Search Results (201)

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

As sea levels rise and extreme weather events become more frequent due to climate change, coastal urban areas are increasingly vulnerable to wave overtopping and flooding. Retrofitting existing vertical seawalls with retreated storm walls represents a key adaptive strategy, especially in the Mediterranean, where steep foreshores and limited public space constrain conventional coastal defenses. This study investigates the effectiveness of storm walls in reducing wave overtopping on vertical walls with steep foreshores (1:7 to 1:10) through high-fidelity numerical simulations using the SWASH model. A comprehensive parametric study, involving 450 test cases, was conducted using Latin Hypercube Sampling to explore the influence of geometric and hydrodynamic variables on overtopping rate. Model validation against Eurotop/CLASH physical data demonstrated strong agreement (r = 0.96), confirming the reliability of SWASH for such applications. Key findings indicate that longer promenades (Gc) and reduced impulsiveness of the wave conditions reduce overtopping. A new empirical reduction factor, calibrated for integration into the Eurotop overtopping equation for plain vertical walls, is proposed based on dimensionless promenade width and water depth. The modified empirical model shows strong predictive performance (r = 0.94) against SWASH-calculated overtopping rates. This work highlights the practical value of integrating storm walls into urban seawall design and offers engineers a validated tool for enhancing coastal resilience. Future research should extend the framework to other superstructure adaptations, such as parapets or stilling basins, to further improve flood protection in the face of climate change. Full article

Land Surface Temperature (LST), as a core variable in the coupling of land–atmosphere energy transfers and ecological responses, relies heavily on the global coverage capacity of thermal infrared remote sensing (TIR-LST) for dynamic monitoring. Currently, the time reconstruction method of the TIR-LST products from China’s Fengyun polar-orbiting satellite under dynamic cloud interference remains under exploration. This study focuses on the Heihe River Basin in western China, and addresses the issue of cloud coverage in relation to the Fengyun-3C (FY-3C) satellite TIR-LST. An innovative spatiotemporal reconstruction framework based on multi-source data collaboration was developed. Using a hybrid ensemble learning framework of random forest and ridge regression, environmental parameters such as vegetation index (NDVI), land cover type (LC), digital elevation model (DEM), and terrain slope were integrated. A downscaling and multi-factor collaborative representation model for land surface temperature was constructed, thereby integrating the passive microwave LST and thermal infrared VIRR-LST from the FY-3C satellite. This produced a seamless LST dataset with 1 km resolution for the period of 2017–2019, with temporal continuity across space. The validation results show that the reconstructed data significantly improves accuracy compared to the original VIRR-LST and demonstrates notable spatiotemporal consistency with MODIS LST at the daily scale (annual R² ≥ 0.88, RMSE < 2.3 K). This method successfully reconstructed the FY-3C satellite’s 1 km level all-weather LST time series, providing reliable technical support for the use of domestic satellite data in remote sensing applications such as ecological drought monitoring and urban heat island tracking. Full article

Urban green areas, while providing numerous benefits, can also produce negative impacts, often referred to as “ecosystem disservices”. While fallen fruits, leaves, and branches may pose tripping hazards, falling trees present a more significant threat to the safety of citizens and buildings. A study was conducted to identify the factors that most influence tree falls, aiming to enhance monitoring and maintenance in high-risk areas and develop preventive felling plans. The analysis was carried out in the city of Venice (Italy) using data from 2019 to 2022. Key variables included daily rainfall and cumulative rainfall over the four days preceding tree falls, minimum temperature, average wind speed and direction, and maximum gust speed on the day of the event and two days prior, as well as detailed information on the affected trees from the municipal GreenSpaces application database (R3GIS). The distribution of fallen trees was assessed in relation to these parameters, and a spatial autocorrelation analysis was performed. The results revealed that tree falls were more frequent during the summer season, coinciding with more intense weather events, especially those characterized by gusts of strong wind (>15 m/s). Street trees and trees in groups, particularly those in parks and densely populated urban areas, were most affected. Tree falls during a single event often occurred in clusters within a radius of approximately 1.5 km. Species analysis indicated that maintaining a diverse mix of tree species could reduce the number of fallen trees, as different species exhibit varying levels of resistance to wind pressure and adaptability to urban conditions. Addressing these findings can help to create more sustainable and livable urban environments, maximizing the benefits of green spaces while mitigating their ecosystem disservices. Full article

