bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Mutation Strategy Selection in Differential Evolution Using Perturbed Adaptive Pursuit
Authors: Bajpai Prathu , Anicho Ogbonnaya , Nagar Atulya K. , Bansal Jagdish Chand 
Published: August 2024

AbstractDiverse mutant vectors play a significant role in the performance of the Differential Evolution (DE). A mutant vector is generated using a stochastic mathematical equation, known as mutation strategy. Many mutation strategies have been proposed in the literature. Utilizing multiple mutation strategies with the help of an adaptive operator selection (AOS) technique can improve the quality of the mutant vector. In this research, one popular AOS technique known as perturbation adaptive pursuit (PAP) is integrated with the DE algorithm for managing a pool of mutation strategies. A community-based reward criterion is proposed that rewards the cumulative performance of the whole population. The proposed approach is called ‘Dynamic Mutation Strategy Selection in Differential Evolution using Perturbed Adaptive Pursuit (dmss-DE-pap)’. The performance of dmss-DE-pap is evaluated over the 30D and 50D optimization problems of the CEC 2014 benchmark test suite. Results are competitive when compared with other state-of-the-art evolutionary algorithms and some recent DE variants.
DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition
Authors: Shianios Demetris , Kolios Panayiotis S. , Kyrkou Christos 
Published: August 2024

AbstractThe integration of Unmanned Aerial Vehicles (UAVs) with artificial intelligence (AI) models for aerial imagery processing in disaster assessment, necessitates models that demonstrate exceptional accuracy, computational efficiency, and real-time processing capabilities. Traditionally Convolutional Neural Networks (CNNs), demonstrate efficiency in local feature extraction but are limited by their potential for global context interpretation. On the other hand, Vision Transformers (ViTs) show promise for improved global context interpretation through the use of attention mechanisms, although they still remain underinvestigated in UAV-based disaster response applications. Bridging this research gap, we introduce DiRecNetV2, an improved hybrid model that utilizes convolutional and transformer layers. It merges the inductive biases of CNNs for robust feature extraction with the global context understanding of Transformers, maintaining a low computational load ideal for UAV applications. Additionally, we introduce a new, compact multi-label dataset of disasters, to set an initial benchmark for future research, exploring how models trained on single-label data perform in a multi-label test set. The study assesses lightweight CNNs and ViTs on the AIDERSv2 dataset, based on the frames per second (FPS) for efficiency and the weighted F1 scores for classification performance. DiRecNetV2 not only achieves a weighted F1 score of 0.964 on a single-label test set but also demonstrates adaptability, with a score of 0.614 on a complex multi-label test set, while functioning at 176.13 FPS on the Nvidia Orin Jetson device.
Investigating the drivers of urban cover-collapse sinkholes in shanghai: analyzing dominant factors and proposing mitigation strategies
Authors: Li Bing , Wang Hanmei , Tang Hang 
Published: August 2024

Abstract
Urban cover-collapse sinkholes pose a significant global challenge due to their destructive impacts. Previous studies have identified groundwater fluctuations, subsurface soil conditions, pipeline leakage, precipitation, and subterranean construction activities as key contributors to these phenomena. However, unique geological settings across different urban environments lead to variations in the primary factors influencing sinkhole formation. This study focuses on Shanghai, a city notable for its extensive urbanization and rich historical context, to explore the dynamics of sinkholes within urbanized areas worldwide. We employ spatial analysis and statistical methods to examine data on sinkholes recorded in the past two decades in Shanghai, correlating these events with the city’s shallow sand layer, ground elevation, and proximity to surface water. Our goal is to identify the dominant factors governing sinkhole occurrence in Shanghai and to lay the groundwork for their effective scientific management and prevention. Key findings indicate that most sinkholes in the area are associated with a thin shallow sand layer, low to moderate ground elevations, and the absence of nearby rivers. Additionally, many sinkholes correlate with subterranean voids within the confined aquifer beneath the cohesive soil layer. The lack of historical river channels, obscured by urban development, also indirectly contributes to sinkhole formation. We recommend enhancing urban river management and drainage systems to mitigate potential damage from water accumulation.
Global Environmental Constraints on Magnetic Reconnection at the Magnetopause From In Situ Measurements
Authors: Michotte de Welle B. , Aunai N. , Lavraud B. , Génot V. , Nguyen G. , Ghisalberti A. , Smets R. , Jeandet A. 
Published: August 2024

