Search Results (222)

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

As an important ecological security barrier on the Tibetan Plateau, southeastern Tibet is crucial to maintaining regional carbon balance under climate change. This study innovatively integrates multi-source remote sensing data (Landsat 8, Sentinel-1, and GEDI) on the Google Earth Engine (GEE) platform, and uses machine learning to model forest carbon storage dynamics from 2019 to 2023. The fusion of multi-source data improves forest vertical structure characterization and makes up for the shortage of single optical data. By comparing machine learning algorithms, the Gradient Boosting model performs excellently (validation set R² = 0.909, RMSE = 26.608 Mg/Ha), achieving high-resolution spatiotemporal mapping. The results show significant spatial heterogeneity; the increase in carbon storage in the central and southern regions is mainly in contrast to the scattered decreases in the eastern and western regions, reflecting vegetation restoration and topographic influence. High-altitude areas are subject to climate restrictions and small changes, while low-altitude areas show significant fluctuations due to human activities. Key drivers were elevation (importance score 22.06), slope (17.00), and temperature (22.04). Land use transformation (such as forest expansion) promotes net carbon accumulation and highlights the effectiveness of regional protection policies. This study provides a scientific basis for targeted ecological management of high-altitude ecosystems. Full article

Hydrogen fuel cell vehicles are one of the most promising alternatives to achieve transport decarbonization targets, thanks to their moderately high efficiency and low refueling time, combined with their zero-exhaust-emission operation. In order to reach reasonable power density figures, fuel cell systems are generally supercharged by radial compressors, which can encounter significant limitations associated with surge and choke operation, especially at high altitudes. Alternatively, the current paper explores the altitude operation of a fuel cell system combined with a Roots compressor. First, the balance of the plant model is built in the Simscape platform, combining a physical and chemical 1D fuel cell model for the stack, calibrated against literature data at different pressure and temperature values, as well as the characteristic maps of the Roots compressor. Then, the model is used to explore the balance-of-plant operation in a working range between 10 and 200 kW and an altitude range between sea level and 5 km. The results show that the compressor is capable of operating around the highest efficiency area (between 60 and 70%) for a wide range of altitude and power conditions, limiting the negative impact of the altitude on the system efficiency to up to 3%. However, once the compressor efficiency falls below 60%, the balance-of-plant performance rapidly drops, overcoming the benefits of the working pressure on the fuel cell stack operation and limiting the peak net power produced. Full article

Urban wind, and particularly turbulence present in the roughness zone near structures, poses a critical challenge for next-generation drones. Complex flow patterns induced by large buildings produce significant disturbances that the vehicle must reject at low altitudes. Traditional turbulence models, such as the von Kármán model, underestimate these localized effects, compromising flight safety. To address this gap, we integrate high-resolution time and spatially varying urban wind fields from Large Eddy Simulations into a flight dynamics simulation framework using vehicle plant models based on configuration geometry and commonly deployed Ardupilot control laws, enabling a detailed analysis of drone responses in urban environments. Our results reveal that high-risk flight zones can be systematically identified by correlating drone response metrics with the spatial distribution of Turbulent Kinetic Energy (TKE). Notably, maximum g-loads coincide with abrupt TKE transitions, underscoring the critical impact of even short-lived wind fluctuations. By coupling advanced computational fluid dynamics with a real-time vehicle dynamics model, this work establishes a foundational methodology for designing safer and more reliable advanced air mobility platforms in complex urban airspaces. This work distinguishes itself from the existing literature by incorporating an efficient vortex lattice aerodynamic solver that supports arbitrary fixed-wing drone platforms through the simple specification of planform geometry and mass properties, and operating full-flights throughout a time and spatially varying urban wind field. This framework enables a robust assessment of stability and control for a wide range of fixed-wing drone platforms operating in urban environments, with delivery drones serving as a representative and practical use case. Full article

