Search Results (1,283)

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. While the 1° high-resolution global TEC model released by MIT offers improved temporal-spatial resolution, it exhibits regions of data gaps. Existing ionospheric image completion methods frequently employ Generative Adversarial Networks (GANs), which suffer from drawbacks such as complex model structures and lengthy training times. We propose a novel high-resolution global ionospheric TEC modeling method based on a Dual-Branch Convolutional Neural Network (DB-CNN) designed for the completion and restoration of incomplete 1°-resolution ionospheric TEC images. The novel model utilizes a dual-branch input structure: the background field, generated using the International Reference Ionosphere (IRI) model TEC maps, and the observation field, consisting of global incomplete TEC maps coupled with their corresponding mask maps. An asymmetric dual-branch parallel encoder, feature fusion, and residual decoder framework enables precise reconstruction of missing regions, ultimately generating a complete global ionospheric TEC map. Experimental results demonstrate that the model achieves Root Mean Square Errors (RMSE) of 0.30 TECU and 1.65 TECU in the observed and unobserved regions, respectively, in simulated data experiments. For measured experiments, the RMSE values are 1.39 TECU and 1.93 TECU in the observed and unobserved regions. Validation results utilizing Jason-3 altimeter-measured VTEC demonstrate that the model achieves stable reconstruction performance across all four seasons and various time periods. In key-day comparisons, its STD and RMSE consistently outperform those of the CODE global ionospheric model (GIM). Furthermore, a long-term evaluation from 2021 to 2024 reveals that, compared to the CODE model, the DB-CNN achieves average reductions of 38.2% in STD and 23.5% in RMSE. This study provides a novel dual-branch input convolutional neural network-based method for constructing 1°-resolution global ionospheric products, offering significant application value for enhancing GNSS positioning accuracy and space weather monitoring capabilities. Full article

Space weather exerts a significant influence on the Earth’s atmosphere, driving a variety of physical processes, including the production of cosmogenic radionuclides. Among these, ⁷Be is a naturally occurring radionuclide formed through spallation reactions induced by cosmic-ray showers interacting with atmospheric constituents, primarily oxygen and nitrogen. Over long timescales, the atmospheric concentration of ⁷Be exhibits a direct correlation with the cosmic-ray flux reaching the Earth and an inverse correlation with solar activity, which modulates this flux via variations of the heliosphere. The large availability of ⁷Be concentration data, resulting from its use as a natural tracer employed in atmospheric transport studies and in monitoring the fallout from radiological incidents such as the Chernobyl disaster, can also be exploited to investigate the impact of space weather conditions on the terrestrial atmosphere and related geophysical processes. The present study analyzes a long-term dataset of monthly ⁷Be activity concentrations in air samples collected at ground level since 1987 at the ENEA Casaccia Research Center in Rome, Italy. In particular, the linear correlation of this time series with the galactic cosmic ray flux on Earth and solar activity have been investigated. Data from a ground-based neutron monitor and sunspot numbers have been used as proxies for galactic cosmic rays and solar activity, respectively. A centered running-mean low-pass filter was applied to the monthly ⁷Be time series to extract its low-frequency component associated with cosmic drivers, which is partially hidden by high-frequency modulations induced by atmospheric dynamics. For Solar Cycles 22, 23, 24, and partially 25, the analysis shows that a substantial portion of the relationship between stratospheric ⁷Be concentrations and cosmic drivers is captured by linear correlation. Within a statistically consistent framework, the evidence supports a correlation between ⁷Be and cosmic drivers consistent with solar-cycle variability. The ⁷Be radionuclide can therefore be regarded as a reliable atmospheric tracer of cosmic-ray variability and, indirectly, of solar modulation. Full article

