## 2 Related Works  
The work proposed in this paper can be classified into several research areas. Since we are exploring possibilities offered by a satellite network, this paper is related to NTNs and their integration with other networks to obtain the SAGINs. However, since we are aiming to endow satellites with on-board processing capabilities, this research also falls into the research field related to the extension of edge computing capability to satellites, and since we are exploring the specific application to EO, it can be also framed in the context of the exploitation of a mega-constellation to process EO data in orbit, with a particular focus on research related to energy aspects in this specific application. Starting from NTNs and SAGINs, in [5], the authors recognized some concerns associated with the integration of LEO mega-constellations and ground networks, such as latency, jitter, unstable routing, and limited network reachability. To address these challenges, the authors proposed an optimization-based solution with the objective of integrating the two network segments while ensuring minimal latency and guaranteeing stable routing. Ref. [19] also focuses on latency and proposes a solution to ensure real-time communications leveraging mega-constellations by appropriately allocating flows and selecting cloud or satellite relay servers. In [20], a deep reinforcement learning-based strategy for traffic offloading is proposed, to overcome the limits of traditional strategies that deal with traffic offloading in highly dynamic topology and traffic scenarios such as the orbital one. Another application of Artificial Intelligence (AI) in this context is given by the authors of [21], focusing on the leveraging of AI techniques in 6G networks to integrate terrestrial networks and NTNs while improving the energy efficiency of maritime networks. Instead, ref. [11] is dedicated to the evaluation of the performance of federated learning techniques applied to LEO constellations for 6G. In line with these efforts, ref. [22] tackles the routing problem in LEO satellite constellations by modeling it as a multi-agent Partially Observable Markov Decision Process (POMDP), and proposes a distributed Q-learning-based approach that operates with only local information. Their results demonstrate low-latency performance comparable to centralized benchmarks, enhanced traffic support under high load, and reduced reliance on the ground segment. Complementarily, ref. [23] addresses learning in constrained LEO environments by introducing FedAAC, a resource-aware federated learning strategy that adaptively tunes aggregation frequency and model compression. Their approach improves training time, energy consumption, and communication overhead, demonstrating its suitability for Orbital Edge Computing scenarios with intermittent connectivity. As far as edge computing application to satellite networks is concerned, this technology has the potential to be used for two purposes: delivering services to terrestrial mobile users through satellite connectivity and facilitating Earth Observation (EO) missions. In the former context, ref. [24] proposes a study of the architectures, technologies, and challenges necessary to extend Mobile Edge Computing to SAGINs. Instead, under the application viewpoint, satellite edge computing to support Internet of Things (IoT) has been investigated in [14], focusing on the related architecture and scheduling strategies, while a framework based on an edge computing capability-endowed SAGIN to support Internet of Vehicles has been proposed in [25], whose authors leverage a deep imitation learning strategy to obtain a task offloading and caching strategy aiming at optimizing both the time needed to complete the task and the use of satellite resources. A collateral research area related to this topic focuses on the application of Network Function Virtualization (NFV) in satellite networks.