Search Results (215)

The conductors of high-speed railway OCSs (Overhead Contact Systems) are susceptible to conductor galloping due to the impact of natural elements such as strong winds, rain, and snow, resulting in conductor fatigue damage and significantly compromising train operational safety. Consequently, monitoring the galloping status of conductors is crucial, and instance segmentation techniques, by delineating the pixel-level contours of each conductor, can significantly aid in the identification and study of galloping phenomena. This work expands upon the YOLO11-seg model and introduces an instance segmentation approach for galloping video and image sensor data of OCS conductors. The algorithm, designed for the stripe-like distribution of OCS conductors in the data, employs four-direction Sobel filters to extract edge features in horizontal, vertical, and diagonal orientations. These features are subsequently integrated with the original convolutional branch to form the FDSE (Four Direction Sobel Enhancement) module. It integrates the ECA (Efficient Channel Attention) mechanism for the adaptive augmentation of conductor characteristics and utilizes the FL (Focal Loss) function to mitigate the class-imbalance issue between positive and negative samples, hence enhancing the model’s sensitivity to conductors. Consequently, segmentation outcomes from neighboring frames are utilized, and mask-difference analysis is performed to autonomously detect conductor galloping locations, emphasizing their contours for the clear depiction of galloping characteristics. Experimental results demonstrate that the enhanced YOLO11-seg model achieves 85.38% precision, 77.30% recall, 84.25% AP@0.5, 81.14% F1-score, and a real-time processing speed of 44.78 FPS. When combined with the galloping visualization module, it can issue real-time alerts of conductor galloping anomalies, providing robust technical support for railway OCS safety monitoring. Full article

In this study, the thermal effectiveness of thermally conductive concrete pavements (TCPs) using silicon carbide (SiC) as a fine aggregate replacement was investigated, compared with that of ordinary Portland cement pavements (OPCPs). The most important purpose of this study is to improve the thermal performance of concrete pavement. Additionally, this study utilized improved thermal properties to enhance the efficiency of pavement heating to prevent icing and snow stacking. Both mixtures met the Korean standards for air content (4.5–6%) and slump (80–150 mm), demonstrating adequate workability. TCP exhibited a higher mechanical performance, with average compressive and flexural strengths of 42.88 MPa and 7.35 MPa, respectively, exceeding the required targets of a 30 MPa compressive strength and a 4.5 MPa flexural strength. The improved strength was mainly attributed to the filler effect and partly due to the van der Waals interactions of the SiC particles. Thermal conductivity tests showed a significant improvement in the TCP (3.20 W/mK), which was approximately twice that of OPCP (1.59 W/mK), indicating an enhanced heat transfer efficiency. In winter field tests, TCP effectively maintained high surface temperatures, overcoming heat loss and outperforming the OPCP. In the site experiment, thermal efficiency was clearly shown in the temperature at the center of the TCP, which was 3.5 °C higher than at the center of the OPCP at the coldest time. These improvements suggest that SiC-reinforced concrete pavements can be practically utilized for effective snow removal and ice mitigation in road systems. Full article

Aircraft icing and snow accumulation are significant threats to flight safety and operational efficiency, necessitating rapid and accurate detection methods. The aim of this study was to develop and comparatively evaluate artificial intelligence (AI) models for the real-time detection of ice and snow on aircraft surfaces using vision systems. A custom dataset of annotated aircraft images under various winter conditions was prepared and augmented to enhance model robustness. Two training approaches were implemented: an automatic process using the YOLOv8 framework on the Roboflow platform and a manual process in the Google Colab environment. Both models were evaluated using standard object detection metrics, including mean Average Precision (mAP) and mAP@50:95. The results demonstrate that both methods achieved comparable detection performance, with final mAP50 values of 0.25–0.3 and mAP50-95 values around 0.15. The manual approach yielded lower training losses and more stable metric progression, suggesting better generalization and a reduced risk of overfitting. The findings highlight the potential of AI-driven vision systems to support intelligent de-icing decision-making in aviation. Future research should focus on refining localization, minimizing false alarms, and adapting detection models to specific aircraft components to further enhance operational safety and reliability. Full article

