Search Results (2,421)

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

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

The formation and evolution of haze layers in planetary atmospheres play a critical role in shaping their chemical composition, radiative balance, and optical properties. In the outer solar system, the atmospheres of Titan and the giant planets exhibit a wide range of compositional and seasonal variability, creating environments favorable for the production of complex organic molecules under low-temperature conditions. Among them, Uranus—the smallest of the ice giants—has, since Voyager 2, emerged as a compelling target for future exploration due to unanswered questions regarding the composition and structure of its atmosphere, as well as its ring system and diverse icy moon population (which includes four possible ocean worlds). Titan, as the only moon to harbor a dense atmosphere, presents some of the most complex and unique organics found in the solar system. Central to the production of these organics are chemical processes driven by low-energy photons and electrons (<50 eV), which initiate reaction pathways leading to the formation of organic species and gas phase precursors to high-molecular-weight compounds, including aerosols. These aerosols, in turn, remain susceptible to further processing by low-energy UV radiation as they are transported from the upper atmosphere to the lower stratosphere and troposphere where condensation occurs. In this review, I aim to summarize the current understanding of low-energy (<50 eV) photon- and electron-induced chemistry, drawing on decades of insights from studies of Titan, with the objective of evaluating the relevance and extent of these processes on Uranus in anticipation of future observational and in situ exploration. Full article

Air–sea interactions play a pivotal role in shaping cyclone development and evolution. In this context, this study investigates the role of ocean optical properties and solar radiation penetration in modulating subsurface heat content and their subsequent influence on the intensity of Mediterranean cyclones. Using a regional coupled ocean–wave–atmosphere model, we conducted sensitivity experiments for Storm Daniel (2023) comparing two solar radiation penetration schemes in the ocean model component: one with a constant light attenuation depth and another with chlorophyll-dependent attenuation based on satellite estimates. Results show that the chlorophyll-driven radiative heating scheme consistently produces warmer sea surface temperatures (SSTs) prior to cyclone onset, leading to stronger cyclones characterized by deeper minimum mean sea-level pressure, intensified convective activity, and increased rainfall. However, post-storm SST cooling is also amplified due to stronger wind stress and vertical mixing, potentially influencing subsequent local atmospheric conditions. Overall, this work demonstrates that ocean bio-optical processes can meaningfully impact Mediterranean cyclone behavior, highlighting the importance of using appropriate underwater light attenuation schemes and ocean color remote sensing data in coupled models. Full article

This study investigates the effects of key atmospheric features on mineral dust emissions and transport in the Sahara–Sahel region, focusing on the Bodélé Depression, during June 2006 and 2011. We use a combination of high-resolution atmospheric simulations (AROME model), satellite observations (MODIS), and reanalysis data (ERA5, ECMWF) to examine the roles of the low-level jet (LLJ), Saharan heat low (SHL), Intertropical Discontinuity (ITD), and African Easterly Jet (AEJ) in modulating dust activity. Our results reveal significant interannual variability in aerosol optical depth (AOD) between the two periods, with a marked decrease in June 2011 compared to June 2006. The LLJ emerges as a dominant factor in dust uplift over Bodélé, with its intensity strongly influenced by local topography, particularly the Tibesti Massif. The position and intensity of the SHL also play crucial roles, affecting the configuration of monsoon flow and Harmattan winds. Analysis of wind patterns shows a strong negative correlation between AOD and meridional wind in the Bodélé region, while zonal wind analysis emphasizes the importance of the AEJ and Tropical Easterly Jet (TEJ) in dust transport. Surprisingly, we observe no significant correlation between ITD position and AOD measurements, highlighting the complexity of dust emission processes. This study is the first to combine climatological context and case studies to demonstrate the effects of African monsoon variability on dust uplift at intra-seasonal timescales, associated with the modulation of ITD latitude position, SHL, LLJ, and AEJ. Our findings contribute to understanding the complex relationships between large-scale atmospheric features and dust dynamics in this key source region, with implications for improving dust forecasting and climate modeling efforts. Full article

Atmospheric and oceanic turbulence can severely degrade the orbital angular momentum (OAM) mode purity of vortex beams in cross-media optical links. Here, we propose a hybrid correction framework that fuses multiscale phase-screen modeling with a lightweight U-Net predictor for phase-distortion—driven solely by measured optical intensity—and augments it with a feed-forward, Gaussian-reference subtraction scheme for iterative compensation. In our experiments, this approach boosts the l = 3 mode purity from 38.4% to 98.1%. Compared to the Gerchberg–Saxton algorithm, the Gaussian-reference feed-forward method achieves far lower computational complexity and greater robustness, making real-time phase recovery feasible for OAM-based communications over heterogeneous channels. Full article

