Search Results (230)

This study investigates the seasonal dynamics of energy balance, evapotranspiration (ET), and Net Ecosystem Production (NEP) in the Dengkou desert ecosystem of Inner Mongolia, China. Using eddy covariance and meteorological data from 2019 to 2022, the research focuses on understanding how these processes interact in one of the world’s most water-limited environments. This arid research area received an average of 109.35 mm per annum precipitation over the studied period, classifying the region as a typical arid ecosystem. Seasonal patterns were observed in daily air temperature, with extremes ranging from −20.6 °C to 29.6 °C. Temporal variations in sensible heat flux (H), latent heat flux (LE), and net radiation (Rn) peaked during summer season. The average ground heat flux (G) was mostly positive throughout the observation period, indicating heat transmission from atmosphere to soil, but showed negative values during the winter season. The energy balance ratio for the studied period was in the range of 0.61 to 0.80, indicating challenges in achieving energy closure and ecological shifts. ET exhibited two annual peaks influenced by vegetation growth and climate change, with annual ET exceeding annual precipitation, except in 2021. Net ecosystem production (NEP) from 2019 to 2020 revealed that the Dengkou desert were a net source of carbon, indicating the carbon loss from the ecosystem. In 2021, the Dengkou ecosystem shifted to become a net carbon sink, effectively sequestrating carbon. However, this was sharply reversed in 2022, resulting in a significant net release of carbon. The study findings highlight the complex interactions between energy balance components, ET, and NEP in desert ecosystems, providing insights into sustainable water management and carbon neutrality strategies in arid regions under climate change effect. Full article

Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base stations with unmanned ground vehicles. To enhance the system’s adaptability, we implement a fluid antenna system (FAS) at the unmanned ground vehicle (UGV) terminal. This innovative model demonstrates exceptional versatility across various wireless communication scenarios through the strategic adjustment of active ports. The inherent dynamic reconfigurability of the FAS provides superior flexibility and adaptability in air-to-ground communication environments. In the paper, we derive and study key performance characteristics like the autocorrelation function (ACF), validating the model’s effectiveness. The results demonstrate that the RIS-FAS collaborative scheme significantly enhances channel reliability while effectively addressing critical challenges in 6G networks, including signal blockage and spatial constraints in mobile terminals. Full article

In recent years, the use of unmanned aerial vehicles (UAVs), commonly known as drones, has significantly surged across civil, military, and commercial sectors. Ensuring reliable and efficient communication between UAVs and between UAVs and base stations is challenging due to dynamic factors such as altitude, mobility, environmental obstacles, and atmospheric conditions, which existing communication models fail to address fully. This paper presents a multi-ray channel model that captures the complexities of the airspace network, applicable to both ground-to-air (G2A) and air-to-air (A2A) communications to ensure reliability and efficiency within the network. The model outperforms conventional line-of-sight assumptions by integrating multiple rays to reflect the multipath transmission of UAVs. The multi-ray channel model considers UAV flights’ dynamic and 3-D nature and the conditions in which UAVs typically operate, including urban, suburban, and rural environments. A technique that calculates the received power at a target UAV within a networked airspace is also proposed, utilizing the reflective characteristics of UAV surfaces along with the multi-ray channel model. The developed multi-ray channel model further facilitates the characterization and performance evaluation of G2A and A2A communications. Additionally, this paper explores the effects of various factors, such as altitude, the number of UAVs, and the spatial separation between them on the power received by the target UAV. The simulation outcomes are validated by empirical data and existing theoretical models, providing comprehensive insight into the proposed channel modelling technique. Full article

