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%7B%22order%22%3A%22desc%22%7D%7D%5D%2C%22_source%22%3A%7B%7D%7D" target="_blank">DOAJ; Ulrichsweb Global Serials Directory; OCLC WorldCat; Index Copernicus; CiteFactor; AcademicKeys; Infobase Index; WorldCat; Google Scholar; Scillit; OUCI Issues and articles from 2016 have a DOI from CrossRef Founder: National Aerospace University «Kharkiv Aviation Institute» ERDPOU Code: 02066769 ROR of the founder: https://ror.org/048j5n646 DOI Prefix: 10.32620/recs Periodicity: 4 issues per year. Editor-in-Chief: Kharchenko Vyacheslav Executive secretary: Editorial office address: 61070, Kharkiv, 17 V. Manka Str., chair 302. E-mail: reks.journal@gmail.com Archives of issues are HERE This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Національний аерокосмічний університет "Харківський авіаційний інститут"
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.
IN MEMORY OF PROFESSOR OLEKSANDR LESHCHENKО
Copyright (c) 2026 Vyacheslav Kharchenko
A MEASURE-BASED METHODOLOGICAL FRAMEWORK FOR CONTINUOUS MAXIMUM COVERAGE: GEOMETRIC FORMULATION AND HYBRID SWARM OPTIMIZATION
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 methods are adapted, interpreted within a unified geometric framework, and provided with evidence-based guidelines for parameter selection and hybridization configuration. The reported numerical results demonstrate the practical applicability of the proposed framework and illustrate typical effects of hybridization on coverage quality under a fixed computational budget. The scientific novelty of the work consists of three contributions. A geometry-agnostic measure-based formulation is developed that simultaneously accommodates arbitrary object shapes, non-convex target regions, forbidden zones of general form, and continuous rotation parameters within a single unified framework, without relying on shape-specific predicates or problem-dependent geometric decompositions. A unified geometric reinterpretation of four swarm intelligence paradigms is established, in which algorithmic update rules are interpreted as coordinated rigid motions in the continuous configuration space, providing a principled geometry-driven basis for algorithm selection depending on the structural properties of the feasible domain. A structured hybridization scheme is formulated for integrating global stochastic search with deterministic local refinement applied to the penalized measure-based coverage functional, with evidence-based guidelines for activation frequency, stopping criteria, and gradient approximation under non-smooth geometric objectives. The proposed framework provides a flexible and extensible methodological foundation for coverage modeling in engineering, energy, and environmental monitoring systems where geometric complexity, forbidden zones, and placement constraints are unavoidable
Copyright (c) 2026 Sergiy Yakovlev, Sergiy Shekhovtsov, Yehor Havryliuk
STRESS-STRAIN STATE OF A LAYER WITH CYLINDRICAL CAVITIES UNDER SPATIALLY DISTRIBUTED LOADS OF INFINITE EXTENT
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 ignoring the mutual influence of cutouts leads to an underestimation of the calculated stresses by more than 20%. Conclusions: The study's results explain the complex interactions among general layer bending, local deformations, and stress-field interference from neighboring concentrators. The significant asymmetry of the stress state confirms the presence of bending moments, and the rapid attenuation of disturbances is consistent with Saint-Venant’s principle. The practical value of the work lies in the potential to use the obtained data to design critical components of aviation technology (integral wing panels, floor power profiles) to increase reliability and optimize weight
Copyright (c) 2026 Nataliia Ukrayinets, Tetyana Alyoshechkina, Vitaly Miroshnikov, Iaroslav Grebeniuk, Vladyslav Demenko, Ihor Arkhypenko
A TOPOLOGY-AWARE UNIFIED MODEL AND METHOD FOR FORECASTING ENERGY CONSUMPTION IN DISTRIBUTED SYSTEMS WITH STATIONARY AND MOBILE DEVICES
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 highlighted some limitations of current methods, such as linear interpolation in load-energy modeling and the neglect of momentary hardware states, including momentary temperature and temperature inertia, which affect performance and consumption. The scientific novelty of the research consists of two parts: a new approach to mathematical modeling that enables extension and scalability, and a more consistent and accurate model of a distributed computing system based on it.