This work presents a high-speed hybrid communication system integrating Underwater Optical Wireless Communication (UOWC), Multimode Fiber (MMF), and Free-Space Optics (FSO) channels, leveraging Orbital Angular Momentum (OAM) beams for enhanced data transmission. A Photodetector, Remodulate, and Forward Relay (PRFR) is employed to enable wavelength conversion from 532 nm for UOWC to 1550 nm for MMF and FSO links. Four distinct OAM beams, each supporting a 5 Gbps data rate, are utilized to evaluate the system’s performance under two scenarios. The first scenario investigates the effects of absorption and scattering in five water types on underwater transmission range, while maintaining fixed MMF length and FSO link. The second scenario examines varying FSO propagation distances under different fog conditions, with a consistent underwater link length. Results demonstrate that water and atmospheric attenuation significantly impact transmission range and received optical power. The proposed hybrid system ensures reliable data transmission with a maximum overall transmission distance of 1125 m (comprising a 25 m UOWC link in Pure Sea (PS) water, a 100 m MMF span, and a 1000 m FSO range in clear weather) in the first scenario. In the second scenario, under Light Fog (LF) conditions, the system achieves a longer reach of up to 2020 m (20 m UOWC link + 100 m MMF span + 1900 m FSO range), maintaining a BER ≤ 10⁻⁴ and a Q-factor around 4. This hybrid design is well suited for applications such as oceanographic research, offshore monitoring, and the Internet of Underwater Things (IoUT), enabling efficient data transfer between underwater nodes and surface stations. Full article

We systematically investigated the anchorage performance of self-drilling anchor bolts in strongly weathered dolomite through integrated field pull-out tests and FLAC3D numerical modeling. The study incorporates symmetry principles in both experimental design and numerical simulations to ensure balanced force distribution and model simplification. Experimental data collected from a slope reinforcement project demonstrated that grouting parameters of 0.8 MPa pressure and 0.8 water–cement ratio achieved an interfacial bond strength of 0.147 MPa, surpassing the recommended value by 22.5%. A modified FLAC3D pile element, calibrated against RS6-01 anchor bolt test data, exhibited improved alignment with load–displacement curves, converging to 272 kN ultimate capacity at 26.1 mm displacement. Symmetrical anchor configurations in the numerical model reduced computational complexity while maintaining accuracy in stress distribution analysis. Through orthogonal experimental design, symmetry-driven parameter optimization identified a 7 m bolt length, 30° installation angle, and 2 m spacing as the most effective configuration. This solution increased the slope safety factor by 19.98% while reducing displacements by 46–62%. The symmetry in anchor spacing and angular alignment contributed to uniform stress redistribution, enhancing slope stability. The findings highlight the synergy between symmetry principles and geotechnical reinforcement strategies. Full article

Extreme ultraviolet (EUV) imagers are key tools to monitor the space environment and forecast space weather. EUV filters are important components to block radiation in the ultraviolet (UV), visible, and near-infrared (IR) regions. In this study, various characterization methods were proposed for the nickel mesh-supported indium (In) filter, and their spectral characteristics were comprehensively studied. The material and thickness of the filter were chosen based on atomic scattering principles, determined through theoretical calculation and software simulation. The metal film was deposited using the vacuum-resistive thermal evaporation method. The measured transmission of the filter was 10.06% at 83.4 nm. The surface elements of the sample were analyzed using X-ray photoelectron spectroscopy (XPS). The surface and cross-sectional morphologies of the filter were observed using a scanning electron microscope (SEM). The impact of the oxide layer and carbon contamination on the filter’s transmittance was investigated using an ellipsometer. A multilayer “In-In₂O₃-C” model was established to determine the thickness of both the oxide layer and carbon contamination layer on the filter. This model introduces the filling factor based on the original model and considers the diffusion of the contamination layer, resulting in more accurate fitting results. The transmittance of the filter in the visible light range was measured using a UV-VIS spectrophotometer, and the measurement error was analyzed. This article provides preparation methods and test methods for the 83.4 nm EUV filter and conducts a detailed analysis of the spectral characteristics of the prepared optical filters, which hold significant value for space exploration applications. Full article

