bluuebunny/crossref_metadata_2025 huggingface dataset:
An Integration of Deep Learning and Transfer Learning for Earthquake-Risk Assessment in the Eurasian Region
Authors: Jena Ratiranjan , Shanableh Abdallah , Al-Ruzouq Rami , Pradhan Biswajeet , Gibril Mohamed Barakat A. , Ghorbanzadeh Omid , Atzberger Clement , Khalil Mohamad Ali , Mittal Himanshu , Ghamisi Pedram
Published: July 2023

The problem of estimating earthquake risk is one of the primary themes for researchers and investigators in the field of geosciences. The combined assessment of spatial probability and the determination of earthquake risk at large scales is challenging. To the best of the authors’ knowledge, there no updated earthquake-hazard-and-risk assessments for the Eurasia region have been published since 1999. Considering that Eurasia is characterized by a seismically active Alpine–Himalayan fault zone and the Pacific Ring of Fire, which are frequently affected by devastating events, a continental-scale risk assessment for Eurasia is necessary to check the global applicability of developed methods and to update the earthquake-hazard, -vulnerability, and -risk maps. The current study proposes an integrated deep-transfer-learning approach called the gated recurrent unit–simple recurrent unit (GRU–SRU) to estimate earthquake risk in Eurasia. In this regard, the GRU model estimates the spatial probability, while the SRU model evaluates the vulnerability. To this end, spatial probability assessment (SPA), and earthquake-vulnerability assessment (EVA) results were integrated to generate risk A, while the earthquake-hazard assessment (EHA) and EVA were considered to generate risk B. This research concludes that in the case of earthquake-risk assessment (ERA), the results obtained for Risk B were better than those for risk A. Using this approach, we also evaluated the stability of the factors and interpreted the interaction values to form a spatial prediction. The accuracy of our proposed integrated approach was examined by means of a comparison between the obtained deep learning (DL)-based results and the maps generated by the Global Earthquake Model (GEM). The accuracy of the SPA was 93.17%, while that of the EVA was 89.33%.
Q-Compensated Gaussian Beam Migration under the Condition of Irregular Surface
Authors: Han Jianguang , Lü Qingtian , Gu Bingluo , Yan Jiayong
Published: July 2023

The viscosity of actual underground media can cause amplitude attenuation and phase distortion of seismic waves. When seismic images are processed assuming elastic media, the imaging accuracy for the deep reflective layer is often reduced. If this attenuation effect is compensated, the imaging quality of the seismic data can be significantly improved. Q-compensated Gaussian beam migration (Q-GBM) is an effective seismic imaging method for viscous media, and it has the advantages of both wave equation and ray-based Q-compensated imaging methods. This study develops a Q-GBM method in visco-acoustic media with an irregular surface. Initially, the basic principles of Gaussian beam in visco-acoustic media are introduced. Then, by correcting the complex-value time of the Gaussian beam in visco-acoustic media, energy compensation and phase correction are carried out for the forward continuation wavefield at the seismic source of the irregular surface and the reverse continuation wavefield at the beam center, which effectively compensates the absorption and attenuation effects of visco-acoustic media on the seismic wavefield. Further, a Q-GBM method under the irregular surface is proposed using cross-correlation imaging conditions. Through migration tests for three numerical models of visco-acoustic media with irregular surfaces, it is verified that our method is an effective depth domain imaging technique for seismic data in visco-acoustic media under the condition of irregular surfaces.
An Experimental Study of the Accuracy and Change Detection Potential of Blending Time Series Remote Sensing Images with Spatiotemporal Fusion
Authors: Wei Jingbo , Chen Lei , Chen Zhou , Huang Yukun
Published: July 2023

Over one hundred spatiotemporal fusion algorithms have been proposed, but convolutional neural networks trained with large amounts of data for spatiotemporal fusion have not shown significant advantages. In addition, no attention has been paid to whether fused images can be used for change detection. These two issues are addressed in this work. A new dataset consisting of nine pairs of images is designed to benchmark the accuracy of neural networks using one-pair spatiotemporal fusion with neural-network-based models. Notably, the size of each image is significantly larger compared to other datasets used to train neural networks. A comprehensive comparison of the radiometric, spectral, and structural losses is made using fourteen fusion algorithms and five datasets to illustrate the differences in the performance of spatiotemporal fusion algorithms with regard to various sensors and image sizes. A change detection experiment is conducted to test if it is feasible to detect changes in specific land covers using the fusion results. The experiment shows that convolutional neural networks can be used for one-pair spatiotemporal fusion if the sizes of individual images are adequately large. It also confirms that the spatiotemporally fused images can be used for change detection in certain scenes.
Enhancing Building Segmentation in Remote Sensing Images: Advanced Multi-Scale Boundary Refinement with MBR-HRNet
Authors: Yan Geding , Jing Haitao , Li Hui , Guo Huanchao , He Shi
Published: July 2023

