The increasing complexity of safety-critical hardware systems demands advanced methods for ensuring functional safety (FuSa). Traditional techniques like ATPG and BIST are intrusive, requiring additional hardware and disrupting operations, making them unsuitable for in-field testing. To address this, for the first time, we propose a machine learning (ML)-driven automated Self-Test Library (STL) generation for seamless in-field testing during idle periods, ensuring uninterrupted fault detection and high system performance. Utilizing reinforcement learning, the STL generates design-specific test patterns, achieving up to $57.57 \%$ improvement in fault coverage and up to $85 \%$ efficiency compared to existing pattern-based testing, enhancing FuSa in mission-critical applications.

This paper investigates using Spire’s grazing-angle GNSS-R data to detect the existence of surface water in the lower Mississippi region. The Signal-to-Noise Ratio (SNR) of the reflected L2 GPS signals acquired using the Spire Global constellation are analyzed over several tracks covering the Mississippi river, along with Normalized Difference Vegetation Index (NDVI) from Sentinel-2 Multispectral Imager (MSI) and backscattering coefficients from Sentinel-1 Synthetic Aperture Radar (SAR) C-band acquisitions. The specular points are segregated on the basis of thresholds based on the GNSS data and associated remote sensing data with correctly classifying the points over water with a true positive rate between 55-70% for L2 SNR thresholds above noise floor.

Hyperspectral sensors measure the radiance spectrum across hundreds of wavelength channels with a resolution typically on the order of 10 nm represented by the full-width-half-maximum (FWHM). The spectra are used in the study of surface materials in the biological, geological and oceanographic sciences to name a few, utilizing quantitative spectroscopic techniques. The instruments developed to measure such data are expensive due to the increased number of bands, and create large datasets that can be difficult to downlink for a given instance. Repeat cycle of space-borne hyperspectral observations of the earth surface is also less than those of multi-spectral sensors. It becomes incumbent to develop mechanisms that could be cost-effective and give desired results. With this aim, spectral Super-Resolution (SR) is attempted on the Airborne Visible and Infra-Red Imaging Spectrometer (AVIRIS) data to reconstruct the hyperspectral band radiance from equally-spaced narrow multi-spectral bands using dictionary learning, followed by denoising using machine learning. The hyperspectral band radiance are first estimated from 30 selected input multi-spectral bands using dictionary trained through K-Singular Value Decomposition (K-SVD), followed by denoising using Random Forest Regression. An overall Signal-to-Noise Ratio (SNR) of 31.58dB is observed from reconstruction after denoising using Random Forest.