## 1 Introduction
In addition, due to the use of IoRT technology in various applications being diverse, there are different requirements for quality of service (QoS). For example, a large-scale IoT system in a disaster situation needs more channel capacity and a high throughput [19]. Meanwhile, during the remote control of rescue robots and other rescue equipment, it is necessary to provide constant services and video streaming services. Moreover, the multi-media traffic for the UAV-assisted emergency care communication networks requires both high reliability and high throughput [20; 21]. Recently, UAV-LEO integrated networks have been suggested in several studies. The work in [11] integrated UAVs with LEO satellites in emergency areas without ground facilities to meet the QoS requirements of different users. The work in [22] analyzed LEO satellite access time, and the LEO satellite orbit direction mainly affects the path planning of the UAV and minimizes the total energy consumption. In [23], the authors employed multiple UAVs as relays, for which deployments and relay schedules were optimized for maximizing the system's energy efficiency along with the power allocation. The work in [24] proposes an integration of LEO-Sat and UAVs for post-disaster assistance. It solves the problem of efficient UAV distribution with fairness and budget constraints. However, the LEO-Sat bandwidth resources are based on the average traffic demands of the LEO-UAV links. The various demands of multiservice traffic are not considered. The work [25] investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network. A DDPG-LSTM-based task offloading and resource allocation algorithm is proposed to solve the problem. According to the above review, most of the existing works focused on the problem of the UAVs' distribution, energy allocation, and bandwidth resource allocation. As the size of the constellation increases and the overlapping range of satellites increases, the selection problem of associated satellites has research value [26]. Meanwhile, the selection method of backhaul links between UAVs and LEO satellites has significant influence on the system performance. However, the selection problem of the backhaul links between UAVs and LEO satellites is rarely discussed in existing works. Moreover, joint quality factors should be designed to satisfy the various demands of multiservice IoRT sensors in the backhaul link selection algorithm. There are several studies about the service quality of UAV-aided or other wireless networks. The work in [27] jointly optimizes energy and throughput through revenue and cost components to boost radio capacity in hotspot zones in UAV-aided cellular networks. The work in [28] proposes a robust optimization approach to solve the capacity uncertainty in communication links and takes into account the loss of transmission capacity due to weather conditions. The proper resource allocation between ground-to-UAV and UAV-to-satellite links is proposed to improve network throughput and reduce latency in [29]. Therefore, it is particularly important to study an adjustable backhaul link selection method between LEO satellites and UAVs based on the different requirements of QoS. For clarity, the main contributions of this work are summarized as follows. 1. The selection problem of the backhaul links between moving UAVs and LEO satellites is solved. The dynamic topology is converted into a static topology based on a snapshot method, and the backhaul link selection problem is formulated as a constrained optimization problem. 2. The optimization problem considering the multiservice of IoRT applications is researched. An adjustable indicator for evaluating system performance, joining both persistence and capacity, is introduced to adapt to different scenarios. Also, it is the optimization objective. A corresponding heuristic algorithm is proposed to solve the optimization problem. 3. Numerical simulations to validate the effectiveness of the proposed algorithm are conducted.