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It is very apparent that the legal framework for Unmanned aircraft system Traffic Management (UTM) needs to be developed as regulators grapple with issues that relate to legal responsibility and accountability for each UTM stakeholder as the proliferation of drones increases. There is a considerable 'legal lacuna' that exists creating much uncertainty within the industry with respect to investment and the direction of innovation. Drones are being utilised today under controlled conditions as technology and ability develops, but with this accelerated pace of technological development, existing regulations soon become limited to address new capabilities and thus become out of date. Policy has become law in many jurisdictions, but policy needs to be developed further to keep pace with demand because safety is paramount. This paper investigates and highlights legal aspects that a regulator and UTM stakeholders have to consider in developing good drone law. It is essential that a properly considered legal framework is developed for many reasons including, but not limited to, increased positive public perception, proliferation of innovation of use cases for Unmanned Aerial Systems, improved environmental impact and improved safety. This paper describes the fundamentals that a well designed and considered legal framework for a UTM system should address, in order to provide much needed certainty that can guide all stakeholders to a regulatory path that leads to safe maximized utility of drones in shared airspace.

The current airspace has limited resource, and the widespread use of Unmanned Aircraft System (UAS) is increasing the density of civilian aircraft that is already crowded with manned aerial vehicles. This increased density in airspace demands to improve the safety, efficiency and capacity of airspace while considering all uncertain parameters that may cause hinderance in aircraft movement like weather and dynamic fluctuations. A systematic analysis of correlations between events and their impacts in air traffic network is a considerable challenge. This paper proposes a methodology that characterizes and identifies the patterns of Unmanned Traffic Management (UTM) airspace based on the analysis of simulated data to improve the performance of UTM network as well as ensuring its safety and capacity. Some sets of metrics are defined to identify the airspace characteristics that include airspace density, capacity and efficiency. The data analysis carried out here, will support risk analysis and improve trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather conditions, loss of safe distances, UAVs' performance, emergency services and airspace structures that may cause deviations from their standard paths.

The aesthetic cultivation of societal recollection is inextricably linked to the approach and preservation of the objects and the mnemonics that accompany them. This perception in modern society includes in its dormitories the historical evolution and retrospect of technology whose origins are pervasive in the realization of social impulses. Nostalgia and memories are no longer moved only through traditional ruins that reflect the aesthetics and sense of social development. In this context, it is important to examine whether video games nowadays can classify the early technological structures into the cradle of modern ruins, the evolutionary course of social memory. This article scrutinizes the multi-dimensional aspects of video games, approaching them as historical and technological achievements and identifying their aesthetic value as objects of the recent past that further stimulate quests lurking in this pixelated romanticism.

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To boost large-scale deployment of unmanned aerial vehicles (UAVs) in the future, a new wireless communication paradigm namely cellular-connected UAVs has recently received an upsurge of interest in both academia and industry. Fifth generation (5G) networks are expected to support this largescale deployment with high reliability and low latency. Due to the high mobility, speed, and altitude of the UAVs there are numerous challenges that hinder its integration with the 5G architecture. Interference is one of the major roadblocks to ensuring the efficient co-existence between UAVs and terrestrial users in 5G networks. Conventional interference mitigation schemes for terrestrial networks are insufficient to deal with the more severe air-ground interference, which thus motivates this paper to propose a new algorithm to mitigate interference. A deep Q-learning (DQL) based algorithm is developed to mitigate interference intelligently through power control. The proposed algorithm formulates a non-convex optimization problem to maximize the Signal to Interference and Noise Ratio (SINR) and solves it using DQL. Its performance is measured as effective SINR against the complement cumulative distribution function. Further, it is compared with an adaptive link technique: Fixed Power Allocation (FPA), a standard power control scheme and tabular Q-learning(TQL). It is seen that the FPA has the worst performance while the TQL performs slightly better. This is since power control and interference coordination are introduced but not as effectively in the TQL method. It is observed that DQL algorithm outperforms the TQL implementation. To solve the severe air-ground interference experienced by the UAVs in 5G networks, this paper proposes a DQL algorithm. The algorithm effectively mitigates interference by optimizing SINR of the air-ground link and outperforms the existing methods. This paper therefore, proposes an effective algorithm to resolve the interference challenge in air-ground links for 5G-connected UAVs.