Abstract. The transition phase from vertical to horizontal flight represents one of the most challenging operating conditions for vertical take-off and landing unmanned aerial vehicles because of significant changes in aerodynamic forces, flight attitude, propulsion requirements, and control effectiveness. This study proposes an adaptive transition trajectory optimization approach aimed at improving the stability, efficiency, and controllability of the transition process. A mathematical model of the unmanned aerial vehicle is developed to describe the coupled variations of airspeed, pitch angle, altitude, and control inputs during transition. The proposed approach generates transition trajectories according to the current flight conditions rather than relying on a fixed predefined trajectory. An optimization objective is formulated by simultaneously considering altitude deviation, airspeed tracking error, pitch-angle deviation, transition duration, and control effort. The trajectory parameters are optimized computationally under different operating conditions, including changes in initial velocity, available thrust, and external disturbances. Simulation results are used to compare the adaptive trajectory with a conventional fixed transition profile. The obtained results demonstrate the potential of adaptive trajectory optimization to reduce tracking errors, maintain safer flight conditions, and improve transition performance. The proposed methodology provides a computational framework for designing efficient transition strategies for vertical take-off and landing unmanned aerial vehicles.
Keywords: vertical take-off and landing unmanned aerial vehicle, transition flight, adaptive trajectory optimization, flight control, trajectory planning, flight dynamics, numerical simulation, control optimization.
Abstract. This article proposes a conceptual model for an integrated energy supply system for Urban Air Mobility (UAM). The aim is to enhance the robustness of the UAM system. UAM energy infrastructure agents include eVTOL aircraft, vertiports equipped with high-capacity charging stations, high-speed charging systems, energy storage systems, and renewable energy sources. Key aspects of the digital transformation of regional renewable energy for UAM include smart grids, artificial intelligence, the Internet of Things (IoT), and charging infrastructure. Vertiport energy systems are integrated with urban power grids and the low-altitude economy. Urban infrastructure development integrates the air mobility system into the broader electric power framework—including infrastructure monitoring—in alignment with Kazakhstan’s civil unmanned aviation development plans for 2025–2031. A geosystem approach was employed as the methodology for examining regional renewable energy resources within the context of urban air mobility. In the final conceptual model, the solution is framed as a spatially integrated 2D vector system: a vertical vector representing the geosystem and a horizontal vector representing the electric power sector. The tasks addressed fall within the realm of cyber-system modeling, utilizing probabilistic, simulation, agent-based, and expert-driven formal foresight tools. Foresight modeling tools were applied to assess priority rankings within the integrated power sector development system. Significance rankings were determined for the following energy development areas: coal-fired power generation, large-scale nuclear power, large-scale hydropower, small-scale hydropower, wind power, and solar power. heat pumps; small-scale nuclear power; integrated renewable energy sources; hydrogen-based power generation. Small-scale nuclear power was given the highest priority, followed by small-scale hydropower. These results are closely linked to the region’s natural-climatic, socio-economic, and geographical characteristics.
Keywords: system, energy sector, cyber-technology, risk, mathematical support, technical support, simulation models.
Abstract. Small spacecraft, especially CubeSat-type nanosatellites, often face the problem of maintaining an energy balance due to their limited size and low power sources. The main advantages of such spacecraft are their relatively low cost, rapid development, and suitability for conducting scientific research and technological experiments. However, their small size and mass impose strict limitations on the power supply system. In this regard, efficient use of energy is considered one of the main engineering tasks in the design and operation of small satellites. This article analyzes modern methods for reducing the energy consumption of payloads and onboard control complexes of small satellites operating in low Earth orbit. First, the structural features of small spacecraft, their main systems, and their energy balance are considered. In addition, the main factors affecting the structure of energy consumption are analyzed, including orbital conditions, the efficiency of solar panels, and the characteristics of battery systems. Then, methods for saving energy in payloads such as optical cameras, radio communication modules, and various scientific sensors are described. These methods include the use of efficient data processing algorithms, periodic activation of devices, the use of low-power operating modes, and the introduction of energy-efficient electronic components. Energy-efficient architectures of onboard control systems, as well as the possibilities of using microcontrollers and specialized processors, are also considered. The latest technological innovations in power supply systems, intelligent energy management algorithms, and dynamic power distribution methods are discussed. Based on data from real CubeSat projects, practical examples of energy optimization are presented. In addition, comparative tables are used to analyze the characteristics of various technical solutions and to propose recommendations for improving the energy efficiency of small satellites in the future.
Keywords: small spacecraft, CubeSat, energy consumption optimization, payload, onboard control system, electrical power system, energy efficiency, low Earth orbit.
