nightmare V.7is primarily distinguished by its aileron and engine system
configuration. Its foremost advantage lies in its near-silent operation while maintaining the
capability to sustain static flight for up to seven seconds. This unique feature allows for precise
cargo or munition drops, affording unparalleled precision and versatility under any
circumstances (Taylor, 2019) (Williams, 2021). A second differentiating factor is the
adaptability of the Black Nightmare V.7 wings, which can seamlessly transition between rigid
and flexible states. This adaptability is facilitated by advanced sensor technology and artificial
intelligence (AI) systems, ensuring optimal wing positioning and flexibility. These distinct
features position the Black Nightmare V.7 as a groundbreaking innovation within the aviation
industry.
Furthermore, the Black Nightmare V.7 operates autonomously, obviating the need for a human
pilot. Its intricate control is managed entirely by the The Full Autonomous Artificial
Intelligence in attack or defence decisions making in military drones’ system box, with human
16
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oversight from a ground-based pilot. The antenna system relies on satellite technology,
guaranteeing superior reception and control. Therefore, this research project aims to introduce
a revolutionary paradigm shift in both the design and functionality of this technology.
The Black Nightmare V.7 represents a pioneering advancement that promises to redefine the
capabilities and applications of UAVs. According to the full autonomous artificial intelligence
in attack or defence decisions making in military drones’ system box (The NeuronDrone-Box),
we can observe an experimental case how 1,000,000 variables are running to build figure 6.
The white colour shows logical and rational conditions and in black colour non-logical and
irrational conditions, The final decision of attack or defence can define to shooting the final
target after the exhausted evaluation of 1,000,000 variables, we are accounting the white colour
of our graphs that represent 70% to take action of attack or defence in different strategic
locations immediately. In this case, we printed in 3D Printer, to give a better view of our graph
physically.
Fig. 7: The Full Autonomous Artificial Intelligence in attack or defence decisions making in
military drones’ System box (The NeuronDrone-Box)
Source: The Author
1.5. Concluding Remarks
In conclusion, this research presents a practical military tool is called the full autonomous
artificial intelligence in attack or defense decisions making in military drones’ system box (The
NeuronDrone-Box) to generate national defense integrated systems. Its innovative design, of
this special box incorporating features like the critical shotting decision system (CSD-System),
Multiple Ailerons System (MAS), Silent Propeller System (SPS), Sensibility Winds System
(BSWS), solar panels for efficient energy supply system (SPEES-System), and a versatile
payload system for air-to-ground and air-to-sea assaults, marks a significant leap forward in
UAV technology. The versatility of the full autonomous artificial intelligence in attack or
defense decisions making in military drones’ system box (The NeuronDrone-Box) and the
Black Nightmare V.7 renders it invaluable for a wide array of military and national emergency
missions, showcasing its boundless utility and potential impact on the industry.
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Reference
Adam, D. (2024). Lethal AI Weapons are here: How can We Control Them? Nature, Vol. 629:
521-523. Available at: https://www.nature.com/articles/d41586-024-01029-0
Anderson, M. R. (2019). Unmanned Aerial Vehicles and the Evolution of Air Warfare.
International Journal of Security Studies, 8(1), 45-59.
Brown, R. D. (2020). The Role of UAVs in Counterterrorism Operations. Journal of Military
Strategy, 35(2), 56-73.
CubePilot (2024). General Information. Available at:
https://www.unmannedsystemstechnology.com/company/cubepilot/
Evans, A. J. (2018). Drone Swarming in Military Operations: Challenges and Solutions.
Journal of Modern Warfare Tactics, 42(4), 213-230.
Johnson, L. M. (2018). Advancements in Drone Technology for Military Reconnaissance.
Aerospace Engineering Research, 11(4), 237-251.
Lee, S. Y. (2020). The Impact of UAV Technology on Military Intelligence. Defense and
Security Studies, 15(2), 123-138.
Martinez, P. S. (2017). Ethical Considerations of Lethal Autonomous Weapons Systems.
Military Ethics Quarterly, 22(1), 89-104.
Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality and Quantity, 51(5):2115-213.
Smith, E. P. (2017). Legal Frameworks for the Use of Military Drones. Harvard International
Law Journal, 30(3), 127-142.
Smith, J. A. (2019). Unmanned Aerial Vehicles in Modern Warfare. Military Technology
Journal, 45(3), 123-138.
Taylor, R. L. (2019). UAVs in Naval Warfare: Current Applications and Future Prospects.
Naval Technology Review, 25(2), 65-80.
VERONTE (2024). General Information. Available at:
https://www.unmannedsystemstechnology.com/company/uavionix-corporation/
Williams, D. H. (2021). The Future of Autonomous Military Drones: Challenges and
Opportunities. Journal of Defense Technology, 16(3), 187-202.
Wolf-Advanced-Technology (2024). General information, available at:
https://www.unmannedsystemstechnology.com/company/wolf-advanced-technology/vpx3ua4500e-vo-wolf-1448/
18
Full Copyright under Mantarraya Negra UAV © 2024
Chapter II
How The Water Drone Propulsion System MAR777
can helps in Case of Rain Floods or Tsunami?
By
Mario Arturo Ruiz Estrada
2.1. Introduction
The new prototype, WATER DRONE PROPULSION SYSTEM MAR777 (refer to Figures 1
and 2), represents a significant leap in maritime technology (Evans, 2018), integrating robotics
(Lee, 2020) with advanced artificial intelligence (Martinez, 2017). This chapter aims to
highlight its distinct features and specifications, showcasing its exceptional capabilities. The
WATER DRONE PROPULSION SYSTEM MAR777 underwent rigorous evaluations, testing
its speed and rotation resistance across various levels (Johnson, 2018). These assessments
confirm its potential as a groundbreaking maritime transportation system, offering superior and
stability compared to conventional propeller and aileron systems.
The precision of the WATER DRONE PROPULSION SYSTEM MAR777 (see Video 1 and
Video 2) stems from its unique features and applications, as detailed in this technical report.
Firstly, we advocate for the implementation of the " Multiple Sensors System (MSS)" (see
Figure 1). Our proposal involves situating all ailerons within the main body structure of the
WATER DRONE PROPULSION SYSTEM MAR777. Simultaneously, the Unmanned
Maritime Vehicle (UMV) introduces an innovative propeller design known as the " Searching
and Rescue Emergency for Rain Floods and Tsunami System (SRERFT-System)" (see Figure
5 and 6). Housed within the main structure, the WATER DRONE PROPULSION SYSTEM
MAR777 boasts two robust motors, complemented by a series of specialized GoPro camras
under water and sea level working in precise synchronization, resulting in a remarkable 99.99%
visualization of areas affected by rain floods or Tsunami during the water navigation.
Furthermore, a novel concept of SRERFT-System) employs artificial intelligence to enhance
operational efficiency (see Figure 6). The WATER DRONE PROPULSION SYSTEM
MAR777is equipped to carry suppliers, medicines, water, and life jackets everywhere and
anytime, further extending its versatility (see Figure 2 and Video 1 and Video 2).
19
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Fig.1: WATER DRONE PROPULSION SYSTEM MAR777
Source: author
20
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 2: WATER DRONE PROPULSION SYSTEM MAR777
Source: author
21
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Fig. 3: WATER DRONE PROPULSION SYSTEM MAR777
Source: author
22
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Fig. 4: WATER DRONE PROPULSION SYSTEM MAR777
Source: author
23
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Fig. 5: WATER DRONE PROPULSION SYSTEM MAR777
Source: author
24
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Fig. 6: WATER DRONE PROPULSION SYSTEM MAR777
Source: author
Video 1: WATER DRONE PROPULSION SYSTEM MAR777
Source: Author
25
Full Copyright under Mantarraya Negra UAV © 2024
Video 2: WATER DRONE PROPULSION SYSTEM MAR777
Source: Author
The strategic placement of cameras and sensors within the WATER DRONE PROPULSION
SYSTEM MAR777 main structure has been executed with remarkable precision, as depicted
in Figure 2. This meticulous placement serves the dual purpose of optimizing rescue and postevaluation or Tsunami within short timeframes. Consequently, the synergy between the
positioning of structure and the sensors are carefully orchestrated to ensure superior
performance. This harmonization has been validated through a series of experiments conducted
across diverse environments and under varying weather conditions (Smith, 2017 and 2019)
(see Video 3 and Video 4).
The WATER DRONE PROPULSION SYSTEM MAR777 is primarily distinguished by its
structure and monitoring system configuration. Its foremost advantage lies in its near-rescue
and post-evaluation operations while maintaining the capability to rescue and evaluate rain
floods and Tsunami in real-time. This unique feature allows for precise cargo drops, affording
unparalleled precision and versatility under any circumstances (Taylor, 2019) (Williams,
2021). A second differentiating factor is the adaptability of the WATER DRONE
PROPULSION SYSTEM MAR777, which can seamlessly transition between rigid (ice) and
soft states (water). This adaptability is facilitated by advanced sensors technology and artificial
intelligence (AI) systems, ensuring optimal positioning and flexibility. These distinct features
position the WATER DRONE PROPULSION SYSTEM MAR777 as a water breaking
innovation within the maritime industry.
Furthermore, the WATER DRONE PROPULSION SYSTEM MAR777 operates
autonomously, obviating the need for a human pilot. Its intricate control is managed entirely
by AI tools, with human oversight from a ground-based pilot. The antenna system relies on
satellite technology, guaranteeing superior reception and control. Therefore, this research
project aims to introduce a revolutionary paradigm shift in both the design and functionality of
26
Full Copyright under Mantarraya Negra UAV © 2024
this technology. The WATER DRONE PROPULSION SYSTEM MAR777 represents a
pioneering advancement that promises to redefine the capabilities and applications of UMVs.
2.2. The Searching and Rescue Emergency for Rain Floods and Tsunami System
(SRERFT-System)
The SRERFT-System follows five fundamental phases:
First Phase: Input and Storage in the SRERFT-System
In this initial phase, two sections are defined: Input and Storage (Refer to Stage 1). The Input
section entails a variety of inputs (IXi) derived from different information resources, including
quantitative data such as wave speed and rainfall. These inputs are further classified as positive
(+) or negative (-) based on the type of information they represent (See Expression 1).
Xi = ƒ (+/-IX1 , +/-IX2 , …, +/-IX∞…) ≡ IXi = ƒ (+/-IXi) where i =1,2,…,∞ (1)
In the Storage of the Mega-Database section, the information inputs (IXi) are recorded in
distinct databases (DBXi) corresponding to i = 1,2,… ∞ (See Expression 2).
DBXi = ƒ (DBX1<+/-IX1>, DBX2<+/-IX2>,…, DBx∞<+/-IX∞>…) (2)
Second Phase: Visualization of SRERFT-System
This phase involves real-time visualization of the SRERFT-System (Refer to Stage 2). It is
based on continuous inputs of information (Ixi) along each respective axis (Xi) from various
Mega-Databases (DBXi) sources. The interconnected relationship between each input of
information (Ixi) and its corresponding axis (Xi) ensures that all multi-dimensional graphs
within the SRERFT-System are continually updated in real-time (See Expression 3).
MD = X1: [+/-Ix1], X2:[+/-Ix2] , …, X∞:[+/-Ix∞]… (3)
Third Phase: Alert of Possible SRERFT-System Failures
In this phase, the system provides alerts for potential failures (Refer to Stage 3). The alerts are
contingent on the position of the multi-dimensional graph within its physical coordinate
system. If the information falls within the negative quadrant, denoted as -Xi = [-IXi], the
SRERFT-System issues possible allocations (See Expression 4 and 5).
If W1: [-IX1], -W2:[-IX2] , …….…, -W∞:[-IX∞]… (4)
then W1: [»Allocation«], -W2:[»Allocation«] , …, -W∞:[»Allocation«] (5)
Inputs in the negative quadrant, denoted as -Wi:[-IXi], are labeled as "Allocations." When the
SRERFT-System identifies an allocation (Ai), it initiates a search (☼) to determine its potential
position within the wave speed (Wi) domain, as outlined in Expression 6.
Ai = -IX1 ☼ A1 : -IX2 ☼ A2 : … : -IX∞ ☼ A∞ (6)
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Fourth Phase: Set of Directions of SRERFT-System
This phase includes two sections related to the Mega-Database of final Directions (Refer to
Stage 4). The Mega-Database of Final Directions is an amalgamation (╦) of numerous
databases (DBXi), each containing a range of possible wave directions and speeds (Wi) (See
Expression 7).
DMi = A1:[ Σ DBX1 <W1>] ╦ A2:[ Σ DBx2 <W2>] ╦ …╦ A∞ [ Σ DBX∞ <W∞>] (7)
Final Phase: Final Output of the SRERFT-System
In this phase, the final output or aileron position by parts (PO) is determined (Refer to Phase
5). This is derived from the last partial differentiation (ƒi) of the extensive list of wave
directions (Sxi). The objective is to refine the strategy for optimum finding survivors and
victims’ (See Expression 9).
ƒ(A1) = A1 ◊ A2 ◊… ◊ A∞
ƒ (A2)’ = A1 ◊ A2 ◊… ◊ A∞
ƒ (A3)’’ = A1◊ A2 ◊… ◊ A∞
ƒ(Ai)i = 0 thus i = 1,2…∞ (9)
3. The Water Drone Propulsion System MAR777 Testing Results
In video 3, we can observe how the water drone propulsion system MAR777 operates in the
water. It demonstrates versatility and a robust structure, enabling navigation in various
conditions and at any time. The MAR777 showcases remarkable durability and structural
integrity.
Video 3: The Water Drone Propulsion System MAR777 Testing
Source: Author
28
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In video 4, we get a clear view from the water drone propulsion system MAR777. We utilize a
GoPro Camera to ensure high-quality video and ample storage capacity for recording in
scenarios such as rain floods or tsunamis. The GoPro camera can be seamlessly connected to
any device, allowing for real-time monitoring of post-rain or tsunami events.
Video 4: The Water Drone Propulsion System MAR777 Testing (See Level Camera)
Source: Author
Finally in video 5, the camera system of the water drone propulsion system MAR777 provides
real-time underwater imagery. The GoPro Camera enables us to effortlessly capture captivating
images and sounds with remarkable clarity at depths of up to 10 meters underwater.
Video 5: The Water Drone Propulsion Ssystem MAR777 Testing (Under Water Camera)
Source: Author
29
Full Copyright under Mantarraya Negra UAV © 2024
2.3. Concluding Remarks
In conclusion, this research underscores the transformative potential of the WATER DRONE
PROPULSION SYSTEM MAR777 in the realm of military maritime. Its innovative design,
incorporating features like the Multiple Sensors System (MSS), Silent Propeller System (SPS),
Sensibility Waves System (SWS) driven by artificial intelligence (AI), and a versatile payload
system for large waves, marks a significant leap forward in UMV technology. The versatility
of the WATER DRONE PROPULSION SYSTEM MAR777 renders it invaluable for a wide
array of military and national emergency missions, showcasing its boundless utility and
potential impact on the industry.
Reference
Anderson, M. R. (2019). Unmanned Maritime Vehicles and the Evolution of Maritime
Warfare. International Journal of Security Studies, 8(1), 45-59.
Brown, R. D. (2020). The Role of UAVs in Counterterrorism Operations. Journal of Military
Strategy, 35(2), 56-73.
Evans, A. J. (2018). Drone Swarming in Military Operations: Challenges and Solutions.
Journal of Modern Warfare Tactics, 42(4), 213-230.
Johnson, L. M. (2018). Advancements in Drone Technology for Military Reconnaissance.
Aerospace Engineering Research, 11(4), 237-251.
Lee, S. Y. (2020). The Impact of UAV Technology on Military Intelligence. Defense and
Security Studies, 15(2), 123-138.
Martinez, P. S. (2017). Ethical Considerations of Lethal Autonomous Weapons Systems.
Military Ethics Quarterly, 22(1), 89-104.
Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality and Quantity, 51(5):2115-213.
Smith, E. P. (2017). Legal Frameworks for the Use of Military Drones. Harvard International
Law Journal, 30(3), 127-142.
Smith, J. A. (2019). Unmanned Aerial Vehicles in Modern Warfare. Military Technology
Journal, 45(3), 123-138.
Taylor, R. L. (2019). UAVs in Naval Warfare: Current Applications and Future Prospects.
Naval Technology Review, 25(2), 65-80.
Williams, D. H. (2021). The Future of Autonomous Military Drones: Challenges and
Opportunities. Journal of Defense Technology, 16(3), 187-202.
