Search Results (124)

Deep learning-based super-resolution (SR) frameworks are widely used in remote sensing applications. However, existing SR models still face limitations, particularly in recovering contours, fine features, and textures, as well as in effectively integrating channel information. To address these challenges, this study introduces a novel residual model named OARN (Optimized Attention Residual Network) specifically designed to enhance the visual quality of low-resolution images. The network operates on the Y channel of the YCbCr color space and integrates LKA (Large Kernel Attention) and OCM (Optimized Convolutional Module) blocks. These components can restore large-scale spatial relationships and refine textures and contours, improving feature reconstruction without significantly increasing computational complexity. The performance of OARN was evaluated using satellite images from WorldView-2, GaoFen-2, and Microsoft Virtual Earth. Evaluation was conducted using objective quality metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Edge Preservation Index (EPI), and Perceptual Image Patch Similarity (LPIPS), demonstrating superior results compared to state-of-the-art methods in both objective measurements and subjective visual perception. Moreover, OARN achieves this performance while maintaining computational efficiency, offering a balanced trade-off between processing time and reconstruction quality. Full article

This study focuses on S32654 super austenitic stainless steel (SASS) and systematically characterizes the morphology of the sigma (σ) phase and the segregation behavior of alloying elements in its as-cast microstructure. High-temperature confocal scanning laser microscopy (HT-CSLM) was employed to investigate the effect of the rare earth element yttrium (Y) on the solidification microstructure and σ phase precipitation behavior of SASS. The results show that the microstructure of SASS consists of austenite dendrites and interdendritic eutectoid structures. The eutectoid structures mainly comprise the σ phase and the γ₂ phase, exhibiting lamellar or honeycomb-like morphologies. Regarding elemental distribution, molybdenum displays a “concave” distribution pattern within the dendrites, with lower concentrations at the center and higher concentrations at the sides; when Mo locally exceeds beyond a certain threshold, it easily induces the formation of eutectoid structures. Mo is the most significant segregating element, with a segregation ratio as high as 1.69. The formation mechanism of the σ phase is attributed to the solid-state phase transformation of austenite (γ → γ₂ + σ). In the late stages of solidification, the concentration of chromium and Mo in the residual liquid phase increases, and due to insufficient diffusion, there are significant compositional differences between the interdendritic regions and the matrix. The enriched Cr and Mo cause the interdendritic austenite to become supersaturated, leading to solid-state phase transformation during subsequent cooling, thereby promoting σ phase precipitation. The overall phase transformation process can be summarized as L → L + γ → γ → γ + γ₂ + σ. Y microalloying has a significant influence on the solidification process. The addition of Y increases the nucleation temperature of austenite, raises nucleation density, and refines the solidification microstructure. However, Y addition also leads to an increased amount of eutectoid structures. This is primarily because Y broadens the solidification temperature range of the alloy and prolongs grain growth perio, which aggravates the microsegregation of elements such as Cr and Mo. Moreover, Y raises the initial precipitation temperature of the σ phase and enhances atomic diffusion during solidification, further promoting σ phase precipitation during the subsequent eutectoid transformation. Full article

This study focuses on the integrated scheduling issues of imaging and data transmission for Earth observation satellites, where each target needs to be imaged and transmitted within a feasible time window. The scheduling process also takes into account the constraints of satellite energy and storage capacity. In this paper, a mixed-integer linear programming (MILP) model for the integrated scheduling of imaging data transmission has been proposed. The MILP model was validated through numerical experiments based on simulation data from SuperView-1 series satellites. Additionally, some neighborhood mechanisms are designed based on the characteristics of the problem. Based on the neighborhood mechanisms, the rule-based large neighborhood search algorithm (RLNS) was designed, which constructs initial solutions through various scheduling rules and iteratively optimizes the solutions using multiple destroying and repairing operators. To address the shortcomings of the overly regular mechanism of the destruction and repair operator for large neighborhood search, we design a genetic algorithms (GA) for tuning the heuristic scheduling rules. The calculation results demonstrate the effectiveness of RLNS and GA, highlighting their advantages over CPLEX in solving large-scale problems. Full article