Abstract. Digital transformation is influencing linguistic communication within the aviation industry; however, linguistic data remains limited. This study examines these changes based on 120 publicly available trilingual texts related to the aviation sector, comparing two time periods: 2018–2019 and 2024–2025. Preprocessing revealed cross-linguistic similarities. Text processing was carried out using a rule-based, Unicode-aware pipeline. Lexical richness was assessed using the MATTR-50 metric, while sentence length and digital terminology were analyzed via five conceptual categories and document-level variance analysis. Independent sample assessment involved Mann-Whitney U-tests with the Holm correction applied across the entire text. Variance analysis of terminology was conducted using Fisher’s tests. The results indicate that sentence length decreased in both Russian and English—though the changes remained statistically significant after the Holm correction—whereas no changes were observed in Kazakh. While digital terminology was present in the earlier period, modern terms place greater emphasis on digital concepts such as AI, modeling, and learning.
Keywords: digital transformation, aviation discourse, multilingual corpus, digital aviation terminology, professional standardization.
Abstract. The article is devoted to the study of the factors determining the ability of a railway carrier to maintain the reliability, continuity and safety of the transportation process with an increase in the volume of transport work. The relevance of the study is due to an increase in traffic volumes, an increase in cargo turnover, an extension of the average range of transportation and an increase in transit load, which leads to an increased impact on infrastructure, rolling stock, dispatch control, repair facilities and the traffic safety system. In these conditions, the sustainability of the railway carrier’s activities should be considered as the ability to ensure stable transportation under the influence of technical, infrastructural, organizational, personnel, financial, digital and external economic factors. At the same time, the paper proves that the stability of a railway carrier cannot be assessed solely by operational or economic indicators, since an increase in transport work must be accompanied by maintaining manageability and an acceptable level of risks. As a methodological basis, a computational and analytical approach is proposed, including an assessment of the dynamics of cargo flows, the calculation of the average range of transportation, the determination of the operational load index, the assessment of the transit component, as well as the calculation of the required reduction in the specific risk of traffic safety. The final result of the assessment is the determination of an integral stability index, the value of which allows us to develop a number of measures to ensure traffic safety. Special attention is paid to the traffic safety coefficient, which is considered as a limiting parameter of the carrier’s integral stability. The practical significance of the methodology lies in the possibility of using calculated indicators to make management decisions aimed at improving the reliability of transportation, preventing traffic safety violations and ensuring the sustainable development of railway carriers in modern conditions.
Keywords: railway carrier, stability, cargo flows, traffic safety, operational load, specific risk.
Abstract. This study aims to develop a reproducible approach to the comprehensive assessment of the economic, environmental, and operational performance of green logistics projects in aviation and to demonstrate its application using an illustrative scenario considered in the context of Kazakhstan. The information base comprises official documents of the International Air Transport Association (IATA), the International Civil Aviation Organization (ICAO), the Bureau of National Statistics of the Republic of Kazakhstan, and the Aviation Administration of Kazakhstan, as well as regulatory legal acts and peer-reviewed scientific publications. The methodology includes the calculation of net present value (NPV), the assessment of life-cycle greenhouse gas emission reductions, the measurement of operational performance, the target-based normalization of individual indicators, and their aggregation using predefined weights. A system of eight key performance indicators (KPIs) is proposed for monitoring purposes. Three aggregated outcomes—economic, environmental, and operational—are used to construct the illustrative composite index. In the scenario calculation for a hypothetical air carrier, a 2% reduction in fuel consumption, a 5% share of sustainable aviation fuel (SAF), and an assumed 65% reduction in the life-cycle emissions of SAF resulted in a 5.19% reduction in emissions, an NPV of USD 3.24 million, and a 6.67% reduction in the average flight–ground cycle time. With equal weights, the composite index was 0.740. Across the four weighting schemes considered, the index ranged from 0.713 to 0.778. The sensitivity analysis of NPV to the SAF price premium indicated a break-even threshold of approximately USD 483 per tonne, with all other parameters held constant. The scientific novelty of the study lies in adapting the composite indicator methodology to the preliminary screening of green aviation logistics projects. The study is limited by the scenario-based rather than corporate nature of the input data; therefore, the actual effectiveness of implementation must subsequently be validated using verified data from a specific airline or airport.
Keywords: green logistics, air transportation, economic efficiency, sustainable aviation fuel, SAF, key performance indicators, KPI, net present value, NPV, greenhouse gas emissions, CORSIA, decarbonization.