30
Full Copyright under Mantarraya Negra UAV © 2024
Chapter III
Water Drone Propulsion System MAR37
By
Mario Arturo Ruiz Estrada
3.1. Introduction
The new prototype, Water Drone Propulsion System MAR37 (refer to Figures 1, 2, 3, and 4),
represents a significant leap in maritime technology (Evans, 2018), integrating robotics (Lee,
2020) with advanced artificial intelligence (Martinez, 2017). This chapter aims to highlight its
distinct features and specifications, showcasing its exceptional capabilities. The Water Drone
Propulsion System MAR37underwent rigorous evaluations, testing its speed and rotation
resistance across various levels (Johnson, 2018). These assessments confirm its potential as a
groundbreaking maritime transportation system, offering superior and stability compared to
conventional propeller and aileron systems.
The precision of the Water Drone Propulsion System MAR37 (see Video 1) stems from its
unique features and applications, as detailed in this technical report. Firstly, we advocate for
the implementation of the "Multiple Sensors System (MSS)" (see Figure 1). Our proposal
involves situating all ailerons within the main body structure of the Water Drone Propulsion
System MAR37. Simultaneously, the Unmanned Maritime Vehicle (UMV) introduces an
innovative propeller design known as the "Silent Propeller System (SPS)" (see Figure 5).
Housed within the main structure, the Water Drone Propulsion System MAR37 boasts 2 robust
motors, complemented by a series of specialized propellers working in precise synchronization,
resulting in a remarkable 99.05% reduction in noise during the water navigation.
Furthermore, a novel concept of "Sensibility Waves System (SWS)" employs artificial
intelligence to enhance operational efficiency (see Figure 6). The Water Drone Propulsion
System MAR37 is equipped to carry suppliers, guns, and munitions to everywhere, further
extending its versatility (see Figure 2 and Video 1).
31
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Fig.1: Water Drone Propulsion System MAR37
Source: author
32
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Fig. 2: Water Drone Propulsion System MAR37
Source: author
33
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Fig. 3: Water Drone Propulsion System MAR37
Source: author
34
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Fig. 4: Water Drone Propulsion System MAR37
Source: author
35
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 5: Water Drone Propulsion System MAR37
Source: author
36
Full Copyright under Mantarraya Negra UAV © 2024
Video 1: Water Drone Propulsion System MAR37
Source: Author
The strategic placement of propellers and ailerons within the Water Drone Propulsion System
MAR37 main structure has been executed with remarkable precision, as depicted in Figure 3.
This meticulous placement serves the dual purpose of optimizing propulsion and enhancing
water stability within short timeframes. Consequently, the synergy between the positioning of
sticks and the noise-reduction propellers is carefully orchestrated to ensure superior
performance. This harmonization has been validated through a series of experiments conducted
across diverse environments and under varying weather conditions (Smith, 2017 and 2019).
The Water Drone Propulsion System MAR37 is primarily distinguished by its aileron and
engine system configuration. Its foremost advantage lies in its near-silent operation while
maintaining the capability to sustain statable floating in any weather. This unique feature
allows for precise cargo or munition drops, affording unparalleled precision and versatility
under any circumstances (Taylor, 2019) (Williams, 2021). A second differentiating factor is
the adaptability of the Water Drone Propulsion System MAR37, which can seamlessly
transition between rigid and flexible states. This adaptability is facilitated by advanced sensor
technology and artificial intelligence (AI) systems, ensuring optimal wing positioning and
flexibility. These distinct features position the Water Drone Propulsion System MAR37 as a
water breaking innovation within the maritime industry.
Furthermore, the Water Drone Propulsion System MAR37 operates autonomously, obviating
the need for a human pilot. Its intricate control is managed entirely by AI tools, with human
oversight from a ground-based pilot. The antenna system relies on satellite technology,
guaranteeing superior reception and control. Therefore, this research project aims to introduce
a revolutionary paradigm shift in both the design and functionality of this technology. The
Water Drone Propulsion System MAR37 represents a pioneering advancement that promises
to redefine the capabilities and applications of UMVs.
37
Full Copyright under Mantarraya Negra UAV © 2024
3.2. The Sensibility Waves System (SWS) Algorithm Theoretical Framework
The Sensibility Waves System (SWS) follows five fundamental phases:
First Phase: Input and Storage in the SWS
In this initial phase, two sections are defined: Input and Storage (Refer to Stage 1). The Input
section entails a variety of inputs (IXi) derived from different information resources, including
quantitative data such as wave speed and rainfall. These inputs are further classified as positive
(+) or negative (-) based on the type of information they represent (See Expression 1).
Xi = ƒ (+/-IX1 , +/-IX2 , …, +/-IX∞…) ≡ IXi = ƒ (+/-IXi) where i =1,2,…,∞ (1)
In the Storage of the Mega-Database section, the information inputs (IXi) are recorded in
distinct databases (DBXi) corresponding to i = 1,2,… ∞ (See Expression 2).
DBXi = ƒ (DBX1<+/-IX1>, DBX2<+/-IX2>,…, DBx∞<+/-IX∞>…) (2)
Second Phase: Visualization of SWS
This phase involves real-time visualization of the SWS (Refer to Stage 2). It is based on
continuous inputs of information (Ixi) along each respective axis (Xi) from various MegaDatabases (DBXi) sources. The interconnected relationship between each input of information
(Ixi) and its corresponding axis (Xi) ensures that all multi-dimensional graphs within the SWS
are continually updated in real-time (See Expression 3).
MD = X1: [+/-Ix1], X2:[+/-Ix2] , …, X∞:[+/-Ix∞]… (3)
Third Phase: Alert of Possible SWS Failures
In this phase, the system provides alerts for potential failures (Refer to Stage 3). The alerts are
contingent on the position of the multi-dimensional graph within its physical coordinate
system. If the information falls within the negative quadrant, denoted as -Xi = [-IXi], the SWS
issues possible allocations (See Expression 4 and 5).
If W1: [-IX1], -W2:[-IX2] , …….…, -W∞:[-IX∞]… (4)
then W1: [»Allocation«], -W2:[»Allocation«] , …, -W∞:[»Allocation«] (5)
Inputs in the negative quadrant, denoted as -Wi:[-IXi], are labeled as "Allocations." When the
SWS identifies an allocation (Ai), it initiates a search (☼) to determine its potential position
within the wave speed (Wi) domain, as outlined in Expression 6.
Ai = -IX1 ☼ A1 : -IX2 ☼ A2 : … : -IX∞ ☼ A∞ (6)
Fourth Phase: Set of Directions of SWS
This phase includes two sections related to the Mega-Database of final Directions (Refer to
Stage 4). The Mega-Database of Final Directions is an amalgamation (╦) of numerous
38
Full Copyright under Mantarraya Negra UAV © 2024
databases (DBXi), each containing a range of possible wave directions and speeds (Wi) (See
Expression 7).
DMi = A1:[ Σ DBX1 <W1>] ╦ A2:[ Σ DBx2 <W2>] ╦ …╦ A∞ [ Σ DBX∞ <W∞>] (7)
Final Phase: Final Output of the SWS
In this phase, the final output or aileron position by parts (PO) is determined (Refer to Phase
5). This is derived from the last partial differentiation (ƒi) of the extensive list of wave
directions (Sxi). The objective is to refine the strategy for optimum stability, minimizing risk
and vulnerability (See Expression 9).
ƒ(A1) = A1 ◊ A2 ◊… ◊ A∞
ƒ (A2)’ = A1 ◊ A2 ◊… ◊ A∞
ƒ (A3)’’ = A1◊ A2 ◊… ◊ A∞
ƒ(Ai)i = 0 thus i = 1,2…∞ (9)
3.3. Concluding Remarks
In conclusion, this research underscores the transformative potential of the Water Drone
Propulsion System MAR37 in the realm of military maritime. Its innovative design,
incorporating features like the Multiple Sensors System (MSS), Silent Propeller System (SPS),
Sensibility Waves System (SWS) driven by artificial intelligence (AI), and a versatile payload
system for large waves, marks a significant leap forward in UMV technology. The versatility
of the Water Drone Propulsion System MAR37 renders it invaluable for a wide array of
military and national emergency missions, showcasing its boundless utility and potential
impact on the industry.
Reference
Anderson, M. R. (2019). Unmanned Maritime Vehicles and the Evolution of Maritime
Warfare. International Journal of Security Studies, 8(1), 45-59.
Brown, R. D. (2020). The Role of UAVs in Counterterrorism Operations. Journal of Military
Strategy, 35(2), 56-73.
Evans, A. J. (2018). Drone Swarming in Military Operations: Challenges and Solutions.
Journal of Modern Warfare Tactics, 42(4), 213-230.
Johnson, L. M. (2018). Advancements in Drone Technology for Military Reconnaissance.
Aerospace Engineering Research, 11(4), 237-251.
Lee, S. Y. (2020). The Impact of UAV Technology on Military Intelligence. Defense and
Security Studies, 15(2), 123-138.
Martinez, P. S. (2017). Ethical Considerations of Lethal Autonomous Weapons Systems.
Military Ethics Quarterly, 22(1), 89-104.
39
Full Copyright under Mantarraya Negra UAV © 2024
Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality and Quantity, 51(5):2115-213.
Smith, E. P. (2017). Legal Frameworks for the Use of Military Drones. Harvard International
Law Journal, 30(3), 127-142.
Smith, J. A. (2019). Unmanned Aerial Vehicles in Modern Warfare. Military Technology
Journal, 45(3), 123-138.
Taylor, R. L. (2019). UAVs in Naval Warfare: Current Applications and Future Prospects.
Naval Technology Review, 25(2), 65-80.
Williams, D. H. (2021). The Future of Autonomous Military Drones: Challenges and
Opportunities. Journal of Defense Technology, 16(3), 187-202.
40
Full Copyright under Mantarraya Negra UAV © 2024
Chapter IV
Water Drone Propulsion System MAR25
By
Mario Arturo Ruiz Estrada
4.1. Introduction
The new prototype, WATER DRONE PROPULSION SYSTEM MAR25 (refer to Figures 1
and 2), represents a significant leap in maritime technology (Evans, 2018), integrating robotics
(Lee, 2020) with advanced artificial intelligence (Martinez, 2017). This chapter aims to
highlight its distinct features and specifications, showcasing its exceptional capabilities. The
WATER DRONE PROPULSION SYSTEM MAR25 underwent rigorous evaluations, testing
its speed and rotation resistance across various levels (Johnson, 2018). These assessments
confirm its potential as a groundbreaking maritime transportation system, offering superior and
stability compared to conventional propeller and aileron systems.
The precision of the WATER DRONE PROPULSION SYSTEM MAR25 (see Video 1) stems
from its unique features and applications, as detailed in this technical report. Firstly, we
advocate for the implementation of the "Multiple Sensors System (MSS)" (see Figure 1). Our
proposal involves situating all ailerons within the main body structure of the WATER DRONE
PROPULSION SYSTEM MAR25. Simultaneously, the Unmanned Maritime Vehicle (UMV)
introduces an innovative propeller design known as the "Silent Propeller System (SPS)" (see
Figure 5). Housed within the main structure, the WATER DRONE PROPULSION SYSTEM
MAR25 boasts a robust motor, complemented by a series of specialized propellers working in
precise synchronization, resulting in a remarkable 99.05% reduction in noise during the water
navigation. Furthermore, a novel concept of "Sensibility Waves System (SWS)" employs
artificial intelligence to enhance operational efficiency (see Figure 6). The WATER DRONE
PROPULSION SYSTEM MAR25is equipped to carry suppliers, guns, and munitions to
everywhere, further extending its versatility (see Figure 2 and Video 1).
41
Full Copyright under Mantarraya Negra UAV © 2024
Fig.1: WATER DRONE PROPULSION SYSTEM MAR25
Source: author
42
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 2: WATER DRONE PROPULSION SYSTEM MAR25
Source: author
43
Full Copyright under Mantarraya Negra UAV © 2024
Video 1: WATER DRONE PROPULSION SYSTEM MAR25
Source: Author
The strategic placement of propellers and ailerons within the WATER DRONE PROPULSION
SYSTEM MAR25 main structure has been executed with remarkable precision, as depicted in
Figure 2. This meticulous placement serves the dual purpose of optimizing propulsion and
enhancing water stability within short timeframes. Consequently, the synergy between the
positioning of sticks and the noise-reduction propellers is carefully orchestrated to ensure
superior performance. This harmonization has been validated through a series of experiments
conducted across diverse environments and under varying weather conditions (Smith, 2017
and 2019).
The WATER DRONE PROPULSION SYSTEM MAR25 is primarily distinguished by its
aileron and engine system configuration. Its foremost advantage lies in its near-silent operation
while maintaining the capability to sustain static flight for up to seven seconds. This unique
feature allows for precise cargo or munition drops, affording unparalleled precision and
versatility under any circumstances (Taylor, 2019) (Williams, 2021). A second differentiating
factor is the adaptability of the WATER DRONE PROPULSION SYSTEM MAR25, which
can seamlessly transition between rigid and flexible states. This adaptability is facilitated by
advanced sensor technology and artificial intelligence (AI) systems, ensuring optimal wing
positioning and flexibility. These distinct features position the WATER DRONE
PROPULSION SYSTEM MAR25 as a water breaking innovation within the maritime
industry.
Furthermore, the WATER DRONE PROPULSION SYSTEM MAR25operates autonomously,
obviating the need for a human pilot. Its intricate control is managed entirely by AI tools, with
human oversight from a ground-based pilot. The antenna system relies on satellite technology,
guaranteeing superior reception and control. Therefore, this research project aims to introduce
a revolutionary paradigm shift in both the design and functionality of this technology. The
44
Full Copyright under Mantarraya Negra UAV © 2024
WATER DRONE PROPULSION SYSTEM MAR25 represents a pioneering advancement
that promises to redefine the capabilities and applications of UMVs.
4.2. The Sensibility Waves System (SWS) Algorithm Theoretical Framework
The Sensibility Waves System (SWS) follows five fundamental phases:
First Phase: Input and Storage in the SWS
In this initial phase, two sections are defined: Input and Storage (Refer to Stage 1). The Input
section entails a variety of inputs (IXi) derived from different information resources, including
quantitative data such as wave speed and rainfall. These inputs are further classified as positive
(+) or negative (-) based on the type of information they represent (See Expression 1).
Xi = ƒ (+/-IX1 , +/-IX2 , …, +/-IX∞…) ≡ IXi = ƒ (+/-IXi) where i =1,2,…,∞ (1)
In the Storage of the Mega-Database section, the information inputs (IXi) are recorded in
distinct databases (DBXi) corresponding to i = 1,2,… ∞ (See Expression 2).
DBXi = ƒ (DBX1<+/-IX1>, DBX2<+/-IX2>,…, DBx∞<+/-IX∞>…) (2)
Second Phase: Visualization of SWS
This phase involves real-time visualization of the SWS (Refer to Stage 2). It is based on
continuous inputs of information (Ixi) along each respective axis (Xi) from various MegaDatabases (DBXi) sources. The interconnected relationship between each input of information
(Ixi) and its corresponding axis (Xi) ensures that all multi-dimensional graphs within the SWS
are continually updated in real-time (See Expression 3).
MD = X1: [+/-Ix1], X2:[+/-Ix2] , …, X∞:[+/-Ix∞]… (3)
Third Phase: Alert of Possible SWS Failures
In this phase, the system provides alerts for potential failures (Refer to Stage 3). The alerts are
contingent on the position of the multi-dimensional graph within its physical coordinate
system. If the information falls within the negative quadrant, denoted as -Xi = [-IXi], the SWS
issues possible allocations (See Expression 4 and 5).
If W1: [-IX1], -W2:[-IX2] , …….…, -W∞:[-IX∞]… (4)
then W1: [»Allocation«], -W2:[»Allocation«] , …, -W∞:[»Allocation«] (5)
Inputs in the negative quadrant, denoted as -Wi:[-IXi], are labeled as "Allocations." When the
SWS identifies an allocation (Ai), it initiates a search (☼) to determine its potential position
within the wave speed (Wi) domain, as outlined in Expression 6.
Ai = -IX1 ☼ A1 : -IX2 ☼ A2 : … : -IX∞ ☼ A∞ (6)
45
Full Copyright under Mantarraya Negra UAV © 2024
Fourth Phase: Set of Directions of SWS
This phase includes two sections related to the Mega-Database of final Directions (Refer to
Stage 4). The Mega-Database of Final Directions is an amalgamation (╦) of numerous
databases (DBXi), each containing a range of possible wave directions and speeds (Wi) (See
Expression 7).
DMi = A1:[ Σ DBX1 <W1>] ╦ A2:[ Σ DBx2 <W2>] ╦ …╦ A∞ [ Σ DBX∞ <W∞>] (7)
Final Phase: Final Output of the SWS
In this phase, the final output or aileron position by parts (PO) is determined (Refer to Phase
5). This is derived from the last partial differentiation (ƒi) of the extensive list of wave
directions (Sxi). The objective is to refine the strategy for optimum stability, minimizing risk
and vulnerability (See Expression 9).
