Search Results (69)

Land degradation issues are getting complicated worldwide. Kazakhstan’s land use has sharply deteriorated over several decades, necessitating comprehensive assessment and restoration. Farmlands in Kazakhstan are grappling with multiple challenges related to climate change, intense anthropogenic disturbances, and aggressive industrial agricultural practices involving monoculture crop production. Soil depletion is widespread in Kazakhstan due to flood erosion and drought expansion, causing desertification. The land sustainability of farmland improvement, including the soil, geology, and water retention assessment, is currently under investigation through our project activities in North Kazakhstan. Nature-based methods for forest plantation along contour strips and topography-based design landscapes are rarely applied or are absent in many rural areas these days. The land use issues have resulted in the loss of the soil moisture protective functions and a reduction in agricultural efficiency. Geodesy geomatics tools were applied for a topography investigation with digital elevation, digital terrain model preparation, and potential retention ponds’ location identification for managed aquifer recharge introduction. The combination of effective water accumulation methods, considering topography, with the development of protective forest shelterbelts should enhance the land use strategies for sustainable development. This strategy is expected to reduce soil erosion, promote moisture accumulation, by improving the soil’s quality as a sponge in water collection, and increase crop yields. Alongside this, a system for developing the retention ponds with managed aquifer recharge locations for proper water collection to improve the agrolandscapes was presented. Full article

Repeated sowing of winter cereals represents one of the adaptive dryland approaches to make more sustainable the rainfed agriculture activities in southern Kazakhstan. This study conducted a multi-year reconstruction of crop transitions using Sentinel-2 imagery for 2018–2025, based on the combined analysis of Normalized Difference Vegetation Index (NDVI) temporal profiles and the Plowed Land Index (PLI), enabling the creation of a field-level harmonized classification set. The transition “spring crop → winter crop” was used as a formal indicator of repeated winter sowing, from which annual repeat layers and an integrated metric, the R-index, were derived. The results revealed a pronounced spatial concentration of repeated sowing in foothill landscapes, where terrain heterogeneity and locally elevated moisture availability promote the recurrent return of winter cereals. Comparison of NDVI composites for the peak spring biomass period (1–20 May) showed a systematic decline in NDVI with increasing R-index, indicating the cumulative effect of repeated soil exploitation and the sensitivity of winter crops to climatic constraints. Precipitation analysis for 2017–2024 confirmed the strong influence of autumn moisture conditions on repetition phases, particularly in years with extreme rainfall anomalies. These findings demonstrate the importance of integrating multi-year satellite observations with climatic indicators for monitoring the resilience of agricultural systems. The identified patterns highlight the necessity of implementing nature-based solutions, including contour–strip land management and the development of protective shelterbelts, to enhance soil moisture retention and improve the stability of regional agricultural landscapes. Full article

In this paper, we investigate the grant-free (GF) accessing for cyber–physical systems (CPSs) over space–air–ground integrated networks (SAGINs) by jointly considering system stability and power consumption. The problem of GF access for CPSs over SAGINs is modeled as a Markov decision process where preamble sequences are chosen to minimize power consumption while guaranteeing system stability. To solve this problem, a distributed multi-agent deep reinforcement learning framework based on factorization technology is proposed. In addition, a local network based on hierarchical reinforcement learning is designed to prevent the explosion of the dimension of the action space, in turn reducing the computational complexity of the proposed algorithm. Finally, the simulation results validate the performance superiority of the proposed scheme in terms of convergence, power consumption and stability compared with the baseline schemes. Full article

The rapid advancement of low-Earth-orbit (LEO) satellite constellations and unmanned aerial vehicles (UAVs) has positioned space–air–ground integrated networks as a key enabler of large-scale IoT services. However, ensuring reliable end-to-end operation remains challenging due to heterogeneous IoT–UAV link conditions and rapidly varying satellite visibility. This work proposes a two-stage optimization framework that jointly minimizes UAV energy consumption during IoT data acquisition and ensures stable UAV–LEO offloading through a demand-aware satellite association strategy. The first stage combines gradient-based refinement with combinatorial path optimization, while the second stage triggers handover only when the remaining offloading demand cannot be met. Simulation results show that the framework reduces UAV energy consumption by over 20% and shortens flight distance by more than 30% in dense deployments. For satellite offloading, the demand-aware strategy requires only 2–3 handovers—versus 7–9 under greedy selection—and lowers packet loss from 0.47–0.60% to 0.13–0.20%. By improving both stages simultaneously, the framework achieves consistent end-to-end performance gains across varying IoT densities and constellation sizes, demonstrating its practicality for future SAGIN deployments. Full article

