Effect Of Fog Research Articles

Fog in western coastal ecosystems: inter-disciplinary challenges and opportunities with example concepts from the Pacific Northwest, USA

Coastal fog occurs along many of the world’s west coast continental environments. It is particularly consequential during summer when an increased frequency of fog co-occurs with the seasonal dryness characteristic of most west coast climate systems, for example, in the Pacific coast of North and South America, the southwestern African coast, and southern coastal Europe. Understanding coastal fog formation and effects has consequences for many disciplines, including the physical (e.g., atmospheric science, oceanography), biological (e.g., biogeography, ecophysiology), and socio-ecological realms (e.g., Indigenous cultural knowledge, public safety, economics). Although research practices differ across disciplines, they share many of the challenges needed to advance fog science. For example, coastal fog remains difficult to reliably monitor when, where, and why it occurs, which adds difficulty to understanding fog’s effects on all facets of the integrated coastal system. These shared challenges provide ripe opportunities for interdisciplinary collaboration, a template with past success in advancing fog-related science that can continue to have success in the future. In this perspectives review, we summarize the current status and frontiers of fog-related science from multiple disciplines, leveraging examples primarily drawn from the Pacific Northwest coastal region of the United States to show how interdisciplinary collaboration is needed to continue to advance our collective understanding of coastal fog formation and effects on west coast environments.

Comparative Impact of Fog and Rainfall on Vegetation in a Foggy Desert

AbstractFog is an important water source that alleviates vegetation water stress, especially for dryland ecosystems. Comprehensive knowledge of fog and rainfall effects can help us better understand dryland vegetation responses to current and future climates. However, the differences between fog and rainfall effects on vegetation are poorly understood. This study compared the effects of fog and rainfall on vegetation greenness changes based on the ground‐level meteorological observations in the Namib Desert and the satellite vegetation index. The vegetation index and its first derivative were utilized to indicate vegetation greenness and its change rate, respectively. Results showed that fog played a more significant role than rainfall in explaining vegetation greenness change rates, while accumulated rainfall was more important than fog in determining vegetation greenness. Soil temperature was an important factor in explaining vegetation greenness changes. These findings offer key insights into how fog and rainfall differentially contribute to vegetation greenness changes.

Multi-Weather Visibility Restoration using MPRNet - Multi-Stage Progressive Image Restoration

This paper introduces a novel approach, termed Multi-Stage Progressive Image Restoration (MSPIR), aimed at addressing the challenging task of restoring images degraded by various adverse weather conditions. Leveraging a combination of advanced algorithms including HI-Net, DCP, and MSCIA, MSPIR offers a comprehensive solution for mitigating the effects of fog, haze, rain streaks, and other atmospheric distortions on image visibility. The methodology follows a multi-stage iterative process, progressively refining image quality and enhancing visibility through successive stages of processing. By integrating deep learning techniques and adaptive context aggregation mechanisms, MSPIR achieves superior results in restoring image clarity and preserving fine details across diverse weather conditions. Experimental evaluations demonstrate the efficacy and robustness of the proposed framework, highlighting its potential for real-world applications in autonomous navigation, surveillance, and remote sensing. Looking ahead, future research directions include optimizing MSPIR for real-time applications, exploring novel deep learning architectures, and integrating additional sensory modalities to further enhance visibility restoration capabilities. Overall, MSPIR represents a significant advancement in multi-weather visibility restoration, offering promising prospects for clearer and safer image processing in various domains.

Performance analysis of the dual-hop mixed RF/UWOC system with fog channel and turbulence based on diversity

ABSTRACT In this paper, the performance of a dual-hop mixed radio frequency (RF)/underwater wireless optical communication (UWOC) system with amplifying and forwarding (AF) relaying is investigated. The diversity combining technology were combined with unifying Fisher-Snedecor ( F ), extended generalized- K (EGK), and α − μ distribution for atmospheric turbulence in foggy conditions to analyze the probability density function (PDF) of RF links. The UWOC link experiences mixed exponential generalized gamma (EGG) fading. By utilizing diversity combination methods and considering the statistical effects of atmospheric turbulence, random fog, and underwater turbulence, we derive the expression for the PDF of the RF/UWOC system. In addition, the tight asymptotic expression for the outage probability and average bit error rate under high signal-to-noise ratio (SNR) is provided. The analytical expressions are verified by Monte Carlo method, and the effects of channel number on RF/UWOC system performance under various atmospheric, underwater and weather conditions are demonstrated.

