Radioelectronic and Computer Systems
type: channel
description: Founded in: 2003 Field of science and issues in focus: The journal reflects the scientific achievements of the scholars of higher education institutions, research institutes, industrial enterprises, potential applicants of academic degrees and titles in the field of electronic, computer and information systems in the following areas: design, technology and organization of production of radio engineering systems and complexes; development of computer systems; the use of information technology and information technology in the management of enterprises, programs and projects. The edition is included in the List of Scientific Professional Publications of Ukraine (Category "A") by the Ministry of Education and Science of Ukraine № 1017 of 27.09.2021. Specialties (ISCED-F 2013): 0533 (104) Physics 0541 (113) Mathematics 0588 (163) Inter-disciplinary programmes and qualifications involving natural sciences, mathematics and statistics 0612 (123, 125, 126) Database and network design and administration 0613 (121, 122) Software and applications development and analysis 0688 (124) Inter-disciplinary programmes and qualifications involving Information and Communication Technologies 0714 (171, 172, 173, 174, 175, 176) Electronics and automation 0716 (173) Motor vehicles, ships and aircraft 0788 (163) Inter-disciplinary programmes and qualifications involving engineering, manufacturing and construction INDEXING: Scopus (from 2019) ISSN: 1814-4225 (Print), 2663-2012 (Online) Media identifier No. R30-01718 (Decision dated 10/26/2023 No. 1146). Indexing: Vernadsky National Library of Ukraine; NRAT;«Ukrainika naukova»; «Dzerelo»; EBSCO; <a href="https://doaj.org/toc/2663-2012?source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22terms%22%3A%7B%22index.issn.exact%22%3A%5B%221814-4225%22%2C%222663-2012%22%5D%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22match_all%22%3A%7B%7D%7D%7D%7D%2C%22size%22%3A100%2C%22sort%22%3A%5B%7B%22created_date%22%3A%…
publisher: Національний аерокосмічний університет "Харківський авіаційний інститут"
language: en-US
publication name: Radioelectronic and Computer Systems
issn: 1814-4225
copyright: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International License that allows others to share (copy and redistribute the material in any medium or format), adapt, remix, transform, and build upon the material: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
items: To 70 anniversary selebration of Professor Oleksii Nikolaev
items: Digital transformation of the occupational health and safety management system in civil aviation through synergetic integration of digital twin and ai agents’ technologies
items: War-driven displacement and COVID-19 in Poland: simulation study using LSTM model
items: Markov modelling of human-machine interaction in an augmented reality environment for UAV/UGV-based hazardous area monitoring systems
items: Methodology for validation of analytical models of two-phase flows in supersonic nozzles using CFD simulation
items: Efficiency analysis of GREEDI algorithm under delta-matroid constraints for subset selection in distributed systems
items: Development of a partially supervised Markov decision-making model for a 3-link collaborative robot-manipulator
items: Explainable artificial intelligence for multimodal sentiment analysis in revitalization project management
items: Per-link collision depth prediction for redundant manipulators in operational environments
items: Model of the intelligent system for prediction of road traffic accidents
items: Information-extreme identification of unmanned aerial vehicles
items: AI-based adaptive management of limited resources in SDN-IoT ecosystems
items: Sea ice extent forecasting using statistical and deep learning models
items: Method of UAV-based inspection of photovoltaic modules using thermal and RGB data fusion
items: A combined approach to pixel-wise classification of satellite images based on LBP, pseudocolor features, and XGBOOST
items: Integrating CVSS, national criticality levels, and MCDA for multi-factor cyber incident prioritization
items: Automating requirements traceability in project documentation using TraceTrend tool: model, design and application
items: Estimation of radio pulse parameters with linear frequency modulation
items: Combined architecture of an analog-to-digital converter with balanced throughput–cost trade-off

To 70 anniversary selebration of Professor Oleksii Nikolaev
type: item
description: To 70 anniversary selebration of Professor Oleksii Nikolaev
creator: Vyacheslav Kharchenko
rights: Copyright (c) 2025 Vyacheslav Kharchenko
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 5

Digital transformation of the occupational health and safety management system in civil aviation through synergetic integration of digital twin and ai agents’ technologies
type: item
description: The subject of this study is the digital transformation of the occupational safety management system in civil aviation. Owing to the country’s unique geopolitical position in the centre of Eurasia, the Republic of Kazakhstan’s rapid growth in cargo and passenger traffic is associated with increasing employee risks, making it critically necessary to review existing occupational safety management systems. Traditional approaches to occupational safety management, which often focus on reactive elimination of consequences, are insufficiently effective in the context of the dynamic development of a high-tech industry, where every incident has serious economic, reputational, and social repercussions. It is possible to improve occupational safety by applying advanced digital technologies, in particular digital twin and AI agent technologies, which are capable of continuously accumulating, processing, and transmitting vast amounts of data in real time through self-learning, creating a comprehensive picture of an aviation enterprise’s entire occupational safety management system’s functioning. This study aims to justify the feasibility of modifying the occupational safety management system in civil aviation in the Republic of Kazakhstan by integrating digital twin and AI agent technologies into key functional processes. Results. The proposed algorithm for the developed AI agent, which is explicitly designed for integration into the occupational safety management systems of aviation enterprises in Kazakhstan, is the key contribution of this study. Its architecture, operating principles, and algorithms for interacting with big data received from digital twins of various elements of the aviation system are described in detail, ranging from the condition of aircraft and ground equipment to the personnel’s psychophysiological indicators and the working environment’s characteristics. This algorithm enables the AI agent to detect anomalies and build predictive models, signalling pot…
creator: Baurzhan Bakirov
creator: Kayrat Koshekov
creator: Abay Koshekov
creator: Nataliia Levchenko
creator: Kazbek Aldamzharov
creator: Rustam Togambayev
rights: Copyright (c) 2025 Baurzhan Bakirov, Kayrat Koshekov, Abay Koshekov, Nataliia Levchenko, Kazbek Aldamzharov, Rustam Togambayev
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 6
ending page: 24
doi: 10.32620/reks.2025.4.01

War-driven displacement and COVID-19 in Poland: simulation study using LSTM model
type: item
description: Russia’s full-scale invasion of Ukraine led to Europe’s largest and fastest displacement since World War II. Poland received the largest inflow. Rapid movement can affect COVID-19 spread and stress testing, reporting, and vaccination systems. Aim: To evaluate whether the invasion-related displacement coincided with short-term departures in Poland’s national COVID-19 cases and deaths using an intervention-anchored counterfactual forecasting approach learned from pre-invasion trends. Object of the study: Daily COVID-19 cases and deaths in Poland. Data come from the WHO dashboard, which spans May 2020 and centers on the 24 February 2022 invasion with a 30-day post-invasion horizon. Methods: We fit a univariate stacked LSTM to pre-invasion data and forecast one step ahead for the first 30 days after 24 February 2022. The network uses LSTM(128) - LSTM(64) - Dense(25) - Dense(1) with a linear output. The timeline is split into training (before 24 January 2022), validation (24 January-23 February 2022), and testing (24 February-+30 days). Each series (cases, deaths) fits six times with different random starts. Accuracy is measured using the mean absolute percentage error (MAPE). Deviations from the counterfactual are summarized as absolute and relative effects over the 30-day window. Results: Observed daily values closely tracked the counterfactual during the first month after the invasion, with only modest, short-lived over-prediction in the middle of the window. Between the validation and test periods, the average MAPE rose from 5.94% to 14.39% for cases and from 5.90% to 14.62% for deaths, reflecting greater short-run uncertainty but no large national-level break. Conclusion: Despite exceptional migration pressure, Poland’s national COVID-19 series did not show a marked divergence from a data-driven counterfactual in the first month after 24 February 2022. Scientific novelty: To the best of our knowledge, this study provides the first Poland-focused, short-horizon, data…
creator: Mykola Butkevych
creator: Ievgen Meniailov
creator: Yaroslav Lutsiv
creator: Bohdan Kraivskyi
creator: Dmytro Chumachenko
rights: Copyright (c) 2025 Mykola Butkevych, Ievgen Meniailov, Yaroslav Lutsiv, Bohdan Kraivskyi, Dmytro Chumachenko
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 25
ending page: 34
doi: 10.32620/reks.2025.4.02

Markov modelling of human-machine interaction in an augmented reality environment for UAV/UGV-based hazardous area monitoring systems
type: item
description: The subject of the study is Markov processes used for the formal description of the dynamics of states of unmanned vehicles controlled by an operator through augmented reality–based human–machine interfaces. Within the scope of the research, unmanned aerial and ground vehicles are considered as complex multi-state technical systems whose functioning is determined both by their technical characteristics and by the specific features of human interaction with the control interface. The aim of the study is to assess the impact of augmented reality–based human–machine interaction interfaces on the error-free decision-making of unmanned system operators, as well as on their responsiveness during control and reaction to changes in system states. The objectives of this study are to develop Markov models for the following scenarios: (a) without considering system failures and operator errors, with full recovery; (b) without considering failures, but allowing for operator errors, with full recovery; (c) considering system failures without operator errors, with full recovery; (d) considering both system failures and operator errors, with full recovery; and (e) without considering failures, allowing for operator errors, with the presence of a redundant unmanned system. The resulting Markov chains are intended to be used for modelling and subsequent comparison of the impact of different operating conditions on the system. As a result of the study, the following were obtained: (a) a classifier of states of unmanned aerial vehicles within a hazardous environment monitoring system based on the possible presence of failures, operator errors, and system redundancy; (b) Markov models for various system operation scenarios; and (c) simulation results of system operation based on the developed Markov models. Conclusion. The scientific novelty is as follows: a method for assessing the availability of monitoring systems with augmented reality–based human–machine interaction interfaces is …
creator: Yevhenii Kanarskyi
creator: Vyacheslav Kharchenko
creator: Oleksandr Orekhov
creator: Yuriy Ponochovnyi
rights: Copyright (c) 2025 Yevhenii Kanarskyi, Vyacheslav Kharchenko, Oleksandr Orekhov, Yuriy Ponochovnyi
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 35
ending page: 54
doi: 10.32620/reks.2025.4.03

Methodology for validation of analytical models of two-phase flows in supersonic nozzles using CFD simulation
type: item
description: The subject matter of this article is the methodology for mathematical modeling of gas-dynamic processes in supersonic converging-diverging nozzles. This study aims to develop and substantiate a comparative validation methodology for assessing the accuracy of simplified analytical isentropic models against high-fidelity computational fluid dynamics (CFD) simulations for two-phase compressible flows involving secondary gas entrainment. The tasks to be solved are as follows: implementing a numerical model of the gas-particle flow within a supersonic nozzle using the finite volume method (ANSYS Fluent); performing analytical calculations of particle velocity and temperature using the one-dimensional isentropic model; and conducting a comparative analysis to identify systematic deviations and substantiate the applicability limits of the simplified analytical approach compared to the numerical solution. The methods used are: the study of gas dynamics of a two-phase flow was studied by numerical modeling using a modern computing package based on the finite volume method ANSYS Fluent, as well as conventional gas dynamics. The following results were obtained: two-phase CFD simulation (carried out in ANSYS Fluent) and a one-dimensional isentropic model were employed to analyze the behavior of nickel particles under varying gas stagnation temperatures (440 °C, 520 °C, 620 °C) and particle diameters (10 μm, 25 μm, 40 μm). The CFD results, which incorporate real gas dynamics, including turbulence, viscous effects, and particle–flow interactions, were compared with analytical results. The CFD results show significantly lower particle velocities (by 50 ± 7%) and higher temperatures (by 22 ± 7%) compared with the isentropic model, primarily due to the inclusion of thermal losses, boundary layer development, and secondary flow effects. The latter arises from the atmospheric entrainment of the carrier gas and powder into the divergent section of the nozzle. These factors disrupt the…
creator: Oleksandr Shorinov
creator: Sergii Polyviany
creator: Nina Savchenko
creator: Roman Ipatov
rights: Copyright (c) 2025 Oleksandr Shorinov, Sergii Polyviany, Nina Savchenko, Roman Ipatov
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 55
ending page: 68
doi: 10.32620/reks.2025.4.04

Efficiency analysis of GREEDI algorithm under delta-matroid constraints for subset selection in distributed systems
type: item
description: The subject matter of the article is the efficiency analysis of greedy optimization algorithms for subset selection in distributed systems under delta-matroid constraints. The goal is to compare the performance of the classical unconstrained greedy algorithm and the GREEDI algorithm with delta-matroid constraints in terms of solution quality, computational characteristics, and scalability. The tasks to be solved are: to implement both algorithms; to perform simulations on synthetic graph datasets with sizes ranging from 10 to 100 nodes; to benchmark computational efficiency and approximation quality; to analyze the impact of delta-matroid constraints on benefit maximization and distributed execution. The methods used are: graph-based modeling, combinatorial optimization under matroid-type constraints, approximation algorithms, and distributed processing frameworks. The following results were obtained: GREEDI consistently provided higher-benefit subsets compared to the unconstrained greedy algorithm, achieving better trade-offs between execution time and solution quality; the distributed processing framework demonstrated scalability for large datasets and supported real-time responsiveness; performance advantages were more pronounced for larger graphs and higher constraint densities. Conclusions. The scientific novelty of the results obtained is as follows: 1) an experimental validation of the GREEDI algorithm under delta-matroid constraints for distributed subset selection was carried out; 2) the influence of such constraints on approximation quality and computational characteristics was quantified; 3) a scalable real-time processing approach for large graph-structured data was proposed, enabling potential applications in sensor deployment, recommendation systems, feature selection, and cache optimization.
creator: Inessa Kulakovska
rights: Copyright (c) 2025 Inessa Kulakovska
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 69
ending page: 82
doi: 10.32620/reks.2025.4.05

