Challenging Weather Conditions Research Articles

Bioinspired, anti-fogging and de-icing transparent surfaces with flexible property

In cold environments, natural phenomena like icing, frost, and fog can significantly impede the functionality of optical windows. Consequently, there is a growing demand for optical windows that demonstrate anti-fogging and de-icing characteristics while maintaining flexibility and transparency with high optical clarity. The development of cost-effective and straightforward anti-fogging and de-icing elements encounters various challenges. The remarkable visual acuity of polar bear eyes in challenging weather conditions can be attributed to a tear film that covers the cornea and the thermoregulatory characteristics of their eyeballs. Inspired by the anti-fogging and anti-icing properties of polar bear eyes, a new flexible, transparent surface with heating capabilities has been developed. To address the limitations of conventional slippery surfaces that solely offer anti-fouling properties, a flexible internal heating unit is integrated into the slippery surface through a layered nesting technique to emulate the homothermal features of polar bear eyeballs. The slippery property of the PDMS surface can be attributed to a gel layer containing an auto-secretory lubricant. To preserve its optical properties, a new processing method that combines liquid metal microfluidics and femtosecond laser processing is employed. This technique can create narrow channels with a high length-to-diameter ratio, thereby reducing the impact on the transmission rate. The sample demonstrates self-cleaning, resistance to dust and pollution, and efficient electrical heating properties. Testing on fogging and freezing validates the practicality and effectiveness of these substances for applications in anti-fogging and de-icing.

Temporal dynamics and extreme events in solar, wind, and wave energy complementarity: Insights from the Polish Exclusive Economic Zone

The Polish Exclusive Economic Zone (EEZ) in the Baltic Sea is an area of increasing strategic importance for Poland's pursuit of renewable energy, especially offshore wind. This research investigates the complementarity among solar, wind, and wave energy resources within the Polish EEZ to examine these energy sources' temporal dynamics, correlations, and extremes. The primary data source corresponds to a 31-year hourly time series dataset from the ERA5 reanalysis, whose reliability was evaluated through performance metrics. The results from complementarity metrics indicate varying levels of association among the three variable renewable energy resources (VRES) in the EEZ, spanning from weak similarity to weak complementarity. The findings of this research indicate that blackouts are most probable at offshore locations during winter and autumn for renewable power systems integrating wind and solar energy, with over 70 % of occurrences within these seasons. The investigation of extreme events highlights critical elements when evaluating VRES and their complementarity. This understanding aids in effectively planning and managing renewable energy systems, ensuring resilience and reliability under challenging weather conditions. Furthermore, while the complementarity may be consistent throughout the entire Polish EEZ, the feasibility and cost of implementing hybrid power systems can significantly vary between locations.

Deep learning and saliency-based parking IoT classification under different weather conditions

The world’s population is growing at an exponential rate, which has created a slew of new issues in our daily lives. Among these challenges is the difficulty of finding available parking spaces. To overcome this obstacle, an effective system for detecting vacant parking spaces is crucial, particularly in densely populated areas. Even though there are numerous parking detecting systems on the market today, they all have their own set of restrictions. There are primarily two types of parking systems available. The first is a hardware-based system that uses hardware to detect whether a space is vacant or occupied, and the second is a smart parking system that uses cameras to classify parking status. Vision-based parking systems are becoming increasingly popular due to benefits such as flexibility and cost efficiency. However, due to challenging weather conditions and various occlusions caused by objects, such systems are challenging to develop. The limitations of a vision-based parking system must be addressed for it to become more accurate. To date, many researchers have attempted to incorporate deep learning-based parking systems with various algorithms but none of them has focused on reducing the complexity of Convolutional Neural Network (CNN) training through unsupervised pre-processing steps. In this study, an attempt is made to reduce the complexity of CNN by integrating salient features as a pre-processing step for normalizing weather conditions. It has been observed that salient features-based pre-processing makes the system more robust in terms of accuracy and time efficiency.

Quantitative Precipitation Estimation Using Weather Radar Data and Machine Learning Algorithms for the Southern Region of Brazil

In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z–R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z–R relationship. The results highlight machine learning’s potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method.

