The increasing number of satellites for specific space tasks makes it difficult for traditional satellite task planning that relies on ground station planning and on-board execution to fully exploit the overall effectiveness of satellites. Meanwhile, the complex and changeable environment in space also poses challenges to the management of multi-satellite systems (MSS). To address the above issues, this paper formulates a mixed integer optimization problem to solve the autonomous task planning for MSS. First, we constructed a multi-agent-based on-board autonomous management and multi-satellite collaboration architecture. Based on this architecture, we propose a hybrid genetic algorithm with simulated annealing (H-GASA) to solve the multi-satellite cooperative autonomous task planning (MSCATP). With the H-GASA, a heuristic task scheduling scheme was developed to deal with possible task conflicts in MSCATP. Finally, a simulation scenario was established to validate our proposed H-GASA, which exhibits a superior performance in terms of computational power and success rate compared to existing algorithms.

Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper proposes a data-driven anomaly detection framework to detect point anomalies. We first propose the Deviation Divide Mean over Neighbors (DDMN) method to figure out the fake anomaly problem caused by data errors in the satellite telemetry data. Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. We applied our approach to the telemetry data collected from sensors on an in-orbit satellite for more than two years and demonstrate its superiority. Moreover, we explored what conditions could lead to false alarms. The approach proposed has been deployed to the ground station to monitor the health status of the in-orbit satellites.

Although in-orbit anomaly detection is extremely important to ensure spacecraft safety, the complex spatial-temporal correlation and sparsity of anomalies in the data pose significant challenges. This study proposes the new multi-task learning-based time series anomaly detection (MTAD) method, which captures the spatial-temporal correlation of the data to learn the generalized normal patterns and hence facilitates anomaly detection. First, four proxy tasks are implemented for feature extraction through joint learning: (1) Long short-term memory-based data prediction; (2) autoencoder-based latent representation learning and data reconstruction; (3) variational autoencoder-based latent representation learning and data reconstruction; and (4) joint latent representation-based data prediction. Proxy Tasks 1 and 4 capture the temporal correlation of the data by fusing the latent space, whereas Tasks 2 and 3 fully capture the spatial correlation of the data. The isolation forest algorithm then detects anomalies from the extracted features. Application to a real spacecraft dataset reveals the superiority of our method over existing techniques, and further ablation testing for each task proves the effectiveness of fusing multiple tasks. The proposed MTAD method demonstrates promising potential for effective in-orbit anomaly detection for spacecraft.

With the increasing number of satellites in orbit, traditional scheduling methods can no longer satisfy the increasing data demands of users. The timeliness of remote sensing images with large data volumes is poor in the backhaul process through low-earth-orbit (LEO) satellite networks. To address the above problems, we propose an edge-computing load-balancing method for LEO satellite networks based on the maximum flow of virtual links. First, the minimum rectangle composed of computing nodes is determined by the source and destination nodes of the transmission task under the configuration of the 2D-Torus topology of LEO satellite networks. Second, edge computing virtual links are established between computing nodes and users. Third, the Ford-Fulkerson algorithm is used to obtain the maximum flow of the topology with virtual links. Finally, a strategy is generated for computing and transmission resource allocation. The simulation results show that the proposed method can optimize the total capacity of the multi-node information backhaul in the remote sensing scenario of LEO satellite networks. The effectiveness of the proposed algorithm is verified in several special scenarios.

This paper proposes a practical life prediction model for Flywheel Systems (FSs) using the Stochastic Hybrid Automaton (SHA) method. The reliability of motors and the performance degradation of bearings are considered key causes of the failure of FSs. The unit flywheel SHA model is established for the failure mechanism, considering burst failure of motors and the accumulated performance degradation of bearings. This prediction model also describes the dynamic relation of lifetime with the configurations of FSs, work modes, and running environments. Monte Carlo simulation results demonstrate that the life distributions of FSs are quite different if the spacecrafts run in various orbits or with different configurations, or under changed work modes. The proposed method provides an engineering reference and guidance for the scheme design and in-orbit mission planning of FSs.

A broadband cascode distributed low-noise amplifier (CDLNA) with a wide operating temperature range is proposed in this work. A series of diodes are used as bias networks to achieve temperature compensation. To enhance the gain level in the passband, the negative resistance network is employed to offset drain transmission line losses. An attenuator based on reverse-biased diodes and resistors is designed to improve stability across the wideband. The circuit is implemented using a 0.15- $\mu $ m GaAs pHEMT process with a chip area of 2.6 $\times$ 1.55 mm $^{2}$ . Test results show that the CDLNA achieves an average gain of 17.5 dB, a linearity index OP1 dB of 14.0 dBm, a minimum noise figure (NF) of 1.2 dB, and an operating frequency range of 0.3–28 GHz. The operating temperature range is $-$ 55 $^{\circ}$ C to 85 $^{\circ}$ C, within which the gain variation is less than 742 ppm/ $^{\circ}$ C, and the NF variation is less than 2667 ppm/ $^{\circ}$ C