bluuebunny/crossref_metadata_2025 huggingface dataset:
Petrological and Mineralogical Characteristics of Exposed Materials on the Floors of the Lavoisier and Surrounding Craters
Authors: Yi Eung Seok , Kim Kyeong Ja , Wöhler Christian , Berezhnoy Alexey A. , Kim Yong Ha , Moon Seulgi
Published: September 2022

Five floor-fractured craters (FFCs), Lavoisier crater and four craters surrounding it (Lavoisier C, Lavoisier E, Lavoisier F, and Lavoisier H), are distributed along the boundary between the northwestern part of the Oceanus Procellarum and the highlands. This study examines the uplifted or exposed materials on the fractured floors of these five impact craters using petrological, mineralogical, and morphological analyses. We inferred the processes that uplifted or exposed the materials from the subsurface to the crater floor using the Chandrayaan-1 Moon Mineralogy Mapper (M3) level 1b (thermally and topographically corrected spectral radiance) data and level 2 (spectral reflectance) data. The elemental abundances, petrological, and mineralogical characteristics of the study regions were mapped. We confirm that mare basalts and dark mantle deposits exist on the floors of these five craters. These two materials (mare basalts and dark mantle deposits) were used to identify minerals exposed on the floor surface of craters using spectral reflectance spectra. Two mineral groups were identified: pigeonite (or orthopyroxene in norite (low-Ca pyroxene) occurred in the craters Lavoisier, Lavoisier F, and Lavoisier H, and subcalcic augite (high-Ca pyroxene) occurs in the craters Lavoisier C and E. Our approach demonstrates that the characterization of uplifted or exposed surface minerals using elemental maps, spectral parameter composite maps, and reflectance spectra can provide information critical for prospective studies involving lunar geology and in situ resource utilization.
Development Status and Service Performance Preliminary Analysis for BDSBAS
Authors: Cao Yueling , Chen Jinping , Liu Li , Hu Xiaogong , Liu Yuchen , Xin Jie , Zhao Liqian , Tian Qiuning , Zhou Shanshi , Wu Bin
Published: September 2022

The BeiDou global navigation satellite system (BDS-3) provides positioning, navigation and timing services for global users, moreover, it provides BDS satellite-based augmentation system (BDSBAS) single-frequency (SF) and dual-frequency multi-constellation (DFMC) services for users in China and its surrounding areas. The BDSBAS SF service is in accordance with Radio Technical Commission for Aeronautics (RTCA) standard protocol (RTCA MOPS) and augment GPS constellation, while the BDSBAS DFMC service is in line with SBAS L5 DFMC standard protocol and is aimed at supporting any combination of BDS/GPS/Galileo/GLONASS constellations, including only a single constellation operation. We introduced the development status of the BDSBAS system, including the system architecture and navigation user algorithms. Based on the GPS measurements, the accuracy, integrity and availability of the BDSBAS SF service were evaluated, and with the BDS measurements, the accuracy of the BDSBAS DFMC service was preliminarily analyzed. The integrity and availability of the BDSBAS DFMC service will be discussed in future work as some of the DFMC integrity parameters are still under discussion for optimization. The results show that, for BDSBAS SF service, the horizontal and vertical position accuracy were about 1.0 m and 2.0 m (95%), respectively, which were improved by 39% and 33%, respectively, compared with the GPS SF position accuracy. For BDSBAS DFMC service, the horizontal and vertical position accuracy were about 0.6 m and 1.2 m (95%), respectively, which were improved by about 25% and 20% compared with the BDS dual-frequency position accuracy. No system integrity risk event was detected during the testing period for BDSBAS SF service. The average availability of the BDSBAS SF service was about 98% which was mainly affected by the availability of ionospheric grid delay corrections.
An Integrated Method for Estimating Forest-Canopy Closure Based on UAV LiDAR Data
Authors: Gao Ting , Gao Zhihai , Sun Bin , Qin Pengyao , Li Yifu , Yan Ziyu
Published: September 2022