Advancements in Deep Learning for Autonomous Driving in Indian Road Conditions

Recent advancements in deep learning techniques tailored for autonomous driving in Indian road conditions are crucial for revolutionizing transportation systems. Indian roads present unique challenges, including unpredictable traffic patterns, diverse road infrastructures, and challenging weather conditions. Deep learning is pivotal in addressing these challenges, focusing on perception, decision-making, and control. Various architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement Learning (DRL), are analyzed for their efficacy in autonomous driving. Sensor fusion techniques enhance perception capabilities, such as lidar, radar, and camera data integration. Additionally, advancements in semantic segmentation and object detection algorithms improve scene understanding and obstacle recognition. Collaborative efforts between academia, industry, and government agencies are essential for accelerating the deployment of autonomous driving technology in Indian road conditions. In conclusion, leveraging cutting-edge deep learning methodologies enables safe and efficient navigation of autonomous vehicles, ushering in a transformative future in transportation systems.

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning.

In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and precise data necessary for these tasks. With the agricultural sector increasingly relying on technological advancements, the integration of innovative solutions is essential. This study presents a novel approach that combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs). The proposed approach demonstrates superior real-time capabilities in fruit detection and counting, utilizing a combination of AI techniques and multi-UAV systems. The core innovation of this approach is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and, ultimately, a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring the high-resolution and accurate detection of targeted objects during UAV operations. Its effectiveness is proven by experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate at 14.7% and a false negative rate at 18.3%, even under challenging weather conditions like cloudiness. Overall, the practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0.

Enhancing InSAR accuracy: Unveiling more accurate displacement fields through 3-D troposphere tomography

Interferometric Synthetic Aperture Radar (InSAR) measurements are susceptible to tropospheric delay induced by changes in tropospheric temperature, pressure, and water vapor. While pressure and temperature temporal variations have a limited impact on phase gradients, variations in water vapor, which contribute to the wet component of tropospheric delay, play a significant role. Therefore, obtaining a more accurate estimation of the wet refractivity of the tropospheric layer is crucial for improving InSAR displacement field accuracy. This paper presents the utilization of Global Navigation Satellite Systems (GNSS) tropospheric tomography for reconstructing three-dimensional (3-D) wet refractivity. Various data sources are employed, including GNSS measurements, ERA5 reanalysis data, radiosonde observations, and Sentinel-1A radar acquisitions. ERA5 reanalysis data and radiosonde observations are used to calculate tropospheric hydrostatic delay and to evaluate the wet refractivity obtained from tomography, respectively. The effectiveness of tomography techniques in mitigating the tropospheric signal in unwrapped interferograms is compared with the total tropospheric delay obtained from Generic Atmospheric Correction Online Service (GACOS) products, which is one of the most common approach for tropospheric correction of InSAR measurements. To ensure a comprehensive analysis, the effects of the correction methods are investigated in two different weather conditions. Based on the results, GNSS tropospheric tomography reduces the mean Root Mean Square Error (RMSE) by about 1.2 cm compared to GACOS products. This technique proves to be highly effective in improving the accuracy of InSAR displacement field estimation, particularly under challenging weather conditions.

RADAR-BASED DETECTION AND RECOGNITION METHODOLOGY OF AUTONOMOUS SURFACE VEHICLES IN CHALLENGING MARINE ENVIRONMENT

This paper presents a methodology that combines radar polarization selection and recognition techniques for navigating objects in atmospheric formations, with a special focus on unmanned surface vehicles (ASVs). The proposed technique utilizes the concept of an energy dissipation matrix to represent these objects as characteristic “shiny dots”. By strategically changing the polarization of the emitted and received electromagnetic waves, the resulting echo energy dissipation matrix is determined. This approach allows the formation of an intensity-based repository of atmospheric formations, which gives SRPC a complete set of tools to account for atmospheric conditions in radar identification of remote objects, including ASVs. The practical application of this technique extends to the improvement of a distinct class of shipborne radar systems optimized for ASVs and their specific navigation requirements. Ultimately, this technology bridges the gap between advanced radar technology and the emerging field of unmanned ground vehicles, providing safer and more proficient navigation in challenging weather conditions.

