## 1 Introduction  
Integrating Unmanned Aerial Vehicles (UAVs) or drones for the monitoring of telecommunication towers or base stations has emerged as a promising technological advancement in recent years. UAVs offer unique advantages, in terms of accessibility, efficiency, and data collection capabilities, enhancing the overall monitoring process [1]. The publications reviewed describe developed algorithms, but their applications were different. For example, in work [2] the researchers collected images of overhead power lines and processed them to analyze and compile them into a unified whole. Complex image processing methods were used for this. In work [3], similarly to [2], image processing was used, but this time for analyzing external power line insulators, with the images collected using drones. Again, in work [4] images were used to search for bird nests built around power poles and wires. In work [5], drones were used slightly differently, being equipped with a gas detector, allowing them to collect leak information while flying over a gas pipeline. Sequential data were then used with an artificial neural network that performed a simple classification task that, in our view, could have been conducted using a basic \"if-else\" condition--i.e., whether a leak was found or not. A somewhat different task was undertaken by the authors of work [6], who developed software for drones to track gas or oil pipelines. Similar tasks are performed in autonomous vehicle tracking competitions, in which line following and photodetectors are used, whereas image processing tools were employed in the article. Similar software was developed in articles [7; 8; 9]. Monitoring and data analytics can be further improved by deploying UAVs equipped with advanced sensors and imaging technologies. These drones can be programmed to conduct automated flight missions, collecting data on electromagnetic radiation levels and specer at various points around the telecommunication towers. The data are then transmitted in real time to ground stations, allowing for immediate analysis and decision making [10; 11]. On the other hand, in articles [10; 11] the focus was on how to efficiently collect data from sensors using drones--this is essentially a classical graph theory problem aimed at finding the optimal flight route between sensors. Articles [12; 13; 14; 15; 16; 17; 18; 19] discussed how antenna characteristics should be measured. Of course, the most accurate measurements would be performed in a controlled laboratory environment, specifically in a specialized RF chamber. However, in our case, measurements were carried out under real flight conditions. We could anticipate some obvious interference and properties, but it was practically impossible to test all scenarios that could influence and to what extent affect our measurement errors. In this case, the other authors used measurement hardware and software that collected data and presented them using standard antenna radiation diagrams. The use of UAVs also enables more frequent and cost-effective monitoring, as they can easily access difficult-to-reach tower locations without the need for physical infrastructure [20]. This accessibility not only enhances the safety of monitoring personnel but also ensures consistent and reliable data collection, especially in remote or hazardous environments. A more interesting solution for precise drone control and photo collection to create a 3D map of a tower was presented in information bulletins [20; 21]. Moreover, UAVs provide a dynamic perspective, allowing for a comprehensive assessment of the towers' surroundings and potential environmental impacts [22]. On the other hand, no algorithms are provided here--only the idea and results are implemented--so comparison is practically impossible. Another work [22], also performed a similar task, but by flying in a circle and visually observing the tower while photographing it. GPS coordinates were attached to the photo.