Deep learning algorithms offer an effective solution to the inefficiencies and poor results of traditional methods for building a footprint extraction from high-resolution remote sensing imagery. However, the heterogeneous shapes and sizes of buildings render local extraction vulnerable to the influence of intricate backgrounds or scenes, culminating in intra-class inconsistency and inaccurate segmentation outcomes. Moreover, the methods for extracting buildings from very high-resolution (VHR) images at present often lose spatial texture information during down-sampling, leading to problems, such as blurry image boundaries or object sticking. To solve these problems, we propose the multi-scale boundary-refined HRNet (MBR-HRNet) model, which preserves detailed boundary features for accurate building segmentation. The boundary refinement module (BRM) enhances the accuracy of small buildings and boundary extraction in the building segmentation network by integrating edge information learning into a separate branch. Additionally, the multi-scale context fusion module integrates feature information of different scales, enhancing the accuracy of the final predicted image. Experiments on WHU and Massachusetts building datasets have shown that MBR-HRNet outperforms other advanced semantic segmentation models, achieving the highest intersection over union results of 91.31% and 70.97%, respectively.
A Model for Estimating the Earth’s Outgoing Radiative Flux from A Moon-Based Radiometer
Authors: Zhang Yuan , Dewitte Steven , Bi Shengshan
Published: July 2023

A Moon-based radiometer can provide continuous measurements for the Earth’s full-disk broadband irradiance, which is useful for studying the Earth’s Radiation Budget (ERB) at the height of the Top of the Atmosphere (TOA). The ERB describes how the Earth obtains solar energy and emits energy to space through the outgoing broadband Short-Wave (SW) and emitted thermal Long-Wave (LW) radiation. In this work, a model for estimating the Earth’s outgoing radiative flux from the measurements of a Moon-based radiometer is established. Using the model, the full-disk LW and SW outgoing radiative flux are gained by converting the unfiltered entrance pupil irradiances (EPIs) with the help of the anisotropic characteristics of the radiances. Based on the radiative transfer equation, the unfiltered EPI time series is used to validate the established model. By comparing the simulations for a Moon-based radiometer with the satellite-based data from the National Institute of Standards and Technology Advanced Radiometer (NISTAR) and the Clouds and the Earth’s Radiant Energy System (CERES) datasets, the simulations show that the daytime SW fluxes from the Moon-based measurements are expected to vary between 194 and 205 Wm−2; these simulations agree well with the CERES data. The simulations are about 5 to 20 Wm−2 smaller than the NISTAR data. For the simulated Moon-based LW fluxes, the range is 251~287 Wm−2. The Moon-based and NISTAR fluxes are consistently 5~15 Wm−2 greater than CERES LW fluxes, and both of them also show larger diurnal variations compared with the CERES fluxes. The correlation coefficients of SW fluxes for Moon-based data and NISTAR data are 0.97, 0.63, and 0.53 for the months of July, August, and September, respectively. Compared with the SW flux, the correlation of LW fluxes is more stable for the same period and the correlation coefficients are 0.87, 0.69, and 0.61 for July to September 2017.
Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision
Authors: Bratic Gorica , Oxoli Daniele , Brovelli Maria Antonia
Published: July 2023

Land cover information plays a critical role in supporting sustainable development and informed decision-making. Recent advancements in satellite data accessibility, computing power, and satellite technologies have boosted large-extent high-resolution land cover mapping. However, retrieving a sufficient amount of reliable training data for the production of such land cover maps is typically a demanding task, especially using modern deep learning classification techniques that require larger training sample sizes compared to traditional machine learning methods. In view of the above, this study developed a new benchmark dataset called the Map of Land Cover Agreement (MOLCA). MOLCA was created by integrating multiple existing high-resolution land cover datasets through a consensus-based approach. Covering Sub-Saharan Africa, the Amazon, and Siberia, this dataset encompasses approximately 117 billion 10m pixels across three macro-regions. The MOLCA legend aligns with most of the global high-resolution datasets and consists of nine distinct land cover classes. Noteworthy advantages of MOLCA include a higher number of pixels as well as coverage for typically underrepresented regions in terms of training data availability. With an estimated overall accuracy of 96%, MOLCA holds great potential as a valuable resource for the production of future high-resolution land cover maps.
Urban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Images
Authors: Wu Hui , Zhuang Minghao , Chen Yuanchi , Meng Chen , Wu Caiyan , Ouyang Linke , Liu Yuhan , Shu Yi , Tao Yuzhong , Qiu Tong , Li Junxiang
Published: July 2023