ƒ(A1) = A1 ◊ A2 ◊… ◊ A∞
ƒ (A2)’ = A1 ◊ A2 ◊… ◊ A∞
ƒ (A3)’’ = A1◊ A2 ◊… ◊ A∞
ƒ(Ai)i = 0 thus i = 1,2…∞ (9)
4.3. Concluding Remarks
In conclusion, this research underscores the transformative potential of the WATER DRONE
PROPULSION SYSTEM MAR25 in the realm of military maritime. Its innovative design,
incorporating features like the Multiple Sensors System (MSS), Silent Propeller System (SPS),
Sensibility Waves System (SWS) driven by artificial intelligence (AI), and a versatile payload
system for large waves, marks a significant leap forward in UMV technology. The versatility
of the WATER DRONE PROPULSION SYSTEM MAR25 renders it invaluable for a wide
array of military and national emergency missions, showcasing its boundless utility and
potential impact on the industry.
Reference
Anderson, M. R. (2019). Unmanned Maritime Vehicles and the Evolution of Maritime
Warfare. International Journal of Security Studies, 8(1), 45-59.
Brown, R. D. (2020). The Role of UAVs in Counterterrorism Operations. Journal of Military
Strategy, 35(2), 56-73.
Evans, A. J. (2018). Drone Swarming in Military Operations: Challenges and Solutions.
Journal of Modern Warfare Tactics, 42(4), 213-230.
Johnson, L. M. (2018). Advancements in Drone Technology for Military Reconnaissance.
Aerospace Engineering Research, 11(4), 237-251.
Lee, S. Y. (2020). The Impact of UAV Technology on Military Intelligence. Defense and
Security Studies, 15(2), 123-138.
46
Full Copyright under Mantarraya Negra UAV © 2024
Martinez, P. S. (2017). Ethical Considerations of Lethal Autonomous Weapons Systems.
Military Ethics Quarterly, 22(1), 89-104.
Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality and Quantity, 51(5):2115-213.
Smith, E. P. (2017). Legal Frameworks for the Use of Military Drones. Harvard International
Law Journal, 30(3), 127-142.
Smith, J. A. (2019). Unmanned Aerial Vehicles in Modern Warfare. Military Technology
Journal, 45(3), 123-138.
Taylor, R. L. (2019). UAVs in Naval Warfare: Current Applications and Future Prospects.
Naval Technology Review, 25(2), 65-80.
Williams, D. H. (2021). The Future of Autonomous Military Drones: Challenges and
Opportunities. Journal of Defense Technology, 16(3), 187-202.
47
Full Copyright under Mantarraya Negra UAV © 2024
Chapter V
Water Drone Propulsion System MAR31
By
Mario Arturo Ruiz Estrada
5.1. Introduction
The new prototype, Water Drone Propulsion System MAR31 (refer to Figures 1, 2, 3, and 4),
represents a significant leap in maritime technology (Evans, 2018), integrating robotics (Lee,
2020) with advanced artificial intelligence (Martinez, 2017). This chapter aims to highlight its
distinct features and specifications, showcasing its exceptional capabilities. The Water Drone
Propulsion System MAR31underwent rigorous evaluations, testing its speed and rotation
resistance across various levels (Johnson, 2018). These assessments confirm its potential as a
groundbreaking maritime transportation system, offering superior and stability compared to
conventional propeller and aileron systems.
The precision of the Water Drone Propulsion System MAR31 (see Video 1) stems from its
unique features and applications, as detailed in this technical report. Firstly, we advocate for
the implementation of the "Multiple Sensors System (MSS)" (see Figure 1). Our proposal
involves situating all ailerons within the main body structure of the Water Drone Propulsion
System MAR31. Simultaneously, the Unmanned Maritime Vehicle (UMV) introduces an
innovative propeller design known as the "Silent Propeller System (SPS)" (see Figure 5).
Housed within the main structure, the Water Drone Propulsion System MAR31 boasts 3 robust
motors, complemented by a series of specialized propellers working in precise synchronization,
resulting in a remarkable 99.05% reduction in noise during the water navigation.
Furthermore, a novel concept of "Sensibility Waves System (SWS)" employs artificial
intelligence to enhance operational efficiency (see Figure 6). The Water Drone Propulsion
System MAR31 is equipped to carry suppliers, guns, and munitions to everywhere, further
extending its versatility (see Figure 2 and Video 1).
48
Full Copyright under Mantarraya Negra UAV © 2024
Fig.1: Water Drone Propulsion System MAR31
Source: author
49
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 2: Water Drone Propulsion System MAR31
Source: author
50
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 3: Water Drone Propulsion System MAR31
Source: author
51
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 4: Water Drone Propulsion System MAR31
Source: author
52
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 5: Water Drone Propulsion System MAR31
Source: author
53
Full Copyright under Mantarraya Negra UAV © 2024
Video 1: Water Drone Propulsion System MAR31
Source: Author
Video 2: Water Drone Propulsion System MAR31
Source: Author
54
Full Copyright under Mantarraya Negra UAV © 2024
The strategic placement of propellers and ailerons within the Water Drone Propulsion System
MAR31 main structure has been executed with remarkable precision, as depicted in Figure 3.
This meticulous placement serves the dual purpose of optimizing propulsion and enhancing
water stability within short timeframes. Consequently, the synergy between the positioning of
sticks and the noise-reduction propellers is carefully orchestrated to ensure superior
performance. This harmonization has been validated through a series of experiments conducted
across diverse environments and under varying weather conditions (Smith, 2017 and 2019).
The Water Drone Propulsion System MAR31 is primarily distinguished by its aileron and
engine system configuration. Its foremost advantage lies in its near-silent operation while
maintaining the capability to sustain statable floating in any weather. This unique feature
allows for precise cargo or munition drops, affording unparalleled precision and versatility
under any circumstances (Taylor, 2019) (Williams, 2021). A second differentiating factor is
the adaptability of the Water Drone Propulsion System MAR31, which can seamlessly
transition between rigid and flexible states. This adaptability is facilitated by advanced sensor
technology and artificial intelligence (AI) systems, ensuring optimal wing positioning and
flexibility. These distinct features position the Water Drone Propulsion System MAR31 as a
water breaking innovation within the maritime industry.
Furthermore, the Water Drone Propulsion System MAR31 operates autonomously, obviating
the need for a human pilot. Its intricate control is managed entirely by AI tools, with human
oversight from a ground-based pilot. The antenna system relies on satellite technology,
guaranteeing superior reception and control. Therefore, this research project aims to introduce
a revolutionary paradigm shift in both the design and functionality of this technology. The
Water Drone Propulsion System MAR31 represents a pioneering advancement that promises
to redefine the capabilities and applications of UMVs.
5.2. The Sensibility Waves System (SWS) Algorithm Theoretical Framework
The Sensibility Waves System (SWS) follows five fundamental phases:
First Phase: Input and Storage in the SWS
In this initial phase, two sections are defined: Input and Storage (Refer to Stage 1). The Input
section entails a variety of inputs (IXi) derived from different information resources, including
quantitative data such as wave speed and rainfall. These inputs are further classified as positive
(+) or negative (-) based on the type of information they represent (See Expression 1).
Xi = ƒ (+/-IX1 , +/-IX2 , …, +/-IX∞…) ≡ IXi = ƒ (+/-IXi) where i =1,2,…,∞ (1)
In the Storage of the Mega-Database section, the information inputs (IXi) are recorded in
distinct databases (DBXi) corresponding to i = 1,2,… ∞ (See Expression 2).
DBXi = ƒ (DBX1<+/-IX1>, DBX2<+/-IX2>,…, DBx∞<+/-IX∞>…) (2)
Second Phase: Visualization of SWS
This phase involves real-time visualization of the SWS (Refer to Stage 2). It is based on
continuous inputs of information (Ixi) along each respective axis (Xi) from various MegaDatabases (DBXi) sources. The interconnected relationship between each input of information
55
Full Copyright under Mantarraya Negra UAV © 2024
(Ixi) and its corresponding axis (Xi) ensures that all multi-dimensional graphs within the SWS
are continually updated in real-time (See Expression 3).
MD = X1: [+/-Ix1], X2:[+/-Ix2] , …, X∞:[+/-Ix∞]… (3)
Third Phase: Alert of Possible SWS Failures
In this phase, the system provides alerts for potential failures (Refer to Stage 3). The alerts are
contingent on the position of the multi-dimensional graph within its physical coordinate
system. If the information falls within the negative quadrant, denoted as -Xi = [-IXi], the SWS
issues possible allocations (See Expression 4 and 5).
If W1: [-IX1], -W2:[-IX2] , …….…, -W∞:[-IX∞]… (4)
then W1: [»Allocation«], -W2:[»Allocation«] , …, -W∞:[»Allocation«] (5)
Inputs in the negative quadrant, denoted as -Wi:[-IXi], are labeled as "Allocations." When the
SWS identifies an allocation (Ai), it initiates a search (☼) to determine its potential position
within the wave speed (Wi) domain, as outlined in Expression 6.
Ai = -IX1 ☼ A1 : -IX2 ☼ A2 : … : -IX∞ ☼ A∞ (6)
Fourth Phase: Set of Directions of SWS
This phase includes two sections related to the Mega-Database of final Directions (Refer to
Stage 4). The Mega-Database of Final Directions is an amalgamation (╦) of numerous
databases (DBXi), each containing a range of possible wave directions and speeds (Wi) (See
Expression 7).
DMi = A1:[ Σ DBX1 <W1>] ╦ A2:[ Σ DBx2 <W2>] ╦ …╦ A∞ [ Σ DBX∞ <W∞>] (7)
Final Phase: Final Output of the SWS
In this phase, the final output or aileron position by parts (PO) is determined (Refer to Phase
5). This is derived from the last partial differentiation (ƒi) of the extensive list of wave
directions (Sxi). The objective is to refine the strategy for optimum stability, minimizing risk
and vulnerability (See Expression 9).
ƒ(A1) = A1 ◊ A2 ◊… ◊ A∞
ƒ (A2)’ = A1 ◊ A2 ◊… ◊ A∞
ƒ (A3)’’ = A1◊ A2 ◊… ◊ A∞
ƒ(Ai)i = 0 thus i = 1,2…∞ (9)
5.3. Concluding Remarks
In conclusion, this research underscores the transformative potential of the Water Drone
Propulsion System MAR31 in the realm of military maritime. Its innovative design,
incorporating features like the Multiple Sensors System (MSS), Silent Propeller System (SPS),
56
Full Copyright under Mantarraya Negra UAV © 2024
Sensibility Waves System (SWS) driven by artificial intelligence (AI), and a versatile payload
system for large waves, marks a significant leap forward in UMV technology. The versatility
of the Water Drone Propulsion System MAR31 renders it invaluable for a wide array of
military and national emergency missions, showcasing its boundless utility and potential
impact on the industry.
Reference
Anderson, M. R. (2019). Unmanned Maritime Vehicles and the Evolution of Maritime
Warfare. International Journal of Security Studies, 8(1), 45-59.
Brown, R. D. (2020). The Role of UAVs in Counterterrorism Operations. Journal of Military
Strategy, 35(2), 56-73.
Evans, A. J. (2018). Drone Swarming in Military Operations: Challenges and Solutions.
Journal of Modern Warfare Tactics, 42(4), 213-230.
Johnson, L. M. (2018). Advancements in Drone Technology for Military Reconnaissance.
Aerospace Engineering Research, 11(4), 237-251.
Lee, S. Y. (2020). The Impact of UAV Technology on Military Intelligence. Defense and
Security Studies, 15(2), 123-138.
Martinez, P. S. (2017). Ethical Considerations of Lethal Autonomous Weapons Systems.
Military Ethics Quarterly, 22(1), 89-104.
Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality and Quantity, 51(5):2115-213.
Smith, E. P. (2017). Legal Frameworks for the Use of Military Drones. Harvard International
Law Journal, 30(3), 127-142.
Smith, J. A. (2019). Unmanned Aerial Vehicles in Modern Warfare. Military Technology
Journal, 45(3), 123-138.
Taylor, R. L. (2019). UAVs in Naval Warfare: Current Applications and Future Prospects.
Naval Technology Review, 25(2), 65-80.
Williams, D. H. (2021). The Future of Autonomous Military Drones: Challenges and
Opportunities. Journal of Defense Technology, 16(3), 187-202.
57
Full Copyright under Mantarraya Negra UAV © 2024
Chapter VI
The Drone Bombardier:
Black Nightmare V.7
By
Mario Arturo Ruiz Estrada
6.1. Introduction
The new prototype, Black Nightmare V.7 drone bombardier (refer to Figures 1 and 4),
represents a significant leap in aviation technology (Evans, 2018), integrating robotics (Lee,
2020) with advanced artificial intelligence (Martinez, 2017). This chapter aims to highlight its
distinct features and specifications, showcasing its exceptional capabilities (refer to Figures 2
and 3). The Black Nightmare V.7 drone bombardier underwent rigorous evaluations, testing its
speed and rotation resistance across various levels (Johnson, 2018). These assessments confirm
its potential as a groundbreaking air transportation system, offering superior propulsion and
stability compared to conventional propeller and aileron systems.
The precision of the Black Nightmare V.7 drone bombardier (see Video 1) stems from its
unique features and applications, as detailed in this technical report. Firstly, we advocate for
the implementation of the "Multiple Ailerons System (MAS)" and Multi-Missiles System
(MM-System) (see Figure 3 and 4). Our proposal involves situating all ailerons within the main
body structure of the Black Nightmare V.7 drone bombardier. Simultaneously, the aircraft
introduces an innovative propeller design known as the "Silent Propeller System (SPS)" (see
Figure 5). Housed within the main structure, the Black Nightmare V.7 drone bombardier boasts
a robust motor, complemented by a series of specialized propellers working in precise
synchronization, resulting in a remarkable 99.05% reduction in noise during departure, flight,
and landing.
Furthermore, a novel concept of "Sensibility Winds System (SWS)" employs artificial
intelligence to enhance operational efficiency (see Figure 5). The Black Nightmare V.7 drone
bombardier is equipped to carry four booms or three missiles for air-air attacks, further
extending its versatility (see Figure 5 and 7 and Video 2 and 3). On top of the Black Nightmare
V.7 drone bombardier, there is a missile designed to intercept and attack any aircraft or object
that attempts to approach from above (see Figure 8).
58
Full Copyright under Mantarraya Negra UAV © 2024
Fig.1: Black Nightmare V.7 drone bombardier
Source: author
59
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 2: Black Nightmare V.7 drone bombardier
Source: author
60
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 3: Black Nightmare V.7 drone bombardier
Source: author
61
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 4: Black Nightmare V.7 drone bombardier
Source: author
62
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 5: Black Nightmare V.7 drone bombardier
Source: author
63
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 6: Black Nightmare V.7 drone bombardier
Source: author
64
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 7: Black Nightmare V.7 drone bombardier
Source: author
65
Full Copyright under Mantarraya Negra UAV © 2024
Fig. 8: Black Nightmare V.7 drone bombardier
Source: author
66
Full Copyright under Mantarraya Negra UAV © 2024
Video 1: Black Nightmare V.7 drone bombardier
Source: Author
Video 2: Black Nightmare V.7 drone bombardier
67
Full Copyright under Mantarraya Negra UAV © 2024
Source: Author
Video 3: Black Nightmare V.7 drone bombardier
Source: Author
The strategic placement of propellers and ailerons within the Black Nightmare V.7 Black
Nightmare V.7 main structure has been executed with remarkable precision, as depicted in
Figure 5. This meticulous placement serves the dual purpose of optimizing propulsion and
enhancing aerial stability within short timeframes. Consequently, the synergy between the
positioning of ailerons and the noise-reduction propellers is carefully orchestrated to ensure
superior performance. This harmonization has been validated through a series of experiments
conducted across diverse environments and under varying weather conditions (Smith, 2017
and 2019).
The Black Nightmare V.7is primarily distinguished by its aileron and engine system
configuration. Its foremost advantage lies in its near-silent operation while maintaining the
capability to sustain static flight for up to seven seconds. This unique feature allows for precise
cargo or munition drops, affording unparalleled precision and versatility under any
circumstances (Taylor, 2019) (Williams, 2021). A second differentiating factor is the
adaptability of the Black Nightmare V.7 wings, which can seamlessly transition between rigid
and flexible states. This adaptability is facilitated by advanced sensor technology and artificial
intelligence (AI) systems, ensuring optimal wing positioning and flexibility. These distinct
features position the Black Nightmare V.7 as a groundbreaking innovation within the aviation
industry.
