Lina Volobuieva

In secondary scientific research, there is often a need to classify publications by content. However, the results of different experts may differ significantly due to different experiences, context, or individual biases. This creates difficulties in the formation of systematic reviews and scientific analytics. The article proposes a method for agreeing expert assessments that takes into account the multi-level classification structure (main and additional classes) and the relative weight of the selected categories. To quantify the differences, a distance metric was used, built by analogy with the Levenshtein distance, with the determination of the cost of editing operations (Replace, Rotate, Add). The proposed approach allows finding a compromise agreed result even with significant differences in assessments. The method was tested on a case of classification of 203 scientific publications on the architecture of on-board computers of CubeSat nanosatellites. The results showed that the use of the method reduces the number of conflict cases and increases the stability of the classification compared to simple majority voting. The proposed approach can be used not only in scientometrics, but also in expert systems and group decision-making tasks.

The embedded computer is a key element of the avionics of both spacecraft and unmanned aerial vehicles (UAVs). Its reactivity determines the system’s ability to ensure that the reaction time to external events meets the specified real-time constraints. This study aims to investigate the reactivity of the microcontroller of the embedded computing system as a component of the overall technical efficiency of the hardware and software complex. The mathematical model of reactivity, as well as methods, tools, and technology for experimental research of the reactivity of a real-time operating system, are the subjects of this study. This study aims to develop and verify a model of the reactivity of embedded computer software for use in nanosatellites and unmanned aerial vehicles. This study substantiated the need to create a set-theoretic model of the reactivity of embedded avionics systems for small spacecraft and unmanned platforms. A constraint analysis (Worst-Case Execution Time, WCET) was performed for typical onboard computer tasks. The requirements for the model were formulated, and its mathematical description and software implementation were developed. Numerical modeling methods were used to verify the simulation results’ compliance in the actual processes in the system. Results. The proposed mathematical model of reactivity considers the stochastic characteristics of event flows and two-stage interrupt processing, allowing the probability of a timely system response to various types of events to be assessed. Experimental verification confirmed the model’s correctness and suitability for analyzing the Boryviter / Falco platform under the control of the FreeRTOS operating system. The scientific novelty lies in the development of a new concept for optimizing the work of the real-time operating system scheduler of UAV and nanosatellites OBC, which is based on a complex mathematical model of energy consumption, computational performance and reactivity of the OBC, which, unlike power consumption control mechanisms in processors with dynamic frequency or voltage regulation, is specifically focused on OBC microcontrollers, in which the frequency and voltage are fixed, and optimization is possible only through the correct choice of the policy for controlling energy-saving modes.Conclusions. The developed model can be used to assess and improve the reactivity of computing systems, including the university nanosatellite Khai-1KA and unmanned aviation systems. Further research is aimed at creating instrumental software for reactivity analysis to support software engineering of new generations of aerospace and unmanned platforms with a high level of autonomy, considering the implementation of AI/ML tools.

The subject of the article is the study and comparison of two approaches to modelling the battery discharge of a CubeSat satellite: analytical using equivalent circuit and machine learning. The article aims to make a reasoned choice of the approach to modelling the battery discharge of a CubeSat satellite. Modelling the battery discharge of a satellite will enable the prediction of the consequences of disconnecting the autonomous power system and ensure the fault tolerance of equipment in orbit. Therefore, the selected study is relevant and promising. This study focuses on the analysis of CubeSat satellite data, based explicitly on orbital data samples of the power system, which include data available at the time of the article’s publication. The dataset contains data on the voltage (mV), current (mA), and temperature (Celsius) of the battery and solar panels attached to the five sides of the satellite. In this context, two approaches are considered: analytical modelling based on physical laws and machine learning, which uses empirical data to create a predictive model. Results: A comparative analysis of the modeling results reveals that the equivalent circuit approach has the advantage of transparency, as it identifies possible parameters that facilitate understanding of the relationships. However, the model is less flexible to environmental changes or non-standard satellite behavior. The machine learning model demonstrated more accurate results, as it can account for complex dependencies and adapt to actual conditions, even when they deviate from theoretical assumptions. However, the model requires prior training on a large amount of data and is less well understood in terms of physical laws. General conclusions. The equivalent circuit approach provides high accuracy and reliability under known conditions, but it is limited when external parameters change. The machine learning approach demonstrates better overall accuracy and stability, especially under variable or unpredictable conditions, but requires a large amount of high-quality data and more complex interpretation. Thus, the most effective approach may be a hybrid one, where the analytical model serves as the basis and machine learning is used as a tool for refining or compensating for inaccuracies.