In the context of ongoing global warming and urbanization, the need for reliable temperature monitoring in urban areas is increasing. Such monitoring serves multiple purposes, including assessing urban heat island (UHI) intensity, evaluating climate adaptation strategies, and supporting heat warning systems. This study utilizes high-resolution urban climate simulations with PALM-4U for calm, clear-sky summer weather conditions and an idealized model domain. The domain represents a dense midrise urban district in Dresden Neustadt, eastern Germany. Areas with air temperatures representative of the pedestrian level within the urban development are determined using a methodology based on a 24-h temporal moving representativity range defined by the temperature’s spatial median value and standard deviation. The method is extended by an evaluation of different temperature sensor heights, addressing practical considerations such as vandalism prevention and space availability. The results highlight the feasibility of representative pedestrian-level air temperature monitoring in densely built-up urban areas, particularly at elevated sensor heights between 2.5 and 6.5 m. It is found that higher sensor heights increase the area suitable for representative pedestrian-level temperature monitoring by up to about 50%. The sensitivity of the results to variations in wind speed and building height is also examined, demonstrating the robustness of the proposed method in clear, calm summer weather conditions. Full article

As contemporary society and the global economy become increasingly dependent on satellite-based systems, the need for reliable and resilient positioning, navigation, and timing (PNT) services has never been more critical. This study investigates the impact of the geomagnetic storm that occurred in May 2024 on the performance of global navigation satellite system (GNSS) low-cost permanent stations. The research evaluates the influence of ionospheric disturbances on both positioning performance and raw GNSS observations. Two days were analyzed: 8 May 2024 (DOY 129), representing quiet ionospheric conditions, and 11 May 2024 (DOY 132), coinciding with the peak of the geomagnetic storm. Precise Point Positioning (PPP) and static relative positioning techniques were applied to data from a low-cost GNSS station (DYVA), supported by comparative analysis using a nearby geodetic-grade station (TRDS00NOR). The results showed that while RMS positioning errors remained relatively stable over 24 h, the maximum errors increased significantly during the storm, with the 3D positioning error nearly doubling on DOY 132. Short-term analysis revealed even larger disturbances, particularly in the vertical component, which reached up to 3.39 m. Relative positioning analysis confirmed the vulnerability of single-frequency (L1) solutions to ionospheric disturbances, whereas dual-frequency (L1+L2) configurations substantially mitigated errors, highlighting the effectiveness of ionosphere-free combinations during storm events. In the second phase, raw GNSS observation quality was assessed using detrended GPS L1 carrier-phase residuals and signal strength metrics. The analysis revealed increased phase instability and signal degradation on DOY 132, with visible cycle slips occurring between epochs 19 and 21. Furthermore, the average signal-to-noise ratio (SNR) decreased by approximately 13% for satellites in the northwest sky sector, and a 5% rise in total cycle slips was recorded compared with the quiet day. These indicators confirm the elevated measurement noise and signal disruption associated with geomagnetic activity. These findings provide a quantitative assessment of low-cost GNSS receiver performance under geomagnetic storm conditions. This study emphasizes their utility for densifying GNSS infrastructure, particularly in regions lacking access to geodetic-grade equipment, while also outlining the challenges posed by space weather. Full article

The Qinghai-Tibet Plateau, known as the “third pole” of the Earth with an average elevation of approximately 4500 m, offers a unique natural laboratory for probing the dynamic behavior of the global electric circuit. In this study, we conduct a comprehensive analysis of near-surface vertical atmospheric electric field (AEF) measurements collected at the Gar Station (80.1° E, 32.5° N; 4259 m a.s.l.) on the western Tibetan Plateau, spanning the period from November 2021 to December 2024. Fair-weather conditions are imposed. The annual mean AEF at Gar is ∼0.331 kV/m, significantly higher than values observed at lowland and plain sites, indicating a pronounced enhancement in atmospheric electricity associated with high-altitude conditions. Moreover, the AEF exhibits marked seasonal variability, peaking in December (∼0.411–0.559 kV/m) and valleying around July–August (∼0.150–0.242 kV/m), yielding an overall amplitude of approximately 0.3 kV/m. We speculate that this seasonal pattern is primarily driven by variations in aerosol concentration. During winter, increased aerosol loading from residential heating and vehicle emissions due to incomplete combustion reduces atmospheric conductivity by depleting free ions and decreasing ion mobility, thereby enhancing the near-surface AEF. In contrast, lower aerosol concentrations in summer lead to weaker AEF. This seasonal decline in aerosol levels is likely facilitated by stronger winds and more frequent rainfall in summer, which enhance aerosol dispersion and wet scavenging, whereas weaker winds and limited precipitation in winter favor near-surface aerosol accumulation. On diurnal timescales, the Gar AEF curve deviates significantly from the classical Carnegie curve, showing a distinct double-peak and double-trough structure, with maxima at ∼03:00 and 14:00 UT and minima near 00:00 and 10:00 UT. This deviation may partly reflect local influences related to sunrise and sunset. This study presents the longest ground-based AEF observations over the Qinghai–Tibet Plateau, providing a unique reference for future studies on altitude-dependent AEF variations and their coupling with space weather and climate processes. Full article