AbstractProgress in locating the X‐line on the magnetopause beyond the atypical due south interplanetary magnetic field (IMF) condition is hampered by the fact that the global plasma and field spatial distributions constraining where reconnection could develop on the magnetopause are poorly known. This work presents global maps of the magnetic shear, current density and reconnection rate, on the global dayside magnetopause, reconstructed from two decades of measurements from Cluster, Double Star, THEMIS and MMS missions. These maps, generated for various IMF and dipole tilt angles, offer a unique comparison point for models and observations. The magnetic shear obtained from vacuum magnetostatic draping is shown to be inconsistent with observed shear maps for IMF cone angles in 12.5° ± 2.5° ≤ |θco| ≤ 45° ± 5°. Modeled maximum magnetic shear lines fail to incline toward the equator as the IMF clock angle increases, in contrast to those from observations and MHD models. Reconnection rate and current density maps are closer together than they are from the shear maps, but this similarity vanishes for increasingly radial IMF orientations. The X‐lines maximizing the magnetic shear are the only ones to sharply turns toward and follow the anti‐parallel ridge at high latitude. We show the behavior of X‐lines with varying IMF clock and dipole tilt angles to be different as the IMF cone angle varies. Finally, we discuss a fundamental disagreement between X‐lines maximizing a given quantity on the magnetopause and predictions of local X‐line orientations.
Soil Microbial Community in 47 Chinese Forest Sites: Biogeographic Patterns and Links With Soil Dissolved Organic Matter
Authors: Zhang Zongxiao , Zhang Qiang , Wang Yinghui , Zhang Peng , Deng Guisen , Sun Guodong , Yang Yuanxi , Jiang Ke , Jiao Shuo , Guo Xue , Wang Junjian 
Published: August 2024

AbstractSoils in forested ecosystems are extremely heterogeneous and represent a critical component of terrestrial ecosystems. Despite their substantial ecological value, the geographic characteristics, ecological processes, and coexistence of microbial communities in forest soils remain poorly understood. Here, we investigated the biodiversity dynamics, environmental influences, community assembly, and co‐occurrence patterns of bacterial and fungal communities in surface and subsurface soils across 47 Chinese forest sites. The biogeographic characteristics determined using high‐throughput sequencing data sets revealed evident spatial patterns of bacterial and fungal α and β diversity, assembly processes, and co‐occurrence relationship, with greater variation in the bacterial than in fungal communities. Both fungal and bacterial communities showed significant spatial separations regulated by community assembly processes, co‐occurrence patterns, and soil variables. The microbial dissimilarity was lower in high latitudes than in low latitudes, which was consistent with the lower deterministic processes and relatively higher co‐occurrence associations in high latitudes than in low latitudes. Additionally, there were significant associations of soil dissolved organic matter (DOM) characteristics (e.g., its content, aromaticity, and molecular weight) with biodiversity dissimilarities, microbial assembly process balances, and microbial co‐occurrence relationships in bacterial and fungal communities; they clearly indicate the key role of DOM in regulating microbial biogeographic patterns in forest soil ecosystems. Collectively, our study enhances the understanding of biogeographic patterns and coexistence theories in forest soil microbial ecosystems.
A Machine Learning Bias Correction on Large‐Scale Environment of High‐Impact Weather Systems in E3SM Atmosphere Model
Authors: Zhang Shixuan , Harrop Bryce , Leung L. Ruby , Charalampopoulos Alexis‐Tzianni , Barthel Sorensen Benedikt , Xu Wenwei , Sapsis Themistoklis 
Published: August 2024

AbstractLarge‐scale dynamical and thermodynamical processes are common environmental drivers of high‐impact weather systems causing extreme weather events. However, such large‐scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high‐impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large‐scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ∼1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high‐impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large‐scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias‐corrected large‐scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large‐scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large‐scale storm environments simulated by low‐resolution climate models.
Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning
Authors: Hammoud Mohamad Abed El Rahman , Raboudi Naila , Titi Edriss S. , Knio Omar , Hoteit Ibrahim 
Published: August 2024

