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Raw datasets of PDA10A receiver signals acquired with MSO2024 oscilloscope for Time Difference of Arrival Optical Wireless Positioning measurements.
The data is stored in csv, numpy and pickle format to be easily imported and processed in Python.
The first dataset consists of measurements on a grid without reflections.
The second dataset are identical measurements but with a corner geometry at the fourth receiver creating diffuse reflections.

This dataset contains measured data from five sensor modules designed for monitoring the oxygen concentration in the air in a hospital environment, especially in rooms where oxygen therapy can potentially occurs. This data is crucial from a safety point of view, as a higher oxygen concentration can increase the risk of fire development.

Mapping millions of buried landmines rapidly and removing them cost-effectively is supremely important to avoid their potential risks and ease this labour-intensive task. Deploying uninhabited vehicles equipped with multiple remote sensing modalities seems to be an ideal option for performing this task in a non-invasive fashion. This report provides researchers with vision-based remote sensing imagery datasets obtained from a real landmine field in Croatia that incorporated an autonomous uninhabited aerial vehicle (UAV), the so-called LMUAV.

This dataset contains adversarial attacks on Deep Learning (DL) when it is employed for the classification of
wireless modulated communication signals. The attack is executed with an obfuscating waveform that is embedded in the
transmitted signal in such a way that prevents the extraction of clean data for training from a wireless eavesdropper. At the
same time it allows a legitimate receiver (LRx) to demodulate the data. The scheme works for both single carrier and multi-carrier