The Yunyao Aerospace Constellation Program is the core project being developed by Yunyao Aerospace Technology Co., Ltd., Tianjin, China. It aims to provide scientific data for weather forecasting, as well as research on the ionosphere and neutral atmosphere. It is expected to launch 90 high time resolution weather satellites. Currently, the Yunyao space constellation provides nearly 16,000 BDS, GPS, GLONASS, and Galileo multi-system occultation profile products on a daily basis. This study initially calculates the precise orbits of Yunyao LEO satellites independently using each GNSS constellation, allowing the derivation of the neutral atmospheric refractive index profile. The precision of the orbit product was evaluated by comparing carrier-phase residuals (ranging from 1.48 cm to 1.68 cm) and overlapping orbits. Specifically, for GPS-based POD, the average 3D overlap accuracy was 4.93 cm, while for BDS-based POD, the average 3D overlap accuracy was 5.18 cm. Simultaneously, the global distribution, the local time distribution, and penetration depth of the constellation were statistically analyzed. BDS demonstrates superior performance with 21,093 daily occultation profiles, significantly exceeding GPS and GLONASS by 15.9% and 121%, respectively. Its detection capability is evidenced by 79.75% of profiles penetrating below a 2 km altitude, outperforming both GPS (78.79%) and GLONASS (71.75%) during the 7-day analysis period (DOY 169–175, 2023). The refractive index profile product was also compared with the ECWMF ERA5 product. At 35 km, the standard deviation of atmospheric refractivity for BDS remains below 1%, while for GPS and GLONASS it is found at around 1.5%. BDS also outperforms GPS and GLONASS in terms of the standard deviation in the atmospheric refractive index. These results indicate that Yunyao satellites can provide high-quality occultation product services, like for weather forecasting. With the successful establishment of the global BDS-3 network, the space signal accuracy has been significantly enhanced, with BDS-3 achieving a Signal-in-Space Ranging Error (SISRE) of 0.4 m, outperforming GPS (0.6 m) and GLONASS (1.7 m). This enables superior full-link occultation products for BDS. Full article

Autonomous driving is a complex and fast-evolving domain at the intersection of robotics, machine learning, and control systems. This paper provides a systematic review of recent developments in reinforcement learning (RL) and imitation learning (IL) approaches for autonomous vehicle control, with a dedicated focus on the CARLA simulator, an open-source, high-fidelity platform that has become a standard for learning-based autonomous vehicle (AV) research. We analyze RL-based and IL-based studies, extracting and comparing their formulations of state, action, and reward spaces. Special attention is given to the design of reward functions, control architectures, and integration pipelines. Comparative graphs and diagrams illustrate performance trade-offs. We further highlight gaps in generalization to real-world driving scenarios, robustness under dynamic environments, and scalability of agent architectures. Despite rapid progress, existing autonomous driving systems exhibit significant limitations. For instance, studies show that end-to-end reinforcement learning (RL) models can suffer from performance degradation of up to 35% when exposed to unseen weather or town conditions, and imitation learning (IL) agents trained solely on expert demonstrations exhibit up to 40% higher collision rates in novel environments. Furthermore, reward misspecification remains a critical issue—over 20% of reported failures in simulated environments stem from poorly calibrated reward signals. Generalization gaps, especially in RL, also manifest in task-specific overfitting, with agents failing up to 60% of the time when faced with dynamic obstacles not encountered during training. These persistent shortcomings underscore the need for more robust and sample-efficient learning strategies. Finally, we discuss hybrid paradigms that integrate IL and RL, such as Generative Adversarial IL, and propose future research directions. Full article