AbstractData assimilation (DA) plays a pivotal role in diverse applications, ranging from weather forecasting to trajectory planning for autonomous vehicles. A prime example is the widely used ensemble Kalman filter (EnKF), which relies on the Kalman filter's linear update equation to correct each of the ensemble forecast member's state with incoming observations. Recent advancements have witnessed the emergence of deep learning approaches in this domain, primarily within a supervised learning framework. However, the adaptability of such models to untrained scenarios remains a challenge. In this study, we introduce a new DA strategy that utilizes reinforcement learning (RL) to apply state corrections using full or partial observations of the state variables. Our investigation focuses on demonstrating this approach to the chaotic Lorenz 63 and 96 systems, where the agent's objective is to maximize the geometric series with terms that are proportional to the negative root‐mean‐squared error (RMSE) between the observations and corresponding forecast states. Consequently, the agent develops a correction strategy, enhancing model forecasts based on available observations. Our strategy employs a stochastic action policy, enabling a Monte Carlo‐based DA framework that relies on randomly sampling the policy to generate an ensemble of assimilated realizations. Numerical results demonstrate that the developed RL algorithm performs favorably when compared to the EnKF. Additionally, we illustrate the agent's capability to assimilate non‐Gaussian observations, addressing one of the limitations of the EnKF.
Block Rotations in NW Iran in Response to the Arabia‐Eurasia Collision Constrained by Paleomagnetism
Authors: Niknam Ali , van der Boon Annique , Rezaeian Mahnaz , Kaymakcı Nuretdin , Langereis Cor 
Published: August 2024

AbstractNorthwest Iran is a seismically active region dominated by NW‐SE trending strike‐slip faults, such as the North Tabriz and Qosha Dagh faults, and smaller NNE‐SSW striking faults. The Bozgush Mountains are shaped by these faults and divided into two domains that show a difference in strike. To quantify rotational tectonic deformation in NW Iran, we performed a paleomagnetic study along three transects of the Bozgush and Qosha Dagh Mountains with 127 sites. Our large new paleomagnetic data set shows that the Bozgush Mountains did not rotate as a single rigid block. In the western domain of the Bozgush Mountains, we find evidence for clockwise vertical axis rotations of ∼40°, while the eastern domain has rotated up to ∼80° clockwise. Declinations of the western Bozgush domain fit well with observed declinations in the Qosha Dagh Mountains. Fault patterns show that the eastern domain of the Bozgush Mountains is divided by a set of NNE‐SSW striking sinistral strike‐slip faults, which created domino‐style blocks that accommodated the additional 40° of rotation. We estimate that these extra rotations have resulted in around 4 km of N‐S shortening and more than 1.5 km of differential uplift.
An Evaluation of Low‐Cost Terrestrial Lidar Sensors for Assessing Hydrogeomorphic Change
Authors: Perks M. T. , Pitman S. J. , Bainbridge R. , Díaz‐Moreno A. , Dunning S. A. 
Published: August 2024

AbstractAccurate topographic data acquired at appropriate spatio‐temporal resolution is often the cornerstone of geomorphic research. Recent decades have seen advances in our ability to generate highly accurate topographic data, primarily through the application of remote sensing techniques. Structure from Motion‐Multi View Stereo (SfM‐MVS) and lidar have revolutionised the spatial resolution of surveys across large spatial extents. Technological developments have led to commercialisation of small form factor (SFF) 3D lidar sensors that are suited to deployment on both mobile (e.g., uncrewed aerial systems), and in fixed semi‐permanent installations. Whilst the former has been adopted, the potential for the latter to generate data suitable for geomorphic investigations has yet to be assessed. We address this gap here in the context of a 3‐month deployment where channel change is assessed in an adjusting fluvial system. We find that SFF 3D lidar sensors generate change detection products comparable to those generated using a conventional lidar system. Areas of no geomorphic change are characterised as such (mean 3D change of 0.014 m compared with 0.0014 m for the Riegl VZ‐4000), with differences in median change in eroding sections of between 0.02 and 0.04 m. We illustrate that this data enables: (a) accurate characterisation of river channel adjustments through extraction of bank long‐profiles; (b) the assessment of bank retreat patterns which help elucidate failure mechanics; and (c) the extraction of water surface elevations. The deployment of this technology will enable a better understanding of processes across a variety of geomorphic systems, as data can be captured in 4D with near real‐time processing.
Improving Sentinel‐3 Altimetry Data With GPD+ Wet Tropospheric Corrections
Authors: Fernandes M. J. , Vieira T. , Lázaro C. , Vasconcellos B. , Aguiar P. 
Published: August 2024