Simulating Civil Society in the Middle East and North Africa (MENA): Fog, Friction, and the Lebanese Port Explosion

Introduction Though the effects of fog and friction on decision-making are frequently simulated in military settings, they are less frequently extended to non-military and non-governmental MENA spaces. This article provides a description and evaluation of a disaster simulation rooted in the real-world event of the August 4, 2020 Lebanese port explosion. The aim of the simulation is to prompt student learning of the decision-making dynamics of CSOs amidst fog and friction in democratizing MENA spaces. Background A brief overview of the Lebanon-CSO landscape is provided. As part of their ruling strategy, sectarian elites engineer fog and friction to co-opt and fragment CSOs. This complex power sharing milieu creates dilemmas for CSOs. The Intervention The simulation was designed for an entire classroom of students to experience the fog and friction in decision-making that CSOs encounter in MENA conditions of limited resources and urgent humanitarian need. Methods and Limitations Student learning, framed by Bloom’s taxonomy of levels of intellectual behavior, was assessed using qualitative entry and exit classroom surveys based on three iterations of the simulation. Qualitative interpretive content analysis was used to categorize survey responses. The sample is not statistically significant given the small class sizes at the College. The study serves as an initial exploration for assessing the promising pedagogical benefits of the Lebanon port explosion simulation. Results Before the simulation, students had knowledge and understanding of CSOs, democratization, and the Lebanese context. After the simulation, student surveys yielded results that evidenced higher- ordered learning. Students discovered the challenge of applying their knowledge of Lebanese CSOs amidst fog. Next, the panel stress- testing and critical reflection essay exercises prompted an analysis and evaluation of CSOs as weak or fraught vehicles for democratization in the region. In sum, fog and friction prompted students to experience and learn how CSOs may subvert democratization in the MENA despite intentions to “do good.” Discussion The Lebanon port explosion survey data indicate the value of simulations as “experience vehicles” that may push students to higher orders of abstracted thinking about CSOs in the MENA. Conclusion Coupled with historical knowledge of co-optation and fragmentation strategies of elites vis-à-vis CSOs in Lebanon, the classroom simulation experience provided a low-cost and high-reward learning opportunity for students to grapple with the constraints and consequences of fog and friction on CSO decision-making.

A study on THz communications between Low Earth Orbit constellations and Earth Stations

A non-terrestrial system that uses Terahertz (THz) frequencies is a potential solution to achieving equal access to the Internet worldwide. This paper describes a non-terrestrial system that consists of a Low Earth Orbit (LEO) constellation, Earth Stations in Motion (ESIMs) and standard Earth stations. We examine the effects of rain, fog, clouds and atmospheric gases for this non-terrestrial system for frequencies between 100-300 GHz. The research findings suggest that the frequency bands between 102 - 109.5 GHz are rather suitable for communication between Earth stations and satellites, including ESIMs, reaching in a critical scenario uplink data rates of up to 2.6 Gbits/s with 0.5 GHz of bandwidth or up to 12 Gbits/s with 5 GHz of bandwidth in uplink. For the downlink, we can reach up to 6 Mbits/s with a transmitted power of 29 dBW, but if we increase the power transmitted by satellites, it is possible to reach up to 25 Gbits/s with 2.5GHz of bandwidth. Under clear, blue-sky conditions, we can achieve a maximum data rate of 17.3 Gbits/s for downlink and uplink. For inter-satellite links (communications between satellites in the same orbit or between different orbits), the frequency bands between 111.8 - 114.25 GHz, 116 - 123 GHz, 174.5 - 182 GHz, 185 - 190 GHz are viable, offering speeds from 1.5 to 2.51 Gbits/s when using a uniform rectangular array with 625 radiating elements. This research provides new findings from the amalgamation of existing literature, which is crucial for the future allocation of optimal frequencies between 100 - 300 GHz for satellite services.