Copyright (c) 2026 Oleksandr Mamchych, Maksym Volk
COMPUTATIONAL ANALYSIS OF COLD SPRAY NOZZLE DESIGN’S EFFECT ON PARTICLE ACCELERATION
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-based nozzle enables earlier gas expansion and a more stable free-jet structure, thereby achieving a higher, significantly more uniform powder particle velocity than the conical nozzle under identical spraying conditions.
Copyright (c) 2026 Oleksandr Shorinov, Nina Savchenko, Oleg Tryfonov, Olga Shypul
TEMPORAL EVENT-DRIVEN SCHEDULED ACTIONS (TESA) ONTOLOGY FOR CYBER PHYSICAL SYSTEMS WITH PRIORITY MECHANISM
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 lightweight ontology for event scheduling and conflict preventing has been developed that strikes a balance between detailed semantics and ease of integration. TESA’s modular structure and reduced semantic overhead enable streamlined adoption and usage with foundational ontologies. At the same time, TESA goes beyond minimal standards like iCalendar by incorporating sufficient domain-neutral semantics for tasks, events, and resource constraints, thereby facilitating more robust scheduling logic. Future tasks in the field of TESA development include refining the ontology’s modular structure for specialized domains, exploring automated alignment techniques with other foundational ontologies on higher level, investigating multi-factor priority mechanisms, and scalability mechanisms for the usage in heterogeneous scheduling environments.
Copyright (c) 2026 Oleksii Atamaniuk, Yevgeniya Sulema
DEVSECOPS PIPELINE FOR PROVIDING SECURITY OF IOT CLIENT INTERFACES
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 architecture. Applying DevSecOps practices at the build level minimizes human-factor risks and increases overall trust in smart device management systems. The proposed solution is scalable and can serve as a foundational element of a cybersecurity strategy for Internet of Things projects
Copyright (c) 2026 Artem Antonenko, Oleh Bondarenko, Oleksandr Golubenko, Nataliia Lashchevska, Olga Morozova
ADAPTIVE KNOWLEDGE REGULARIZATION FOR CONTINUAL LEARNING IN TRANSFORMER ARCHITECTURES
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 comparable computational resources. The scientific novelty of proposed approach consists in development of layer-selective latent regularization framework for continual language modeling which integrates an attention with forgetting mechanism with preserving domain-invariant representations through statistical alignment in latent space for reducing forgetting in continual learning scenarios. Unlike existing approaches to continual learning that consider model regularization either on parameter or memory level (by using previous data), the proposed approach moves regularization into representation space, where it uses both direct regularization via proposed uncertainty-based regularization and indirect regularization via attention with forget gate, ensuring the models’ possibility of stable continual language modeling in non-stationary environments.
Copyright (c) 2026 Arsentii Kriukov, Yurii Pushkarenko
IMPLEMENTATION OF THE TEXT SIMILARITY EVALUATION METHOD WITH A WEIGHTED APPROACH TO IMPROVE THE ACCURACY OF FAKE NEWS CLASSIFICATION
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. The scientific novelty lies in replacing uniform semantic pooling with an embedding-norm-based weighting mechanism, offering a computationally efficient and highly accurate solution for real-world disinformation detec
Copyright (c) 2026 Iryna Ilina, Kyrylo Reuka
MODEL FOR PRODUCT STRATEGY ANALYSIS OF AN IT COMPANY USING FUZZY INFERENCE
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 for evaluating and analyzing product strategy, based on the KPI system of the IT company, has been developed for the first time. Implementing this model in the strategic analysis of an IT product will enable the development of a product strategy aligned with the IT company's strategic goals. 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 across three aspects: financial results, the level of customer orientation of the IT product, and the effectiveness of sales and marketing activities. As a result of the model’s implementation, an analysis of the product strategy is conducted to assess the feasibility of its further implementation to achieve the IT company’s strategic objective.