AbstractThe provision of accurate wet tropospheric corrections (WTC), accounting for the delay of the radar pulses caused mostly by the atmospheric water vapor in the altimeter‐range observations, is pivotal for the full exploitation of altimeter‐derived surface heights. The WTC is best retrieved by measurements from Microwave Radiometers (MWR) on board the same altimeter mission. However, these instruments fail to provide valid WTC over land and ice and under rainy conditions. The GNSS‐derived Path Delay Plus (GPD+) algorithm has been designed to provide WTC over these surfaces where the onboard MWR WTC is invalid. This study focuses on the estimation of enhanced GPD+ WTC for the Copernicus Sentinel‐3A and Sentinel‐3B satellites, for the latest Baseline Collection 005.02 (BC005.2), spanning the period since the beginning of the missions until March 2023. GPD+ corrections are being provided operationally since 2022 and have been adopted as the default WTC in the calculation of the sea level anomaly (SLA). Compared to previous versions, the BC005.2 GPD+ WTC features improved data combination procedures, possesses a larger percentage of points estimated from observations, a better intermission alignment and reduced systematic differences among ascending and descending passes. Overall, GPD+ WTC are consistent, calibrated corrections, valid over all points present in the Non Time Critical marine product, allowing to recover, on average, about 17% of the altimeter observations with valid SLA, which otherwise, most of them would be rejected. Impacts of these WTC are most significant over coastal and inland water regions, at high latitudes and during rain events.
How Energy Dissipation Mode Controls the Evolution of Multiple Plane‐Strain Hydraulic Fractures Under Isotropic Stresses
Authors: Cheng Shaoyi , Wu Bisheng , Zhang Xi , Huppert Herbert E. , Jeffrey Robert G. 
Published: August 2024

AbstractThe geometrical prediction of multiple hydraulic fractures fed from a single fluid source is a major challenge due to transient stress interference among fractures and nonlinear coupling between rock deformation and fluid flow. Here we find that the evolution of multiple hydraulic fractures under isotropic stresses is controlled by a dimensionless toughness, which measures the ratio of the energy dissipated in rock fracturing to that dissipated in viscous fluid flow. The existence of a relation between the dimensionless toughness and the dimensionless length of the arrested fractures is demonstrated by using a bifurcation analysis. The numerical results show that the fractures tend to grow simultaneously in the viscosity‐dominated regime, and the scaling remains effective when the number of fractures varies. This study provides a quantitative and efficient tool for predicting the fracture pattern in engineered and natural fracture systems.
Improving the Predictability of the US Seasonal Surface Temperature With Convolutional Neural Networks Trained on CESM2 LENS
Authors: An Yujay , Kim Hyemi 
Published: August 2024

AbstractTo better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TSUS) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TSUS predictions. For predicting TSUS, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TSUS during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TSUS, suggesting that the CNN models are able to capture the ENSO‐TSUS teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TSUS is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TSUS forecast skill from subseasonal to seasonal timescales.
Optimizing Radar Functionality: Development of a Steerable Patch Antenna Array With Enhanced Bandwidth
Authors: Alanazi Mubarak , Assisi Aladdin 
Published: August 2024

AbstractToday, wireless communication systems need an antenna with a high gain, efficiency and beamsteering, as well as broadband capability, especially important in radar communications. The array antenna is commonly used in many applications due to its advantages, such as high gain and wide bandwidth. This paper presents an advanced design of a horizontally steerable planar antenna array intended for significant radar applications. A novel structure is used to create a compact array antenna of 8 × 3 elements. The design features a comprehensive 60° steering sector alongside a notable 12% bandwidth (4.45─5.42 GHz), using 8 × 3 planar array in an novel configuration. A three‐element series‐fed vertical array is utilized, employing aperture feeds with precisely sized patches to maximize performance. A detailed description of the design and refinement process of this array is presented, with emphasis on its exceptional capabilities for horizontal steering and bandwidth efficiency. By employing series‐fed vertical arrays with variable patch dimensions, we have successfully developed an antenna array that meets the stringent bandwidth requirements essential for radar technology, thereby enhancing the operational versatility of radar systems.
Metastable Helium Lidar for Thermosphere and Lower Exosphere Measurements: Instrument Description and Initial Results
Authors: Zhao Ruocan , Liu Zhenwei , Xue Xianghui , Zhou Hang , Wang Zhaofeng , Liu Yingyu , Li Jiangtao , Sun Dongsong , Lan Jiaxin , Chen Tingdi , Zhao Dongfeng , Dou Xiankang 
Published: August 2024

AbstractIn this work, we present a metastable helium lidar system for the measurements of metastable helium He(23S) density in the thermosphere and lower exosphere. This lidar system consists of a high power 1,083 nm pulsed laser, a 1 m aperture laser beam expander, six 1 m aperture receiving telescopes and a superconducting nanowire single‐photon detector (SNSPD). This system realizes metastable helium density detection up to 1,000 km. Daily rapid variation of metastable helium density within several hours was measured with a height resolution of 50 km for the first time. It demonstrates the capability of ground‐based lidar for continuous height‐resolved detection of the atmospheric metastable helium in the height range of 200–1,000 km. This is a promising tool to help study the coupling of neutral and ionized atmosphere in this height range and further providing observation basis for space weather prediction.