Development of a partially supervised Markov decision-making model for a 3-link collaborative robot-manipulator
type: item
description: The subject are mathematical models of decision-making under uncertainty in a production environment with human presence. The research objectives: is to form a safe and effective policy for controlling the motion of a three-link collaborative robot-manipulator, by developing a mathematical model of a partially observable Markov decision process (POMDP). Methods: methodology of partially observable Markov processes (POMDP), numerical modeling, approximation of the expected reward, comparative analysis of scenarios with different risk parameters. Results: the implemented model is able to form an adaptive policy for the manipulator's behavior taking into account incomplete information about the person's position; the dependence of optimal actions on the probability distribution of the human position and the intensity of the risk penalty is demonstrated; the influence of the difference in rewards between the fast movement and stop modes on the choice of actions is shown. Conclusions: the developed POMDP model can be used as a basis for building high-level adaptive control of a collaborative manipulator in a shared workspace with a human. The proposed approach has the prospect of being implemented in flexible production systems and cyber-physical complexes, in particular, taking into account dynamic risk reassessment and integration with computer vision algorithms.
creator: Vladyslav Yevsieiev
creator: Dmytro Gurin
creator: Sergii Kulish
creator: Yuliia Voloshyn
rights: Copyright (c) 2025 Vladyslav Yevsieiev, Dmytro Gurin, Sergii Kulish, Yuliia Voloshyn
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 83
ending page: 94
doi: 10.32620/reks.2025.4.06

Explainable artificial intelligence for multimodal sentiment analysis in revitalization project management
type: item
description: This study focuses on the development and evaluation of an explainable artificial intelligence (XAI) framework for multimodal sentiment analysis, specifically applied to territorial revitalization project management. The research addresses the critical problem of “black box” AI models, whose lack of transparency hinders their adoption by project managers who require trustworthy information for high-stakes decision-making in complex social environments. The goal of this study is to propose and rigorously validate a novel framework for multimodal sentiment analysis that is tailored to provide transparent, trustworthy, and actionable insights for decision-making in territorial revitalization project management. The tasks to be solved include developing a hybrid XAI technique that fuses insights from cross-modal attention and gradient-based attribution, designing a cohesive, user-centric explanation format combining highlighted text and image heatmaps, constructing a custom RevitalizeSent-MM dataset for this specific domain, and empirically evaluating the framework’s predictive accuracy and, crucially, the fidelity of its explanations. The methods used involve a transformer-based Multimodal Sentiment Analysis (MSA) model using BERT and ViT with cross-modal attention for information fusion. The explainability component is a hybrid XAI technique that integrates cross-modal attention analysis with Integrated Gradients to assign importance scores to input features. Evaluation was performed using standard classification metrics for performance and the “Accuracy Drop on Perturbation” metric for explanation fidelity. The results confirmed the efficacy of the framework. The multimodal model demonstrated superior accuracy over unimodal baselines, and the proposed XAI method achieved significantly higher fidelity than naive explanation approaches, demonstrating its ability to accurately reflect the model’s internal reasoning. The scientific novelty lies in three areas: the develo…
creator: Serhii Dolhopolov
creator: Yuliia Riabchun
creator: Maksym Delembovskyi
creator: Oleksandr Molodid
rights: Copyright (c) 2025 Serhii Dolhopolov, Yuliia Riabchun, Maksym Delembovskyi, Oleksandr Molodid
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 95
ending page: 111
doi: 10.32620/reks.2025.4.07

Per-link collision depth prediction for redundant manipulators in operational environments
type: item
description: The subject matter of this study is a collision checking for redundant robotic manipulators operating in variable environments, which remains a significant computational bottleneck in motion planning. The goal of this study is to improve computational efficiency of collision checking for multi-joint robotic manipulators in sampling-based motion planning, while preserving functional safety. This is achieved by developing and evaluating a learning-based method that predicts per-link penetration depth and serves as a statistical pre-filter rather than a replacement for exact collision checking. The tasks are as follows: 1) to propose a novel input representation that fuses the manipulator's kinematic state with localized geometric context extracted from the environment via voxel grids; 2) to design and implement a hybrid neural network architecture combining a fully-connected projection layer with a Kolmogorov-Arnold Network (KAN); 3) to train the network on a large, procedurally generated dataset of diverse collision scenarios; and 4) to evaluate the model's regression accuracy, classification performance, and computational speedup over a direct physics-based checker. The following results were obtained: the trained model achieves high regression accuracy with a low Mean Squared Error of 0.000148 on the test set; the model achieves promising classification results with a per-link recall of 93.01%, which is an important indicator for its use as a pre-filter capable of screening out the majority of hazardous states; computational speedup - performance benchmarks for a batch of 8192 states show that the proposed approach, including data preparation and inference, is approximately 3.7 times faster than a direct physics-based checker. Conclusions. The scientific novelty of results obtained is as follows: 1) a neural network architecture combining fully-connected and Kolmogorov–Arnold Network layers is proposed for predicting per-link collision depth of a redundant manipula…
creator: Andrii Medvid
creator: Vitaliy Yakovyna
rights: Copyright (c) 2025 Andrii Medvid, Vitaliy Yakovyna
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 112
ending page: 126
doi: 10.32620/reks.2025.4.08