Necropsy findings, meat control pathology and causes of loss in semi-domesticated reindeer (Rangifer tarandus tarandus) in northern Norway

BackgroundReindeer herding in Norway is based on traditional Sámi pastoralism with the animals free ranging throughout the year. The animals move over large areas in varying terrain and often in challenging weather conditions. Winter crises, such as difficult grazing conditions caused by icing or large amounts of snow, are survival bottlenecks for reindeer. Calves are especially vulnerable, and many may die from starvation during winter crises. Predation and starvation are the predominant narratives to explain losses, however, carcasses are difficult to find and often little remains after scavenging and decay. Documentation of the causes of death is therefore scarce.ResultsIn this study, we investigated the cause of reindeer mortality in Troms and Finnmark, Nordland and Trøndelag during 2017–2019. Necropsies (n = 125) and organ investigation (n = 13) were performed to document cause of death. Body condition was evaluated using visual fat score and bone marrow fat index. A wide range of causes of death was detected. The diagnoses were categorized into the following main categories: predation (n = 40), emaciation (n = 35), infectious disease (n = 20), trauma (n = 11), feeding related disease (n = 5), neoplasia (n = 4), others (n = 6) and unknown (n = 17). Co-morbidities were seen in a number of diagnoses (n = 16). Reindeer herders are entitled to economic compensation for reindeer killed by endangered predators, but a lack of documentation leads to a gap between the amount of compensation requested and what is awarded. An important finding of our study was that predators, during winter, killed animals in good as well as poor body condition. Emaciation was also shown to be associated with infectious diseases, and not only attributable to winter grazing conditions.ConclusionsThis study highlights the importance of examining dead reindeer to gain knowledge about why they die on winter pasture. The work presented herein also shows the feasibility and value of increased documentation of reindeer losses during winter.

A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

A Robust Intelligent System for Text-Based Traffic Signs Detection and Recognition in Challenging Weather Conditions

CDRSHNET: VARIANCE-GUIDED MULTISCALE AND SELF-ATTENTION FUSION WITH HYBRID LOSS FUNCTION TO RESTORE TRAFFIC-SIGN IMAGES CAPTURED IN ADVERSE CONDITIONS

This paper proposes a CDRSHNet (CodecDirtyRainyShadowHazeNetwork) architecture with a fusion of self-attention (SA) and variance-guided multiscale attention (VGMA) mechanism to restore traffic sign images captured in challenging weather conditions including raindrops, shadows, haze, blurry images from dirty camera lenses and codec errors. The SA captures global dependencies whereas VGMA enhances the representation by emphasizing informative channels and spatial locations. To enhance the image quality hybrid loss function is proposed that combines the Gradient Magnitude Similarity Deviation (GMSD) and Charbonnier loss. The CDRSHNet is trained on a dataset of real and synthesized images. Its performance is evaluated on the average Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) on Test RID (Real Image Dataset) and Test SID (Synthesized Image Dataset). CDRSHNet achieved an average SSIM of 0.978 and an average PSNR of 39.58 on Test RID. On Test SID, the average SSIM is 0.963, and the average PSNR is 39.46

A Robust Lane Detection Algorithm Adaptable to Challenging Weather Conditions

A Robust Lane Detection Algorithm Adaptable to Challenging Weather Conditions

Solar-Powered Adsorption-Based Multi-Generation System Working under the Climate Conditions of GCC Countries: Theoretical Investigation

In this study, transient modelling for a solar-powered adsorption-based multi-generation system working under the climatic conditions of the Gulf Cooperation Council (GCC) countries is conducted. Three cities are selected for this study: Sharjah in the United Arab Emirates, Riyadh in Saudi Arabia, and Kuwait City in Kuwait. The system comprises (i) evacuated tube solar collectors (ETCs), (ii) photovoltaic-thermal (PVT) solar collectors, and (iii) a single-stage double-bed silica gel/water-based adsorption chiller for cooling purposes. A MATLAB code is developed and implemented to theoretically investigate the performance of the proposed system. The main findings of this study indicate that among the selected cities, based on the proposed systems and the operating conditions, Riyadh has the highest cooling capacity of 10.4 kW, followed by Kuwait City, then Sharjah. As for the coefficient of performance (COP), Kuwait City demonstrates the highest value of 0.47. The electricity generated by the proposed system in Riyadh, Kuwait City, and Sharjah is 31.65, 31.3, and 30.24 kWh/day, respectively. Furthermore, the theoretical results show that at 18:00, the overall efficiency of the proposed system reaches about 0.64 because of the inclusion of a storage tank and its feeding for the adsorption chiller. This study analyzes the feasibility of using a combination of ETCs and PVT collectors to drive the adsorption chiller system and produce electricity in challenging weather conditions.

