Atmospheric and Oceanic Sciences

The dilatancy of marine sands highly depends on the complex cyclic stress paths caused by waves. A series of the axial-torsional coupling cyclic loading tests are performed on the saturated marine sands under isotropically consolidated condition by using the Hollow Cyclic Apparatus (HCA). The dilatant behavior of saturated sands is investigated under complex stress paths, as well as the correspondent mathematical model. The results are summarized as follows: The volumetric strain of sands is composed of a completely reversible component and an irreversible component. The cyclic stress path has significant effects on the development of volumetric strain. The equivalent cyclic stress ratio (ESR), which is defined as the ratio of the mean value of the maximum stress in a loading cycle to the initial effective confining pressure can be used as an index to quantitatively characterize the cyclic stress paths of the soil sample under wave-induced axial-torsional loading. The accumulated volumetric strain ( ε vd,ir ) increment may be uniquely correlated to the applied ESR, which accumulates linearly with the increase of ESR. By introducing ESR, A stress-dependent normalized ε vd,ir incremental model of the saturated sands under complex cyclic loading was presented. Retrospective simulation of a laboratory test using the proposed model shows good agreement, calibrating the reliability of the model. However, the modified Byrne model significantly underestimates the volumetric strain accumulation of the saturated marine sands under the axial-torsional coupling cyclic loading, which was built on the data of direct shear tests. The proposed model provides a practical tool for estimating the long-term accumulation of volumetric strain and consequent settlement in offshore foundation soils, such as those supporting wind turbines or pipelines, under the action of complex storm-wave loading.

As remote tropical mountain regions often lack open data and traditional methods of collecting hydrometeorological data are not always feasible, this study validates an alternative participatory monitoring approach for collecting hydrometeorological data in mountainous regions in Ecuador, Honduras and Tanzania. Volunteers used analog low-cost sensors to measure air temperature, relative humidity, rainfall and water level. The measurements were validated with photos taken alongside the measurements. Data from selected stations were additionally validated against automatic sensor data using different metrics, such as the mean absolute error (MAE). In addition, errors made by frequent and non-frequent participants were compared, assessing the performance of these two target groups. In the period between May 2023 and May 2025 a total of 2,982 observations were received, whereby the majority were submitted by frequent participants (84.4%). A comparison between frequent and non-frequent users showed that the former measured with higher accuracy. The comparison with automatic sensor data showed a correlation for all parameters ranging from 0.42 to 0.96. The best results in terms of accuracy were achieved for air temperature (MAE: 0.74 °C–1.65 °C) and water level (MAE: 0.04–0.08 m). On the other hand, a high deviation was found for relative humidity (MAE: 16.76%–31.69%). This deviation was corrected by applying linear regression, resulting in moderate deviation (MAE: 5.45%–9.50%). Rainfall had a MAE ranging from 2.55 to 3.10 mm. This was mainly attributed to the low measurement frequency and the limited capacity of the rain gauges. Overall, the study showed ambivalent results, where analog thermometers and water level gauges can be considered the most promising alternatives to automatic sensor measurements. However, the hygrometers only provided moderate measurement quality, while the rain gauges used were too small to cover all rainfall in the periods analyzed.

The application of biochar in soil has demonstrated benefits in reducing greenhouse gas emissions, improving soil properties, enriching soil microbial communities, and effectively adsorbing pollutants to limit their mobility. This study focuses on the adsorption capacity of biochar for pollutants, specifically targeting Cr(VI) and atrazine. The research investigates the ability of biochar to immobilize Cr(VI) and atrazine within soil environments and explores how acidification of biochar can enhance its adsorption capacity for atrazine. The mechanism behind the enhanced adsorption capacity of acid-modified biochar is also examined. The results indicate that applying just 1% biochar can significantly improve the soil system’s capacity to immobilize Cr(VI). Fine-grained biochar shows a markedly higher adsorption and fixation capacity for Cr(VI), exhibiting up to three times the adsorption amount compared to larger biochar particles under certain conditions, with minimal desorption under acid rain leaching. Acidification was found to enhance the adsorption capacity of biochar for atrazine under certain conditions. Both the pre- and post-acidification biochar adsorption isotherms fit the Freundlich model, and adsorption capacity was notably affected by temperature, increasing with rising temperatures. The adsorption kinetics of pre-acid-modified biochar align with the Elovich model, whereas post-acidification biochar follows a pseudo-second-order kinetic model. The enhanced adsorption capacity of acid-modified biochar for atrazine is attributed to an increase in surface area, pore size, and pore volume, providing more adsorption sites and stronger van der Waals forces. Additionally, acidification alters the surface charge of biochar, leading to strong electrostatic attraction between biochar and atrazine.