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Furthermore, the Black Nightmare V.7 operates autonomously, obviating the need for a human
pilot. Its intricate control is managed entirely by AI tools, with human oversight from a groundbased pilot. The antenna system relies on satellite technology, guaranteeing superior reception
and control. Therefore, this research project aims to introduce a revolutionary paradigm shift
in both the design and functionality of this technology. The Black Nightmare V.7 represents a
pioneering advancement that promises to redefine the capabilities and applications of UAVs.
6.2. The Sensibility Winds System (SWS) Algorithm Theoretical Framework
The Sensibility Winds System (SWS) follows five fundamental phases:
First Phase: Input and Storage in the SWS
In this initial phase, two sections are defined: Input and Storage (Refer to Stage 1). The Input
section entails a variety of inputs (IXi) derived from different information resources, including
quantitative data such as wind speed and rainfall. These inputs are further classified as positive
(+) or negative (-) based on the type of information they represent (See Expression 1).
Xi = ƒ (+/-IX1 , +/-IX2 , …, +/-IX∞…) ≡ IXi = ƒ (+/-IXi) where i =1,2,…,∞ (1)
In the Storage of the Mega-Database section, the information inputs (IXi) are recorded in
distinct databases (DBXi) corresponding to i = 1,2,… ∞ (See Expression 2).
DBXi = ƒ (DBX1<+/-IX1>, DBX2<+/-IX2>,…, DBx∞<+/-IX∞>…) (2)
Second Phase: Visualization of SWS
This phase involves real-time visualization of the SWS (Refer to Stage 2). It is based on
continuous inputs of information (Ixi) along each respective axis (Xi) from various MegaDatabases (DBXi) sources. The interconnected relationship between each input of information
(Ixi) and its corresponding axis (Xi) ensures that all Multi-Dimensional graphs within the SWS
are continually updated in real-time (See Expression 3).
MD = X1: [+/-Ix1], X2:[+/-Ix2] , …, X∞:[+/-Ix∞]… (3)
Third Phase: Alert of Possible SWS Failures
In this phase, the system provides alerts for potential failures (Refer to Stage 3). The alerts are
contingent on the position of the Multi-Dimensional graph within its physical coordinate
system. If the information falls within the negative quadrant, denoted as -Xi = [-IXi], the SWS
issues possible allocations (See Expression 4 and 5).
If W1: [-IX1], -W2:[-IX2] , …….…, -W∞:[-IX∞]… (4)
then W1: [»Allocation«], -W2:[»Allocation«] , …, -W∞:[»Allocation«] (5)
Inputs in the negative quadrant, denoted as -Wi:[-IXi], are labeled as "Allocations." When the
SWS identifies an allocation (Ai), it initiates a search (☼) to determine its potential position
within the wind speed (Wi) domain, as outlined in Expression 6.
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Ai = -IX1 ☼ A1 : -IX2 ☼ A2 : … : -IX∞ ☼ A∞ (6)
Fourth Phase: Set of Directions of SWS
This phase includes two sections related to the Mega-Database of final Directions (Refer to
Stage 4). The Mega-Database of Final Directions is an amalgamation (╦) of numerous
databases (DBXi), each containing a range of possible wind directions and speeds (Wi) (See
Expression 7).
DMi = A1:[ Σ DBX1 <W1>] ╦ A2:[ Σ DBx2 <W2>] ╦ …╦ A∞ [ Σ DBX∞ <W∞>] (7)
Final Phase: Final Output of the SWS
In this phase, the final output or aileron position by parts (PO) is determined (Refer to Phase
5). This is derived from the last partial differentiation (ƒi) of the extensive list of wind
directions (Sxi). The objective is to refine the strategy for optimum stability, minimizing risk
and vulnerability (See Expression 9).
ƒ(A1) = A1 ◊ A2 ◊… ◊ A∞
ƒ (A2)’ = A1 ◊ A2 ◊… ◊ A∞
ƒ (A3)’’ = A1◊ A2 ◊… ◊ A∞
ƒ(Ai)i = 0 thus i = 1,2…∞ (9)
6.3. Concluding Remarks
In conclusion, this research underscores the transformative potential of the Black Nightmare
V.7 in the realm of military aviation. Its innovative design, incorporating features like the
Multiple Ailerons System (MAS), Silent Propeller System (SPS), Sensibility Winds System
(BSWS) driven by artificial intelligence (AI), solar panels for efficient energy supply, and a
versatile payload system for air-to-ground and air-to-sea assaults, marks a significant leap
forward in UAV technology. The versatility of the Black Nightmare V.7 renders it invaluable
for a wide array of military and national emergency missions, showcasing its boundless utility
and potential impact on the industry.
Reference
Anderson, M. R. (2019). Unmanned Aerial Vehicles and the Evolution of Air Warfare.
International Journal of Security Studies, 8(1), 45-59.
Brown, R. D. (2020). The Role of UAVs in Counterterrorism Operations. Journal of Military
Strategy, 35(2), 56-73.
Evans, A. J. (2018). Drone Swarming in Military Operations: Challenges and Solutions.
Journal of Modern Warfare Tactics, 42(4), 213-230.
Johnson, L. M. (2018). Advancements in Drone Technology for Military Reconnaissance.
Aerospace Engineering Research, 11(4), 237-251.
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Lee, S. Y. (2020). The Impact of UAV Technology on Military Intelligence. Defense and
Security Studies, 15(2), 123-138.
Martinez, P. S. (2017). Ethical Considerations of Lethal Autonomous Weapons Systems.
Military Ethics Quarterly, 22(1), 89-104.
Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality and Quantity, 51(5):2115-213.
Smith, E. P. (2017). Legal Frameworks for the Use of Military Drones. Harvard International
Law Journal, 30(3), 127-142.
Smith, J. A. (2019). Unmanned Aerial Vehicles in Modern Warfare. Military Technology
Journal, 45(3), 123-138.
Taylor, R. L. (2019). UAVs in Naval Warfare: Current Applications and Future Prospects.
Naval Technology Review, 25(2), 65-80.
Williams, D. H. (2021). The Future of Autonomous Military Drones: Challenges and
Opportunities. Journal of Defense Technology, 16(3), 187-202.
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Chapter VII
Hydronescarrier
By
Mario Arturo Ruiz Estrada
7.1. Aquatic Drones: Overview
The main idea behind the creation of the Hydronescarrier was to enable faster attacks on distant
targets in the sea. The primary objective is to defend or attack targets in the sea or lakes with
greater precision and efficiency. The structure of any Hydronescarrier is based on the principles
of battleship construction, featuring a strong, balanced platform and different levels between
the main structure and equipment. Thus, the fundamental design of the Hydronescarrier is a
naval fortress capable of carrying Hydrones, supplies (water, food, and medicines), and heavy
guns. Hydronescarrier play a crucial role in geopolitical and national security, particularly in
managing national maritime borders. The aquatic smart platform, as essential weapons in wars,
can have a significant impact on military technological advancements in the navy of any
country. We can say that the intensive use of aquatic smart platform in war helps reduce human
and material losses in the sea military missions.
The initial stages of Hydronescarrier development began in 2018 when we build small and
simple prototypes, we did it from small flouting platforms until the construction of complex
structures. After many years of testing and experimentation, we built the first Hydronescarrier
in year 2024. In the case of Hydronescarrier, we can reference the use of aquatic smart platform
in the military field. These aquatic smart platforms can be used extensively for reconnaissance
and defense. However, the weaknesses of aquatic smart platform included their vulnerability
to attacks in critical weather conditions and stability originated by heavy rains, large waves,
and strong winds in the open sea.
Finally, this research chapter presents a new concept of Hydronescarrier using aquatic smart
platforms. The chapter is divided into three major sections: Section-1: Aquatic Drones:
Overview; Section-2: An Introduction to the; Section-3: The MAR107X, MAR107Y, and
MAR107Z aquatic smart platform respectively. Each section discusses its fully autonomous
artificial intelligence systems for attack or defense decision-making, collectively known as the
NeuronDrone-Box and the Mega-NeuronDrone-Box respectively.
7.2. An Introduction to the Hydronescarrier
This basic manual introduces the first aquatic drone’s carrier prototype, called Hydronescarrier,
as part of the National Sea Platforms Defense System (NSPD-System). This new concept of
military sea defense is based on a large platform (65 feet wide, 100 feet long, and 15 feet high).
On top of it, every place is precisely marked to allocate aquatic smart platforms, radars,
antennas, missiles, guns, fans, batteries, and chargers to supply energy in different parts of the
Hydronescarrier (See the Gallery of 8 photos below and 1 video). On the top part, we have one
full floor with his five large rotative disks tracks in the Hydronescarrier with fifty-five aquatic
smart platforms strategically located according to different missions: Track-1: the quick strike
reconnaissance and rescue (8 aquatic smart platforms MAR107X); Track-2: Military
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equipment and basic suppliers’ delivery (16 aquatic smart platforms under the uses of
MAR107Y); Track-3: defense or attack system (31 aquatic smart platforms MAR107Z)
distributed in kamikaze aquatic smart platforms and light artillery aquatic smart platforms.
Each rotative disks tracks in the same floor of the Hydronescarrier is moving fast to departure
or docking a large number of aquatic smart platforms according to different mission(s). At the
same time, the same floor has four elevators to accommodate and parking four aquatic smart
platforms, at the same time, another four elevators for departures of aquatic smart platforms
respectively. Under each rotative disks track is a system of lane with mini-wagons to secure
each aquatic smart platforms during arriving or departure from the Hydronescarrier.
Additionally, we have fifty-five aquatic smart platforms split in the same floor with his rotative
disks track for immediate action, ten in the aquatic smart platforms parking area, eight elevators
who bring the aquatic smart platforms to a small compartment before departure and docking
from the Hydronescarrier, and eight elevators can carry sixteen aquatic smart platforms
simultaneously in each elevator to deliver the aquatic smart platforms fast and efficiently for
action. All these aquatic smart platforms are using artificial intelligence. They are equipped
with light munitions, missiles, and powerful bombs. (See the Gallery of 8 photos and 1video)
below. Additionally, we have many main gates access for each floor can use it electricity or
manually in case of emergencies to replace any in action during emergencies. For a better
understanding of our new set of aquatic smart platforms, more details are provided in fourth
section of this manual. Each aquatic smart platforms have its own NeuronDrone-Box. This
allows for autonomous actions in defense or attack, based on signals from powerful radars and
antennas (located in the corners of the Hydronescarrier) and satellite images.
The main structure of the Hydronescarrier is made from light yet strong special materials.
Inside the structure are two mega-batteries located in the middle, charged constantly by solar
energy. These batteries supply power to large motors, aquatic smart platforms chargers (which
automatically charge aquatic smart platforms in their parking spots), lights, computer systems,
fans, radars, and the main brain of the Hydronescarrier. The main brain operates under the
integration of seventy aquatic smart platforms under the uses of the Mega-NeuronDroneBoxes. This system is referred to as the Mega-NeuronDrone-Box. The Mega-NeuronDroneBox programming is presented in this manual both mathematically and graphically. The MegaNeuronDrone-Box controls the internal systems (auto-pilots, propeller power, radars,
instructions, Hydronescarrier positioning, and energy levels) and external systems (the set of
aquatic smart platforms).
The Hydronescarrier has two large fans to supply cold air and maintain a consistent temperature
throughout its structure. Additionally, the two large motors and propellers behind ensure a
constant speed to keep this mega-structure stable in the sea, maintaining perfect balance at any
weather condition, carrying seventy aquatic smart platforms. We remind you that all aquatic
smart platforms are managed by the Full Autonomous Artificial Intelligence in Attack or
Defense Decision-Making system in Military Drones.
The groundbreaking Hydronescarrier prototype boasts a range of distinctive military features
and applications, which are detailed in this report. Firstly, we advocate for the implementation
of this new drone sea carrier. This involves strategically placing all equipment and aquatic
smart platforms within the main body structure of the Hydronescarrier. Additionally, the
Hydronescarrier features an innovative design as part of the National Sea Platforms Defense
System (NSPD-System). The Hydronescarrier carries a total of five missile systems: two longrange missiles (transatlantic), one medium-range missile (intercontinental), and one hundred
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short-range missiles (perimeter of five kilometers) stored inside the main structure of the
Hydronescarrier. These missiles are controlled by artificial intelligence, which conducts a
preliminary and exhaustive evaluation to ensure their optimal use anytime and anywhere.
In a bid for sustainability, the Hydronescarrier is equipped with solar panels, ensuring a
continuous charge to support its two powerful motors, drone chargers, equipment, and
computer systems simultaneously. Notably, the Hydronescarrier is capable of carrying seventy
aquatic smart platforms, heavy munitions, bombs, and five missiles. Finally, its versatile
capabilities render the Hydronescarrier indispensable for a wide array of military and national
emergency missions (See the Gallery of 8 photos below and 1 video).
7.3. Mega-NeuronDrone-Box: Algorithm
Initially, the Mega-NeuronDrone-Box proposes a new graphical modeling to visualize a large
amount of data in the same graphical space (Ruiz Estrada, 2017). Firstly, the MegaNeuronDrone-Box shows five types of systems: (a.) Nano-systems (j); (b.) Micro-systems (k);
(c.) Sub-systems (L); (d.) General-systems (m); (e.) Mega-system (ξ) (see Video 2). The first
type of systems in the Mega-NeuronDrone-Box is called the Nano-systems. The Nano-systems
(j) are represented graphically by a very small circle that keep one single vertical straight axis
that is pending among all nano-endogenous variables (αi<R:ϛ>). Each nano-endogenous variable
keeps its specific sub-coordinate system that is based on two points of reference. First is the
origin position line (R) that is distributed in a perimeter of 360° degrees or 2π at the Nanosystem (j). Hence, each degree (or angle) represents the origin position line of each nanoendogenous variable in the same Nano-system (j). We always need to plot the first nanoendogenous variable at the origin position line zero (R0) in the degree 0° then we need to plot
the next nano-endogenous variable in the next degree level. Accordingly, it is depended on the
number of nano-endogenous variables in analysis in the same Nano- system (j) (see Expression
2). On the other hand, the second point of reference in the sub-coordinate system in the Nanosystem (j) is the disk level “ϛ” that is the number of systems inside of the Nano-system (j). In
our case, “ϛ” always is represented by a positive real number between 0 to ∞…This implies
that exist “∞” number of small disks within the Nano-system (j). In fact, we are able to plotting
each nano-endogenous variable according to expression 1 on the top of the platform of each
Nano-system (j) respectively.
(αi<R:ϛ>)
(1)
In the case of the origin position line (R) is necessary first to calculate the origin position line
distance rate (ΔR).
ΔR = 360°/n
(2)
Where “n” represents the number of nano-endogenous variables in analysis in this specific
Nano-system (j). Therefore, the origin position line next position (Rt+1) starts from degree 0°
plus ΔR until (R) arrives to 360° degrees.
(Rt+1) = R0(0° + ΔR)+ … R∞ (360°)
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Thus, the (Rt+1) is the space or degrees that exist between each nano-endogenous variable in
the Nano-system (j). In this case, we always need to assume that (Rt+1) keeps a closed interval
according to expression 4.
[0° ≥ (Rt+1) ≤ 360°]
(4)
Subsequently, the calculation of the nano-exogenous variable in the same Nano-system (j) is
based on the nano-arithmetic mean (αj). Initially, the αj is equal to the total sum of all nanoendogenous variables (∑αi<R:ϛ>) divided by “i” in each Nano-system (j) according to
Expression 5.
∞
αj = ∑ αi <R,ϛ>
i=1
(5)
i
Nevertheless, we like to remind that “i” is the total number of nano-endogenous variables in
analysis in the same Nano-system (j). From a graphical view, the Nano-system (j) requests that
the αj and all nano-endogenous variables (αi<R:ϛ>) need to be joined by straight lines until yields
an asymmetric spiral-shaped geometric figure with n-faces (See Video 2). Meanwhile, any
change of any nano-endogenous variable in the same Nano-system (j) can generate
immediately a high impact on the nano-exogenous variable in our case we are referring to the
nano-arithmetic mean “αj”. Hence, we can visualize graphically how a large number of nanoendogenous variables are moving all the time in different spaces within the Nano-system (j).
At the same time, it is possible to observe how all these nano-endogenous variables can affect
directly in the nano-exogenous variable behavior continuously. The second type of disk in the
Mega-NeuronDrone-Box is the Micro-system (k). The Micro-system (k) is built by a large
number of Nano-systems (j= 1,2,3,…∞). The main indicator in the Micro-system (k) is based
on the calculation of the micro-arithmetic mean (Ӎk). The Ӎk measurement is based on the
total sum of all nano-arithmetic mean (αj) in the same Micro- system (k) level divided by the
total number of Nano-system (j) in the same Micro-system (k) according to expression 6.
