Les satellites qui reçoivent les paquets AIS (Automatic Identification System) émis depuis des zones de trafic maritime dense subissent les effets d’une surcharge du canal AIS due au nombre élevé des navires. Les collisions entre messages AIS peuvent alors empêcher leur détection. Dans cet article, nous nous intéressons à l’amélioration du processus d’annulation successive d’interférences à partir de l’algorithme de détection élémentaire d’un message AIS. Pour cela, nous considérons la combinaison de l’algorithme de Viterbi à liste parallèle (PLVA) avec le détecteur du code CRC (contrôle de redondance cyclique) dont nous avons établi l’efficacité dans une étude précédente. L’intérêt du PLVA dans un récepteur par annulation successive d’interférences est démontré au travers des simulations avec un modèle simple mais suffisament réaliste du système AIS.

Polar codes with large kernels offer improved error-correction performance but suffer from high decoding complexity due to costly marginalization operations. In this work, we propose neural network (NN)-based approximations of these marginalization functions, enabling efficient decoding for arbitrary linear kernels. We show that training on all-zero partial codewords is sufficient for linear kernels, significantly reducing the dataset size and allowing for smaller, faster NN models. Our method is evaluated within a Successive Cancellation (SC) decoder, being compatible with any blocklength, code rate, or SNR. Experimental results for kernel size 16 demonstrate strong performance and substantial complexity savings over exact marginalization.

Forest fires, intensified by climate change, threaten tropical ecosystems by accelerating biodiversity loss, releasing carbon emissions, and altering hydrological cycles. Continuous detection of fire-induced forest loss is therefore critical. However, commonly used optical-based methods often face limitations, particularly due to cloud cover and coarse spatial resolution. This study explores the use of C-band Sentinel-1 Synthetic Aperture Radar (SAR) time series, combined with Bayesian Online Changepoint Detection (BOCD), for detecting and continuously monitoring fire-induced vegetation loss in forested areas. Three BOCD variants are evaluated: two single-polarization approaches individually using VV and VH reflectivities, and a dual-polarization approach (BOCD) integrating both channels. The analysis focuses on a fire-affected area in Baixo Uraim (Paragominas, Brazil), supported by field-validated reference data. BOCD performance is compared against widely used optical products, including MODIS and VIIRS active fire and burned area data, as well as Sentinel-2-based difference Normalized Burn Ratio (dNBR) assessments. Results indicate that BOCD achieves spatial accuracy comparable to dNBR (88.2% agreement), while enabling detections within a delay of three Sentinel-1 acquisitions. These findings highlight the potential of SAR-based BOCD for rapid, cloud-independent monitoring. While SAR enables continuous detection regardless of atmospheric conditions, optical imagery remains essential for characterizing the type and severity of change.

In this communication, we propose a new Bayesian framework to characterize the concentration parameter of the von Mises distribution. To achieve this, we equip this parameter with a Lie group structure. We design a Lie group (LG) estimator by incorporating prior information modeled by a Gaussian distribution on R+. This estimator is determined using a dedicated optimization algorithm on R+. The performance of this estimator is then evaluated by computing a new expression of the Bayesian Cram´er-Rao bound on the Lie group (LG-BCRB) R+. The consistency between the proposed estimator and the LGBCRB is validated through numerical simulations by comparing it with the Bayesian Mean Squared Error.

Satellites receiving Automatic Identification System (AIS) packets in dense areas are particularly prone to AIS channel overload due to the extensive number of vessels. Thus a failure of detection might be caused by the collisions among AIS messages. To improve the detection capability, we propose to exploit the presence of the cyclic redundancy check (CRC) in AIS frames by using the parallel list Viterbi algorithm (PLVA) instead of the classical Viterbi algorithm (VA) often used for decoding AIS signals. The performance of combining the PLVA with AIS post processing including the CRC is studied with two detectors, one coherent and the other differential, in two channel models: a single-user AWGN channel and a more realistic multiple-access AIS channel. We also show the impact of the PLVA parameters on the success recovery rate. The simulation results show that the resulting procedure can significantly improve the packet error rate (PER) at the cost of a limited increase of the computational complexity. The proposed technique could be applied to improve the performance of interference cancellation receivers by significantly lowering the AIS decoding threshold.

