## 5 Conclusions and Future Works  
The authors of this paper built a drone with commercial off-the-shelf components capable of making use of several Galileo services that provide a comparative advantage when compared to other components that use different solutions that implement other components or GNSSs. Using a UAV as a starting point, additional parts were included to provide additional functionalities, like measuring signal levels in mobile network communications, data logging or information visualization via dashboards. The tests that were carried out confirmed a prototype that could make use of most of these characteristics. At the end of Section 2, a hypothesis was formulated that was pivotal to the paper: is it possible to develop a solution capable of providing an inexpensive open-source tool to perform reliable mobile network measurements making use of Galileo as the GNSS? This hypothesis was taken as the main challenge by the authors of this paper due to the fact that, as had been previously explained, regular procedures to take mobile network measurements are slower (it takes a longer time to cover the area to be measured with a team of people, even if they are using vehicles, because they are confined to roads or have to be careful about rocky or hilly ground areas), more expensive (the cost of having an expert or a team of experts performing the measurements is usually higher than a UAV pilot and the UAV operating in an area) and less effective (even equipped with the suitable equipment, a team of humans cannot take as many precise measures in a three-dimensional space as a UAV flying to the specific points required). While the idea of using a UAV to take those measurements looked appealing on paper, it was necessary to implement a system that could be used for experimentation in the real world, as there were other parameters (weather, landing, flight, accuracy with Galileo GNSS, etc.) that could be scarcely determined with a theoretical approach. The work shown in this paper proved that the hypothesis formulated (that is to say, the challenge that was presented in this paper that the studied state-of-the-art failed to completely solve) could be answered in an affirmative manner and paves the way for further development in this application domain. As far as future studies are concerned, it must be considered that the chosen solution proved its usability to acquire information from signal levels in the environment and could be upgraded in the future with the purpose of improving the accuracy of the data based on the positioning data that is obtained from Galileo. The use of the program Geo++ Rinex could be considered in future works as the logger application to obtain information, as it can be combined with the Galileo system regarding satellite positions that can be obtained from the NAVCAST server. In this way, updates regarding Galileo satellite positioning can be obtained. In addition to this, the possibility of building a UAV with an MDAS subsystem already integrated after its manufacturing could also be an important enhancement to the already existing solution. The files that have been created about the tests carried out and the displayed information are available in [[https://github.com/jrodrimo/GALENCODER/tree/main/logs](https://github.com/jrodrimo/GALENCODER/tree/main/logs)]([https://github.com/jrodrimo/GALENCODER/tree/main/logs](https://github.com/jrodrimo/GALENCODER/tree/main/logs)). MATLAB files containing the source code of the GUI are included there as well (accessed on 27 December 2022). [PERSON] designed, develop and tested all parts related to the MDAS, whereas [PERSON] worked on every aspect relate to the UAV and G.S. developed the GUI. [PERSON] contributed by writing Sections 2-4 and [PERSON] wrote Sections 1 and 5 and checked the document for spelling errors and typos. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable.