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: IN MEMORY OF PROFESSOR OLEKSANDR LESHCHENKО
items: A MEASURE-BASED METHODOLOGICAL FRAMEWORK FOR CONTINUOUS MAXIMUM COVERAGE: GEOMETRIC FORMULATION AND HYBRID SWARM OPTIMIZATION
items: STRESS-STRAIN STATE OF A LAYER WITH CYLINDRICAL CAVITIES UNDER SPATIALLY DISTRIBUTED LOADS OF INFINITE EXTENT
items: A TOPOLOGY-AWARE UNIFIED MODEL AND METHOD FOR FORECASTING ENERGY CONSUMPTION IN DISTRIBUTED SYSTEMS WITH STATIONARY AND MOBILE DEVICES
items: COMPUTATIONAL ANALYSIS OF COLD SPRAY NOZZLE DESIGN’S EFFECT ON PARTICLE ACCELERATION
items: TEMPORAL EVENT-DRIVEN SCHEDULED ACTIONS (TESA) ONTOLOGY FOR CYBER PHYSICAL SYSTEMS WITH PRIORITY MECHANISM
items: DEVSECOPS PIPELINE FOR PROVIDING SECURITY OF IOT CLIENT INTERFACES
items: ADAPTIVE KNOWLEDGE REGULARIZATION FOR CONTINUAL LEARNING IN TRANSFORMER ARCHITECTURES
items: IMPLEMENTATION OF THE TEXT SIMILARITY EVALUATION METHOD WITH A WEIGHTED APPROACH TO IMPROVE THE ACCURACY OF FAKE NEWS CLASSIFICATION
items: MODEL FOR PRODUCT STRATEGY ANALYSIS OF AN IT COMPANY USING FUZZY INFERENCE
items: ENHANCING UAV CLASSIFICATION WITH RADIO FREQUENCY SIGNALS USING A HYBRID CONVOLUTIONAL-LSTM ARCHITECTURE
items: ADAPTIVE LEARNING OF FAULT‑TOLERANT SOFTWARE ENGINEERING FOR UAV AND NANOSATELLITE ONBOARD COMPUTING
items: A MODEL-BASED APPROACH TO ASSOCIATIVE TESTING TECHNOLOGY FOR EVALUATING COGNITIVE COMPETENCIES IN EDUCATION
items: A MACHINE LEARNING-BASED METHOD FOR ASSESSING THE FUNCTIONAL STABILITY OF DISTRIBUTED INFORMATION SYSTEMS THROUGH TOPOLOGICAL STRUCTURE
items: AN APPROACH TO ENHANCING HYBRID CYBERATTACK NETWORK SURVIVABILITY DURING BLACKOUTS
items: A BLOCKCHAIN-BASED ELECTRONIC VOTING SYSTEM WITH AUTOMATED LEADERLESS BFT CONSENSUS
items: iTZBEI: AN INTEGRAL SECURITY METRIC FOR SDN ARCHITECTURES INTEGRATING ZERO TRUST AND BLOCKCHAIN
items: VERKLE-FRI: A NOVEL ARCHITECTURE FOR STATELESS AND QUANTUM-RESISTANT VECTOR COMMITMENTS

IN MEMORY OF PROFESSOR OLEKSANDR LESHCHENKО
type: item
description: IN MEMORY OF PROFESSOR OLEKSANDR LESHCHENKО
creator: Vyacheslav Kharchenko
rights: Copyright (c) 2026 Vyacheslav Kharchenko
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 5
ending page: 5
doi: 10.32620/%x

A MEASURE-BASED METHODOLOGICAL FRAMEWORK FOR CONTINUOUS MAXIMUM COVERAGE: GEOMETRIC FORMULATION AND HYBRID SWARM OPTIMIZATION
type: item
description: Today, technical systems that rely on spatial monitoring, autonomous decision-making, and distributed sensing are widely used in industry, energy, environmental protection, telecommunications, and security. Many modern infrastructures, such as sensor networks, robotic platforms, surveillance systems, and environmental monitoring grids, require reliable geometric coverage of a given territory under operational constraints. Achieving high-quality coverage in the presence of forbidden zones, irregular boundaries, and heterogeneous service requirements represents an important scientific and practical challenge. The goal of this study is to develop a measure-based methodological framework for the formulation and computational treatment of continuous maximum coverage problems involving geometric objects of arbitrary shapes. To achieve this goal, the paper addresses the formalization of continuous coverage problems through set measures and configuration parameters, the construction of scalable methods for evaluating coverage areas with controllable accuracy, and the integration of global and local optimization mechanisms as computational instruments within the proposed approach, rather than as objects of algorithmic comparison. The subject of the research concerns methodological principles of hybrid swarm-based optimization applied to high-dimensional geometric configurations of covering objects. The proposed framework combines concepts from computational geometry, inclusion–exclusion formulas, Monte Carlo sampling, and population-based optimization, which are interpreted as geometric motion models in a continuous parameter space. The study provides several important methodological results. A general nonlinear optimization formulation of the continuous maximum coverage problem based on set measures is constructed, and computational schemes for estimating coverage areas under different accuracy–complexity trade-offs are introduced. Several families of hybrid swarm-based met…
creator: Sergiy Yakovlev
creator: Sergiy Shekhovtsov
creator: Yehor Havryliuk
rights: Copyright (c) 2026 Sergiy Yakovlev, Sergiy Shekhovtsov, Yehor Havryliuk
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 6
ending page: 22
doi: 10.32620/reks.2026.2.01

STRESS-STRAIN STATE OF A LAYER WITH CYLINDRICAL CAVITIES UNDER SPATIALLY DISTRIBUTED LOADS OF INFINITE EXTENT
type: item
description: The subject of this article is the stress and strain fields in an isotropic elastic layer in contact with rigid supports and weakened by a system of longitudinal cylindrical cavities. The goal of this study is to mathematically model the stress-strain state of an elastic layer with a specified number of cylindrical cavities; to develop a mathematical model and improve an analytical-numerical approach based on the generalized Fourier method for the exact determination of stress fields in an elastic layer with a specified number of cylindrical cavities under the action of infinitely distributed spatial loads. Tasks: to formulate a boundary value problem of elasticity theory for a multiconnected layer considering loads that do not decay at infinity; to construct a general solution of the Lamé equation by the superposition method using the auxiliary problem introduction algorithm; to reduce the boundary value problem to solving a quasi-regular infinite system of linear algebraic equations; to perform a numerical analysis for a layer with three cavities and to evaluate the effect of concentrators on stress distribution. The generalized Fourier method is applied to a layer in the Cartesian coordinate system and cavities in local cylindrical coordinate systems. The scientific novelty lies in the creation of an improved approach that combines the generalized Fourier method with the auxiliary problem algorithm. For the first time, the stress distribution and stress concentration patterns in the vicinity of holes for spatially distributed loads acting on an infinite length have been established. This made it possible to obtain the following results: correctly take into account loads that do not have a periodic structure and do not attenuate at infinity; for an aluminum layer with three cavities, accurate pictures of the distribution of stress tensor components were obtained and zones of their maximum concentration were determined; analysis of the stress state showed that igno…
creator: Nataliia Ukrayinets
creator: Tetyana Alyoshechkina
creator: Vitaly Miroshnikov
creator: Iaroslav Grebeniuk
creator: Vladyslav Demenko
creator: Ihor Arkhypenko
rights: Copyright (c) 2026 Nataliia Ukrayinets, Tetyana Alyoshechkina, Vitaly Miroshnikov, Iaroslav Grebeniuk, Vladyslav Demenko, Ihor Arkhypenko
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 23
ending page: 36
doi: 10.32620/reks.2026.2.02

A TOPOLOGY-AWARE UNIFIED MODEL AND METHOD FOR FORECASTING ENERGY CONSUMPTION IN DISTRIBUTED SYSTEMS WITH STATIONARY AND MOBILE DEVICES
type: item
description: The subject of this research is energy consumption modeling and forecasting in distributed computing systems using Entity-Component Architecture (ECA). This task focuses on improving the method of quantifying and predicting the energy demands of a distributed computing system by simulating all components of this system, such as computational units (CPUs, GPUs), data transmission interfaces (LAN, USB interfaces), and network infrastructure (LAN switches, routers) in heterogeneous environments. Method validation is based on the stationary computing network (SCN) and mobile computing network (MCN) cases. This study addresses the inherent limitations of traditional algebraic models by advocating for a modular, scalable, and flexible ECA-based framework that integrates direct and indirect power expenditures of distributed computational elements to facilitate energy profiling. The experimental validation showed that the proposed ECA model achieves high prediction accuracy. In SCN configurations, simulations matched real-world energy measurements within 4.4% for CPU-only tasks, while CPU+GPU tasks showed a larger discrepancy of 20.2% due to GPU underutilization. Conversely, the MCN scenario, involving mobile devices and USB connections, resulted in minimal deviations of 3.5% in CPU-only mode and approximately 10.1% in CPU+GPU mode. These results underscore the model's robustness in accurately forecasting energy usage across diverse computational settings. We conclude that the effective transition from conventional algebraic models to a flexible and extendable ECA approach significantly enhanced the methodology’s modularity, scalability, and universality. Major achievements include refining computational benchmarking practices by generalizing computing payloads, adopting all-inclusive energy measurements, and highlighting the energy-efficiency advantage of computations on mobile platforms over traditional stationary hardware for both CPU and GPU tasks. This research has hig…
creator: Oleksandr Mamchych
creator: Maksym Volk
rights: Copyright (c) 2026 Oleksandr Mamchych, Maksym Volk
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 37
ending page: 52
doi: 10.32620/reks.2026.2.03

COMPUTATIONAL ANALYSIS OF COLD SPRAY NOZZLE DESIGN’S EFFECT ON PARTICLE ACCELERATION
type: item
description: The subject of this article is the computational analysis of the effect of nozzle geometry on gas dynamics and particle acceleration in cold gas dynamic spraying (CGDS). This study aims to compare the performance of conventional conical nozzles and Method of Characteristics (MOC)-based bell-shaped nozzles in accelerating particles to the critical velocities required for effective coating deposition. The tasks to be solved are as follows: design and model conical and MOC nozzles; conduct CFD simulations of compressible two-phase gas–particle flows in both geometries; analyze velocity, pressure, and temperature distributions along the nozzle and jet regions; and evaluate particle acceleration and velocity distribution for aluminum powder. The following methods were used: numerical modeling of gas–particle flow employing the Navier–Stokes equations with the RNG k–ε turbulence model in ANSYS Fluent 2025 R1, along with Lagrangian particle tracking; computational design of nozzles using MATLAB code based on the Method of Characteristics; and comparison of flow fields and particle dynamics between nozzle configurations. The results show that both nozzles operate under underexpanded jet conditions, but the MOC nozzle accelerates the carrier gas and particles earlier, resulting in more uniform velocity fields and reduced velocity lag between the gas and the dispersed phase. For 25 µm aluminum particles, the MOC nozzle achieves higher particle velocities than the conical nozzle, indicating greater potential for plastic deformation and improved coating adhesion. Additionally, higher exit gas velocities and more stable jet structures were observed in the MOC nozzle, despite the presence of more pronounced boundary-layer vorticity. Conclusions. The scientific novelty of the results lies in establishing the gas-flow acceleration patterns with aluminum powder particles in conical and MOC-based nozzles, optimized for aluminum deposition at 2.5 MPa gas pressure and 650 °C. The MOC-b…
creator: Oleksandr Shorinov
creator: Nina Savchenko
creator: Oleg Tryfonov
creator: Olga Shypul
rights: Copyright (c) 2026 Oleksandr Shorinov, Nina Savchenko, Oleg Tryfonov, Olga Shypul
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 53
ending page: 64
doi: 10.32620/reks.2026.2.04

TEMPORAL EVENT-DRIVEN SCHEDULED ACTIONS (TESA) ONTOLOGY FOR CYBER PHYSICAL SYSTEMS WITH PRIORITY MECHANISM
type: item
description: This study aims to create a lightweight ontology to extend OWL time with scheduling and event-driven actions, which is critical for the tightly coupled complex systems modeling during the design of cyber-physical programs like Digital Twins. In addition, the developed ontology should be compatible with foundational ontologies like BFO, SUMO, UFO, and DOLCE. Moreover, the priority mechanism for resolving conflicted actions should be created for the new ontology. The subject of this paper is the development and evaluation of ontologies with scheduling and conflict-resolving mechanisms. This paper aims to develop and evaluate a new ontology that provides scheduling and event-driven actions with conflict resolution mechanisms and is compatible with foundational ontologies. The primary focus was on limiting the halts created by conflicted actions during the ontology reasoner run. The tasks to be solved are as follows: to collect the requirements for the new ontology, to describe core elements of the ontology-modeled system to be compatible with foundational ontologies, and to describe and evaluate new ontology. Methods involve designing the test system for the testing, its formalization for modeling the system using proposed and other ontologies, and software engineering techniques to run and collect the data during experiments. The results demonstrate that the proposed TESA achieves a significant reduction in cognitive and developmental effort, characterized by a 70% decrease in Halstead effort metrics compared to ISO-standard PSL and prevents the reasoner from facing halts during reasoning. Under high-density conflict scenarios, TESA exhibited superior temporal stability, sustaining sub-second reasoning latency while traditional methodologies underwent exponential performance degradation, with execution times exceeding 10 seconds. Moreover, the proposed ontology is lightweight and easily compatible with foundational ontologies. Conclusions. For the first time, a lightw…
creator: Oleksii Atamaniuk
creator: Yevgeniya Sulema
rights: Copyright (c) 2026 Oleksii Atamaniuk, Yevgeniya Sulema
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 65
ending page: 82
doi: 10.32620/reks.2026.2.05