Experimental investigations on an FSO-fiber converged communication system under fog and turbulence

This paper presents experimental results for a free-space optical-fiber converged (FSO-FC) communication system under varying turbulence and foggy conditions. Based on the experimental measurements, we statistically characterize the FSO channel under different levels of turbulence and fog in terms of their respective probability density functions (PDFs). Our experimental PDFs fit well with the theoretical PDFs proposed in the literature. We experimentally evaluate the average bit error rate (ABER) of the considered FSO-FC communication system under different levels of turbulence and fog. Further, to compensate for the effects of turbulence and fog, we use multimode fiber as the receiver and compare the results with single-mode fiber as the receiver. Finally, we show the improvement in ABER under varying turbulence and fog by adding cyclic redundancy check bits to data bits.

Hierarchical prior-guided quality assessment method for underwater images

Many underwater image enhancement (UIE) algorithms have been proposed to improve the quality of the underwater image. However, the lack of effective objective image quality assessment (IQA) methods has hindered the further development of UIE algorithms. Unlike traditional distorted images, underwater images usually contain natural distortions such as color cast and fog effects induced by light absorption and scattering. In addition, due to the complexity of underwater scenes, the enhanced results generated using existing UIE algorithms may have artificial distortions such as under-enhancement, over-enhancement, and detail loss. Thus, it is difficult for traditional IQA methods to comprehensively capture quality-related features in complicated underwater conditions. Based on this, this paper proposes a new hierarchical prior-guided quality assessment method for underwater images with the aid of deep learning technology, which first decomposes the underwater image into the base layer and detail layer. Then, the two layers are taken as input of feature extraction network for extracting the luminance information and texture information of the image, respectively. Meanwhile, two pseudo-reference images are used to guide model training so as to obtain luminance and texture features more accurately and efficiently. Finally, the two types of features are fused to obtain the quality score of the underwater image. Extensive experiments have been conducted on two publicly available underwater image datasets, and the experimental results show that the proposed method outperforms other underwater image quality assessment methods.

Fog Computing-Based Smart Consumer Recommender Systems

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC “smart” applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the “fog-based recommender systems” opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper.

The effect of fog on atmospheric electric fields

Naturally occurring electric fields exist in the atmosphere as part of the global electric circuit. These fields are produced by global phenomena, but they are strongly modified by local conditions. One such local effect is fog, which is the reduction in visibility by droplets suspended in the air. For over a century, it has been known that fog can often result in larger values of electric field compared to that in clear air conditions. However, the physical processes controlling the electric field and its variability in fog are still not fully understood. More detailed information on this is required to assess whether electric field measurements can be used as a fog prediction method, which was suggested many decades ago but is still an open question. Furthermore, improving the understanding of electrical processes in fog will also help in related areas of research such as cloud electrification processes and the impact of charge on the behaviour of aerosol and droplets.This study aims to fully characterise the electric field in fog using an extensive dataset of over 17 years, measured from the University of Reading Atmospheric Observatory, UK. This encompasses electric field, visibility, and meteorological data from over a hundred fog events, which allows us to examine the behaviour of electric fields in fog conditions statistically, rather than only focusing on individual case studies as is often the case in the literature. The long series of data from the site allows the variability in the electrical data during fog events to be more fully characterised than previously.

Bit Error Rate Performance in CDMA Free Space Optical Links with SIK Receiver under Fog Conditions

This article presents a novel analytical method for evaluating fog's impact on Bit Error Rate (BER) in an Optical Code Division Multiple Access (OCDMA) Free Space Optical (FSO) connection. The investigation employs an optical encoder and direct detection receiver employing a balanced photo detector with Sequence Inverse Keying (SIK) to explore fog effects. By scrutinizing multi-access interference (MAI) current and signal current values at the SIK receiver's output under varied fog conditions, we deduce BER expressions for diverse scenarios. These encompass different fog thickness levels, code lengths, simultaneous users, and system parameters. The results reveal substantial OCDMA degradation due to fog thickness and users. Also, the system incurs a marked power penalty at a specific BER, especially with higher fog and user density. However, we identify a potential solution: increasing code length, effectively mitigating fog-induced performance decline.