Abstract Ocean acoustic thermometry is simulated in the North Atlantic using two global ocean state estimates to assess and elucidate its potential contributions to the Global Ocean Observing System. Acoustic data were simulated for transatlantic acoustic propagation from Cabo Verde to Bermuda, a 4183-km archetypal path across the mid-Atlantic Ridge. Acoustic scattering by internal waves was simulated using a range-dependent, stochastic model added to the ocean state estimates. While the Ridge blocks acoustic propagation traveling deeper than about 3000 m, and internal-wave sound-speed scintillations cause prohibitive scattering of later-arriving rays, it is apparent that O(10) resolvable acoustic ray arrivals on this path are likely. These rays generally cycled between 500 and 3000 m. The details of acoustic scattering by the small-scale processes like internal waves, together with patches of warm and salty Mediterranean water, require new observations. A simple inverse was applied to simulated travel-time data derived from a 26-year state estimate. The uncertainty of the tomographic estimates of temperature, averaged over basin-wide range and between 500–3000-m depth, was about 3 m°C. As shown by Dushaw (2019), a sparse, basin-scale array of such acoustic measurements would substantially reduce the uncertainty of basin-averaged temperature, while providing excellent temporal resolution and some depth resolution. The improvements to ocean resolution when tomographic information is combined with all other data in ocean state estimates, particularly in abyssal regions, have yet to be determined.

In the sensor array signal reception system, improving the accuracy of weak-signal detection is crucial. Traditional fixed-step algorithms struggle to balance the convergence rate (CR) and low steady-state error (SSE) owing to their inherent trade-off limitations. To address this limitation, we propose a novel variable-step-size least-mean-square (VSS-LMS) algorithm based on a modified versoria function, specifically redesigned to enhance curvature characteristics. This approach establishes dynamic coupling between error statistics and step-size factors through nonlinear mapping. It derives closed-loop equations governing parameters (α, β, and γ) relative to the smoothed instantaneous error correlation function. Consequently, an adaptive feedback system is constructed to achieve real-time adjustment through optimal step-size generation. The optimal parameters (α, β, and γ) are determined through empirical enumeration and analysis of their impact on algorithmic performance. Comparative evaluations against established VSS-LMS algorithms confirm performance: the proposed algorithm accelerates convergence while maintaining a low SSE, and exhibits robust signal recovery capabilities under low-SNR conditions with diverse interference types.

A compact and polarization-insensitive hexa-band metamaterial absorber (MMA) is designed, fabricated, and experimentally validated for S, C, X, and Ku band applications. The proposed unit cell consists of two square rings, two hexagonal rings, and two diamond-shaped copper resonators printed on an FR-4 dielectric substrate with a thickness of 1.6 mm. The structure exhibits six distinct absorption peaks at 2.178, 5.484, 8.391, 11.811, 15.858, and 18.689 GHz, with corresponding absorptivities of 99%, 98%, 99%, 99%, 99%, and 97%, respectively. The compact unit cell of 12.5 × 12.5 mm2 achieves high absorption efficiency due to strong electromagnetic coupling among the resonators. Simulated and measured results show strong agreement, confirming the accuracy of the design. Owing to its four-fold symmetric geometry, the absorber maintains stable performance under varying polarization angles and incidence angles up to 60° for both TE and TM polarizations. The electric field, magnetic field, and surface current distributions are analyzed to explain the absorption mechanism at each resonant frequency. The proposed MMA demonstrates multiband functionality, angular stability, and high absorptivity within a simple and low-cost design, making it a promising candidate for stealth, air traffic control, and satellite communication applications.

Reservoir boundary distance measurement is a key technology in geosteering drilling. In this field, it is difficult to balance detection precision and depth. This paper proposes a method to measure reservoir boundary distance using a drill-attached impulse sound source equipped with a reflector. The COMSOL Multiphysics (COMSOL) is used to construct a while-drilling reservoir model with a reflector and verify the model’s effectiveness through the real-axis integration method. Under this model, the dimensions of the reflector are analyzed, the relative ranging error under different distances is calculated, and source distance combinations and reservoir interface dip angles are considered. Moreover, the effectiveness of this method is verified through the results of ranging for two sets of actual geological parameters. These results show that the rotating parabolic reflector (depth 45 mm, opening radius 12.2 mm) has a good energy bunching effect. When the dominant excitation frequency of the sound source is 8 kHz, and the source distance combination is 2 m and 4 m, the minimum relative ranging error for the reservoir boundary at 7 m is 2.1%. The relative error becomes smaller when the reservoir boundary dip angle and source distance are smaller. When the source distance is 2 m or 7 m, and the dip angle is between [−20, 20] degrees, the relative error is below 15%. Simulations with actual formation parameters indicate that the proposed method attains good ranging precision.

