## 2 Related Works
### Study of the State-of-the-Art
[17] described what the authors defined as: large-scale statistical modelling of air-to-air wireless UAV channels. This statistical modelling was created by using, among other features, propagation characterization of wireless communications enabled by Software Defined Radio (SDR) capabilities. As far as testing was concerned, two commercially available hexacopters were used as the transmitter and the receiver; both were equipped with a mounted channel measurement box. The purpose of the study fell close to the application domain that we have defined for our proposal, but there are some significant differences: the solution put forward by us is more oriented towards mobile network signal measurements, rather than modelling of wireless channels, and an end-user GUI is provided for better understanding the collected information. Furthermore, communications in the reviewed paper followed an A2A pattern, whereas the information is displayed in a GUI running on a computer in our study, thus using an Air-to-Ground (A2G) paradigm. UAVs also play a major role in electromagnetic coupling measurements, as it was shown in [18]. The authors of this paper described how UAVs operate in congested wireless environments that, when used in certain application domains (i.e., mobile base stations) exacerbate electromagnetic compatibility (EMC) and/or electromagnetic interference (EMI). They claim that: _\"To avoid undertesting and overtesting in EMC measurements, there is a strong need for a computational tool that can guide experimental measurements by predicting and quantifying the frequency and the orientation that causes the maximum electromagnetic coupling to a complex DUT_ [Device Under Test]\". To accomplish this, a simplified model was createdwhere a UAV was represented by a 220 millimetres edge length with four wires attached to the patch's corners, making a 45 degrees angle with the edge of the square patch. A Characteristic Mode Analysis (CMA) was performed afterwards, with modal significance, currents and field/radiation patterns for the simplified model being studied. Overall, this piece of research touched on a compelling topic that is of great interest when having several devices working together with wireless communications, but its scope and purposes were significantly different from the ones put forward in our study. It was described in [19] how multi-temporal UAV-obtained data could be used for measuring even rill and inter-rill erosion on the European loess belt. The usage of Digital Surface Models (DSMs) obtained as data acquired from a UAV was put forward to evaluate these erosion levels and take any required action, which could be as accurate as having less than 1 centimetre error in the imagery collected. The tool used for data acquisition was the UAV Falcon 8 (which received this name from being an octocopter), which, due to its hardware features (active stabilizing camera, capability to record data, camera with a zoom lens, etc.) was deemed suitable for this task. Furthermore, a Terrestrial Laser Scanning (TLS) device was used for further accuracy and reliability in error assessments of the aerial data. By means of this tool, it was determined with high precision what kind of erosion takes place during rainfall days in spring or thunderstorms in summer, reaching the conclusion that the greatest erosion volumes occur because of rill erosion instead of inter-rill erosion. As with previous papers, while the objective of this one was set in a rather different environment than the one used in our own study, it proved that UAVs can be used for different purposes that go beyond monitoring with a portable camera. More related to concepts of smart farming and precision agriculture, the paper authored by [PERSON] et al. [20] discussed the integration of _\"an airborne multi-spectral imaging sensor which is able to simultaneously capture three visible and three near-infrared channels\"_ in a UAV.