This paper investigates the super-resolution reconstruction technology of coarse granular particle images for embankment construction in earth/rock dams based on Real-ESRGAN, aiming to improve the quality of low-resolution particle images and enhance the accuracy of particle shape analysis. The paper begins with a review of traditional image super-resolution methods, introducing Generative Adversarial Networks (GAN) and Real-ESRGAN, which effectively enhance image detail recovery through perceptual loss and adversarial training. To improve the generalization ability of the super-resolution model, the study expands the morphological database of earth/rock dam particles by employing a multi-modal data augmentation strategy, covering a variety of particle shapes. The paper utilizes a dual-stage degradation model to simulate the image degradation process in real-world environments, providing a diverse set of degraded images for training the super-resolution reconstruction model. Through wavelet transform methods, the paper analyzes the edge and texture features of particle images, further improving the precision of particle shape feature extraction. Experimental results show that Real-ESRGAN outperforms other traditional super-resolution algorithms in terms of edge clarity, detail recovery, and the preservation of morphological features of particle images, particularly under low-resolution conditions, with significant improvement in image reconstruction. In conclusion, Real-ESRGAN demonstrates excellent performance in the super-resolution reconstruction of coarse granular particle images for embankment construction in earth/rock dams. It can effectively restore the details and morphological features of particle images, providing more accurate technical support for particle shape analysis in civil engineering. Full article

Thermochemical nonequilibrium significantly affects the accurate simulation of the aerothermal environment surrounding a hypersonic reentry vehicle entering Earth’s atmosphere during deep space exploration missions. The different heat transfer modes corresponding to each internal energy mode and chemical diffusion have not been sufficiently analyzed. The existing dimensionless correlations for stagnation point aerodynamic heating do not account for thermochemical nonequilibrium effects. This study employs an in-house high-fidelity solver PHAROS (Parallel Hypersonic Aerothermodynamics and Radiation Optimized Solver) to simulate the hypersonic thermochemical nonequilibrium flows over a standard sphere under both super-catalytic and non-catalytic wall conditions. The total stagnation point heat flux and different heating modes, including the translational–rotational, vibrational–electronic, and chemical diffusion heat transfers, are all identified and analyzed. Stagnation point aerodynamic heating correlations have been modified to account for the thermochemical nonequilibrium effects. The results further reveal that translational–rotational and chemical diffusion heat transfers dominate the total aerodynamic heating, while vibrational–electronic heat transfer contributes only about 5%. This study contributes to the understanding of aerodynamic heating principles and thermal protection designs for future hypersonic reentry vehicles. Full article

As the planet most similar to Earth in the solar system, Mars holds an important role in exploring significant scientific problems, such as the evolution of the solar system and the origins of life. Research on Mars mainly rely on planetary remote sensing technology, among which thermal infrared remote sensing is of great studying significance. This technology enables the recording of Martian thermal radiation properties. However, the current spatial resolution of Mars thermal infrared remote sensing images remains relatively low, limiting the detection of fine-scale thermal anomalies and the generation of higher-precision surface compositional maps. While updating extraterrestrial exploration satellites can help enhancing the spatial resolution of thermal infrared images, this method entails high cost and long update cycles, making improvement difficult to conduct in the short term. To address this issue, this paper proposes a super-resolution reconstruction method for Mars thermal infrared remote sensing images integrating multi-source data. First, based on the principle of domain adaptation, we introduced a method using highly correlated visible light images as auxiliary to enhance the spatial resolution of thermal infrared images. Then, a multi-sources data integration method is designed to constrain the thermal radiation flux of resulting images, ensuring the radiation distribution remains consistent with the original low-resolution thermal infrared images. Through both subjective and objective evaluations, our method is demonstrated to significantly enhance the spatial resolution of existing Mars thermal infrared images. It optimizes the quality of existing data, increasing the resolution of the original thermal infrared images by four times. In doing so, it not only recovers finer texture details to produce better visual effects than typical super-resolution methods, but also maintains the consistency of thermal radiation flux, with the error after applying the consistency constraint reduced by nearly tenfold, ensuring the applicability of the results for scientific research. Full article