Recent exploration efforts in the Qiongdongnan Basin have revealed hydrocarbon resources within granitic basement rocks in buried hill traps. However, the formation mechanisms and primary controlling factors of these reservoirs remain poorly understood. In this study, we utilized data from six wells in the Qiongdongnan Basin, including sidewall cores, thin sections, imaging logging, and seismic reflection profiles, to analyze the petrological characteristics, pore systems, and fracture networks of the deep basement reservoir. The aim of our study was to elucidate the reservoir formation mechanisms and identify the key controlling factors. The results indicate that the basement lithology is predominantly granitoid, intruded during the late Permian to Triassic. These rocks are characterized by high felsic mineral content (exceeding 90% on average), with them possessing favorable brittleness and solubility properties. Fractures identified from sidewall cores and interpreted from image logging can be categorized into two main groups: (1) NE-SW trending conjugate shear fractures with sharp dip angles and (2) NW-SE trending conjugate shear fractures with sharp angles. An integrated analysis of regional tectonic stress fields suggests that the NE-trending fractures and associated faults were formed by compressional stresses related to the Indosinian closure of the ancient Tethys Ocean. In contrast, the NW-trending fractures and related faults resulted from southeast-directed compressional stresses during the Yanshanian subduction event. During the subsequent Cenozoic extensional phase, these fractures were reactivated, creating effective storage spaces for hydrocarbons. The presence of calcite and siliceous veins within the reservoir indicates the influence of meteoric water and magmatic–hydrothermal fluid activities. Meteoric water weathering exerted a depth-dependent dissolution effect on feldspathoid minerals, leading to the formation of fracture-related pores near the top of the buried hill trap during the Mesozoic exposure period. Consequently, the combination of high-density fractures and dissolution pores forms a vertically layered reservoir within the buried hill trap. The distribution of potential hydrocarbon targets in the granitic basement is closely linked to the surrounding tectonic framework. Full article

In this study, we analyzed the spatiotemporal characteristics of pedestrian behavior in street spaces using pedestrian count data—specifically, the number of pedestrians passing in front of infrared sensors installed throughout the downtown area. The analysis focused on three main questions: (1) whether the thermal environment affects pedestrian behavior, (2) how to characterize the spatiotemporal patterns of pedestrian activity, and (3) how to effectively present the results to urban planners and designers. A temporal and spatial analysis method was examined using hourly pedestrian count data over one year at more than 100 locations in the street canyon. The temporal characteristics of the pedestrian count data were classified into weekday and weekend clusters according to the peak hours within a day. The spatial characteristics of the pedestrian count data were clearly defined by distance from the station, office district, and commercial district, according to peak commuting, shopping, etc. Results from principal component analysis and cluster analysis did not reveal a significant influence of the thermal environment on the temporal variation in pedestrian counts. Instead, the data suggested that weekday versus weekend distinctions were the primary determinants of daily and annual patterns, while seasonal and weather-related factors had relatively minor effects. The analytical approach developed in this study represents a valuable and practical contribution that may be applicable to other urban contexts as well. Full article

Global navigation satellite systems provide important data sets that can be used to study the influence of various space weather factors. We analyzed the effects of the main phase of the May 2024 extreme geomagnetic storm on the ionosphere and GPS kinematic precise point positioning (PPP). ROTI and global ionospheric maps showed the ionospheric dynamics. The auroral oval expanded up to low latitudes: up to 30°N in the American sector and up to 45°N in the European–Asian sector during the main phase of the geomagnetic storm. The ROTI peaked at 2 TECU/min, which is four times as much against the background. The equatorial anomaly crest intensified considerably (up to 200 TECU) and shifted poleward in the American sector. The counter-propagation finally caused the equatorial anomaly to cross the auroral oval boundary. The ROTI correlated with errors in the kinematic PPP. Positioning errors increased 1.5–5 times at the boundary of the auroral oval. Increased positioning errors propagated according to the shift of the auroral oval boundary. The geomagnetic storm significantly affected the positioning and the ionosphere, threatening various applications based on navigation and communication. Full article