Individual tree detection for urban forests in subtropical environments remains a great challenge due to the various types of forest structures, high canopy closures, and the mixture of evergreen and deciduous broadleaved trees. Existing treetop detection methods based on the canopy-height model (CHM) from UAV images cannot resolve commission errors in heterogeneous urban forests with multiple trunks or strong lateral branches. In this study, we improved the traditional local-maximum (LM) algorithm using a dual Gaussian filter, variable window size, and local normalized correlation coefficient (NCC). Specifically, we adapted a crown model of maximum/minimum tree-crown radii and an angle strategy to detect treetops. We then removed and merged the pending tree vertices. Our results showed that our improved LM algorithm had an average user accuracy (UA) of 87.3% (SD± 4.6), an average producer accuracy (PA) of 82.8% (SD± 4.1), and an overall accuracy of 93.3% (SD± 3.9) for sample plots with canopy closures less than 0.5. As for the sample plots with canopy closures from 0.5 to 1, the accuracies were 78.6% (SD± 31.5), 73.8% (SD± 10.3), and 68.1% (SD± 12.7), respectively. The tree-height estimation accuracy reached more than 0.96, with an average RMSE of 0.61 m. Our results show that the UAV-image-derived CHM can be used to accurately detect individual trees in mixed forests in subtropical cities like Shanghai, China, to provide vital tree-structure parameters for precise and sustainable forest management.
How Important Is Satellite-Retrieved Aerosol Optical Depth in Deriving Surface PM2.5 Using Machine Learning?
Authors: Tian Zhongyan , Wei Jing , Li Zhanqing
Published: July 2023

PM2.5 refers to the total mass concentration of tiny particulates in the atmosphere near the surface, obtained by means of in situ observations and satellite remote sensing. Given the highly limited number of ground observation stations of inhomogeneous distribution and an ill-posed remote sensing approach, increasing efforts have been devoted to the application of machine-learning (ML) models to both ground and satellite data. A key satellite-derived parameter, aerosol optical thickness (AOD), has been most commonly used as a proxy of PM2.5, although their correlation is fraught with large uncertainties. A critical question that has been overlooked concerns how much AOD helps to improve the retrieval of PM2.5 relative to its uncertainty incurred concurrently. The question is addressed here by taking advantage of high-density PM2.5 stations in eastern China to evaluate the contributions of AOD, determined as the difference in the accuracy of PM2.5 retrievals with and without AOD for varying densities of PM2.5 stations, using four popular ML models (i.e., Random Forest, Extra-trees, XGBoost, and LightGBM). Our results reveal that as the density of monitoring stations decreases, both the feature importance and permutation importance of satellite AOD demonstrate a consistent upward trend (p < 0.05). Furthermore, the ML models without AOD exhibit faster declines in overall accuracy and predictive ability compared with the models with AOD assessed using the sample-based and station-based (spatial) independent cross-validation approaches. Overall, a 10% reduction in the number of stations results in an increase of 0.7–1.2% and 0.6–1.2% in uncertainty in estimated and predicted accuracies, respectively. These findings attest to the indispensable role of satellite AOD in the PM2.5 retrieval process through ML because it can significantly mitigate the negative impact of the sparse distribution of monitoring sites. This role becomes more important as the number of PM2.5 stations decreases.
A Review of Forest Height Inversion by PolInSAR: Theory, Advances, and Perspectives
Authors: Xing Cheng , Wang Hongmiao , Zhang Zhanjie , Yin Junjun , Yang Jian
Published: July 2023

Forests cover approximately one-third of the Earth’s land surface and constitute the core region of the carbon cycle on Earth. The paramount importance and multi-purpose applications of forest monitoring have gained widespread recognition over recent decades. Polarimetric synthetic aperture radar interferometry (PolInSAR) has been demonstrated as a promising technique to retrieve the forest height over large areas with a limited cost. This paper presents an overview of forest height inversion (FHI) techniques based on PolInSAR data. Firstly, we introduce the basic theories of PolInSAR and FHI procedures. Next, we review the established data-based algorithms for single-baseline data and describe innovative techniques related to multi-baseline data. Then, the model-based algorithms are also introduced with their corresponding forest scattering models under multiple data acquisition modes. Subsequently, a case study is presented to demonstrate the applicable scenarios and advantages of different algorithms. Model-based algorithms can provide accurate results when the scene and forest properties are well understood and the model assumptions are valid. Data-based algorithms, on the other hand, can handle complex scattering scenarios and are generally more robust to uncertainties in the input parameters. Finally, the prospect of forest height inversion was analyzed. It is our hope that this review will provide guidelines to future researchers to enhance further FHI algorithmic developments.
Discarding–Recovering and Co-Evolution Mechanisms Based Evolutionary Algorithm for Hyperspectral Feature Selection
Authors: Liao Bowen , Li Yangxincan , Liu Wei , Gao Xianjun , Wang Mingwei
Published: July 2023