∞
Ӎk = ∑ αj
j=1
(6)
J
The next step is to build the Micro-system (k) graphical representation that requests the uses
of Ӎk and all αj need to be joined by straight lines until yields an asymmetric spiral-shaped
geometric figure. Moreover, we need to find the Sub-system (L) main indicator that is based
on the sub-arithmetic mean (δL). The sub-arithmetic mean (δL) is equal to the total sum of all
micro-arithmetic mean (Ӎk) in the same Sub-systems (L) divided by the total number of Microsystems (k) in the same Sub-system (L) according to expression 7.
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∞
∑ Ӎk
δL =
k=1
(7)
k
The fourth type of disk in the Mega-NeuronDrone-Box is the General-systems (m) that is based
on the combination of all Sub-systems (L). The General-systems (m) main indicator is explained
in expression 8. In fact, the general-arithmetic mean (Ξm) is equal to the total sum of all subarithmetic mean (δL) divided by the total of Sub-systems (L) in the same General-systems.
Ξm =
∞
∑ δL
L=1
(8)
L
Notwithstanding, the Mega-system (ξ) is formed by a large number of General-systems (m). In
the Mega-system, it is possible to observe the final arithmetic mean in the Mega-NeuronDroneBox that is called the mega-arithmetic mean. The mega-arithmetic mean can be affected
anytime from the most remote nano-arithmetic mean (αj) that is located in some far Nanosystem (j), Micro-system (k), Sub-system (L), and General-system (m) respectively. The megaarithmetic mean is expressed in Expression 9.
ξ=
∞
∑ Ξm
m=1
(9)
m
Nonetheless, the “m” is equal to the total number of General-systems (m) in the Mega-system
(ξ). The idea to propose this multidimensional coordinate system is to generate a deep analysis
of networks that can be monitored and analyzed within the same graphical space (Ruiz Estrada,
2017). The Mega-NeuronDrone-Box keeps always five types of arithmetic mean: (i) nanoarithmetic mean (αj); (ii) micro-arithmetic mean (Ӎk); (iii) sub-arithmetic mean (δL); (iv)
general-arithmetic mean (Ξm); (v) mega-arithmetic mean (ξ). Hence, the mega-arithmetic mean
(ξ) is a single value that is plotted on its single vertical straight axis that is pending among all
General-systems (m) (Video 2). Finally, the Mega-NeuronDrone-Box introduces a formal and
general coordinate system that can locate any endogenous or exogenous variable (point) in any
General-system (m); Sub-systems (L); Micro-systems (k); Nano-systems (j); origin position line
(R); systems level (ϛ) within the Mega-system (ξ) see expression 10.
(m, L, k, j, R, ϛ)
(10)
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Fig. 1. Hydronescarrier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 2. Hydronescarrier (Hydrones Position)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig.3. Hydronescarrier (Hydrones Access)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig.4. Hydronescarrier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 5. Hydronescarrier (Missile System)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig.6. Hydronescarrier (Missile System)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 7. Hydronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig.8. Hydronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Video 1. Hydronescarrier
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Video 2. Mega-NeuronDrone-Box
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
7.4. Introduction to MAR107X aquatic smart platform, MAR107Y aquatic smart
platform, and MAR107Z aquatic smart platform
This section of the technical report presents MAR107X aquatic smart platform, MAR107Y
aquatic smart platform, and MAR107Z aquatic smart platform. The MAR107X aquatic smart
platform stands out due to its unique features and applications, as detailed in this technical
report. Firstly, we advocate for the implementation of the "Multiple Sensors System (MSS)."
The MAR107X aquatic smart platform boasts two robust motors, complemented by a series of
specialized GoPro cameras underwater and at sea level, working in precise synchronization.
Consequently, the synergy between the structure's positioning and the sensors is carefully
orchestrated to ensure superior performance. This harmonization has been validated through a
series of experiments conducted across diverse environments and under varying weather
conditions (see Video 3). The MAR107X aquatic smart platform is primarily distinguished by
its structure and monitoring system configuration. Its foremost advantage lies in its weapons
system, which includes kamikaze bombs and light munition guns for fast attacks under any
circumstances. A second differentiating factor is the adaptability of the MAR107X aquatic
smart platform, which can seamlessly transition between rigid (ice) and soft states (water). This
adaptability is facilitated by advanced sensor technology and artificial intelligence (AI)
systems, using fully autonomous AI for attack or defense decision-making in the military
drones' system box (The NeuronDrone-Box), ensuring optimal positioning and flexibility.
These distinct features position the MAR107X aquatic smart platform as a groundbreaking
innovation within the maritime military industry.
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Video 3: MAR107X Aquatic Smart Platform
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
The MAR107Y Aquatic Smart Platform (see Video 4) is characterized by its three rotors and
strong plastic structure. The main advantage of the MAR107Y is its capability to navigate
anywhere and anytime. However, its disadvantage lies in its limited range due to battery power
and ESC resistance. Additionally, the limitations of the battery systems cause difficulties,
particularly delays during departure and arrival in distant areas. The MAR107Y also uses highresolution GoPro cameras. The MAR107Y is large enough to carry military cargo supplies. We
propose that the MAR107Y can serve as a new military aquatic smart platform, capable of
delivering necessary provisions such as water, food, medicine, guns, munitions, lights, and
communication systems (radios) to soldiers. In essence, the MAR107Y offers the following
features: (1) a large number of storage compartments; (2) efficient and fast charging systems;
and (3) fully autonomous artificial intelligence for attack or defense decision-making in the
military drones’ system box (The NeuronDrone-Box).
Video 4: MAR107Y Aquatic Smart Platform
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
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In this section, we present the MAR107Z Aquatic Smart Platform (see Video 5) designed for
surveillance and spying missions related to national security. This platform utilizes fully
autonomous artificial intelligence for attack or defense decision-making within the military
drones’ system box (The NeuronDrone-Box). The MAR107Z features a compact structure with
two powerful electric engines, enabling it to navigate longer distances for various assigned
missions. The MAR107Z is a small but highly capable smart hydrone, powered by two large
electric engines and engineered with sophisticated software and hardware systems. These
systems provide immense navigation and operational capabilities through fully autonomous AI
for attack or defense decision-making in the NeuronDrone-Box. The MAR107Z includes a
compact structure, long-distance navigation capabilities, autonomous GPS systems, HD GoPro
cameras, efficient battery systems, and impressive endurance.
Video 5: MAR107Z Aquatic Smart Platform
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
7.5. Full Autonomous Artificial Intelligence in Attack or Defense Decisions Making in
Military Drones Box: The NeuronDrone-Box
The first section presents the full autonomous artificial intelligence in attack or defence
decisions making in military drones’ system box (The NeuronDrone-Box) (See Figure 9) to
adapt to any drone to the main control system of any drone (3 different control systems sources
such as the Cubepilot Ecosystem, VERONTE Autopilots, and Wolf-Advanced-Technology
centralized in a single box using backups systems among all these three control systems
according to different environments and circumstances.
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Fig. 9. The Full Autonomous Artificial Intelligence in Attack or Defense Decisions
Making in Military Drones’ System Box (The NeuronDrone-Box)
Source: Author
The Cube Orange from Cubepilot Ecosystem is (See Video 6) shows a high flexibility of its
uses and adaptation in any drone, we are using as the first control system in the full autonomous
artificial intelligence in attack or defense decisions making in military drones’ system box (The
NeuronDrone-Box).
Video 6: The Cube Orange
Source: The Cubepilot Ecosystem (2024)
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The second control system in the full autonomous artificial intelligence in attack or defense
decisions making in military drones’ system box (The NeuronDrone-Box) is the George UAV
Autopilot from VERONTE Autopilot (VERONTE, 2024) (See Video 7).
Video 7: The George UAV Autopilot
Source: VERONTE (2024)
The third control system in the full autonomous artificial intelligence in attack or defense
decisions making in military drones’ system box (The NeuronDrone-Box) is the VPX3UA4500E-VO (Wolf-1448) from Wolf Advanced Technology (Wolf Advanced Technology),
2024) give a full support to the full autonomous artificial intelligence in attack or defense
decisions making in military drones’ system box (The NeuronDrone-Box) (see Video 8).
Video 8: VPX3U-A4500E-VO (WOLF-1448)
Source: (Wolf Advanced Technology, 2024)
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Finally, the full autonomous artificial intelligence in attack or defense decisions making in
military drones’ system box (The NeuronDrone-Box) tries to allocate three control systems to
generate more precision in any attack or defense systems with a high precision.
7.6. The Attacks or Defense Decision System (ADD-System) Algorithm: Theoretical
Framework
The Attacks & Defense Making Decisions System (A&DMD-System) follows five
fundamental phases: First Phase: Input and Storage in the possible attacks and Defense plots
In this initial phase, two sections are defined: Input and Storage (Refer to Stage 1). The Input
section entails a variety of inputs (IXi) derived from different information resources (espionage
information), including quantitative data such as weak targets and high population density
places. These inputs are further classified as positive (+) or negative (-) based on the type of
information they represent (See Expression 1).
Xi = ƒ (+/-IX1, +/-IX2, …, +/-IX∞…) ≡ IXi = ƒ (+/-IXi) where i =1,2,…,∞ (1)
In the Storage of the Mega-Database section, the information inputs (IXi) are recorded in
distinct databases (DBXi) corresponding to i = 1,2,… ∞ (See Expression 2).
DBXi = ƒ (DBX1<+/-IX1>, DBX2<+/-IX2>,…, DBx∞<+/-IX∞>…) (2)
Second Phase: Visualization of possible targets
This phase involves real-time visualization of the possible attacks and timing enemies can
arrive it (Refer to Stage 2). It is based on continuous inputs of information (Ixi) along each
respective axis (Xi) from various Mega-Databases (DBXi) sources. The interconnected
relationship between each input of information (Ixi) and its corresponding axis (Xi) ensures
that all multi-dimensional graphs within the possible targets are continually updated in realtime (See Expression 3).
MD = X1: [+/-Ix1], X2:[+/-Ix2] , …, X∞:[+/-Ix∞]… (3)
Third Phase: Alert of Possible Attacks and Defense Failures
In this phase, the system provides alerts for potential failures (Refer to Stage 3). The alerts are
contingent on the position of the multi-dimensional graph within its physical coordinate
system. If the information falls within the negative quadrant, denoted as -Xi = [-IXi], the SWS
issues possible allocations (See Expression 4 and 5).
If W1: [-IX1], -W2:[-IX2] , …….…, -W∞:[-IX∞]… (4)
then W1: [»Possible Attack«], -W2:[»Possible Attack«] , …, -W∞:[»Possible Attack«] (5)
Inputs in the negative quadrant, denoted as -Wi:[-IXi], are labeled as "Weak Locations." When
the targets and defense identify an allocation (Ai), it initiates a search (☼) to determine its
potential position within the attack speed (Wi) domain, as outlined in Expression 6.
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Ai = -IX1 ☼ A1 : -IX2 ☼ A2 : … : -IX∞ ☼ A∞ (6)
Fourth Phase: Set of Directions of attacks and defense
This phase includes two sections related to the Mega-Database of final Directions (Refer to
Stage 4). The Mega-Database of Final Directions is an amalgamation (╦) of numerous
databases (DBXi), each containing a range of possible attacks and defense points directions
and speeds (Wi) (See Expression 7).
DMi = A1:[ Σ DBX1 <W1>] ╦ A2:[ Σ DBx2 <W2>] ╦ …╦ A∞ [ Σ DBX∞ <W∞>] (7)
Final Phase: Final Output of the targets and Defense
In this phase, the final output or attack and defense position by places (PO) is determined (Refer
to Phase 5). This is derived from the last partial differentiation (ƒi) of the extensive list of attack
and defense directions (Sxi). The objective is to refine the strategy for optimum stability,
minimizing risk and vulnerability of attack. (See Expression 9).
ƒ(A1) = A1 ◊ A2 ◊… ◊ A∞
ƒ (A2)’ = A1 ◊ A2 ◊… ◊ A∞
ƒ (A3)’’ = A1◊ A2 ◊… ◊ A∞
ƒ(Ai)i = 0 thus i = 1,2…∞ (9)
Initially, the mega-data disks coordinate space (Ruiz Estrada, 2017) is crucial part of the
Attacks or Defense Decision System (ADD-System) Algorithm. We have a powerful an
analytical graphical modeling to visualize and analyze a large amount of data. Firstly, this
specific coordinate space shows one single vertical straight axis that is pending among all
endogenous variables (the final decision of attack or defense: Shotting). Hence, we are
available to plotting our endogenous variable on this single vertical straight axis that is
represented by αV+/- (Different factors are taking in consideration to attack or defend: Shotting).
Secondly, each exogenous variable in analysis is represented by its specific coordinate system
to attack or defend: Shotting such as βΦi:ζj. Where “Φi” represents the sub-space level in
analysis, in this case either from sub-space level zero (SS0°) to sub-space level infinite (SS360°);
“ζj” represents the disk level in analysis at the same quadrant of exogenous variables (in our
case, from disk level j=1, disk level j=2, disk level j=3,…, to disk level j=∞…). In fact, we
assume that all exogenous variables are using only real positive numbers -rational factors of to
decide or not shotting- (R+). In order to plot different exogenous variables in the mega-data
disks coordinate space, each value need to be plotted directly on its radial subspace in analysis
(Φi) and disk level in analysis (ζj) respectively. Each “i” is a radius that emanates from the
origin and in defined by the angle which can range from 0 to just before 360°, a theoretical
infinite range of shotting. Each disk is a concentric circle that starts from the origin outwards
towards a theoretical infinite value. At the same time, all these values plotted in different axis
levels in analysis (Φi) and disk levels in analysis (ζj) need to be joined with its endogenous
variable “αV+/-” until we build a series of coordinates to attack or defend (Shotting). All these
coordinates need to be joined by straight lines until yields an asymmetric spiral-shaped
geometrical figure with n-faces (see Figure 10) and disk levels in analysis (ζj) need to be joined
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together by straight lines directly to the endogenous variable αV+/- (final target to shotting) until
a cone-shaped figure with n-faces is built. It is important to mention at this juncture that the
endogenous variables “αV+/-” is fixed according to any change associated with its
corresponding exogenous variables (precision levels) in βΦi:ζj, where i = {0°, 1°, 2°,…,360°}
and j = {0, 1, 2,…,∞…} , αV+/-. Hence, we can imagine a large number of exogenous variables
moving all the time in different positions within its radius in real time continuously (decision
of attack and defense (shotting). At the same time, we can visualize how all these exogenous
variables directly affect on the behavior the endogenous variable (αV+/-) (Final Target to
shotting) simultaneously. αV+/- is fixed according to any change can be occurred among the
infinite exogenous variables in βΦi:ζj, where i = {0°, 1°, 2°,…,360°} and j = {0, 1, 2,…,∞…},
YV+/-. Hence, we can imagine a large number of infinite exogenous variables moving all the
time in different positions within its radius in real time continuously. At the same time, we can
visualize how all these exogenous variables (rational or irrational decisions evaluation) are
affecting directly on the behavior of the endogenous variable (αV+/-) (final decision to attack or
defense: Shooting) simultaneously. Moreover, the endogenous variable (αV+/-) can fluctuate
freely (see Figure 10). In our case, the endogenous variables (αV+/-) can show positive/negative
final decisions of attack or according to our multidimensional coordinate space. In the case of
exogenous variables, these can only experience non-negative properties. The mega-data disks
multivariable random coordinate space in vertical position is represented by:
(βΦi:ζj, αV+/-) where βΦi:ζj ≥ 0; i = θ° ; j =R+ ≥ 0; αV+/-= R+/αV+/- = ƒ (βΦi:ζj)
(10)
(11)
Fig. 10: The NeuronDrone-Box
Source: Ruiz Estrada (2017)
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Hence, this algorithm apply a specific trigonometry function such as the tangent
/tan(βφi:Ψj/αh+/-)/ or /tan(βΦi:ζj/αv+/-)/ and inverse tangent tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj
/αv+/-)-1. Initially, the calculation of the tangent /tan(βφi:Ψj/αh+/-)/ or /tan(βΦi:ζj/αh+/-)/ is
equal to β (adjacent) divided by α (opposite). Our graphical modeling applies absolute value to
eliminate negative values in the construction of our new coordinate space (Final attack:
Shooting). The main objective to calculate the inverse tangent tan(βφi:Ψj/αh+/-)-1 or
tan(βΦi:ζj/αv+/-)-1 is to find each angle that is located into the mega-data disks coordinate space
in vertical and horizontal position. Therefore, the tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj/αv+/-)-1can help
us to study easily the relationship between αh+/- or αv+/- (opposite) and βφi:Ψj or βΦi:ζj (adjacent)
in different periods of analysis. In fact, we are establishing three different parameters are
followed by (i) the representative area to attack or defense that keep angles between 50° and
40°; (ii) the acceptable area to attack or defense is located between 65°/51° and 41°/25°; (iii)
non-representative area to attack or defense that is fixed between 65°/90° and 26°/0° (see
Figure 63). We like to mention that each tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj/αv+/-)-1 is located
between 0° and 90° (See Figure 11).
