Search Results (234)

The population dynamics of high-altitude mountainous areas are shaped by a complex interplay of socioeconomic and environmental drivers. Despite their significance, such regions have received limited scholarly attention. This research identifies and examines the principal determinants of population changes in the high-altitude mountainous zones of Sichuan Province, China. Utilizing a robust quantitative framework, we introduce the Sustainable Population Migration Index (SPMI) to systematically analyze the migration potential over two decades. The findings indicate healthcare accessibility as the most significant determinant influencing resident and rural population changes, while economic factors notably impact urban populations. The SPMI reveals a pronounced deterioration in migration attractiveness, decreasing by 0.27 units on average from 2010 to 2020. Furthermore, a fixed-effects panel regression confirmed the predictive capability of SPMI regarding population trends, emphasizing its value for demographic forecasting. We also develop a Digital Twin-based Simulation and Decision-support Platform (DTSDP) to visualize policy impacts effectively. Scenario simulations suggest that targeted enhancements in healthcare and infrastructure could significantly alleviate demographic pressures. This research contributes critical insights for sustainable regional development strategies and provides an effective tool for informed policymaking. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

In both scientific and industrial fields, there has been a notable increase in attention toward High-Altitude Pseudo-Satellites (HAPSs) in recent years. This surge is driven by their distinct advantages over traditional satellites and Remotely Piloted Aircraft Systems (RPASs). These benefits are particularly evident in critical areas such as intelligent transportation systems, surveillance, remote sensing, traffic and environmental monitoring, emergency communications, disaster relief efforts, and the facilitation of large-scale temporary events. This review provides an overview of key aspects related to the propellers and propulsion systems of HAPSs. To date, propellers remain the most efficient means of propulsion for high-altitude applications. However, due to the unique operational conditions at stratospheric altitudes, propeller design necessitates specific approaches that differ from those applied in conventional applications. After a brief overview of the propulsion systems proposed in the literature or employed by HAPSs, focusing on both the technical challenges and advancements in this emerging field, this review integrates theoretical foundations, historical design approaches, and the latest multi-fidelity optimization techniques to provide a comprehensive comparison of propeller design methods for HAPSs. It identifies key trends, including the growing use of CFD-based simulations methodologies, which contribute to notable performance improvements. Additionally, the review includes a critical assessment of experimental methods for performance evaluation. These developments have enabled the design of propellers with efficiencies exceeding 85%, offering valuable insights for the next generation of high-endurance, high-altitude platforms. Full article

The integration of reconfigurable intelligent surfaces (RISs) with non-terrestrial networks (NTNs), particularly those enabled by unmanned aerial vehicles (UAVs) or drone-based platforms, has emerged as a transformative approach to enhance 6G connectivity in high-mobility scenarios. UAV-assisted NTNs offer flexible deployment, dynamic altitude control, and rapid network reconfiguration, making them ideal candidates for RIS-based signal optimization. However, the high mobility of UAVs and their three-dimensional trajectory dynamics introduce unique challenges in maintaining robust, low-latency links and seamless handovers. This paper presents a comprehensive performance analysis of RIS-assisted UAV-based NTNs, focusing on optimizing RIS phase shifts to maximize the signal-to-interference-plus-noise ratio (SINR), throughput, energy efficiency, and reliability under UAV mobility constraints. A joint optimization framework is proposed that accounts for UAV path loss, aerial shadowing, interference, and user mobility patterns, tailored specifically for aerial communication networks. Extensive simulations are conducted across various UAV operation scenarios, including urban air corridors, rural surveillance routes, drone swarms, emergency response, and aerial delivery systems. The results reveal that RIS deployment significantly enhances the SINR and throughput while navigating energy and latency trade-offs in real time. These findings offer vital insights for deploying RIS-enhanced aerial networks in 6G, supporting mission-critical drone applications and next-generation autonomous systems. Full article