Forest-canopy closure (FCC) reflects the coverage of the forest tree canopy, which is one of the most important indicators of forest structure and a core parameter in forest resources investigation. In recent years, the rapid development of UAV LiDAR and photogrammetry technology has provided effective support for FCC estimation. However, affected by factors such as different tree species and different stand densities, it is difficult to estimate FCC accurately based on the single-tree canopy-contour method in complex forest regions. Thus, this study proposes a method for estimating FCC accurately using algorithm integration with an optimal window size for treetop detection and an optimal algorithm for crown-boundary extraction using UAV LiDAR data in various scenes. The research results show that: (1) The FCC estimation accuracy was improved using the method proposed in this study. The accuracy of FCC in a camphor pine forest (Pinus sylvestris var. mongolica Litv.) was 89.11%, with an improvement of 6.77–11.25% compared to the results obtained from other combined conditions. The FCC accuracy for white birch (White birch platyphylla Suk) was about 87.53%, with an increase of 3.25–8.42%. (2) The size of the window used for treetop detection is closely related to tree species and stand density. With the same forest-stand density, the treetop-detection window size of camphor pine was larger than that of white birch. The optimal window size of camphor pine was between 5 × 5~11 × 11 (corresponding 2.5~5.5 m), while that of white birch was between 3 × 3~7 × 7 (corresponding 1.5~3.5 m). (3) There are significant differences in the optimal-canopy-outline extraction algorithms for different scenarios. With a medium forest-stand density, the marker-controlled watershed (MCW) algorithm has the best tree-crown extraction effect. The region-growing (RG) method has better extraction results in the sparse areas of camphor pine and the dense areas of white birch. The Voronoi tessellation (VT) algorithm is more suitable for the dense areas of camphor pine and the sparse regions of white birch. The method proposed in this study provides a reference for FCC estimation using high-resolution remote-sensing images in complex forest areas containing various scenes.
Hail Climatology in the Mediterranean Basin Using the GPM Constellation (1999–2021)
Authors: Laviola Sante , Monte Giulio , Cattani Elsa , Levizzani Vincenzo
Published: September 2022

The impacts of hailstorms on human beings and structures and the associated high economic costs have raised significant interest in studying storm mechanisms and climatology, thus producing a substantial amount of literature in the field. To contribute to this effort, we have explored the hail frequency in the Mediterranean basin during the last two decades (1999–2021) on the basis of hail occurrences derived from the observations of the microwave radiometers on board satellites of the Global Precipitation Measurement Constellation (GPM-C) from 2014 (date of GPM Core Observatory launch) onwards and merging multiple other satellite platforms prior to 2014. According to the MWCC-H method, two hail event categories (hail and super hail) are identified, and their spatiotemporal distributions are evaluated to identify the hail development areas in the Mediterranean and the corresponding monthly climatology of hail occurrences. Our results show that the northern sectors of the domain (France, Alpine Region, Po Valley, and Central-Eastern Europe) tend to be hit by hailstorms from June to August, while the central sectors (from Spain to Turkey) are more affected as autumn approaches. The trend analysis shows that the mean number of hail events over the entire domain tends to substantially increase, showing a higher increment during 2010–2021 than during 1999–2010. This behavior was particularly enhanced over Southern Italy and the Balkans. Our findings point to the existence of “sub-hotspots”, i.e., Mediterranean regions most susceptible to hail events and thus possibly more vulnerable to climate change effects.
Rapid Target Detection of Fruit Trees Using UAV Imaging and Improved Light YOLOv4 Algorithm
Authors: Zhu Yuchao , Zhou Jun , Yang Yinhui , Liu Lijuan , Liu Fei , Kong Wenwen
Published: September 2022

The detection and counting of fruit tree canopies are important for orchard management, yield estimation, and phenotypic analysis. Previous research has shown that most fruit tree canopy detection methods are based on the use of traditional computer vision algorithms or machine learning methods to extract shallow features such as color and contour, with good results. However, due to the lack of robustness of these features, most methods are hardly adequate for the recognition and counting of fruit tree canopies in natural scenes. Other studies have shown that deep learning methods can be used to perform canopy detection. However, the adhesion and occlusion of fruit tree canopies, as well as background noise, limit the accuracy of detection. Therefore, to improve the accuracy of fruit tree canopy recognition and counting in real-world scenarios, an improved YOLOv4 (you only look once v4) is proposed, using a dataset produced from fruit tree canopy UAV imagery, combined with the Mobilenetv3 network, which can lighten the model and increase the detection speed, combined with the CBAM (convolutional block attention module), which can increase the feature extraction capability of the network, and combined with ASFF (adaptively spatial feature fusion), which enhances the multi-scale feature fusion capability of the network. In addition, the K-means algorithm and linear scale scaling are used to optimize the generation of pre-selected boxes, and the learning strategy of cosine annealing is combined to train the model, thus accelerating the training speed of the model and improving the detection accuracy. The results show that the improved YOLOv4 model can effectively overcome the noise in an orchard environment and achieve fast and accurate recognition and counting of fruit tree crowns while lightweight the model. The mAP reached 98.21%, FPS reached 96.25 and F1-score reached 93.60% for canopy detection, with a significant reduction in model size; the average overall accuracy (AOA) reached 96.73% for counting. In conclusion, the YOLOv4-Mobilenetv3-CBAM-ASFF-P model meets the practical requirements of orchard fruit tree canopy detection and counting in this study, providing optional technical support for the digitalization, refinement, and smart development of smart orchards.
Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco
Authors: Kriese Jennifer , Hoeser Thorsten , Asam Sarah , Kacic Patrick , Da Ponte Emmanuel Da , Gessner Ursula
Published: September 2022