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High-intensity interval training (HIIT) provides substantial cardiovascular benefits; however, precise monitoring typically requires expensive devices. These systems are feasible in research laboratories but are costly for schools and the fitness industry. Low-cost, validated devices are required to facilitate broader implementation. A cross-sectional study was conducted with 213 students (173 men and 40 women) from the Catholic University of Valencia, Spain. The participants completed an HIIT protocol consisting of five 3 min blocks. Heart rate (HR) was recorded using a Moofit HW401 armband (ANT+ technology). Ratings of perceived exertion (RPE, Omni-Res scale) and the Wint index were also obtained. Pearson correlations were computed between reserve heart rate (HRr), RPE, and Wint index during the warm-up phases (T1, T2) and HIIT, stratified by sex, age, and body mass index (BMI). HRr was strongly correlated with the Wint index (r = 0.95, p < 0.0001) and moderately correlated with RPE (r = 0.235, p = 0.001). No significant sex differences were observed (men 83.66 ± 8.18% vs. women 82.31 ± 10.89%; p > 0.05). Correlations were weaker in participants with extreme BMI values (n < 10, obese). The Moofit HW401 armband showed consistent agreement between HRr, RPE, and Wint index during HIIT, supporting its practical use for group monitoring in educational settings, pending formal validation against gold standards.

Accurate spatiotemporal alignment of multi-view video streams is essential for a wide range of dynamic-scene applications such as multi-view 3D reconstruction, pose estimation, and scene understanding. However, synchronizing multiple cameras remains a significant challenge, especially in heterogeneous setups combining professional- and consumer-grade devices, visible and infrared sensors, or systems with and without audio, where common hardware synchronization capabilities are often unavailable. This limitation is particularly evident in real-world environments, where controlled capture conditions are not feasible. In this work, we present a low-cost, general-purpose synchronization method that achieves millisecond-level temporal alignment across diverse camera systems while supporting both visible (RGB) and infrared (IR) modalities. The proposed solution employs a custom-built LED Clock that encodes time through red and infrared LEDs, allowing visual decoding of the exposure window (start and end times) from recorded frames for millisecond-level synchronization. We benchmark our method against hardware synchronization and achieve a residual error of 1.34 ms RMSE across multiple recordings. In further experiments, our method outperforms light-, audio-, and timecode-based synchronization approaches and directly improves downstream computer vision tasks, including multi-view pose estimation and 3D reconstruction. Finally, we validate the system in large-scale surgical recordings involving over 25 heterogeneous cameras spanning both IR and RGB modalities. This solution simplifies and streamlines the synchronization pipeline and expands access to advanced vision-based sensing in unconstrained environments, including industrial and clinical applications.

Image-Based Visual Servoing (IBVS) systems often suffer from instability due to measurement noise, modeling errors, and external disturbances. To address these issues, this study proposes a Visual Predictive Control framework integrating Radial Basis Function (RBF) and Extended Kalman Filter (EKF) coupled state-disturbance estimation and task-oriented K-means clustering. First, a feedback linearization Model Predictive Control (MPC) law is designed to handle system nonlinearities and physical constraints. Second, a coupled estimation mechanism is established where the EKF suppresses noise while the RBF network learns lumped disturbances. Crucially, to optimize network efficiency, a task-oriented K-means clustering method is introduced to select RBF centers based on the nominal IBVS path. Lyapunov analysis confirms the Uniformly Ultimately Bounded (UUB) stability. Simulation results demonstrate that the proposed method significantly reduces estimation errors and improves tracking accuracy compared to traditional schemes. Ultimately, this approach enhances the robustness and engineering practicality of robotic visual servoing through the deep coordination of control and estimation.