With the improvement of spectral resolution, the redundant information in the hyperspectral imaging (HSI) datasets brings computational, analytical, and storage complexities. Feature selection is a combinatorial optimization problem, which selects a subset of feasible features to reduce the dimensionality of data and decrease the noise information. In recent years, the evolutionary algorithm (EA) has been widely used in feature selection, but the diversity of agents is lacking in the population, which leads to premature convergence. In this paper, a feature selection method based on discarding–recovering and co-evolution mechanisms is proposed with the aim of obtaining an effective feature combination in HSI datasets. The feature discarding mechanism is introduced to remove redundant information by roughly filtering the feature space. To further enhance the agents’ diversity, the reliable information interaction is also designed into the co-evolution mechanism, and if detects the event of stagnation, a subset of discarded features will be recovered using adaptive weights. Experimental results demonstrate that the proposed method performs well on three public datasets, achieving an overall accuracy of 92.07%, 92.36%, and 98.01%, respectively, and obtaining the number of selected features between 15% and 25% of the total.
Improving the Accuracy of Vehicle Position in an Urban Environment Using the Outlier Mitigation Algorithm Based on GNSS Multi-Position Clustering
Authors: Kim Hak Ju , Kim Yong Hun , Lee Joo Han , Park So Jin , Ko Bo Sung , Song Jin Woo
Published: July 2023

In this paper, we propose a multi-position cluster-based weighted position estimation method that minimizes the influence of multipath (MP)/non-line-of-sight (NLOS) signals using a global navigation satellite system (GNSS) receiver. The proposed method is suitable for positioning passenger cars, particularly in urban driving environments. Density-based spatial clustering of applications with noise (DBSCAN)-based clustering is performed by generating multi-position data through a subset of observable satellites and analyzing the density characteristics of position data generated by line-of-sight (LOS) satellite signals from the generated multi-position data. To estimate the optimal position through clustered data, we propose a method by constructing a weighted model through Doppler-based velocity measurements, which is robust to MP delay signals compared to code-based pseudorange measurements. In addition, to prevent unnecessary clustering points from having weights, the predicted range is selected based on the nonholonomic constraint of the vehicle. The proposed algorithm was quantitatively validated by selecting a region in an actual urban environment where the MP/NLOS error could occur significantly. It was confirmed that the accuracy of vehicle position was improved by approximately 24% by the proposed method compared to existing positioning solutions.
Sea Surface Roughness Determination from Grazing Angle GPS Ocean Observations and Scatterometry Simulations
Authors: Høeg Per , Carlström Anders
Published: July 2023

Measurements of grazing angle GNSS-R ocean reflections combined with meteorological troposphere data are used for retrieval of ocean wave heights and surface roughness parameters. The observational results are compared to multiphase screen simulations for the same atmosphere conditions. The retrieved data from observations and simulations give equal results within the error bounds of the methods. The obtained ocean mean wave-heights are almost proportional to the square of the wind speed when applying a first-order approximation model to the high-wave-number part of the measured GNSS-R power spectra. The spectral variances from the measurements link directly to the ocean surface roughness, which is also verified by the performed multiple phase-screen wave propagation simulations. Thus, grazing angle GNSS-R techniques are an efficient method for determining the ocean state and the conditions in the boundary layer of the troposphere.
Towards Feature Decoupling for Lightweight Oriented Object Detection in Remote Sensing Images
Authors: Deng Chenwei , Jing Donglin , Han Yuqi , Deng Zhiyuan , Zhang Hong
Published: July 2023

Recently, the improvement of detection performance always relies on deeper convolutional layers and complex convolutional structures in remote sensing images, which significantly increases the storage space and computational complexity of the detector. Although previous work has designed various novel lightweight convolutions, when these convolutional structures are applied to remote sensing detection tasks, the inconsistency between features and targets as well as between features and tasks in the detection architecture is often ignored: (1) The features extracted by convolution sliding in a fixed direction make it difficult to effectively model targets with arbitrary direction distribution, which leads to the detector needing more parameters to encode direction information and the network parameters being highly redundant; (2) The detector shares features from the backbone, but the classification task requires rotation-invariant features while the regression task requires rotation-sensitive features. This inconsistency in the task can lead to inefficient convolutional structures. Therefore, this paper proposed a detector that uses the Feature Decoupling for Lightweight Oriented Object Detection (FDLO-Det). Specifically, we constructed a rotational separable convolution that extracts rotational equivariant features while significantly compressing network parameters and computational complexity through highly shared parameters. Next, we introduced an orthogonal polarization transformation module that decomposes rotational equivariant features in both horizontal and vertical orthogonal directions, and used polarization functions to filter out the required features for classification and regression tasks, effectively improving detector performance. Extensive experiments on DOTA, HRSC2016, and UCAS-AOD show that the proposed detector can achieve the best performance and achieve an effective balance between computational complexity and detection accuracy.
A Spatially Self-Adaptive Multiparametric Anomaly Identification Scheme Based on Global Strong Earthquakes
Authors: Jiao Zhonghu , Hao Yumeng , Shan Xinjian
Published: July 2023