The Space–Air–Ground Integrated Network (SAGIN), a core architecture for 6G, faces formidable routing challenges stemming from its high-dynamic topological evolution and strong heterogeneous resource characteristics. Traditional protocols like OSPF suffer from excessive convergence latency due to frequent topology updates, while existing intelligent methods such as DQN remain confined to a passive reactive decision-making paradigm, failing to leverage spatiotemporal predictability of network dynamics. To address these gaps, this study proposes an adaptive routing algorithm (GCN-T-PPO) integrating a GCN-Transformer hybrid encoder, Particle Swarm Optimization (PSO), and Proximal Policy Optimization (PPO) with spatiotemporal attention. Specifically, the GCN-Transformer encoder captures spatial topological dependencies and long-term temporal traffic evolution, with PSO optimizing hyperparameters to enhance prediction accuracy. The PPO agent makes proactive routing decisions based on predicted network states (next K time steps) to adapt to both topological and traffic dynamics. Extensive simulations on real dataset-parameterized environments (CelesTrak TLE data, CAIDA 100G traffic statistics, CRAWDAD UAV mobility models) demonstrate that under 80% high load and bursty Pareto traffic, GCN-T-PPO reduces end-to-end latency by 42.4% and packet loss rate by 75.6%, while improving QoS satisfaction rate by 36.9% compared to DQN. It also outperforms SOTA baselines including OSPF, DDPG, D2-RMRL, and Graph-Mamba. Ablation studies validate the statistical significance (p < 0.05) of key components, confirming the synergistic gains from spatiotemporal joint modeling and proactive decision-making. This work advances SAGIN routing from passive response to active prediction, significantly enhancing network stability, resource utilization efficiency, and QoS guarantees, providing an innovative solution for 6G global seamless coverage and intelligent connectivity. Full article

In Kazakhstan’s Akmola Region, rural households face heightened vulnerability from climate change, driven by reliance on weather-dependent resources and amplified risks of extreme precipitation events, prolonged dry spells, and progressive soil degradation—further intensified by limited adaptive capacity and inequities affecting women-led or ethnic minority families. This study conducted stratified household surveys across four agricultural districts, developed a tailored Livelihood Vulnerability Index (LVI) incorporating shelterbelt presence, condition, and perceived effects, alongside readiness for hydrological surface recovery (contour–strip organisation, swales/valokany, and tree–shrub planting). Results revealed an average LVI of 0.45–0.55, which was higher (+10–15%) in marginalized groups; testing pathways showed correlations (r = 0.65, p < 0.05) with water security, soil condition, income stability, and hazard reduction, with potential LVI reductions of 15–25% through integrated measures. District-specific recommendations include implementing the Potapenko–Lukin method on slopes <5% with valokany (width 80 cm, depth 1.5 m, spacing 100–500 m), endemic plantings, and biomaterial, supported by subsidies (488,028 tenge/ha/year) and GIS monitoring, to enhance resilience and equity in steppe and forest–steppe farming. Full article

Homography estimation of infrared and visible light images is a key visual technique that enables drones to perceive their environment and perform autonomous localization in low-altitude environments. Its potential lies in integration with edge computing and 5G technologies, enabling real-time control of drones within air–ground integrated networks. However, research on homography estimation techniques for low-altitude dynamic viewpoints remains scarce. Additionally, images in low-altitude scenarios suffer from issues such as blurring and jitter, presenting new challenges for homography estimation tasks. To address these issues, this paper proposes a light-weight homography estimation method, LFHomo, comprising two components: two anti-blurring feature extractors with non-shared parameters and a lightweight homography estimator, LFHomoE. The anti-blurring feature extractors introduce in-verse residual layers and feature displacement modules to capture sufficient contextual information in blurred regions and to enable lossless and rapid propagation of feature information. In addition, a spatial-reduction-based channel shuffle and spatial joint attention module is designed to suppress redundant features introduced by lossless transmission, allowing efficient extraction and refinement of informative features at low computational cost. The homography estimator LFHomoE adopts a CNN–GNN hybrid architecture to efficiently model geometric relationships between cross-modal features and to achieve fast prediction of homography matrices. Meanwhile, we construct and annotate an unregistered infrared and visible image dataset from drone perspectives for model training and evaluation. Experimental results show that LFHomo maintains great registration accuracy while significantly reducing model size and inference time. Full article