Background/Objectives: Schizothorax curvilabiatus, a typical highland polyploid species within the subfamily Schizothoracinae, holds economic value and ecological research significance. Currently, there are no related genomic studies. To obtain its genetic information and lay the foundation for subsequent whole-genome map construction, this study conducted a genome survey analysis, preliminary genome assembly, microsatellite identification, repeat sequence annotation, mitochondrial genome characterization, and phylogenetic relationship research. Methods: DNA was sequenced on a DNBSEQ-T7 platform to obtain paired-end genomic data. The genome was analyzed using GCE, and the draft genome was assembled with SOAPdenovo. Microsatellites were identified using MISA, and the mitochondrial genome was assembled with NOVOPlasty. Genome features were analyzed, and phylogenetic trees were constructed using PhyloSuite and MEGA. Results: The genome size was estimated at 2.53 Gb, with a heterozygosity of 6.55% and 47.66% repeat sequences. A 1.324 Gb preliminary genome draft was obtained, with repeat sequences comprising 47.17%, the majority being DNA transposons (24.64%). Dinucleotide repeats were most abundant (46.91%), followed by mononucleotide repeats (38.31%), with A/T and AC/GT being the most frequent. A complete mitochondrial genome of 16,589 bp was assembled, and a 939 bp D-loop was annotated. Phylogenetic relationships among genera in the Schizothoracinae subfamily were also clarified. Conclusions: This study provides the latest molecular data for analysis of the S. curvilabiatus genome and its related populations, and for the first time offers genomic resources for research on genomic adaptive evolution and polyploidization in high-altitude environments. Full article

The aerial–terrestrial integrated Internet of Things (ATI-IoT) utilizes both aerial platforms (e.g., drones and high-altitude platform stations) and terrestrial networks to establish comprehensive and seamless connectivity across diverse geographical regions. The integration offers significant advantages, including expanded coverage in remote and underserved areas, enhanced reliability of data transmission, and support for various applications such as emergency communications, vehicular ad hoc networks, and intelligent agriculture. However, due to the inherent openness of wireless channels, ATI-IoT faces potential network threats and attacks, and its security issues cannot be ignored. In this regard, incorporating physical layer security techniques into ATI-IoT is essential to ensure data integrity and confidentiality. Motivated by the aforementioned factors, this review presents the latest advancements in ATI-IoT that facilitate physical layer security. Specifically, we elucidate the endogenous safety and security of wireless communications, upon which we illustrate the current status of aerial–terrestrial integrated architectures along with the functions of their components. Subsequently, various emerging techniques (e.g., intelligent reflective surfaces-assisted networks, device-to-device communications, covert communications, and cooperative transmissions) for ATI-IoT enabling physical layer security are demonstrated and categorized based on their technical principles. Furthermore, given that aerial platforms offer flexible deployment and high re-positioning capabilities, comprehensive discussions on practical applications of ATI-IoT are provided. Finally, several significant unresolved issues pertaining to technical challenges as well as security and sustainability concerns in ATI-IoT enabling physical layer security are outlined. Full article

Semantic communication technology, as it allows for source data meaning extraction and the transmission of appropriate semantic information only, has the potential to extend Shannon’s paradigm, which is concerned with the reproduction of a message from one location to another, regardless of its meaning. Nevertheless, some user terminals (UTs) may experience inadequate service due to their geolocation in reference to the base stations, which may entirely affect the accuracy of transmission and complicate deployment and implementation. A High-Altitude Platform Station (HAPS) serves as a key enabler for the deployment of wireless broadband in inaccessible areas, such as in coastal, desert, and mountainous areas. This paper proposes a novel HAPS relay-based semantic communication scheme, named DeepSTAS, which leverages deep learning techniques to enhance transmission accuracy. The proposed scheme focuses on attention-based semantic signal decoding, denoising, and forwarding modes; thus, called a CSA-DCGAN SDF HAPS relay network. The simulation results reveal that the proposed system with attention mechanisms significantly outperforms the system without attention mechanisms, both in peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index (MS-SSIM); the proposed system can achieve a 2 dB gain when leveraging the attention mechanisms, and a PSNR of 38.5 dB can be obtained, with an MS-SSIM exceeding 0.999 at an approximate SNR of only 20 dB. The system provides considerable performance, more than 37 dB, and a corresponding MS-SSIM close to 0.999 at an estimated SNR of 20 dB when the CIFAR-100 dataset is considered and an MS-SSIM of 0.965 at an approximate SNR of only 10 dB on the Kodak dataset. The proposed system holds promise to maintain consistent performance even at low SNRs across various channel conditions. Full article