Earthquake forecasting aims to determine the likelihood of a damaging earthquake occurring in a particular area within a period of days to months. This provides ample preparation time for potential seismic hazards, resulting in significant socioeconomic benefits. Surface and atmospheric parameters derived from satellite thermal infrared observations have been utilized to identify pre-earthquake anomalies that may serve as potential precursors for earthquake forecasting. However, the correlation between these anomalies and impending earthquakes remains a significant challenge due to high false alarm and missed detection rates. To address this issue, we propose a spatially self-adaptive multiparametric anomaly identification scheme based on global strong earthquakes to establish the optimal recognition criteria. Each optimal parameter exhibits significant spatial variability within the seismically active region and indicates transient and subtle anomaly signals with a limited frequency of occurrences (<10 for most regions). In comparison to the fixed criterion for identifying anomalies, this new scheme significantly improves the positive Matthew’s correlation coefficient (MCC) values from ~0.03 to 0.122–0.152. Additionally, we have developed a multi-parameter anomaly synthesis method based on the best MCC value of each parameter anomaly. On average, the MCC increased from 0.143 to 0.186, and there are now more earthquake-prone regions with MCC values > 0.5. Our research emphasizes the critical importance of a multiparametric system in earthquake forecasting, where each geophysical parameter can be assigned a distinct weight, and the findings specifically identify OLR, including all-sky and clear-sky ones, as the most influential parameter on a global scale, highlighting the potential significance of OLR anomalies for seismic forecasting. Encouraging results imply the effectiveness of utilizing multiparametric anomalies and provide some confidence in advancing our knowledge of operational earthquake forecasting with a more quantitative approach.
Asymmetric Drifter Trajectories in an Anticyclonic Mesoscale Eddy
Authors: Tuo Pengfei , Hu Zhiyuan , Chen Shengli , Hu Jianyu , Yu Peining
Published: July 2023

The influences of sea surface wind on the oceanic mesoscale eddy are complex. By integrating our self-developed surface drifters with satellite observations, we examined the influence of sea surface wind on the distribution of water masses and biomass within the interior of an anticyclonic eddy. Ten drifters were deployed in the northern South China Sea in the spring of 2021. Eventually, six were trapped in an anticyclonic mesoscale eddy for an extended period. Interestingly, the drifters’ trajectories were not symmetric around the eddy center, displaying a significant offset of the distance from the wind turns to the southerly wind. Particle tracking experiments demonstrated that this departure could mainly be attributed to wind-driven ageostrophic currents. This is due to the strength of wind-driven ageostrophic currents being more comparable to geostrophic currents when accompanied by a deflection between the directions of the wind-driven current and the eddy’s translation. The drifters’ derived data indicated that sub-mesoscale ageostrophic currents within the eddy contributed to this asymmetric trajectory, with Ekman and non-Ekman components playing a role. Furthermore, the evolution of ocean color data provided corroborating evidence of these dynamic processes, highlighting the importance of ageostrophic processes within mesoscale eddies.
Remote Sensing Neural Radiance Fields for Multi-View Satellite Photogrammetry
Authors: Xie Songlin , Zhang Lei , Jeon Gwanggil , Yang Xiaomin
Published: July 2023

Neural radiance fields (NeRFs) combining machine learning with differentiable rendering have arisen as one of the most promising approaches for novel view synthesis and depth estimates. However, NeRFs only applies to close-range static imagery and it takes several hours to train the model. The satellites are hundreds of kilometers from the earth. Satellite multi-view images are usually captured over several years, and the scene of images is dynamic in the wild. Therefore, multi-view satellite photogrammetry is far beyond the capabilities of NeRFs. In this paper, we present a new method for multi-view satellite photogrammetry of Earth observation called remote sensing neural radiance fields (RS-NeRFs). It aims to generate novel view images and accurate elevation predictions quickly. For each scene, we train an RS-NeRF using high-resolution optical images without labels or geometric priors and apply image reconstruction losses for self-supervised learning. Multi-date images exhibit significant changes in appearance, mainly due to cars and varying shadows, which brings challenges to satellite photogrammetry. Robustness to these changes is achieved by the input of solar ray direction and the vehicle removal method. NeRFs make it intolerable by requiring a very long time to train an easy scene. In order to significantly reduce the training time of RS-NeRFs, we build a tiny network with HashEncoder and adopted a new sampling technique with our custom CUDA kernels. Compared with previous work, our method performs better on novel view synthesis and elevation estimates, taking several minutes.
Retrieval of Chla Concentrations in Lake Xingkai Using OLCI Images
Authors: Fu Li , Zhou Yaming , Liu Ge , Song Kaishan , Tao Hui , Zhao Fangrui , Li Sijia , Shi Shuqiong , Shang Yingxin
Published: July 2023

Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake’s eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R2 of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality.
Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer
Authors: Li Shijie , Wu Tong , Zhong Kai , Zhang Xianzhong , Sun Yue , Zhang Yijian , Wang Yu , Li Xinqi , Xu Degang , Yao Jianquan
Published: July 2023

Lidar is important active remote sensing equipment in the field of atmospheric environment detection. However, the detection range of lidar is severely limited by the dynamic range of photodetectors. To solve this problem, atmospheric lidars are often equipped with two or more channels to receive signals from different altitude ranges, where gluing the multi-channel echo signals becomes a key issue for accurate data inversion. In this paper, a multi-channel signal gluing algorithm based on the Improved Gray Wolf Optimizer (IGWO) and Neighborhood Rough Set (NRS), named IGWO-RSD, is proposed. The fitness function F is formed by three objective functions: correlation coefficient R, regression stability coefficient S and mean fit deviation D. All three objective functions are obtained from the data itself and do not rely on prior information. The weights of the objective functions R, S and D are pre-trained by NRS, and IGWO is used to optimize the fitness function F. With ground-based aerosol lidar data, all-day signal gluing experiments are performed, where IGWO-RSD demonstrates obvious advantages in stability, accuracy and applicability in lidar signal processing compared with NRSWNSGA-II.
Modeling and Implementation of a Joint Airborne Ground Penetrating Radar and Magnetometer System for Landmine Detection
Authors: Lee Junghan , Lee Haengseon , Ko Sunghyub , Ji Daehyeong , Hyeon Jongwu
Published: July 2023

We modeled and implemented a joint airborne system integrating ground penetrating radar (GPR) and magnetometer (MAG) models specifically for landmine detection applications. We conducted both simulations and experimental analyses of the joint airborne GPR and MAG models, with a focus on detecting the metallic components of different types of landmines, including antitank (AT) M15 metallic, antipersonnel (AP) M16 metallic, and AT M19 plastic (minimum-metal) landmines. The GPR model employed the finite-difference time-domain (FDTD) method and was evaluated using a singular value decomposition (SVD) and Kirchhoff migration (KM) with matched filtering (MF). These advanced techniques enabled the automatic identification and precise focusing of the reflected hyperbolic signals emitted by the landmines while considering cross-range resolution. Additionally, the MAGs were utilized based on the magnetic dipole model with a de-trend and a spatial median filtering method to estimate the magnetic anomaly of the landmines while considering various data spatial intervals. The joint airborne GPR and MAG system was implemented by combining and integrating the GPR and MAG models for experimental validation. Through this comprehensive approach, which included experiments, simulations, and data processing, the design parameters of the final system were obtained. These design parameters can be used in the development and application of landmine detection systems based on airborne GPR and MAG technology.
Regional Study on the Oceanic Cool Skin and Diurnal Warming Effects: Observing and Modeling
Authors: Liu Zhenyu , Yang Minglun , Qu Liqin , Guan Lei
Published: July 2023

The cool skin and diurnal warming effects are important factors affecting the vertical temperature gradient in the upper ocean. Accurately understanding skin effects is of great significance for studying ocean–atmosphere modeling and climate change. The skin models need to be validated for their applicability under different oceanic conditions to improve their accuracy. Shipboard measurements from August 2015 to October 2018 in the Northwest Pacific Ocean are used to validate some of the current models. The results show that the Tropical Ocean-Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) cool skin model obtains a mean cool skin value of −0.25 K, which is close to the averaged observed value of −0.23 K. A significant positive correlation between the sea–air temperature difference and the amplitude of the cool skin effect is observed in this study. Three diurnal warming models are discussed and compared. The profiles of ocean surface heating (POSH) model performed the best and was the closest one to the observation. The mean temperature differences bewteen the COARE and POSH models are close to 0 K, while the other model shows the overestimation with a mean temperature difference of 0.21 K. The measurements and validations of the thermal skin effects in this study can be useful for regional research on the air–sea interaction and upper ocean gradients.
Study of Atmospheric Aerosol in the Baikal Mountain Basin with Shipborne and Ground-Based Lidars
Authors: Nasonov Sergei , Balin Yurii , Klemasheva Marina , Kokhanenko Grigorii , Novoselov Mikhail , Penner Ioganes
Published: July 2023