The sixth-generation (6G) system has been attracting increasing attention from both industry and academia, with the space–air–ground integrated network (SAGIN) identified as one of its key applications. This study investigates a SAGIN framework tailored for deployment in remote areas. To address the differing needs of users with emergency and routine tasks, an offloading strategy is proposed that enables direct offloading for emergency tasks and optimized UAV-assisted offloading for routine tasks. Additionally, considering the limited satellite coverage duration, a reliability mechanism for task offloading is designed. The study formulates a task offloading optimization problem aimed at maximizing the completion rate of routine tasks—while reducing their energy consumption and latency—under the premise of guaranteeing the completion of emergency task offloading. The problem is modeled as a Markov Decision Process (MDP). To solve it, a D-MAPPO reinforcement learning algorithm is proposed, which integrates the Dirichlet distribution with the Multi-Agent Proximal Policy Optimization (MAPPO) framework. Simulation results show that, compared with the MAPPO and PPO algorithms, the delay is reduced by 38% and 31%, respectively, while the energy consumption is reduced by 7% and 48%, respectively. Full article

This paper presents a comprehensive review of integrated terahertz (THz) and free-space optical (FSO) communication systems, focusing on their potential to address the escalating demands for high-capacity, long-distance, and ultra-reliable transmission in future six-generation (6G) and space–air–ground integrated networks (SAGIN). The study systematically examines recent advancements in three critical areas: channel modeling, transmission performance, and integrated system architectures. Specifically, it analyzes THz and FSO channel characteristics, including attenuation mechanisms, turbulence effects, pointing errors, and noise sources, and compares their complementary strengths under diverse atmospheric conditions. Key findings reveal that THz communication achieves transmission rates up to several Tbps over distances of several kilometers but is constrained by molecular absorption and weather-induced attenuation, while FSO offers superior bandwidth-distance products yet suffers from turbulence-induced fading, posing significant reliability challenges. The integration of THz and FSO through adaptive switching strategies (e.g., hard and soft switching) demonstrates enhanced reliability and spectral efficiency, with experimental results showing seamless data rates exceeding Tbps in hybrid systems. However, challenges persist in transceiver hardware integration, algorithmic optimization, and dynamic resource allocation. The review concludes by identifying future research directions, including the development of unified channel models, shared architectures, and intelligent switching algorithms to achieve robust integrated communication infrastructures. Full article

Security is paramount in space–air–ground integrated networks (SAGINs) due to their inherent openness and the broadcast characteristics of wireless transmission. In this paper, we propose a secure downlink transmission scheme with NOMA-based mixed FSO/RF communications for SAGINs. Specifically, the satellite communicates with two ground users through an unmanned aerial vehicle (UAV) relay, where FSO and RF transmissions are adopted for the satellite–relay and relay–user links, respectively. Furthermore, the NOMA technique is integrated to further enhance secrecy performance. Subsequently, exact closed-form expressions for the secrecy outage probability of the downlink transmission link in SAGINs are derived. Finally, Monte Carlo simulations are performed to validate the effectiveness of the proposed secure downlink transmission scheme and the accuracy of the analytical expressions. Full article

