We are delighted and proud to congratulate Benjamin Holló for winning the HTE Fekete László Award this year. The goal of the award’s founders, including eNET, is to support Hungarian innovators year after year, thereby contributing to the development of the domestic ICT (Information and Communication Technology sector) sector. Benjamin, a former member of the eNET team, worked in the drone industry. Recently, he has earned the HTE Fekete László Award for his outstanding work in improving the efficiency of solar power plants. With his team, he developed a complex, innovative system that combines drone data collection, 5G and Starlink technology, and artificial intelligence to detect faulty or contaminated solar panels quickly and precisely. Thanks to their development, power plant owners can achieve significant cost savings while maximizing renewable energy production. The speed and automation of this new system make it an outstanding solution on an international level, attracting well-deserved attention from the professional community. In an interview conducted on the occasion of the award, we explore how Benjamin turned his dreams into a commercially viable reality and how the experiences he gained at eNET played a role in this journey. We would like to congratulate you once again on this outstanding achievement! How did you feel when you found out you had won the HTE Fekete László Award? Thank you very much! I was completely surprised by the award because winning wasn’t really my goal when I applied. I was more curious about how industry professionals would react to our project and what their opinions would be, even from a business perspective. So, when I found out we had won the award, it was an especially big surprise for me. I’m very happy that such a prestigious professional community recognized our work. Could you tell us a little about your current work at Qamcom? What tasks do you deal with on a daily basis? This is a high-tech company that develops solutions in the space industry, telecommunications, and AI-based applications. The company’s portfolio includes 5G network solutions, private gigabit wireless networks, hardware development, as well as data processing and AI applications. My daily work revolves around supporting these developments and participating in new projects. Recently, I have mainly focused on product management for our solar panel project, but I am also involved in various consulting tasks. These include IT and security solutions, as well as drone detection projects. Additionally, I regularly attend industry events, maintain relationships with clients, and generate new projects to further expand our company’s portfolio. Could you please share a bit about your experiences at eNET, particularly in the drone industry? How did these experiences help you in your later projects? I worked at eNET for about 2.5 years, and although I recently left the company, I gained a lot of valuable experience, especially in the drone industry. One of the most important projects I led professionally was the UTM and Drone Research and Consultancy project. It was a collaboration involving three consortium members, with eNET taking the leading role. The project aimed to find solutions that enable the safe and large-scale management of drones. During the project, we worked on integrating ground networks, artificial intelligence, and algorithms to solve the challenges of efficient drone management. Although my primary focus at eNET was on drone technology and AI, the experiences I gained there had a significant impact on my decision to later focus on solar farm maintenance. It was during my time at eNET that the idea took shape in my mind that renewable energy, and particularly the sustainability of solar panels, is a key area where we can offer effective solutions by applying drones and AI. Why did you choose solar panels? Renewable energy, especially solar panels, has always been a topic of great interest to me. I’ve been following economic trends and noticed that this solution is becoming increasingly widespread and cost-effective. During my time at eNET, I kept a close eye on this market, attended conferences, and even spoke with solar farm owners and maintenance professionals in my free time. That’s when I realized that the field of maintenance is still in its infancy. In many cases, the detection of issues was done manually, which was not only time-consuming but also prone to errors. I came to the realization that as solar farms continue to grow, manual maintenance and fault detection will no longer be sustainable. These parks are located in vast areas and faults can only be measured when the system is in operation, so often it is necessary to walk around the plants in 35-40 degree heat to measure their performance. For solar farm owners, it’s crucial to keep every panel operational at all times to avoid revenue losses caused by shading, overgrown vegetation, or other contaminants like bird droppings. However, if faulty panels are not maintained in time, they may eventually fail and a complete cell replacement may be necessary. This gave me the idea to use drones for automated fault detection and apply artificial intelligence to perform diagnostics quickly, efficiently, and without any human error. Could you tell us about the solution you developed? How did you integrate drones, AI, and other technologies into the system? Our solution is capable of identifying faults in solar panels automatically, without human intervention. The system uses computer vision, artificial intelligence, and advanced algorithms to segment and analyze the panels in detail. Each panel is examined individually, and we build a database of faulty units, enabling quick and precise problem detection. Our automated system allows on-site measurements to be processed immediately. Since data processing relies on server capacity, it is not dependent on human resources, making the process extremely fast. A detailed report is generated, including a list of faulty panels, GPS coordinates, and map files that can be downloaded to a phone. This makes it possible for maintenance staff to go out to the site even in the evening to fix the source of the problem. We use drones for aerial