Strapdown gravity systems are increasingly employed in airborne geophysical exploration and geodetic studies due to advantages such as ease of installation, wide dynamic range, and adaptability to various platforms, including airplanes, helicopters, and large drones. This study presents results from an airborne gravity survey conducted over the northeastern sector of Sardinia (Italy), using a high-resolution strapdown gravity ensuring an accuracy of approximately 1 mGal. Data were collected at an average altitude of 1800 m with a spatial resolution of 3.0 km. The survey focused on the Sos Enattos area near Lula (Nuoro province), a candidate site for the Einstein Telescope (ET), a third-generation gravitational wave observatory. The ideal site is required to be geologically and seismically stable with a well-characterized subsurface. To support this, we performed a new gravity survey to complement existing geological and seismic data aimed at characterizing the mid-to-shallow crustal structure of Sos Enattos. Results show that the strapdown system effectively detects gravity anomalies linked to crustal sources down to ~3.5 km, with particular emphasis within the 1–2 km depth range. Airborne gravity data reveal higher frequency anomalies than those resolved by the EGM2008 global gravity model and show good agreement with local terrestrial gravity data. Forward modeling of the gravity field suggests a crust dominated by alternating high-density metamorphic rocks and granitoid intrusions of the Variscan basement. These findings enhance the geophysical understanding of Sos Enattos and support its candidacy for the ET site. Full article

To better understand the temperature changes in face slab concrete and address challenges such as delayed curing and outdated methods in complex and variable environments, this study investigates the use of visualization and real-time feedback control in concrete construction. The conducted study systematically develops an intelligent curing control system for face slab concrete based on multi-source measured data. A tailored multi-source data acquisition scheme was proposed, supported by an IoT-based transmission framework. Cloud-based data analysis and feedback control mechanisms were implemented, along with a decoupled front-end and back-end system platform. This platform integrates essential functions such as two-way communication with gateway devices, data processing and analysis, system visualization, and intelligent curing control. In conjunction with the ongoing Maerdang concrete face rockfill dam (CFRD) project, located in a high-altitude, cold-climate region, an intelligent curing system platform for face slab concrete was developed. The platform enables three core visualization functions: (1) monitoring the pouring progress of face slab concrete, (2) the early warning and prediction of temperature exceedance, and (3) dynamic feedback and adjustment of curing measures. The research outcomes were successfully applied to the intelligent curing of the Maerdang face slab concrete, providing both theoretical insight and practical support for achieving scientific and precise curing control. Full article

The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise wind estimation. This study introduces a machine learning (ML) approach to improve the accuracy of the wind speed and direction estimation using UAVs. The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. The experiments reveal that incorporating a 10 s smoothing window yields a root mean square error (RMSE) value of 0.39 m/s for the wind speed (horizontal) and an even lower bias (≤0.069 m/s) when using a 60 s smoothing window, representing a marked improvement over traditional techniques. These results are particularly promising at longer smoothing windows (>50 s), where the ML-based approach achieves superior accuracy compared to LiDAR measurements. The findings underscore the potential of integrating machine learning with UAV-based wind measurement systems to achieve higher precision and reliability in wind characterization. Full article

The rapid growth of the private space industry has intensified the demand for lightweight, efficient, and cost-effective photovoltaic technologies. Metal halide perovskite solar cells (PSCs) offer high power conversion efficiency (PCE), mechanical flexibility, and low-temperature solution processability, making them strong candidates for next-generation space power systems. However, exposure to extreme thermal cycling, high-energy radiation, vacuum, and ultraviolet light in space leads to severe degradation. This study addresses these challenges by introducing three key design strategies: self-healing perovskite compositions that recover from radiation-induced damage, gradient buffer layers that mitigate mechanical stress caused by thermal expansion mismatch, and advanced encapsulation that serves as a multifunctional barrier against space environmental stressors. These approaches enhance device resilience and operational stability in space. The design strategies discussed in this review are expected to support long-term power generation for low-cost satellites, high-altitude platforms, and deep-space missions. Additionally, insights gained from this research are applicable to terrestrial environments with high radiation or temperature extremes. Perovskite solar cells represent a transformative solution for space photovoltaics, offering a pathway toward scalable, flexible, and radiation-tolerant energy systems. Full article

