Earth and Environmental Sciences

Synthetic aperture radar (SAR) target images are highly sensitive to aspect angle, while practical data acquisition usually provides only sparse observations over limited viewpoints. This leads to severe data scarcity at unseen aspect angles and makes cross-angle generation prone to scattering-structure distortion and background statistical mismatch. Existing SAR image generation methods either focus on distribution matching without sufficiently exploiting scattering-related structural cues, or emphasize angle conditioning while failing to jointly preserve physically plausible dominant scattering-response variations and realistic background speckle statistics at unseen aspect angles. To address this issue, we propose a physically consistent framework for SAR image generation at unseen aspect angles. The proposed method introduces an ASC-inspired sparse scattering-structure prior to approximate the dominant scattering responses in the SAR image plane. Rather than performing full parametric ASC inversion, this prior serves as a differentiable and angle-aware structural proxy that guides the generator toward synthesizing SAR images with structurally plausible scattering layouts. In addition, a dual-consistency scheme is introduced to jointly enforce target-region scattering consistency and background-region statistical consistency, thereby improving the physical realism of the generated results in both the target and background regions. Extensive experiments under strict unseen-angle interpolation and hold-out protocols demonstrate that the proposed method consistently outperforms representative baselines in image fidelity, target-region scattering consistency, background statistical consistency, and angle-conditioned consistency. Further visualization and ablation studies verify the critical role of the ASC-inspired sparse scattering-structure prior in physically consistent SAR view completion.

Microcystin-LR (MC-LR), a natural toxin produced by cyanobacteria, poses a significant threat to human health and ecological systems. Rapid and accurate quantification of MC-LR plays a critical role in the early warning systems and environmental risk assessment of eutrophic water bodies. Herein, we identified a unique UV-induced degradation mechanism of MC-LR mediated by a newly developed ratiometric fluorescence probe ASS12 . Under UV irradiation, the probe undergoes a specific reaction with MC-LR, during which its fluorescence color changes sequentially from yellow to red and ultimately to blue within 20 min. This dynamic response enables visual detection of MC-LR by the naked eye and significantly enhances the ratiometric fluorescence signal for MC-LR at the I 465 / I 555 ratio by 77-fold. For the first time, we found that the long alkyl chain and aldehyde group of the probe serve as key molecular recognition sites during MC-LR detection. Furthermore, the ratiometric fluorescence signal of the probe exhibits a strong correlation with the concentration of MC-LR across different regions of plateau lakes, effectively reflecting the spatial distribution patterns of MC-LR within these aquatic environments. Using a multivariate linear parametrization model linking the fluorescence ratio and water quality parameters, the distribution of MC-LR in lakes can be determined. This study provides a valuable theoretical foundation and technical approach for the development of novel MC-LR monitoring tools and the visual assessment of water eutrophication.

Underground mining operations depend heavily on vertical shafts for access, ventilation, and ore transport, making their structural integrity and safety critical to overall mine performance. Traditional shaft inspections, though rigorous, are limited by human accessibility, environmental hazards, and subjective evaluation. This study presents the development and initial testing of a novel unmanned aerial vehicle (UAV) system designed specifically for shaft inspections in deep mining environments. The research focuses on the GG-1 shaft in Kwielice, Poland—the country’s deepest operational shaft—where challenging conditions such as high ventilation airflow, confined geometry, and absence of GNSS signals necessitated innovative solutions. A custom-built hexacopter equipped with high-resolution cameras and photogrammetric capabilities was deployed to capture detailed visual and spatial data. This article presents complementary path of UAV evolution, from concept, early development stage and results without positioning system through to the description of final results including positioning system and all six cameras until results of high-altitude flights. Results demonstrate that UAV-based inspection can deliver sufficient precision for identifying structural irregularities, documenting shaft infrastructure, and enhancing safety monitoring. The findings highlight the potential of UAV technology as a complementary tool to conventional inspections, offering improved data quality, reduced risk to personnel, and a new approach to shaft maintenance.