Model of the intelligent system for prediction of road traffic accidents
type: item
description: This study aims to determine the prerequisites for the occurrence of road traffic accidents, analyze the most dangerous maneuvers of motor vehicles that can lead to hazardous situations, and develop the most effective method for promptly informing the driver about potential danger. The goal of this study is to develop an information system that ensures timely notification of drivers about possible road traffic accidents in designated hazardous areas. The tasks include: investigating existing computer vision models for classification and object tracking tasks and determining the most suitable ones for deployment on a single-board computer Nvidia Jetson, while examining their performance and technical limitations; developing an optimized solution for the prompt notification of drivers about danger; creating an algorithm for detecting potential vehicle collisions that integrates computer vision methods and mathematical modeling; developing a comprehensive danger warning system based on the obtained results and testing its functionality. The following methods were applied in this study: a process-based approach to investigate the mechanisms of road traffic accident occurrence, statistical analysis of hazardous areas and maneuvers, and performance analysis of computer vision models for real-time object detection and tracking and driver notification. Additionally, road situations were simulated and modeled using the BeamNG.tech environment. The results include the development of a methodology based on computer vision and mathematical models for identifying hazardous situations on the road and the creation of an approach for prompt notification of road users using cloud technologies, IoT devices, and the GeoHash algorithm. An information system that allows drivers to receive warnings about potential hazards along their route has been proposed. Conclusions: this study confirms the successful development of a software system for forecasting and notifying drivers about the ri…
creator: Oleksandr Byzkrovnyi
creator: Kirill Smelyakov
creator: Anastasiya Chupryna
rights: Copyright (c) 2025 Oleksandr Byzkrovnyi, Kirill Smelyakov, Anastasiya Chupryna
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 127
ending page: 142
doi: 10.32620/reks.2025.4.09

Information-extreme identification of unmanned aerial vehicles
type: item
description: This research aims to detect and identify unmanned aerial vehicles (UAVs) by analyzing the network traffic they transmit to their ground control station. The relevance of this topic arises from the need to develop highly efficient UAV identification systems, given their widespread use across the military, civilian, and commercial sectors. Effective solutions for detecting, identifying, and classifying these devices are required. This study aims to develop models and methods that enable machine-learning-based UAV identification systems by analyzing incoming and outgoing data traffic using an extremely intelligent information technology. A methodology is proposed for applying extreme information technology to UAV identification. This methodology involves employing pattern recognition techniques and designing intelligent information systems. The scientific novelty lies in introducing an approach to modeling an intelligent system capable of learning to identify UAVs based on traffic characteristics analysis. The research methodology includes the following steps: (1) forming a training dataset based on UAV traffic parameters, (2) building a UAV identification system using an information-extreme approach, and (3) training the system under standard operating conditions of digital radio communication channels used by UAVs. The results demonstrate that the proposed intelligent information system provides high UAV identification accuracy. Testing achieved an average identification accuracy of 86%. Conclusions. The proposed UAV identification system is based on an innovative approach to network traffic analysis using information-extreme intelligent technology. The results confirm its effectiveness for identification tasks under standard regulated traffic characteristics. The obtained results have practical significance for developing monitoring and protection systems in various fields against potential threats associated with UAV usage.
creator: Igor Shelehov
creator: Dmytro Prylepa
creator: Dmytro Olefirenko
creator: Roman Krytskyi
rights: Copyright (c) 2025 Igor Shelehov, Dmytro Prylepa, Dmytro Olefirenko, Roman Krytskyi
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 143
ending page: 153
doi: 10.32620/reks.2025.4.10

AI-based adaptive management of limited resources in SDN-IoT ecosystems
type: item
description: The subject of the study is the integration of artificial intelligence (AI) methods into software-defined networks (SDN) for adaptive control of access to limited resources within the infrastructure of Internet of Things (IoT) ecosystems. The goal of this work is to develop a model and architectural solution for a hybrid Cloud-SDN-IoT framework with embedded AI components, enabling the intelligent allocation of network and computing resources, and to experimentally validate the improvement of the fair distribution of a limited IoT resource across different traffic patterns in an emulation environment. The main tasks of the research are: 1) to analyze modern approaches to energy-efficient resource management and security in SDN-IoT networks; 2) to create the architecture of a hybrid Cloud-SDN-IoT framework that combines centralized SDN network control with the flexibility of cloud infrastructure; 3) to develop an experimental methodology using machine learning components to improve resource allocation and reduce load imbalance among competing clients; 4) to evaluate the system’s efficiency in relation to the stated objectives and the fair distribution of limited IoT resources by assessing the request distribution and the accuracy of detecting resource access violations. The paper proposes an improved three-layer SDN architecture model incorporating AI-based analytics: the IoT infrastructure layer, the SDN control layer, and the cloud application layer. The experimental part was implemented in a virtual Linux environment using Mininet and Ryu, where the trained AI model makes decisions about allocating the limited resource. The experimental results demonstrated that integrating the AI module into the SDN controller workflow increases the accuracy of detecting resource access violations, reduces load imbalance among clients, and improves the stability of real-time request distribution. Conclusions. The scientific novelty of the obtained results lies in the development …
creator: Anatolii Banar
creator: Heorhii Vorobets
rights: Copyright (c) 2025 Anatolii Banar, Heorhii Vorobets
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 154
ending page: 170
doi: 10.32620/reks.2025.4.11

