Апаратно-програмне забезпечення бортових комп'ютерів наносупутників cubesat: систематизований огляд літератури

CubeSat nanosatellites have gained significant popularity due to their advantages, including modular architecture, short development cycles, relatively low launch costs, and qualification requirements for the project team, which enable them to successfully fulfill a wide range of tasks in educational and scientific missions. The number of available publications, covering the entire spectrum of scientific and production aspects of modern CubeSat nanosatellite platforms, is several hundred works per year, but their fragmentation and focus on individual narrow topics or specific subsystems of nanosatellites complicate a systematic understanding of the problems and approaches to solving them. This article aims to present a structured and systematic review of the literature on CubeSats over the past decade and to highlight emerging research trends for near future. The article analyzes existing scientific publications addressing the life cycle processes of CubeSat on-board computer hardware and software. To solve this complex problem, the authors employed an approach that involves automatic clustering of selected publications based on keyword analysis, expert classification using annotation analysis, and subsequent alignment of automatic and expert evaluations. The results of a systematic review and analysis of the literature reveal that the majority of publications are to some extent related to software engineering, while topics such education and training receive little attention. The study found that expert classification can partially reduce the impact of automatic clustering errors, and the ability to attribute a publication to several classes ranked by importance enables the analysis of the interdisciplinary nature of the publication. However, expert classification creates additional difficulties associated with the coordination of subjective expert opinions. The paper identifies key research areas in this field, contemporary approaches, hardware and software solutions, and outlines promising research trends. In particular, it was found that the key clusters to focus on are systems engineering and standards, hardware and software, computer architecture and reliability, and the use of artificial intelligence and machine learning.