Ground-level ozone (O₃) pollution is a problem when managing air quality in China, and biogenic volatile organic compounds (BVOCs) are key precursors of O₃ formation. Vegetation type and temperature influence BVOC emissions, yet the differences in emissions across vegetation types and their temperature responses still exhibit significant uncertainties. This study was focused on the Sichuan Basin in China. It used the G95 model to develop a high-resolution BVOC emission inventory, allowing the analysis of emission characteristics for different vegetation types. The study also used a temperature sensitivity algorithm to assess how temperature changes affect BVOC emissions. The impact of these emissions on regional O₃ formation potential (OFP) was then quantified using the OFP method. The results show significant differences in BVOC emissions across vegetation types. Forests at the basin edges (mixed, broad-leaved, and coniferous) have much higher emission intensity (10.5 t/km²) than agricultural areas in the center of the basin (0.15 t/km²). In terms of composition, monoterpenes (MON) mainly dominate mixed and coniferous forests (42.28% and 58.37%, respectively), while isoprene (ISOP) dominates broad-leaved forests (64.02%). The study found that temperature generally increases BVOC emissions, which vary by vegetation type. Broad-leaved forests have the highest temperature sensitivity (3.94%), much higher than agricultural vegetation (0.03%). BVOC emissions exhibit a seasonal pattern of “high in summer, low in winter” and a spatial pattern of “high at the edges, low at the center”. Temperature also influences emission intensity and composition, thus driving variations in the potential for O₃ formation. Seasonally, different vegetation types show structural changes in OFP contribution. Broad-leaved forests, dominated by ISOP, show a significant increase in summer contribution (+8.0%), becoming the main source of O₃ precursors. In contrast, mixed forests, dominated by MON, show a clear decrease in summer contribution (−6.3%). Full article

This study evaluated the impact of a ground channel ventilation system on seasonal ammonia emissions in a swine-finishing barn over three distinct seasons: summer, late autumn, and winter. The ground channel system tempered inlet air, cooling it during summer and warming it during colder seasons, maintaining stable room temperatures despite external fluctuations. During summer, the ground channel reduced the incoming air temperature from 26.9 °C to 22.5 °C, contributing to steady barn temperatures (28.0 °C) and mitigating ammonia emissions, which reached 111.0 ± 23.6 g day⁻¹ AU⁻¹. In late autumn and winter, it warmed the inlet air from 4.7 °C and −0.7 °C to 8.1 °C and 6.8 °C, respectively, maintaining stable room temperatures (25.1 °C and 24.3 °C). Ammonia emissions remained consistent across seasons, with 125.0 ± 37.3 g day⁻¹ AU⁻¹ in late autumn and 107.1 ± 20.5 g day⁻¹ AU⁻¹ in winter. Thus, ammonia emissions showed no seasonal differences, highlighting the system’s effectiveness in balancing ventilation rates with emissions. During late autumn and winter, it improved air quality without compromising thermal comfort for the swine. In summer, the reduced ventilation demand lowered ammonia emissions, supporting the effective management of ammonia emissions year-round. Future research should investigate the system’s effects on other gases and slurry pit temperatures. 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

Unmanned aerial vehicles (UAVs), owing to their flexible coverage expansion and dynamic adjustment capabilities, hold significant application potential across various fields. With the emergence of urban low-altitude air traffic dominated by UAVs, the integrated aviation information network combining UAVs and manned aircraft has evolved into a complex space–air–ground integrated Internet of Things (IoT) system. The application of 5G/6G network technologies, such as cloud computing, network function virtualization (NFV), and edge computing, has enhanced the flexibility of air traffic services based on service function chains (SFCs), while simultaneously expanding the network attack surface. Compared to traditional networks, the aviation information network integrating UAVs exhibits greater heterogeneity and demands higher service reliability. To address the failure issues of SFCs under attack, this study proposes an efficient SFC recovery method for recovery rate optimization (ERRRO) based on virtual network functions (VNFs) migration technology. The method first determines the recovery order of failed SFCs according to their recovery costs, prioritizing the restoration of SFCs with the lowest costs. Next, the migration priorities of the failed VNFs are ranked based on their neighborhood certainty, with the VNFs exhibiting the highest neighborhood certainty being migrated first. Finally, the destination nodes for migrating the failed VNFs are determined by comprehensively considering attributes such as the instantiated SFC paths, delay of physical platforms, and residual resources. Experiments demonstrate that the ERRRO performs well under networks with varying resource redundancy and different types of attacks. Compared to methods reported in the literature, the ERRRO achieves superior performance in terms of the SFC recovery rate and delay. Full article