The Paraguayan Chaco is one of the most rapidly deforested areas in Latin America, mainly due to cattle ranching. Continuously forested windbreaks between agricultural areas and forest patches within these areas are mandatory to minimise the impact that the legally permitted logging has on the ecosystem. Due to the large area of the Paraguayan Chaco, comprehensive in situ monitoring of the integrity of these landscape elements is almost impossible. Satellite-based remote sensing offers excellent prerequisites for large-scale land cover analyses. However, traditional methods mostly focus on spectral and texture information while dismissing the geometric context of landscape features. Since the contextual information is very important for the identification of windbreak gaps and central forests, a deep learning-based detection of relevant landscape features in satellite imagery could solve the problem. However, deep learning methods require a large amount of labelled training data, which cannot be collected in sufficient quantity in the investigated area. This study presents a methodology to automatically classify gaps in windbreaks and central forest patches using a convolutional neural network (CNN) entirely trained on synthetic imagery. In a two-step approach, we first used a random forest (RF) classifier to derive a binary forest mask from Sentinel-1 and -2 images for the Paraguayan Chaco in 2020 with a spatial resolution of 10 m. We then trained a CNN on a synthetic data set consisting of purely artificial binary images to classify central forest patches and gaps in windbreaks in the forest mask. For both classes, the CNN achieved an F1 value of over 70%. The presented method is among the first to use synthetically generated training images and class labels to classify natural landscape elements in remote sensing imagery and therewith particularly contributes to the research on the detection of natural objects such as windbreaks.
InSAR Atmospheric Delay Correction Model Integrated from Multi-Source Data Based on VCE
Authors: Li Xiaobo , Wang Xiaoya , Chen Yanling
Published: September 2022

With the rapid development of interferometric synthetic aperture radar (InSAR) measurement technology, its measurement accuracy requirements are increasing. Atmospheric delay errors must be corrected, especially in the case of crustal deformation monitoring, the 20% variation of tropospheric water vapor among InSAR pairs generally produces range from 10 cm to 14 cm deformation errors. Such errors can be of the same magnitude as the annual changes in crustal deformation, or even greater, masking crustal deformation information and seriously affecting the results of crustal deformation monitoring. Therefore, in order to obtain a more accurate InSAR atmospheric delay correction model, this paper calculated and integrated atmospheric delays that were estimated by different sources, including the 37 pressure levels of the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF)) numerical weather prediction model, ECMWF Reanalysis v5 (ERA5), and Global Navigation Satellite System (GNSS) measurement data from the crustal movement observation network of China, based on the variance component estimation (VCE) weighting method. The results showed that the integrated model, based on the VCE method, is better than the generic atmospheric correction online service (GACOS) model for InSAR measuring of crustal deformation. The precision in monitoring crustal deformations was improved by approximately 5 mm, the correlation coefficient of atmospheric delay errors and crustal deformations improved from 0.287 to 0.347, and accuracy improved by approximately 25%. However, the improvement in accuracy was limited because of system error decoherence that was induced by atmospheric noise caused by abundant vegetation or snow cover. Therefore, in order to achieve more accurate results, we recommend the adoption of the multi-source integrated atmospheric delay correction model, based on the VCE method, for InSAR high-precision measuring of crustal deformation and seismic activities.
Coherent Targets Parameter Estimation for EVS-MIMO Radar
Authors: Ding Xueke , Hu Ying , Liu Changming , Wan Qun
Published: September 2022