Hybrid electric vertical take-off and landing (HEVTOL) flying vehicles serve as effective platforms for efficient transportation, forming a cornerstone of the emerging low-altitude economy. However, the current lack of co-optimization methods for powertrain component sizing and energy controller design often leads to suboptimal HEVTOL performance. To address this, this paper proposes a hierarchical manifold-enhanced Bayesian evolutionary optimization (HM-BEO) approach for HEVTOL systems. This framework employs lightweight manifold dimensionality reduction to compress the decision space, enabling Bayesian optimization (BO) on low-dimensional manifolds for a global coarse search. Subsequently, the approximate Pareto solutions generated by BO are utilized as initial populations for a non-dominated sorting genetic algorithm III (NSGA-III), which performs fine-grained refinement in the original high-dimensional design space. The co-optimization aims to minimize fuel consumption, battery state-of-health (SOH) degradation, and manufacturing costs while satisfying dynamic and energy management constraints. Evaluated using representative HEVTOL duty cycles, the HM-BEO demonstrates significant improvements in optimization efficiency and solution quality compared to conventional methods. Specifically, it achieves a 5.3% improvement in fuel economy, a 7.4% mitigation in battery SOH degradation, and a 1.7% reduction in system manufacturing cost compared to standard NSGA-III-based optimization. Full article

To further enhance the stability and security of image transmission in FSO (Free Space Optics) aviation platforms, this paper proposes a communication transmission scheme that integrates a chaotic image encryption system with the HAP (high-altitude platform) environment. This scheme effectively combines the chaotic image encryption algorithm with the atmospheric turbulence channel transmission process, improving the anti-interference capabilities and security of HAP optical communication for image transmission. First, a five-dimensional hyperchaotic system with complex dynamic characteristics is introduced, and the system’s chaotic behaviors and dynamic properties are explored. The improved system model incorporates chaotic mapping and DNA coding techniques, forming a robust chaotic image encryption system, whose performance is experimentally validated. Next, the feasibility of integrating the chaotic image encryption system with HAP optical communication is discussed. A detailed description of the corresponding turbulence model and test conditions is provided. To verify the scheme’s feasibility, plaintext images of varying sizes are selected for experiments, comparing the transmission performance of both unencrypted and encrypted images under three turbulence levels: weak, medium, and strong. The impact on image communication quality is quantitatively analyzed using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index measure). Finally, the effect of malicious interception attacks, caused by noise interference from different levels of atmospheric turbulence, is examined. The robustness and feasibility of the proposed scheme are validated, providing a promising approach for integrating HAP optical communication’s anti-turbulence capabilities with chaotic image encryption. Full article

The growing demand for bandwidth-intensive vehicular applications—particularly ultra-high-definition streaming and immersive panoramic video—is pushing current network infrastructures beyond their limits, especially in urban areas with severe congestion and degraded user experience. To address these challenges, we propose an aerial-assisted vehicular network architecture that integrates 6G base stations, distributed massive MIMO networks, visible light communication (VLC), and a heterogeneous aerial network of high-altitude platforms (HAPs) and drones. At its core is a context-aware dynamic bandwidth allocation algorithm that intelligently routes infotainment data through optimal aerial relays, bridging connectivity gaps in coverage-challenged areas. Simulation results show a 47% increase in average available bandwidth over conventional first-come-first-served schemes. Our system also satisfies the stringent latency and reliability requirements of emergency and live infotainment services, creating a sustainable ecosystem that enhances user experience, service delivery, and network efficiency. This work marks a key step toward enabling high-bandwidth, low-latency smart mobility in next-generation urban networks. Full article

As urban populations expand and congestion intensifies, traditional ground transportation struggles to satisfy escalating mobility demands. Unmanned Electric Vertical Take-Off and Landing (eVTOL) aircraft, as a key enabler of Urban Air Mobility (UAM), leverage low-altitude airspace to alleviate ground traffic while offering environmentally sustainable solutions. However, supporting high bandwidth, real-time video applications, such as Virtual Reality (VR), Augmented Reality (AR), and 360° streaming, remains a major challenge, particularly within bandwidth-constrained metropolitan regions. This study proposes a novel Unmanned Aerial Vehicle (UAV)-enabled video streaming architecture that integrates 6G wireless technologies with intelligent routing strategies across cooperative airborne nodes, including unmanned eVTOLs and High-Altitude Platform Systems (HAPS). By relaying video data from low-congestion ground base stations to high-demand urban zones via autonomous aerial relays, the proposed system enhances spectrum utilization and improves streaming stability. Simulation results validate the framework’s capability to support immersive media applications in next-generation autonomous air mobility systems, aligning with the vision of scalable, resilient 3D transportation infrastructure. Full article