The accurate and reliable positioning of unmanned aerial vehicles (UAVs) in urban environments is crucial for urban air mobility (UAM) application, such as logistics, surveillance, and disaster management. However, global navigation satellite systems (GNSSs) often fail in densely populated areas due to signal reflections (multipath propagation) and obstructions non-line-of-sight (NLOS), causing significant positioning errors. To address this, we propose a machine learning (ML) framework that integrates 5G position reference signals (PRSs) to correct UAV position estimates. A dataset was generated using MATLAB’s UAV simulation environment, including estimated coordinates derived from 5G time of arrival (TOA) measurements and corresponding actual positions (ground truth). This dataset was used to train a fully connected feedforward neural network (FNN), which improves the positioning accuracy by learning patterns between predicted and actual coordinates. The model achieved significant accuracy improvements, with a mean absolute error (MAE) of 1.3 m in line-of-sight (LOS) conditions and 1.7 m in NLOS conditions, and a root mean squared error (RMSE) of approximately 2.3 m. The proposed framework enables real-time correction capabilities for dynamic UAV tracking systems, highlighting the potential of combining 5G positioning data with deep learning to enhance UAV navigation in urban settings. This study addresses the limitations of traditional GNSS-based methods in dense urban environments and offers a robust solution for future UAV advancements. Full article

With the increasing development of 6th-generation (6G) air-to-ground (A2G) communications, the combination of millimeter-wave (mmWave) and multiple-input multiple-output (MIMO) technologies can offer unprecedented bandwidth and capacity for unmanned aerial vehicle (UAV) communications. The introduction of new technologies will also make the UAV channel characteristics more complex and variable, posing higher requirements for UAV channel modeling. This paper presents a novel predictive channel modeling method based on Transformer architecture by integrating data-driven approaches with UAV air-to-ground channel modeling. By introducing the mmWave and MIMO into UAV communications, the channel data of UAVs at various flight altitudes is first collected. Based on the Transformer network, the typical UAV channel characteristics, such as received power, delay spread, and angular spread, are then predicted and analyzed. The results indicate that the proposed predictive method exhibits excellent performance in prediction accuracy and stability, effectively addressing the complexity and variability of channel characteristics caused by mmWave bands and MIMO technology. This method not only provides strong support for the design and optimization of future 6G UAV communication systems but also lays a solid communication foundation for the widespread application of UAVs in intelligent transportation, logistics, and other fields in the future. Full article

A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is difficult due to (1) the coarse scale of common remote sensing data, which do not observe the key environments beneath urban tree canopies, and, (2) conversely, the immense labor of intense, location-specific, ground-based survey campaigns. This work tested whether remotely sensed urban heat merged with land cover heterogeneity and shade/sun fractions, if combined at a sufficiently fine scale so as to be linearly additive, would enable simple and accurate statistical modeling of street-scale urban air temperatures with minimal empirical fitting. We used ground-based thermography of a sample of 12 residential streetscapes in Portland, Oregon, to characterize the land surface temperatures (LST_g) of eleven common urban surface cover types when sun-exposed and in shade. Surfaces were cooler in shade than sun, but with surface-specific differences not explained by greenery nor (im)perviousness. Also, surfaces on streetscapes with more canopy cover, even when sun-exposed at midday, remained significantly cooler than comparable sun-exposed surfaces on streets with less canopy cover, indicating the key significance of partial diurnal shading, not typically accounted for in urban thermal statistical models. We used high-resolution orthoimagery to quantify the area of each surface cover type within each streetscape and computed an area-weighted average surface temperature (T_s), accounting for sun/shade heterogeneity. The data revealed a significant, nearly 1:1 relationship between calculated T_s values and sun-shielded air temperatures (T_a). In contrast, relationships of T_a to tree coverage, impervious area, or the LST_g of dominant surface cover types were all statistically insignificant. These results suggest that statistical models may more reliably bridge the gap between remote sensing urban surface temperatures and reliable predictions of street-scale air temperatures if (1) analysis is at a sufficiently high resolution (e.g., <10 m) to avoid some of the known scale-dependence of urban thermal environments and enable simple weighted linear models, and (2) distinctions between thermal contributions of sunlit and shaded surfaces are included along with the influence of diurnal shading. Such models may provide effective and low-cost predictions of local UHIs and help inform effective street-level approaches to mitigating urban heat. Full article