As an emerging technique for detection, electromagnetic vector sensor multiple-input multiple-output (EVS-MIMO) radar has attracted extensive interest recently. This paper focuses on the coherent targets issue in EVS-MIMO radar, and a spatial smoothing estimator is developed to estimate the multiple parameters. It first recovers the rank of the array data via forward spatial smoothing. Then, it estimates the elevation angles via the rotational invariance technique. Combined with the vector cross-product method, the azimuth angles are obtained. Thereafter, with the previously achieved direction angles, the polarized parameters are acquired by using the least squares technique. Unlike the existing polarization smoothing techniques, the proposed estimator is able to estimate the two-dimensional direction parameter. Furthermore, it can provide a polarized parameter of the target. In addition, the proposed estimator is computationally efficient, since it offers closed-form and automatically paired solutions to all the parameters. Numerical experiments are carried out to show its superiority and effectiveness.
3D Reconstruction of Remote Sensing Mountain Areas with TSDF-Based Neural Networks
Authors: Qi Zipeng , Zou Zhengxia , Chen Hao , Shi Zhenwei
Published: September 2022

The remote sensing 3D reconstruction of mountain areas has a wide range of applications in surveying, visualization, and game modeling. Different from indoor objects, outdoor mountain reconstruction faces additional challenges, including illumination changes, diversity of textures, and highly irregular surface geometry. Traditional neural network-based methods that lack discriminative features struggle to handle the above challenges, and thus tend to generate incomplete and inaccurate reconstructions. Truncated signed distance function (TSDF) is a commonly used parameterized representation of 3D structures, which is naturally convenient for neural network computation and computer storage. In this paper, we propose a novel deep learning method with TSDF-based representations for robust 3D reconstruction from images containing mountain terrains. The proposed method takes in a set of images captured around an outdoor mountain and produces high-quality TSDF representations of the mountain areas. To address the aforementioned challenges, such as lighting variations and texture diversity, we propose a view fusion strategy based on reweighted mechanisms (VRM) to better integrate multi-view 2D features of the same voxel. A feature enhancement (FE) module is designed for providing better discriminative geometry prior in the feature decoding process. We also propose a spatial–temporal aggregation (STA) module to reduce the ambiguity between temporal features and improve the accuracy of the reconstruction surfaces. A synthetic dataset for reconstructing images containing mountain terrains is built. Our method outperforms the previous state-of-the-art TSDF-based and depth-based reconstruction methods in terms of both 2D and 3D metrics. Furthermore, we collect real-world multi-view terrain images from Google Map. Qualitative results demonstrate the good generalization ability of the proposed method.
Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
Authors: Müller Vitali , Hauk Markus , Misfeldt Malte , Müller Laura , Wegener Henry , Yan Yihao , Heinzel Gerhard
Published: September 2022

The GRACE Follow-On satellite mission measures distance variations between its two satellites in order to derive monthly gravity field maps, indicating mass variability on Earth on a scale of a few 100 km originating from hydrology, seismology, climatology and other sources. This mission hosts two ranging instruments, a conventional microwave system based on K(a)-band ranging (KBR) and a novel laser ranging instrument (LRI), both relying on interferometric phase readout. In this paper, we show how the phase measurements can be converted into range data using a time-dependent carrier frequency (or wavelength) that takes into account potential intraday variability in the microwave or laser frequency. Moreover, we analyze the KBR-LRI residuals and discuss which error and noise contributors limit the residuals at high and low Fourier frequencies. It turns out that the agreement between KBR and LRI biased range observations can be slightly improved by considering intraday carrier frequency variations in the processing. Although the effect is probably small enough to have little relevance for gravity field determination at the current precision level, this analysis is of relevance for detailed instrument characterization and potentially for future more precise missions.
Combined Use of GPR and Other NDTs for Road Pavement Assessment: An Overview
Authors: Elseicy Ahmed , Alonso-Díaz Alex , Solla Mercedes , Rasol Mezgeen , Santos-Assunçao Sonia
Published: September 2022