The observations of atmospheric, oceanic, and sea ice data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition were used to analyze the influence of snow redistribution and melt-pond processes on the evolution of sea ice thickness (SIT) in 2019 and 2020. To mitigate the effect of missing atmospheric observations from the time of the expedition, we used ERA5 atmospheric reanalysis along the MOSAiC drift trajectory to force the single-column sea ice model Icepack. SIT simulations from six combinations of two melt-pond schemes and three snow-redistribution configurations of Icepack were compared with observations and analyzed to investigate the sources of model–observation discrepancies. The three snow-redistribution configurations are the bulk scheme, the snwITDrdg scheme, and one simulation conducted without snow redistribution. The bulk scheme describes snow loss from level ice to leads and open water, and snwITDrdg describes wind-driven snow redistribution and compaction. The two melt-pond schemes are the TOPO scheme and the LVL scheme, which differ in the distribution of melt water. The results show that Icepack without snow redistribution simulates excessive snow–ice formation, resulting in an SIT thicker than that observed in spring. Applying snow-redistribution schemes in Icepack reduces snow–ice formation while enhancing the congelation rate. The bulk snow-redistribution scheme improves the SIT simulation for winter and spring, while the bias is large in simulations using the snwITDrdg scheme. During the summer, Icepack underestimates the sea ice surface albedo, resulting in an underestimation of SIT at the end of simulation. The simulations using the TOPO scheme are characterized by a more realistic melt-pond evolution compared to those using the LVL scheme, resulting in a smaller bias in SIT simulation. Full article

Aiming at the problems of serious pavement temperature diseases, low efficiency and high loss of ice-breaking methods, high occupancy rate of waste tires and the low utilization rate and insufficient durability of rubber particles, this paper aims to improve the service level of roads and ensure the safety of winter pavements. A pavement material with high efficiency, low carbon and environmental friendliness for active snow melting and ice breaking is developed. Firstly, NaOH, NaClO and KH550 were used to optimize the treatment of rubber particles. The hydrophilic properties, surface morphology and phase composition of rubber particles before and after optimization were studied, and the optimal treatment method of rubber particles was determined. Then, the optimized rubber particles were used to replace the natural aggregate in the polyurethane gel mixture by the volume substitution method, and the optimum polyurethane gel dosages and molding and curing processes were determined. Finally, the influence law of the road performance of RPGM was compared and analyzed by means of an indoor test, and the ice-breaking effect of RPGM was explored. The results showed that the contact angles of rubber particles treated with three solutions were reduced by 22.5%, 30.2% and 36.7%, respectively. The surface energy was improved, the element types on the surface of rubber particles were reduced and the surface impurities were effectively removed. Among them, the improvement effect of the KH550 solution was the most significant. With the increase in rubber particle content from 0% to 15%, the dynamic stability of the mixture gradually increases, with a maximum increase of 23.5%. The maximum bending strain increases with the increase in its content. The residual stability increases first and then decreases with the increase in rubber particle content, and the increase ranges are 1.4%, 3.3% and 0.5%, respectively. The anti-scattering performance increases with the increase in rubber content, and an excessive amount will lead to an increase in the scattering loss rate, but it can still be maintained below 5%. The fatigue life of polyurethane gel mixtures with 0%, 5%, 10% and 15% rubber particles is 2.9 times, 3.8 times, 4.3 times and 4.0 times higher than that of the AC-13 asphalt mixture, respectively, showing excellent anti-fatigue performance. The friction coefficient of the mixture increases with an increase in the rubber particle content, which can be increased by 22.3% compared with the ordinary asphalt mixture. RPGM shows better de-icing performance than traditional asphalt mixtures, and with an increase in rubber particle content, the ice-breaking ability is effectively improved. When the thickness of the ice layer exceeds 9 mm, the ice-breaking ability of the mixture is significantly weakened. Mainly through the synergistic effect of stress coupling, thermal effect and interface failure, the bonding performance of the ice–pavement interface is weakened under the action of driving load cycle, and the ice layer is loosened, broken and peeled off, achieving efficient de-icing. Full article

