Low Earth Orbit (LEO) satellite communication networks have become an important means to provide internet access services for areas with limited infrastructure. Compared with the Geostationary Earth Orbit (GEO) satellites, the LEO satellites have limited on-board communication caching and calculating resources. Furthermore, the distribution of traffic requests is dynamically changing and uneven due to the relative movement between the LEO satellites and the ground. Therefore, how to schedule the multi-dimensional resources is an important issue for the LEO satellite communication networks. Beam-hopping is an efficient approach to improve the resource utilization by dynamically allocating time, power, and frequency according to the traffic requests. This paper proposes an efficient multi-dimensional resource allocation mechanism for beam-hopping in LEO satellite networks, which simultaneously satisfies the GEO interference avoidance. First, we construct the beam-hopping model of LEO satellites, and formulate the resource optimization problem. Second, we provide the weighted greedy strategy to determine the illumination pattern. In order to reduce the search space, the cells are clustered to non-interference clusters. Then, an improved genetic algorithm is provided to jointly allocate the communication resources. Finally, we construct various simulations to evaluate our proposed mechanism. Compared with the random-BH, polling-BH and traditional genetic algorithm, our algorithm achieves better performance in terms of both system throughput, access success rate, average delay and fairness between cells. The performance improvement is more significant in scenarios where traffic demand is unevenly distributed.

On-orbit object detection has received extensive attention in the field of artificial intelligence (AI) in space research. Deep-learning-based object-detection algorithms are often computationally intensive and rely on high-performance devices to run. However, those devices usually lack space-qualified versions, and they can hardly meet the reliability requirement if directly deployed on a satellite platform, due to software errors induced by the space environment. In this paper, we evaluated the impact of space-environment-induced software errors on object-detection algorithms through large-scale fault injection tests. Aside from silent data corruption (SDC), we propose an extended criterial SDC-0.1 to better quantify the effect of the transient faults on the object-detection algorithms. Considering that a bit-flip error could cause severe detection result corruption in many cases, we propose a novel automated model hardening with reinforcement learning (AMHR) framework to solve this problem. AMHR searches for error-sensitive kernels in a convolutional neural network (CNN) through trial and error with a deep deterministic policy gradient (DDPG) agent and has fine-grained modular-level redundancy to increase the fault tolerance of the CNN-based object detectors. Compared to other selective hardening methods, AMHR achieved the lowest SDC-0.1 rates for various detectors and could tremendously improve the mean average precision (mAP) of the SSD detector by 28.8 in the presence of multiple errors.

In recent years, satellites have exhibited a trend toward increased intelligence, leading to the need for the on-orbit deployment of artificial intelligence (AI) algorithms. Intelligent applications require heterogeneous computing to achieve accelerated processing. At the same time, employing a centralized onboard computing system can reduce the overhead associated with intra-satellite data interaction and management. In this context, this paper proposes an onboard heterogeneous computing framework compatible with the satellite management system. The design employs a chip-level heterogeneous architecture, utilizing CPU, GPU, and FPGA as the computational units. To manage and utilize the heterogeneous computing resources, the satellite management software structure, based on a multi-core processor, is expanded to form a heterogeneous computing software framework. This framework includes dependency libraries for intelligent applications and introduces an intelligent application management mechanism. The satellite management system, along with a database and intelligent application, is deployed on a hardware platform based on the Yulong810A multi-core heterogeneous processor. Test results demonstrate that the satellite management software, database, and intelligent applications function cooperatively and complete computational tasks within the required timeframes, thereby validating the fundamental capabilities of the proposed onboard heterogeneous computing system.