## 1 Introduction  
In existing networks, communication between User Equipments (UEs) is accomplished through terrestrial base stations. However, this communication service is limited to the coverage area of the installed base station, and it cannot be provided outside that region. To address these limitations, a communication method called Non-Terrestrial-Network (NTN) 3 GPP was proposed. NTN utilizes satellites capable of providing communication services across all regions, enabling coverage in areas not served by ground-based base stations. NTN leverages satellites, High-Altitude Platforms (HAPs), and Unmanned Aerial Vehicles (UAVs) as airborne mediums to offer services to users in locations such as airplanes, mountainous regions, and the sea, where traditional ground-based base stations cannot provide coverage [1, 2]. The use of diverse types of satellites and HAPs is expected to foster connectivity in various industries, including scenarios involving natural disasters, seaborne base stations, and construction sites [3]. Various scenarios utilizing NTN have been presented. Among them, an air-to-ground system has been proposed, enabling communication with airborne objects by utilizing ground networks while also providing ground network services in the air [4]. Additionally, research is being conducted on the NB-IoT (Narrowband Internet of Things) that supports IoT applications within NTN, as well as eMTC (enhanced Machine Type Communication) [5]. Over time, NTN is evolving to offer communication services or an expanded range of communication services to the public based on 5G technology, and numerous studies are currently underway in this domain. Covered also are the development of 5G NTN standards and the challenges of satellite 5G network integration technology and an overview of the state of the art in LEO satellite access [6; 7]. There are numerous factors to consider when applying NTN. It should be capable of supporting terrestrial networks and be adaptable to various industries [8]. Moreover, as technology progresses, studies are being conducted to incorporate Artificial Intelligence (AI) and Machine Learning (ML) models into communication systems [9]. Several papers have explored the application of ML models in different fields relevant to communication systems [10; 11]. Additionally, research is underway on AI/ML management in communication systems, including methods that leverage data exchanged during handover processes [11; 12; 13; 14]. Several considerations arise when high-altitude satellites or UAVs are utilized to provide services to ground terminal nodes. Among them, the handover of the control plane is crucial for ensuring service continuity when a User Equipment (UE) moves from one cell served by a base station to a neighboring cell, or transitions from a receiving base station to a neighboring base station's cell [15; 16]. However, applying the handover metric used in terrestrial networks to Non-Terrestrial-Network (NTN) poses a challenge. In a Terrestrial Network (TN), the distance between the UE and the base station is relatively close, allowing handover indicators to be measured at relatively short distances and used for triggering handovers. In contrast, in the NTN system, satellites can be positioned at altitudes ranging from 2000 km to 35,786 km above sea level, requiring considerably longer distances than in TN [17]. This difference in distance poses a significant challenge in developing handover mechanisms and metrics that are suitable for NTN's unique characteristics. Furthermore, satellites provide services while moving at a constant speed in the Earth's orbit. Due to this constant motion, the User Equipment (UE) receiving service from such a satellite experiences frequent handovers, even if it remains stationary. This frequent handover leads to a degradation in communication service quality and poses challenges in ensuring continuous service quality. Additionally, when a high-altitude satellite provides service to a ground-based UE, a propagation delay occurs.