Wall-climbing robots have broad application potential in industrial equipment inspection, chemical storage tank maintenance, and high-altitude operations. However, their practical implementation is challenged by the robots’ adhesion requirements in complex wall environments. This study uses a systematic methodology integrating computational simulation and experimental validation to design and optimize a magnetic adsorption system for wall-climbing robots. Firstly, an adjustable suspended magnetic adhesion unit is designed to achieve intelligent control of a wall-climbing robot’s adhesion force on a wall surface. The Maxwell software (AnsysEM21.1) is used to simulate and analyze the critical parameters of the magnetic adsorption unit, including the thickness of the magnet and yoke, as well as the distance and angle between the magnet and the wall surface. Then, a magnetic wheel is designed for the wall-climbing robot based on the optimization of the structure and parameters of the magnetic adhesion unit. The absorption and demagnetization of the magnetic wheels are achieved by rotating the magnetic absorption unit. Subsequently, the simulation results are verified on the experimental platform, and adhesion performance tests are conducted on both standard flat surfaces and inclined walls. The results show that the optimized single magnetic adhesion unit gives the wall-climbing robot an adhesion force of 2767 N under normal working conditions, with a simulation experiment error margin as low as 8.3%. These results both provide theoretical guidance and highlight practical methodologies for developing high-performance magnetic adsorption systems in complex operational environments. Full article

High-altitude wetlands in the Ecuadorian Andes are key ecosystems for water regulation and biodiversity conservation but remain poorly monitored due to persistent cloud cover and complex terrain. This study aims to develop a multitemporal approach to map and monitor these wetlands under challenging environmental conditions. We integrated Sentinel-1 (SAR) and Sentinel-2 (multispectral) satellite imagery within the Google Earth Engine platform, applying a Random Forest classifier and soil moisture estimation through the Water Cloud Model. Results show that using only multispectral data underestimated wetland extent (18,919 ha in 2022; 4.7% of the area). In contrast, integrating radar and multispectral data enabled dynamic analysis, identifying 2023 as the peak year (28,972 ha; 7.2%), with the highest monthly coverage in April (6.7%). Soil moisture estimates showed stable monthly wetland extents (15.3–15.9%), with a maximum of 3065 ha in January–February, and demonstrated a strong link with cumulative rainfall patterns. This integrated approach offers a reliable method for high-resolution, seasonal wetland monitoring in cloud-prone mountain environments, supporting data-driven conservation and land management strategies. Full article

As sensor technology integrates into modern life, diverse sensing devices have become essential for collecting critical data that enables human–machine interfaces such as autonomous vehicles and healthcare monitoring systems. However, the growing number of sensor devices places significant demands on network capacity, which is constrained by the limitations of radio frequency (RF) technology. RF-based communication faces challenges such as bandwidth congestion and interference in densely populated areas. To overcome these challenges, a combination of RF with free-space optical (FSO) communication is presented. FSO is a laser-based wireless solution that offers high data rates and secure communication, similar to fiber optics but without the need for physical cables. However, FSO is highly susceptible to atmospheric turbulence and conditions such as fog and smoke, which can degrade performance. By combining the strengths of both RF and FSO, a hybrid FSO/RF system can enhance network reliability, ensuring seamless communication in dynamic urban environments. This review examines hybrid FSO/RF systems, covering both theoretical models and real-world applications. Three categories of hybrid systems, namely hard switching, soft switching, and relay-based mechanisms, are proposed, with graphical models provided to improve understanding. In addition, multi-platform applications, including autonomous, unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites, are presented. Finally, the paper identifies key challenges and outlines future research directions for hybrid communication networks. Full article