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The deep learning-based profiling attacks have received significant attention for their potential against masking-protected devices. Currently, additional capabilities like exploiting only a segment of the side-channel traces or having knowledge of the specific countermeasure scheme have been granted to attackers during the profiling phase. In case either capability is removed, a practical profiling attack faces great difficulty and complexity. 

Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities.

This paper proposes a Model-Based Online Learning (MBOL) framework for waveform optimization in integrated sensing and communications (ISAC) systems. In particular, the MBOL framework is proposed to enhance the ISAC performance under dynamic environmental conditions. Unlike Model-Free Online Learning (MFOL) methods, our approach leverages a rich structural knowledge of sensing, communications, and radio environments, offering better explainability and sample efficiency.

We introduce ICE-TIDE, a method for cryogenic electron tomography (cryo-ET) that simultaneously aligns observations and reconstructs a high-resolution volume. The alignment of tilt series in cryo-ET is a major problem limiting the resolution of reconstructions. ICE-TIDE relies on an efficient coordinate-based implicit neural representation of the volume which enables it to directly parameterize deformations and align the projections.

While snapshot hyperspectral cameras are cheaper and faster than imagers based on pushbroom or whiskbroom spatial scanning, the output imagery from a snapshot camera typically has different spectral bands mapped to different spatial locations in a mosaic pattern, requiring a demosaicing process to be applied to generate the desired hyperspectral image with full spatial and spectral resolution.

Single-satellite geolocation achieves effective localization of ground electromagnetic interference (EMI) signals with a low cost compared to the multi-satellite counterparts. In such systems, the Doppler and Doppler rate are commonly exploited to extract the information of the ground EMI sources and the constrained Unscented Kalman filter (cUKF) is found effective to provide instantaneous EMI locations over time.