Utah, USA, relies heavily on snowpack for water to sustain its growing population. Scientists and policy makers are exploring and proposing several potential sustainable solutions to improving flow to the Great Salk Lake as it recently has significantly declined in size, including removing tree canopy. This study examines the influence of tree canopy coverage, climate, and topography on snow water equivalent (SWE) within the Great Salt Lake Watershed. Using SNOTEL data, NLCD land use/land cover rasters, t-tests, and multiple linear regression (MLR), the study analyzed SWE variability in relation to canopy density, winter precipitation, elevation, temperature, and latitude. Initial t-tests showed significant differences in SWE between sites with canopy coverage below and above 70%, yet tree canopy was excluded as a significant predictor in the MLR model. Instead, SWE was primarily explained by mean winter precipitation, elevation, average winter high temperatures, and latitude. Additionally, canopy change analysis of the 2018 Pole Creek Fire in the Jordan River watershed showed no significant changes in SWE following canopy loss. This study highlights the dominant role of climatic factors in influencing snowpack dynamics on a watershed scale. It also provides important data for sustainable watershed and forestry management and a framework for understanding snowpack responses to climate and land cover changes in saline lake ecosystems. Full article

Sustained reductions in terrestrial water storage (TWS) have been observed globally using Gravity Recovery and Climate Experiment (GRACE) satellite data since 2002. However, the underlying mechanisms remain incompletely understood due to limited record lengths and data discontinuity. Recently, explainable artificial intelligence (XAI) has provided robust tools for unveiling dynamics in complex Earth systems. In this study, we employed a deep learning technique (Long Short-Term Memory network, LSTM) to reconstruct global TWS dynamics, filling gaps in the GRACE record. We then utilized the Local Interpretable Model-agnostic Explanations (LIME) method to uncover the underlying mechanisms driving observed TWS reductions. Our results reveal a consistent decline in the global mean TWS over the past 22 years (2002–2024), primarily influenced by precipitation (17.7%), temperature (16.0%), and evapotranspiration (10.8%). Seasonally, the global average of TWS peaks in April and reaches a minimum in October, mirroring the pattern of snow water equivalent with approximately a one-month lag. Furthermore, TWS variations exhibit significant differences across latitudes and are driven by distinct factors. The largest declines in TWS occur predominantly in high latitudes, driven by rising temperatures and significant snow/ice variability. Mid-latitude regions have experienced considerable TWS losses, influenced by a combination of precipitation, temperature, air pressure, and runoff. In contrast, most low-latitude regions show an increase in TWS, which the model attributes mainly to increased precipitation. Notably, TWS losses are concentrated in coastal areas, snow- and ice-covered regions, and areas experiencing rapid temperature increases, highlighting climate change impacts. This study offers a comprehensive framework for exploring TWS variations using XAI and provides valuable insights into the mechanisms driving TWS changes on a global scale. Full article

Glaciers worldwide are in retreat, and their meltwater can modulate mountain aquifers. We examined whether mass loss of the Juncal Norte Glacier (central Chile) has affected groundwater storage in the Juncal River basin between 1990 and 2022. Recession-curve modeling of daily streamflow shows no statistically significant trend in basin-scale groundwater reserves (τ = 0.06, p > 0.05). In contrast, glacier volume declined significantly (−3.8 hm³/yr, p < 0.05), and precipitation at the nearby Riecillos station fell sharply during the 2008–2017 megadrought (p < 0.05) but exhibited no significant change beforehand. Given the simultaneous decreases in meteoric inputs (rain + snow) and glacier mass, one would expect groundwater storage to decline; its observed stability therefore suggests that enhanced glacier-melt recharge may be temporarily offsetting drier conditions. Isotopic evidence from comparable Andean catchments supports such glacio-groundwater coupling, although time lags of months to years complicate detection with recession models alone. Hence, while our results do not yet demonstrate a direct glacier–groundwater link, they are consistent with the hypothesis that ongoing ice loss is buffering aquifer storage. Longer records and tracer studies are required to verify this mechanism and to inform sustainable water resources planning. Full article