The rapid evolution of wireless communications has introduced new possibilities for the digital transformation of maritime operations. As 5G begins to take shape in selected nearshore and port environments, the forthcoming 6G promises to unlock transformative capabilities across the entire maritime domain, integrating Terrestrial/Non-Terrestrial Networks (TN/NTN) to form a space-air-ground-sea-underwater system. This paper presents a comprehensive review of how 6G-enabling technologies can be adapted to address the unique challenges of Maritime Communication Networks (MCNs). We begin by outlining a reference architecture for heterogeneous MCNs and reviewing the limitations of existing 5G deployments at sea. We then explore the key technical advancements introduced by 6G and map them to maritime use cases such as fleet coordination, just-in-time port logistics, and low-latency emergency response. Furthermore, the critical Artificial Intelligence/Machine Learning (AI/ML) concepts and algorithms are described to highlight their potential in optimizing maritime functionalities. Finally, we propose a set of resource optimization scenarios, including dynamic spectrum allocation, energy-efficient communications and edge offloading in MCNs, and discuss how AI/ML and learning-based methods can offer scalable, adaptive solutions. By bridging the gap between emerging 6G capabilities and practical maritime requirements, this paper highlights the role of intelligent, resilient, and globally connected networks in shaping the future of maritime communications. Full article

Combined air pollution of ozone and PM_2.5 often occurs in coal-based cities of China such as Taiyuan City. In this study, the Weather Research and Forecasting/Chemistry (WRF-Chem) model was employed to simulate a combined high air pollution event of ozone and PM_2.5 in Taiyuan City from 20 May to 29 May 2015,with the maximum daily 8-hour average (MDA8) for ozone exceeding 140 ppbv and PM_2.5 concentrations surpassing $200\mu \mathrm{g}{\mathrm{m}}^{-3}$. We further investigated the drivers of the combined air pollution in Taiyuan during the polluted period. The simulation results showed that the model can well simulate the combined pollution event in Taiyuan, with assessment parameters within reasonable ranges. Moreover, by analyzing the observational data and simulations, the major factors causing the PM_2.5 pollution in Taiyuan during this time period were suggested to be local emissions and pollutant transport from the North China Plain (NCP) located to the east of Taiyuan. In addition, unfavorable meteorological and geographical conditions in Taiyuan also play important roles in forming severe PM_2.5 pollution. Regarding the ozone pollution in Taiyuan, we suggest that the mechanism dominating the pollution event is that of ozone-rich air being transported to Taiyuan at high altitudes and then mixed downwards, resulting in an increase of the ground-level ozone in Taiyuan. Furthermore, we found local emissions and emissions from Taiyuan Basin and Henan Province, which are located to the south of Taiyuan, contributing significantly to the ozone pollution in Taiyuan City during this time. Full article