Abstract. Effective science communication plays a crucial role in enhancing public understanding of Quaternary science. One promising strategy involves highlighting the interconnectedness of Quaternary sites, archaeology, and human culture. Despite the recent increased focus on science communication within the geosciences, the significance and effectiveness of highlighting such geocultural connections in communicating about Quaternary geoheritage sites have rarely been examined experimentally. This study evaluates the effectiveness of including geocultural context in educational videos for communicating the significance of Quaternary geoheritage sites in United Arab Emirates (UAE) and Oman. An online experiment was conducted to evaluate the effects of videos produced with input from academics, museum professionals, and heritage administrators from the region. The study compares the impact of two different 9 min videos developed in collaboration with academics, museum professionals, and heritage administrators from the region – one emphasising the geocultural context, and the other focusing solely on Quaternary science –. The impacts on participants' knowledge, interest, and perception of Quaternary geoheritage sites were assessed. The videos enhanced participants' self-reported knowledge of Quaternary geoheritage sites and increased their interest. Although the statistical results remain tempered by uncertainty, the overall pattern suggests that geocultural framing can foster a stronger and more durable sense of the importance of conserving Quaternary geoheritage, especially among people with less prior knowledge of such sites. The number of participants of this study is small and demographically limited to highly educated, relatively young adults with pro-nature attitudes, but this study demonstrates the value of integrating geocultural context in communicating the importance of Quaternary science and raising awareness of Quaternary geoheritage.

