## 2 Related Work  
At present, there are some studies on computation offloading in STN. [PERSON] et al. [17] proposed a double computation offloading algorithm based on STN, in which tasks are assigned to edge servers at the lowest cost, so as to minimize system energy consumption and reduce task offloading delay. [PERSON] et al. [18] proposed the satellite MEC, which allows UEs without a nearby MEC server to enjoy MEC services through satellite links. It also designed a cooperative computation offloading model to realize STN parallel computing, which minimizes user perceived delay and system energy consumption by optimizing task scheduling. [PERSON] et al. [19] designed a space-air-ground integrated network (SAGIN) architecture, which includes Internet of Things (IoT) devices, unmanned aerial vehicles (UAVs), and satellites. UAVs have edge servers, and satellites provide access to cloud servers. A learning-based computing offloading approach was proposed, thus converging fast and reducing total cost. [PERSON] et al. [20] proposed an architecture named satellite-terrestrial integrated edge computing network (STEN), which includes edge computing clusters, terrestrial MEC platforms, and satellite MEC platforms. The former two are closer to UEs, but have the limited service scope. The latter has the large service scope, but is further from UEs, the transmission delay is higher. [PERSON] et al. [21] designed a double intelligent satellite-terrestrial integrated network. The satellite MEC server assists the terrestrial MEC server to compute tasks offloaded by users. A task migration strategy based on greedy algorithm was proposed, so as to achieve load balancing and reduce system delay. [PERSON] et al. [22] proposed a Ka/Q-band satellite-terrestrial integrated network (STIN), where satellites are equipped with transparent repeaters without computing capabilities. Satellites receive tasks offloaded by Internet of remote things (IoRT) devices, and then forward tasks to ground stations. A learning-based computation offloading approach was proposed, thus improving the total reward, increasing the number of tasks satisfied, and decreasing the power consumption. [PERSON] et al. [23] proposed an MEC framework for the satellite-terrestrial IoT. With the assistance of satellite-terrestrial terminals, mobile devices of IoT can offload tasks to LEO satellites for computing. It also proposed an energy-efficient algorithm of computation offloading and resource allocation, so as to minimize the weighted sum of energy consumption of IoT mobile devices. [PERSON] et al. [24] proposed a coded computation offloading strategy (CCOS) in STIN, thus migrating ultra-dense computing tasks on the ground to distributed satellite constellations. The access satellite receives tasks and transmits them to other satellites without providing computing services. The effect of stragglers is alleviated, and the delay-energy tradeoff cost is minimized. [PERSON] et al. [25] designed a satellite-terrestrial double edge computing network architecture, where the computing capability of overloaded terrestrial MEC server is supplemented by LEO satellite MEC server. The former executes tasks in serial, the latter executes tasks in parallel. The local computing of IoT nodes is not considered. A joint optimization method of BS task execution order and satellite resource allocation was proposed, which increases the profit of MEC resource providers, and meanwhile satisfies QoE requirements of tasks. However, as far as we know, there are few studies on satellite-terrestrial cooperation, where a satellite as both a relay and a computing unit, assisting UEs in computing tasks.