Snow cover variations significantly affect the stability of regional water supply and terrestrial ecosystems in arid northwest China. This study comprehensively evaluates snow resource changes since 2000 by integrating multisource remote sensing datasets and analyzing four key indicators: snow cover area (SCA), snow phenology (SP), snow depth (SD), and snow water equivalent (SWE). The results reveal a slight downtrend in SCA over the past two decades, with an annual decline rate of 7.13 × 10³ km². The maximum SCA (1.28 × 10⁶ km²) occurred in 2010, while the minimum (7.25 × 10⁵ km²) was recorded in 2014. Spatially, SCA peaked in December in the north and January in the south, with high-altitude subregions (Ili River Basin (IRB), Tarim River Region (TRR), North Kunlun Mountains (NKM), and Qaidam Basin (QDB)) maintaining stable summer snow cover due to low temperatures and high precipitation. Analysis of snow phenology indicates a significant shortening of snow cover duration (SCD), with 62.40% of the study area showing a declining trend, primarily driven by earlier snowmelt. Both SD and SWE exhibited widespread declines, affecting 75.09% and 84.85% of the study area, respectively. The most pronounced SD reductions occurred in TRR (94.44%), while SWE losses were particularly severe in North Tianshan Mountains (NTM, 94.61%). The total snow mass in northwest China was estimated at 108.95 million tons, with northern Xinjiang accounting for 66.24 million tons (60.8%), followed by southern Xinjiang (37.44 million tons) and the Hexi Inland Region (5.27 million tons). Consistency analysis revealed coherent declines across all indicators in 55.56% of the study area. Significant SD and SCD reductions occurred in TRR and Tuha Basin (THB), while SWE declines were widespread in NTM and IRB, driven by rising temperatures and decreased snowfall. The findings underscore the urgent need for adaptive strategies to address emerging challenges for water security and ecological stability in the region. Full article

Snow cover is an important freshwater source in many mountain ranges around the world and is heavily affected by climate change, often leading to reduced overall snow cover availability and duration as well as shifts in seasonality. To monitor these changes and long-term trends, the analysis of remote sensing is a commonly used tool, as data are available consistently and for long time series. In this study we acquired and processed the whole archive of available Landsat data between 1985 and 2024 for two catchments in the Chilean Andes, Aconcagua and Río Maipo, located in the Valparaíso and Santiago de Chile metropolitan regions, respectively. We generated monthly Snow Line Elevation (SLE) time series from the entire archive for both catchments and performed trend analyses on these time series. Strong positive long-term SLE change rates of 11.25 m per year for the Aconcagua catchment and 9.85 m to 15.65 m per year for the Río Maipo catchment were detected, indicating a decrease in snow cover as well as available freshwater from snowmelt. The projection to the year 2050 revealed a potential loss of snow covered area of up to 42% during summer months, with the SLE receding up to 231 m. Full article

Snow water equivalent (SWE), the amount of water generated when a snowpack melts, has been used to study the impacts of climate change on the cryosphere processes and snow cover dynamics during the winter season. In most analyses, high-temporal-resolution SWE and SD data are aggregated into monthly and yearly averages to detect and characterize changes. Aggregating snow measurements, however, can magnify the modifiable aerial unit problem, resulting in differing snow trends at different temporal resolutions. Time series analysis of gridded SWE data holds the potential to unravel the impacts of climate change and global warming on daily, weekly, and monthly changes in snow during the winter season. Consequently, this research presents a high-temporal-resolution analysis of changes in the SWE across the cold regions of Canada. A Siamese UNet (Si-UNet) was developed by modifying the model’s last layer to incorporate the structural similarity (SSIM) index. The similarity values from the SSIM index are passed to a contrastive loss function, where the optimization process maximizes SSIM index values for pairs of similar SWE images and minimizes the values for pairs of dissimilar SWE images. A comparison of different model architectures, loss functions, and similarity metrics revealed that the SSIM index and the contrastive loss improved the Si-UNet’s accuracy by 16%. Using our Si-UNet, we found that interannual SWE declined steadily from 1979 to 2018, with March being the month in which the most significant changes occurred (R² = 0.1, p-value < 0.05). We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks. Full article