Petrochemical complexes are spatially expansive and host diverse emission sources, making accurate monitoring of volatile organic compounds (VOCs) challenging using conventional two-dimensional methods. This study introduces Mobile-extraction Differential Optical Absorption Spectroscopy (Me-DOAS), a real-time, three-dimensional remote sensing technique for assessing benzene emissions in the Ulsan petrochemical complex, South Korea. A vehicle-mounted Me-DOAS system conducted monthly measurements throughout 2024, capturing data during four daily intervals to evaluate diurnal variation. Routes included perimeter loops and grid-based transects within core industrial zones. The highest benzene concentrations were observed in February (mean: 64.28 ± 194.69 µg/m³; geometric mean: 5.13 µg/m³), with exceedances of the national annual standard (5 µg/m³) in several months. Notably, nighttime and early morning sessions showed elevated levels, suggesting contributions from nocturnal operations and meteorological conditions such as atmospheric inversion. A total of 179 exceedances (≥30 µg/m³) were identified, predominantly in zones with benzene-handling activities. Correlation analysis revealed a significant relationship between high concentrations and specific emission sources. These results demonstrate the utility of Me-DOAS in capturing spatiotemporal emission dynamics and support its application in exposure risk assessment and industrial emission control. The findings provide a robust framework for targeted management strategies and call for integration with source apportionment and dispersion modeling tools. 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

This study aimed to investigate the effect of wildfire episodes on air quality in terms of particulate matter (PM) and carbonaceous compound concentration in ambient air, and to assess deviations from typical annual patterns. The sampling was performed at a rural background site near the Adriatic coast in Croatia through 2024. To better understand contributions caused by fire events, the levels of organic carbon (OC), elemental carbon (EC), black carbon (BC), pyrolytic carbon (PyrC), optical carbon (OptC), water-soluble organic carbon (WSOC), levoglucosan (LG), mannosan (MNS), and galactosan (GA) were determined in PM₁₀ and PM_2.5 fractions (particles smaller than 10 µm and 2.5 µm, respectively). The annual mean concentrations of PM₁₀ and PM_2.5 were 14 µg/m³ and 8 µg/m³, respectively. During the fire episode, the PM_2.5 mass contribution to the total PM₁₀ mass exceeded 65%. Total carbon (TC) and OC increased by a factor of 7, EC and BC by 12, PyrC by 8, and WSOC by 12. The concentration of LG reached 1.219 μg/m³ in the PM₁₀ fractions and 0.954 μg/m³ in the PM_2.5 fractions, representing a 200-fold increase during the fire episode. Meteorological data were integrated to assess atmospheric conditions during the fire episode, and the specific ratios between fire-related compounds were analyzed. Full article

The spatial heterodyne spectrometer is an interferometric spectrometer specifically designed for particular detection targets, capable of achieving ultra-high spectral resolution within a designated spectral range. As the demand for signal detection accuracy continues to increase, the extraction of accurate target spectra from spatial heterodyne interferograms has become increasingly important. This paper applies a deep neural network to the spectral reconstruction of specific spatial heterodyne interferograms. The spectral reconstruction model, SRDNN, was trained using CO₂ data simulated by the SCIATRAN radiative transfer model and the principles of spatial heterodyne spectroscopy. The results indicate that SRDNN has excellent CO₂ spectral reconstruction performance, with an evaluation index $R^2$ of 0.9943 and an $MSE$ of 0.00021. The average difference between the reconstructed spectra and the target spectra is only 0.371%. Furthermore, the method was further validated using experimental data obtained from a spatial heterodyne spectrometer. The remarkable spectral reconstruction results and excellent evaluation indicators once again demonstrated the universality and effectiveness of the method. Finally, the robustness of the method was studied using noisy experimental data. The results demonstrate that the method can accurately reconstruct spectra from interferograms with slight noise without requiring additional processing, simplifying the spectral reconstruction process. This work is expected to provide novel methods and effective solutions for the spectral reconstruction of specific targets detected by spatial heterodyne spectrometers. Full article

Ultrahigh resolution fine particulate matter (PM_2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate 30 m resolution PM_2.5 mass concentrations over urban areas from Landsat-8 and Sentinel-2A/B satellite measurements. The algorithm utilized aerosol optical depth (AOD) products retrieved from the Landsat-8 OLI and Sentinel-2 MSI measurements from 2017 to 2020, combined with multi-source auxiliary data to establish a PM_2.5-AOD relationship model across China. The results showed an overall high coefficient of determination (R²) of 0.82 and 0.76 for the model training accuracy based on samples and stations, respectively. The model prediction accuracy in Beijing and Wuhan reached R² values of 0.86 and 0.85. Applications in both cities demonstrated that ultrahigh resolution PM_2.5 has significant advantages in resolving fine-scale spatial patterns of urban air pollution and pinpointing pollution hotspots. Furthermore, an analysis of point source pollution at a typical heavy pollution emission enterprise confirmed that ultrahigh spatial resolution PM_2.5 can accurately identify the diffusion trend of point source pollution, providing fundamental data support for refined monitoring of urban air pollution and air pollution prevention and control. Full article

