## 2 Related Works  
Recently, there have been many studies on edge computing for satellites. In [17], the authors studied the edge cloud resource scheduling problem of space-air-ground integrated networks (SAGIN). The authors proposed an improved Two_Arch2 algorithm to optimize the resource-scheduling strategy of the SAGIN network to improve the service capabilities of the internet of vehicles. In [18], the authors proposed a satellite edge computing network architecture that supports the IoT and constructed a multi-objective optimization problem that considers system delay, computing power, and energy consumption. The authors proposed a slicing-based scheduling strategy to optimize the offloading sequence and a number of offloading tasks. In [19], the authors studied a hybrid LEO satellites and MEO satellite network for IoT. In order to solve the problem of satellite load imbalance, the authors formulated a joint optimization problem of computing and communication resources and proposed an optimization algorithm based on deep reinforcement learning to solve it. In [20], the authors studied the service chain optimization problem in satellite edge computing scenarios. Aiming to minimize transmission delay, the authors designed two algorithms, an approximation algorithm and an online algorithm. In addition, there are also studies on HAP drones carrying edge servers and providing edge computing services. [PERSON] et al. [21] studied HAP drone and ground network collaboration to provide computing offloading services for ground transportation. Considering the joint optimization of cache, computing, and communication resources, the authors used multi-agent reinforcement learning and the Lagrange multiplier method to solve the problem, which effectively reduced task processing delay. In [22], the authors introduced NOMA technology and deployed edge servers on HAP to provide computing offloading services for ground users. Taking into account power, transmission bandwidth, and maximum tolerated delay constraints, the authors proposed a transmission and deployment joint optimization algorithm based on successive convex approximation to minimize system energy consumption. In [23], the authors also studied the HAP network that supports NOMA. The authors proposed a power control algorithm based on DDPG to reduce energy consumption and task processing delay. In [24], the authors considered edge computing and wireless power transfer at the same time. In addition to offloading tasks to HAP drones for processing, ground IoT devices can also charge themselves through ground access points. The authors considered the problem of maximizing computing power while minimizing IoT energy consumption and designed a heuristic algorithm to solve the problem. Research on edge computing for collaboration between HAP drones and satellites mainly focuses on resource scheduling between HAP drones and satellites. In [25], the authors studied the task offloading and resource allocation problem in the scenario where HAP carries an edge server and cooperates with the LEO satellite network. The HAP drone can directly provide computing services to ground vehicles or forward data to the ground center for processing through LEO satellites. In [26], the authors studied the application prospects of machine learning in resource scheduling problems in space-space-ground integrated networks. The authors designed an optimization algorithm based on a deep neural network to realize intelligent user scheduling in space-space-ground integrated networks. In [27], the authors studied the edge computing network where HAP and satellites collaborate. Under this framework, users' tasks can be processed collaboratively by HAP and satellites. The author proposes a task offloading and resource allocation algorithm based on block coordinate descent to improve network service capabilities.