Accurate identification of microplastic polymers in marine environments is essential for tracing pollution sources, understanding ecological impacts, and guiding mitigation strategies. This study presents a comprehensive, explainable-AI framework that uses Raman spectroscopy to classify pristine and weathered microplastics versus biological materials. Using a curated spectral library of 78 polymer specimens—including pristine, weathered, and biological materials—we benchmark seven supervised machine learning models (Decision Trees, Random Forest, k-Nearest Neighbours, Neural Networks, LightGBM, XGBoost and Support Vector Machines) without and with Principal Component Analysis for binary classification. Although k-Nearest Neighbours and Support Vector Machines achieved the highest single metric accuracy (82.5%), k NN also recorded the highest recall both with and without PCA, thereby offering the most balanced overall performance. To enhance interpretability, we employed SHapley Additive exPlanations, which revealed chemically meaningful spectral regions (notably near 700 cm⁻¹ and 1080 cm⁻¹) as critical to model predictions. Notably, models trained without Principal Component Analysis provided clearer feature attributions, suggesting improved interpretability in raw spectral space. This pipeline surpasses traditional spectral matching techniques and also delivers transparent insights into classification logic. Our findings can support scalable, real-time deployment of AI-based tools for oceanic microplastic monitoring and environmental policy development. Full article

Ocean surface wind fields are crucial for marine environmental research and applications in weather forecasting, ocean disaster monitoring, and climate change studies. However, traditional wind retrieval methods often struggle with modeling complexity and ambiguity due to the nonlinear nature of geophysical model functions (GMFs), leading to increased computational costs and reduced accuracy. To tackle these challenges, this study establishes a sea surface wind field retrieval model employing a backpropagation (BP) neural network, which integrates multi-angular observations from the Wind Radar (WindRAD) sensor aboard the Fengyun-3E (FY-3E) satellite. Experimental results show that the proposed model achieves high precision in retrieving both wind speed and direction. The wind speed model achieves a root-mean-square error (RMSE) of 1.20 m/s for the training set and 1.00 m/s for the selected test set when using ERA5 data as the reference, outperforming the official WindRAD products. For wind direction, the model attains an RMSE of 23.99° on the training set and 24.58° on the test set. Independent validation using Tropical Atmosphere Ocean (TAO) buoy observations further confirms the model’s effectiveness, yielding an RMSE of 1.29 m/s for wind speed and 24.37° for wind direction, also surpassing official WindRAD products. The BP neural network effectively captures the nonlinear relationship between wind parameters and radar backscatter signals, showing significant advantages over traditional methods and maintaining good performance across different wind speeds, particularly in the moderate range (4–10 m/s). In summary, the method proposed herein significantly enhances wind field retrieval accuracy from space; it has the potential to optimize satellite wind field products and improve global wind monitoring and meteorological forecasting. Full article

Alteration is a common metamorphic process in igneous formations and recorded geological information in different times and spaces. Owing to its unique location, the igneous rocks of the buried hills in the northern South China Sea exhibit complex lithology and alteration patterns resulting from multi-phase tectonic, magmatic, and climatic influences. Here, we report buried hills igneous rock samples with both hydrothermal alteration and weathering leaching. Based on multi-scale geophysical–chemical data—including scanning electron microscopy, core slice identification, petrophysical–chemical experiments, zircon dating, wireline logs, element cutting logs, seismic profiles, and others—we analyzed the multi-scale alteration characteristics of buried hills igneous rocks and proposed a four-stage alteration model related to Earth activities. Results demonstrate that tectonic movements develop continuous cracks enabling hydrothermal alteration, while burial-hill uplift facilitates weathering leaching. We further find that multi-phase tectonic movements and associated magmatic activities not only influence global hydrothermal cycles but also govern elemental migration patterns, driving distinct alteration mechanisms in these igneous rocks—including plagioclase metasomatism, hornblende replacement, and carbonate dissolution. Additionally, we identify the Cretaceous arid–cold climate as the primary controller for generating chlorite-dominated hydrothermal alteration products. These multi-scale alteration characteristics confirm Late Jurassic Pacific Plate subduction and Cretaceous South China Plate orogeny and may indicate an earlier initial expansion of the South China Sea. Full article

