The recent developments in satellite network research and space-based artificial intelligence applications highlight a significant shift towards optimizing network efficiency, enhancing on-board processing capabilities, and improving the accuracy of space object recognition and pose estimation. Innovations in Low Earth Orbit (LEO) satellite networks focus on leveraging the dynamic nature of satellite movements for distributed learning and network topology optimization, aiming to maximize network capacity and minimize latency. On-board image processing using AI models is being advanced to address the challenges posed by the constraints of the satellite environment, enabling more efficient Earth observation. Furthermore, the generation of synthetic datasets for training models to recognize and estimate the pose of Resident Space Objects (RSOs) represents a breakthrough in space situational awareness, crucial for collision avoidance and debris removal operations.

Noteworthy papers include:

• A novel split learning architecture for LEO satellites that utilizes their cyclical movement for distributed model training, significantly reducing computational burden and energy consumption.
• An innovative framework for generating synthetic datasets of RSO imagery, combining image recovery and pose estimation techniques to improve the accuracy of space object recognition.
• The introduction of the Dynamic Time-Expanded Graph-based Optimal Topology Design (DoTD) algorithm, which optimizes LEO satellite network topologies over time to enhance network efficiency and resilience.
• A Combined Routing Protocol (CRP) for ad hoc networks that integrates the strengths of AODV and GPSR protocols, reducing packet delivery time and improving route stability.
• A study comparing centralized and distributed routing schemes in large-scale satellite networks, demonstrating the advantages of distributed routing in dynamic network conditions.