Search Results (912)

Free-space optical (FSO) communications have emerged as a promising complement to conventional radio-frequency (RF) systems due to their high bandwidth, low interference, and license-free spectrum. Visible-light FSO communication, using laser diodes or LEDs, offers potential for short-range data links, but performance is highly wavelength-dependent under varying atmospheric conditions. This study presents an experimental evaluation of three visible laser diodes at 650 nm (red), 532 nm (green), and 405 nm (violet), focusing on their optical output power, quantum efficiency, and modulation behavior across a range of driving currents and frequencies. A custom laboratory testbed was developed using an Atmega328p microcontroller and a Visual Basic control interface, allowing precise control of current and modulation frequency. A silicon photovoltaic cell was employed as the optical receiver and energy harvester. The results demonstrate that the 650 nm red laser consistently delivers the highest quantum efficiency and optical output, with stable performance across electrical and modulation parameters. These findings support the selection of 650 nm as the most energy-efficient and versatile wavelength for short-range, cost-effective visible-light FSO communication. This work provides experimentally grounded insights to guide wavelength selection in the development of energy-efficient optical wireless systems. Full article

Produce supply chains play a critical role in ensuring fruits and vegetables reach consumers efficiently, affordably, and at optimal freshness. In recent decades, hub-and-spoke network models have emerged as valuable tools for optimizing sustainable cold food supply chains. Traditional optimization efforts typically focus on removing inefficiencies, minimizing lead times, refining inventory management, strengthening supplier relationships, and leveraging technological advancements for better visibility and control. However, the majority of models rely on deterministic approaches that overlook the inherent uncertainties of crop yields, which are further intensified by climate variability. Rising atmospheric CO₂ concentrations, along with shifting temperature patterns and extreme weather events, have a substantial effect on crop productivity and availability. Such uncertainties can prompt distributors to seek alternative sources, increasing costs due to supply chain reconfiguration. This research introduces a stochastic hub-and-spoke network optimization model specifically designed to minimize transportation expenses by determining optimal distribution routes that explicitly account for climate variability effects on crop yields. A use case involving a cold food supply chain (CFSC) was carried out using several weather scenarios based on climate models and real soil data for California. Strawberries were selected as a representative crop, given California’s leading role in strawberry production. Simulation results show that scenarios characterized by increased rainfall during growing seasons result in increased yields, allowing distributors to reduce transportation costs by sourcing from nearby farms. Conversely, scenarios with reduced rainfall and lower yields require sourcing from more distant locations, thereby increasing transportation costs. Nonetheless, supply chain configurations may vary depending on the choice of climate models or weather prediction sources, highlighting the importance of regularly updating scenario inputs to ensure robust planning. This tool aids decision-making by planning climate-resilient supply chains, enhancing preparedness and responsiveness to future climate-related disruptions. Full article

The time series prediction of visibility in terms of various meteorological variables, such as relative humidity, temperature, atmospheric pressure, and wind speed, is presented in this paper using Single-Variable Regression Analysis (SVRA), Artificial Neural Network (ANN), and Hybrid Adaptive Neuro-fuzzy Inference System (ANFIS) techniques for several sub-tropical locations. The initial method used for the prediction of visibility in this study was the SVRA, and the results were enhanced using the ANN and ANFIS techniques. Throughout the study, neural networks with various algorithms and functions were trained with different atmospheric parameters to establish a relationship function between inputs and visibility for all locations. The trained neural models were tested and validated by comparing actual and predicted data to enhance visibility prediction accuracy. Results were compared to assess the efficiency of the proposed systems, measuring the root mean square error (RMSE), coefficient of determination (R²), and mean bias error (MBE) to validate the models. The standard statistical technique, particularly SVRA, revealed that the strongest functional relationship was between visibility and RH, followed by WS, T, and P, in that order. However, to improve accuracy, this study utilized back propagation and hybrid learning algorithms for visibility prediction. Error analysis from the ANN technique showed increased prediction accuracy when all the atmospheric variables were considered together. After testing various neural network models, it was found that the ANFIS model provided the most accurate predicted results, with improvements of 31.59%, 32.70%, 30.53%, 28.95%, 31.82%, and 22.34% over the ANN for Durban, Cape Town, Mthatha, Bloemfontein, Johannesburg, and Mahikeng, respectively. The neuro-fuzzy model demonstrated better accuracy and efficiency by yielding the finest results with the lowest RMSE and highest R² for all cities involved compared to the ANN model and standard statistical techniques. However, the statistical performance analysis between measured and estimated visibility indicated that the ANN produced satisfactory results. The results will find applications in Optical Wireless Communication (OWC), flight operations, and climate change analysis. Full article

