We investigate the evolution of the reliability of a mobile communication network through its capacity to avoid and withstand faults (called robustness) and to maintain proper functioning if faults occur nevertheless (called resilience). Our case study focuses on a swarm of nanosatellites orbiting the Moon and operating as a distributed space interferometer. The objective of this study is to evaluate the impact of graph division techniques on the robustness and resilience of the system, simultaneously. A high reliability level is key to guaranteeing proper quality of service by recovering from impairments while preserving the primary function or mission of the mobile network. By analyzing the effects of exploration and random selection algorithms on the network reliability, our results show that fair graph division significantly improves robustness metrics such as routing cost and network efficiency, while introducing a measurable tradeoff with specific resilience metrics such as path redundancy and disparity. In addition, our analysis highlights the superior performance of sequential exploration algorithms, such as MIRW, in optimizing robustness while preserving a decent level of resilience.

This study investigates the synergistic use of dual-polarization Sentinel-1 time series for forest loss monitoring across mixed land-cover types. While VH polarization generally provides higher contrast between intact vegetation and deforested areas, VV polarization is assumed to be more effective over areas characterized by vegetation remnants left after clearing. These insights motivate the development of fpol-BOCD, an unsupervised Bayesian polarimetric change detection method with Near Real-Time (NRT) capabilities. The algorithm is designed to leverage the complementary scattering properties of both polarizations, improving detection sensitivity across diverse land-cover conditions. Notably, fpol-BOCD jointly processes VH and VV Sentinel-1 data with comparable computational complexity relative to single-polarization approaches. Evaluation is performed using two datasets of forest loss events, extracted from the MapBiomas Alerta reference dataset for 2020 in the Cerrado and Amazon biomes in Brazil. One dataset focuses on small-scale clearings (0.1-2 hectares), while the other includes a randomly selected subset of larger deforested patches. The method improves detection accuracy by approximately 10% compared to single-polarization BOCD methods in areas likely containing residual vegetation after clearing, e.g., small disturbances in the Cerrado and large ones in the Amazon, while producing very few false alarms relative to the simple merging of single-polarization alerts. Additionally, fpol-BOCD outperforms the operational optical-based GLAD-L alerts in the Cerrado, achieving a 47% increase in true detections In the Amazon, a comparison with the operational SAR-based RADD alerts shows a 34% increase in true positives for small clearings. The detection speed of fpol-BOCD matches single-polarization BOCD methods. Temporal analysis shows a peak in forest loss during the dry season, with unexpected early wet-season increases likely linked to a lengthening of the dry period. Overall, results highlight the value of polarimetric data for efficient, more accurate forest loss monitoring in complex tropical environments.

As analytic nonlinearities cannot simulate correctly passive intermodulation (PIM) behavior, a nonanalytic power law behavioral model has been proposed to explain the non-classical variation of product power versus carrier power. This model has then been applied to many different measurement cases. This has successfully explained some published measures, and it has been used to propose new measurements and predict their results. It seems that the behavioral model may be linked to an underlying physical model of the nonlinearity responsible for the PIM.

In the multiple measurement vector (MMV) model, sparse signal recovery (SSR) is formulated as identifying the common support set of sparse signal vectors using a collection of measurement vectors obtained via a shared, known overcomplete dictionary. The sparse signal vectors or their powers may be estimated along with the support set using Sparse Bayesian Learning (SBL) framework. This talk focuses on the Type-II (marginal) likelihood in SBL, which reduces to a covariance‑learning (CL) problem: estimating the sparse signal powers and their support using the measurements’ second‑order statistics. Building on successive convex approximation (SCA) and majorization–minimization (MM), we propose two fast covariance-learning algorithms for solving the type-II likelihood, the CL-SCA and CL-MM algorithms. These methods iteratively estimate the sparse signal powers and typically require an order of magnitude fewer iterations than the standard SBL expectation maximization (EM) algorithm. Numerical experiments show consistent gains in SSR over state-of-the-art baselines. We also demonstrate an application to joint activity detection and channel estimation (JADCE) for massive random access. Finally, we discuss robustness extensions that move beyond the Gaussian assumptions of classical SBL.

In this communication, we propose a novel Cramer-Rao bound for matrix Lie groups, based on the exact modeling of Gaussian distributions on Lie groups (LG-ExCRB), different from those in the literature based on a simplifying approximation. To achieve this, we design a generic expression and develop new analytical formulas of the Fisher Information matrix. Then, closed-form expressions are given for two Lie groups of interest in engineering applications, SO(3) and SE(2). The proposed LG-ExCRB is validated numerically by comparison with the LG-CRB derived from the approximate modeling.

Time-delay and Doppler estimation are among the most critical operations for synchronizing wireless communication systems, as well as for applications such as radar and global navigation satellite system (GNSS). Typically, a maximum likelihood estimator (MLE) is employed to initialize the time-delay and Doppler parameters. How ever, due to the high computational complexity of the MLE, sub-optimal algorithms are often used for subsequent parameter tracking. In this paper, we propose a novel low-complexity Fisher-scoring estimator, which is a variant of the Newton-Raphson method. Under the band-limited assumption, we derive closed-form expressions for the Fisher Information Matrix (FIM) and the gradient, both of which depend solely on the received signal samples. The performance of the proposed estimator is evaluated against the Cram´er-Rao Bound (CRB), demonstrating asymptotic convergence and achieving the same performance as the MLE after a certain number of iterations.

Cet article traite de la mise en œuvre d’un système d’annulation active et adaptative du bruit, applicable aux essais d’émission rayonnée de compatibilité électromagnétique (CEM) ainsi qu’à des applications plus larges. Sous certaines conditions, le système peut isoler les différentes sources de bruit et construire des filtres adaptatifs numériques afin d’éliminer ces interférences. Les résultats de mesures en laboratoire et en environnement ouvert montrent la capacité à supprimer différents types de bruit (blanc, à bande étroite ou plus spécifiques d’un environnement réel) et à restituer un signal situé bien en dessous de ce bruit parasite.

Aviation GNSS receivers are critical for safety but challenging to test due to nonlinear behaviors, environmental variability, and timing-dependent faults. This paper presents a correlation-guided fuzz testing framework that integrates sensitivity analysis into hybrid search-based workflows, enabling systematic, interpretable, and data-driven robustness evaluation. Unlike prior approaches that focus on isolated software modules or simulated environments, the proposed framework targets system-level, real-time behaviors of hardware-integrated receivers. Step changes, ramps, and transient peaks are prioritized as indicators of anomalies, including spoofing and jamming events. Initial evaluations demonstrate the feasibility of focusing fuzz testing on high-impact inputs to improve efficiency and interpretability. Future work will validate the framework in larger-scale campaigns, including scenarios influenced by the South Atlantic Anomaly and controlled jamming or spoofing, aiming to provide a scientifically rigorous yet practically usable approach for safety-critical GNSS systems.