This paper explores the role of metrology in the assessment of image quality in the field of radiomics. Image Quality Assessment (IQA) is central to ensuring the reliability and reproducibility of radiomic analyses, as it directly affects the accuracy of feature extraction and segmentation, ultimately impacting diagnostic outcomes. From the analysis of approximately 20,000 papers sourced from three databases (PubMed, Scopus, IEEE Xplore), last searched in December 2025, the need for standardized imaging protocols and quality control measures emerges as a critical theme. Studies were included if they involved radiomic feature extraction and evaluated the impact of image quality variations on feature robustness and no formal risk-of-bias assessment was performed. A total of 105 studies were included, covering different medical imaging modalities. Across the included studies, noise, motion, acquisition and reconstruction parameters, and other artifacts consistently emerged as major sources of radiomic feature instability. Indeed, in most papers, IQA is neglected, while the effect of poor-quality images is reported. This research identifies and discusses the relevant issues reported in clinical practice, as well as the main metrics adopted for image quality evaluation. Through a comprehensive review of current literature and an analysis of emerging trends, this paper highlights the urgent need for innovative solutions in image quality metrics tailored to radiomics applications.

The increasing depth and complexity of underground metal mining has raised ventilation energy demands and safety risks, driving the need for intelligent and more adaptive ventilation systems. Ventilation on Demand (VOD) systems dynamically adjust airflow using real-time operational and environmental data to improve energy efficiency while maintaining safety. Although VOD has been applied for over a decade, deeper and more extreme mining environments associated with critical minerals extraction introduce new challenges and opportunities. VOD systems rely on the tight integration of hardware, sensing, optimization-based control, and flexible infrastructure as mining operations evolve. The application of Artificial Intelligence (AI) introduces significant opportunities to further enhance and adapt VOD systems to these emerging challenges. This work presents a comprehensive review of the state of the art in AI integration within VOD technologies, covering sensing and prediction models, control strategies, and optimization frameworks aimed at improving energy efficiency, safety, and overall system performance. Findings show an increasing use of hybrid deep learning architectures, such as CNN-LSTM and Bi-LSTM, for forecasting, as well as AI-enabled optimization methods for sensor and actuator placement. Key research gaps include a reliance on narrow AI models, limited long-term predictive capabilities for maintenance and strategic planning, and a predominance of simulation-based validation over real-world field deployment. Future research directions include the integration of generative and generalized AI approaches, along with human–cyber–physical system (Human-CPS) designs, to enhance robustness and reliability under the uncertain and dynamic conditions characteristic of deep underground mining environments.

Ancient Chinese architecture, with its typical symmetrical structures, curved roofs, and upturned eaves presenting a unique architectural aesthetic, is a treasure of Chinese culture. Recently, unmanned aerial vehicle oblique photogrammetry and laser scanning technology have greatly facilitated the realistic replication of ancient buildings and have become crucial data sources for the HBIM of ancient buildings. However, parameter extraction and geometric model representation are more difficult because of the curved surfaces and upturned eaves of traditional Chinese roofs. As symmetrical features are typical of ancient Chinese architecture, the parameter quantity and modelling difficulty of the model representation can be effectively reduced by recognizing the symmetrical structure of traditional Chinese roofs and using “mirror replication” to quickly generate the other half of the model. Accurate symmetry detection and highly efficient parameter extraction are crucial for the HBIM of traditional Chinese roofs. Therefore, in this study, a deep learning network, namely, TCRSym-Net, is proposed to identify the symmetry from point clouds of traditional Chinese roofs. Each roof point cloud is then relocated and reoriented to obtain longitudinal and cross sections, and parametric modelling scripts are coded in Dynamo to model traditional Chinese roofs via curve lofting and solid Boolean operations. The experimental results reveal that the symmetry detection network is effective for symmetry detection, and five different types of traditional Chinese roofs are successfully recreated, which confirms the dependability of the method.

This paper proposes an acoustic analysis system to help improve saxophone performance skills. The system combines direct support for performance movements by a robot with indirect support by presenting performance information. By sensing the performance audio and performing real-time acoustic analysis, the system presents the learner with information about their performance and their playing habits. The performance information presented to the learner includes pitch, volume, and playing timing. For performance habit analysis, a Markov model with pitch as the state and an internal probability parameter that indicates the quality of the performance evaluation as the pitch transitions are defined. In the experiment, we conducted a pilot study targeting experienced saxophone players and a beginner saxophone player to verify the effectiveness of the proposed system. The experiment showed that the MAE of the played pitch was significantly reduced by using the proposed system.