Wheel‐based 5G fronthaul/backhaul hybrid MDM‐NGPON/FSO system under Gamma‐Gamma turbulence and weather effects

SummaryThis work presents the bidirectional and symmetric 40 Gbps wheel architecture comprising 16 channels‐based bidirectional hybrid next generation passive optical network with free space optics (NGPON/FSO) system using mode division multiplexing (MDM) scheme. The system incorporates a low‐cost high bandwidth and integrated time and wavelength division multiplexing with orthogonal frequency division multiplexing (TWDM‐OFDM) for data transmission over mixed fiber‐FSO links to various end users at donut modes 0 and 1. This system enhances the fifth generation (5G)‐based networks reliability, survivability, and flexibility, especially in rural places where the core fiber link may be damaged/destroyed under diverse turbulent and weather conditions. The obtained results depict the faithful multimode fiber range of 100 km with fixed FSO range of 100 m at −17 dBm receiver sensitivity and low power penalty of 0.2 dB at 10−9 bit error rate. Maximum FSO range of 2800 m under weak‐to‐strong Gamma‐Gamma turbulence and the effect of fog, rain, snow, and snow with rain can be obtained successfully with fixed 50 km fiber length. Also, the proposed wheel architecture provides superior performance over hybrid ring‐mesh architecture with high split ratio of 1:64. Moreover, the system can sustain up to 70 remote nodes at symmetric 10 Gbps throughput providing high gain, high optical signal‐to‐noise ratio, and low noise figure. The comparative literature work also reveals that the proposed architecture offers high bandwidth, security, long‐reach, and cost‐effectiveness in 5G fronthaul/backhaul networks.

Visual-Based Children and Pet Rescue from Suffocation and Incidence of Hyperthermia Death in Enclosed Vehicles.

Over the past several years, many children have died from suffocation due to being left inside a closed vehicle on a sunny day. Various vehicle manufacturers have proposed a variety of technologies to locate an unattended child in a vehicle, including pressure sensors, passive infrared motion sensors, temperature sensors, and microwave sensors. However, these methods have not yet reliably located forgotten children in the vehicle. Recently, visual-based methods have taken the attention of manufacturers after the emergence of deep learning technology. However, the existing methods focus only on the forgotten child and neglect a forgotten pet. Furthermore, their systems only detect the presence of a child in the car with or without their parents. Therefore, this research introduces a visual-based framework to reduce hyperthermia deaths in enclosed vehicles. This visual-based system detects objects inside a vehicle; if the child or pet are without an adult, a notification is sent to the parents. First, a dataset is constructed for vehicle interiors containing children, pets, and adults. The proposed dataset is collected from different online sources, considering varying illumination, skin color, pet type, clothes, and car brands for guaranteed model robustness. Second, blurring, sharpening, brightness, contrast, noise, perspective transform, and fog effect augmentation algorithms are applied to these images to increase the training data. The augmented images are annotated with three classes: child, pet, and adult. This research concentrates on fine-tuning different state-of-the-art real-time detection models to detect objects inside the vehicle: NanoDet, YOLOv6_1, YOLOv6_3, and YOLO7. The simulation results demonstrate that YOLOv6_1 presents significant values with 96% recall, 95% precision, and 95% F1.

A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors.

In this work, we introduce a novel approach to model the rain and fog effect on the light detection and ranging (LiDAR) sensor performance for the simulation-based testing of LiDAR systems. The proposed methodology allows for the simulation of the rain and fog effect using the rigorous applications of the Mie scattering theory on the time domain for transient and point cloud levels for spatial analyses. The time domain analysis permits us to benchmark the virtual LiDAR signal attenuation and signal-to-noise ratio (SNR) caused by rain and fog droplets. In addition, the detection rate (DR), false detection rate (FDR), and distance error derror of the virtual LiDAR sensor due to rain and fog droplets are evaluated on the point cloud level. The mean absolute percentage error (MAPE) is used to quantify the simulation and real measurement results on the time domain and point cloud levels for the rain and fog droplets. The results of the simulation and real measurements match well on the time domain and point cloud levels if the simulated and real rain distributions are the same. The real and virtual LiDAR sensor performance degrades more under the influence of fog droplets than in rain.