Sea ice extent forecasting using statistical and deep learning models
type: item
description: The subject matter of the article is the forecasting of time series of sea ice extent using statistical and deep learning methods. Sea ice extent is one of the most important indicators of climate change. Today, there are trends towards melting glaciers, which leads to a rise in sea level and, in turn, creates a significant threat of flooding of coastal regions around the globe. In addition, melting glaciers affect the flora and fauna of the Arctic and Antarctic regions, as well as economic stability in the world, covering economic development and food security. The spheres of agriculture, tourism, logistics are directly dependent on climate change, therefore, forecasting future changes is critically important for stability and sustainable development. The article analyzes the main trends in the change in sea ice extent. The goal of the study is to increase the reliability of long-term forecasting by designing a framework that covers the full forecasting cycle from data analysis to the use of predictive statistical methods and deep learning techniques. The tasks of the article are to conduct a comparative analysis of statistical methods and deep learning methods and their evaluation for the task of forecasting the area of sea ice distribution. The study used forecasting methods based on statistical models and deep learning. A study was conducted on the use of different approaches to forecasting future changes in a time series based on statistical methods, deep learning methods and ensemble models. The results obtained allow to evaluate the performance of models in the short term and an approach to long-term forecasting was formed. The use of autoregressors and deep learning methods is proposed to create a reliable long-term forecast. The comparison of the performance of the methods was carried out for the Northern and Southern Hemispheres. Conclusions. The scientific novelty of the results obtained is as follows: the method of forecasting time series of sea ice dist…
creator: Tetiana Hovorushchenko
creator: Olga Pavlova
creator: Vitalii Alekseiko
rights: Copyright (c) 2025 Tetiana Hovorushchenko, Olga Pavlova, Vitalii Alekseiko
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 171
ending page: 185
doi: 10.32620/reks.2025.4.12

Method of UAV-based inspection of photovoltaic modules using thermal and RGB data fusion
type: item
description: The Subject matter of the article is the design and experimental evaluation of an intelligent edge-cloud cyber-physical system for automated inspection of photovoltaic (PV) modules in utility-scale solar power plants based on multi-palette thermal infrared and RGB imagery acquired by unmanned aerial vehicles (UAVs). The Goal of this study is to enhance PV defect detection accuracy and operational efficiency by designing a novel method that converts raw, overlapping sensor data into a compact, geo-referenced inventory of actionable defects. The goal is achieved by systematically increasing detection mean Average Precision (mAP) and reducing false positive rates caused by data redundancy, while simultaneously minimizing bandwidth usage. The Tasks to be solved include: developing a palette-invariant thermal representation that suppresses dependence on pseudo-color rendering and camera internal parameters; fusing this robust thermal stream with contrast-enhanced RGB data using an adaptive gated mechanism that can down-weight unreliable modalities; implementing an on-board active perception loop that re-orients the UAV gimbal for additional views of ambiguous, small-area anomalies; designing a geo-spatial clustering and de-duplication module that merges repeated detections into unique fault events suitable for integration with SCADA and GIS tools; and quantifying the benefits of the proposed architecture on public benchmarks and real-world field trials in comparison with single-modality baselines. The Methods employed include deep convolutional neural networks based on a YOLOv11m-seg instance segmentation backbone trained with a palette-consistency regularization term, gated feature-level fusion of thermal and RGB embeddings, Rodrigues-based calculation of corrective gimbal rotations for adaptive re-acquisition, density-based spatial clustering with the haversine distance metric for geographic de-duplication of detections, and statistical performance analysis using mAP, …
creator: Andrii Lysyi
creator: Anatoliy Sachenko
creator: Pavlo Radiuk
creator: Mykola Lysyi
creator: Oleksandr Melnychenko
creator: Diana Zahorodnia
rights: Copyright (c) 2025 Andrii Lysyi, Anatoliy Sachenko, Pavlo Radiuk, Mykola Lysyi, Oleksandr Melnychenko, Diana Zahorodnia
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 186
ending page: 205
doi: 10.32620/reks.2025.4.13