The results of long-term lidar studies of the peculiarities of the vertical structure of atmospheric aerosols over Lake Baikal are presented. The paper provides an analysis of data obtained over the period from 2010 to 2022. The studies were carried out under both the background conditions and the extreme natural conditions associated with severe wildfires in Siberia. The parameters of the lidars used in regular summer expeditions to Lake Baikal are briefly described. The data analysis shows that the vertical structure of the aerosol in the lower troposphere up to 2000 m above Baikal in summer is often a stable structure of several aerosol layers tens to hundreds of meters thick. There can be no mixing of layers because the water in the lake is very cold and the aerosol does not rise to higher layers while the air is warming up during the day. The difference is shown between the spatiotemporal structures of aerosol plumes from local wildfires within the lake area and from distant sources. The Angstrom parameter and the aerosol optical depth are calculated for different atmospheric conditions: ηβ = 1.57 ± 0.16 and τ = 0.09 for background conditions; ηβ = 1.41 ± 0.07 and τ = 0.64 for the cases of the observation of smoke aerosol from distant wildfires; and ηβ = 1.05 ± 0.08 and τ = 0.25 for the cases of the observation of smoke aerosol from nearby wildfires.
The New Exponentiated Half Logistic-Harris-G Family of Distributions with Actuarial Measures and Applications
Authors: Warahena-Liyanage Gayan , Oluyede Broderick , Moakofi Thatayaone , Sengweni Whatmore
Published: July 2023

In this study, we introduce a new generalized family of distributions called the Exponentiated Half Logistic-Harris-G (EHL-Harris-G) distribution, which extends the Harris-G distribution. The motivation for introducing this generalized family of distributions lies in its ability to overcome the limitations of previous families, enhance flexibility, improve tail behavior, provide better statistical properties and find applications in several fields. Several statistical properties, including hazard rate function, quantile function, moments, moments of residual life, distribution of the order statistics and Rényi entropy are discussed. Risk measures, such as value at risk, tail value at risk, tail variance and tail variance premium, are also derived and studied. To estimate the parameters of the EHL-Harris-G family of distributions, the following six different estimation approaches are used: maximum likelihood (MLE), least-squares (LS), weighted least-squares (WLS), maximum product spacing (MPS), Cramér–von Mises (CVM), and Anderson–Darling (AD). The Monte Carlo simulation results for EHL-Harris-Weibull (EHL-Harris-W) show that the MLE method allows us to obtain better estimates, followed by WLS and then AD. Finally, we show that the EHL-Harris-W distribution is superior to some other equi-parameter non-nested models in the literature, by fitting it to two real-life data sets from different disciplines.
Revitalising Metallic Materials: A Path towards a Sustainable Circular Economy
Authors: Ashraf Farhan , Lodh Arijit , Pagone Emanuele , Castelluccio Gustavo M.
Published: July 2023

Improving materials’ productivity and reusability can advance circularity by reducing extraction and enabling efficient fully recyclable parts and systems. However, the pursuit of circular materials often focuses on the choice among reusing, repairing, or recycling materials, with limited consideration for techniques that can proactively revitalise materials. Consequently, the adoption of preventive material therapies remain relatively scarce and unexplored. This work discusses the potential for revitalising metallic materials with preventive maintenance prior to detectable damage and we identify techniques that can effectively prolong the structural lifespan of metallic components. By analysing the literature and considering the eco-footprint and implementation feasibility within the aerospace sector, this study ranks approaches based on their circularity impact and provides valuable insights to guide future research in the field of materials maintenance.
China’s Digital Economy and Enterprise Labor Demand: The Mediating Effects of Green Technology Innovation
Authors: Sun Zhaoqing , Liu Jianxu , Tansuchat Roengchai
Published: July 2023

In the context of the new revolution of science and technology, the digital economy not only exerts a significant and profound impact on the scale of enterprise labor demand, but also increasingly becomes a new engine driving green technology innovation in companies. In this paper, we use the micropanel data of Chinese A-share-listed companies from 2011 to 2020 to assess the impact of the digital economy on the scale of enterprise labor demand and the role played by green technology innovation within it. The study finds that: (1) the digital economy significantly expands the labor demand of enterprises, with a more pronounced expansion effect observed at the lower and higher quantiles of the labor demand scale. (2) Green technology innovation can exert a significant intermediary effect between the digital economy and the scale of enterprise labor demand. (3) There is significant heterogeneity in the impact of the digital economy on the labor demand of enterprises and the mediating effect of green technology innovation. the expansion effect of the digital economy on the scale of labor demand is more pronounced in enterprises located in the western region and characterized by rapid industry technological changes in small-to-medium-sized companies, while the mediating effect of green technology innovation is more pronounced in enterprises located in the eastern region, characterized by slow industry technological changes in large-sized companies. (4) Among the segmented indicators of the digital economy, cloud computing technology, big data technology, and digital technology applications significantly expand the scale of enterprise labor demand. The empirical results of this paper have important theoretical and policy implications for understanding the role of the digital economy in promoting labor force employment and achieving green sustainable development.
Circular Bioeconomy Practices in the Greek Pig Sector: The Environmental Performance of Bakery Meal as Pig Feed Ingredient
Authors: Melas Lefteris , Batsioula Maria , Malamakis Apostolos , Patsios Sotiris I. , Geroliolios Dimitris , Alexandropoulos Evangelos , Skoutida Stamatia , Karkanias Christos , Dedousi Anna , Kritsa Maria-Zoi , Sossidou Evangelia N. , Banias George F.
Published: July 2023