Fig. 11: tan(β/α)-1
Source: Ruiz Estrada (2017)
Finally, all tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj/αv+/-)-1 results are organized in descendent order from
the smallest angle to the largest angle. Finally, we transfer all these results to the mega-data
disks coordinate space in vertical and horizontal position to visualize the behavior of all angles
that help us to appreciate clearly the behavior of multi-data analysis before to attack or defense
any target.
However, MAR107X aquatic smart platform, MAR107Y aquatic smart platform, and
MAR107Z aquatic smart platform distinguished by structures and AI pilot autofocus systems
configuration under the uses of the full autonomous artificial intelligence in attack or defence
decisions making in military drones’ system box (The NeuronDrone-Box). This unique feature
allows for any military mission affording unparalleled precision and versatility under any
circumstances to surveillance and spying missions, military cargo, and military defense system under
the uses of sophisticate guns system (kamikaze -bombs systems and light munition guns for fast
attacks to enemy under any circumstances).
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Furthermore, the MAR107X aquatic smart platform, MAR107Y aquatic smart platform, and
MAR107Z aquatic smart platform operates autonomously, obviating the need for a human
pilot. Its intricate control is managed entirely by the The Full Autonomous Artificial
Intelligence in attack or defence decisions making in military drones’ system box, with human
oversight from a ground-based pilot. The antenna system relies on satellite technology,
guaranteeing superior reception and control. Therefore, this research project aims to introduce
a revolutionary paradigm shift in both the design and functionality of this technology.
The MAR107X aquatic smart platform, MAR107Y aquatic smart platform, and MAR107Z
aquatic smart platform represents a pioneering advancement that promises to redefine the
capabilities and applications of UAVs. According to the full autonomous artificial intelligence
in attack or defence decisions making in military drones’ system box (The NeuronDrone-Box)
(See Video 9), we can observe an experimental case how 1,000,000 variables are running to
build figure 12. The white colour shows logical and rational conditions and in black colour
non-logical and irrational conditions, The final decision of attack or defence can define to
shooting the final target after the exhausted evaluation of 1,000,000 variables (see Video 9),
we are accounting the white colour of our graphs that represent 70% to take action of attack or
defence in different strategic locations immediately. In this case, we printed in 3-D Printer, to
give a better view of our graph physically.
Fig. 12: The Full Autonomous Artificial Intelligence in attack or defence decisions making in
military drones’ System box (The NeuronDrone-Box)
Source: The Author
Video 9. NeuronDrone-Box
Source: The Author
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7.7. Concluding Remarks
In conclusion, this research presents a new military defense system is called the
Hydronescarrier to generate a strong national sea defense integrated system. Its innovative
design, of this special war machine incorporating features like a powerful squadron of
MAR107X aquatic smart platform, MAR107Y aquatic smart platform, and MAR107Z aquatic
smart platform with a critical shotting decision system (CSD-System), solar panels for efficient
energy supply system (SPEES-System), the versatility of the the Hydronescarrier (structure
and equipment) with the Mega-NeuronDrone-Box that control all NeuronDrone-Box of reach
hydrone in board of the Hydronescarrier. At the same time, the NeuronDrone-Box in each
NeuronDrone-Box renders it invaluable for a wide array of military and national security
emergency missions, showcasing its boundless utility and potential impact on the military
industry.
Reference
CubePilot (2024). General Information. Available at:
https://www.unmannedsystemstechnology.com/company/cubepilot/
Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality and Quantity, 51(5):2115-213.
VERONTE (2024). General Information. Available at:
https://www.unmannedsystemstechnology.com/company/uavionix-corporation/
Wolf-Advanced-Technology (2024). General information, available at:
https://www.unmannedsystemstechnology.com/company/wolf-advanced-technology/vpx3ua4500e-vo-wolf-1448/
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Chapter VIII
Mantarraya Negra Dronescarrier
By
Mario Arturo Ruiz Estrada
8.1. Aircraft Carrier: Overview
The main idea behind the creation of the aircraft carrier was to enable faster attacks on distant
targets. The primary objective is to defend or attack targets by air with greater precision and
efficiency. The structure of any aircraft carrier is based on the principles of battleship
construction, featuring a strong, balanced platform and different levels between the main
structure and the airstrip (Model Carrier, 1947). Thus, the fundamental design of the aircraft
carrier is a naval fortress capable of carrying aircraft (fighters and strike aircraft), helicopters,
bombers, gunships, soldiers, supplies, and heavy guns.
Military aircraft carriers play a crucial role in geopolitical and economic control, particularly
in managing trade and transit channels (Horowitz, 2010). Airplanes, as essential weapons in
wars, have a significant impact on military technological advancements in aeronautics
(Hanieski, 1973). Their intensive use in war helps reduce human and material losses in land
military missions (The Science News-Letter, 1924).
The initial stages of aircraft carrier development began in 1910, when private pilot Eugene Ely
took off from a platform on the deck of the U.S. cruiser Birmingham. The following year, he
landed on a platform built on top of the battleship Pennsylvania. After many years of testing,
the U.S. Navy built its first aircraft carrier, the USS Langley, in 1923. The British were the
pioneers in creating aircraft carriers during WWI, with the HMS Argus and Hermes (Scientific
American, 1924). This was later followed by the Americans and Japanese in WWII.
In the case of air carriers, we can reference the use of Zeppelins by the French, German, and
American armies in WWI. These early air heavy cargo carriers were used extensively for
reconnaissance, bombing, military cargo transport, and as aircraft carriers (U.S.S. “Wright” —
Our First Balloon-and-Airplane Carrier, 1922). However, the weaknesses of Zeppelins
included their vulnerability to attacks and volatility.
Finally, this research chapter presents a new concept of military carriers using drones. The
chapter is divided into three major sections: Section-1: Aircraft Carrier: Overview; Section-2:
The Drones Air Carrier: Mantarraya Negra Dronescarrier; Section-3: The Black Nightmare
Drone Bombardier V.7. Each section discusses the fully autonomous artificial intelligence
systems for attack or defense decision-making, collectively known as the NeuronDrone-Box
and the Mega- NeuronDrone-Box respectively.
8.2. The Drones Air Carrier System: Mantarraya Negra Dronescarrier
This basic manual introduces the first drone air carrier prototype, called Mantarraya Negra
Dronescarrier, as part of the National Air Platforms Defense System (NAPD-System). This
new concept of military air defense is based on a flying fixed platform (82 feet wide, 115 feet
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long, and 12 feet high). On top of it, every place is precisely marked to allocate drones, radars,
antennas, fans, batteries, and chargers to supply energy in different parts of the Mantarraya
Negra Dronescarrier (See the Gallery of 26 photos below and 6 videos).
On the top part, we have two landing tracks with four access points, strategically located
according to wind speed and direction, perfectly situated in the middle part of the Mantarraya
Negra Dronescarrier. Each landing track measures 75 feet long and 20 feet wide. Under each
landing track is a system of magnets to secure each drone during landing or departure from the
Mantarraya Negra Dronescarrier.
Additionally, we have eighteen Nightmare Dream Drone Bombardiers (four on the landing
tracks for immediate action, ten in the drone parking area, two for vertical takeoffs (defenders),
and two for replacement in case of failures in any of the ten drones parked next to the landing
track). All these drones are self-repairing using robotics and artificial intelligence. They are
equipped with heavy munitions, missiles, and powerful bombs. Therefore, we have eighteen
drones parked next to the landing tracks (See the Gallery of 26 photos and 6 videos) below.
Additionally, we have two vertical takeoff landing tracks in each corner and two Nightmare
Dream Drone Bombardiers to replace any in action during emergencies. For a better
understanding of our new UAV or drone, more details are provided in section no. 4 of this
manual. Each Nightmare Dream Drone Bombardier has its own independent Full Autonomous
Artificial Intelligence in Attack or Defense Decision-Making system: the NeuronDrone-Box.
This allows for autonomous actions in defense or attack, based on signals from powerful radars
and antennas (located in the corners of the Mantarraya Negra Dronescarrier) and satellite
images.
The main structure of the Mantarraya Negra Dronescarrier is made from light yet strong special
materials. Inside the structure are two mega-batteries located in the middle, charged constantly
by solar energy. These batteries supply power to large motors, drone chargers (which
automatically charge drones in their parking spots), lights, computer systems, fans, radars, and
the main brain of the Mantarraya Negra Dronescarrier. The main brain operates under the
integration of twenty Full Autonomous Artificial Intelligence in Attack or Defense DecisionMaking systems in Military Drones Boxes: The NeuronDrone-Boxes. This system is referred
to as the Mega-NeuronDrone-Box. The integral autonomous artificial system programming is
presented in this manual both mathematically and graphically. The Mega-NeuronDrone-Box
controls the internal systems (auto-pilots, propeller power, radars, instructions, Mantarraya
Negra Dronescarrier positioning, and energy levels) and external systems (the set of drones).
The Mantarraya Negra Dronescarrier has two large fans to supply cold air and maintain a
consistent temperature throughout its structure. Additionally, the two mega-motors and
propellers on the bottom ensure a constant speed to keep this mega-structure stable in the air,
maintaining perfect balance at any altitude up to 40,000 feet, carrying eighteen drones. We
remind you that all drones are managed by the Full Autonomous Artificial Intelligence in
Attack or Defense Decision-Making system in Military Drones (Ruiz Estrada, 2024).
The groundbreaking Mantarraya Negra Dronescarrier prototype boasts a range of distinctive
military features and applications, which are detailed in this report. Firstly, we advocate for the
implementation of this new drone air carrier. This involves strategically placing all equipment
and drones within the main body structure of the Mantarraya Negra Dronescarrier.
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Additionally, the Mantarraya Negra Dronescarrier features an innovative design as part of the
National Air Platforms Defense System (NAPD-System). This system incorporates two
powerful motors within the main structure, accompanied by a series of specialized propellers
that operate in precise synchronization to reduce departure and landing noise levels by an
impressive 99.5%. Furthermore, a cutting-edge concept called the "Sensibility Winds System
(SWS)" is integrated, leveraging artificial intelligence for enhanced performance.
The Mantarraya Negra Dronescarrier carries a total of five missile systems: two long-range
missiles (transatlantic), one medium-range missile (intercontinental), and one hundred shortrange missiles (perimeter of five kilometers) stored inside the main structure of the Mantarraya
Negra Dronescarrier. These missiles are controlled by artificial intelligence, which conducts a
preliminary and exhaustive evaluation to ensure their optimal use anytime and anywhere.
In a bid for sustainability, the Mantarraya Negra Dronescarrier is equipped with solar panels,
ensuring a continuous charge to support its two powerful motors, drone chargers, equipment,
and computer systems simultaneously. Notably, the Mantarraya Negra Dronescarrier is capable
of carrying eighteen drones, heavy munitions, bombs, and three missiles (air-to-air). Finally,
its versatile capabilities render the Mantarraya Negra Dronescarrier indispensable for a wide
array of military and national emergency missions (See the Gallery of 26 photos below and 6
videos).
8.2.1. Mega-NeuronDrone-Box: Algorithm
Initially, the Mega-NeuronDrone-Box proposes a new graphical modeling to visualize a large
amount of data in the same graphical space (Ruiz Estrada, 2017). Firstly, the MegaNeuronDrone-Box shows five types of systems: (a.) Nano-systems (j); (b.) Micro-systems (k);
(c.) Sub-systems (L); (d.) General-systems (m); (e.) Mega-system (ξ) (see Video 7). The first
type of systems in the Mega-NeuronDrone-Box is called the Nano-systems. The Nano-systems
(j) are represented graphically by a very small circle that keep one single vertical straight axis
that is pending among all nano-endogenous variables (αi<R:ϛ>). Each nano-endogenous variable
keeps its specific sub-coordinate system that is based on two points of reference. First is the
origin position line (R) that is distributed in a perimeter of 360° degrees or 2π at the Nanosystem (j). Hence, each degree (or angle) represents the origin position line of each nanoendogenous variable in the same Nano-system (j). We always need to plot the first nanoendogenous variable at the origin position line zero (R0) in the degree 0° then we need to plot
the next nano-endogenous variable in the next degree level. Accordingly, it is depended on the
number of nano-endogenous variables in analysis in the same Nano- system (j) (see Expression
2). On the other hand, the second point of reference in the sub-coordinate system in the Nanosystem (j) is the disk level “ϛ” that is the number of systems inside of the Nano-system (j). In
our case, “ϛ” always is represented by a positive real number between 0 to ∞…This implies
that exist “∞” number of small disks within the Nano-system (j). In fact, we are able to plotting
each nano-endogenous variable according to expression 1 on the top of the platform of each
Nano-system (j) respectively.
(αi<R:ϛ>)
(1)
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In the case of the origin position line (R) is necessary first to calculate the origin position line
distance rate (ΔR).
ΔR = 360°/n
(2)
Where “n” represents the number of nano-endogenous variables in analysis in this specific
Nano-system (j). Therefore, the origin position line next position (Rt+1) starts from degree 0°
plus ΔR until (R) arrives to 360° degrees.
(Rt+1) = R0(0° + ΔR)+ … R∞ (360°)
(3)
Thus, the (Rt+1) is the space or degrees that exist between each nano-endogenous variable in
the Nano-system (j). In this case, we always need to assume that (Rt+1) keeps a closed interval
according to expression 4.
[0° ≥ (Rt+1) ≤ 360°]
(4)
Subsequently, the calculation of the nano-exogenous variable in the same Nano-system (j) is
based on the nano-arithmetic mean (αj). Initially, the αj is equal to the total sum of all nanoendogenous variables (∑αi<R:ϛ>) divided by “i” in each Nano-system (j) according to
Expression 5.
∞
αj = ∑ αi <R,ϛ>
i=1
(5)
i
Nevertheless, we like to remind that “i” is the total number of nano-endogenous variables in
analysis in the same Nano-system (j). From a graphical view, the Nano-system (j) requests that
the αj and all nano-endogenous variables (αi<R:ϛ>) need to be joined by straight lines until yields
an asymmetric spiral-shaped geometric figure with n-faces (See Video 6). Meanwhile, any
change of any nano-endogenous variable in the same Nano-system (j) can generate
immediately a high impact on the nano-exogenous variable in our case we are referring to the
nano-arithmetic mean “αj”. Hence, we can visualize graphically how a large number of nanoendogenous variables are moving all the time in different spaces within the Nano-system (j).
At the same time, it is possible to observe how all these nano-endogenous variables can affect
directly in the nano-exogenous variable behavior continuously. The second type of disk in the
Mega-NeuronDrone-Box is the Micro-system (k). The Micro-system (k) is built by a large
number of Nano-systems (j= 1,2,3,…∞). The main indicator in the Micro-system (k) is based
on the calculation of the micro-arithmetic mean (Ӎk). The Ӎk measurement is based on the
total sum of all nano-arithmetic mean (αj) in the same Micro- system (k) level divided by the
total number of Nano-system (j) in the same Micro-system (k) according to expression 6.
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∞
Ӎk = ∑ αj
j=1
(6)
J
The next step is to build the Micro-system (k) graphical representation that requests the uses
of Ӎk and all αj need to be joined by straight lines until yields an asymmetric spiral-shaped
geometric figure. Moreover, we need to find the Sub-system (L) main indicator that is based
on the sub-arithmetic mean (δL). The sub-arithmetic mean (δL) is equal to the total sum of all
micro-arithmetic mean (Ӎk) in the same Sub-systems (L) divided by the total number of Microsystems (k) in the same Sub-system (L) according to expression 7.
δL =
∞
∑ Ӎk
k=1
(7)
k
The fourth type of disk in the Mega-NeuronDrone-Box is the General-systems (m) that is based
on the combination of all Sub-systems (L). The General-systems (m) main indicator is explained
in expression 8. In fact, the general-arithmetic mean (Ξm) is equal to the total sum of all subarithmetic mean (δL) divided by the total of Sub-systems (L) in the same General-systems.
∞
Ξm = ∑ δL
L=1
(8)
L
Notwithstanding, the Mega-system (ξ) is formed by a large number of General-systems (m). In
the Mega-system, it is possible to observe the final arithmetic mean in the Mega-NeuronDroneBox that is called the mega-arithmetic mean. The mega-arithmetic mean can be affected
anytime from the most remote nano-arithmetic mean (αj) that is located in some far Nanosystem (j), Micro-system (k), Sub-system (L), and General-system (m) respectively. The megaarithmetic mean is expressed in Expression 9.