This study investigates the impact on the Earth’s ionosphere of a severe geomagnetic storm (Dst $\sim -212$ nT) that began on 23 April 2023 at around 17:37 UT according to very low-frequency (VLF, 3–30 kHz) or low-frequency (LF, 30–300 kHz) radio signals and ionosonde data. We analyze VLF/LF signals received by SuperSID monitors located in mid-latitude (Europe) and low-latitude (South America, Colombia) areas across nine different propagation paths in the Northern Hemisphere. Mid-latitude regions exhibited a daytime amplitude perturbation, mostly an increase, by ∼3–5 dB during the storm period, with a subsequent recovery after 7–8 days post April 23. In contrast, signals received in low-latitude regions (UTP, Colombia) did not show significant variation during the storm-disturbed days. We also observe that the 3-hour average of foF2 data declined by up to 3 MHz on April 23 and April 24 at the European Digisonde stations. However, no significant variation in foF2 was observed at the low-latitude Digisonde stations in Brazil. Both the VLF and ionosonde data exhibited anomalies during the storm period in the European regions, confirming that both D- and F-region ionospheric perturbation was caused by the severe geomagnetic storm. Full article

Cross-calibration is an essential technique for calibrating Earth observation satellite sensors, which involves taking nearly simultaneous images of a ground target to compare an uncalibrated sensor to a well-calibrated reference sensor. This study introduces the hyperspectral Trend-to-Trend (T2T) cross-calibration technique utilizing EPICS Cluster 13 Global Temporally Stable (Cluster 13-GTS) as the calibration target, offering better temporal stability than previous targets used in T2T cross-calibration by an absolute difference of 0.4%, between coefficients of variation across all bands excluding CA band. A multispectral sensor-specific normalized hyperspectral profile was developed using the EO-1 Hyperion hyperspectral profile over Cluster 13-GTS to improve Spectral Band Adjustment Factor (SBAF) estimation, capturing sensor-specific Relative Spectral Response (RSR) variations and introducing the ability to use the multispectral sensor-specific hyperspectral profile for calibrating future satellite sensors like Landsat Next with super-spectral bands. SBAFs were derived from EO-1 Hyperion normalized to multispectral sensors, which were interpolated to 1 nm, ensuring precise spectral band adjustments following a Monte Carlo simulation approach for uncertainty quantification. Results show that reference sensor-specific hyperspectral profiles at 1 nm spectral resolution improve SBAF accuracy and exhibit total uncertainty within 5.8% across all bands and all sensor pairs with L8 as the reference sensor. These findings demonstrate that integrating reference sensor-specific high-resolution hyperspectral data and stable calibration targets improves T2T cross-calibration accuracy, supporting future super-spectral missions such as Landsat Next. Full article

Satellite remote sensing plays a crucial role in monitoring Earth’s ecosystems, yet satellite-derived data often suffer from limited spatial resolution, restricting the availability of accurate and precise data for atmospheric modeling and climate research. Errors and biases may also be introduced into applications due to the use of data with insufficient spatial and temporal resolution. In this work, we propose a deep learning-based Super-Resolution (SR) framework that leverages land cover information to enhance the spatial accuracy of Biogenic Volatile Organic Compound (BVOC) emissions, with a particular focus on isoprene. Our approach integrates land cover priors as emission drivers, capturing spatial patterns more effectively than traditional methods. We evaluate the model’s performance across various climate conditions and analyze statistical correlations between isoprene emissions and key environmental information such as cropland and tree cover data. Additionally, we assess the generalization capabilities of our SR model by applying it to unseen climate zones and geographical regions. Experimental results demonstrate that incorporating land cover data significantly improves emission SR accuracy, particularly in heterogeneous landscapes. This study contributes to atmospheric chemistry and climate modeling by providing a cost-effective, data-driven approach to refining BVOC emission maps. The proposed method enhances the usability of satellite-based emissions data, supporting applications in air quality forecasting, climate impact assessments, and environmental studies. Full article