In the field of underwater acoustics, two methods are usually used to study the propagation of acoustic signals in seawater media. The first method is wave theory, which applies rigorous mathematical methods, combined with known fixed solution conditions, to solve the wave equation and study the change of amplitude and phase of acoustic signals in space. The second method is ray theory, in which the propagation of sound waves in a seawater medium is regarded as the propagation of sound lines in the medium in the high-frequency case; the change of sound intensity, the propagation time, and the propagation range of the sound lines in space are studied. Due to the approximation of ray theory, it is difficult to apply it in low-frequency shallow water conditions, and the importance of wave theory is particularly prominent in the context of the increasingly low frequency of sonar action. How to be able to solve the fluctuation equations accurately and quickly has become the focus of research by scholars in various countries.As shown in Figure 1, under the assumption of linear acoustics, the basic control equation of underwater sound propagation, the wave equation (WE), can be obtained according to the equation of motion, continuity equation, and state equation. Due to the spatial and temporal complexity of the wave equation, it is difficult to solve it directly. Generally, the strategy adopted in practice is to convert the Fourier transform to the frequency domain to obtain the Helmholtz equation (HE) (Jensen et al., 2011).Approaches to solving the Helmholtz equation are divided into two methods: direct and indirect. Solving the Helmholtz equation directly is difficult in practical applications, and the amount of computation is still staggering even today, despite the rapid development of computers. Liu et al. (2021) used the secondorder and fourth-order difference formats to solve the Helmholtz equation directly for Lloyd's mirror example with analytical solution, which took more than 1 hour and more than 1,000 iterations to reach convergence on a computer with 360 CPU cores, and the accuracy basically meets the requirements compared with the analytical solution, which is less usable in the actual sound field calculation.Most of the solution ideas are solved by various simplified theories of Helmholtz equations; in the process of long-term exploration, simplified models based on the wavenumber integration (WI) theory (Schmidt and Glattetre, 1985), the normal mode (NM) theory (Godin, 1992), and the parabolic equation (PE) theory (Collis et al., 2008) are used. At present, most of the numerical solution methods are based on the development of the above theories or a combination of each other; these methods have their own advantages and disadvantages, and restrictions on the use of conditions, and there is no model that can be applied in any case.Traditional numerical methods for solving partial differential equations (PDEs) are essentially solved discretely. For example, the finite difference method (FDM) (Stephen, 1988) solves partial differential equations by replacing differentiation with a difference approximation of the derivatives at grid points. The finite element method (FEM) (Thompson, 2006) solves the problem by dividing the solution domain into a finite number of small elements and then approximating the functional form of the solution on the elements. The finite volume method (FVM) (Fogarty and LeVeque, 1999) is based on the concept of control volume and divides the solution domain into multiple control volumes. The spectrum method (SM) (Tu et al., 2023) uses global basis functions (e.g., sine and cosine functions) to approximate the solution. Methods such as the boundary element method (BEM) (Lu et al., 2008) are widely used for solving PDEs, transforming the problem into integral equations on the boundary and discretizing only the boundary. These numerical discretization methods are currently widely used in underwater acoustic propagation model calculations and have an irreplaceable role for a short period of time.With the continuous development of artificial intelligence (AI) and deep learning (DL), the application of AI is not only limited to traditional tasks such as computer vision, natural language processing, and speech recognition; the application of AI in various disciplines is more and more extensive, and the combination of AI and other disciplines has become one of the main research directions in this field. The combination of artificial intelligence and other disciplines has become one of the main research directions in this field. One of the applications of combining AI with mathematics, physics, and other disciplines is solving partial differential equations (Han et al., 2018).In 1989, Cybenko (Cybenko, 1989) proved that a perceptron neural network with hidden layers has the ability to approximate any function when the activation function is a Sigmoid function, and in 1991, Hornik et al. (1989) further proved that the same applies when the activation function is any non-constant function. This is the fundamental theoretical basis of deep learning, universal approximation theorem (UAT), which describes the property that feedforward neural networks with a sufficient number of hidden units can approximate any continuous function to arbitrary accuracy, which is also known as the theoretical basis for neural networks to be able to solve PDEs.With the rapid development of computer hardware technology and the arrival of the era of big data and deep learning, deep learning frameworks such as TensorFlow (Dean and Monga, 2015), PyTorch (Paszke, 2019), and PaddlePaddle (Ma et al., 2019) have been gradually generated, perfected, and developed, and AI is rapidly becoming a powerful tool for solving complex scientific problems. In particular, the application of AI is opening up new possibilities in the field of solving PDEs. The core of the emerging field of artificial intelligence for partial differential equations (AI4PDE) lies in the use of neural networks and other machine learning techniques to approximate the solution of PDEs, a method called a deep learning solver. Deep learning solvers are able to learn from data and automatically capture complex patterns and features of the problem to provide faster and more accurate solutions than traditional numerical methods. Raissi et al. 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Introduction Coordinating multiple unmanned surface vehicles (USVs) in coastal waters requires simultaneous consideration of COLREGs compliance, real-time response, energy efficiency, fault tolerance, and semantic scene understanding. Existing approaches typically address only part of this problem and provide limited support for integrated fleet-level coordination. Methods This paper proposes NSC-Marine, a neuro-symbolic framework that conceptually addresses these coupled constraints. It combines multimodal causal perception, LLM-based rule reasoning, energy-aware motion planning, and distributed fault reconfiguration within a dual-rate control architecture. To improve deployment safety, semantic reasoning is used to generate structured constraints, while low-level motion execution remains under deterministic planning and control. Results The framework is evaluated strictly within a physics-based simulation environment with approximately 70% overall fidelity, involving 1,000 trials of 20–100 USVs under Beaufort Scale 3–5 conditions. Under these simulated conditions, NSC-Marine achieves 88.7% ± 2.3% COLREGs compliance, 82.3% mission success under compound faults, and 13.6% energy reduction relative to the RLCA baseline, while maintaining a 320 ms critical-path latency. Discussion These metrics reflect an idealized simulation baseline and must not be generalized to physical deployment readiness. Real-world performance remains unvalidated, and staged hardware-in-the-loop testing and field trials are required to characterize the system’s actual behavior under physical disturbances.