DEVSECOPS PIPELINE FOR PROVIDING SECURITY OF IOT CLIENT INTERFACES
type: item
description: This article examines the processes for ensuring the information security of client interfaces (Front-end) in Internet of Things (IoT) systems. This study aims to develop and implement an automated DevSecOps pipeline integrated into the Gulp build environment to detect and block code vulnerabilities at the earliest stages of development (the Shift Left concept). The tasks are as follows: 1) analyze technological opportunities and challenges of implementing DevSecOps for IoT web interfaces; 2) analyze possible threats and vulnerabilities inherent in client-side code; 3) analyze existing approaches to integrating SAST tools into automated workflows; 4) analyze options for using standard automation tools (such as Gulp) to solve security tasks; 5) propose a DevSecOps pipeline architecture for the security of client interfaces; 6) propose a sequence of critical components and experimentally verify the effectiveness of the proposed solution. Based on the set tasks, the following results were obtained. An analysis of security problems in modern JavaScript-based IoT interfaces was conducted, and the need for automated code control was substantiated. An architecture for a DevSecOps pipeline is proposed using Gulp.js as an automation tool and a configured ESLint as a SAST scanner. A Gulp plugin ("wrapper") has been developed and implemented, providing continuous code monitoring during development. The system successfully identifies dangerous patterns (e.g., the use of eval functions or vulnerable setTimeout constructions) and blocks such code from entering the final release (Artifact). The proposed approach does not create significant overhead on the development process but guarantees compliance with security policies. Conclusions. The main contribution and scientific novelty of the results lie in creating an adaptive, easily integrated protection mechanism for the IoT ecosystem by integrating Static Application Security Testing (SAST) tools directly into the Gulp task runner…
creator: Artem Antonenko
creator: Oleh Bondarenko
creator: Oleksandr Golubenko
creator: Nataliia Lashchevska
creator: Olga Morozova
rights: Copyright (c) 2026 Artem Antonenko, Oleh Bondarenko, Oleksandr Golubenko, Nataliia Lashchevska, Olga Morozova
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 83
ending page: 94
doi: 10.32620/reks.2026.2.06

ADAPTIVE KNOWLEDGE REGULARIZATION FOR CONTINUAL LEARNING IN TRANSFORMER ARCHITECTURES
type: item
description: The subject matter of the article is development of latent representation regularization mechanism in transformer-based architecture under conditions of continuous learning with domain shifts. Modern language models achieve high quality in static learning scenarios, but they remain limited in long-term operation cases, incremental adaptation to new domains and lacking resistance to catastrophic forgetting, especially in the absence of access to previously observed data. This paper explores the possibility of overcoming these limitations by combining uncertainty inspired regularization and forgetting attention mechanisms into one transformer architecture. The goal of the study is to design, implement and validate transformer-based architecture with multi-level representation regularization mechanism that can help transformer-based language models efficiently adapt to alternative data distribution while retaining previously acquired knowledge. The proposed approach aims to achieve an equilibrium between model adaptability and stability in continual learning without requiring complete model retraining or legacy data retention. The tasks to be solved in this study include: formalization of an unified conceptual latent representations regularization method that combines Bayesian uncertainty inspired latent representations regularization with head-wise attention scaling in attention mechanism; implementation of this method into the transformer model; creating the experimental case of continuous language modeling with sequential domain shifts; give quantitative estimation of model forgetting and stability in prediction quality; compare the proposed model to classical naive fine-tuning, LoRA and parameter regularization methods. The conclusions demonstrate that the proposed method achieves lower forgetting, lower perplexity on previously learned domains and a better stability–plasticity trade-off than naive fine-tuning, LoRA and Elastic Weight Consolidation, while requiring…
creator: Arsentii Kriukov
creator: Yurii Pushkarenko
rights: Copyright (c) 2026 Arsentii Kriukov, Yurii Pushkarenko
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 95
ending page: 111
doi: 10.32620/reks.2026.2.07

IMPLEMENTATION OF THE TEXT SIMILARITY EVALUATION METHOD WITH A WEIGHTED APPROACH TO IMPROVE THE ACCURACY OF FAKE NEWS CLASSIFICATION
type: item
description: The article examines how to enhance automated fake news detection accuracy using a novel semantic-aware text-similarity evaluation method. Traditional frequency-based models often fail to capture subtle semantic manipulations in the modern context of digital disinformation and hybrid warfare. This study addresses this gap by combining GloVe word embeddings with a specialized weighting approach. The goal of this study is to develop a highly effective classification methodology that integrates pre-trained GloVe embeddings with a norm-based weighted similarity metric to improve the identification accuracy of fake news. The achievement of this goal is quantitatively validated through measurable improvements in Accuracy, F1-score, Precision, Recall, and Log-loss compared to standard unweighted baselines. The tasks to be addressed include formulating a robust mathematical method to calculate word importance based on the L2-norm of semantic vectors, applying Manhattan distance for nuanced similarity scoring, and seamlessly integrating this feature into a comprehensive machine learning pipeline to ensure flexibility across various algorithms. The methods used in this study encompass extensive data preprocessing on a balanced dataset of 44,898 news records. Text vectorization is performed using 50-dimensional GloVe embeddings. Then, the semantic features are used to train and evaluate four supervised classifiers: Logistic Regression, Random Forest, XGBoost, and Support Vector Classifier (SVC) across incremental dataset sizes and under simulated noise conditions. Conclusions. The empirical results demonstrate that the proposed weighted similarity approach significantly improves fake news classification. Specifically, the method yields an average increase of 3–4% in Accuracy and up to a 4% improvement in F1-scores across all evaluated models. Furthermore, the proposed approach significantly reduces the impact of linguistic noise and maintains strong probabilistic confidence. T…
creator: Iryna Ilina
creator: Kyrylo Reuka
rights: Copyright (c) 2026 Iryna Ilina, Kyrylo Reuka
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 112
ending page: 125
doi: 10.32620/reks.2026.2.08