Vehicle classification based on audio-visual feature fusion with low-quality images and noise

In Intelligent Transport Systems (ITS), vision is the primary mode of perception. However, vehicle images captured by low-cost traffic cameras under challenging weather conditions often suffer from poor resolution and insufficient detail representation. On the other hand, vehicle noise provides complementary auditory features that offer advantages such as environmental adaptability and a large recognition distance. To address these limitations and enhance the accuracy of low-quality traffic surveillance classification and identification, an effective audio-visual feature fusion method is crucial. This paper presents a research study that establishes an Urban Road Vehicle Audio-visual (URVAV) dataset specifically designed for low-quality images and noise recorded in complex weather conditions. For low-quality vehicle image classification, the paper proposes a simple Convolutional Neural Network (CNN)-based model called Low-quality Vehicle Images Net (LVINet). Additionally, to further enhance classification accuracy, a spatial channel attention-based audio-visual feature fusion method is introduced. This method converts one-dimensional acoustic features into a two-dimensional audio Mel-spectrogram, allowing for the fusion of auditory and visual features. By leveraging the high correlation between these features, the representation of vehicle characteristics is effectively enhanced. Experimental results demonstrate that LVINet achieves a classification accuracy of 93.62% with reduced parameter count compared to existing CNN models. Furthermore, the proposed audio-visual feature fusion method improves classification accuracy by 7.02% and 4.33% when compared to using single audio or visual features alone, respectively.

Machine Learning Analysis of Autonomous Vehicle Sensors Under Extreme Conditions in Alaska

Abstract Autonomous vehicles are part of an expanding industry that encompasses various interdisciplinary fields such as dynamic controls, thermal engineering, sensors, data processing, and artificial intelligence. Exposure to extreme environmental conditions, such as changes to temperature and humidity, affects sensor performance. To address potential safety concerns related to sensor perception used in autonomous vehicles in extremely cold real-world situations, specifically Alaska, examination of frosts and water droplets impact on vehicle optical sensors is conducted in both real-world and laboratory-controlled settings. Machine learning models are utilized to determine the vision impediment levels. Potential hardware and software tools are then introduced as solutions for the environmental impacts. Through this research, a better understanding of the potential caveats and algorithm solutions can be suggested to improve autonomous driving, even under challenging weather conditions.

Evaluation of An Aerial Application of Duet Hd® Against Aedes Dorsalis and Culex Tarsalis in Rural Habitats of the Great Salt Lake, Utah.

The Salt Lake City Mosquito Abatement District (SLCMAD) has been conducting aerial applications using an organophosphate insecticide against adult mosquitoes for several decades. In order to evaluate a potential rotation product, aerial applications of Duet HD™, a pyrethroid, were conducted under operational conditions against wild populations of Aedes dorsalis and Culex tarsalis and against colony strains of Cx. pipiens and Cx. quinquefasciatus. The erratic wind patterns of the greater Salt Lake area did not prevent sufficient droplet deposition flux at 9 monitoring locations spread across a 5,120-acre (2,072 ha) spray block within rural habitats. Three separate aerial application trials showed great efficacy against Ae. dorsalis. In contrast, Cx. tarsalis exhibited inconsistent treatment-associated mortalities, suggesting the presence of less susceptible or resistant field populations as a result of spillover from agricultural or residential pyrethroid usage. Bottle bioassays to diagnose pyrethroid resistance using field-collected Cx. tarsalis indicated that some populations of this species, especially those closest to urban edges, failed to show adequate mortality in resistance assays. Despite challenging weather conditions, Duet HD worked reasonably well against susceptible mosquito species, and it may provide a crucial role as an alternative for organophosphate applications within specific and sensitive areas. However, its area-wide adoption into control applications by the SLCMAD could be problematic due to reduced impacts on the most important arboviral vector species, Cx. tarsalis, in this area. This study demonstrates the importance of testing mosquito control products under different operational environments and against potentially resistant mosquito populations by municipal mosquito control districts.