Abstract. The paper examines approaches to determining the angular position of objects by establishing the direction of propagation of radio waves coming from radio emission sources or formed as a result of reflection from objects. The study is carried out within the framework of grant funding for the scientific and technical project “Development of a hardware and software complex for passive direction finding of radio emission sources of aircraft based on artificial intelligence technology” (contract No. 332/23-25 dated 03.10.2025, project No. AR 327016/0225). Research is also carried out in accordance with the decision of the First Deputy Minister of Defense of the Armed Forces of the Republic of Kazakhstan – Chief of the General Staff of the Armed Forces of the Republic of Kazakhstan dated November 18, 2023, No. 62-3-4068. Within the framework of this area, the Department of Fundamentals of Military Radio Engineering and Electronics conducts a comprehensive study of modern methods for passively determining the direction to radio emission sources. In modern radio direction finding, the accurate and rapid determination of the angular coordinates of radio emission sources, including active jammers, remains a critical task. This requires finding a compromise between measurement accuracy, unambiguous positioning, and equipment noise immunity under complex multipath propagation conditions. This paper conducts a comparative theoretical analysis of amplitude and phase direction-finding methods to systematize their technical limitations and identify ways to overcome them. Using methods of mathematical analysis of radio engineering systems and analytical synthesis of specialized literature, the dependence of equipment sensitivity on the geometry of antenna systems is evaluated. Additionally, numerical modeling of the phase direction-finding method was conducted using the Monte Carlo method by varying the d/λ ratio, signal-to-noise ratio, and signal arrival angle. The analysis shows that the classical amplitude maximum method has low direction-finding sensitivity. Phase methods, conversely, provide higher accuracy; however, as the distance between antennas increases, they encounter the problem of bearing ambiguity. As a result of the study, it is concluded that to achieve the required accuracy and unambiguous measurements, it is most expedient to use combined multichannel approaches. Furthermore, the implementation of modern digital signal processing algorithms (e.g., the MUSIC algorithm) and the use of adaptive antenna arrays can compensate for the impact of destabilizing multipath factors.
Keywords: amplitude direction finding, phase direction finding, detection zone, active jammer, signal, bearing, noise immunity.
Abstract. Not all authentication log events require the same analysis priority; therefore, ranking them using temporal and behavioral context is relevant to information security. This study examines temporal and behavioral features for ranking authentication events by relative risk. The objective is to assess the contribution of behavioral representation to ranking quality and robustness, statistical validity, explainability, and calibration, and to test the technical feasibility of behavioral prioritization on real-world logs. Methods included baseline model comparison, representation ablation, chronological train/validation/test splitting, empirical random-ranking comparison, robustness and statistical analyses, explainability, calibration, low-and-slow temporal-horizon sensitivity, and behavioral feature-group ablation; NDCG@100 was the primary ranking metric.In the synthetic experiment, R2_behavioral_core achieved a higher mean NDCG@100 than R0_minimal (0.126952 vs. 0.071314) and outperformed it in 10 of 12 scenario–model combinations. Across 60 paired observations, mean ΔNDCG@100 was +0.046803 with a 95% confidence interval of [0.035444, 0.057751]. Empirical random-ranking analysis provided an additional reference for interpreting the absolute ranking scores: for Logistic Regression with R2_behavioral_core, NDCG@100 was 0.189052 versus a random mean of 0.010045 in the baseline scenario and 0.145514 versus 0.004943 in the rare-attacks scenario. The behavioral advantage remained condition-dependent and weakened under low-and-slow activity. Explainability and calibration were used to characterize score contributions, explanation stability, calibration quality, and ranking preservation.
For 1,760,511 real-world log events, prioritization scores and unique ranks were calculated from five past-only behavioral features, satisfying all 9/9 temporal/representation integrity requirements. Predictive effectiveness was not evaluated because verified security labels were unavailable. The synthetic results support the incremental ranking value of temporal and behavioral context, while the real-world analysis demonstrates the technical feasibility of implementing the corresponding past-only behavioral prioritization procedure. Confirming predictive effectiveness in operational settings requires temporal external validation using logs with verified security labels and semantic field documentation.
Keywords: authentication events, behavioral analysis, risk ranking, temporal validation, security event prioritization, machine learning, explainability.
Abstract: The work is devoted to the development and software implementation of a specialized web interface for data processing, visualization and predictive analysis of corrosion resistance of protective coatings. The article presents a mathematical apparatus combining the equations of diffusion of aggressive media through polymer matrices (Fick’s second law), the kinetics of electrochemical dissolution of a metal substrate (Tafel equation) and empirical models of degradation of protective layers. Based on the proposed formalisms using Python, the Streamlit framework, and the Pandas, NumPy, and Plotly libraries, an interactive software package was designed. The developed tool makes it possible to automate the input and filtering of experimental data, perform multifactorial regression analysis, build 2D and 3D graphical dependencies, and predict the estimated uptime of coatings. The applied benefit of this work is the formation of a convenient digital twin, which helps to significantly reduce the number of experiments in the process of searching for optimal characteristics of insulating compounds.
