## 2 Related Work  
In recent decades, aerial applications in wireless communications have attracted active research due to their maneuvering, flying, and hovering capabilities [2; 3]. These functions enable their applications as flying BSs [21], forwarding communication links from GBSs to remote users in non-rural areas [22], providing rescue services in post-disaster areas [5], strengthening communications in hotspot areas using aerial mounted reconfigurable intelligent surfaces (RISs) [6], etc.  
On the other side, LEO-Sat has emerged as a promising technology for providing wireless communication services in recent years. These satellites orbit the Earth at a low altitude and can cover a relatively small area, allowing for high-speed, low-latency communication links. LEO-Sat systems are particularly attractive for applications such as sixth-generation (6G) networks, Internet of Things (IoT) connectivity, and other emerging wireless communication technologies. Regarding LEO-enabled wireless communications, in [8], a comprehensive overview of the physical and logical links, architectural and technological components needed to integrate LEO constellations into fifth generation (5G) and beyond 5G (B5G) systems was provided. In [10], a three-dimensional (3D) constellation optimization technique for a high-density LEO-based terrestrial-satellite communication model was proposed to optimize the number of satellites per constellation while attaining the minimum required backhaul per terminal. In [11], a load-balanced routing scheme was introduced to handle the LEO-Sat constellation framework. The work in [12] analyzed the downlink throughput of the LEO-Sat network that uses hopping spot beams. The authors presented upper and lower bounds on the downlink throughput based on the number of spot beams, interference restrictions, and coverage area. In [13], the authors discussed a user-centric handover methodology for ultra-dense LEO-Sat constellations to address the frequent handover problem and provide a higher quality of service. In [14], a low-complex massive multiple input multiple output (mMIMO) transmission technique with full frequency reuse (FFR) in LEO-Sat communication was proposed. The work of [15] investigated the joint optimization in LEO-Sat networks with multi-access edge computing servers to minimize the total delay of ground IoT devices.  
Recently, a few researchers focused on integrating LEO-Sat and aerials to expand the coverage range. In [16], a multiplayer deep reinforcement learning (DRL) technique was utilized to optimize millimeter-wave (mmWave) and free-space optical (FSO) links of combined LEO-Sat and aerials. In [17], the authors suggested benefiting from aerials and LEO-Sat for information gathering in 6G networks. The focus was on minimizing the total energy cost while satisfying the data transmission demands, and the issue was solved using algorithms incorporating LEO-Sat-assisted aerial trajectory design. In [18], the authors leveraged LEO-Sat and aerials' caching for content delivery in ground systems to enhance connections and increase user capacity. The joint optimization problem of cache position, resource management, and aerial path was treated using an algorithm based on block coordinate descent and successive convex approximation methods. In [23], the authors of this paper considered the same problem of LEO-assisted aerial distribution but without considering the problem of jointly optimizing the LEO-Sat bandwidth and aerial TX power like the current work.