Satellite communications are envisioned as a key enabler for ubiquitous coverage in future 6G networks, yet the broadcast nature renders them vulnerable to eavesdropping, especially given the long-distance transmissions and associated high uncertainties. In this paper, we propose the physical layer security enhancement for multi-beam satellite communications with the assistance of an aerial reconfigurable intelligent surface (ARIS). Considering the high dynamics and uncertainties of channels, we characterize the channel distribution with moment-based ambiguity sets. Accordingly, a distributionally robust secrecy rate optimization is formulated through joint design of transmit and reflection beamforming. We then introduce a conditional value-at-risk-based reformulation to convert the probabilistic constraints into deterministic forms. An alternating optimization framework is subsequently employed to iteratively update the transmit and reflective beamforming vectors until convergence. Simulation results demonstrate that the proposed distributionally robust scheme significantly enhances secrecy performance, and maintains reliable performance across various channel error distributions.

Satellite communication is envisioned as a key enabler of future 6G networks, yet its wide coverage with high link attenuation poses significant challenges for physical layer security. In this paper, we investigate secure multi-beam, multi-group satellite communications assisted by aerial reconfigurable intelligent surfaces (ARISs). To maximize the sum of achievable multicast rates among the groups while constraining wiretap rates, we formulate a joint optimization problem involving transmission and reflection beamforming, ARIS-group association, and ARIS deployment. Due to the mixed-integral and non-convex nature of the formulated problem, we propose to decompose the problem and employ the block coordinate descent framework that iteratively solves the subproblems. Simulation results demonstrate that the proposed ARIS-assisted multi-beam satellite system provides a notable improvement in secure communication performance under various network scenarios, offering useful insights into the deployment and optimization of intelligent surfaces in future secure satellite networks.

This paper addresses the enhancement of physical layer security in multibeam satellite systems through the deployment of aerial reconfigurable intelligent surfaces (ARIS). We aim to maximize the sum achievable rate across multiple groups, subject to constraints on wiretap rates, by jointly optimizing the transmission beamforming and ARIS passive beamforming. We propose an alternating optimization framework, where the transmission beamforming and passive beamforming are optimized using semidefinite programming. Simulation results demonstrate that the proposed ARIS-assisted multibeam satellite systems can significantly enhance secure communication performance under various eavesdropping scenarios.

The space-air-ground integrated network (SAGIN) reveals enormous potential towards ubiquitous access with pros-perous applications for future 6G wireless networks, yet the limited energy presents a significant challenge towards the efficient operation of SAGIN. In this paper, we propose to employ an un-manned aerial vehicle (UAV) to approach the ground nodes to help alleviate the computation burden, where the computed results are then forwarded to the satellite for remote use. We formulate the problem to minimize the weighted energy consumption in terms of data offloading, computation, and results forwarding, along with the UAV propulsion energy, while jointly investigating the transmissions, scheduling, computation, and trajectory strategy design. The problem is then decomposed and solved in a block coordinate descent framework. Simulation results demonstrate that the proposed joint optimization scheme effectively reduces the overall energy consumption compared to benchmark approaches.

Non-terrestrial network (NTN)-based integrated communication and computation empowers various emerging applications with global coverage. Yet this vision is severely challenged by the energy issue given the limited energy supply of NTN nodes and the energy-consuming nature of communication and computation. In this paper, we investigate the energy-efficient integrated communication and computation for the ground node data through a NTN, incorporating an unmanned aerial vehicle (UAV) and a satellite. We jointly consider ground data offloading to the UAV, edge processing on the UAV, and the forwarding of results from UAV to satellite, where we particularly address the uncertainties of the UAV-satellite links due to the large distance and high dynamics therein. Accordingly, we propose to minimize the weighted energy consumption due to data offloading, UAV computation, UAV transmission, and UAV propulsion, in the presence of angular uncertainties under Gaussian distribution within the UAV-satellite channels. The formulated problem with probabilistic constraints due to uncertainties is converted into a deterministic form by exploiting the Bernstein-type inequality, which is then solved using a block coordinate descent framework with algorithm design. Simulation results are provided to demonstrate the performance superiority of our proposal in terms of energy sustainability, along with the robustness against uncertain non-terrestrial environments.

With increasing human space activities, detecting Resident Space Objects (RSOs) has become critical for space monitoring and on-orbit missions. Traditional optical sensors struggle in space environments due to extreme illumination variations and motion blur. Event cameras, bio-inspired sensors that asynchronously record per-pixel brightness changes, offer high temporal resolution, wide dynamic range, and low power consumption, making them promising for orbital sensing yet underexplored in this context. In this work, we present the first systematic study of spaceborne event-based space object detection. To address the scarcity of event data, we construct a large-scale dataset named Event-based SPAcecraft Recognition leveraging Knowledge of Space Environment (E-SPARK) by applying affine transformations and advanced event simulators to existing datasets. Building on this dataset, we propose a lightweight multi-scale MetaFormer backbone called M2Former together with an area-aware loss (AAL) tailored for small object detection. These components are integrated into the Real-Time Detection Transformer (RT-DETR) framework, a Transformer-based detector known for its robustness but higher computational cost compared to You Only Look Once (YOLO) models. Our design reduces parameters and complexity by over 50% while maintaining comparable detection accuracy. In addition, we design an improved data augmentation strategy that enriches supervision density and data diversity, further boosting detection performance. Experiments on both synthetic and real event data demonstrate that our method achieves state-of-the-art performance and strong generalization. These results highlight the potential of event cameras as a reliable sensing modality for spaceborne detection. The dataset, code, and supplementary materials are publicly available at: https://iamie-vision.github.io/M2Former/.

Multi-source frequency-hopping (FH) signal sorting without prior information remains a challenging problem. Con-ventional multi-source FH signal sorting is a two-stage scheme based on parameter estimation and signal classification, in which the low accuracy of parameter estimation will degrade signal sorting performance. Existing parameter estimation methods rely heavily on prior information about the signal and are susceptible to noise. This letter proposes a data-driven context-level FH signal parameter extractor (FHExt) to alleviate the mentioned limits by considering the correlations between pixels within signal areas and learning-based signal detection. To verify the sorting performance of the FHExt-based framework, an accurately labeled FH signal parameter extraction and sorting (FHES) dataset is developed. Experiments reveal that the FHExt-based framework outperforms benchmarks in terms of accuracy and mean average precision (mAP) in fully-blind scenarios. In addition, the FHExt-based framework can be adapted to semi-blind scenarios by slightly adjusting the post-processing method.

With the growth of interest in the attack and defense of deep neural networks, researchers have increasingly focused on the robustness of their application to devices with limited memory, in order to deal with unknown-budget (blind) adversarial attacks under different compression ratios. We analyze the existing pruning methods and find that the robustness of the pruned models varies drastically with different pruning processes, and the robustness of the pruned model with adversarial training exhibits a high sensitivity to the budget of the adversarial examples. These methods cannot obtain models that are comprehensively robust when confronting blind adversarial attacks with different compression ratios. To address this problem, we propose an approach called blind adversarial pruning (BAP) that introduces the approach of blind adversarial training into the gradual pruning process, to ultimately obtain pruned models with comprehensive robustness under different compression ratios. The experimental results obtained using BAP for pruning classification models based on several benchmarks demonstrate the competitive performance of this method; the robustness of BAP models is more stable compared to various pruning processes, and BAP exhibits better comprehensive robustness against blind adversarial attacks.