Keywords: corrosion resistance, software solution, computer simulation, Python, Streamlit, polymer films, regression methods, graphical data visualization.
Abstract. Pressure-pulsation measurements in aircraft testing depend on the sensing element, connecting passages, housing cavities and supply circuit. This study separates their effects on frequency response, local temperature compensation and uncertainty under explicit assumptions relevant to semiconductor sensors intended for extreme conditions. Thin-plate and second-order models, viscothermal tube transfer matrices and an additive decibel uncertainty model are checked through limiting cases, independent calculations and Monte Carlo propagation. A single bandwidth boundary cannot jointly identify natural frequency and damping. A lower frequency estimate near 195 kHz requires an assumed 65 kHz bandwidth and damping of 0.01–0.10. For a neck 0.8 mm in diameter and 1 mm long, with a 1 mm³ cavity, the complete cascade has a peak at 25.728 kHz and a bandwidth of 7.981 kHz. A 20 mm probe of radius 0.40 mm reduces the bandwidth to 0.981 kHz. Cavity thermal losses and diaphragm acoustic back-loading are neglected. With assumed temperature coefficients and a 6 V supply, the restricted topology provides 13.846 percent of the required local compensation; zeroing the first derivative does not establish stability across the temperature range. Two illustrative budgets give expanded uncertainties of 2.491 and 0.980 dB for a coverage factor of two, and 95 percent interval half-widths of 2.214 and 0.950 dB. The results are conditional computational examples, without new experimental validation or verified accuracy claims for a particular sensor. The work provides a basis for testing the model assumptions and for designing a subsequent measurement protocol.
Keywords: piezoresistive sensor, frequency response, cavity resonance, acoustic probe, parameter identification, temperature compensation, measurement uncertainty, numerical verification.
Abstract. Political communication has migrated to social platforms, where opinions are expressed through short, code-switched, and rhetorically dense messages. Conventional sentiment analysis systems, tuned for product reviews, transfer poorly to this domain because political polarity is entangled with stance, irony, and discourse structure. This article surveys the principal families of sentiment analysis methods—lexicon-based, classical machine learning, deep neural, and transformer-based approaches—and introduces PoliSent, a hybrid discourse-aware architecture for political sentiment analysis on the Internet. PoliSent unifies three complementary signals: multilingual contextual embeddings produced by an XLM-RoBERTa encoder, a domain-specific political lexicon (PoliLex) with valence shifters and negation handling, and a graph-attention module operating over Universal Dependencies parses. A learned gating mechanism fuses these signals, and a multi-task objective jointly predicts sentiment, stance, and rhetorical tactic. We evaluate the framework on PoliWeb-CA, a newly annotated corpus of 18,500 Kazakh- and Russian-language political messages collected from microblogs, messaging channels, and news comment threads. PoliSent attains a macro-F1 of 0.904 and a Matthews correlation coefficient of 0.857, surpassing a fine-tuned KazRoBERTa baseline by 4.6 macro-F1 points and outperforming all lexicon, classical, and deep baselines by a wide margin (p < 0.01, paired bootstrap). Ablation studies confirm that each architectural component contributes measurably, with the dependency-graph attention and lexicon augmentation jointly responsible for a 5.8-point improvement over the contextual backbone. The results demonstrate that explicit linguistic structure remains valuable even in the era of large pretrained encoders, particularly for low-resource and morphologically rich political discourse.
Keywords: sentiment analysis, political discourse, opinion mining, natural language processing, transformer models, graph attention networks, stance detection, low-resource languages, Kazakh; multilingual NLP.
Abstract. The objective of this study is to improve the quality of agricultural engineering for cattle feed production in Eastern Kazakhstan. Kazakhstan’s agricultural production is considered a high-risk area, and therefore, producer and consumer risks of agricultural products are used as key criteria for agricultural engineering quality. Implementing these tasks in this scientific and production context is particularly challenging due to the insufficient scientific study of the subject area, particularly in terms of regional natural, climatic, and geographic aspects. In modern digital production management systems, proactive monitoring and control are crucial. This study explores a new approach that transforms management into an agricultural engineering system, in which risks are considered as criteria for the digital maturity of the business environment. In livestock farming, feed quality plays a crucial role, and monitoring it is impossible without the use of modern digital solutions at all stages of feed production. To qualitatively evaluate control processes based on digital maturity criteria in both integral and differential formats, this paper proposes a new model based on the principles of fuzzy control agents, which improves the quality of decision-making.
Keywords: agricultural engineering, feed production, risk, digital maturity, control, model.