Kalmukhamed Tazhen

The formation of TiB reinforcement during the sintering of Ti–TiB₂ powder mixtures depends on reaction kinetics and processing conditions, which influence the phase composition available for subsequent thermomechanical treatment. This study presents a reproducible computational assessment of TiB formation in a nominal Ti–5 wt.% TiB₂ composite subjected to vacuum sintering for 4 h at 1050, 1150, 1200, 1250, and 1350 °C. A published two-pathway Avrami kinetic model was implemented and evaluated against 25 reported conversion observations, yielding a root-mean-square error of 0.0419 and a mean absolute error of 0.0312 in the conversion fraction. Under the original kinetic parameters, the model predicts nearly complete TiB₂ conversion at all five temperatures after 4 h. Stoichiometric mass balance indicates a theoretical maximum of approximately 8.44 wt.% TiB and 91.56 wt.% residual Ti matrix. A kinetic sensitivity analysis was conducted to examine the influence of reduced effective reaction rates on the predicted phase fractions. The results demonstrate that the nominal sintering conditions alone are insufficient to establish a unique phase composition when the kinetic parameters are transferred to powders with different particle sizes and compaction histories. The proposed computational framework provides a transparent basis for evaluating reaction-related processing uncertainties and identifying conditions requiring experimental verification before hot rolling. The analysis does not independently establish densification, reinforcement morphology, or mechanical properties, and its applicability to the investigated powder system remains subject to material-specific validation.

Reliable wireless connectivity in underground public transportation can vary with train position because rolling stock modifies propagation paths and local interference conditions. This study proposes a transport-state-aware radio-planning framework for a three-node distributed antenna system (DAS) in a reference underground metro station. A three-dimensional 3.5 GHz reference station model, three operating states (empty station, train at platform, and train entering), a discrete set of 69 candidate antenna positions, and multi-objective placement/power optimization are combined to evaluate received power, SNIR, joint service coverage, and serving-area balance. The optimized configuration increased worst-state joint service coverage from 66.12% to 68.26%, improved worst-state P5 received power from −71.92 to −71.49 dBm, and reduced aggregate transmit power from 753.57 to 682.49 mW. The mean serving-area coefficient of variation decreased from 0.202 to 0.114. Threshold analysis showed that the optimized layout was advantageous at moderate and high SNIR requirements but not at a relaxed 5 dB criterion. The results support treating underground DAS deployment as a transport-state-aware infrastructure-planning problem rather than a static geometric-spacing problem.

As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The main contribution is the explicit coupling of logistics-related supervisory variables with the predictive control problem and the diagnostic feature space. Buffer occupancy, transport delay, and logistics-induced waiting state are incorporated into an augmented reduced-order model to support constrained control and health-state interpretation. The framework is evaluated through a comparative simulation-based feasibility study using a low-order model of a robotic production axis affected by disturbances, degradation, and logistics-related constraints. The proposed approach is compared with classical feedback control, predictive control without diagnostics, and predictive control with diagnostics but without explicit intralogistics coupling. In the reduced-order simulation scenario, the proposed method achieved the lowest mean RMSE of 0.330 ± 0.015 and the lowest mean constraint violation rate of 3.133 ± 0.280% across 40 repeated simulation runs. However, the improvement in nominal tracking accuracy over the strongest diagnostic-assisted MPC baseline was marginal. Adding logistics-related diagnostic features improved mean accuracy from 0.848 ± 0.014 to 0.874 ± 0.012 and mean F1-score from 0.844 ± 0.016 to 0.872 ± 0.013. The main advantage of the proposed architecture was observed in reliability- and continuity-oriented indicators, including reduced downtime, lower final damage accumulation, fewer cooling cycles, and improved differentiation between machine-related and logistics-induced abnormal conditions.