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 rising popularity of UAVs and other autonomous control systems coupled with real-time operating systems has increased the complexity of developing systems with the proper robustness, performance, and reactivity. The growing demand for more sophisticated computational tasks, proportionally larger payloads, battery limitations, and smaller take-off mass requires higher energy efficiency for all avionics and mission computers. This paper aims to develop a technique for experimentally studying the indicators of reactivity and energy consumption in a computing platform for unmanned aerial vehicles (UAVs). The paper provides an experimental assessment of the ‘Boryviter 0.1’ computing platform, which is implemented on the ATSAMV71 microprocessor and operates under the open-source FreeRTOS operating system. The results are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. This paper provides experimental results of measurements of the energy consumed by the microcontroller and estimates of the reduction in system energy consumption due to additional time costs for suspending and resuming the computer’s operation. The results show that the ‘Boryviter 0.1’ computing platform can be used as a UAV mission computer for typical flight control tasks requiring real-time computing under the influence of external factors. As a further work direction, we plan to investigate the proposed energy-saving algorithms within the planned NASA F’Prime software flight framework. Such an investigation, which should use the mission computer’s actual flight computation load, will help to qualify the obtained energy-saving methods and their implementation results.

The rising popularity of UAVs and other autonomous control systems, coupled with real-time operating systems, has increased the complexity of developing systems with the proper robustness, performance, and reactivity. Concurrently, the growing demand for more sophisticated computational tasks, proportionally bigger payloads, battery limitations, and smaller take-off mass necessitates higher energy efficiency for all avionics and mission computers. The purpose of this paper is to develop the technology for experimental studies of indicators of reactivity and energy consumption of the computing platform for unmanned aerial vehicles (UAVs). The paper provides an experimental assessment of the ’Boryviter’ 0.1 computing platform, which is implemented on the ATSAMV71 microprocessor and operates under the open-source FreeRTOS operating system. The results obtained are the basis for developing algorithms and energy-efficient design strategies for the mission computer to solve the optimization problem. The paper provides experimental results of measurements of the energy consumed by the microcontroller and estimates of the reduction in system energy consumption due to additional time costs for suspending and resuming the computer’s operation. The results show that the ’Boryviter’ 0.1 computing platform can be used as a UAV mission computer for typical flight control tasks that require real-time computing under the influence of external factors. As the further work direction, authors plan to carry out the investigation of the proposed energy saving algorithms in scope of the planned NASA’s F’Prime software flight framework. Such an investigation that should be done with the real flight computation load for the mission computer, will help to qualify the obtained energy saving methods and their implementation results.

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.

The subject of study in this article is a method for industrial equipment vibration diagnostics that uses discrete Fourier transform and Allan variance to increase precision and accuracy of industrial equipment vibration diagnostics processes. We propose IoT-oriented solutions based on smart sensors. The primary objectives include validating the practicality of employing platform-oriented technologies for vibro-diagnostics of industrial equipment, creating software and hardware solutions for the IoT platform, and assessing measurement accuracy and precision through the analysis of measurement results in both time and frequency domains. The IoT system architecture for industrial equipment vibration diagnostics consists of three levels. At the autonomous sensor level, vibration acceleration indicators are obtained and transmitted via a BLE digital wireless data transmission channel to the second level, the hub, which is based on a BeagleBone single-board microcomputer. The computing power of BeagleBone is sufficient to work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the state of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is used. The verification of the accuracy and precision of the vibration diagnostics system was carried out on the developed stand. A comparison of the expected and measured results in the frequency and time domains confirms the correct operation of the entire system.