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. In this work, considering that traditional data processing methods in air traffic control systems in areas with complex terrain—mountainous and foothill regions—often lack sufficient accuracy, which can lead to a decrease in the level of air transport safety and efficiency, it is proposed to develop methods for increasing data processing accuracy under complex noise conditions in mountainous and foothill areas.Taking into account the specific features of the terrain and technical limitations, the main factors affecting the reduction in data processing accuracy operating in mountainous and foothill conditions were identified, including meteorological, radio-technical, topographical, and anthropogenic interference. At the same time, criteria for assessing data processing accuracy under interference loads, as well as system reliability parameters depending on the degree of distortion of information flows, have been introduced and new results have been obtained, which will ensure increased safety and efficiency of air traffic in complex conditions.
Keywords: mountainous regions, interference, navigation, system, terrain, noise.
Abstract. Improving the efficiency of the transportation process on the railways of Kazakhstan by optimizing the Train Formation plan (TPP) is an important task in the context of growing transit flows, especially in such key divisions as the Almaty Transportation Department. Traditional methods of calculating the TFR used in the regional Transportation Management Center (RCMP-2) often do not provide global optimization and high transit of wagon flows at complex, branched landfills. The purpose of the study is to develop and test a method of dynamic transit forecasting to create optimal PFPS at the landfills of the Almaty branch.
The work is based on a mathematical model that formalizes the process as a multi-stage optimization problem with a transit maximization criterion (T_r). The main difference between the method is that is a calculation from the destination station to the departure station, a step-by-step selection of conditionally optimal solutions for combining or distributing car flows based on a comparison of car-hour savings. Detailed algorithms and flowcharts have been developed for linear directions and adjacent polygons suitable for implementation in an automated control system (ACS). The use of the method had a significant impact. At 15 stations and two adjacent landfills, it was possible to obtain optimal PFPS, saving 13,790 car-hours. A comparative analysis of the linear route of 6 stations shows that the proposed method allows increasing the number of flows arriving at the destination station by trains by 25-30%, and the number of transit stations from 5 to 8 compared with the traditional method. Doubling the capacity of the flows at the entrance to the landfill led to savings of 3.5-4 times per wagon–hour. The method of dynamic transit forecasting is a fundamentally new approach to the organization of carriage flows. It provides a significant increase in transitivity, reduction of sorting operations and acceleration of cargo delivery. The developed algorithms are adapted for implementation in the automated control system RCUP-2 of the Almaty branch of JSC NC KTZ and allow real-time automation of the OS correction process, which creates the basis for the transition to intelligent operational management systems.
Keywords: train formation plan, transit, dynamic forecasting, optimization, carriage flow, railway landfill, algorithm, saving car hours, JSC NC KTZ.
Abstract. With the digitalization of the economy and increasing demands on the efficiency of logistics processes, the importance of methods for optimizing transport flows and resource allocation is growing. One key tool for solving such problems is the linear programming transport model, which minimizes the total cost of transportation between suppliers and consumers. Forming an initial feasible baseline plan is of particular importance when solving a transportation problem, since the quality of the initial solution directly affects the speed of subsequent optimization and computational costs. This article provides a comparative analysis of methods for creating an initial baseline plan for a transportation problem used in logistics systems and digital supply chains. The relevance of the study stems from the need to reduce transportation costs and improve the computational efficiency of optimization algorithms in the context of growing logistics networks. This paper examines the northwest corner, minimum cost, Vogel approximation, and dual preference methods. The methods were evaluated through computational experiments using criteria such as the initial plan cost, the number of subsequent optimization iterations, computational complexity, and the degree of approximation to the optimal solution. It was found that the quality of the initial baseline plan has a significant impact on the efficiency of solving a transportation problem. The Vogel approximation method demonstrated the most consistent results, ensuring minimal deviation from the optimal solution and a reduced number of optimization iterations compared to other approaches. The scientific novelty of this study lies in its comprehensive comparative evaluation of methods for generating an initial baseline plan using a set of quantitative performance criteria in the context of digitalization of logistics processes. The practical significance of the results lies in the potential application of the findings in the development of digital decision support systems, automation of transportation planning, and optimization of logistics costs.
Keywords: transport problem, initial baseline plan, transport logistics, Vogel method, transportation optimization, linear programming, transport costs, digitalization of logistics.
Abstract. The research is devoted to solving the urgent problem of modernization of technological cycles at mining and processing plants. The main focus of the article is on automation of the primary stage of separation of fossil raw materials. Today, in many enterprises, quality control of rock mass is based on visual inspection and is carried out by the operator. It is generally believed that with this approach, the human factor introduces subjectivity into the assessment and reduces the accuracy of impurity fixation. That is why the article explores the possibility of implementing computer vision systems for operational sorting.