The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions. Full article

Background/Objectives: As bacteriophage-based strategies to control bacterial pathogens continue to gain momentum, phage therapy is increasingly being explored across various fields. In the poultry industry, efforts to minimize the public health impact of Salmonella have spurred growing interest in phage applications, particularly as prophylactic and disinfecting agents. Although the disinfecting potential of bacteriophages has been recognized, in-depth studies examining their efficacy under varying environmental conditions remain limited. This study focused on evaluating the effectiveness of bacteriophages as disinfecting agents against biofilm-forming Salmonella Infantis under different environments. Methods: A comprehensive screening of biofilm-producing strains was conducted using Congo Red Agar and 96-well plate assays. Two strains with distinct biofilm-forming capacities were selected for further analysis under different environmental conditions: aerobic and microaerobic atmospheres at both 25 °C and 37 °C. The resulting biofilms were then treated with four phage preparations: three individual phages and one phage cocktail. Biofilm reduction was assessed by measuring optical density and CFU/well. Additionally, scanning electron microscopy was used to visualize both untreated and phage-treated biofilms. Results: The results demonstrated that all S. Infantis strains were capable of forming biofilms (21/21). All three phage candidates exhibited biofilm-disrupting activity and were able to lyse biofilm-embedded Salmonella cells. Notably, the lytic efficacy of the phages varied depending on environmental conditions, highlighting the importance of thorough phage characterization prior to application. Conclusions: These findings underscore that the effectiveness of bacteriophages as surface disinfectants can be significantly compromised if inappropriate phages are used, especially in the presence of biofilms. Full article

100 nm thick TiO₂/TiN bilayers with varying thickness ratios were deposited via reactive sputtering of a Ti target in controlled oxygen and nitrogen atmospheres. Post-deposition annealing in air at 600 °C was performed to induce nitrogen diffusion through the oxygen-deficient TiO₂ layer. The resulting changes in morphology and chemical environment were investigated in detail using transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and UV-Vis spectroscopy. Detailed TEM and XPS analyses have confirmed nitrogen diffusion across the TiO₂ layer, with surface nitrogen concentration and the ratio of interstitial to substitutional nitrogen dependent on the TiO₂/TiN mass ratio. Optical studies demonstrated modifications in optical constants and a reduction of the effective bandgap from 3.2 eV to 2.6 eV due to new energy states introduced by nitrogen doping. Changes in surface free energy induced by nitrogen incorporation showed a correlation to nitrogen doping sites on the surface, which had positive effects on overall photocatalytic activity. Photocatalytic activity, assessed through methylene blue degradation, showed enhancement attributed to nitrogen doping. Additionally, deposition of a 5 nm gold layer on the annealed sample enabled investigation of synergistic effects between nitrogen doping and gold incorporation, resulting in further improved photocatalytic performance. These findings establish the TiO₂/TiN bilayer as a versatile platform for supporting thin gold films with enhanced photocatalytic properties. Full article

The microstructure and mechanical properties of welded joints of API 5L X-52 steel plates cladded with AISI 316L-Si austenitic stainless steel were evaluated. The gas metal arc welding process with pulsed arc (GMAW-P) and controlled arc oscillation were used to join the bimetallic plates. After the root welding pass, buttering with an ERNiCrMo-3 filler wire was performed and multi-pass welding followed using an ER70S-6 electrode. The results obtained by optical and scanning electron microscopy indicated that the shielding atmosphere, welding parameters, and electric arc oscillation enabled good arc stability and proper molten metal transfer from the filler wire to the sidewalls of the joint during welding. Vickers microhardness (HV) and tensile tests were performed for correlating microstructural and mechanical properties. The mixture of ERNiCrMo-3 and ER70S-6 filler materials presented fine interlocked grains with a honeycomb network shape of the Ni–Fe mixture with Ni-rich grain boundaries and a cellular-dendritic and equiaxed solidification. Variation of microhardness at the weld metal (WM) in the middle zone of the bimetallic welded joints (BWJ) is associated with the manipulation of the welding parameters, promoting precipitation of carbides in the austenitic matrix and formation of martensite during solidification of the weld pool and cooling of the WM. The BWJ exhibited a mechanical strength of 380 and 520 MPa for the yield stress and ultimate tensile strength, respectively. These values are close to those of the as-received API 5L X-52 steel. Full article