To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retinex theory and the dark channel prior is proposed in this paper. The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. At the same time, the atmospheric light intensity is accurately calculated by the Otsu algorithm and edge constraints, which effectively suppresses the halo artifacts and color deviation of the sky region in the dark channel a priori defogging algorithm. The experiments based on self-collected data and public datasets show that the algorithm in this paper presents better detail preservation ability (the visible edge ratio is minimally improved by 0.1305) and color reproduction (the saturated pixel ratio is reduced to about 0) in the subjective evaluation, and the average gradient ratio of the objective indexes reaches a maximum value of 3.8009, which is improved by 36–56% compared with the classical DCP and Tarel algorithms. The method provides a robust image defogging solution for computer vision systems under complex meteorological conditions. Full article

Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. This study presents a novel algorithm to retrieve day and night CBH using infrared observations of the geostationary Advanced Himawari Imager (AHI). The algorithm is featured by integrating deep learning techniques with a physical model. The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. Then, a physical model is introduced to relate cloud geometric thickness (CGT) to CWP by constructing a look-up table of effective cloud water content (ECWC). Thus, the CBH can be obtained by subtracting CGT from CTH. The results demonstrate good agreement between our AHI CBH retrievals and the spaceborne active remote sensing measurements, with a mean bias of −0.14 ± 1.26 km for CloudSat-CALIPSO observations at daytime and −0.35 ± 1.84 km for EarthCARE measurements at nighttime. Additional validation against ground-based millimeter wave cloud radar (MMCR) measurements further confirms the effectiveness and reliability of the proposed algorithm across varying atmospheric conditions and temporal scales. Full article

The conversion of CO₂ into CH₄ through the Sabatier reaction is one of the key processes that can reduce CO₂ emissions into the atmosphere. This work aims to develop Ni-Al aerogel-based thermo-photocatalysts with large specific surface areas prepared using a sol–gel method and subsequent supercritical drying in CO₂. Different Al/Ni molar ratios were selected for the development of the catalysts, characterized using ICP-OES, N₂ adsorption–desorption isotherms, XRD, H₂-TPR, TEM, UV-Vis DRS, and XPS techniques. Thermo-photocatalytic activity tests were performed in a photoreactor with two different light sources (λ = 365 nm, λ = 470 nm) at a temperature range from 300 °C to 450 °C and a pressure of 10 bar. The catalyst with the highest Ni loading (AG 1/3) produced the best catalytic results, reaching CO₂ conversion and CH₄ selectivity levels of 82% and 100%, respectively, under visible light at 450 °C. In contrast, the catalysts with the lowest nickel loading produced the lowest results, most likely due to their low amounts of active Ni. These results suggest that supercritical drying is an efficient method for developing active thermo-photocatalysts with high Ni dispersion, suitable for Sabatier reactions under mild reaction conditions. Full article

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions. Full article

Nanoscale zinc sulfide (ZnS) powders have attracted considerable interest due to their unique properties and diverse applications in various fields, including wastewater treatment, optics, electronics, photocatalysis, and solar systems. In this study, nano-powder ZnS was chemically synthetized starting from Zn powder, diluted HCl, and laboratory-prepared Na2S. The obtained ZnS was studied using an SEM coupled with EDS, XRD analysis, UV–Visible spectroscopy, and FTIR techniques. The XRD results showed that the synthesized nanoscale ZnS powder was approximately 2.26 nm. Meanwhile, the EDS and XRD patterns confirmed the high purity of the obtained ZnS powder. In addition, the ZnS powder was compacted and sintered in an argon atmosphere at 400 °C for 8 h to prepare the required pellets for thin-film deposition via E-beam evaporation. The microscopic structure of the sintered pellets was investigated using the SEM/EDS. Furthermore, the optical properties of the deposited thin films were studied using UV–Visible spectroscopy in the wavelength range of 190–1100 nm and the FTIR technique. The bandgap energies of the deposited thin films with thicknesses of 111 nm and 40 nm were determined to be around 4.72 eV and 5.82 eV, respectively. This article offers a facile production route of high-purity ZnS powder, which can be compacted and sintered as a suitable source for thin-film deposition. Full article

