This article is dedicated to the study of airborne GNSS-R signal processing techniques for water body detection and edge localization using a low-altitude airborne carrier with high rate reflectivity measurements. A GNSS-R setup on-board a carrier with reduced size and weight was developed for this application. We develop a radar technique for automatic GNSS signal segmentation in order to differentiate in-land water body surfaces based on the reflectivity measurements associated to different areas of reflection. Such measurements are derived from the GNSS signal amplitudes. We adapt a transitional model to characterize the changes in the measurements of the reflected GNSS signals from one area to another. We propose an on-line/off-line change detection algorithm for GNSS signal segmentation. A real flight experimentation took place in the context of this work obtaining reflections from different surfaces and landforms. We show, using the airborne GNSS measurements obtained, that the proposed radar technique detects in-land water body surfaces along the flight trajectory with high temporal (50 Hz ) and spatial resolution (order of 10 to 100 m${}^2$). We also show that we can localize the edges of the detected water body surfaces at meter accuracy.

We present the MPEG standardization activity on redundant encoding and packaging for live segmented media. The standardization includes profiling the Dynamic Adaptive Streaming over HTTP (DASH) Media Presentation Description for ingest, storage and redundant packaging applications. Further, a Common Media Application Format (CMAF) segment and track format is defined to support redundant encoding and packaging using a common timeline relative to the Unix epoch. The standardization is still ongoing and we solicit feedback from academic and industry practitioners.

Measurement of soil moisture content on a global scale have gained increased interest over the years, due to its essential role in agriculture and most importantly in predicting the occurrence of natural disasters. This paper is dedicated to a study on GNSS Reflectometry (GNSS-R) using a low-altitude airborne carrier for soil moisture estimation with 1 ms rate of carrier-to-noise ratio observations. The principle of GNSS-R is the exploitation of L-band navigation signals as sources of opportunity to characterize the earth surface, because the reflected signals are often affected by the nature of the reflective surface. To scan large regional surface areas and quickly reach the areas to monitor, a dynamic GNSS-R system is considered.The GNSS-R setup used in this study consists of RHCP and LHCP antennas mounted on the nose of a gyrocopter and of a Syntony front-end GNSS receiver. In addition, the gyrocopter is equipped with a signal digitizer and mass storage devices for digitizing and storing the base-band GNSS direct and reflected signals along the flight. A drone sensors board is also attached to the gyrocopter, which records the gyrocopter’s attitude and position at 1000 Hz rate along its trajectory. To cope with the rapid displacement of the satellites’ footprints along the receiver trajectory, high rate (1 ms) of carrier-to-noise ratio observations are processed with the data collection of the base-band RHCP and LHCP signals.In the context of the study, it is very important to localize the reflective surfaces (satellites’ footprints) from which each processed signal has reflected, and thus detect which areas were scanned during the flight. The link between the reflected signals and the satellites' footprints is based on the GPS time, attitude and position provided by the drone board and the GPS time extracted from the digitized GNSS signals. We show that these parameters allow to determine, at ms rate, the satellites’ footprints locations (i.e. the surface areas) from which each signal has reflected at a specific GPS Time. A Geographic Information System is developed based on this principle to map the measurements obtained from the GNSS-R airborne setup along a real receiver trajectory. The ultimate aim of this study is to link the obtained GNSS-R measurements with the scanned surfaces to provide a soil moisture mapping of the studied area.

Intelligent sensing systems are increasingly used in wearable devices, enabling advanced tasks across various application domains including robotics and human–machine interaction. Ensuring these systems are energy autonomous is highly demanded, despite strict constraints on power, memory and processing resources. To meet these requirements, embedded neural networks must be optimized to achieve a balance between accuracy and efficiency. This paper presents an integrated approach that combines Hardware-Aware Neural Architecture Search (HW-NAS) with optimization techniques—weight reshaping, quantization, and their combination—to develop efficient classifiers for a multisensory glove. HW-NAS automatically derives 1D-CNN models tailored to the NUCLEO-F401RE board, while the additional optimization further reduces model size, memory usage, and latency. Across three datasets, the optimized models not only improve classification accuracy but also deliver an average reduction of 75% in inference time, 69% in flash memory, and more than 45% in RAM compared to NAS-only baselines. These results highlight the effectiveness of integrating NAS with optimization techniques, paving the way towards energy-autonomous wearable systems.