With the frequent occurrence of extreme weather events worldwide, the compound frequency of drought and flood events has significantly increased, imposing multidimensional pressures on agricultural water resource management. Agricultural water consumption accounts for approximately 70%, with severe waste, as a large amount of water is lost during transmission and distribution. Faced with increasingly severe and frequent extreme weather, traditional drainage systems may become unsustainable. Identifying the factors influencing drainage time is crucial for efficient drainage. The MIKE URBAN model has significant potential in farmland waterlogging simulation and drainage network optimization. This study validated the model’s accuracy based on infiltration well overflow capacity experiments, with Average Relative Error (ARE) values of 2.29%, 6.52%, 4.41%, 3.17%, 4.37%, and 5.69%, demonstrating good simulation accuracy. The MIKE URBAN model was used to simulate drainage networks, explore factors affecting drainage time, establish an annual cost system for the drainage network, and optimize the network using a genetic algorithm with the objective of minimizing annual costs. Research findings: There is a clear negative correlation between the maximum inflow of infiltration wells and drainage time. As inflow increases, drainage becomes faster, but beyond 0.0075 m³/s (27 m³/h), the efficiency gains level off. This indicates that selecting infiltration wells with at least a 20% opening ratio is essential. Similarly, increasing the collector’s diameter enhances drainage efficiency significantly, though the effect follows a diminishing return pattern. While smaller lateral spacing improves local water collection, it may lead to flow congestion if the collector is undersized; conversely, larger spacing increases drainage paths and delays, even if the collector is large. An optimal spacing range of 100–150 m is suggested alongside the collector diameter. Lateral diameter also affects performance: increasing it reduces drainage time, but the benefit plateaus around 200 mm, making further enlargement cost-ineffective. The genetic algorithm helped to optimize the drainage network design. Utilizing the genetic algorithm, the drainage network was optimized in just 15 iterations. The fitness function value rapidly decreased from 351,000 CNY to 55,000 CNY and then stabilized, reducing the annual cost from 59,640.67 CNY to 45,337.86 CNY—a 24% savings—highlighting the approach’s effectiveness in designing efficient and economical farmland drainage systems. This study has shown that the simulation-based optimization of drainage networks provides a more rational and cost-effective approach to planning drainage infrastructure. Full article

Accurate prediction of extreme atmospheric conditions is essential for various scientific and engineering applications, ranging from environmental monitoring to space weather forecasting and urban climate resilience. This study introduces an empirical approach to predict maximum atmospheric pressure and temperature using an empirical model based on statistical parameters. The model incorporates key inputs such as the mean value, standard deviation, integral time scale, and a variability factor, denoted as b, to capture application-specific uncertainties. The methodology is applied to two distinct atmospheric scenarios: (i) forecasting maximum atmospheric pressure using data from 29 global monitoring stations, and (ii) predicting maximum temperature around isolated structures within unstable boundary layers, leveraging insights from Large Eddy Simulation (LES) data. The results indicate that the model performs robustly across diverse conditions, with the b parameter exhibiting a wide range of values depending on the specific atmospheric setting. The comparison between model predictions and observed data demonstrates excellent agreement, validating the model’s applicability in extreme value prediction. These findings reinforce the empirical model’s potential for integration into computational fluid dynamics (CFD) simulations, enhancing the predictive capabilities of Reynolds-Averaged Navier-Stokes (RANS) methodologies. Furthermore, the model’s ability to generalize across different atmospheric processes highlights its significance in advancing our understanding of meteorological extremes. Full article