MODEL FOR PRODUCT STRATEGY ANALYSIS OF AN IT COMPANY USING FUZZY INFERENCE
type: item
description: The subject of study is the strategic analysis process of product strategy based on an IT company’s key performance indicators. The objective is to improve the product strategy’s analysis process by implementing a model for evaluating and analyzing the product strategy based on the IT company’s key performance indicators system. The tasks are to investigate methods and tools for strategic analysis of IT product strategy; to develop a model for evaluating an IT company’s product strategy based on a system of key performance indicators, which characterizes the results of strategy implementation in three aspects: financial results, level of customer orientation of the product; effectiveness of the sales team and marketing activities; and to investigate the results of implementing the model for improving the product strategy to achieve the IT company’s strategic goals. Methods of data normalization, fuzzy logic, and the Mamdani method for constructing a fuzzy inference system were used. The following results were obtained. Unlike existing models, the developed model of strategic analysis of product strategy is the result of a hierarchical combination of fuzzy inference systems for evaluating product strategy in three aspects: financial result, level of customer-orientation of IT product, and effectiveness of sales and marketing activities. As a result of the model implementation, an analysis of the product strategy is conducted regarding the possibility of its further implementation to achieve the IT company’s strategic objective. The use of a strategic analysis model based on fuzzy inference systems enables the product manager to obtain product strategy assessments and analyze KPIs, which generally represent the financial and market results of strategy implementation. Ways to improve the product strategy, considering the IT company’s goals, are identified based on the analysis results. Conclusions. The scientific novelty of the results obtained is as follows: a model f…
creator: Volodymyr Moskalenko
creator: Marina Grinchenko
creator: Valentyna Moskalenko
rights: Copyright (c) 2026 Volodymyr Moskalenko, Marina Grinchenko, Valentyna Moskalenko
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 126
ending page: 149
doi: 10.32620/reks.2026.2.09

ENHANCING UAV CLASSIFICATION WITH RADIO FREQUENCY SIGNALS USING A HYBRID CONVOLUTIONAL-LSTM ARCHITECTURE
type: item
description: This research aims to automate the classification of small UAVs using radio frequency signals. The object of this research is to classify UAVs by radio frequency signature using combined machine learning models. It focuses on a combination of machine learning-based models to achieve better classification results when efficient learning and high accuracy are difficult to achieve due to temporal dependency in complex signals. This study aims to enhance UAV classification models by optimizing a convolutional neural network architecture with the addition of recurrent layers. Methods described in this research propose a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model that combines convolutional and recurrent layers to extract spatially and temporally dependent features from UAV RF signals. Compared to traditional CNNs, the Convolutional Long-Short-Term Deep Neural Network (CLDNN) model reduces false positives across classes and improves classification performance (recall) for closely related or noisy signal classes. However, the complexity and computational costs of the CLDNN model are acknowledged, underscoring challenges for systems with limited processing power. The tasks of this research are as follows: 1) to review the current state-of-the-art approaches in the classification of small UAVs; 2) to outline the problem statement; 3) to create a test dataset for the proposed drone models; 4) to adapt CLDNN model for drone classification tasks; 5) to provide a comparison between CNN baseline and the proposed model. Conclusions. The experimental results show that the proposed CNN–LSTM model significantly improves classification accuracy compared with the baseline CNN architecture, especially for classes prone to misclassification. The integration of convolutional layers, which are effective at extracting local features, and recurrent LSTM layers, which are effective at modeling sequential dependencies, enables the combined architecture to reduc…
creator: Oleksii Shevchenko
creator: Oleksandr Bezsonov
creator: Oleg Rudenko
rights: Copyright (c) 2026 Oleksii Shevchenko, Oleksandr Bezsonov, Oleg Rudenko
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 150
ending page: 157
doi: 10.32620/reks.2026.2.10

