## 2 Related Work  
Non-terrestrial networks, particularly satellite networks, were proposed to be integrated with terrestrial networks in the early 2030s. Moreover, the most anticipated function of the STN was to provide coverage to rural and remote areas [18]. The authors in [19] proposed satellite MEC. MEC servers deployed at LEO satellites enabled devices without a nearby MEC server to enjoy MEC services through satellite links. In addition, a cooperative computation offloading model was designed to realize parallel computation in STNs, which minimized user-perceived delay and system energy consumption by optimizing task scheduling. Through radio frequency (RF) communication or optical communication, ISLs can realize data communication, interconnection, and information relay. Moreover, compared with RF ISLs, optical ISLs have the advantages of broader bandwidth, lower energy consumption, and lower interference [20]. According to [21], inter-satellite laser technologies can establish ISLs and achieve transmission between satellites at a high speed. With the aid of optical or visible light communication systems, LEO satellites can achieve connection through intra-plane ISLs and inter-plane ISLs [22]. In [23], some satellites temporarily formed a task group, where the access satellite was responsible for task and resource scheduling, while others were responsible for computing tasks. The authors in [24] proposed a double-edge computing STN architecture, where an access satellite can send tasks to other satellites for computing. They also proposed a double-edge computation offloading algorithm to minimize system energy consumption and reduce task offloading delay. In [25], a double-edge intelligent integrated STN was designed. Satellite MEC servers were grouped by ISLs, and they can assist terrestrial MEC servers in computing tasks. A task migration strategy based on a greedy algorithm was proposed to achieve load balancing and reduce system delay. The authors in [26] proposed a hybrid cloud and edge computing LEO satellite network architecture, where the tasks of ground users can be completed in one of three places, i.e., the ground user themselves, the LEO satellite, or the terrestrial cloud server. They also came up with a distributed algorithm to minimize the sum energy consumption of the ground users. The authors in [27] proposed an MEC framework for the terrestrial-satellite IoT. With the assistance of terrestrial-satellite terminals, IoT mobile devices can offload tasks to LEO satellites for computing. They also proposed an energy-efficient algorithm for computation offloading and resource allocation, thereby minimizing the weighted sum of energy consumption of IoT mobile devices. Most of the above works investigate the satellite-terrestrial cooperation or the inter-satellite cooperation in the presence of terrestrial networks, where satellites usually assist terrestrial entities. However, there are few works that investigate inter-satellite cooperation to assist users in computing tasks in areas not covered by terrestrial networks. Algorithms for computation offloading include heuristic algorithms, meta-heuristic algorithms, game theory-based algorithms, etc. [28]. Algorithms for resource allocation include convex optimization algorithms, heuristic algorithms, game theory-based algorithms, etc. Convex optimization algorithms are widely investigated and can easily obtain suboptimal solutions. However, they are complex and not easy to implement in actual systems. Heuristic algorithms are efficient and can quickly obtain solutions, but they are prone to fall into the local optimum. Meta-heuristic algorithms have too many parameters, which are not easy to adjust. Game theory-based algorithms are simple, flexible, and easy to implement. However, the obtained solution may not be globally optimal and the Nash equilibrium needs to be reached through continuous iterations [29]. The GWO algorithm is a meta-heuristic algorithm with few parameters.