As global climate change intensifies, the frequency and severity of extreme weather events continue to rise. However, research on semi-outdoor and transitional spaces remains limited, and transportation stations are typically not fully enclosed. Therefore, it is crucial to gain a deeper understanding of the environmental needs of users in these spaces. This study employs machine learning (ML) algorithms and the SHAP (SHapley Additive exPlanations) methodology to identify and rank the critical factors influencing outdoor thermal comfort at tram stations. We collected microclimatic data from tram stations in Guangzhou, along with passenger comfort feedback, to construct a comprehensive dataset encompassing environmental parameters, individual perceptions, and design characteristics. A variety of ML models, including Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Random Forest (RF), and K-Nearest Neighbors (KNNs), were trained and validated, with SHAP analysis facilitating the ranking of significant factors. The results indicate that the LightGBM and CatBoost models performed exceptionally well, identifying key determinants such as relative humidity (RH), outdoor air temperature (Ta), mean radiant temperature (Tmrt), clothing insulation (Clo), gender, age, body mass index (BMI), and the location of the space occupied in the past 20 min prior to waiting (SOP20). Notably, the significance of physical parameters surpassed that of physiological and behavioral factors. This research provides clear strategic guidance for urban planners, public transport managers, and designers to enhance thermal comfort at tram stations while offering a data-driven approach to optimizing outdoor spaces and promoting sustainable urban development. Full article

Used for soil and weathered rocks, soil nails are rigid reinforcements positioned at certain angles on the ground to provide slope stability. A rigid reinforcement element placed in a well filled with cement grout mix after completing drilling will generate adherence stress between the grout-mixed nail bar and soil. Due to this stress, load is transferred to the soil along the soil–grout interaction surface. In the case discussed herein, the slope at the parcel border needed to be made steeper in order to accommodate the construction of a facility in the Taşkısığı region of Sakarya province. Soil-nailed walls, which are inexpensive and suitable for weathered rocks, were needed as a support system because the slope was too steep to support itself. Support system performance was measured using two inclinometers and two soil nail pull-out tests conducted on different sections observed during and after construction. Contrary to the design-phase prediction, it was determined that the stresses started to dampen in the region closer to the slope-facing zone. Field measurement data and numerical analysis revealed that higher parameters than necessary were selected. In this context, sensitivity and parameter analyses were carried out using the Hoek–Brown constitutive model. The GSI value was re-evaluated and found to be compatible with the observation results obtained from the field performance. Since the retaining wall performance observed was higher than expected, geometric parametric analysis of the structural elements was performed; high safety coefficients were found across variations. The effects of the inclination of the slope, nail length, nail spacing, and nail slope design parameters on the safety coefficient and horizontal displacement were examined. The optimal design suggested nail lengths of 4.00 m, a spacing of 1.60 m, and slopes of 20°. It was discovered that the effect of the inclination degree of the slope on the safety coefficient was lower than expected. The results revealed that a more economical design with a similar safety factor can be obtained by shortening the lengths of the nails. Full article

The sea surface displays dynamic characteristics, such as waves and various formations. As a result, images of the sea surface usually have few stable feature points, with a background that is often complex and variable. Moreover, the sea surface undergoes significant changes due to variations in wind speed, lighting conditions, weather, and other environmental factors, resulting in considerable discrepancies between images. These variations present challenges for identification using traditional methods. This paper introduces an algorithm based on the phase-consistency model. We utilize image data collected from a specific maritime area with a high-frame-rate surface array infrared camera. By accurately detecting images with identical names, we focus on the subtle texture information of the sea surface and its rotational invariance, enhancing the accuracy and robustness of the matching algorithm. We begin by constructing a nonlinear scale space using a nonlinear diffusion method. Maximum and minimum moments are generated using an odd symmetric Log–Gabor filter within the two-dimensional phase-consistency model. Next, we identify extremum points in the anisotropic weighted moment space. We use the phase-consistency feature values as image gradient features and develop feature descriptors based on the Log–Gabor filter that are insensitive to scale and rotation. Finally, we employ Euclidean distance as the similarity measure for initial matching, align the feature descriptors, and remove false matches using the fast sample consensus (FSC) algorithm. Our findings indicate that the proposed algorithm significantly improves upon traditional feature-matching methods in overall efficacy. Specifically, the average number of matching points for long-wave infrared images is 1147, while for mid-wave infrared images, it increases to 8241. Additionally, the root mean square error (RMSE) fluctuations for both image types remain stable, averaging 1.5. The proposed algorithm also enhances the rotation invariance of image matching, achieving satisfactory results even at significant rotation angles. Full article