Roads are the main transportation system in any country and, therefore, must be maintained in good physical condition to provide a safe and seamless flow to transport people and goods. However, road pavements are subjected to various defects because of construction errors, aging, environmental conditions, changing traffic load, and poor maintenance. Regular inspections are therefore recommended to ensure serviceability and minimize maintenance costs. Ground-penetrating radar (GPR) is a non-destructive testing (NDT) technique widely used to inspect the subsurface condition of road pavements. Furthermore, the integral use of NDTs has received more attention in recent years since it provides a more comprehensive and reliable assessment of the road network. Accordingly, GPR has been integrated with complementary NDTs to extend its capabilities and to detect potential pavement surface and subsurface distresses and features. In this paper, the non-destructive methods commonly combined with GPR to monitor both flexible and rigid pavements are briefly described. In addition, published work combining GPR with other NDT methods is reviewed, emphasizing the main findings and limitations of the most practical combination methods. Further, challenges, trends, and future perspectives of the reviewed combination works are highlighted, including the use of intelligent data analysis.
An Improved Retrieval Method for Porphyra Cultivation Area Based on Suspended Sediment Concentration
Authors: Cheng Yinhe , Sun Yue , Peng Lin , He Yijun , Zha Mengling
Published: September 2022

The rapid expansion of Porphyra farming in China lends considerable urgency to developing a satellite remote sensing retrieval method to monitor its cultivation, in order to promote sustainable economic development and protective utilization of ecosystem-oriented marine natural resources. For medium-resolution satellite imagery such as HY-1C images, pixel-by-pixel techniques are appropriate; however, many factors affect the retrieval accuracy of the Porphyra cultivation area. In coastal regions, Porphyra and suspended sediment radiate a similar spectrum, which inevitably causes errors in the identification of the Porphyra. To improve the overall retrieval accuracy of Porphyra cultivation area from medium-resolution HY-1C images, we considered suspended sediment concentration (SSC) as an independent variable and constructed a new model in conjunction with high-resolution Sentinel-2 satellite images using a linear regression method in Haizhou Bay, China. A comparative analysis was performed with a traditional random forest classification algorithm and pixel-based dichotomy model in different SSC seawater. The results showed that the new model expressed the best ability to supervise Porphyra cultivation area, and its overall relative error and root mean square deviation, whether in area or in validation sample points, were the lowest among the models. The experiment was performed by removing the SSC variable while using the same processes as in the new model, and the results indicate that the SSC played an important role in new model, which is suitable to be applied to coastal seawater containing more suspended sediment, as in the HY-1C coastal zone image. The application of the new model on temporal change in the retrieved results was indirectly verified as effective. This study provides an effective method to exactly extract Porphyra cultivation area in the coastal sea using medium-resolution HY-1C satellite imagery.
Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China
Authors: Meng Qingyan , Liu Wenxiu , Zhang Linlin , Allam Mona , Bi Yaxin , Hu Xinli , Gao Jianfeng , Hu Die , Jancsó Tamás
Published: September 2022

Urban environments have a strong influence on the land surface temperature (LST) in urban areas. Understanding the relationship between LST and urban environmental factors can help develop effective strategies to reduce high LSTs in urban areas, which is critical for mitigating the urban heat island effect. Previous studies have focused on the correlation between LST and the environmental factors that drive its formation, without considering the influences of the neighboring environment and the vertical expansion of highly urbanized areas. Notably, the correlation between LST and its neighboring environment in different seasons remains unclear. In this study, we selected central Beijing in China as our study area and employed the moving window method to characterize the environmental factors of the neighboring environment of the central LST cell. We explored eight environmental factors from three layers: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI), building density (BD), building height (BH), building volume (BV), sky view factor (SVF), and road density (RD). The Pearson correlation and extreme gradient boosting (XGB) regression methods were applied to measure the correlation between LST and the different factors in moving windows of different sizes. The results indicated that the correlation between NDVI, MNDWI, and LST was considerably different in the winter and other seasons. However, NDBI was positively correlated with LST in all seasons, although the correlation was strongest/weakest in summer/winter. Among building-related factors, BD and BH were more strongly correlated with LST, and the positive/negative correlation between BD/BH and LST was stronger in summer/winter. The correlation between LST and its neighboring environment varied with increasing window size, and this variation differs significantly between winter and other seasons. In spring, summer, and autumn, the strength of the correlation between LST and its neighboring environment showed an “inverted V” pattern with increasing window size. The optimal spatial scales to explore the influence of neighboring environments on the LST of 30-m cells were 210 m and 270 m. This study revealed the seasonal correlation between LST and its neighboring environment while explaining the variation at a spatial scale. Notably, this study can provide a new perspective for understanding the driving mechanism of the urban thermal environment, while contributing to its scientific optimization and management.
Detailed Processes of Tidal Flat Geomorphology Evolution Based on Time-Series Satellite Images
Authors: Wang Lihua , Yang Ziheng , Sun Weiwei , Fan Li , Tan Benhua , Zhou Yunxuan
Published: September 2022