With the widespread adoption of space–air–ground integrated networks (SAGINs) in next-generation wireless communications, intelligent reflecting surfaces (IRSs) have emerged as a key technology for enhancing system performance through passive link reinforcement. This paper addresses the prevalent issue of channel state information (CSI) uncertainty in practical systems by constructing an IRS-assisted multi-hop SAGIN communication model. To capture the performance degradation caused by channel estimation errors, a norm-bounded uncertainty model is introduced. A simulated annealing (SA)-based phase optimization algorithm is proposed to enhance system robustness and improve worst-case communication quality. Simulation results demonstrate that the proposed method significantly outperforms traditional multiple access strategies (SDMA and NOMA) under various user densities and perturbation levels, highlighting its stability and scalability in complex environments. Full article

With the rapid growth of user service demands, space–air–ground integrated networks (SAGINs) face challenges such as limited resources, complex connectivity, diverse service requirements, and no-fly zone (NFZ) constraints. To address these issues, this paper proposes a joint optimization approach under NFZ constraints, maximizing system utility by simultaneously optimizing user association, unmanned aerial vehicle (UAV) trajectory, and slice resource allocation. Due to the problem’s non-convexity, it is decomposed into three subproblems: user association, UAV trajectory optimization, and slice resource allocation. To solve them efficiently, we design the iterative SAC-MS algorithm, which combines matching game theory for user association, sequential convex approximation (SCA) for UAV trajectory, and soft actor–critic (SAC) reinforcement learning for slice resource allocation. Simulation results show that SAC-MS outperforms TD3-MS, DDPG-MS, DQN-MS, and hard slicing, improving system utility by 10.53%, 13.17%, 31.25%, and 45.38%, respectively. Full article

This paper discusses the use of additional ultrasonic fuel treatment technology to reduce sulfur oxide emissions from marine diesel exhaust gases. The research was conducted on a Bulk Carrier vessel with a deadweight of 64,710 tons with the main engine YMD MAN BW 6S50ME-C9.7 and three auxiliary diesel generators CMP-MAN 5L23/30H. The exhaust gases from all engines were treated for sulfur impurities using a scrubber system. It was stated that the combined use of the exhaust gas scrubber system and ultrasonic fuel treatment technology (compared to scrubber-only exhaust gas cleaning) results in a reduction in carbon dioxide CO₂ and sulfur dioxide SO₂ emissions, along with their ratio SO₂/CO₂. The additional ultrasonic fuel treatment technology has had the most significant effect on sulfur-containing components, leading to a substantial decrease in SO₂ emissions from exhaust gases. For various operating conditions of ship diesel engines, a reduction in CO₂ emissions of 2.9–7.5% and a reduction in SO₂ emissions of 9.3–33.1% were established. This achieved a reduction of 6.3 to 23.7% in the SO₂/CO₂ ratio, a critical parameter for evaluating the performance of the scrubber system in exhaust gas cleaning, as mandated by the provisions of Annex VI of MARPOL. The requirements of the international conventions MARPOL and SOLAS were adhered to during the experiments. Full article

Satellite monitoring of agricultural crops plays a crucial role in ensuring food security and in the sustainable management of agricultural resources, particularly in regions dominated by rainfed farming, such as the Turkestan region of Kazakhstan. Many satellite monitoring tasks rely on remote identification of different types of cultivated crops. In developing the proposed method, we accounted for the temporal characteristics of crop growth and development in various climatic zones of rainfed agriculture, analyzed the dynamics of the Normalized Difference Vegetation Index (NDVI) together with ground-based data, and identified effective time periods and patterns for successful crop recognition. This study aims to develop and comparatively assess two methods for the automatic identification of cultivated crops in rainfed zones using Sentinel-2 satellite data for the years 2018 and 2022. The first method is based on detailed classification of pre-digitized field boundaries, providing high accuracy in satellite-based mapping. The second method represents a fully automated approach applied to large rainfed areas, emphasizing operational efficiency and scalability. The results obtained from both methods were validated against official national statistics, ground-based field surveys, and farm-level data. The findings indicate that the field-boundary-based method delivers significantly higher accuracy (average accuracy of 91.1%). While the automated rainfed-zone approach demonstrates lower accuracy (78%), it still produces acceptable results for large-scale monitoring, confirming its suitability for rapid assessment of sown areas. This research highlights the trade-off between the accuracy achieved through detailed field boundary digitization and the efficiency provided by an automated, scalable approach, offering valuable tools for agricultural production management. Full article

The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin. Full article