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.
Absract. In the context of increasing air traffic intensity and the growing proportion of night and long-haul flights, the management of crew fatigue is becoming a critically important factor in ensuring flight safety. Crew fatigue is recognized as a significant factor affecting the reliability of flight operations and decision-making processes. At the same time, traditional working time limitations do not provide a comprehensive assessment of fatigue-related risks, which necessitates the implementation of integrated approaches. Modern methods, including heart rate variability (HRV) analysis and simulation modeling, enable a more accurate assessment of the functional state of crew members and facilitate the identification of fatigue formation patterns. The research problem lies in the limitations of the prescriptive approach based on Flight Time Limitations (FTL), which does not account for physiological and circadian characteristics of humans. The aim of this study is to develop recommendations for assessing crew fatigue levels in airlines of the Republic of Kazakhstan based on an integrated model consistent with international standards of ICAO, IATA, and EASA. The objectives include the analysis of international fatigue management approaches, the development of a mathematical model of the fatigue index, its statistical validation, and the formulation of practical recommendations. The research methods include multiple and logistic regression, ROC analysis, calculation of sensitivity and specificity, Monte Carlo simulation, and heart rate variability (HRV) analysis. The results demonstrated the superiority of the integrated FRMS model (AUC = 0.623) compared to the prescriptive FTL-based model (AUC = 0.574). Simulation modeling showed a reduction in fatigue risk by up to 17% under optimized scheduling conditions. A statistically significant relationship between fatigue, night workload, and decreased HRV was identified. It is concluded that the implementation of an integrated fatigue index within the safety management systems of airlines in the Republic of Kazakhstan is justified. The proposed recommendations enable a transition to proactive fatigue risk management and are consistent with international standards.
Keywords: crew fatigue, fatigue risk management, safety management system, working time limitations, heart rate variability, biomathematical modeling analysis, aviation safety.
Abstract. This article examines approaches to improving the reliability and resilience of information exchange for unmanned systems, taking into account the presence of external interference. The primary issue addressed in this article is the analysis of natural and artificial factors that may affect the operation of data transmission channels. Emphasis is also placed on the impact of artificial electromagnetic disturbances. The paper’s scientific innovation is that it considers the unmanned system as a multi-agent environment. The agents are the operator, the aircraft, and the external environment. The goal of the study is to improve the reliability of the data exchange channel by implementing Visible Light Communication technology. To achieve this goal, the authors formalized quantitative control assessment methods, developed a structural and functional model of the control system, and conducted simulation tests to assess risks. An analysis of experimental data and computer modeling demonstrated that Visible Light Communication, operating in the infrared range, provides a stable information exchange channel over a distance of up to 3 kilometers. This result demonstrates the potential of using this technology to build a noise-resistant and secure information exchange channel for unmanned systems.
Keywords: Unmanned aerial vehicle system, Visible Light Communication technology, risk assessment, simulation model, control system model.
Abstract. This work presents a mathematical model for determining the coordinates of integrated groups of unmanned aerial vehicles (UAVs). The main application areas of UAVs and promising directions for the development of unmanned aviation systems, integrating complexes of remotely controlled vehicles, are analyzed. To improve the stability and reliability of interaction in such systems, stable structures of integrated groups are proposed. Special attention is given to methods for modeling integrated UAV groups, including mathematical description and computer simulation. The structures of integrated groups are analyzed, taking into account the criteria of topological stability during movement and the reliability of data transmission channels based on visible light communication (VLC) technologies. To obtain results, mathematical descriptions and corresponding computer models of integrated UAV groups were developed, meeting the criteria of topological stability and reliability of data transmission channels. Modeling was performed in Matlab R2020b, where computer models of UAV swarms were constructed in a three-dimensional coordinate system.
Additionally, the work considers different types of topologies for integrated UAV groups and their impact on the stability and reliability of system performance.
Keywords: method, group of unmanned aerial vehicles, control, model, data transmission channels, artificial intelligence.
Abstract. This paper explores modern techniques to process and enhance the reliability of radar signals amid strong noise and interference typical for the secondary surveillance radar (SSR) band at 1030/1090 MHz. Air-traffic radar systems play a critical role in ensuring safe and efficient airspace management, however, their performance degrades under strong noise and interference typical of the 1030/1090 MHz band. A comprehensive analysis of filtering and adaptive signal processing algorithms was conducted to improve the signal-to-noise ratio, stabilize target response parameters, and lower false detection rates. For objective comparison, both traditional and advanced digital filtering methods were evaluated. Special focus was placed on comparing the Median, Butterworth, and recursive Kalman filters, implemented and tested in MATLAB with synthetic data and simulated radar scenarios. The effects of filter order, bandwidth, adaptation coefficients, and sampling interval on signal reconstruction quality were investigated. Using statistical, correlation, and probabilistic analyses, an enhanced adaptive threshold detection method was developed, which accounts for the non-stationary background noise, temporal signal correlations, amplitude fluctuations, and dynamic interference parameters in real time. Results demonstrate that combining and recursively applying these filters greatly enhances the robustness of secondary radars against random and systematic interference, reduces estimation error variance, and improves radar reliability. The practical significance lies in the potential integration of these methods into intelligent air-traffic management systems, which can enhance interference immunity, aircraft identification accuracy, and overall radar surveillance quality in civil aviation.
Keywords: secondary radar, adaptive filtering, Kalman filter, Butterworth filter, median processing, digital signal processing, signal interference, reliability of radar surveillance.