A combined approach to pixel-wise classification of satellite images based on LBP, pseudocolor features, and XGBOOST
type: item
description: The subject of the article is pixel-wise classification of Sentinel-2 satellite imagery represented as three-channel data mapped to the RGB color space for convenient visualization, with specific attention to the challenges posed by sensor noise and lossy compression artifacts typical for satellite data. The goal is to develop and validate a classification approach that maintains high accuracy under substantial noise and compression, by combining Local Binary Patterns (LBP) texture descriptors with pseudocolor features and employing an efficient ensemble classifier. The tasks to be addressed are: to design a compact feature representation that integrates LBP-based texture information with pseudocolor; to train and tune an XGBoost classifier on these features and compare its performance with baselines that rely on pseudocolor information alone and with simple neural network models; to assess robustness to noise and compression artifacts across a range of compression levels. The methods used include extraction of LBP descriptors to capture local texture patterns, construction of pseudocolor features from RGB-mapped Sentinel-2 channels, and concatenation of these descriptors into joint feature vectors. An XGBoost algorithm is employed to build the classification model. Model effectiveness is evaluated using the F1 score as the primary metric under varying noise and compression conditions. Visual inspection of the resulting classification maps is used to corroborate quantitative results and to analyze spatial consistency and error patterns. Conclusions. The scientific novelty of the results is as follows: for the first time in the context of Sentinel-2 pixel-wise classification, the use of LBP in combination with XGBoost has been systematically investigated and substantiated for BPG lossy compression scenarios at the optimal operating point (OOP) or nearby; it has been experimentally established that there is a substantial gain in classification accuracy for heterogeneo…
creator: Maksym Rybnytskyi
creator: Sergii Kryvenko
creator: Volodymyr Lukin
creator: Volodymyr Rebrov
rights: Copyright (c) 2025 Maksym Rybnytskyi, Sergii Kryvenko, Volodymyr Lukin, Volodymyr Rebrov
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 206
ending page: 219
doi: 10.32620/reks.2025.4.14

Integrating CVSS, national criticality levels, and MCDA for multi-factor cyber incident prioritization
type: item
description: This integrated approach aims to prevent suboptimal prioritization, ensure effective resource allocation, and expedite the recovery of information systems. Methodology: the proposed methodology establishes a hierarchical, multi-factor prioritization approach. It integrates the quantitative technical severity assessment provided by CVSS with Ukraine’s national criticality levels. This dual-layer scoring is further supplemented by a structured tie-breaking mechanism using additional attributes to achieve precise prioritization. A structured dataset was constructed, covering nationwide impact, economic consequences, information-related impacts, functional consequences, recovery capabilities, and system/network specifics. A prioritization methodology was developed, involving five key phases: incident registration, data verification, correlation and aggregation, criticality assessment, and tie-breaking. Dedicated software was implemented to simulate the algorithm within the CERT-UA environment, enabling real-time registration, evaluation, and visualization of prioritized incidents. The simulation tested the algorithm’s effectiveness in handling incident inflows and its potential to streamline response efforts. Conclusions: this study presents a robust and novel multi-factor methodology that overcomes the insufficient granularity of existing national criticality levels. Introducing a hierarchical tie-breaking mechanism, the approach provides CERT-UA with a clear, decisive, and efficient tool for incident prioritization. Simulation and pilot implementations confirm the algorithm’s practical value and immediate applicability within the existing operational environments, significantly enhancing the ability of national-level response teams to mitigate the negative impacts of cyber threats. The system’s simplicity and adaptability ensure its applicability within existing operational environments, while its tie-breaking mechanism minimizes the risk of suboptimal prioritization.…
creator: Dmytro Uzlov
creator: Sergiy Yakovlev
creator: Olena Tolstoluzka
creator: Oleksandr Kopytsia
creator: Sergiy Burchenko
rights: Copyright (c) 2025 Dmytro Uzlov, Sergiy Yakovlev, Olena Tolstoluzka, Oleksandr Kopytsia, Sergiy Burchenko
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 220
ending page: 235
doi: 10.32620/reks.2025.4.15

Automating requirements traceability in project documentation using TraceTrend tool: model, design and application
type: item
description: The object of the study is a formalized model of requirements traceability in project documentation for hardware-software systems. The subject matter of the research encompasses the application of mathematical modeling and tool-based approaches to automate the traceability process, focusing on the design and functionality of the TraceTrend software tool. The primary goal of the study is to improve the quality and integrity of requirements management by implementing traceability mechanisms that ensure logical consistency, hierarchical correctness, and complete test coverage across all project documentation stages. The research tasks include: identifying challenges related to manual requirements tracing in safety-critical domains; constructing a formal mathematical model based on set theory, binary relations, and directed graphs; defining binary matrices for requirement inheritance and test coverage; developing automated analysis techniques for traceability conditions; integrating the model into the TraceTrend tool; and demonstrating its applicability through a real-world case study. The study employed the following methods: mathematical modeling of binary relations, model-based testing, static analysis of documentation structures, and the use of Boolean matrix operations for verifying coverage and consistency. As a result of the research, a formal model of requirements traceability was created and implemented in the TraceTrend tool. The tool enables automated extraction of requirement identifiers, construction of traceability matrices, and verification of coverage and logical completeness. The application of TraceTrend has shown its effectiveness in identifying undocumented requirements, broken dependencies, and gaps in test coverage early in the project lifecycle. Conclusions. The integration of formal models and traceability tools significantly strengthens the reliability and auditability of requirements management processes in engineering projects. TraceTrend has …
creator: Oleg Odarushchenko
creator: Olena Odarushchenko
creator: Oleksii Striuk
creator: Viacheslav Shamanskyi
creator: Petro Hroza
rights: Copyright (c) 2025 Oleg Odarushchenko, Olena Odarushchenko, Oleksii Striuk, Viacheslav Shamanskyi, Petro Hroza
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 236
ending page: 246
doi: 10.32620/reks.2025.4.16