∞
ξ = ∑ Ξm
m=1
(9)
m
Nonetheless, the “m” is equal to the total number of General-systems (m) in the Mega-system
(ξ). The idea to propose this multidimensional coordinate system is to generate a deep analysis
of networks that can be monitored and analyzed within the same graphical space (Ruiz Estrada,
2017). The Mega-NeuronDrone-Box keeps always five types of arithmetic mean: (i) nano98
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arithmetic mean (αj); (ii) micro-arithmetic mean (Ӎk); (iii) sub-arithmetic mean (δL); (iv)
general-arithmetic mean (Ξm); (v) mega-arithmetic mean (ξ). Hence, the mega-arithmetic mean
(ξ) is a single value that is plotted on its single vertical straight axis that is pending among all
General-systems (m) (see Figure 8 and Prototype 1). Finally, the Mega-NeuronDrone-Box
introduces a formal and general coordinate system that can locate any endogenous or
exogenous variable (point) in any General-system (m); Sub-systems (L); Micro-systems (k);
Nano-systems (j); origin position line (R); systems level (ϛ) within the Mega-system (ξ) see
expression 10.
(m, L, k, j, R, ϛ)
(10)
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Fig. 1. Mantarraya Negra Dronescarrier (bottom)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 2. Mantarraya Negra Dronescarrier (Bottom part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig.3. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig.4. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 5. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig.6. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig.7. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 8. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 9. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 10. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada
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Fig. 11. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 12. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 13. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 14. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 15. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 16. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig.17. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 18. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 19. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 20. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 21. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 22. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 23. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 24. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 25. Mantarraya Negra Dronescarrier (Top part)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
Fig. 26. Mantarraya Negra Dronescarrier (Comparison)
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Video 1. Mantarraya Negra Dronescarrier
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
Video 2. Mantarraya Negra Dronescarrier
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Video 3. Mantarraya Negra Dronescarrier
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
Video 4. Mantarraya Negra Dronescarrier
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Video 5. Mantarraya Negra Dronescarrier Missile System
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
Video 6. Mega-NeuronDrone-Box
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Video 7. Mega-NeuronDrone-Box
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
8.3. Introduction to the Black Nightmare V.7 Drone Bombardier
The new prototype, Black Nightmare V.7 drone bombardier (refer to Figures 27 and 30),
represents a significant leap in aviation technology (Evans, 2018), integrating robotics (Lee,
2020) with advanced artificial intelligence (Martinez, 2017). This chapter aims to highlight its
distinct features and specifications, showcasing its exceptional capabilities (refer to Figures 27
and 28). The Black Nightmare V.7 drone bombardier underwent rigorous evaluations, testing
its speed and rotation resistance across various levels (Johnson, 2018). These assessments
confirm its potential as a groundbreaking air transportation system, offering superior
propulsion and stability compared to conventional propeller and aileron systems.
The precision of the Black Nightmare V.7 drone bombardier (see Video 8) stems from its
unique features and applications, as detailed in this technical report. Firstly, we advocate for
the implementation of the "Multiple Ailerons System (MAS)" and Multi-Missiles System
(MM-System) (see Figure 28 and 29). Our proposal involves situating all ailerons within the
main body structure of the Black Nightmare V.7 drone bombardier. Simultaneously, the aircraft
introduces an innovative propeller design known as the "Silent Propeller System (SPS)" (see
Figure 30). Housed within the main structure, the Black Nightmare V.7 drone bombardier
boasts a robust motor, complemented by a series of specialized propellers working in precise
synchronization, resulting in a remarkable 99.05% reduction in noise during departure, flight,
and landing.
Furthermore, a novel concept of "Sensibility Winds System (SWS)" employs artificial
intelligence to enhance operational efficiency (see Figure 30). The Black Nightmare V.7 drone
bombardier is equipped to carry four booms or three missiles for air-air attacks, further
extending its versatility (see Figure 31 & 32 and Video 9 & 10). On top of the Black Nightmare
V.7 drone bombardier, there is a missile designed to intercept and attack any aircraft or object
that attempts to approach from above (see Figure 33 and 34).
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Fig. 27: Black Nightmare V.7 drone bombardier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 28: Black Nightmare V.7 drone bombardier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 29: Black Nightmare V.7 drone bombardier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 30: Black Nightmare V.7 drone bombardier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 31: Black Nightmare V.7 drone bombardier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 32: Black Nightmare V.7 drone bombardier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 33: Black Nightmare V.7 drone bombardier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Fig. 34: Black Nightmare V.7 drone bombardier
Photo by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Video 8: Black Nightmare V.7 drone bombardier
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
Video 9: Black Nightmare V.7 drone bombardier
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
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Video 10: Black Nightmare V.7 drone bombardier
Video by Dr. Mario Arturo Ruiz Estrada (c) 2024
The strategic placement of propellers and ailerons within the Black Nightmare V.7 Black
Nightmare V.7 main structure has been executed with remarkable precision, as depicted in
Figure 5. This meticulous placement serves the dual purpose of optimizing propulsion and
enhancing aerial stability within short timeframes. Consequently, the synergy between the
positioning of ailerons and the noise-reduction propellers is carefully orchestrated to ensure
superior performance. This harmonization has been validated through a series of experiments
conducted across diverse environments and under varying weather conditions (Smith, 2017
and 2019).
The Black Nightmare V.7is primarily distinguished by its aileron and engine system
configuration. Its foremost advantage lies in its near-silent operation while maintaining the
capability to sustain static flight for up to seven seconds. This unique feature allows for precise
cargo or munition drops, affording unparalleled precision and versatility under any
circumstances (Taylor, 2019) (Williams, 2021). A second differentiating factor is the
adaptability of the Black Nightmare V.7 wings, which can seamlessly transition between rigid
and flexible states. This adaptability is facilitated by advanced sensor technology and artificial
intelligence (AI) systems, ensuring optimal wing positioning and flexibility. These distinct
features position the Black Nightmare V.7 as a groundbreaking innovation within the aviation
industry.
Furthermore, the Black Nightmare V.7 operates autonomously, obviating the need for a human
pilot. Its intricate control is managed entirely by AI tools, with human oversight from a groundbased pilot. The antenna system relies on satellite technology, guaranteeing superior reception
and control. Therefore, this research project aims to introduce a revolutionary paradigm shift
in both the design and functionality of this technology. The Black Nightmare V.7 represents a
pioneering advancement that promises to redefine the capabilities and applications of UAVs.
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8.4. Full Autonomous Artificial Intelligence in Attack or Defense Decisions Making
in Military Drones Box: The NeuronDrone-Box
The first section presents the full autonomous artificial intelligence in attack or defence
decisions making in military drones’ system box (The NeuronDrone-Box) (See Figure 35) to
adapt to any drone to the main control system of any drone (3 different control systems sources
such as the Cubepilot Ecosystem, VERONTE Autopilots, and Wolf-Advanced-Technology
centralized in a single box using backups systems among all these three control systems
according to different environments and circumstances.
Fig. 35. The Full Autonomous Artificial Intelligence in Attack or Defense Decisions
Making in Military Drones’ System Box (The NeuronDrone-Box)
Source: Author
The Cube Orange from Cubepilot Ecosystem is (See Video 11) shows a high flexibility of its
uses and adaptation in any drone, we are using as the first control system in the full autonomous
artificial intelligence in attack or defense decisions making in military drones’ system box (The
NeuronDrone-Box).
Video 11: The Cube Orange
Source: The Cubepilot Ecosystem (2024)
The second control system in the full autonomous artificial intelligence in attack or defense
decisions making in military drones’ system box (The NeuronDrone-Box) is the George UAV
Autopilot from VERONTE Autopilot (VERONTE, 2024) (See Video 12).
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Video 12: The George UAV Autopilot
Source: VERONTE (2024)
The third control system in the full autonomous artificial intelligence in attack or defense
decisions making in military drones’ system box (The NeuronDrone-Box) is the VPX3UA4500E-VO (Wolf-1448) from Wolf Advanced Technology (Wolf Advanced Technology),
2024) give a full support to the full autonomous artificial intelligence in attack or defense
decisions making in military drones’ system box (The NeuronDrone-Box) (see Video 13).
Video 13: VPX3U-A4500E-VO (WOLF-1448)
Source: (Wolf Advanced Technology, 2024)
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Finally, the full autonomous artificial intelligence in attack or defense decisions making in
military drones’ system box (The NeuronDrone-Box) tries to allocate three control systems to
generate more precision in any attack or defense systems with a high precision.
8.5. The Attacks or Defense Decision System (ADD-System) Algorithm: Theoretical
Framework
The Attacks & Defense Making Decisions System (A&DMD-System) follows five
fundamental phases: First Phase: Input and Storage in the possible attacks and Defense plots
In this initial phase, two sections are defined: Input and Storage (Refer to Stage 1). The Input
section entails a variety of inputs (IXi) derived from different information resources (espionage
information), including quantitative data such as weak targets and high population density
places. These inputs are further classified as positive (+) or negative (-) based on the type of
information they represent (See Expression 1).
Xi = ƒ (+/-IX1, +/-IX2, …, +/-IX∞…) ≡ IXi = ƒ (+/-IXi) where i =1,2,…,∞ (1)
In the Storage of the Mega-Database section, the information inputs (IXi) are recorded in
distinct databases (DBXi) corresponding to i = 1,2,… ∞ (See Expression 2).
DBXi = ƒ (DBX1<+/-IX1>, DBX2<+/-IX2>,…, DBx∞<+/-IX∞>…) (2)
Second Phase: Visualization of possible targets
This phase involves real-time visualization of the possible attacks and timing enemies can
arrive it (Refer to Stage 2). It is based on continuous inputs of information (Ixi) along each
respective axis (Xi) from various Mega-Databases (DBXi) sources. The interconnected
relationship between each input of information (Ixi) and its corresponding axis (Xi) ensures
that all multi-dimensional graphs within the possible targets are continually updated in realtime (See Expression 3).
MD = X1: [+/-Ix1], X2:[+/-Ix2] , …, X∞:[+/-Ix∞]… (3)
Third Phase: Alert of Possible Attacks and Defense Failures
In this phase, the system provides alerts for potential failures (Refer to Stage 3). The alerts are
contingent on the position of the multi-dimensional graph within its physical coordinate
system. If the information falls within the negative quadrant, denoted as -Xi = [-IXi], the SWS
issues possible allocations (See Expression 4 and 5).
If W1: [-IX1], -W2:[-IX2] , …….…, -W∞:[-IX∞]… (4)
then W1: [»Possible Attack«], -W2:[»Possible Attack«] , …, -W∞:[»Possible Attack«] (5)
Inputs in the negative quadrant, denoted as -Wi:[-IXi], are labeled as "Weak Locations." When
the targets and defense identify an allocation (Ai), it initiates a search (☼) to determine its
potential position within the attack speed (Wi) domain, as outlined in Expression 6.
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Ai = -IX1 ☼ A1 : -IX2 ☼ A2 : … : -IX∞ ☼ A∞ (6)
Fourth Phase: Set of Directions of attacks and defense
This phase includes two sections related to the Mega-Database of final Directions (Refer to
Stage 4). The Mega-Database of Final Directions is an amalgamation (╦) of numerous
databases (DBXi), each containing a range of possible attacks and defense points directions
and speeds (Wi) (See Expression 7).
DMi = A1:[ Σ DBX1 <W1>] ╦ A2:[ Σ DBx2 <W2>] ╦ …╦ A∞ [ Σ DBX∞ <W∞>] (7)
Final Phase: Final Output of the targets and Defense
In this phase, the final output or attack and defense position by places (PO) is determined (Refer
to Phase 5). This is derived from the last partial differentiation (ƒi) of the extensive list of attack
and defense directions (Sxi). The objective is to refine the strategy for optimum stability,
minimizing risk and vulnerability of attack. (See Expression 9).
ƒ(A1) = A1 ◊ A2 ◊… ◊ A∞
ƒ (A2)’ = A1 ◊ A2 ◊… ◊ A∞
ƒ (A3)’’ = A1◊ A2 ◊… ◊ A∞
ƒ(Ai)i = 0 thus i = 1,2…∞ (9)
Initially, the mega-data disks coordinate space (Ruiz Estrada, 2017) is crucial part of the
Attacks or Defense Decision System (ADD-System) Algorithm. We have a powerful an
analytical graphical modeling to visualize and analyze a large amount of data. Firstly, this
specific coordinate space shows one single vertical straight axis that is pending among all
endogenous variables (the final decision of attack or defense: Shotting). Hence, we are
available to plotting our endogenous variable on this single vertical straight axis that is
represented by αV+/- (Different factors are taking in consideration to attack or defend: Shotting).
Secondly, each exogenous variable in analysis is represented by its specific coordinate system
to attack or defend: Shotting such as βΦi:ζj. Where “Φi” represents the sub-space level in
analysis, in this case either from sub-space level zero (SS0°) to sub-space level infinite (SS360°);
“ζj” represents the disk level in analysis at the same quadrant of exogenous variables (in our
case, from disk level j=1, disk level j=2, disk level j=3,…, to disk level j=∞…). In fact, we
assume that all exogenous variables are using only real positive numbers -rational factors of to
decide or not shotting- (R+). In order to plot different exogenous variables in the mega-data
disks coordinate space, each value need to be plotted directly on its radial subspace in analysis
(Φi) and disk level in analysis (ζj) respectively. Each “i” is a radius that emanates from the
origin and in defined by the angle which can range from 0 to just before 360°, a theoretical
infinite range of shotting. Each disk is a concentric circle that starts from the origin outwards
towards a theoretical infinite value. At the same time, all these values plotted in different axis
levels in analysis (Φi) and disk levels in analysis (ζj) need to be joined with its endogenous
variable “αV+/-” until we build a series of coordinates to attack or defend (Shotting). All these
coordinates need to be joined by straight lines until yields an asymmetric spiral-shaped
geometrical figure with n-faces (see Figure 36) and disk levels in analysis (ζj) need to be joined
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together by straight lines directly to the endogenous variable αV+/- (final target to shotting) until
a cone-shaped figure with n-faces is built. It is important to mention at this juncture that the
endogenous variables “αV+/-” is fixed according to any change associated with its
corresponding exogenous variables (precision levels) in βΦi:ζj, where i = {0°, 1°, 2°,…,360°}
and j = {0, 1, 2,…,∞…} , αV+/-. Hence, we can imagine a large number of exogenous variables
moving all the time in different positions within its radius in real time continuously (decision
of attack and defense (shotting). At the same time, we can visualize how all these exogenous
variables directly affect on the behavior the endogenous variable (αV+/-) (Final Target to
shotting) simultaneously. αV+/- is fixed according to any change can be occurred among the
infinite exogenous variables in βΦi:ζj, where i = {0°, 1°, 2°,…,360°} and j = {0, 1, 2,…,∞…},
YV+/-. Hence, we can imagine a large number of infinite exogenous variables moving all the
time in different positions within its radius in real time continuously. At the same time, we can
visualize how all these exogenous variables (rational or irrational decisions evaluation) are
affecting directly on the behavior of the endogenous variable (αV+/-) (final decision to attack or
defense: Shooting) simultaneously. Moreover, the endogenous variable (αV+/-) can fluctuate
freely (see Figure 36). In our case, the endogenous variables (αV+/-) can show positive/negative
final decisions of attack or according to our multidimensional coordinate space. In the case of
exogenous variables, these can only experience non-negative properties. The mega-data disks
multivariable random coordinate space in vertical position is represented by:
(βΦi:ζj, αV+/-) where βΦi:ζj ≥ 0; i = θ° ; j =R+ ≥ 0; αV+/-= R+/αV+/- = ƒ (βΦi:ζj)
(10)
(11)
Fig. 36: The NeuronDrone-Box
Source: Ruiz Estrada (2017)
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Hence, this algorithm apply a specific trigonometry function such as the tangent
/tan(βφi:Ψj/αh+/-)/ or /tan(βΦi:ζj/αv+/-)/ and inverse tangent tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj
/αv+/-)-1. Initially, the calculation of the tangent /tan(βφi:Ψj/αh+/-)/ or /tan(βΦi:ζj/αh+/-)/ is
equal to β (adjacent) divided by α (opposite). Our graphical modeling applies absolute value to
eliminate negative values in the construction of our new coordinate space (Final attack:
Shooting). The main objective to calculate the inverse tangent tan(βφi:Ψj/αh+/-)-1 or
tan(βΦi:ζj/αv+/-)-1 is to find each angle that is located into the mega-data disks coordinate space
in vertical and horizontal position. Therefore, the tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj/αv+/-)-1can help
us to study easily the relationship between αh+/- or αv+/- (opposite) and βφi:Ψj or βΦi:ζj (adjacent)
in different periods of analysis. In fact, we are establishing three different parameters are
followed by (i) the representative area to attack or defense that keep angles between 50° and
40°; (ii) the acceptable area to attack or defense is located between 65°/51° and 41°/25°; (iii)
non-representative area to attack or defense that is fixed between 65°/90° and 26°/0° (see
Figure 63). We like to mention that each tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj/αv+/-)-1 is located
between 0° and 90° (See Figure 37).