ADAPTIVE LEARNING OF FAULT‑TOLERANT SOFTWARE ENGINEERING FOR UAV AND NANOSATELLITE ONBOARD COMPUTING
type: item
description: This article discusses intelligent, computer-based learning methods for teaching reliability engineering principles to onboard computing platforms used in unmanned aerial vehicles and nanosatellites. This study focuses on integrating mathematical reliability models, simulation-based engineering tasks, and formal verification tools into an adaptive educational environment for safety-critical aerospace systems. This study aims to develop an educational methodology that combines adaptive intelligent tutoring with formal verification techniques to train students to design, analyze, and formally prove the correctness of reliability-related algorithms used in onboard computing systems. The tasks to be solved are: defining a competency model for fault-tolerant onboard computing, designing parameterized simulation-based learning tasks, integrating fault-injection scenarios into software quality assessment, and establishing correctness-critical computational kernels for adaptive tutoring. Additional tasks include developing a learner‑modeling mechanism for individualized trajectories and validating reliability-related algorithms used in education. The methods used include: Monte‑Carlo simulation of failure processes, analytic consistency checks, formal verification with Lean, Isabelle/HOL, and Rocq, fault‑injection-driven software quality evaluation, and adaptive knowledge‑tracing–based task sequencing. The proposed approach is demonstrated through an OnBoard1000 case study. The current validation is limited to a proof-of-concept prototype, reproducible computational checks, hidden-test diagnostics, and a prespecified classroom pilot protocol; completed classroom outcome data will be identified future work. Conclusions. The proposed approach demonstrates that combining simulation-driven engineering tasks with formal verification activities encourages students to validate the accuracy of their algorithms and reliability models rather than relying solely on numerical results. …
creator: Ihor Turkin
creator: Andriy Chukhray
creator: Oleksandr Yevdokymov
creator: Oleksandr Nosykov
rights: Copyright (c) 2026 Ihor Turkin, Andriy Chukhray, Oleksandr Yevdokymov, Oleksandr Nosykov
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 158
ending page: 172
doi: 10.32620/reks.2026.2.11

A MODEL-BASED APPROACH TO ASSOCIATIVE TESTING TECHNOLOGY FOR EVALUATING COGNITIVE COMPETENCIES IN EDUCATION
type: item
description: The subject of the article is the development of a methodology for designing and utilizing Word Association Tests (WAT) to assess competencies within academic disciplines. The goal of this study is to establish the theoretical and technological foundations of an information system for associative testing, aimed at enabling a more structurally informed and diagnostically sensitive evaluation of the theoretical competence of learners. These tests are constructed on the basis of associative fields related to specific aspects of a discipline, allowing for the assessment of the structure and quality of cognitive representations in a learner’s mind. This study focuses on developing a comprehensive set of models and methods that underpin the technology of associative test construction and defines the methodological mechanisms through which such tests enhance the analytical capabilities of theoretical knowledge assessment. The proposed tests enable the evaluation of learners' depth of understanding of specific theoretical components of a discipline. The methods incorporate an approach for identifying underlying patterns and hypotheses relevant to the construction of WATs tailored to particular thematic areas. A systematic analysis is performed to explore the local cognitive features of learners and to generate micro-thesauri corresponding to individual disciplinary aspects. The frame theory is employed as a structural foundation for modeling the testing procedure. The following key results were obtained: (1) a conceptual model of the associative testing domain was developed to systematize all phases of the testing process; (2) an ontology model was created to integrate semantic elements of test questions into a coherent system; (3) a micro-thesaurus model was designed to represent specific disciplinary aspects and support the generation of associative test matrices across various academic fields; (4) a novel method for assessing associative test performance was proposed, le…
creator: Ihor Shevchenko
creator: Olena Poddubei
creator: Vladyslav Amosov
rights: Copyright (c) 2026 Ihor Shevchenko, Olena Poddubei, Vladyslav Amosov
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 173
ending page: 185
doi: 10.32620/reks.2026.2.12

A MACHINE LEARNING-BASED METHOD FOR ASSESSING THE FUNCTIONAL STABILITY OF DISTRIBUTED INFORMATION SYSTEMS THROUGH TOPOLOGICAL STRUCTURE
type: item
description: The subject of this research is the application of machine learning to detect the functional stability of distributed information systems based on the numerical characteristics of their topology. This study aims to improve the accuracy of verifying the condition whose fulfillment ensures the functional stability of considered distributed information systems. The task of this research is to improve the accuracy of machine learning-based verification of the condition whose fulfillment ensures the functional stability of a considered distributed information system. The main result of the study is the development of an ensemble classifier based on multiple heterogeneous machine learning models, enabling superior accuracy compared to that reported in related studies on the same or similar tasks. Unlike other studies on applying machine learning to assess information system functional stability, the proposed classifier architecture allows sequential use of sets of machine learning models. In other words, the first set of models receives a group of input parameters and estimates certain additional parameters. The obtained estimates are then passed to another set of models, which produces the final result. This approach enables the discovery of deeper dependencies between input and output parameters, which in turn yields higher accuracy. Such an approach differs significantly from the one commonly used in practice, where all models receive the same input parameters, produce the same type of output, and aggregate their outputs depending on the specific task. Conclusions. The study's outcome is a trained and tested ensemble machine learning model that, based on several numerical characteristics, detects whether the condition is met for the considered distributed information system to be regarded as functionally stable. The ensemble machine learning model obtained in this work can be applied at the design stage of information systems. When used as demonstrated here, the machin…
creator: Stanislav Dovgyi
creator: Oleh Kopiika
creator: Oleg Barabash
creator: Yurii Kravchenko
creator: Andriy Makarchuk
rights: Copyright (c) 2026 Stanislav Dovgyi, Oleh Kopiika, Oleg Barabash, Yurii Kravchenko, Andriy Makarchuk
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 186
ending page: 194
doi: 10.32620/reks.2026.2.13

