## 1 Introduction
Regulation and compliance standards require a continuous update of UAV's software to ensure continued adherence to legal and safety requirements [35], constituting a challenging chore. Where a UAV relies on remote communication systems for flight control and data transmission [36], any loss of communication (e.g., when UAVs operate beyond signal range limitations) poses a significant risk to the control and data transmission quality of the UAV (e.g., delayed responses and degraded data). Sensors inside a UAV, on the other hand, can be subject to failures [37], especially those that require precise calibration for accurate data interpretation [38]. Calibration errors or time-induced drifts lead to inaccurate readings (e.g., non-invasive current sensors reflect a false image over the actual current norm inside overhead electrical cables), hence compromising the performance of data collection [39]. In relation to the power systems inside a UAV, batteries (i.e., the main power source in a drone) can fail, with a sudden discharge or cell malfunctions [40]. Thus, the resulting loss of power can potentially limit the UAV's flight time [41], and in the worst cases, causes the UAV to shut down and crash. On the other hand, environmental conditions [42] (i.e., weather) can impact UAV performance. For instance, proper UAV performance is affected by strong winds [43], heavy rain [44], and extreme temperatures as follows:  
* Water-related challenges (i.e., rain) result in UAV operational limitations [45] as water can leak into the UAV, permanently damaging sensitive electronic components. * Humidity: high levels of air humidity induce condensation and water accumulation inside a UAV. * High temperatures: the performance of semiconductors inside a UAV is greatly affected by high temperatures. With the mentioned challenges and obstacles objecting to proper UAV performance, the more detailed the knowledge about different UAV subsystems is, the better the overall design will be. In other terms, a detailed analysis of the hierarchies between the electro-mechanical components of a UAV, in addition to the sensory parts, with algorithms and data processing, serves as a predictive UAV maintenance scheme. Eventually, the performance of a UAV will be optimized by better decision-making (i.e., in the design part) and problem-solving (i.e., after a fault occurrence). In accordance, there are many drone-related literature reviews in the field, which serve in turn as informative backgrounds about different aspects of UAVs. In [46], the established review took only into consideration the interactionsbetween a UAV and the payload (i.e., suspended loads), whereas in [47], the state-of-the-art focused mainly on drone detection and classification techniques. From another perspective, the review in [48] identifies current gaps in the application of UAVs for the creation of 3D models in the contexts of urban planning and historic monuments preservation. The review in [49] took into consideration the diverse possible applications of drones in healthcare applications, whereas in [50], the main focus was on the study of marine mammals (i.e., individual estimation, body conditions and biometrics, behavioral patterns, etc.). On the other hand, the review in [51] examined the drone-integrated Geographic Information System (GIS) in different fields, differently from the work in [52], which focused on UAVs' potential to advance climate change research and monitoring. Although the work in [53] mainly accounted for the UAV remote sensing of crop species, it included some of the multispectral sensors used in such applications, thus enriching the informative background about UAVs. In a more general approach, the review in [54] considered a structured presentation of the recent trends in the UAV field, classifying them according to their flight characteristics, showcasing the potential areas for further development, and addressing the hardware/software within a UAV.