Copyright (c) 2026 Volodymyr Moskalenko, Marina Grinchenko, Valentyna Moskalenko
ENHANCING UAV CLASSIFICATION WITH RADIO FREQUENCY SIGNALS USING A HYBRID CONVOLUTIONAL-LSTM ARCHITECTURE
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 reduce false positives. Accordingly, this hybrid structure is fairly effective for signal classification problems because it captures spatially localized features and temporal contextual information within the input. This study confirms that the CNN-LSTM model is efficient at classifying UAV radio-frequency signals and has great potential for practical applications. Possible future research directions include optimizing the model's computational efficiency by handling greater interference and noisier data.
Copyright (c) 2026 Oleksii Shevchenko, Oleksandr Bezsonov, Oleg Rudenko
ADAPTIVE LEARNING OF FAULT‑TOLERANT SOFTWARE ENGINEERING FOR UAV AND NANOSATELLITE ONBOARD COMPUTING
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. This supports the development of rigorous engineering reasoning required in safety-critical domains such as onboard computing in aerospace. Scientific novelty consists of integrating formal theorem-proving tools into an adaptive intelligent tutoring framework for reliability engineering education. The contribution is not a new knowledge-tracing algorithm but a domain-specific methodological layer that connects mission profiles and operating conditions, reliability assumptions and models, architectural redundancy decisions, and parameterized learning tasks with formally verified reference computations and adaptive task selection. In the proposed approach, formal proofs serve as a learning mechanism that teaches students to verify the correctness properties of algorithms and reliability models used in fault-tolerant onboard computing systems.
Copyright (c) 2026 Ihor Turkin, Andriy Chukhray, Oleksandr Yevdokymov, Oleksandr Nosykov
A MODEL-BASED APPROACH TO ASSOCIATIVE TESTING TECHNOLOGY FOR EVALUATING COGNITIVE COMPETENCIES IN EDUCATION
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, leveraging an additional feature space defined by auto-association map coefficients, providing a formal basis for more fine-grained analytical evaluation of associative relations; (5) a frame-based model of the associative testing procedure was developed, offering a structured framework for implementation and facilitating software development, allowing software developers to quickly master the subject area; and (6) a general methodology for test construction was described, requiring minimal web resources. The proposed approach demonstrates a measurable improvement in assessment adequacy, as evidenced by a reduced discrepancy between automated test results and expert evaluation. In particular, WAT-based assessment approximates expert judgment more closely than conventional testing methods, indicating reduced assessment bias, higher diagnostic precision, and greater ability to accurately capture the learner’s cognitive structure. Conclusions: The scientific novelty of this research lies in the formalization and integration of a comprehensive methodological framework for associative testing that is applicable across educational and research contexts. This framework includes a system-level conceptual model, an ontology model that organizes semantic elements into a unified structure, and a micro-thesaurus model for a specific discipline-specific aspect that reflects the learner’s mental map and enables the generation of associative test matrices. The proposed method for evaluating associative responses differs from existing approaches in that it introduces a new metric in the form of auto-association map coefficients, providing a formalized representation of associative alignment rather than an empirical performance indicator. This enables a more precise structural analysis of the learner’s associative connections within a thematic micro-thesaurus compared to the instructor’s thesaurus. The frame-based model further supports structured implementation, and the overall methodology for determining cognitive structures (mental maps) has potential applications in psychology and sociology. The results confirm that the proposed approach improves the reliability and validity of competence assessment by more closely aligning automated evaluation with expert judgment.