AN APPROACH TO ENHANCING HYBRID CYBERATTACK NETWORK SURVIVABILITY DURING BLACKOUTS
type: item
description: The subject of the study is the development process of SOHO (Small Office / Home Office) electronic communications networks based on a compact GPON (Gigabit-capable Passive Optical Network) mini OLT (Optical Line Terminal) with one PON (Passive Optical Network) port for up to 32 ONU/ONT (Optical Network Unit/Optical Network Terminal) subscriber terminals. This study aims to develop and validate an enhanced network survivability approach under hybrid cyberattacks during blackouts. The research tasks are as follows: a formal model of SOHO access survivability should be developed that considers the UPS energy budget and the structure of active nodes; a hybrid risk model in which energy failures are combined with cyber compromise should be formalized; the monitoring methodology IDS+SIEM (Intrusion Detection System + Security Information and Event Management) with machine learning should be developed; a comparative analysis of Ethernet and GPON architectures should be performed; practical recommendations for the implementation, diagnostics and streamlining of management should be formulated in accordance with the principles of Zero Trust and the requirements of information security management, taking into account the human factor and the ease of use of controls in SOHO. The following results were obtained. The survivability model was developed that combines the power backup circuit (UPS autonomy assessment), the availability of the active circuit, and a probabilistic model of the risk of compromising control components, including firmware attacks related to SNMP (Simple Network Management Protocol) and SSL/TLS (Secure Sockets Layer/Transport Layer Security) vulnerabilities in embedded software. A monitoring methodology that integrates IDS and SIEM with deep-learning-based analytical model classes was developed and represented by AE+LSTM-type (Autoencoder + Long Short-Term Memory) telemetry analysis and CNN+LSTM-type (Convolutional Neural Network + LSTM) firmware-oriented…
creator: Serhii Zybin
creator: Dmytro Prokopovych-Tkachenko
creator: Rostyslav Palahusynets
creator: Yuliia Khokhlachova
creator: Anton Herasymenko
rights: Copyright (c) 2026 Serhii Zybin, Dmytro Prokopovych-Tkachenko, Rostyslav Palahusynets, Yuliia Khokhlachova, Anton Herasymenko
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 195
ending page: 219
doi: 10.32620/reks.2026.2.14

A BLOCKCHAIN-BASED ELECTRONIC VOTING SYSTEM WITH AUTOMATED LEADERLESS BFT CONSENSUS
type: item
description: The article discusses a decentralized electronic voting system based on blockchain technology. This study aims to improve the performance and fault tolerance of blockchain-based electronic voting systems by introducing the Automated Leaderless Byzantine Fault Tolerance (AL-BFT) consensus protocol. This study aims to develop and evaluate an electronic voting system model that applies the proposed AL-BFT consensus mechanism in a permissioned peer-to-peer network. The methods used include computer modeling of a peer-to-peer (P2P) network, implementation of a decentralized ledger, and experimental load testing of the consensus protocol. System performance is evaluated using key metrics, such as transaction latency, throughput (requests per second), fault tolerance threshold, and scalability. The study results include the development of a conceptual architecture for the electronic voting system, the identification of its core components, and the analysis of their interactions to ensure data integrity and the reliability of voting results. At each stage of the electoral process, data security is considered, and additional protection mechanisms are analyzed to enhance system robustness. Eliminating the leader election phase from the consensus process is a key feature of the proposed approach, thereby reducing coordination overhead and enabling more efficient agreement among nodes. The proposed AL-BFT protocol reduces transaction latency and improves throughput while maintaining the fault tolerance level of traditional Byzantine Fault Tolerance-based approaches. The results confirm improved efficiency compared to classical leader-based consensus mechanisms, particularly in small permissioned blockchain networks. Conclusions. A practical implementation of the system has been developed and tested under real simulated load conditions. The proposed solution ensures stable system operation and reliable consensus formation. The system can be effectively applied to university elec…
creator: Maksym Holikov
creator: Mariia Rodinko
creator: Dmytro Uzlov
creator: Artem Bronnikov
rights: Copyright (c) 2026 Maksym Holikov, Mariia Rodinko, Dmytro Uzlov, Artem Bronnikov
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 220
ending page: 233
doi: 10.32620/reks.2026.2.15

