Orbital maneuver detection is critical for space situational awareness, yet it remains challenging due to the complex and dynamic nature of satellite behaviors. This paper proposes a novel Multi-Level Firing Spiking Neural Network (MLF-SNN) for detecting orbital maneuvers based on changes in satellite orbital parameters. The MLF-SNN incorporates multiple firing thresholds and a leaky integrate-and-fire (LIF) neuron model to enhance temporal feature extraction and classification performance. The MLF-SNN encodes time-dependent input features, which include variations in orbital elements, and subsequently processes these features through a multi-layer spiking architecture. A surrogate gradient approach is adopted during training to enable end-to-end backpropagation through the spiking layers. Experimental results on real satellite data demonstrate that the proposed method achieves improved recall in maneuver detection compared to conventional approaches, effectively reducing false alarms and missed detections. The work highlights the potential of MLF-SNN in processing time-series spatial data and offers a robust solution for autonomous satellite behavior analysis.

The delay, load, reliability and other factors of Software Defined Satellite Network are affected by the dynamic network topology. The location and number of controllers deployed are important factors affecting the performance of satellite networks. Aiming at multi-objective constraints such as delay, load and reliability, the controller deployment strategy is studied, which can achieve the optimal performance of the overall network. This paper proposes a multi-level controller deployment strategy for software defined satellite network, which uses GEO satellites to achieve global state coordination, and combines MEO and LEO satellites to build a multi-level control architecture. The strategy constructed a multi-objective optimization model including propagation delay, controller load and link reliability, and proposed an improved multi-objective particle swarm optimization algorithm to search the optimal solution. Simulation results show that compared with GA algorithm with K-means initialization and the adaptive multi-objective particle swarm optimization algorithm, the proposed strategy can reduce the average delay and controller load rate, while improving the network reliability. This provides a reference for the construction of software-defined multi-level satellite networks.

In this letter, a polarization filtering based transmission (PFBT) scheme is proposed to enhance the spectrum efficiency in wireless communications. In such scheme, the information is divided into several parts and each is conveyed by a polarized signal with a unique polarization state (PS). Then, the polarized signals are added up and transmitted by the dual-polarized antenna. At the receiver side, the oblique projection polarization filters (OPPFs) are adopted to separate each polarized signal. Thus, they can be demodulated separately. We mainly focus on the construction methods of the OPPF matrix when the number of the separate parts is 2 and 3 and evaluate the performance in terms of the capacity and the bit error rate. In addition, we also discuss the probability of the signal separation when the number of the separate parts is equal or greater than 4. Theoretical results and simulation results demonstrate the performance of the proposed scheme.

To address systemic challenges including decentralized construction, functional redundancy, and network heterogeneity in space-based information systems in the New Space Age, For the first time, this study formulates a unified, modular, and service-oriented digital infrastructure specifically tailored for integrated space-based information support. The architecture integrates four core components: 1) A localized hardware infrastructure ensures autonomous and controllable space information infrastructure; 2) A multisource data governance infrastructure establishes unified data resource pools; 3) An integrated space-terrestrial network protocol system constructs elastic transmission frameworks; 4) A microservice-oriented infrastructure achieves cross-domain functional integration of communication, navigation, and remote sensing (CNR) systems. The proposed architecture innovatively combines localized autonomous hardware, multisource data governance, an elastic space-terrestrial network protocol system, and cross-domain microservices for Communication, Navigation, and Remote Sensing (CNR) integration—moving beyond separate subsystem approaches seen in previous studies. Practical applications demonstrate its capability to enable flexible networking and synergistic operations of CNR systems, providing scalable technological foundations for constructing smart Earth, advancing space economy, and fostering aerospace scientific innovation. The research establishes a “horizontal convergence and vertical integration” architectural paradigm for space digital infrastructure, offering novel solutions for enhancing space-based intelligence support system effectiveness.

The reinforcement learning framework for multi-hop relational paths is one of the effective methods for solving knowledge graph incompletion. However, these models are associated with limited performances attributed to delayed rewards and far-fetched search trajectories. To overcome these challenges, we propose the searching window and multi-policy agent. The searching window provides a large action space, so that the agent can backtrack based on the newly obtained information and establish a local graph instead of a path chain. Based on the searching window, a double long short-term memory (DBL-LSTM) policy network is introduced to encode the local graph and relation sequence, after which the encoding information is used by the agent to select a correct entity to grow the local graph. Furthermore, multi-policy agent separately infers the local graph through three different policy networks, then, all local graphs are integrated into an information-rich local graph. Experiments using the WN18RR dataset revealed that local graph reasoning with searching window had greater rewards than path reasoning, the proposed DBL-LSTM policy network improved all HITS@N(N = 1,3,5,10) compared to prior works, and that the multi-policy agent achieved higher hit rates than single-policy agent.