## 1 Introduction  
Beyond visual line of sight (BVLOS) operation of an unmanned aerial system (UAS) is one of the key factors to obtaining the full benefits of a UAS business. Examples of BVLOS operations are UAS cargo delivery, environmental monitoring, and surveillance. UAS-based cargo delivery has several advantages over conventional vehicles, including a constant and high travel speed, no need for physical road infrastructure, directness of travel, and no exposure to traffic and congestion. This logistics system is preferable for the emerging market of e-commerce in an urban area and emergency medical supply in a rural area where the companies and customers benefit from saving time and cost. However, safety and privacy issues regarding UAV-based logistics are the main barrier to this application [1]. A UAS can also be used for environmental monitoring with mounted sensors to measure the conditions of environmental parameters, such as water, air, electromagnetic, light, and heat [2]. For example, this remote sensing application could be used for a risk assessment of the rising temperature effect on certain locations, especially on their populations [3]. Furthermore, a machine learning technique can be used to improve the detection of environmental conditions based on images captured from a UAV flight [4]. Another popular application of UAVs is surveillance. This application has a wide range of users from travelers, hobbyists, news reporters, security officers, and military personnel. The point of view of UAVs that humans have never been able to experience becomes one of the advantages, besides being scalable and flexible with more comprehensive surveillance coverage. However, this application also encounters several challenges, such as limited processing and power resources, and disturbance in transmission signals [5; 6]. These UAS applications and operations will require a UAS traffic management (UTM) system that maintains the safety level and monitors the traffic situation and communicates the information to all the stakeholders on the ground. Cellular communication is one of the proposed communication channels to be used in the UTM system. In an area where UAS traffic is high, conflict detection is crucial to maintain the safety level of UAS operations. The conflict is defined as \"an event in which two or more aircraft experience a loss of minimum separation\" [7]. Conflict detection can be classified into two types, which are strategic and tactical conflict detection. The strategic aim is that a planned UAS trajectory must be free of all known conflicts in 4-dimensional space prior to the departure. Strategic detection may be applied using trajectory- or volume-based representation of operations, while tactical detection involves a separation service that helps the UAV to avoid near-term conflicts during flight. Tactical conflict detection may take the form of a real-time sense and avoidance system that reacts to UAV traffic in the vicinity. This system could be deployed over the network, onboard the UAV, or a combination of both [8]. It is predicted that the current aviation communication channel is not suitable for UTM purposes where a higher degree of automation is expected. The new communication channel could allow machine learning and artificial intelligence to be involved in the decision-making of future UTM systems. However, safety- and risk-based evaluations must be carefully conducted as part of the certification process. Thus, machine learning techniques could be implemented at the strategic level first [8]. An example of these techniques is a deep reinforced learning algorithm to assist the UAV in avoiding static or moving obstacles during flight [9] and a random forest algorithm to detect and classify objects from images captured by the UAV [10]. There was some research conducted to solve this issue.