## 4 System Testing and Performance
### System Deployment in the Real Scenario
As shown in Figure 11, a maximum flight length of 26 min and 9 s can be obtained, with a more conservative figure of near 21 min for a safe flight that will deplete only 80% of the battery. Although these figures are largely theoretical, they were useful to get an idea of how long a flight could be. Additionally, information about energy consumption for the most important components of the drone could be calculated. Again, Figure 11 shows how 30.4 Amperes is the maximum current drawn from the battery at full flying load, whereas the Maximum power consumption expected from the UAV would be 337.44 Watts. Other compelling information (current drawn from the battery at selected flying load, charger specifications, etc.) has been obtained as well. Figure 11: Information calculated for UAV flight, according to the UAV and battery capabilities. As the data were collected by using the Network Cell Info program, in the end, there was a significant degree of adjustability in the information retrieved, depending on the program that was used. Regardless of the application used in the MDAS, or the program written for such a purpose, there are several key information elements (signal power, location of the measurement, network operator, cell identifiers, etc.) that must be included in a mandatory manner. As can be inferred from the previous information, collecting information with UAV flights is plausible and the data collected is of interest. Furthermore, it can be obtained with a significantly cheaper and faster solution rather than using a team of technicians and/or engineers with their equipment. However, as appealing as this solution might be, it requires further processing after the information has been collected. The data collected must be visualized in a way that can be used by end users, regardless of their knowledge on the topics shown in this paper. As previously described, that is why a GUI was developed to display the most relevant data. The data collected under these experiments can be used as an example on how the GUI works, so that the information provided can be further extended: to begin with, the data collected from the CSV files obtained from the application installed in the smartphone used as MDAS will be loaded to the GUI by using a regular file explorer tab. Once the file has been loaded, information pre-processing will take place so that noise-related values are removed. Figure 12 shows how the GUI can display several pieces of relevant information, such as carrier-to-noise ratios for the two carriers used in data transmission for the Galileo GNSS and the signal level obtained as a representation of the collected data. Information shown in this figure is strictly related to the data collected by the drone, so it is a representation of the data shown in Figure 10, along with further data displayed in the GitHub link shown in [37]. As can be seen, data can be displayed in a user-friendly manner so that end users of the system will be able to infer knowledge from the drone missions in a fast way. There are also other actions that can be carried out with the GUI. As explained in Section 3, signal measurements can be represented over a map to better grasp the implications of the collected data in terms of actual coverage. As depicted in Figure 12, the information collected shows signal power measurements over a map, which reveals what locations or infrastructures are better covered by the 4G LTE signal and which ones have poorer signal values. Figure 12: Information displayed by the GUI.