Rapid and reliable seismic monitoring requires accurate waveform inference, together with robustness to noise, incomplete sensing, and unstable predictions. This study investigates a quality-controlled, waveform-centered, AI-assisted framework for seismic event detection, P- and S-phase picking, graph-aware inter-station refinement, and rapid hazard-related characterization. The framework includes optional DAS, MEMS, and high-rate GNSS branches; however, the primary empirical validation is based on real waveform-centered IRIS records from the Almaty seismic region, not on a fully synchronized multimodal field deployment. The dataset includes seven seismic stations, HHZ waveforms sampled at 100 Hz, 219 seismic events, 1260 event traces, and 240 s P-centered windows from 1 January 2023 to 31 December 2024. Optional auxiliary branches are evaluated through controlled branch-availability, reduced-input, fallback, and stress-test scenarios. Under the standard-condition benchmark, the proposed framework achieved a precision of 0.941, recall of 0.932, F1 score of 0.936, false-alarm rate of 0.051, detection latency of 173 ms, and P- and S-pick mean absolute errors of 31 ms and 54 ms. Under controlled low-SNR testing, it retained an F1 score of 0.846. The findings support waveform-centered, quality-controlled monitoring, while broader cross-domain and fully synchronized multimodal validation remain necessary.

The object of the study is the onboard diagnostic process in CubeSat nanosatellite systems, with particular emphasis on fault detection based on real-time analysis of multivariate telemetry data under real-time embedded operating conditions. The problem statement is the gap between the relatively low computational expenses and predictability of classical FDIR approaches and their limited ability to detect complex anomalies, and, conversely, the greater anomaly sensitivity and lower embedded performance of machine-learning models. A hybrid onboard diagnostic architecture that integrates telemetry acquisition, telemetry preprocessing, statistical feature extraction, a deterministic FDIR branch, a lightweight machine-learning branch, and decision fusion into one onboard diagnostics session has been elaborated. The proposed hybrid onboard diagnostic architecture has been evaluated based on hardware-in-the-loop testing using representative telemetry and fault cases for CubeSat systems. The evaluation metrics were the fault-detection accuracy, the detection latency, the CPU consumption, the memory consumption, and the resistance to telemetry noise. The hybrid onboard diagnostic architecture was compared to the conventional threshold-based FDIR system and standalone machine learning algorithms under the same HIL conditions. In the reported experiment setup, the proposed hybrid onboard diagnostic architecture demonstrated the fault-detection accuracy of 94%, while the classical FDIR method provided 71%, and the standalone machine-learning approach provided 88%. The average detection latency was decreased to 83 ms, in contrast to 120 ms and 95 ms, respectively. The embedded solution requires only 17.6% CPU and 58 KB of memory. Under the highest telemetry-noise level, the fault-detection accuracy of the proposed hybrid onboard diagnostic architecture decreases to 80%, whereas the standalone machine-learning and the classical FDIR baselines provide only 65% and 43% fault-detection accuracy, respectively.

Energy-efficient and resilient decentralized unmanned aerial vehicles (UAV) swarm operation in global navigation satellite system (GNSS) denied environments remains challenging because propulsion demand, communication load, and onboard inference are tightly coupled at the mission level. Although prior studies have examined some of these components separately, their joint evaluation within adaptive decentralized swarms remains limited under degraded navigation conditions. This study proposes an energy-aware adaptive communication-topology framework integrated with lightweight edge artificial intelligence (AI)-assisted navigation for decentralized UAV swarms operating without reliable GNSS support. The approach combines a unified mission-level energy-accounting structure for propulsion, communication, and onboard inference, a residual-energy-aware topology adaptation mechanism for preserving swarm connectivity, and a convolutional neural network-long short-term memory (CNN–LSTM) based edge-AI navigation module for improving localization robustness. The framework was evaluated in 1200 s Robot Operating System 2 (ROS2)–Gazebo–PX4 simulation scenarios against fixed topology and extended Kalman filter (EKF)-based baselines. Under the adopted simulation assumptions, the proposed configuration achieved a 22.7% reduction in total energy consumption, with the largest decrease observed in the communication-energy component, while preserving positive algebraic connectivity across all evaluated runs. The edge-AI module yielded a 4.8% root mean square error (RMSE) reduction relative to the EKF baseline, indicating a modest but meaningful improvement in localization performance. These results support the feasibility of integrated energy-aware swarm coordination in GNSS-denied environments; however, they should be interpreted as simulation-based evidence under the adopted modeling assumptions, and further high-fidelity propagation modeling, broader learning validation, and hardware-in-the-loop studies remain necessary.

