The field of Earth Observation and satellite networking is witnessing significant advancements, driven by the increasing capabilities of Agile Earth Observation Satellites and the development of more efficient satellite constellations. Researchers are exploring innovative approaches to optimize satellite scheduling, onboard processing, and real-time monitoring, aiming to provide high-quality information and reduce network latency. Furthermore, the design of Low Earth Orbit satellite networks is being improved through the optimization of inter-satellite link connections, leading to minimized average shortest path lengths and enhanced network performance. The application of multi-agent reinforcement learning is also gaining traction, enabling autonomous coordination in multi-satellite systems and improving the efficiency of spatial reuse in wireless local area networks. Notable papers in this area include:

• A study on scheduling Agile Earth Observation Satellites with onboard processing and real-time monitoring, which proposed a constructive heuristic method and achieved a 10% increase in resolution and 83% reduction in monitoring frequency variance.
• A research on minimum-hop constellation design for Low Earth Orbit satellite networks, which established lower bounds for average shortest path lengths and presented constructions for achieving near-optimal performance.
• A paper on hierarchical multi-agent reinforcement learning for coordinated spatial reuse in next-generation WLANs, which demonstrated improved throughput and latency across various network topologies.
• A case study on multi-agent reinforcement learning for autonomous multi-satellite Earth Observation, which evaluated the training stability and performance of state-of-the-art algorithms and provided practical guidelines for improving policy learning in decentralized EO missions.