Copyright (c) 2026 Ihor Shevchenko, Olena Poddubei, Vladyslav Amosov
A MACHINE LEARNING-BASED METHOD FOR ASSESSING THE FUNCTIONAL STABILITY OF DISTRIBUTED INFORMATION SYSTEMS THROUGH TOPOLOGICAL STRUCTURE
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 machine learning models trained in the study can be integrated into software for the design and simulation of information systems. The method for combining these machine learning models is of particular interest. As described in the paper, the approach to organizing models within the ensemble opens new possibilities for building highly accurate classifiers. The principal scientific novelty of the obtained results lies in a new approach to constructing ensemble machine learning models. Unlike classical ensembles, such as random forests, the ensemble described in this study employs a set of machine learning models to compute estimates of certain parameters, which are then used as inputs to another set of models. This particular approach to organizing the ensemble most likely improved the quality of solving the task, as demonstrated within the study.
Copyright (c) 2026 Stanislav Dovgyi, Oleh Kopiika, Oleg Barabash, Yurii Kravchenko, Andriy Makarchuk
AN APPROACH TO ENHANCING HYBRID CYBERATTACK NETWORK SURVIVABILITY DURING BLACKOUTS
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 analysis. Comparative modeling of Ethernet Everywhere and GPON architectures with passive branching was performed for 5–20–32 connection scaling scenarios. Numerical examples of calculations, comparative tables of metrics, and the practical algorithm for diagnosing typical installation errors (APC/UPC, IN/OUT splitter, incompatibility of standards) were provided. The results were interpreted using Zero Trust principles for small networks and the prospects of multimodal AI for correlating SIEM events with firmware artifacts and physical optical channel telemetry were identified. Practical recommendations regarding segmentation, update policies, and privilege minimization for access control in the SOHO segment were offered. Conclusions. This study determines that enhancing network survivability is an important and urgent task. The scientific novelty of the results obtained is as follows: the integrated monitoring methodology with machine learning that ensure the transition from component composition and operational assumptions to numerical metrics of survivability, availability, and detection was developed; the formal survivability model was developed considering the UPS energy budget and the structure of active nodes was developed; the risk of hybrid events in which energy failures are combined with cyber compromise was formalized. Areas for further research: failure localization methodology; methodologies related to Zero Trust; multimodal AI analysis; decision support under conditions of limited SOHO resources.
Copyright (c) 2026 Serhii Zybin, Dmytro Prokopovych-Tkachenko, Rostyslav Palahusynets, Yuliia Khokhlachova, Anton Herasymenko
A BLOCKCHAIN-BASED ELECTRONIC VOTING SYSTEM WITH AUTOMATED LEADERLESS BFT CONSENSUS
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 elections, organizational voting, and other scenarios that require transparency, security, and manipulation resistance
Copyright (c) 2026 Maksym Holikov, Mariia Rodinko, Dmytro Uzlov, Artem Bronnikov
iTZBEI: AN INTEGRAL SECURITY METRIC FOR SDN ARCHITECTURES INTEGRATING ZERO TRUST AND BLOCKCHAIN
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 state. Scientific novelty. This study introduces a unified SDN + ZTA + Blockchain framework for network security, formalizes a transaction-level algorithm that directly links access decisions with distributed audit procedures, and proposes the iTZBEI metric as the first integral indicator for evaluating the integration’s effectiveness in dynamic network environments.
Copyright (c) 2026 Oleksandr Pidpalyi, Oleksandr Romanov, Larysa Globa, Anton Romanov, Anton Marinov, Mykola Nesterenko
VERKLE-FRI: A NOVEL ARCHITECTURE FOR STATELESS AND QUANTUM-RESISTANT VECTOR COMMITMENTS
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 replacing pairing-based assumptions with hash-based proximity testing; 2) a formal security proof reducing security to hash collision resistance and FRI soundness; 3) quantified efficiency-security trade-offs; 4) a viable pathway for quantum-resistant infrastructure in blockchains, software distribution, and government communications.
Copyright (c) 2026 Giorgi Akhalaia, Maksim Iavich, Razvan Bocu