## 1 Introduction  
Satellite networks has recently received increasing attention and been regarded as an important component for future sixth-generation (6G) network architectures [1]. They have global coverage capability and high robustness, providing communication access for IoT devices widely distributed on the ground [2]. These characteristics mean that satellite networks remedy the defects of terrestrial networks in many scenarios [3; 4; 5]. Due to limited computing resources, IoT devices usually rely on cloud servers to process generated data beyond local capabilities [6]. Unfortunately, a long distance exists between cloud platforms and devices, leading to high communication latency, making it hard to deal with latency-sensitive applications. Meanwhile, with the rapid growth of IoT devices and data, transferring excessive data poses a challenge to network affordability [7], which also causes troubles in handling massive computation-intensive applications. Mobile edge computing (MEC) makes up for the above shortcomings, providing a new computing paradigm by deploying computing resources close to the terminal device [8]. In MEC, all undesired transmissions involving long distance and excessive data between the cloud and terminals are avoided [9], which significantly alleviates network congestion and improves response time for latency-sensitive applications. The widespread IoT devices motivate the convergence of satellite networks and terrestrial networks. Furthermore, since embedding MEC servers in satellites can provide a computing service for IoT devices, even in remote and depopulated areas, SEC has received extensive attention [10; 11; 12; 13; 14]. Many computation-intensive requirements, such as scientific applications [15], large-scale image mosaicking for reconnaissance [16], and object detection in images [17], represented as applications, can be further decomposed into several tasks with data dependence. Figure 1 illustrates a task offloading scenario with data-dependent constraints in SEC. Processing these tasks can be extremely challenging because tasks are interdependent rather than isolated from each other. Subsequent tasks must wait until the results of all predecessor tasks are available. Thus, the offloading strategy plays a critical role in SEC systems. It determines the assignment of edge servers to specific tasks, improving resource utilization, enhancing coordination, and reducing energy consumption. The dynamic topology of satellite networks [18], combined with the constant changes in routing and intersatellite link (ISL) lengths, amplifies the challenge of this problem. Essentially, task offloading with data-dependent constraints in SEC is an NP-hard combinatorial optimization problem [19]. The complexity of the solution process is affected by the size of the problem, such as the number of satellites, applications, and tasks. Hence, the main challenges of this problem are creating an appropriate mathematical model and discovering an efficient algorithm. Numerous researchers have contributed significant progress to SEC. [PERSON] et al. [10] pioneered the SEC concept and designed a cooperative computation offloading model. [PERSON] et al. [11] proposed a task offloading strategy based on Lyapunov optimization. [PERSON] et al. [12] designed a search matching-based algorithm to solve the dynamic satellite selection problem that improves the load balance of satellite resources. [PERSON] et al. [13] proposed a deep-imitation-learning-driven offloading and caching algorithm to achieve real-time decision making. [PERSON] et al. [14] presented a greedy-strategy-based task allocation algorithm for LEO satellite networks. Some scholars considered satellite-terrestrial links: [PERSON] et al. [7] proposed a LEO satellite network that combined hybrid cloud and edge computing, taking into account satellite coverage time and computation capabilities. [PERSON] et al. [20] designed a framework for terrestrial-satellite IoT and divided computational offloading into ground and space segments.