CNES is involved in the protection of Radio Astronomy near the far side of the Moon, in a zone defined by ITU as the Shielded Zone of the Moon (SZM). The 2483.5-2500 MHz band has been chosen for lunar in-situ PNT notably since it is the only band recommended by SFCG (Space Frequency Coordination Group) for lunar in situ PNT. This band is also the only GNSS band recommended by SFCG for radiocommunications from Martian orbit to Martian surface. CNES proposed this band for lunar and Martian frequencies to SFCG. Regarding the protection of Radio Astronomy in Shielded Zone of the Moon (SZM), which is more or less the far side of the Moon and above, this 2483.5-2500 MHz band is well adapted, while it is not the case for any part of the other GNSS bands used on Earth: both RNSS L and C bands constitute each an important threat for Radio Astronomy in the SZM. SFCG issued two recommendations concerning the protection of lunar in-situ PNT in its 2483.5-2500 MHz band: Recommendation SFCG 32-2R6, so called “Freqs for lunar region”, and Recommendation SFCG 43-1, so called “Protection of lunar S-band PNT”. Obeying both SFCG RECs 32-2R6 and 43-1simultaneously is mandatory to ensure protection of lunar in-situ PNT from wireless WIFI and 3GPP (like 4G, 5G, …) lunar surface links. Adjacent to the 2483.5-2500 MHz in-situ lunar PNT band recommended by SFCG 32-2R6, the bands 2400-2480 MHz and 2503.5-2655 MHz are among the bands recommended for lunar surface wireless systems. This means that there is a minimum of 3.5 MHz mandatory guard bands on each side of the 2483.5-2500 MHz PNT band in SFCG 32-2R6 for the protection of lunar in-situ PNT. The SFCG REC 43-1 recommends the PNT devices to implement filtering, and that each lunar surface wireless system should not generate an aggregated PFD exceeding -121dBW/m²/MHz at the input of the PNT receiving antenna. The SFCG REC 32-2R6 recommends the Wireless device to implement filtering when necessary to avoid Out Of Band harmfull interference to PNT. The paper details these 2 SFCG recommendations which are fundamental for protection of in-situ lunar PNT. It provides some rules to the implementers to respect both SFCG recommendations. A model of PNT receiver response to interference has been developed by TéSA. Different cases are considered, such as astronauts on the lunar surface in a suit equipped with wireless and PNT devices and related antennas on their backpack, with the wireless transmitters (WIFI and 5G) interfering with the PNT reception. Technical justifications of the PFD limit of SFCG REC 43-1 are also provided. These explanations and rules are valid for in-situ lunar PNT, like the AFS (Augmented Forward Service) of LunaNet, but also for the baseline of the future Chinese insitu lunar PNT service. This paper presents the Wireless to PNT interference simulator developed by TéSA. The interference results from this simulator were used by CNES to participate to the elaboration of REC 32-2R6 and REC 43-1 in order to contribute protecting lunar in-situ PNT and, consequently, Radio Astronomy in the SZM. The SFCG recommendation applicable in the Mars region is REC 22-1R4, “Frequency assignment guidelines for communications in the Mars region” , so called “Freqs for Mars region”. In addition to the 2483.5-2500 MHz orbit to surface band, REC 22-1R4 recommends several surface wireless bands, including 2400-2480 MHz and 2503.5-2620 MHz (likely to be extended up to 2655 MHz in a next version). CNES showed that there would also be Radio Astronomy issues with GNSS L and C bands if one of them were broadcast by a Martian radiocom constellation, since Mars is regularly visible from the Shielded Zone of the Moon. The protection measures for a Martian in-situ PNT in 2483.5-2500 MHz would then be similar to the ones described for lunar in-situ PNT systems. This paper introduces the CCSDS Standard for lunar and Martian 3GPP and WIFI wireless links. This CCSDS Standard specifies to comply with the described SFCG recommendations. The paper finally concludes the systematic need to conduct system studies for each lunar wireless network, combining wireless and PNT, and involving wireless to PNT interference computations.

Cet article présente une extension d’une approche plug-and-play pour le recalage de nuages de points 3D. Le problème de recalage de nuages de points 3D est formulé comme un problème inverse, et une approche plug-and-play est utilisée pour conjointement débruiter et recaler les nuages de points. Dans cet article, nous proposons d’optimiser la transformation de recalage en exploitant la structure de groupe de Lie de la transformation rigide SE(3). Des expériences menées sur des nuages de points LiDAR sont présentées mettant en évidence l’amélioration de la méthode par rapport à une méthode existante.

Cet article présente un nouvel algorithme EM (Expectation-Maximization) pour le recalage robuste de nuages de points 2D–3D issus d’une caméra et d’une carte de référence. Nous nous intéressons à l’estimation conjointe des paramètres d’intérêt (i.e., orientation et position de la caméra), de la proportion d’observations aberrantes et de la variance du bruit de mesure. L’approche proposée repose sur un modèle statistique intégrant des variables latentes permettant de gérer les associations inconnues entre points 2D, points 3D et observations aberrantes, via un modèle de mélange. Des résultats obtenus à partir de données synthétiques montrent l’intérêt de cette démarche en termes de rapidité de convergence de l’algorithme proposé et de robustesse face aux mesures aberrantes.

Un réseau IP peut être représenté sous la forme d'un graphe où les noeuds sont des routeurs et les arêtes des liens de communication IP. Ce graphe aide à analyser les interactions et les flux d'information au sein du réseau. Chaque routeur, agissant comme une file d'attente, gère le trafic avec une capacité de mémoire tampon et un débit de sortie. Lorsque le trafic entrant dépasse cette capacité, une congestion se produit, dégradant le service. Identifier ces goulots d'étranglement est crucial pour évaluer la performance du réseau. Cet article explore une méthode de partitionnement de graphe permettant de regrouper les flux partageant un goulot commun à l'aide d'un modèle probabiliste plus général que ceux de la littérature.

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.

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.

This work studies a new Expectation-Maximization (EM) algorithm for solving the 2D-3D registration problem, which consists of estimating the position and orientation of a camera using a 3D map and a 2D image of the same scene. This algorithm associates each image feature coordinate to one vector of the 3D map using the pinhole camera model or to a class of outliers, making the registration robust to the presence of abnormal image features. It iteratively improves the camera pose by estimating the associations between the image features and the 3D map coordinates (using a robust mixture model) and minimizing the reprojection errors between the image and map points. Experimental results demonstrate that the proposed EM algorithm achieves competitive results in both absolute position and orientation compared to the Iterative Closest Point (ICP) approach.