In this study, experiments were conducted to etch SiO₂ and Si₃N₄ by introducing N₂ at flow rates of 0, 2, 4, 6, and 8 sccm into a CF₄/O₂ plasma. OES (Optical Emission Spectroscopy) data were systematically collected and analyzed under each condition to understand the impact of N₂ addition on plasma chemistry. Machine learning techniques were applied to identify specific OES wavelengths that are critical to the etch rate and selectivity of both materials. Furthermore, the importance of the selected wavelengths was determined using XAI (Explainable Artificial Intelligence) methods.

This project aims to generate smart home IoT datasets (especially Zigbee traffic data) in order to support research on smart home IoT network and device profiling, behaviour modelling, characterization, and security analysis. The Zigbee traffic data is captured in a real house with two Zigbee networks containing over 25 Zigbee devices which monitor the daily activities inside the house. The captured Ethernet traffic data from Home Assistant also contains the status data of several non-Zigbee IoT devices such as printers, a smart thermostat, and entertainment devices.

Pressure sensors based on fiber Bragg gratings in side-hole optical fiber enable remote monitoring of pressure at multiple points within many otherwise inaccessible environments. However, sensors fabricated from side-hole fiber drawn from a preform have limitations in their design and material composition. The design of such sensors is a compromise between achieving good sensitivity, while also minimizing splice losses due to mode-mismatch.

This is a dataset collected in the sea trials at YAS Island, Abu Dhabi, UAE on January, 30 2024 at 17:34 February, 05 2024 at 16:36. The test were conducted in sea code 1~2 on January, 30, and sea code 3 on Feb, 05. The primary aim of these experiments was to investigate the effectiveness of the proposed method in estimating the position of the target station when significant amount of sea wave turbulence are present.