Floating plastic debris can provide long-lived substrates for attached organisms, but reconstructing the drift history of small consumer items remains difficult. Here we report a colonized plastic bottle cap collected in the northwest Pacific. The cap hosted a tube-building polychaete and an associated assemblage including benthic foraminifera. We combined (1) a census of the fouling community, (2) chamber-level δ 18 O and δ 13 C measurements from two Rosalina globularis tests, and (3) Lagrangian drift simulations driven by surface currents to constrain the cap's likely trajectory and timescale. The assemblage comprised nine taxa and 307 individuals, and was strongly dominated by spirorbid tubes ( Spirorbis sp.; 76.5%). For specimen #021, δ 18 O-derived temperatures were 26.0 °C for the pooled early chambers (p–f-4), 26.8 °C for f-3, 29.9 °C for f-2, 23.3 °C for f-1, and 22.3 °C for the final chamber; the final-chamber estimate was close to the in situ sea-surface temperature at collection (21.7–21.8 °C). The final chamber of specimen #005 yielded an estimated temperature of 27.0 °C but should be treated as a reference value because of its very small carbonate mass. Drift simulations suggested that trajectories reaching the sampling site within ∼1–3 months most frequently originated from the Philippine region and were transported northward by the Kuroshio system. This multi-proxy approach illustrates how benthic biofoulers, including foraminifera, can help reconstruct the dispersal history of small plastic items and highlights the potential for occasional long-distance transport of coastal benthic taxa on tiny rafts.

Understanding shear rate-dependent mechanical behavior and fracture evolution is essential for evaluating the stability of rock engineering structures under shear loading. In this study, direct shear tests and real-time acoustic emission (AE) monitoring were conducted on sandstone. Three shear rates were used: 0.015, 0.03, and 0.06 mm/s. All tests were performed under a constant normal stress of 5 MPa. The results show that shear rate strongly affects the shear response of sandstone. As shear rate increases, the shear strength and residual strength increase by approximately 15.28% and 24.52%, respectively. This rate-strengthening behavior is mainly related to the time-dependent propagation of microcracks and inertial effects under rapid loading. AE activity also shows clear rate-dependent characteristics. The cumulative AE event count decreases with increasing shear rate, whereas the average energy per event increases. This indicates that rapid loading suppresses the number of microcracking events but promotes more intense energy release from individual events. Frequency-domain analysis shows a transition from a multi-band frequency distribution at low shear rates to low-frequency dominance at high shear rates. This shift reflects the change from distributed microcracking to localized shear band development. The Gaussian Mixture Model (GMM) classification of RA-AF parameters further confirms this transition. With increasing shear rate, the proportion of shear cracks increases from 34.30% to 57.42%, while the proportion of tensile cracks decreases from 65.70% to 42.58%. AE source localization also shows a clear spatial transition. AE events change from a dispersed volumetric distribution at low shear rates to concentrated localization along the shear band at high shear rates.

The BepiColombo spacecraft, designed by ESA and JAXA, is currently in its cruise phase toward Mercury. Among the scientific investigations is the Mercury Orbiter Radio-science Experiment (MORE), which will exploit a multi-frequency microwave tracking system with an advanced Ka-band transponder to achieve its objectives pertaining to Mercury’s geodesy and fundamental physics. Leveraging precise measurements from this state-of-the-art radio tracking system, MORE is expected to provide new insights into Mercury’s interior, refining and expanding upon the findings of the MESSENGER mission. This work evaluates the performance of MORE’s gravity and rotation experiment, specifically assessing how BepiColombo’s improved radio tracking data can reduce uncertainties in the determination of Mercury’s gravity field, Love number k2, and rotational state. Differently from previous covariance analyses, this work includes errors in the dynamical model to assess the experiment’s performance under controlled mismodeling conditions. We present the results of a numerical simulation covering BepiColombo’s extended two-year orbital phase, with scientific operations set to begin in 2027.