Fig. 37: tan(β/α)-1
Source: Ruiz Estrada (2017)
Finally, all tan(βφi:Ψj/αh+/-)-1 or tan(βΦi:ζj/αv+/-)-1 results are organized in descendent order from
the smallest angle to the largest angle. Finally, we transfer all these results to the mega-data
disks coordinate space in vertical and horizontal position to visualize the behavior of all angles
that help us to appreciate clearly the behavior of multi-data analysis before to attack or defense
any target.
However, the strategic placement of propellers and ailerons within the Black Nightmare V.7
Black Nightmare V.7 main structure has been executed with remarkable precision, as depicted
in Figure 3. This meticulous placement serves the dual purpose of optimizing propulsion and
enhancing aerial stability within short timeframes. Consequently, the synergy between the
positioning of ailerons and the noise-reduction propellers is carefully orchestrated to ensure
superior performance. This harmonization has been validated through a series of experiments
conducted across diverse environments and under varying weather conditions (Smith, 2017
and 2019).
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The Black Nightmare V.7 is primarily distinguished by its aileron and engine system
configuration. Its foremost advantage lies in its near-silent operation while maintaining the
capability to sustain static flight for up to seven seconds. This unique feature allows for precise
cargo or munition drops, affording unparalleled precision and versatility under any
circumstances (Taylor, 2019) (Williams, 2021). A second differentiating factor is the
adaptability of the Black Nightmare V.7 wings, which can seamlessly transition between rigid
and flexible states. This adaptability is facilitated by advanced sensor technology and artificial
intelligence (AI) systems, ensuring optimal wing positioning and flexibility. These distinct
features position the Black Nightmare V.7 as a groundbreaking innovation within the aviation
industry.
Furthermore, the Black Nightmare V.7 operates autonomously, obviating the need for a human
pilot. Its intricate control is managed entirely by the The Full Autonomous Artificial
Intelligence in attack or defence decisions making in military drones’ system box, with human
oversight from a ground-based pilot. The antenna system relies on satellite technology,
guaranteeing superior reception and control. Therefore, this research project aims to introduce
a revolutionary paradigm shift in both the design and functionality of this technology.
The Black Nightmare V.7 represents a pioneering advancement that promises to redefine the
capabilities and applications of UAVs. According to the full autonomous artificial intelligence
in attack or defence decisions making in military drones’ system box (The NeuronDrone-Box)
(See Video 14), we can observe an experimental case how 1,000,000 variables are running to
build figure 38. The white colour shows logical and rational conditions and in black colour
non-logical and irrational conditions, The final decision of attack or defence can define to
shooting the final target after the exhausted evaluation of 1,000,000 variables (see Video 13),
we are accounting the white colour of our graphs that represent 70% to take action of attack or
defence in different strategic locations immediately. In this case, we printed in 3-D Printer, to
give a better view of our graph physically.
Fig. 38: The Full Autonomous Artificial Intelligence in attack or defence decisions making in
military drones’ System box (The NeuronDrone-Box)
Source: The Author
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Video 14. NeuronDrone-Box
8.6. Concluding Remarks
In conclusion, this research presents the first drones air military carriers and a new Drones
system is called the Mantarraya Negra Dronescarrier to generate a strong national air defense
integrated system. Its innovative design, of this special war machine incorporating features like
a powerful squadron of Black Nightmare V.7 drone bombardier (18 units) with a critical
shotting decision system (CSD-System), Multiple Ailerons System (MAS), Silent Propeller
System (SPS), Sensibility Winds System (BSWS), solar panels for efficient energy supply
system (SPEES-System), and a versatile payload system for air-to-ground and air-to-sea
assaults, marks a significant leap forward in UAV technology. The versatility of the the
Mantarraya Negra Dronescarrier (structure and equipment) with the NeuronDrone-Box. At the
same time, the NeuronDrone-Box in each Black Nightmare V.7 drone bombardier renders it
invaluable for a wide array of military and national emergency missions, showcasing its
boundless utility and potential impact on the industry.
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Chapter IX
The Black&White
Non-linear Irregular Strokes
Camouflage System
By
Mario Arturo Ruiz Estrada
9.1. A Short Review About Camouflage System: Overview
The idea of camouflage (Behrens, 1978, 1980, 1988) has its origins in France in 1914, initiated
by the famous artist Lucien-Victor Guirand de Scévola and other artists. The origins of
camouflage were driven by military needs rather than artistic reasons, aiming to create
uniforms that could hide military personnel and equipment. Subsequently, the British followed,
with the Americans adopting the practice later. Camouflage, which mixes art and science in its
creation, has a long history rooted in its visual effects on the enemy in war (modern military
strategy) and hunting (from prehistoric times) (Baert, 2018; Bhatnagar & Gupta, 1941).
Initially, natural camouflage (Friedmann, 1943; Murphy, 1919) was the adaptation used by
animals to avoid predators or ambush prey. Classic examples include the zebras stripes, the
chameleons color -changing skin, and the Arctic foxs seasonal fur changes (Skelhorn & Rowe,
2016; Stevens & Merilaita, 2009). The natural camouflage system is based on coloration and
pattern. In prehistoric times, early humans used natural materials like mud, leaves, and animal
skins to blend into their environments during hunting (Talas, Baddeley, & Cuthill, 2017).
Additionally, indigenous tribes used body painting in war to psychologically affect the enemy.
Formal military camouflage systems did not appear until the 18th and 19th centuries, when
uniforms shifted from bright colors to more subdued hues. During World War I (WWI), colors
were used on ships, tanks, and airplanes, which were painted with bold, geometric patterns to
confuse enemy rangefinders. Artists and designers were employed to create effective
concealment for troops, equipment, and infrastructure (Covert, 2007).
In World War II (WWII), armies from both the Axis and Allied forces developed complex
camouflage patterns for uniforms, vehicles, and installations. Patterns like Germany’s
"splinter" and the US "frog-skin" emerged. During the Cold War, new materials, including
synthetic fabrics and better dyes, allowed for more durable and effective concealment designed
for specific environments like jungles, deserts (Forsyth, 2014), and urban settings.
In the 21st century, digital camouflage systems use pixelated designs to break up outlines more
effectively at various distances, using new materials that provide concealment across multiple
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spectra, including infrared and thermal. Ongoing research is focused on materials that can
dynamically change color and pattern, inspired by animals like octopuses and cuttlefish.
Modern camouflage systems also consider concealment from electronic detection, using
materials that can block or distort radar and other sensors. Finally, we present the black-andwhite non-linear irregular strokes camouflage system, which uses algorithms and is applied in
the Black Nightmare Drone Bomber V.7 to demonstrate how this new type of camouflage
works. In this paper, we present the formal mathematical framework, pictures, and videos to
provide a comprehensive understanding of how the black-and-white non-linear irregular
strokes camouflage system operates.
9.2. An Introduction to the black&White Non-linear Irregular Strokes Camouflage
System: Algorithm
Initially, the Black&White non-linear irregular strokes camouflage system proposes a new
graphical algorithm modeling to compute a large amount of data (temperatures and weather
levels) and adapt a mixed of two colors (black or white) to camouflage System any military
transport, equipment, or structure (see Figure 4 and 5). Firstly, this specific coordinate space
shows millions of white non-linear strokes that is spreading in a black surface without any
order or origins. Hence, we are available to configurate in white strokes on a black surface
according to different temperature levels and αV+/- in evaluation is represented by its specific
white stroke under a chaos disorders βΦi:ζj. Where “Φi” different allocations in different white
strokes in analysis, in this case either from white stroke zero (WS0°) to white stroke infinite
(WS360°); “ζj” represents different white strokes on the black surface in analysis at the same
quadrant. In fact, we assume that all white strokes are located according to the temperature’s
levels. In order to plot different white strokes in the black surface, each white stroke to be
configurated directly on its black surface (ζj) respectively. Each “i” is a white stroke that
emanates from any origin and in defined by the angle which can range from 0 to just before
360°, a theoretical infinite range. Each white stroke is a non-linear stroke that starts from any
origin outwards towards a theoretical infinite value. At the same time, all these white strokes
plotted in different spaces in the black surfaces in analysis (Φi) and levels in analysis (ζj) need
to be joined together “αV+/-” until we build a single new surface under the mix of white strokes
on the black surface. All these white strokes need to be joined together until yields a nonasymmetric surface-shaped geometrical figure with n-spaces (see Figure 1, 2, and 3) and
(Video 1 and Video 2). It is important to mention at this juncture that the white strokes “αV+/-”
is fixed according to any change associated with its corresponding temperature levels in βΦi:ζj,
where i = {0°, 1°, 2°,…,1000°} and j = {0, 1, 2,…,∞…} , αV+/-. Hence, we can imagine a large
number of white strokes moving all the time in different positions within its radius in real time
continuously. At the same time, we can visualize how all these exogenous any restriction
directly on the behavior the final color (αV+/-). αV+/- is variable according to any change can be
occurred in infinite temperature levels in βΦi:ζj, () where i = {0°, 1°, 2°,…,1000°} and j = {0, 1,
2,…,∞…}, YV+/-. Hence, we can imagine a large number of white strokes moving all the time in
different positions within a large black surface in real time continuously (Ruiz Estrada, 2017).
The black&white non-linear irregular strokes camouflage system is represented by:
(βΦi:ζj -teneoerature levels-, αV+/- -Colors stock-) where βΦi:ζj ≥ 0; i = θ° ; j =R+ ≥ 0; αV+/-= R+/αV+/- = ƒ (βΦi:ζj)
(2)
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(1)
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Fig. 1: The Black&White Non-linear Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 2: The black&White Non-linear Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 3: The black&White Non-linear Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 4: The black&White Non-linear Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 5: The black&White Non-linear Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Video 1: The black&White Non-linear Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
Video 2: The black&White Non-linear Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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9.3. The Black Nightmare V.7 Drone Bombardier using the Black & White Non-linear
Irregular Strokes Camouflage System
The new prototype, Black Nightmare V.7 drone bombardier (refer from Figures 6 to 12),
represents a significant leap in aviation technology, integrating robotics with advanced
artificial intelligence. This paper aims to highlight its distinct features and specifications,
showcasing its exceptional capabilities. The Black Nightmare V.7 drone bombardier
underwent rigorous evaluations, testing its speed and rotation resistance across various levels.
These assessments confirm its potential as a groundbreaking air transportation system, offering
superior propulsion and stability compared to conventional propeller and aileron systems. A
major feature of the black-and-white non-linear irregular strokes camouflage system is its
application of Artificial Intelligence (AI) through the NeuronDrone-Box, which serves as the
main master control system. This system dynamically changes the camouflage of the Black
Nightmare V.7 drone bombardier's main structure.
The precision of the Black Nightmare V.7 drone bombardier (see Video 3) stems from its
unique features and applications, as detailed in this technical report. Firstly, we advocate for
the implementation of the "Multiple Ailerons System (MAS)" and Multi-Missiles System
(MM-System) (see from Figure 6 to 12). Our proposal involves situating all ailerons within the
main body structure of the Black Nightmare V.7 drone bombardier. Simultaneously, the aircraft
introduces an innovative propeller design known as the "Silent Propeller System (SPS)" (see
Video 3). Housed within the main structure, the Black Nightmare V.7 drone bombardier boasts
a robust motor, complemented by a series of specialized propellers working in precise
synchronization, resulting in a remarkable 99.05% reduction in noise during departure, flight,
and landing. Furthermore, a novel concept of "Sensibility Winds System (SWS)" employs
artificial intelligence to enhance operational efficiency. The Black Nightmare V.7 drone
bombardier is equipped to carry four booms or three missiles for air-air attacks, further
extending its versatility (see Video 3). On top of the Black Nightmare V.7 drone bombardier,
there is a missile designed to intercept and attack any aircraft or object that attempts to approach
from above. The strategic placement of propellers and ailerons within the Black Nightmare V.7
Black Nightmare V.7 drone bombardier main structure has been executed with remarkable
precision, as depicted in Video 3. This meticulous placement serves the dual purpose of
optimizing propulsion and enhancing aerial stability within short timeframes. Consequently,
the synergy between the positioning of ailerons and the noise-reduction propellers is carefully
orchestrated to ensure superior performance. This harmonization has been validated through a
series of experiments conducted across diverse environments and under varying weather
conditions.
The Black Nightmare V.7 drone bombardier is primarily distinguished by its aileron and engine
system configuration. Its foremost advantage lies in its near-silent operation while maintaining
the capability to sustain static flight for up to seven seconds. This unique feature allows for
precise cargo or munition drops, affording unparalleled precision and versatility under any
circumstances. A second differentiating factor is the adaptability of the Black Nightmare V.7
drone bombardier wings, which can seamlessly transition between rigid and flexible states.
This adaptability is facilitated by advanced sensor technology and artificial intelligence (AI)
systems, ensuring optimal wing positioning and flexibility. These distinct features position the
Black Nightmare V.7 drone bombardier as a groundbreaking innovation within the aviation
industry. Furthermore, the Black Nightmare V.7 drone bombardier operates autonomously,
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obviating the need for a human pilot. Its intricate control is managed entirely by AI tools, with
human oversight from a ground-based pilot. The antenna system relies on satellite technology,
guaranteeing superior reception and control. Therefore, this research project aims to introduce
a revolutionary paradigm shift in both the design and functionality of this technology. The
Black Nightmare V.7 drone bombardier represents a pioneering advancement that promises to
redefine the capabilities and applications of UAVs.
Fig. 6: The Black Nightmare V.7 Drone Bombardier using the black&White Non-linear
Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 7: The Black Nightmare V.7 Drone Bombardier using the black&White Non-linear
Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 8: The Black Nightmare V.7 Drone Bombardier using the black&White Non-linear
Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 9: The Black Nightmare V.7 Drone Bombardier using the black&White Non-linear
Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 10: The Black Nightmare V.7 Drone Bombardier using the black&White Non-linear
Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 11: The Black Nightmare V.7 Drone Bombardier using the black&White Non-linear
Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Fig. 12: The Black Nightmare V.7 Drone Bombardier using the black&White Non-linear
Irregular Strokes Camouflage System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
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Video 3: The Black Nightmare V.7 Drone Bombardier using the black&White Non-linear
Irregular Strokes Camouflage System System
Source: Dr. Mario Arturo Ruiz Estrada © 2024
9.4. Concluding Remarks
In conclusion, this research underscores the transformative potential uses of the the
black&White Non-linear Irregular Strokes Camouflage System System in the realm of military
camouflage System systems for sea, land, and air military equipment and transportation. Its
innovative design, incorporating features like a new camouflage System driven by artificial
intelligence (AI) using the NeuronDrone-Box. The versatility of the the black&white nonlinear irregular strokes camouflage system is possible to be observed in the Black Nightmare
V.7 drone bombardier.
References
Baert, B. (2018). Camouflage. In S. Heremans (Ed.), Fragments (Vol. 14, pp. 51–55). Peeters
Publishers.
Behrens, R. R. (1978). On Visual Art and Camouflage. Leonardo, 11(3), 203–204.
Behrens, R. R. (1980). Camouflage, Art and Gestalt. The North American Review, 265(4), 8–
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Behrens, R. R. (1988). The Theories of Abbott H. Thayer: Father of Camouflage System.
Leonardo, 21(3), 291–296.
Bhatnagar, S. S., & Gupta, N. N. S. (1941). Camouflage Paints. Current Science, 10(11),
475–478.
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Covert, C. T. (2007). Art at War: Dazzle Camouflage. Art Documentation: Journal of the Art
Libraries Society of North America, 26(2), 50–56.
Forsyth, I. (2014). Designs on the desert: camouflage, deception and the militarization of
space. Cultural Geographies, 21(2), 247–265.
Friedmann, H. (1943). The Natural History Background of Camouflage. The American
Biology Teacher, 5(8), 171–177.
Murphy, R. C. (1919). Marine Camouflage. The Brooklyn Museum Quarterly, 6(1), 34–53.
Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics:
Econographicology.” Quality and Quantity, 51(5):2115-2139.
Skelhorn, J., & Rowe, C. (2016). Cognition and the evolution of camouflage. Proceedings:
Biological Sciences, 283(1825), 1–7.
Stevens, M., & Merilaita, S. (2009). Introduction: Animal Camouflage: Current Issues and
New Perspectives. Philosophical Transactions: Biological Sciences, 364(1516), 423–427.
Talas, L., Baddeley, R. J., & Cuthill, I. C. (2017). Cultural evolution of military camouflage.
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