Layered SOTIF Analysis and 3σ-Criterion-Based Adaptive EKF for Lidar-Based Multi-Sensor Fusion Localization System on Foggy Days

The detection range and accuracy of light detection and ranging (LiDAR) systems are sensitive to variations in fog concentration, leading to the safety of the intended functionality-related (SOTIF-related) problems in the LiDAR-based fusion localization system (LMSFLS). However, due to the uncontrollable weather, it is almost impossible to quantitatively analyze the effects of fog on LMSFLS in a realistic environment. Therefore, in this study, we conduct a layered quantitative SOTIF analysis of the LMSFLS on foggy days using fog simulation. Based on the analysis results, we identify the component-level, system-level, and vehicle-level functional insufficiencies of the LMSFLS, the corresponding quantitative triggering conditions, and the potential SOTIF-related risks. To address the SOTIF-related risks, we propose a functional modification strategy that incorporates visibility recognition and a 3σ-criterion-based variance mismatch degree grading adaptive extended Kalman filter. The visibility of a scenario is recognized to judge whether the measurement information of the LiDAR odometry is disturbed by fog. Moreover, the proposed filter is adopted to fuse the abnormal measurement information of the LiDAR odometry with IMU and GNSS. Simulation results demonstrate that the proposed strategy can inhibit the divergence of the LMSFLS, improve the SOTIF of self-driving cars on foggy days, and accurately recognize the visibility of the scenarios.

Asymptotic Ergodic Capacity Analysis for FSO Communication between Mobile Platforms in Maritime Environments

With the rapid development of wireless communication technologies, free-space optical (FSO) communication has gained much attention in recent years. The FSO technology, which is usually used for communication in terrestrial environments, is expanding its regime to communication in maritime environments. In this study, we analyze the ergodic capacity of FSO communication between mobile platforms in maritime environments under foggy conditions, pathloss, and pointing error. More specifically, based on the moment expressions, we derive the closed-form expression of asymptotic ergodic capacity using conventional detection techniques (i.e., heterodyne and intensity modulation/direct detection). The derived analytical results are cross-verified with simulation results via Monte Carlo simulations. The results show that the combined effects of fog with pathloss and pointing error degrade the FSO performance and that it is important to apply the appropriate detection technique according to the communication environment.

Accelerated Aging of Polymeric Composites Based on Waste with TiO2 Fillers

This study aimed to investigate the properties of composites based on polymeric waste with organic and inorganic fillers before and after accelerated aging for indoor or outdoor applications. The composites were obtained by compression molding from waste: tire rubber as a matrix, polyethene terephthalate (PET), and high-density polyethylene (HDPE) as organic fillers and TiO2 nanoparticles as inorganic fillers. The critical issue in ensuring good mechanical properties is the interface between the components of the composites. The effects of moisture, temperature, UV radiation, and salt fog on the mechanical properties of composites were evaluated. The modifications at the structural and molecular levels were evidenced by changes in FTIR, XRD spectra, contact angle measurements, and AFM analysis. To evaluate the durability performance of waste composites in this study, before and after accelerated aging samples were tested. The mechanical properties of the polymer composites with organic/inorganic filler materials depend on the interface between the organic–organic phase and/or the organic–inorganic phase, the processing temperature, and the characteristics of accelerated aging. Thus, the salt fog absorbed by the composite materials leads to a significant decrease in the values of the elasticity modules; the values of elongation at the break of the samples improve significantly with the decrease in the temperature of maintaining the samples; and the interactions between the polymer and the inorganic filler materials have the effect of increasing the energy of molecular cohesion and, implicitly, the thermal energy required to activate the thermal degradation mechanism.