Estimation of radio pulse parameters with linear frequency modulation
type: item
description: The subject of this article is the process of estimating the parameters of a pulse signal with linear frequency modulation (LFM) used in airborne radar systems, particularly in synthetic aperture radars (SAR). The goal of this study is to synthesize algorithms for the optimal estimation of the key parameters of an LFM signal (i.e., carrier frequency, modulation frequency change rate, pulse length, and radio pulse envelope) and to develop a block diagram of a radar receiver that implements these algorithms. The tasks to be solved are as follows: build a mathematical model of a signal with linear frequency modulation emitted by a radar, an observation equation, and a likelihood functional; synthesize algorithms for estimating the parameters of an LFM signal using the maximum likelihood method; and develop a block diagram of a receiver based on the synthesized algorithms. The solutions to these tasks are based on the statistical theory of radio engineering systems and computer simulation. The following results were obtained: 1) algorithms for estimating the carrier frequency, frequency change rate, pulse length, and radio pulse envelope were synthesized; 2) simulations showed high noise robustness of the algorithms (up to a signal-to-noise ratio of –30 dB); 3) a block diagram of the radar was designed, which implements the synthesized algorithms and refines the estimated parameters in feedback. Conclusions. The scientific novelty of the obtained results is as follows: algorithms for estimating the parameters of both the point (carrier frequency, modulation frequency change rate, pulse length) and time characteristics (radio pulse envelope) of pulse LFM signals have been obtained using the maximum likelihood method. For the first time, it has been shown that estimating the pulse width requires solving a transcendental equation, and estimating the envelope requires smoothing in a sliding window. The obtained results expand the application of the maximum likelihood method…
creator: Volodymyr Pavlikov
creator: Maksym Peretiatko
creator: Volodymyr Trofymenko
creator: Denys Kolesnikov
rights: Copyright (c) 2025 Volodymyr Pavlikov, Maksym Peretiatko, Volodymyr Trofymenko, Denys Kolesnikov
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 247
ending page: 260
doi: 10.32620/reks.2025.4.17

Combined architecture of an analog-to-digital converter with balanced throughput–cost trade-off
type: item
description: The object of research in this article is a combined architecture of analog-to-digital converters (ADCs), which is built by integrating a low-resolution flash ADC with a successive approximation register (SAR) ADC. Flash ADCs provide extremely high conversion speeds but suffer from a significant drawback: the resolution cost per bit increases exponentially with increasing bit depth. In contrast, SAR ADCs are characterized by low cost per bit, but their inherently sequential conversion mechanism limits their conversion speed. This study investigates a combined ADC architecture designed to effectively merge the advantages of flash and SAR ADCs, thereby maximizing economic efficiency per resolution bit. The core hypothesis is that using a flash ADC of relatively low resolution for initial rapid coarse conversion, followed by a SAR ADC for precise computation of the residual analog signal, can significantly reduce the overall cost of implementing high-resolution ADCs. The research objectives include analyzing the characteristics of flash and SAR ADCs, determining the optimal combination of their respective resolutions, developing the operational algorithm of the proposed combined ADC, creating a mathematical model in MATLAB Simulink, and evaluating its technical and economic performance. The results demonstrate that the optimal combination is a flash ADC with a resolution of 4–5 bits paired with an 8–10-bit SAR ADC. This configuration significantly lowers the cost per bit compared with traditional high-resolution flash ADCs while maintaining a considerably higher conversion speed compared with SAR ADCs of equivalent resolution. The simulation results indicated that integral nonlinearity (INL) and differential nonlinearity (DNL) values of the proposed ADC did not exceed ±0.5 LSB, confirming high conversion accuracy. In addition, we show that the energy-per-conversion figure remains unchanged relative to pure flash and pure SAR solutions in isolation. Furthermore, the eco…
creator: Oleksiy Azarov
creator: Oleksandr Dudnyk
creator: Maksym Obertiukh
creator: Oleksandr Lukashuk
creator: Oleksandr Murashchenko
rights: Copyright (c) 2025 Oleksiy Azarov, Oleksandr Dudnyk, Maksym Obertiukh, Oleksandr Lukashuk, Oleksandr Murashchenko
date: 2025-12-08
publication date: 2025-12-08
volume: 2025
number: 4
starting page: 261
ending page: 271
doi: 10.32620/reks.2025.4.18