Maintenance dredging is a common practice in estuaries to ensure navigability and support port operations, but it also represents a widespread anthropogenic disturbance. Its effects on planktonic communities, however, remain poorly understood, particularly for mesozooplankton, a key component linking primary producers and higher trophic levels. This study evaluated the short-term responses of mesozooplankton communities and environmental variables to a maintenance dredging operation in the Guadalquivir estuary (SW Spain). Sampling was conducted at two sites with contrasting salinity conditions (Salinas: polyhaline and Puntalete: mesohaline) across three distinct phases: pre-, during, and post-dredging. Among the environmental variables analysed, pH showed a significant but transient decrease during dredging, while chlorophyll-a varied significantly among sampling phases. Turbidity tended to increase during dredging, although differences were not significant, and most other physicochemical variables remained largely unchanged. Overall, the observed environmental patterns were compatible with a short-term dredging signal but could not be confidently distinguished from the high natural temporal variability characteristic of the estuary. Biologically, the mesozooplankton community exhibited contrasting site-specific responses. In the polyhaline zone, species richness declined significantly and community composition shifted, with only partial recovery after dredging. Conversely, the mesohaline zone maintained a stable community composition but showed a marked decline in total abundance, representing the clearest biological response observed during dredging. These contrasting responses observed between sites suggest that the local ecological context may influence how estuarine ecosystems respond to short-term dredging-related disturbances.

In Unmanned Aerial Vehicle (UAV) object detection tasks, complex lighting conditions and variable weather render robust all-weather perception challenging when relying solely on the visible modality. Although infrared modalities can provide complementary information, the reliability of individual modalities is highly scene-dependent. Existing multimodal detection methods typically adopt static fusion strategies, which ignore spatial heterogeneity of modal reliability and under-explore spatial-frequency collaborative representation, thus limiting detection robustness in dynamic environments. To address these issues, this paper proposes a Dual-domain Enhanced Adaptive Fusion Network (DEAF-Net), with two core innovative modules to tackle the above challenges. First, the Dual Domain Progressive Refinement (DDPR) module mitigates feature degradation caused by poor imaging conditions via the joint design of frequency-domain learnable filtering and scale-aware contextual refinement in the spatial domain, effectively suppressing noise, enhancing textures, and yielding a purified feature basis for fusion. Second, the Consistency–Discrepancy Guided Fusion (CDGF) strategy leverages the selective scanning mechanism of VMamba to model consistent and differential patterns across modalities, dynamically generates local modal contribution maps for adaptive fusion, and integrates global scene prior via entropy weights for calibration. Extensive experiments on the DroneVehicle and VEDAI datasets show that DEAF-Net outperforms mainstream multimodal detection methods, achieving mAP@0.5 scores of 81.9% and 76.2%, respectively, while delivering improved robustness in low-light, dense fog, and sparse-category scenarios.

Detecting small targets in Unmanned Aerial Vehicle (UAV) imagery is challenging due to low pixel coverage, complex backgrounds, and information loss during downsampling. Existing detectors lack explicit mechanisms for enhancing weak target signals. We propose GHF-DETR, a Transformer-based detector featuring three collaboratively designed modules. First, a Heterogeneous Multi-Path Convolutional Network (HMC) backbone uses partial convolution and gated linear units to reduce computational redundancy while maintaining discrimination of small-object features. Second, a Dynamic Multi-Scale Focusing (DMSF) module integrates learned offset alignment with multi-kernel depthwise convolutions for cross-scale feature fusion. Third, a High-Frequency Selective Preservation (HSP) downsampling module combines space-to-depth convolution with 2D Discrete Wavelet Transform (DWT) to compensate for information loss in both spatial and frequency domains. On VisDrone2019, GHF-DETR achieves 33.1% mAP@0.5 and 18.6% mAP@0.5:0.95 with 15.4 GFLOPs and 7.59 M parameters, improving over the DFINE-n baseline by 5.4% and 3.1%, respectively, with AP_S reaching 10.1%. Generalization is validated on NWPU VHR-10. These results demonstrate that GHF-DETR achieves a favorable accuracy–efficiency balance for efficient UAV small-object detection.