Large-scale magnetic flux ropes (MFRs) usually become visible during an eruption and are the core structures of coronal mass ejections, but the nature of MFRs is still a mystery. Here, we identify a large transequatorial MFR that spans across NOAA 13373 (in the Northern Hemisphere) and NOAA 13374 (in the Southern Hemisphere). Here, NOAA 13373 is a growing, newly emerging active region with a leading sunspot moving rapidly to the southwest, and it is surrounded by a highly dynamic moving magnetic feature (MMF), while NOAA 13374 is a decaying active region with a tiny leading negative sunspot and a large fading area. Recurrent reconnection, which occurs under the MFRs around the leading sunspot of NOAA 13373, results in local energy release, appearing as local EUV brightening, and it is related to the appearance of a transequatorial MFR. The appearance of this MFR involves several stages: EUV brightening, the slow rising and expansion of the MFR and its hosted filament, and, eventually, fading and shrinking. These observations demonstrate that a large-scale MFR can exist for a long-term period and that MMFs play a key role in building up free energy and triggering small-scale reconnections in the lower atmosphere. The energy released by these reconnection events is insufficient for triggering the eruption of an MFR but results in local disturbances. Full article

Visible light communication (VLC) is considered to be one of the key technologies for advancing the next-generation intelligent transportation systems (ITSs). However, in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) VLC, the line-of-sight (LOS) link for communication is often obstructed by vehicle mobility. To address this issue and enhance system performance, a novel V2I–VLC system is proposed and analyzed in this study. The system targets mountain road traffic scenarios employing optical reflecting intelligent surfaces (RISs). To emphasize the practicality of the study, the effects of atmospheric turbulence (AT) and weather conditions are also considered in the channel modeling. Further, the closed-form expressions for average path loss, channel capacity, and outage probability are derived. Furthermore, a novel closed-form expression is also derived for the properties of RIS, which can be used to calculate the required number of RIS elements to achieve a target energy efficiency. In the performance analysis, the accuracy of the derived theoretical expression is validated by numerical simulation, and the effectiveness of the RIS-aided V2I–VLC system is evaluated. Moreover, with a reasonable number of required RIS elements, the system performance in terms of path loss is improved by more than 23.5% on average over the existing studies. Full article

Mangrove ecosystems are crucial coastal habitats that support life and regulate the Earth’s atmosphere. However, these ecosystems face prominent threats due to anthropogenic activities and environmental constraints. For instance, the Saudi Arabian coast is particularly vulnerable to species extinction and biodiversity loss due to the fragility of the ecosystem; this highlights the need to understand the spatial and temporal dynamics of mangrove forests in desert environments. Hence, this is the first national study to quantify mangrove forests and analyze at-risk zone-based land use along Saudi Arabian coasts over 40 years. Thus, the primary contents of this research were (1) to produce a new long-term dataset covering the entire Saudi coastline, (2) to identify the patterns, analyze the trends, and quantify the change of mangrove areas, and (3) to determine vulnerability zoning of mangrove area-based land use and transportation networks. This study used Landsat satellite imagery via Google Earth Engine for national-scale mangrove mapping of Saudi Arabia between 1985 and 2024. Visible and infrared bands and seven spectral indices were employed as input features for the random forest classifier. The two classes used were mangrove and non-mangrove; the latter class included non-mangrove land-use and land-cover areas. Then, the study employed the output mangrove mapping to delineate vulnerable mangrove forest-based land use. The overall results showed a substantial increase in mangrove areas, ranging from 27.74 to 59.31 km² in the Red Sea and from 1.05 to 8.65 km² in the Arabian Gulf between 1985 and 2024, respectively. However, within this decadal trend, there were noticeable periods of decline. The spatial coverage of mangroves was larger on Saudi Arabia’s western coasts, especially the southwestern coasts, than on its eastern coasts. The overall accuracy, conducted annually, ranged between 91.00% and 98.50%. The results also show that expanding land uses and transportation networks within at-risk zones of mangrove forests may have a high potential effect. This study aimed to benefit the government, conservation agencies, coastal planners, and policymakers concerned with the preservation of mangrove habitats. Full article