The successful integration of photovoltaic (PV) generators in cities requires careful planning that accounts for possible factors influencing their operation. Numerous authors have extensively studied these factors; however, the urban environment has its unique characteristics. This study aims to conduct a narrative review of the most common and influential urban factors that impact the operation of PV modules and explore potential mitigation strategies. Based on preliminary knowledge on the topic, a methodology was proposed according to which they are classified into two categories: those enhanced by the urban environment and those specific to it. A total of 97 studies, mostly from the last decade, were selected based on the relevance and impact criteria. Shading, soiling, and snow were analyzed in an urban context, followed by different urban-specific factors, such as the urban landscape, pollution, and the limitations of PV mounting spots, which can lead to more than 50% performance losses. The performed review also identified the key and most promising approaches for mitigation of the abovementioned factors, such as electrostatic dust cleaning and forward bias current snow removal. Furthermore, recommendations for urban landscape planning were made in the context of PV integration. This review could also be useful for designers and operators of urban PV facilities by providing them with basic guidelines for their optimization. Full article

The meteorology-driven multiscale behavior of snow depth over the Tibetan Plateau was investigated via analyzing the spatio-temporal variability of snow depth over 28 intraseasonal continuous snow cover regions. By employing power spectra and the Kullback–Leibler (K-L) distance, the spectral similarities between snow depth and meteorological factors were examined at scales of 5 km, 10 km, 20 km, and 50 km across seasons from 2008 to 2014. Results reveal distinct seasonal and scale-dependent dynamics: in spring and winter, snow depth exhibits lower spectral variance with scale breaks around 50 km, emphasizing the critical roles of precipitation, atmospheric moisture, and temperature, with lower K-L distances at smaller scales. Summer shows the highest spatial variance, with snow depth primarily influenced by wind and radiation, as indicated by lower K-L distances at 15–45 km. Autumn demonstrates the lowest spatial heterogeneity, with windspeed driving snow redistribution at finer scales. The alignment between spatial variance maps and power spectra implies that snow depth data can be effectively downscaled or upscaled without significant loss of spatial information. These findings are essential for improving snow cover modeling and forecasting, particularly in the context of climate change, as well as for effective water resource management and climate adaptation strategies in this strategically vital plateau. Full article

To further investigate the heat transfer characteristics of electric heating snow-melting pavement, this study developed two finite element models of such systems and conducted small-scale field experiments. An analysis was performed on the snow-melting pavement systems’ temperature field, temperature change rate, and gradient distribution during summer and winter, with entransy dissipation introduced to further analyze the heat transfer characteristics of asphalt snow-melting pavement. The results indicate that during system shutdown in summer and winter, the pavement structure exhibits reduced heat transfer capacity, leading to progressive decreases in the temperature variation rate and gradient with depth. The primary heat transfer loss occurs in the asphalt layer, with entransy dissipation predominantly concentrated during summer daylight and winter nighttime. During winter operation, the cable heat source modifies the temperature field distribution and gradient, which alters entransy dissipation. Installing an insulation layer improves snow-melting efficiency, and operating the system from 00:00 to 05:00 effectively prevents pavement icing. Full article

Current advancements in image processing technologies have led to significant progress; however, adverse weather conditions, including haze, snow, and rain, often degrade image quality, which in turn impacts the performance of deep learning-based image matching algorithms. Most existing methods attempt to correct blurred images prior to target detection, which increases network complexity and may result in the loss of potentially crucial information. To better integrate image restoration and image matching tasks, this paper presents DeMatchNet, an end-to-end integrated network framework that seamlessly combines the feature fusion attention network for single image dehazing (FFA-Net) dehazing module with the detector-free local feature matching with transformers (LoFTR) feature matching module. The proposed framework first designs an attention-based feature fusion module (FFM), which effectively merges the original hazy features with the dehazed features. This ensures that the generated dehazed features not only have improved visual quality, but also provide higher-quality input for subsequent feature matching. Subsequently, a feature alignment module (FA) performs scale and semantic adjustments on the fused features, enabling efficient sharing with the LoFTR module. This deep collaboration between dehazing and feature matching significantly reduces computational redundancy and enhances the overall performance. Experimental results on synthetic hazy datasets (based on MegaDepth and ETH3D) and real-world hazy datasets demonstrate that DeMatchNet outperforms the existing methods in terms of matching accuracy and robustness, showcasing its superior performance under challenging weather conditions. Full article