## 5 Application Scenarios
The white paper in [209], discussing of an EU vision of the upcoming 6G network ecosystem, describes NTN nodes as \"computing and storage in the sky\" for task scheduling, task offloading, and caching capabilities [26; 50; 209; 214]. Generally speaking, NTN nodes can be seen as data centers in the air, which are supposed to strongly leverage AI-based techniques [206; 207]. SAGINs can also be described through the lens of service-oriented networks [50], in which the focus is moved from coverage, user access, and data exchanges to the possibility of offering guaranteed services to final users. This means that on-demand reconfigurability must be possible to tailor the network configuration at any time to adapt to the requirements of the services to be provided. SDN and NFV are key technologies in this matter, and flexible components, such as UAVs, are crucial to recompose the so-called _service function chain_ accordingly to the considered requirements. A 3D network architecture with moving elements poses several challenges in terms of mobility management: node movements must be carefully considered and predicted to minimize, e.g., link interruptions that impact user services. Another research line that has seen a recent revamped interest in the scientific community is represented by indoor localization, more precisely by the possibility to provide services offering a continuum indoor-outdoor localization and positioning [204; 205; 215]. SAGINs have the potential to support both localization and positioning and location-aware services, which will be of paramount importance for autonomous vehicles and in environments in which purely GNSS-based services cannot work (e.g., indoor, urban canyons) [205]. The case of autonomous vehicles is challenging from several viewpoints, especially looking at deployment, coverage, and capacity issues of the roadside infrastructure [202]. The network comprising both autonomous vehicles and roadside units is referred to as Internet of Vehicles (IoV), in which services similar to real-time autonomous driving assistance, collision avoidance, and traffic management, among others, are key services to be made available [203]. Those services require real-time data exchanges in most of the cases, thus calling for the use of edge-cloud computing in a synergic manner [50], offering low delay, caching, and offloading capabilities at the edge, complemented by significant storage capabilities and computational power at the cloud level. The paradigm of IoV is inspired by IoT, which sees a plethora of application scenarios of interest described in the literature, especially when considering the interplay of UAVs and satellites. IoT is described as the means to collect data from sensors or RFID [216; 217] and to send control messages to actuators in [200]. The assumption is that the smart objects are remote, dispersed over a wide geographical area, or are inaccessible; thus, the airborne segment is a viable if not the only option to connect them. The concept of IoT is specialized into what the authors define as Internet of Remote Things (IoRT) [200], and it is of interest for smart grids, environmental monitoring, and emergency scenarios. Several additional scenarios can be read in [212], such as military ones for dull, dirty, and dangerous operations; or in the case of disasters for recovery and support operations, as performed in Haiti in 2013 for goods delivery and for providing temporary connectivity because of the unavailability of the terrestrial infrastructure; for real-time traffic monitoring, as also proposed in [218], to assist in the case of heavy road congestion; finally, to enable local weather forecasting and monitoring [219], removing the need for fixed stations. The success of UAVs can be explained by the increasingly low prices, among other factors, which makes them an ideal option for several applications, such as fire detection and control or search and rescue operations [105], in addition to those already mentioned above.