Nickel (Ni) ultrathin films exhibit phase-dependent electrical, magnetic, and optical characteristics that are significantly influenced by deposition methods. However, these films are inherently prone to rapid oxidation, with the oxidation rate dependent on substrate, temperature, and deposition parameters. The focus of this research is to investigate the temporal oxidation of RF-sputtered Ni ultrathin films on Corning glass under ambient atmospheric conditions and its impact on their structural, surface, and optical characteristics. Controlled film thicknesses were achieved through precise manipulation of deposition parameters, enabling the analysis of oxidation-induced modifications. Atomic force microscopy (AFM) revealed that films with high structural integrity and surface uniformity are exhibiting roughness values (Rq) from 0.679 to 4.379 nm of corresponding thicknesses ranging from 4 to 85 nm. Scanning electron microscopy (SEM) validated the formation of Ni grains interspersed with NiO phases, facilitating SPR-like effects. UV-visible spectroscopy is demonstrating thickness-dependent spectral (plasmonic peak) shifts. Finite Difference Time Domain (FDTD) simulations corroborate the observed thickness-dependent optical absorbance and the resultant shifts in the absorbance-induced plasmonic peak position and bandgap. Increased NiO presence primarily drives the enhancement of electromagnetic (EM) field localization and the direct impact on power absorption efficiency, which are modulated by the tunability of the plasmonic peak position. Our work demonstrates that controlled fabrication conditions and optimal film thickness selection allow for accurate manipulation of the Ni oxidation process, significantly altering their optical properties. This enables the tailoring of these Ni films for applications in transparent conductive electrodes (TCEs), magneto-optic (MO) devices, spintronics, wear-resistant coatings, microelectronics, and photonics. Full article

The sterility of ophthalmic drugs is a fundamental requirement for ensuring patient safety, and as such, it is subject to stringent regulatory standards. However, significant gaps remain regarding the effect of sterilization techniques on the impurity profile and relative content of active pharmaceutical ingredients (API). Previous research involving a set of five APIs used in ophthalmic preparations (dexamethasone, methylprednisolone, aciclovir, tetracycline hydrochloride, and triamcinolone) demonstrated that gamma irradiation led to the formation of specific impurities in the corticosteroids, dexamethasone and methylprednisolone. This study aims to further explore the effect of both gamma and electron beam (E-beam) irradiation on the impurity profiles of these APIs under varying conditions, with and without dry ice. The analyses were conducted using high-performance liquid chromatography with ultraviolet/visible light (UV/VIS) detection and the effect of sterilization conditions was assessed in accordance with the assay and related substances test outlined in the European Pharmacopoeia (Ph. Eur.). Additionally, this study investigated whether exposure in a controlled atmosphere with reduced oxygen or water content could mitigate the formation of impurities and influence the stability of the compounds. The results indicated a protective effect of low-temperature and low-oxygen environments during both gamma and E-beam irradiation but no effect of dry conditions. Full article

The Earth’s degassing is an important factor in evaluating global carbon budget estimates and understanding the carbon cycle. As a result, numerous studies have focused on this topic. However, current estimates predominantly focus on subaerial CO₂ emissions and CO₂ deep submarine emissions, particularly along mid-ocean ridges (MORs), whereas very few and only spatially limited estimates of shallow submarine CO₂ emissions have been reported, despite being widespread features of the seafloor. This study reports the results of measuring the dissolved CO₂ concentrations in shallow submarine environments along the coast of Vulcano Island (Aeolian Islands, Italy). For the areas exhibiting the highest concentrations, we calculated the amount of diffuse degassing by computing the sea–air CO₂ flux. The results revealed extremely high dissolved CO₂ concentrations, reaching up to 24 vol.% in areas with visible hydrothermal activity, including one location far from the island’s main crater. Notably, elevated CO₂ levels were also detected in areas with minimal or no apparent hydrothermal discharge, indicating the occurrence of diffuse degassing processes in these areas. In addition, the calculated diffuse degassing flux was comparable in magnitude to the CO₂ flux directly emitted into the atmosphere from the island’s main bubbling pools. Full article

This paper presents a new high-speed RGB image transmission system over Free-Space Optics (FSO) channel employing Optical Code Division Multiple Access (OCDMA) with Permutation Vector (PV) codes. Four RGB images are transmitted simultaneously at 10 Gbps per image, achieving a total capacity of 40 Gbps. The system’s performance is evaluated under various atmospheric conditions, including three fog levels and real-world visibility data from Alexandria city, Egypt. Image Quality Assessment (IQA) metrics, including Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), correlation coefficients, and Structural Similarity Index Measure (SSIM), are evaluated for both unfiltered and median-filtered images. The results show significant degradation in image quality due to transmission distance and atmospheric attenuation. In Alexandria’s clear atmospheric conditions, the system achieves a maximum transmission range of 15 km with acceptable visual quality, while the range is reduced to 2.6 km, 1.6 km, and 1 km for Low Fog (LF), Medium Fog (MF), and Heavy Fog (HF), respectively. At these distances, the RGB images achieve minimum SNR, RMSE, and SSIM values of 7.27 dB, 47.66, and 0.20, respectively, with further improvements when applying median filtering. Full article