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. This study aims to explore the potential of implementing blockchain-based verification in the language training of aviation technical specialists. The research objective is to find a transparent, reliable, and professionally validated tool for assessing student performance in language training. The research methodology utilized a quasi-experimental approach, comparing control and experimental groups, which underwent introductory and final testing. Within this approach, experiments were conducted to assess the results of individual components, learning success, the verification index, and student confidence in the assessment system. The results demonstrated that the proposed technology significantly enhances competency development, improves academic performance and competence, and increases trust in the assessment procedures. An additional finding was a positive correlation between the verification index and English language proficiency. In conclusion, the proposed technology serves as a reliable performance measurement tool and a pedagogical mechanism for assessing competencies. This study contributes to the development of transparent, verifiable, and professional models for teaching English for specific technical purposes.
Keywords: blockchain based verification, aviation English, aircraft maintenance education, competence assessment, digital credentials.
Abstract. Unstable approaches remain one of the most persistent safety risks in commercial aviation and are strongly associated with long landing and runway excursion events. While conventional safety monitoring practices primarily rely on threshold-based exceedance detection within stabilized approach criteria, considerably less attention has been given to the underlying energy management processes that precede such outcomes. This study presents an empirical analysis of Quick Access Recorder (QAR) data collected over a twelve-month operational period from a mixed fleet of Boeing 737 NG and MAX aircraft. Flights were classified into nominal and risk subsets, and systematic differences in key approach energy management parameters were examined across multiple altitude bands during the approach phase using a variability-oriented analytical framework. The results demonstrate that Fast on Approach should not be interpreted as an isolated speed exceedance, but rather as a progressive degradation of approach energy management developing well before stabilized approach criteria are formally violated. Fast on Approach was consistently associated with sustained speed deviations, increased thrust modulation, elevated pitch variability, and a higher likelihood of excess energy being carried into the landing phase. An association between Fast on Approach and long landing outcomes was observed, supporting the interpretation of excess approach speed as an intermediate undesired aircraft state linking early energy management deviations to adverse runway outcomes. The study proposes a risk-based, variability-oriented perspective on approach energy management that complements traditional threshold-based monitoring logic. The findings have direct implications for flight data monitoring systems, pilot training programs, and proactive safety management practices aimed at early identification of approach energy management degradation.
Keywords: unstable approach, energy management, fast on approach, long landing, flight data monitoring, threat and error management, runway excursion risk.
Abstract. Airfield areas are a source of significant aviation emissions, negatively impacting air quality in nearby residential areas. This study examines the development and experimental validation of a UAV-based digital airspace smoke monitoring platform. The primary focus of the study is the concentration of soot and aerosol particles formed during the combustion of kerosene aviation fuel, which negatively impacts human health and the environment. The developed platform uses a capacitive sensor that records changes in the dielectric properties of the airspace, converting them into an electrical signal processed by Arduino and Raspberry Pi microcontrollers capable of digital recording and data transmission. Field experiments were conducted with UAV flights along a three-dimensional cylindrical trajectory at altitudes of 20-100 m. The platform was also calibrated with a Meta-01MP-0.1 smoke meter. A graphical calibration curve for the sensor output voltage versus the smoke coefficient was constructed, and the results were processed using a mathematical approach of correlation and regression analysis. A 3D model in Python and the Plotly library were created to visualize the distribution of aerosol particles and gas pollutants. The measured data confirm the effectiveness of the presented platform for air monitoring and predictive assessment of smoke levels in real time, making it suitable for use in environmental monitoring systems in airport areas and other urbanized areas.
Keywords: air pollution, digital platform, smoke, 3D air distribution model, UAV, environmental impact assessment, environmental monitoring.
Abstract. Project Relevance. Nowadays, Low Earth Orbit (LEO) has become the most popular destination for nanosatellites. However, this orbit presents specific challenges: a satellite passes over a ground station at a very high speed (over 28,000 km/h). Within this short window – only 10-15 minutes – all vital collected data must be transmitted to the ground without loss. Therefore, considering the limited power and small size of nanosatellites, developing a reliable and efficient telemetry system is a crucial task.
Object and Objectives. Our research focuses on communication systems for small satellites. The main objective is to collect data from the nanosatellite’s onboard sensors (temperature, pressure, orientation data), combine them into specialized digital packets, and ensure error-free transmission to the ground station. Additionally, we aim to enable the ground operator to interpret this data instantly by displaying it as user-friendly real-time graphs. Implementation Methods. We chose the ESP32 microcontroller for this project due to its high performance and energy efficiency. To transmit data over long distances, we use the LoRa (Long Range) radio module. This technology is ideal for receiving signals from LEO because of its high interference immunity. On the ground station side, Python is used for data processing and visualization. Our Python-based software reads the incoming codes from the satellite and transforms them into «live» graphical displays in real-time.
Results and Conclusion. As a result of this work, we have developed a fully functional communication model between a prototype nanosatellite and a ground station. This model is capable of real-time data collection, processing, and visualization. Our platform serves as a highly convenient experimental base for testing and fine-tuning telemetry systems before the assembly of actual spacecraft. Thus, we have practically demonstrated an effective communication algorithm for satellites in Low Earth Orbit.
Keywords: nanosatellite, mock-up satellite, low altitude, telemetry, ESP32 microcontroller, LoRa radio module, sensors, Earth Station.