Super-Earth b and sub-Neptunes c and d are orbiting about the M3.0V dwarf TOI-270 in that order from the star. Their global resonant chain (3:5, 1:1, 2:1) is extremely surprising because planet d appears to be the only known planet occupying the 2:1 resonant orbit without participating in a Laplace resonance (LR) or another planet intervening between the 1:1 and 2:1 orbits as in HD 110067. We do not believe that TOI-270 d is an exception to the empirical rule calling for 2:1 vacancy except in 1:2:4 LRs and Laplace-like 2:3:4 chains. Instead, a LR might exist in this system, and we searched (to no avail) the TESS light curves of TOI-270 for hints of an outer planet that would complete the LR chain. Alternative explanations would be an unknown planet more massive than planet c ($M_{\mathrm{c}}=6.20M_{\oplus }$) establishing the actual 1:1 orbit, or planet b residing in the 1:2 Laplace orbit with a period shorter by 0.53 days. However, these possibilities are ruled out by current data. This leaves only one other option to explore: the observed orbits could be in a stable ${\displaystyle \frac{3}{5}}$:1:2 resonant chain. Preliminary calculations do not preclude this possibility that should be investigated further by numerical orbit integrations. To this end, we determine two potentially resonant angles, $\phi$ and $\widehat{\phi }$, related via the Laplace phase ${\phi }_{\mathrm{L}}$ by $\widehat{\phi }={\phi }_{\mathrm{L}}+2\phi$. In contrast, HD 110067 is shown to have planets d-e-f in a Laplace-like 1:${\displaystyle \frac{3}{2}}$:2 resonance with phase $\phi =2{\phi }_{\mathrm{L}}$ precisely. Full article

In the past, measures of the “Earth-likeness” of exoplanets have been qualitative, considering an abiotic Earth, or requiring discretionary choices of what parameters make a planet Earth-like. With the advent of high-resolution exoplanet spectroscopy, there is a growing need for a method of quantifying the Earth-likeness of a planet that addresses these issues while making use of the data available from modern telescope missions. In this work, we introduce an informational–entropic metric that makes use of the spectrum of an exoplanet to directly quantify how Earth-like the planet is. To illustrate our method, we generate simulated transmission spectra of a series of Earth-like and super-Earth exoplanets, as well as an exoJupiter and several gas giant exoplanets. As a proof of concept, we demonstrate the ability of the information metric to evaluate how similar a planet is to Earth, making it a powerful tool in the search for a candidate Earth 2.0. Full article

Because of variations in the amount of solar energy that reaches the Earth’s surface, the output of solar power plants can undergo significant variability in the electricity generated. To solve this conundrum, modeling the parametric forecast of short-scale solar energy across Mozambique’s Mid-North region was the goal of this study. The parametric model applied consists of machine learning models based on the parametric analysis of all atmospheric, geographic, climatic, and spatiotemporal elements that impact the fluctuation in solar energy. It highlights the essential importance of the exact management of the interferential power density of each parameter influencing the availability of super solar energy. It enhances the long and short forecasts, estimates and scales, and geographic location, and provides greater precision, compared to other forecasting models. We selected eleven Mid-North region sites that collected data between 2019 and 2021 for the validation sample. The findings demonstrate a significant connection in the range of 0.899 to 0.999 between transmittances and irradiances caused by aerosols, water vapor, evenly mixed gases, and ozone. Uniformly mixed gases exhibit minimal attenuation, with a transmittance of about 0.985 in comparison to other atmospheric constituents. Despite the increased precision obtained by parameterization, the area still offers potential for solar application, with average values of 25% and 51% for clear skies and intermediate conditions, respectively. The estimated solar energy allows the model to be evaluated in any reality since it is within the theoretical irradiation spectrum under clear skies. Full article