The object of the study is an autonomous high-altitude airship platform. This type of high-altitude platform is designed for long-term telecommunication retransmission at altitudes of 18–25 km. The main problem lies in the insufficient stability of flight at the height of the stratosphere and the limited energy capabilities of aircraft. An integrated intelligent control system and energy management have been developed to solve the problems. This architecture in portable form includes a sensor module, an STM32 microcontroller, a LoRa telemetry channel, a mathematical model of dynamics taking into account lift, aerodynamic drag, external factors and atmospheric parameters, and most importantly, a hybrid adaptive PID-fuzzy controller was proposed. Additionally, a model of the energy balance was proposed, which directly interacts with solar panels, energy consumption of drives and battery operation. The simulation data obtained shows that the use of the PID-fuzzy controller provides a significant increase in the stability of the platform. The transition time is reduced by 44.4%, the maximum deviation from the set altitude and flight path is reduced by 56.5%, and the average drive consumption is reduced by 20–22%. The energy balance model demonstrates that the developed system is capable of retaining up to 46% of the battery charge after 24 hours of battery life, which is 18% higher compared to a system without adaptive energy management. The practical significance of the project lies in the possibility of using the developed system as part of autonomous telecommunications networks, balloons, an emergency communication system, remote sensing of the earth, as well as in elements of promising 6G and NTN networks.

The object of this study is a stratospheric airship-based telecommunication platform, employed as a high-altitude platform station (HAPS), designed to operate at altitudes of 20–30 km and provide broadband connectivity in regions with limited terrestrial infrastructure such as rural and remote areas of the Republic of Kazakhstan. The key research problem is to ensure stable connectivity of HAPS-based telecommunication platforms under strong stratospheric winds, with limited payload capacity and energy resources, while developing a scalable network architecture for multi-airship coordination. This paper proposes a network concept based on modular nano-airships, which reduces drag, enhances maintainability, and ensures continuous service. Calculations of lifting capacity, aerodynamic drag of different envelope shapes, energy balance, and the coverage radius of a single station were performed. Experimental tests of a prototype confirmed the feasibility of using the sub-GHz band (433 MHz) to provide long-range communication under ground test conditions, where signal attenuation was found to be minimal compared to higher frequencies. Due to the obtained characteristics, the hypothesis of employing a group of smaller airships instead of a single large carrier was confirmed. This is explained by their reduced sensitivity to wind loads, flexibility in network configuration, and lower operational risks. Unlike traditional satellite systems, which are expensive to launch and maintain, stratospheric airships can be recovered, repaired, and redeployed at relatively low cost, offering an economically viable solution for developing regions. The results can be applied in the creation of national communication networks for remote and sparsely populated areas of the Republic of Kazakhstan, in emergency response operations, and as a complementary layer to satellite constellations. The proposed concept demonstrates that modular HAPS networks are a realistic and scalable alternative, capable of providing broadband access under real-world atmospheric and geographic constraints

The object of the study is the methods of radio frequency resource management in stratospheric communication systems based on high altitude platform stations (HAPS). The problem addressed is the limited radio frequency spectrum, frequency overlap with fifth- and sixth-generation (5G/6G) networks, and the high probability of interference, which complicate efficient spectrum utilization and coordination. The obtained results indicate that within the frequency bands recommended by the International Telecommunication Union (ITU) — 21.4–22.0 GHz, 24.25–27.5 GHz, 47.2–47.5 GHz, and 47.9–48.2 GHz — the probability of interference reaches up to 70% in the 27.5–28.35 GHz band. By applying cognitive radio (CR) technology, interference levels decreased by 60%, and spectrum utilization efficiency increased by 35%. Dynamic spectrum access (DSA) improved spectrum efficiency by 30–45%, while spectrum sharing methods enhanced it by 40–60%. A brief explanation of the results shows that the proposed management approaches significantly increase the efficiency of radio frequency resource use and substantially reduce interference. For example, at a bandwidth of 100 MHz and a signal-to-noise ratio (SNR) of 10, the channel capacity reached approximately 332 Mbps. The distinctive features of the results lie in the comprehensive use of modern technologies that effectively address spectrum scarcity and interference issues, ensuring compatibility of HAPS with existing terrestrial and satellite communication systems. The proposed approaches are suitable for implementation in international and national spectrum coordination and licensing frameworks aimed at expanding broadband connectivity in underserved regions