As sixth-generation mobile communication (6G) advances towards ultra-high speed and global coverage, visible light communication (VLC) has emerged as a crucial complementary technology due to its ultra-high bandwidth, low power consumption, and immunity to electromagnetic interference. Modulation format recognition (MFR) plays a vital role in the dynamic optimization and adaptive transmission of VLC systems, significantly influencing communication performance in complex channel environments. This paper systematically reviews the research progress in MFR for VLC, comparing the theoretical frameworks and limitations of traditional likelihood-based (LB) and feature-based (FB) methods. It also explores the advancements brought by deep learning (DL) technology, particularly in enhancing noise robustness, classification accuracy, and cross-scenario adaptability through automatic feature extraction and nonlinear mapping. The findings indicate that DL-based MFR substantially enhances recognition performance in intricate channels via multi-dimensional feature fusion, lightweight architectures, and meta-learning paradigms. Nonetheless, challenges remain, including high model complexity and a strong reliance on labeled data. Future research should prioritize multi-domain feature fusion, interdisciplinary collaboration, and hardware–algorithm co-optimization to develop lightweight, high-precision, and real-time MFR technologies that align with the 6G vision of space–air–ground–sea integrated networks. Full article

In 5G-and-beyond (B5G) Internet of Things (IoT) networks, the integration of intelligent reflecting surfaces (IRSs) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) techniques can significantly improve signal quality and increase network capacity. However, a single fixed IRS lacks the dynamic adjustment capability to flexibly adapt to complex environmental changes and diverse user demands, while mmWave MIMO is constrained by limited coverage. Motivated by these challenges, we investigate the application of drone-mounted IRS-assisted MIMO communications in B5G IoT networks, where multiple IRS-equipped drones are deployed to provide real-time communication support. To fully exploit the advantages of the proposed MIMO-enabled air-to-ground integrated information transmission framework, we formulate a joint optimization problem involving beamforming, phase shift design, and drone deployment, with the objective of maximizing the sum of achievable weighted data rates (AWDRs). Given the NP-hard nature of the problem, we develop an iterative optimization algorithm to solve it, where the optimization variables are tackled in turn. By employing the quadratic transformation technique and the Lagrangian multiplier method, we derive closed-form solutions for the optimal beamforming and phase shift design strategies. Additionally, we optimize drone deployment by using a distributed discrete-time convex optimization approach. Finally, the simulation results show that the proposed scheme can improve the sum of AWDRs in comparison with the state-of-the-art schemes. Full article

This study presents a preliminary analysis of an innovative system that combines indoor air conditioning with water recovery and storage. The device integrates Peltier cells with a horizontal Earth-to-Air Heat Exchanger (EAHX), exploiting the ground stable temperature to enhance cooling and promote condensation. Warm, humid air is pre-cooled via the geothermal pipe, then split by a fan into two streams: one passes over the cold side of the Peltier cells for cooling and dehumidification, while the other flows over the hot side and heats up. The two airstreams are then mixed in a water storage tank, which also serves as a thermal mixing chamber to regulate the final air temperature. The analysis investigates the influence of soil thermal conditions on condensation within the horizontal pipe and the resulting cooling effect in indoor spaces. A hybrid simulation approach was adopted, coupling a 3D model implemented in COMSOL Multiphysics^® with a 1D analytical model. Boundary conditions and meteorological data were based on the Typical Meteorological Year (TMY) for Palermo. Two scenarios were considered. In Case A, during the hours when air conditioning is not operating (between 11 p.m. and 9 a.m.), air is circulated in the exchanger to pre-cool the ground and the air leaving the exchanger is rejected into the environment. In Case B, the no air is not circulated in the heat exchanger during non-conditioning periods. Results from the June–August period show that the EAHXs reduced the average outdoor air temperature from 27.81 °C to 25.45 °C, with relative humidity rising from 58.2% to 66.66%, while maintaining nearly constant specific humidity. The system exchanged average powers of 102 W (Case A) and 96 W (Case B), corresponding to energy removals of 225 kWh and 212 kWh, respectively. Case A, which included nighttime soil pre-cooling, showed a 6% increase in efficiency. Condensation water production values range from around 0.005 g/s with one Peltier cell to almost 0.5 g/s with seven Peltier cells. As the number of Peltier cells increases, the cooling effect becomes more pronounced, reducing the output temperature considerably. This solution is scalable and well-suited for implementation in developing countries, where it can be efficiently powered by stand-alone photovoltaic systems. Full article