Nighttime Rainy Season Traffic Analysis: Vehicle Detection, Tracking, and Counting with YOLOv8 and DeepSORT

This research focuses on developing a reliable computer vision system for accurately tracking traffic density in India during the rainy season. The system uses deep learning-based techniques to handle the difficulties associated with vehicle detection and tracking. The three modules are vehicle detection, tracking, and vehicle counting. Vehicles are initially identified using the YOLOv8 algorithm, a state-of-the-art deep learning detector. Subsequently, the DeepSORT algorithm is utilized for multi-object tracking to ensure accurate and robust tracking of various objects, including cars, buses, trucks, bikes, and pedestrians. The importance of accurate vehicle counting and speed measurement is emphasized, especially during bad weather. An independently compiled dataset of Indian rainy conditions is used to assess the proposed computer vision system. The outcomes demonstrate the system's capability to accurately identify, track, count, and estimate the speeds of vehicles. These features offer insightful information for traffic analysis, including flow monitoring, congestion detection, and other associated traffic challenges. This study makes a contribution to the field of computer vision-based traffic monitoring and offers potential applications in transportation management systems under challenging weather conditions.

Raindrop-Removal Image Translation Using Target-Mask Network with Attention Module

Image processing plays a crucial role in improving the performance of models in various fields such as autonomous driving, surveillance cameras, and multimedia. However, capturing ideal images under favorable lighting conditions is not always feasible, particularly in challenging weather conditions such as rain, fog, or snow, which can impede object recognition. This study aims to address this issue by focusing on generating clean images by restoring raindrop-deteriorated images. Our proposed model comprises a raindrop-mask network and a raindrop-removal network. The raindrop-mask network is based on U-Net architecture, which learns the location, shape, and brightness of raindrops. The rain-removal network is a generative adversarial network based on U-Net and comprises two attention modules: the raindrop-mask module and the residual convolution block module. These modules are employed to locate raindrop areas and restore the affected regions. Multiple loss functions are utilized to enhance model performance. The image-quality assessment metrics of proposed method, such as SSIM, PSNR, CEIQ, NIQE, FID, and LPIPS scores, are 0.832, 26.165, 3.351, 2.224, 20.837, and 0.059, respectively. Comparative evaluations against state-of-the-art models demonstrate the superiority of our proposed model based on qualitative and quantitative results.

The Application of CNN-Based Image Segmentation for Tracking Coastal Erosion and Post-Storm Recovery

Coastal erosion due to extreme events can cause significant damage to coastal communities and deplete beaches. Post-storm beach recovery is a crucial natural process that rebuilds coastal morphology and reintroduces eroded sediment to the subaerial beach. However, monitoring the beach recovery, which occurs at various spatiotemporal scales, presents a significant challenge. This is due to, firstly, the complex interplay between factors such as storm-induced erosion, sediment availability, local topography, and wave and wind-driven sand transport; secondly, the complex morphology of coastal areas, where water, sand, debris and vegetation co-exists dynamically; and, finally, the challenging weather conditions affecting the long-term small-scale data acquisition needed to monitor the recovery process. This complexity hinders our understanding and effective management of coastal vulnerability and resilience. In this study, we apply Convolutional Neural Networks (CNN)-based semantic segmentation to high-resolution complex beach imagery. This model efficiently distinguishes between various features indicative of coastal processes, including sand texture, water content, debris, and vegetation with a mean precision of 95.1% and mean Intersection of Union (IOU) of 86.7%. Furthermore, we propose a new method to quantify false positives and negatives that allows a reliable estimation of the model’s uncertainty in the absence of a ground truth to validate the model predictions. This method is particularly effective in scenarios where the boundaries between classes are not clearly defined. We also discuss how to identify blurry beach images in advance of semantic segmentation prediction, as our model is less effective at predicting this type of image. By examining how different beach regions evolve over time through time series analysis, we discovered that rare events of wind-driven (aeolian) sand transport seem to play a crucial role in promoting the vertical growth of beaches and thus driving the beach recovery process.

WRF Parameterizations of Short-Term Solar Radiation Forecasts for Cold Fronts in Central and Eastern Europe

The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by photovoltaic systems depends. This study presents the forecast effects for one of the most challenging weather conditions in modelling, occurring in central and eastern Europe. The dates of the synoptic situations were selected from 2021 and 2022. Simulations were carried out for 18 days with a cold front and, in order to verify the model configuration, for 2 days with a warm front, 2 days with an occlusion front and 2 days with a high pressure situation. Overall, 24 forecasts were made for each of the three parameterizations of the Weather Research and Forecasting model. The data were compared with the values measured in situ at the station performing the actinometric measurements belonging to Germany’s National Meteorological Service. This paper presents the spatial distribution of the global horizontal irradiance parameters for several terms to explain the differences between the results of the different simulations.