Picking fruits from tall fruit trees manually is laborious and inefficient. Rotary-wing drones, a low-altitude carrier platform, can enhance the picking efficiency for tall fruit trees when combined with picking robotic arms. However, during the operation of rotary-wing drones, the wind field changes dramatically, and the center of gravity of the drone shifts at the moment of picking, leading to poor aerodynamic stability and making it difficult to achieve optimized attitude control. To address the aforementioned issues, this paper constructs a drone and wind field testing platform and employs the Lattice Boltzmann Method and Large Eddy Simulation (LBM-LES) algorithm to solve the high-dynamic, rapidly changing airflow field during the transient picking process of the drone. The aerodynamic structure of the drone is optimized by altering the rotor spacing and duct intake ratio of the harvesting drone. The simulation results indicate that the interaction of airflow between the drone’s rotors significantly affects the stability of the aerodynamic structure. When the rotor spacing is 2.8R and the duct ratio is 1.20, the lift coefficient is increased by 11% compared to the original structure. The test results from the drone and wind field experimental platform show that the rise time ($t_r$) of the drone is shortened by 0.3 s, the maximum peak time ($t_p$) is reduced by 0.35 s, and the adjustment time ($t_s$) is accelerated by 0.4 s. This paper, by studying the transient wind field of the harvesting drone, clarifies the randomness of the transient wind field and its complex vortex structures, optimizes the aerodynamic structure of the harvesting drone, and enhances its aerodynamic stability. The research findings can provide a reference for the aerodynamic optimization of other types of drones. Full article

Geothermal resources are crucial for sustainable energy development, yet accurately detecting geothermal anomalies in complex terrains remains a significant challenge. This study develops a multi-source thermal infrared approach to enhance geothermal anomaly detection using Landsat 8 and ASTER land surface temperature (LST) data. The Yangbajing–Yangyi Basin in Tibet, characterized by high altitude and rugged topography, serves as the study area. Landsat 8 winter time-series data from 2013 to 2023 were processed on the Google Earth Engine (GEE) platform to generate multi-year average LST images. After water body removal and altitude correction, a local block thresholding method was applied to extract daytime geothermal anomalies. For nighttime data, ASTER LST products were analyzed using global, local block, elevation zoning, and fault buffer strategies to extract anomalies, which were then fused using Dempster–Shafer (D–S) evidence theory. A joint daytime–nighttime analysis identified stable geothermal anomaly regions, with results closely aligning with known geothermal fields and borehole distributions while predicting new potential anomaly zones. Additionally, a 21-year time-series analysis of MODIS nighttime LST data identified four significant thermal anomaly areas, interpreted as potential magma chambers, whose spatial distributions align with the identified anomalies. This multi-source approach highlights the potential of integrating thermal infrared data for geothermal anomaly detection, providing valuable insights for exploration in geologically complex regions. Full article

In Japan, the DEURAS system has been deployed to detect and locate illegal radio sources that either exceed permissible transmission power limits or operate on unauthorized frequencies. This system utilizes receiving antennas installed on high-rise buildings and radio towers to capture radio signals and estimate the location of the transmission source. However, in densely built urban environments, the accuracy of location estimation is often compromised due to signal reflections and diffractions. Additionally, in large-scale disasters such as earthquakes, terrestrial infrastructure may be severely damaged, making it essential to develop a localization system that operates independently of ground-based stations. To overcome these limitations, this study proposes a localization system based on a high-altitude-platform station (HAPS), which operates at an altitude of approximately 20 km. The feasibility and effectiveness of the proposed system are evaluated through numerical simulations, considering various environmental conditions. The results demonstrate that HAPS-based localization significantly improves positioning accuracy, offering a robust and high-precision alternative for radio source detection, particularly in scenarios where traditional ground-based systems are unreliable or unavailable. Full article

