Search Results (5,891)

Geosynthetics have increasingly been applied to geotechnical engineering works due to their numerous advantages, including cost-effectiveness and their significant role in sustainable development. When geosynthetics are used as reinforcement in earth structures, such as embankments, retaining walls and bridge abutments, soil–geosynthetic interface shear behavior is a critical parameter involved in the design. This paper presents a series of monotonic and cyclic/post-cyclic pullout tests carried out to examine the apparatus scale effect on the pullout response of a geogrid embedded in two different soils. To assess the small-scale equipment feasibility, comparisons were made between pullout test parameters derived from small- and large-scale equipment. The test results indicate that, under a low confining stress of 25 kPa, using a smaller-sized apparatus results in lower values of geogrid pullout resistance and maximum mobilized shear stress, but higher values of confined tensile stiffness at low strains. On the other hand, as the confining stress increases (i.e., 50 kPa and 100 kPa), the difference between the results becomes less significant and similar trends are observed regardless of the equipment type. Adopting small-scale equipment enables obtaining soil–reinforcement interaction parameters using test procedures that are less time-consuming than those associated with large-scale pullout tests. However, proper scale effect correction factors may be considered for more consistent estimates of the interface strength parameters under low normal stress values. Full article

Clouds and other data artefacts frequently limit the retrieval of key variables from remotely sensed Earth observations. We train a natural language processing (NLP)-inspired algorithm with high-fidelity ocean simulations to accurately reconstruct masked or missing data in sea surface temperature (SST) fields—one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the resulting model, referred to as Enki, repeatedly outperforms previously adopted inpainting techniques by up to an order of magnitude in reconstruction error, while displaying exceptional performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masked percentages of at least 40% show reconstruction errors of less than the known uncertainty of this sensor (root mean square error (RMSE) $\lesssim 0.1$ K). We attribute Enki’s success to the attentive nature of NLP combined with realistic SST model outputs—an approach that could be extended to other remotely sensed variables. This study demonstrates that systems built upon Enki—or other advanced systems like it—may therefore yield the optimal solution to mitigating masked pixels in in climate-critical ocean datasets sampling a rapidly changing Earth. Full article

Calibrating optical sensors has become a priority to maintain data quality and ensure consistency among sensors from different agencies. Achieving and monitoring radiometric calibration often involves the identification of temporally stable targets on the Earth’s surface. Although some locations across North Africa have traditionally been used as primary targets for calibration purposes, it is crucial to explore alternative options to account for potential changes in these sites over time. This study conducted a global assessment of pixel-level temporal stability using Landsat 8 OLI data, with the primary goal of identifying regions suitable for global radiometric calibration efforts. This work followed a two-stage approach, including the testing and selection of an effective combination of statistical tests to differentiate between temporally stable and unstable pixels and the generation of a worldwide mosaic of temporally stable pixels through a per-pixel statistical analysis employing a combination of Spearman’s rho and Pettitt’s test for assessing long-term trends and detecting change points. Notably, comparing the temporal mean top-of-atmosphere (TOA) reflectance before and after applying the generated temporal filter to a site with documented unstable pixels revealed a substantial reduction in mean variation, up to 6%. In addition, slopes observed in the pre-filter mean TOA reflectance, ranging between −0.002 and −0.005, became zero or near-zero and statistically insignificant after the temporal filter was applied, demonstrating a reduction in total uncertainties by 3 to 4%. These findings evidence the potential of this work, placing it as a potential foundation in the continuous search to identify additional targets for global radiometric calibration efforts. Full article

In the context of the current information age, leveraging Earth observation (EO) technology and spatial analysis methods enables a more accurate understanding of the characteristics of small towns. This study conducted an in-depth analysis of the urban morphology of small towns in the Qinba Mountain Area of Southern Shaanxi by employing large-scale data analysis and innovative urban form measurement methods. The U-Net3+ model, based on deep learning technology, combined with the concave hull algorithm, was used to extract and precisely define the boundaries of 31,799 buildings and small towns. The morphological characteristics of the town core were measured, and the core areas of the small towns were defined using calculated tessellation cells. Hierarchical clustering methods were applied to analyze 12 characteristic indicators of 89 towns, and various metrics were calculated to determine the optimal number of clusters. The analysis identified eight distinct clusters based on the towns’ morphological differences. Significant morphological differences between the small towns in the Qinba Mountain Area were observed. The clustering results revealed that the towns exhibited diverse shapes and distributions, ranging from irregular and sparse to compact and dense forms, reflecting distinct layout patterns influenced by the unique context of each town. The use of the morphometric method, based on cellular and biological morphometry, provided a new perspective on the urban form and deepened the understanding of the spatial structure of the small towns from a micro perspective. These findings not only contribute to the development of quantitative morphological indicators for town development and planning but also demonstrate a novel, data-driven approach to conventional urban morphology studies. Full article

Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies. Full article

Cement-stabilized rammed earth (CSRE) is a variation of the traditional rammed earth building material, which has been used since ancient times, strengthened by the addition of a stabilizer in the form of Portland cement. This article compares the compressive strength of CSRE determined from specimens cored from structural walls and those molded in the laboratory. Both types of specimens underwent a 120-day curing period. The tests were conducted on specimens with various grain sizes and cement content. An analysis of variance (ANOVA) was performed on the obtained results to determine whether it is possible to establish a conversion factor between the compressive strength values obtained from laboratory-molded cubic samples and those from cored samples extracted from the CSRE structure. The study revealed that the compressive strength of CSRE increases significantly over the curing period, with substantial strength gains observed up to 120 days. The results indicated no statistically significant difference in the mean unconfined compressive strength (UCS) between cubic and cored specimens for certain mixtures, suggesting that a shape coefficient factor may not be necessary for calculating CSRE compressive strength in laboratory settings. However, for other mixtures, normal distribution was not confirmed. These findings have implications for the standardization and practical application of CSRE in construction, highlighting the need for longer curing periods to achieve optimal strength and the potential to simplify testing protocols. Full article

Large-scale cosmic filaments connect galaxies, clusters, and voids. They are permeated by magnetic fields with a variety of topologies. Cosmic rays with energies up to ${10}^{20}\mathrm{eV}$ can be produced in astrophysical environments associated with star-formation and AGN activities. The fate of these cosmic rays in filaments, which cannot be directly observed on Earth, are rarely studied. We investigate the high-energy processes associated with energetic particles (cosmic rays) in filaments, adopting an ecological approach that includes galaxies, clusters/superclusters, and voids as key cosmological structures in the filament ecosystem. We derive the phenomenology for modelling interfaces between filaments and these structures, and investigate how the transfer and fate of energetic cosmic ray protons are affected by the magnetism of the interfaces. We consider different magnetic field configurations in filaments and assess the implications for cosmic ray confinement and survival against hadronic pion-producing and photo-pair interactions. Our analysis shows that the fate of the particles depends on the location of their origin within a filament ecosystem, and that filaments act as ‘highways’, channelling cosmic rays between galaxies, galaxy clusters, and superclusters. Filaments can also operate as cosmic ‘fly paper’, capturing cosmic ray protons with energies up to ${10}^{18}\mathrm{eV}$ from cosmic voids. Our analysis predicts the presence of a population of ∼${10}^{12}$–${10}^{16}\mathrm{eV}$ cosmic ray protons in filaments and voids accumulated continually over cosmic time. These protons do not suffer significant energy losses through photo-pair or pion production, nor can they be cooled efficiently. Instead, they form a cosmic ray fossil record of the power generation history of the Universe. Full article

Moho tomography is important for studying the deep Earth structure and geodynamics, and fiber borehole strainmeters are broadband, low-noise, and attractive tools for seismic observation. Recently, many studies have shown that fiber optic seismic sensors can be used for subsurface structure imaging based on ambient noise cross-correlation, similar to conventional geophones. However, this array-dependent cross-correlation method is not suitable for fiber borehole strainmeters. Here, we developed a Moho imaging scheme for the characteristics of fiber borehole strainmeters based on ambient noise autocorrelation. S-wave reflection signals were extracted from the ambient noise through a series of processing steps, including phase autocorrelation (PAC), phase-weighted stacking (PWS), etc. Subsequently, the time-to-depth conversion crustal thickness beneath the station was calculated. We applied our scheme to continuous four-component recordings from four fiber borehole strainmeters in Lu’an, Anhui Province, China. The obtained Moho depth was consistent with the previous research results. Our work shows that this method is suitable for Moho imaging with fiber borehole strainmeters without relying on the number of stations. Full article

This study investigates the impact of varying extrusion ratios on the microstructure and mechanical properties of AlSiCuFeMnYb alloy. Following hot extrusion, significant enhancements are observed in the microstructure of the cast rare earth aluminium alloy. Within the cross-sectional microstructure, the α-Al phase is reduced in size, and its dendritic morphology is eliminated. The morphology of the eutectic Si phase transitions from long strips to short rods, fine fibres, or granular forms. Similarly, the Fe-rich phase changes from a coarse skeletal and flat noodle shape to small strips and short skeletal forms resembling Chinese characters. The CuAl₂ phase evolves from large blocks to smaller blocks and granular forms, while the Yb (Ytterbium)-rich rare earth phase shifts from large blocks to smaller, more uniformly distributed blocks. In the longitudinal section, the structure aligns into strips along the extrusion direction, with the spacing between these strips decreasing as the extrusion ratio increases. At an extrusion ratio of 22.56, the alloy demonstrates superior mechanical properties with a tensile strength of 325.50 MPa, a yield strength of 254.44 MPa, a hardness of 143.90 HV, and an elongation of 15.47%. These represent improvements of 27.8%, 36.5%, 38.9%, and 236.4%, respectively, compared with the as-cast rare earth alloy. In addition, the fracture surface of the extruded rare earth alloy exhibits obvious ductile fracture characteristics. Additionally, the alloy undergoes dynamic recrystallisation and dislocation entanglement during hot extrusion. The emergence of a twinned Si phase and a dynamically precipitated nanoscale CuAl₂ phase are critical for enhancing deformation strengthening, modification strengthening, and dynamic precipitation strengthening of the extruded alloys. Full article