Equation of state (EoS) parameters of hexagonal close-packed iron (hcp-Fe), the dominant core component in large terrestrial planets, is crucial for studying interior structures of super-Earths. However, EoS parameters at interior conditions of super-Earths remain poorly constrained, and extrapolating from Earth’s core conditions introduces significant uncertainties at TPa pressures. Here, we compiled experimental static and dynamic compression data and theoretical data up to 1374 GPa and 12,000 K from the literature to refine the EoS of hcp-Fe. Using the third-order Birch–Murnaghan and Mie–Grüneisen–Debye equations, we obtained V₀ (unit-cell volume) = 6.756 (10) cm³/mol, K_T0 (isothermal bulk modulus) = 174.7 (17) GPa, $K_{T0}^{\prime }$ (pressure derivative of K_T0) = 4.790 (14), θ₀ (Debye temperature) = 1209 (73) K, γ₀ (Grüneisen parameters) = 2.86 (10), and q (volume-independent constant) = 0.84 (5) at ambient conditions. These parameters were then incorporated into an interior model of CoRoT-7b and Kepler-10b, which includes four solid compositional layers (forsterite, MgSiO₃ perovskite, post-perovskite, and hcp-Fe). The model yields the core mass fractions (CMF) of 0.1709 in CoRoT-7b and 0.2216 in Kepler-10b, suggesting a Mars-like interior structure. Extrapolation uncertainties (±10–20% in density) can change CMF by −12.6 to 21.2%, highlighting the necessity of precise EoS constraints at the super-Earth interior conditions. Full article

Accurate digital soil organic carbon mapping is of great significance for regulating the global carbon cycle and addressing climate change. With the advent of the remote sensing big data era, multi-source and multi-temporal remote sensing techniques have been extensively applied in Earth observation. However, how to fully mine multi-source remote sensing time-series data for high-accuracy digital SOC mapping remains a key challenge. To address this challenge, this study introduced a new idea for mining multi-source remote sensing time-series data. We used 413 topsoil organic carbon samples from southern Xinjiang, China, as an example. By mining multi-source (Sentinel-1/2) remote sensing time-series data from 2017 to 2023, we revealed the temporal variation pattern of the correlation between Sentinel-1/2 time-series data and SOC, thereby identifying the optimal time window for monitoring SOC using Sentinel-1/2 data. By integrating environmental covariates and a super ensemble model, we achieved high-accuracy mapping of SOC in Southern Xinjiang, China. The results showed the following aspects: (1) The optimal time windows for monitoring SOC using Sentinel-1/2 data were July–September and July–August, respectively; (2) the modeling accuracy using multi-source sensor data integrated with environmental covariates was superior to using single-source sensor data integrated with environmental covariates alone. In the optimal model based on multi-source data, the cumulative contribution rate of Sentinel-2 data is 51.71% higher than that of Sentinel-1 data; (3) the stacking super ensemble model’s predictive performance outperformed the weight average and simple average ensemble models. Therefore, mining the optimal time windows of multi-source remote sensing data and environmental covariates, driven a super ensemble model, represents a high-accuracy strategy for digital SOC mapping. Full article

The accurate measurement of high-resolution solar spectral irradiance (SSI) and its variations at the top of the atmosphere is crucial for solar physics, the Earth’s climate, and the in-orbit calibration of optical satellites. However, existing space-based solar spectral irradiance instruments achieve high-precision SSI measurements at the cost of spectral resolution, which falls short of meeting the requirements for identifying fine solar spectral features. Therefore, this paper proposes a new method for reconstructing high-resolution solar spectral irradiance based on a residual channel attention network. This method considers the stability of SSI spectral features and employs residual channel attention blocks to enhance the expression ability of key features, achieving the high-accuracy reconstruction of spectral features. Additionally, to address the issue of excessively large output features from the residual channel attention blocks, a scaling coefficient adjustment network block is introduced to achieve the high-accuracy reconstruction of spectral absolute values. Finally, the proposed method is validated using the measured SSI dataset from SCIAMACHY on Envisat-1 and the simulated dataset from TSIS-1 SIM. The validation results show that, compared to existing scaling coefficient adjustment algorithms, the proposed method achieves single-spectrum super-resolution reconstruction without relying on external data, with a Mean Absolute Percentage Error (MAPE) of 0.0302% for the reconstructed spectra based on the dataset. The proposed method achieves higher-resolution reconstruction results while ensuring the accuracy of SSI. Full article