Key Exchange mechanisms (KE or KEMs) such as the Diffie-Hellman protocol have proved to be a cornerstone conciliating the efficiency of symmetric encryption and the practicality of public key primitives. Such designs however assume the non-compromission of the long term asymmetric key in use. To relax this strong security assumption, and allow for modern security features such as Perfect Forward Secrecy (PFS) or Post Compromise Security (PCS), Ratcheted-KE (RKE) have been proposed. This work proposes to turn the Hamming Quasi-Cyclic (HQC) cryptosystem into such a Ratcheted-KE, yielding the first code-based such construction. Interestingly, our design allows indifferently one party to update the key on-demand rather than the other, yielding a construction called bi-directionnal RKE, which compares favorably to generic transformations. Finally, we prove that the resulting scheme satisfies the usual correctness and key-indistinguishability properties, and suggest concrete sets of parameters, assuming different reallife use cases.

Routing in nanosatellites swarms presents distinct challenges, including variable node availability, constrained bandwidth, and dynamic topology. Strategies like delay-tolerant networking (DTN) can be advantageous, as they adapt to intermittent connectivity by storing and forwarding data when connections are established. Moreover, geographic routing protocols that exploit satellite positions can improve efficiency, while machine learning approaches may optimize routing decisions based on changing network conditions. What about hybrid approaches that may combine some of these methods? Basically, the crucial question is where to begin. The primary challenge for nanosatellites network designers is to determine which routing strategies to test prior to deployment. Given the vast number of existing routing protocols, testing all of them is not possible. This problem motivates the present study, which share the authors' experiences on selecting the most suitable routing algorithms for a given nanosatellites swarm. In particular, the study reports how the use of graph theory metrics helps in restricting the set of routing algorithms to be considered for network characterization and protocol selection.

High resolution spectrometers are composed of different optical elements and detectors that must be modeled as accurately as possible. Specifically, accurate estimates of Instrument Spectral Response Functions (ISRFs) are critical in order not to compromise the retrieval of trace gas concentrations from spectral measurements. Currently, parametric models are used to estimate these response functions. However, these models cannot always take into account the diversity of ISRF shapes that are encountered in practical applications. This paper studies a new ISRF estimation method based on a sparse representation of the ISRF in a dictionary. The proposed method is shown to be very competitive when compared to parametric models, yielding up to one order of magnitude smaller normalized ISRF estimation errors. The method is applied to different high-resolution spectrometers, demonstrating its reproducibility for multiple remote sensing missions.

Leveraging carrier phase observations within Global Navigation Satellite Systems receivers allows centimeter-level positioning accuracy. However, carrier phase observations are significantly affected by additive noise, which is assumed to follow a von Mises distribution, thereby degrading the performance of phase-based positioning estimators. To improve the modeling of carrier phase observations, we propose a novel approach that constrains the parameters of the von Mises distri-bution-specifically, the angular location modeling the phase and its dispersion parameter $\kappa$ modeling the noise-to evolve within the Lie group space $S O(2) \times \mathbb{R}^{+}$. To estimate these parameters, we employ a Lie group maximum likelihood estimator, solved through a Newton algorithm on Lie groups. This approach demonstrates advantages in terms of robustness and precision, especially when dealing with a small number of observations, compared to traditional Euclidean-based methods.

Plug-and-play algorithms have shown impressive results on imaging inverse problems, such as registration, super-resolution, denoising and inpainting. These methods rely on a neural network denoiser to learn an implicit prior of the image to be estimated. This paper investigates a new plug-and-play approach for 3D point cloud registration, which is crucial for a wide range of applications such as urban planning, archaeology and autonomous vehicles. The 3D point cloud registration problem is formulated as an inverse problem whose unknowns are the image to be estimated and the transformation between the two point clouds. A plug-and-play approach using an alternating optimization strategy is proposed for solving the registration problem. Experiments conducted on synthetic data and Li-DAR point clouds are presented showing the potential of the method.