In satellite altimetry calibration, the atmosphere’s integrated water vapor content has been customarily derived through the Global Navigation Satellite Systems (GNSS), principally over land where the satellite radiometer is not operational. Progressively, several alternative methods have emerged to estimate this wet troposphere component with ground instruments, alternative satellite sensors, and global models. For any ground calibration facility, integration of various approaches is required to arrive at an optimum value of a calibration constituent and in accordance with the strategy of Fiducial Reference Measurements (FRM). In this work, different estimation methods and instruments are evaluated for wet troposphere delays, especially when transponder and corner reflectors are employed at the Permanent Facility for Altimetry Calibration of the European Space Agency. Evaluation includes, first, ground instruments with microwave radiometers and radiosondes; second, satellite sensors with the Ocean Land Color Instrument (OLCI) and the Sea Land Surface Temperature Radiometer (SLSTR) of the Copernicus Sentinel-3 altimeter, as well as the TROPOMI spectrometer on the Sentinel-5P satellite; and finally with global atmospheric models, such as the European Center for Medium-Range Weather Forecasts. Along these lines, multi-sensor and redundant values for the troposphere delays are thus integrated and used for the calibration of Sentinel-6 MF and Sentinel-3A/B satellite altimeters. All in all, the integrated water vapor value of the troposphere is estimated with an FRM uncertainty of ±15 mm. In the absence of GNSS stations, it is recommended that the OLCI and SLSTR measurements be used for determining tropospheric delays in daylight and night operations, respectively. Ground microwave radiometers can also be used to retrieve tropospheric data with high temporal resolution and accuracy, provided that they are properly installed and calibrated and operated with site-specific parameters. Finally, the synergy of ground radiometers with instruments on board other Copernicus satellites should be further investigated to ensure redundancy and diversity of the produced values for the integrated water vapor. Full article

The Mount Wudang architectural complex, recognized as a UNESCO World Cultural Heritage site, extensively utilizes green schist as the building material in its rock temple structures. Due to prolonged exposure to weathering and moisture, effective surface protection of these stones is crucial for their preservation. Inspired by the lotus leaf, femtosecond laser fabrication of bioinspired micro/nanostructures offers a promising approach for imparting hydrophobicity to stone surfaces. However, green schist is a typical heterogeneous material primarily composed of quartz, chlorite, and muscovite, and it contains metal elements, such as Fe and Ni. These pronounced compositional differences complicate laser–material interactions, posing considerable challenges to the formation of stable and uniform micro/nanostructures. To address this issue, we performed systematic femtosecond laser scanning experiments on green schist surfaces using a 100 kHz, 40 μJ laser with a 30 μm spot diameter, fabricating microgrooves under various process conditions. Surface morphology and EDS mapping analyses were conducted to elucidate the ablation responses of quartz, chlorite, and muscovite under different groove spacings (100 μm, 80 μm, 60 μm, and 40 μm) and scan repetitions (1, 2, 4, 6, 8, 10). The results revealed distinct differences in energy absorption, material ejection, and surface reorganization among these minerals, significantly influencing the formation mechanisms of laser-induced structures. Based on optimized parameters (60 μm spacing, 2–6 passes), robust and repeatable micro/nanostructures were successfully produced, yielding superhydrophobic performance with contact angles exceeding 155°. This work offers a novel strategy for interface control in heterogeneous natural stone materials and provides a theoretical and technical foundation for the protection and functional modification of green schist in heritage conservation. Full article