iTZBEI: AN INTEGRAL SECURITY METRIC FOR SDN ARCHITECTURES INTEGRATING ZERO TRUST AND BLOCKCHAIN
type: item
description: The subject matter of the article is the iTZBEI (Integrated Trust–ZTA–Blockchain SDN Efficiency Index) – a novel composite metric for quantitative security assessment of software-defined networks (SDN) integrating Zero Trust Architecture (ZTA) and Blockchain technologies. The relevance of the research is determined by the fact that the centralized SDN control model generates critical vulnerabilities, including DDoS attacks, unauthorized routing manipulation, and insider threats – for which no unified quantitative evaluation framework currently exists. The study introduced a formalized aggregated security metric that enables continuous monitoring and comparative assessment across all components of the SDN–ZTA–Blockchain architecture. The tasks to be solved include: (1) identification of principal SDN attack vectors; (2) formalization of a transaction-processing algorithm covering the full access lifecycle; (3) definition of nine local security indicators; and (4) construction of the iTZBEI index with justified weighting coefficients. The methods used combine mathematical formalization of access control processes, cryptographic transaction verification, and experimental emulation of attack scenarios in a Mininet–OpenDaylight–Hyperledger Fabric environment. Conclusions. The obtained results of the article consist in the development of a functional algorithm that performs dynamic verification of user requests, makes adaptive authorization decisions according to the principles of least privilege, and records these decisions in an immutable distributed ledger. A metrics system is proposed, including local indicators such as the Continuous Authorisation Integrity Score (CAIS), the Blockchain Audit Integrity Score (BAIS), and the Local Policy Integrity (LPI). On this basis, the generalized Integrated Trust and Zero-Trust Blockchain Evaluation Index (iTZBEI) is described as an aggregated metric for comparative evaluation and continuous monitoring of the network’s security st…
creator: Oleksandr Pidpalyi
creator: Oleksandr Romanov
creator: Larysa Globa
creator: Anton Romanov
creator: Anton Marinov
creator: Mykola Nesterenko
rights: Copyright (c) 2026 Oleksandr Pidpalyi, Oleksandr Romanov, Larysa Globa, Anton Romanov, Anton Marinov, Mykola Nesterenko
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 234
ending page: 252
doi: 10.32620/reks.2026.2.16

VERKLE-FRI: A NOVEL ARCHITECTURE FOR STATELESS AND QUANTUM-RESISTANT VECTOR COMMITMENTS
type: item
description: The subject matter of the article is the cryptographic integrity of digital authentication systems facing quantum computing threats, specifically focusing on post-quantum alternatives and efficient authenticated data structures. The goal is to design and formally analyze VERKLE-FRI—a hybrid architecture synthesizing Verkle tree proof-size reduction with FRI-based quantum-resistant commitments, establishing a scalable, stateless, and quantum-secure framework. The tasks are: analyze limitations of hash-based signatures and Merkle trees; evaluate polynomial commitment schemes (KZG, Bulletproofs, FRI, lattice-based); propose a hybrid Verkle-FRI design; develop a formal security proof against classical and quantum adversaries; execute complexity analysis with concrete implementation parameters. The methods used are: theoretical cryptographic analysis, formal security modeling via reductionist proofs, algebraic methods over finite fields, polynomial interpolation, random oracle model, FRI protocol with DEEP-FRI optimization, Merkle trees, vector commitments, and asymptotic complexity analysis. The following results were achieved: a novel architecture where Verkle node vectors are polynomial-encoded, committed via Merkle trees over FRI codewords, and verified through FRI with out-of-domain sampling. A formal proof establishes λ-bit quantum security using 2λ-bit hash functions. Complexity yields proof size O(λ log² N), prover time O(λ N log N), and verifier time O(λ log N). Concrete 128-bit quantum parameters include SHA3-512, field size ≈2²⁵⁵, branching factor 256, and 128 FRI rounds, achieving soundness error ≤2⁻¹²⁷. For a concrete benchmark authenticating 2²⁶ elements, a traditional Merkle proof requires ≈0.8 KB, whereas our VERKLE-FRI proof requires ≈180 KB. While larger, this provides quantum resistance and eliminates the trusted setup, a critical trade-off for long-term security. Conclusions. Scientific novelty consists in: 1) the first hybrid Verkle-FRI architecture …
creator: Giorgi Akhalaia
creator: Maksim Iavich
creator: Razvan Bocu
rights: Copyright (c) 2026 Giorgi Akhalaia, Maksim Iavich, Razvan Bocu
date: 2026-08-12
publication date: 2026-08-12
volume: 2026
number: 2
starting page: 253
ending page: 269
doi: 10.32620/reks.2026.2.17