The research focuses on the development and testing of a binary classification method for digital images, which makes it possible to effectively separate streams into the target product (coal) and waste rock. In the framework of this work, the Random Forest algorithm was chosen as an architectural solution, the hyperparameters of which were optimized by the lattice search method. During the preliminary tests, the algorithm showed stable results in dusty conditions and changing lighting. To train and test the model, a data set of 4027 images of the mountain range was collected. The experiment was based on a comparative analysis of the proposed method with the methods of convolutional neural network (CNN), logistic regression and decision tree. The results confirmed the potential of this method. The model achieved a classification accuracy of 96.5% with an F1-score of 0.896 and a coal detection completeness of 85.7%. It has been found that with accuracy comparable to convolutional networks, the chosen algorithm has an advantage in resource efficiency and the ability to work on Edge devices without a GPU, providing performance of 30-35 FPS. The research results allow us to conclude that the achieved indicators, as well as the stability of the algorithm, make it possible to successfully integrate it into the monitoring system. The proposed solution can become the basis of an autonomous control system at a mining and processing plant without human intervention.
Keywords: computer vision, machine learning, Random Forest, rock classification, conveyor automation, coal industry.
Abstract. This paper examines an approach to building an event-driven serverless architecture for a distributed information system operating under uneven and peak load conditions. Modern digital services are characterized by sharp fluctuations in the intensity of incoming requests, which requires maintaining operational stability, acceptable response times, and the ability to quickly scale computing resources. Traditional monolithic and container-based solutions in such environments often require upfront capacity reservations or respond to load surges with delays. The goal of this study is to develop and experimentally evaluate an architectural solution in which request processing is organized as a stream of independent events using a serverless computing model. To this end, a formalized event model and input load generation scheme are proposed, enabling the reproduction of both normal operating modes and short-term peak impacts. The effectiveness was assessed based on a series of controlled computational experiments under various load scenarios. The key metrics used were average processing latency, the p95 metric, system throughput, and the error rate during periods of increased activity. The results obtained during the study demonstrate that as the load increases, the increase in latency is manageable, and the system maintains operability during short-term overloads. This allows us to consider the proposed approach as a promising solution for scalable distributed services.
Keywords: event-driven architecture, serverless computing, distributed information systems, scalability, peak load, tail latency, performance.
Abstract. This paper presents an experimental evaluation of the efficiency of the proposed digital signature based on a Verkle tree using the Chinese Remainder Theorem. A software implementation of the algorithms for key generation, signature formation, and verification has been developed. In the proposed scheme, the Verkle tree is used for compact representation of commitments, while the Chinese Remainder Theorem is applied to optimize modular computations and improve the computational efficiency of signing and verification operations. An analysis of the time characteristics of the algorithms was carried out, and complexity indicators were obtained. Experimental results were obtained on a fixed computing platform with multiple test runs to ensure statistical reliability. A comparative analysis was performed with a digital signature based on the classical polynomial commitment scheme Kate–Zaverucha–Goldberg (KZG) in terms of the main signature parameters and execution time. The obtained results demonstrate the potential of using the Verkle tree in combination with the Chinese Remainder Theorem for constructing compact and computationally efficient digital signatures.
Keywords: Verkle tree, vector commitment, polynomial commitment, Chinese Remainder Theorem, digital signature, authentication, verification.
Abstract. In recent years, the problem of air pollution has become more and more acute, especially for industrial regions. The constant growth of environmental monitoring data requires not only their accumulation, but also effective intelligent processing. One of the key tasks is the timely detection of abnormal values that can indicate both real emissions of pollutants and errors in measuring systems.
In this paper, an algorithm for detecting anomalies in the atmospheric air monitoring system is proposed, based on a combination of statistical methods and machine learning algorithms. This approach allows you to take into account both simple emissions and more complex, hidden patterns in the data. For primary filtration, the methods of Z-score and interquartile range (IQR) were used, and for a more in-depth analysis, the Isolation Forest algorithm was used, which is able to effectively work with multidimensional ecological time series. The novelty of the study lies in the hybrid decision procedure that combines statistical filtering, unsupervised anomaly detection and meteorological-context interpretation for industrial air pollution monitoring data.
Particular attention is paid to the construction of the system architecture, which is implemented using cloud technologies. This provides the ability to process large amounts of data coming from monitoring sensors, as well as analyze them in near real time. The algorithm was tested on data from the city of Ust-Kamenogorsk, including indicators of the concentration of the main pollutants and meteorological parameters. The results showed that the proposed hybrid approach achieved higher performance than individual methods, reaching Precision = 0.94, Recall = 0.91 and F1-score = 0.92. At the same time, the system is able to automatically record sharp deviations associated with industrial emissions, weather conditions or technical failures.
The practical significance of the work lies in the possibility of introducing the proposed algorithm into environmental information systems and smart city solutions. Its application makes it possible to improve the quality of monitoring, the efficiency of response and the validity of management decisions in the field of environmental protection.
Keywords: atmospheric air monitoring, data anomalies, machine learning, isolation forest, environmental monitoring, cloud technologies.