Food systems and, to an extent, the pig sector are major contributors of greenhouse gas (GHG) emissions globally. At the same time, significant amounts of waste are produced from the food sector. The aim of this study is to examine the implementation of circular bio-economy practices in the Greek pig sector to improve its environmental performance. More specifically, in collaboration with a pig farm in Northern Greece and a waste management company, the collection and processing of bakery by-products was organized to produce bakery meal (BM) to integrate it in the diets of fattening and growing pigs. Using Life Cycle Assessment (LCA) methodology, the environmental performance of 20% BM inclusion in pig diets was examined in comparison with the conventional feedstock. BM experimentally replaced corn, wheat, barley, and soya bean from conventional feedstock. The Life Cycle Inventory (LCI) was based on the yearly average values of feed and energy consumption to produce 1 kg of living weight of pig on the pig farm. Life Cycle Impact Assessment (LCIA) was conducted with SimaPro v3.5, using Recipe Midpoint Hierarchical v1.6. The LCIA calculations exhibited that BM inclusion in pig diets can lead to significant land occupation decrease, approximately 30%, which is mostly related to reduced wheat and soya beans. The reduction of cultivated croplands also led to reduced fertilizer and pesticide application, which improved marine eutrophication and freshwater ecotoxicity impact by 20% while it significantly reduced risks of human carcinogenic toxicity by 25%. Moreover, the Greek pig sector exhibited a 5% capacity for overall improvement of its environmental performance, which relies on minimizing logistics when the pig farm conducted collection and processing of by-products. A basic assumption of this study is the assessment of bakery by-product quantities in the wider region of the pig farm. The availability of by-products, based on the reported experience, was limited, and the reliability of the supply was frequently disrupted. As such, the supply chain model of the central hub for the collection and processing of bakery by-products is proposed as more efficient for regulating logistic challenges and availability.
A Parameter Sensitivity Analysis of Hydropower Units under Full Operating Conditions Considering Turbine Nonlinearity
Authors: Liu Dong , Wei Xinxu , Zhang Jingjing , Hu Xiao , Zhang Lihong
Published: July 2023

A parameter sensitivity analysis is an important part of the stability study of hydro turbine regulation systems, which helps operators to deepen their understanding of the characteristics and connections among the various parts of these systems. Considering that large hydropower stations undertake an essential regulation task in the power grid, the safety and stability of their operation cannot be ignored. To this end, taking a unit in a giant hydropower station in China as an example, a hydraulic–mechanical–electrical coupling model of the hydraulic turbine regulation system is established. A comprehensive parameter sensitivity indicator and parameter sensitivity analysis framework are proposed. On this basis, the sensitivity of the main system variables to parameter changes under full operating conditions is investigated by considering two different control modes of the unit (i.e., corresponding to different grid types). The results show that the sensitivity of the system state to the mechanical parameters of the generator is the highest in the power control mode, while the sensitivity to the electrical parameters of the generator and excitation system is higher in the frequency control mode. The sensitivity of the system with these key parameters also shows different patterns of change with a change in the unit operating conditions. The relevant findings can provide some theoretical guidance for the operation of hydropower stations and help to reduce the risk of system instability.
Mechanical Properties and Influencing Factors of Shield Cutting Existing Station Supporting Piles
Authors: Guan Xiaoming , Liu Zeliang , Xu Huawei , Liu Yanchun , Ling Xianzhang , Ding Hao , Ren Sihao , Lu Ruiquan , Yu Ke , Miao Jijun
Published: July 2023

Based on the shield-cutting existing station supporting piles project of Zhengzhou metro line 6, the process and mechanism of concrete in the process of pile cutting were analyzed by establishing a three-dimensional model of concrete with a cutter. The magnitude and variation rules of cutting force, penetration force, and tangential force were explored. The variation rules of cutting force with four factors of cutting speed, cutting depth, cutter width, and tool rake angle were explored. The correctness of the numerical model and results were verified by the theoretical analysis method. Finally, the significance of the influencing factors of pile cutting was studied by means of range analysis and variance analysis using the orthogonal test method, and the cutter parameters were optimized. The cutting mechanism is that the front concrete is compressed and crushed to produce a dense core. The cutting force increases rapidly to its maximum value in a short time during the cutting process. The cutting force is always larger than the penetration force and tangential force. The cutter contact force basically remains unchanged with the change in cutting speed; however, it increases with the increase in cutting depth and cutter width. The significant order of the three influencing factors is cutting depth, cutter width, and rake angle. It is suggested that the cutting depth be adjusted preferentially, followed by the cutter width. And the principle of rake angle should be considered last.