폭염저감기술에 따른 외부공간의 기온 저감에 대한 연구 : 쿨링포그의 더위 저감효과 검증을 위한 실증실험

To provide Social Overhead Capital (SOC) for daily human lives, local governments are preparing various measures against heat waves, such as supplying Heat Wave Mitigation Facilities and supporting the vulnerable. Cooling Fog is a recent trend of Heat Wave Mitigation Facilities, but South Korea has limited data on reducing body temperature through direct application of cooling matter on the body, and most research on Cooling Fog has addressed technical aspects. In this study, to confirm the effect of Cooling Fog on the human body, experiments were conducted. First, the change of environmental factors in the space around the Cooling Fog was compared with an unaffected area. Second, a survey was conducted targeting people who use Cooling Fog to review its sensory effects. Finally, the effect of Cooling Fog was quantitatively verified by linking and analyzing related data. The air temperature and radiation temperature were lower in the area of Cooling Fog, but humidity was high. The sensory survey confirmed that Cooling Fog produced a sensation of cooler temperature compared to the area without Cooling Fog, as assessed using Thermal Sensation Vote (TSV), Heat Score, and Apparent Temperature (AT). In addition, the area of Cooling Fog was reported to have higher Thermal Comfort than the area without.

Spatial variability of throughfall in heavily fogged old-growth Fagus orientalis forests is controlled by fog precipitation and stand structural characteristics

Ecological impacts of the spatial heterogeneity of throughfall (TF) in forest ecosystems are recognized in ecohydrology. The objective of this study was to evaluate the spatial variability of TF and to quantify the effects of forest cover, fog, and gross rainfall (GR) characteristics on TF. Data were collected from May 2016 to November 2017 on an event basis in an intact, old-growth oriental beech (Fagus orientalis L.) stand in northern Iran. Over the measurement time period, 22 rainfall events were observed (total rain = 648 mm). Because the region experienced considerable fog, TF was greater than 100% of GR in 14 rainfall events with negative interception loss (total = -24.1 mm), causing low interception values, and 8 rainfall events were found with positive I (total = 25.5 mm). The low TF spatial variability and increasing TF depth observed in this study were related to increases in rainfall depth and intensity. TF was significantly and positively correlated with LAI and significantly and negatively correlated with canopy openness for rainfall events of more than 50 mm. The high amount of TF depth highlighted the role of LAI to enhance fog interception and the role of fog precipitation to increase TF in the study site. Spatial continuity at an effective range of 3-8 m was observed by variogram analyses. The increasing spatial continuity of TF with increasing GR observed in this study, along with the larger effective range for GR >50 mm and correspondingly similar LAI range, suggest an effect of LAI on the spatial patterning of TF for larger GR. Therefore, this heavily fogged ecosystem received considerable TF due to fog interception, and higher LAI increased TF for larger rainfall events. Our findings can optimize forest management practices by accounting for the effect of Fagus orientalis trees on the local hydrological processes of the Hyrcanian mountain forests.

AI based temperature reduction effect model of fog cooling for human thermal comfort: Climate adaptation technology

This study developed a gradient-boosted regression tree-based artificial intelligence (AI) model––temperature reduction effect AI model (TREAM)––to determine the temperature reduction effect of fog cooling that varies with weather conditions. According to the trend of global warming, our society is suffering from serious damage from urban heat islands, especially negatively affecting human health and thermal comfort. Therefore, it is very important to develop and evaluate adaptive technology for providing pleasant thermal comfort to humans. This study select fog cooling as adaptive technology for human thermal comfort, and indoor and outdoor simulation were performed using STAR CCM+, a computational fluid dynamics(CFD) program. Moreover, transient analysis to validate the outdoor model of CFD and parametric study to identify the correlation between the environmental factors and fog cooling were performed. When initial temperature set as 45 °C at a relative humidity of 90% and wind speed of 1 m/s, a temperature reduction of 8.92% can be obtained. Regardless of the temperature and humidity conditions, the temperature reduction effect of fog cooling was ∼1% if the wind speed increased above 5 m/s. This study contributed to the quantitative analysis of the temperature reduction effect according to the change of environmental factors.