As a subclass of nanosatellites, CubeSats have changed the game’s rules in the scientific research industry and the development of new space technologies. The main success factors are their cost effectiveness, relative ease of production, and predictable life cycle. CubeSats are very important for training future engineers: bachelor’s and master’s students of universities. At the same time, using CubeSats is a cost-effective method of nearest space exploration and scientific work. However, many issues are related to efficient time-limited development, software and system-level quality assurance, maintenance, and software reuse. In order to increase the flexibility and reduce the complexity of CubeSat projects, this article proposes a “hybrid” development model that combines the strengths of two approaches: the agile-a-like model for software and the waterfall model for hardware. The paper proposes a new computing platform solution, “Falco SBC/CDHM”, based on Microchip (Atmel) ATSAMV71Q21 with improved performance. This type of platform emphasizes low-cost space hardware that can compete with space-grade platforms. The paper substantiates the architecture of onboard software based on microservices and containerization to break down complex software into relatively simple components that undergraduates and graduates can handle within their Master’s studies, and postgraduates can use for scientific space projects. The checking of the effectiveness of the microservice architecture and the new proposed platform was carried out experimentally, involving the time spent on executing three typical algorithms of different algorithmic complexities. Algorithms were implemented using native C (Bare-metal) and WASM3 on FreeRTOS containers on two platforms, and performance was measured on both “Falco” and “Pi Pico” by Raspberry. The experiment confirmed the feasibility of the complex application of the “hybrid” development model and microservices and container-based architecture. The proposed approach makes it possible to develop complex software in teams of inexperienced students, minimize risks, provide reusability, and thus increase the attractiveness of CubeSat student projects.

CubeSats development has become a fastgrowing industry during the last 20 years. Mainly it became available due to the general use of the COTS hardware and software components. Using COTS hardware and software solutions brought speed and easiness to the development process but led to many non-successful missions. Today, with the costs and availability of COTS hardware, memory and computation power resources are no longer the main bottleneck in designing a nanosatellite. This change opened a significant breakthrough in using more complex data structures and models in the CubeSat software platform components. These models from the broader spectrum of more extensive data footprint applications allow CubeSat developers to benefit from extra metadata and not just a non-parametrized binary chunk of bits and bytes. This paper proposes a data model for representing and handling the constants, measurement, and calculation results in the software for CubeSat. Along with the data model, processing methods are proposed that provide a framework for improving the quality of open-source software projects for experimental development and further support of CubeSat student nanosatellites. The basis of the concept is an effort to improve the methods and means of automatic verification and validation of nanosatellite software when it is the data handling correctness that determines the mission success of the satellite. Proposed in the conceptual data model and data processing methods provide the basis for static and dynamic data validation.

The object of this study was to study the energy consumption of the on-board computer of the CubeSat nanosatellite. The subject of this study is the on-board computer energy consumption model and its energy efficiency optimization method. The purpose of this work was to develop a model of an on-board computer’s energy consumption and a method of optimizing its energy efficiency, followed by an experimental verification of the effectiveness of this method. Task: justify the feasibility of finding new methods for optimizing the energy efficiency of the on-board computer of the CubeSat nanosatellite; prepare for the experiment, namely, develop a model of the on-board computer's energy consumption and a method of optimizing its energy efficiency, develop an experiment plan, and conduct measurement methodology; conduct experimental research and present the main results of the experiment; provide a meaningful interpretation of the obtained experimental results; generalize the conclusions, formulate the advantages and disadvantages of this work, and propose directions for further research. Conclusions. The power consumption of the on-board computers of nanosatellites must be reduced by hardware and software means. This study examines the effectiveness of known energy-saving methods for the authors' "Falco SBC 1.0" computing platform. These methods include dynamic frequency scaling (DFS), the race-to-dark (RTD) algorithm, and the combined algorithm, which proved to be the most effective. The results were idealized because they did not consider OS overhead. The proposed methodology can be used to evaluate other platforms. The following studies will consider the following areas: energy consumption in various processor energy-saving modes, overhead costs of multi-threaded real-time operating systems, and power management of non-processor components. Solving these problems remains an important area of scientific research.

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.

The technology of building CubeSat-class nanosatellites has created a breakthrough in exploring near space for research and education. The availability of commercial solutions for the rapid integration of hardware and software of a CubeSat under development and the launch of such a nano-satellite into Earth orbit as a parasitic payload has made this technology accessible and attractive to universities worldwide. Despite this popularity, the statistics of nanosatellite failures after launch require a systematic approach to their design and construction and the selection of off-the-shelf (COTS) components. The object of the study is to determine the overall efficiency of standardized network interfaces when used in the onboard network of CubeSat nanosatellites. The subject of the article is a method for comprehensively evaluating the effectiveness of standardized network interfaces for data exchange in the onboard network of CubeSat nanosatellites, taking into account typical scenarios for data and command exchange. The aim of the study was to substantiate the method of expert evaluation of the technical efficiency of standardized network interfaces for data exchange in the onboard network of CubeSat nanosatellites, taking into account the following factors: energy efficiency, multi-mastering capability, resistance to electromagnetic interference, redundancy, coverage following the OSI network model, data transmission speed, and complexity of network technology implementation by software. Objectives: To analyze modern methods of organizing onboard avionics networks of nanosatellites of the CubeSat class, formulate an expert comparison model, and compare the selected information onboard networks following the model and the list of selected networks and their protocols. Results: an expert model consisting of a 7-factor comparison was created, modern and most widely used in the industry onboard networks were selected, and each network was compared according to the proposed model. Conclusions: The two most common and efficient models, I2C and CAN, were selected as recommended for use in the avionics of the KhAI1Sat nanosatellite and the “Boryviter”/”Falco” onboard computer being developed by the authors. Although the difference in power consumption between these two types of networks is significant, each has certain advantages in terms of noise immunity, multicasting, and application.