This paper describes a flexible and seamless processor for Unmanned Aerial Vehicle (UAV)-borne Synthetic Aperture Radar (SAR) imagery. When designing a focusing algorithm for large-scale and high-resolution SAR images, efficiency and accuracy are two mandatory aspects to consider. The proposed processing scheme is based on a modified version of Fast Factorized Back-Projection (FFBP), in which the factorization procedure is interrupted on the basis of a computational cost analysis to reduce the number of complex operations at its minimum. The algorithm gains efficiency in the case of low-altitude platforms, where there are significant variations in azimuth resolution, but not in the case of conventional airborne missions, where the azimuth resolution can be considered constant in the swath. The algorithm’s performance is derived by assessing the number of complex operations required to focus an SAR image. Two scenarios are tackled in a numerical simulation: a UAV-borne SAR with a short synthetic aperture and a wide field of view, referred to as the ground-based-like (GBL) scenario, and a classical stripmap scenario. In both cases, we consider mono-static and bi-static radar configurations. The results of the numerical simulations show that the proposed algorithm outperforms FFBP in the stripmap scenario while achieving the same performance as FFBP in the GBL scenario. In addition, the algorithm is validated thanks to an experimental UAV-borne SAR campaign in the X-band. Full article

Herein, a recently developed UAV/Drone approach as a new vector for the collection of airborne particulate matter is reported. In this study, airborne particle emissions from plumes generated in a holiday fireworks display were collected. A platform fabricated using a 3D printer was mounted on the drone, which allowed for particulate capture using double-sided carbon tape attached to aluminum disks. The drone platform was used to trap airborne samples from two types of plumes: high-altitude sampling (HAS), which relates to professional fireworks, and low-altitude sampling (LAS), associated with personal fireworks. Collected samples were studied using a Scanning Electron Microscope alongside Electron Dispersal X-ray Spectroscopy (EDX) for elemental composition analysis. The overall findings regarding the physical morphology reveal several key observations. Firstly, particles from professional fireworks are significantly larger and more spheroidal than those from personal fireworks. Secondly, both types of fireworks show a consistent trend in which some of the larger particles have finer particulates deposited on their surfaces. Lastly, the plumes produced by both types contain spheres that are either solid, hollow or exhibit a core–shell structure. EDX analysis revealed the presence of various types of metals within the samples. EDX analysis shows that the samples collected from the HAS and LAS contain particulates with common elements. However, the samples from the plume of professional fireworks appear to have Ba, Mg, and Fe compared to the samples from personal fireworks. These elements are known to be used in powerful fireworks to create colored displays. A proposed mechanism for particulate growth in fireworks is proposed and discussed. Full article

The variation in the background temperature field in aquatic environments plays a crucial role in the detection of thermal signatures of maritime moving targets. To elucidate the influence of various meteorological and hydrological parameters on the background temperature field of water bodies, this study employs the COARE 3.0 model to analyze the relationship between the net heat flux at the air–water interface and the characteristics of the cool skin layer. By examining the diurnal fluctuations of environmental parameters, the diurnal variation patterns of the cool skin layer properties are investigated. A dynamic temperature field testing platform was established in an outdoor pool to measure air–water volume variables and validate the accuracy of the water temperature field calculation model. The findings indicate that the cool skin phenomenon is indeed present in natural aquatic environments. The properties of the cool skin layer are predominantly affected by factors such as wind speed, the specific humidity gradient between the near-surface and high-altitude regions, and the temperature gradient between these regions. The temperature of the cool skin layer is typically a few tenths of K lower than that of the subsurface water, with a thickness generally ranging from 2 to 5 mm. Full article