Abstract. The article discusses the issues of increasing the efficiency of paint and varnish coating operations on aircraft samples using a flexible industrial robot. The subject of the research is the technological processes of automated application of paint and varnish materials on the surface of aircraft structures of complex geometric shape, as well as methods for improving the quality of coating and productivity of painting work through the use of robotic complexes. The aim of the study is to develop and evaluate a flexible robot control method that improves the efficiency of aircraft painting operations by optimizing the trajectory of the working tool, maintaining the required distance to the surface to be painted and evenly distributing the paint and varnish material over the entire processing area. The developed method is based on the use of a digital model of the painted object, adaptive trajectory planning algorithms for the manipulator and a mathematical apparatus for spatial positioning, which allows taking into account the complex configuration of aircraft surfaces. The method is based on the principles of robotic control of technological processes, computer modeling and automated control of coating parameters in real time. In contrast to the classical model of manual application of paint coatings, the developed method provides higher accuracy in positioning spray equipment, reducing the influence of the human factor, increasing the stability of coating thickness and reducing the consumption of paint and varnish materials. In addition, the use of a flexible robot makes it possible to shorten the duration of the technological cycle, increase the safety of work and ensure the processing of hard-to-reach areas of aircraft structures while maintaining the required coating quality. The results obtained confirm the prospects of introducing robotic technologies into the production and maintenance processes of aviation equipment.
Keywords: flexible robot, paint coatings, aviation equipment, robotic painting, automation of technological processes, optimization of motion trajectory, digital surface model.
Abstract. Traditional PID controllers remain widely used in embedded flight control and stabilization systems. However, in small aircraft Attitude and Heading Reference Systems (AHRS), classical PID approaches are insufficient under sensor noise, drift, vibration, and energy limitations typical for lightweight avionics platforms.
This paper proposes a context-aware, risk-sensitive PID framework for AHRS modernization. The controller integrates sensor reliability estimation, energy-aware modulation, and multi-objective optimization into the PID decision logic. The method reduces oscillatory corrections caused by gyroscope and accelerometer noise while preserving attitude tracking accuracy. Simulation results demonstrate reduced integrated control energy, lower multi-objective cost, improved stability, and enhanced robustness under sensor uncertainty typical for MEMS-based AHRS systems. The proposed structure transforms PID from a purely error-compensation mechanism into an intelligent stabilization module suitable for modern small aircraft avionics.
Keywords: PID controller, AHRS modernization, small aircraft stabilization, contextual control, risk-aware control, energy-efficient avionics, sensor reliability, multi-objective optimization.
Abstract. This paper considers the problem of automated assessment of atmospheric transparency in urban conditions based on images obtained from an unmanned aerial vehicle, using computer vision and deep learning methods. The study explores an approach focused on analyzing visual signs of smoke near the horizon, where the concentration of aerosol pollution is usually most pronounced. For the experimental study, a specialized dataset was formed, including aerial photographs of the urban atmosphere of Almaty taken in January-February 2024, followed by spatial division of the images into nine sectors and manual visual assessment of the transparency level on a discrete scale. This method of marking allowed us to record the spatial heterogeneity of pollution within a single frame and take into account the differences between the sky background, the horizon line, and urban development. Based on the pre-trained MobileNetV2 architecture, two model variants were implemented — classification and regression — which made it possible to compare discrete and continuous approaches to the interpretation of visual information. A comparative analysis showed that the classifier provides higher accuracy of strict class matching (83.9%), while the regression model, when rounding predictions to whole values, demonstrates higher accuracy within a tolerance of ±1 class (97.2%) and a lower level of systematic errors. The results confirm the promise of using UAVs in combination with computer vision methods for local monitoring of atmospheric transparency and highlight the potential of this approach as a supplement to traditional ground-based environmental monitoring systems in urban environments, which is particularly relevant given the limited density of stationary stations.
Keywords: air quality monitoring, UAV data; computer vision; atmospheric transparency, smog; deep learning.
Abstract. The continuous growth of unstructured textual data volumes in aircraft maintenance systems creates a demand for automated analysis methods. Traditional defect categorization using standard codes is often insufficiently detailed to reveal the true root causes of failures, as routine operations and critical malfunctions are frequently combined within a single system category. The subject of this study is the semantic structure of textual fault descriptions related to the exterior lighting system (ATA 33–40) of an aircraft fleet of a single model. The objective of the study is to develop and validate a method for the automatic identification of latent operational patterns and failure modes without the use of labeled data. The methodological foundation of the research is probabilistic topic modeling based on the Latent Dirichlet Allocation (LDA) algorithm. To improve model quality, a specialized text preprocessing procedure was implemented, including the expansion of industry-specific abbreviations and the removal of contextual noise. The optimal model configuration was determined through quantitative analysis of the topic coherence metric (Cv) and an assessment of topic semantic stability. Experimental results show that a six-topic model provides the highest level of interpretability. Analysis of the resulting clusters made it possible to identify design-related defect occurrence zones and to classify failures according to their manifestation type. Latent subgroups corresponding to electrical circuit failures and mechanical damage to structural components were automatically identified. The proposed approach enables the transformation of unstructured maintenance personnel records into detailed diagnostic information, thereby creating opportunities to improve maintenance programs and to transition toward predictive reliability management of specific aircraft subsystems.
Keywords: aircraft, maintenance, external lighting, textual descriptions, natural language processing, topic modeling, Latent Dirichlet Allocation.