As cities grow, they increase in complexity, requiring the effective use of land resources. Cycling is generally regarded as an alternative transport mode to support the development of the cities of tomorrow. In response to urbanization, in many cities worldwide, a common concern associated with investing in cycling networks is the resulting use after such investment. This study uses a continuous longitudinal dataset of daily cycling counts from January 2018 to June 2024 to assess bicycle volumes across three of New Zealand’s largest cities. The results reveal that the relationship between distance and cycle count is not uniform across space, with some areas showing a negative effect between distance and cycling, and others showing a positive one. A global OLS model hides these complexities, as shown in the geographically weighted regression (GWR) model. The coefficients for distance (−0.49) and precipitation (−95.23) in the global OLS are higher, and do not reveal the non-uniformity between cities, wheras themultiple GWR coefficients for distance range between −0.57 and −0.47 and precipitation between −33.47 and −97.63. The results reveal that cycling volume demonstrates lower sensitivity to changes in distance compared to variations in weather conditions. At the city level, there are notable intercity differences in sensitivity. The variability in the coefficients across locations suggests that, although distance and precipitation have general effects, local factors, such as infrastructure quality, topography, weather adaptation measures, and cultural attitudes toward cycling, play a critical role in modulating these relationships. The findings highlight the complexity of spatial interactions and emphasize the need for localized interventions when planning cycling networks. Full article

The industrial sector is a major contributor to energy-related CO₂ emissions in Europe, making the transition to renewable energy solutions essential. Decarbonization strategies integrate renewable energy sources, power-to-heat technologies, and energy storage systems into existing production sites to enhance sustainability and flexibility. However, a key challenge lies in designing energy systems that remain robust under long-term operational uncertainties. Usually the design of each energy system component is discrete, as it is manufactured in a predetermined size. Classical state-of-the-art coupled design and operational optimization methods are based on continuous design variables, which might give sub-optimal solutions. This study overcomes this limitation by employing novel, computationally efficient robust quantum-classical discrete-design methods. Traditional approaches often optimize operations for a single year due to the computational limitations of operational optimization algorithms, leading to designs that lack robustness. By incorporating long-term operational uncertainties, this approach ensures that selected energy-system configurations minimize both CO₂ emissions and costs while maintaining resilience to variations in weather conditions and demand fluctuations. Robust discrete designs which consider operational uncertainties show 12% less global warming impact (GWI) with 27% higher total annualized cost (TAC) compared to designs based on operational optimization without uncertainty. A novel quantum-assisted non-dominated sorting genetic algorithm (QANSGA-II) shows accuracy up to 90%, which leads to 27% less computational effort than the NSGA-II algorithm. This novel method can help industries to search larger and more optimal robust discrete-design spaces for making decarbonization decisions. Full article

The rapid development of intelligent high-speed railways (HSRs) has significantly improved the transportation efficiency of modern transit systems, while also imposing higher bandwidth demands on mobile communication systems. Free-space optical (FSO) communication technology, as a promising solution, can effectively meet the high-speed data transmission requirements in intelligent HSR scenarios. In this paper, we consider an intelligent HSR system based on beamwidth-adaptive FSO communication and investigate the coverage performance of the system. Different from the circular cells used in traditional radio frequency wireless communication systems, this paper focuses on the coverage problem of narrow-strip-shaped cells in HSR systems based on FSO communication. When the transmitter emits a wide beam, the channel gain includes geometric loss, atmospheric attenuation, and atmospheric turbulence. When the transmitter emits a narrow beam, the channel gain includes pointing error, atmospheric attenuation, and atmospheric turbulence. To adapt the width of the transmitter’s beam, we propose a beamwidth-adaptive HSR system and a beamwidth-adaptive method. Furthermore, we derive closed-form expressions of the edge coverage probability (ECP) and the percentage of cell coverage area (CCA), where the ECP is the probability that the received signal-to-noise ratio at the cell edge is greater than or equal to a given threshold, and the percentage of CCA dictates the percentage of locations within a cell that are not in outage. The accuracy of the derived theoretical expressions is validated through Monte-Carlo simulations. The average relative error of the ECP between theoretical and simulation results is only 0.035%, and the corresponding error of the percentage of CCA is 0.087%. In addition, the impacts of factors such as cell diameter, transmission power, signal-to-noise ratio threshold, and weather visibility on coverage performance are also discussed. Full article