The thermal expansion of four alkaline-earth borates, namely Ca₃B₂O₆ (0D), CaB₂O₄ (1D), Sr₃B₁₄O₂₄ (2D) and CaB₄O₇ (3D), has been studied by in situ high-temperature powder X-ray diffraction (HTXRD). Strong anisotropy of thermal expansion is observed for the structures of Ca₃B₂O₆ (0D) and CaB₂O₄ (1D) built up from BO₃ triangles only; these borates exhibit maximal expansion perpendicular to the BO₃ plane, i.e., along the direction of weaker bonding in the crystal structure. Layered Sr₃B₁₄O₂₄ (2D) and framework CaB₄O₇ (3D) built up from various B–O groups expand less anisotropically. The thermal properties of the studied compounds compared to the other alkaline-earth borates are summarized depending on the selected structural characteristics like anion dimensionality, residual charge per one polyhedron (BO₃ BO₄), cationic size and charge, and structural complexity. For the first time, these dependencies are established as an average for both types of polyhedra (triangle and tetrahedron) occurring in the same structure at the same time. The most common trends identified from these studies are as follows: (i) melting temperature decreases with the dimensionality of the borate polyanion, and more precisely, as the residual charge per one polyhedron (triangle or tetrahedron) decreases; (ii) volumetric expansion decreases while the degree of anisotropy increases weakly when the residual charge decreases; (iii) both trends (i) and (ii) are most steady within borates built by triangles only, while borates built by both triangles and tetrahedra show more scattered values. Full article

As satellite capabilities have evolved and new observation requirements have emerged, satellites have become essential tools in disaster relief, emergency monitoring, and other fields. However, the efficiency of satellite scheduling still needs to be enhanced. Learning and optimization are symmetrical processes of solving problems. Learning problem knowledge could provide efficient optimization strategies for solving problems. A knowledge-guided parallel hybrid local search algorithm (KG-PHLS) is proposed in this paper to solve time-dependent agile Earth observation satellite (AEOS) scheduling problems more efficiently. Firstly, the algorithm uses heuristic algorithms to generate initial solutions. Secondly, a knowledge-based parallel hybrid local search algorithm is employed to solve the problem in parallel. Meanwhile, data mining techniques are used to extract knowledge to guide the construction of new solutions. Finally, the proposed algorithm has demonstrated superior efficiency and computation time through simulations across multiple scenarios. Notably, compared to benchmark algorithms, the algorithm improves overall efficiency by approximately 7.4% and 8.9% in large-scale data scenarios while requiring only about 60.66% and 31.89% of the computation time of classic algorithms. Moreover, the proposed algorithm exhibits scalability to larger problem sizes. Full article

On 20 May 2012, an M_w 5.8 earthquake, followed by an M_w 5.6 event nine days later, struck the Emilia-Romagna region in northern Italy, causing substantial damage and loss of life. Post-mainshock, several water-related phenomena were observed, such as changes in the groundwater levels in wells, the expulsion of sand–water mixtures, and widespread liquefaction evidence such as sand boils and water leaks from cracks. We analyzed the Earth’s surface displacement during and after the Emilia 2012 seismic sequence using synthetic aperture radar images from the COSMO-SkyMed satellite constellation. This analysis revealed post-seismic ground subsidence between the Sant’Agostino and Mirabello villages. Specifically, the displacement time series showed a slight initial uplift followed by rapid subsidence over approximately four to five months. This widespread ground displacement pattern likely stemmed from the extensive liquefaction of saturated sandy layers at depth. This phenomenon typically induces immediate post-seismic subsidence. However, the observed asymptotic subsidence, reaching about 2.1 cm, suggested a time-dependent process related to post-liquefaction consolidation. To test this hypothesis, we analytically estimated the consolidation subsidence resulting from earthquake-induced excess pore pressure dissipation in the layered soil deposits. The simulated subsidence matched the observed data, further validating the significant role of excess pore pressure dissipation induced by earthquake loading in post-seismic ground subsidence. Full article

Carbon assimilation and wood production are influenced by environmental conditions and endogenous factors, such as species auto-ecology, age, and hierarchical position within the forest structure. Disentangling the intricate relationships between those factors is more pressing than ever due to climate change’s pressure. We employed the 3D-CMCC-FEM model to simulate undisturbed forests of different ages under four climate change (plus one no climate change) Representative Concentration Pathways (RCP) scenarios from five Earth system models. In this context, carbon stocks and increment were simulated via total carbon woody stocks and mean annual increment, which depends mainly on climate trends. We find greater differences among different age cohorts under the same scenario than among different climate scenarios under the same age class. Increasing temperature and changes in precipitation patterns led to a decline in above-ground biomass in spruce stands, especially in the older age classes. On the contrary, the results show that beech forests will maintain and even increase C-storage rates under most RCP scenarios. Scots pine forests show an intermediate behavior with a stable stock capacity over time and in different scenarios but with decreasing mean volume annual increment. These results confirm current observations worldwide that indicate a stronger climate-related decline in conifers forests than in broadleaves. Full article

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO₂ emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a “core–subcore–periphery” emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers. Full article

As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth’s surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial–spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models. Full article