Personalized federated learning (pFL) aims to address data heterogeneity by training client-specific models. However, it faces two critical challenges under few-shot conditions. First, existing methods often overlook the hierarchical structure of neural representations, limiting their ability to balance generalization and personalization. Second, recent approaches incorporate representation-level inductive biases that typically rely on rigid assumptions, such as fixed perturbation patterns or compact class clusters, making them vulnerable to distribution shifts in federated environments. To overcome these limitations, we propose pFedH2A, a novel hierarchical framework incorporating brain-inspired mechanisms, tailored for personalized federated learning in few-shot scenarios. First, we design a dual-branch hypernetwork (DHN) that employs two structurally distinct branches to generate aggregation weights. Each branch is biased toward capturing either low-level shared features or high-level personalized representations, enabling fine-grained personalization by mimicking the brain’s division of perceptual and representational processing. Second, we introduce a relation-aware module that learns an adaptive similarity function for each client, supporting few-shot classification by measuring whether a pair of samples belongs to the same class without relying on rigid prototype assumptions. Extensive experiments on public image classification datasets demonstrate that pFedH2A outperforms existing pFL baselines under few-shot scenarios, validating its effectiveness.

Soil organic matter (SOM) is a key indicator of arable land quality and the global carbon cycle; accurate regional-scale SOM estimation is vitally significant for sustainable agricultural development and climate change research. This study evaluates a multisource data-fusion approach for improving cropland SOM prediction in Yucheng City, Shandong Province, China. We applied a Local Energy Weighted Discrete Wavelet Transform (LEW-DWT) to fuse multi-temporal Landsat 8 imagery (2014–2023). Quantitative analysis (e.g., Information Entropy and Average Gradient) demonstrated that LEW-DWT effectively preserved high-frequency spatial details and texture features of fragmented croplands better than traditional DWT and simple splicing methods. These were combined with 41 environmental predictors to construct composite Ev–Tn–Mm features (environmental variables, temporal NDVI features, and multi-temporal multispectral information). Random Forest (RF) and Convolutional Neural Network (CNN) models were trained and compared to assess the contribution of the fused data to SOM mapping. Key findings are: (1) Comparative analysis showed that the LEW-DWT fusion strategy achieved the lowest spectral distortion and highest spatial fidelity. Using the fused multitemporal dataset, the CNN attained the highest predictive performance for SOM (R2 = 0.49). (2) Using the Ev–Tn–Mm features, the CNN achieved R2 = 0.62, outperforming the RF model (R2 = 0.53). Despite the limited sample size, the optimized shallow CNN architecture effectively extracted local spatial features while mitigating overfitting. (3) Variable importance analysis based on the RF model reveals that mean soil moisture is the primary single variable influencing the SOM, (relative importance 15.22%), with the NDVI phase among time-series features (1.80%) and the SWIR1 band among fused multispectral bands (1.38%). (4) By category, soil moisture-related variables contributed 45.84% of total importance, followed by climatic factors. The proposed multisource fusion framework offers a practical solution for regional SOM digital monitoring and can support precision agriculture and soil carbon management.

As a non-contact identification technology, RFID (Radio Frequency Identification) is widely used in various Internet of Things applications. However, RFID systems are highly vulnerable to diverse attacks due to the openness of communication links between readers and tags, leading to serious security and privacy concerns. Numerous RFID authentication protocols have been designed that employ hash functions and symmetric cryptography to secure communications. Despite these efforts, such schemes generally exhibit limitations in key management flexibility and scalability, which significantly restricts their applicability in large-scale RFID deployments. Confronted with this challenge, public key cryptography offers an effective solution. Taking into account factors such as parameter size, computational complexity, and resistance to quantum attacks, the NTRU algorithm emerges as one of the most promising choices. Since the NTRU signature algorithm is highly complex and requires large parameters, there are currently only a few NTRU encryption-based RFID authentication protocols available, all of which exhibit significant security flaws—such as supporting only one-way authentication, failing to address public key distribution, and so on. Moreover, performance evaluations of the algorithm in these contexts are often incomplete. This paper proposes a mutual authentication protocol for RFID based on the NTRU encryption algorithm to address security and privacy issues. The security of the protocol is analyzed using the BAN-logic tools and some non-formalized methods, and it is further validated through simulation with the AVISPA tool. With the parameter set (N, p, q) = (443, 3, 2048), the NTRU algorithm can provide 128 bits of post-quantum security strength. This configuration not only demonstrates greater foresight at the theoretical security level but also offers significant advantages in practical energy consumption and computation time when compared to traditional algorithms such as ECC, making it a highly competitive candidate in the field of post-quantum cryptography.