In-depth understanding of long-term evolution of mega deltas with a large population is of great significance for national sustainability development programs. However, due to insufficient satellite images and its low temporal resolution, previous studies have only roughly explored the long-term evolution. To reveal the detailed delta evolution processes, Jiuduansha (JDS) upon the Yangtze estuary, which is one of the world’s mega deltas, was taken as an example. Based on the continuous time series of 792 satellite images between 1965 and 2021, this study combined the K-means classification, probability statistics, and GIS spatial analysis to generate the annual probability waterlines. Furthermore, the continuous time series of geomorphological features, position of waterlines and centroid change were determined. The results showed that the JDS exposed area presented a rapid growth trend. Specifically, in the early period (1965–1998), it presented a natural evolution state of “the head erosion, the tail deposition, and extending to the southeast”. In the recent period (1999–2021), the evolution state changed into the engineering-dominated evolution state of “erosion in the south, deposition in the north, continuous extension to the southeast, and overall northward movement”. The accelerated deposition of JDS over the recent 20 years has been mainly attributed to human activities, including the construction of Deep-water Navigation Channels and the vegetation promoting silting, followed by the upstream and downstream sediment sources. Current results suggest the JDS is likely to be more elongated in the SE-NW direction, with erosion in the southern region.
An Adaptive and Accurate Method for Rotational Angular Velocity Estimation of Rotor Targets via Fourier Coefficient Interpolation
Authors: Wang Wantian , Zhang Jiahao , Wu Hao , Meng Jin
Published: September 2022

As a special micro-motion feature of rotor targets, rotational angular velocity can provide a discriminant basis for target classification and recognition. In this paper, an adaptive and accurate method is proposed for estimating the rotational angular velocity of rotor targets via a Fourier coefficient interpolation algorithm that is based on modified frequency index residue initialization. The negative frequency complex exponential signal component is removed at each iteration to eliminate the estimation bias caused by spectrum superposition and improve the estimation accuracy. The frequency index residue initialization is modified, based on a normalized Fourier spectrum, to improve the estimable range of the rotational angular velocity and reduce the computational complexity of the algorithm. The simulation results show that the estimation performance of the proposed method achieves the Cramér–Rao lower bound and outperforms state-of-the-art methods in terms of the estimable range and estimation accuracy of the rotational angular velocity.
A Dynamic Self-Tuning Maximum Correntropy Kalman Filter for Wireless Sensors Networks Positioning Systems
Authors: Liao Tianrui , Hirota Kaoru , Wu Xiangdong , Shao Shuai , Dai Yaping
Published: September 2022

To improve the accuracy of the maximum correntropy Kalman filter (MCKF) in wireless sensors networks (WSNs) positioning, a dynamic self-tuning maximum correntropy Kalman filter (DSTMCKF) is proposed, where innovation and the sensors information of the WSNs are used to adjust the noise covariance matrices, and the maximum correntropy criterion is the criterion for the filter’s optimality. By dynamically adjusting the noise covariance matrices, the DSTMCKF ensures that the correntropy distribution is accurate in the presence of non-Gaussian noise (NGN), thus improving its ability to handle the NGN. In simulation and real environment positioning experiments, the DSTMCKF is used to compare with the MCKF, variable kernel width–maximum correntropy Kalman filter (VKW-MCKF) and robust minimum error entropy Kalman filter (R-MEEKF). Among the four filters, the DSTMCKF has the highest accuracy, and the error of the DSTMCKF is reduced by 34.5, 42.9 and 40.0%, respectively, compared with the MCKF, VKW-MCKF and R-MEEKF in the real-world environment positioning experiment. The application of the DSTMCKF in WSNs positioning systems improves the stability of the control systems because of the rising positioning accuracy, which makes WSNs positioning systems more widely used in scenarios requiring high stability, such as automatic parking.
Establishment and Extension of a Fast Descriptor for Point Cloud Registration
Authors: Zhao Lidu , Xiang Zhongfu , Chen Maolin , Ma Xiaping , Zhou Yin , Zhang Shuangcheng , Hu Chuan , Hu Kaixin
Published: September 2022

Point cloud registration (PCR) is a vital problem in remote sensing and computer vision, which has various important applications, such as 3D reconstruction, object recognition, and simultaneous localization and mapping (SLAM). Although scholars have investigated a variety of methods for PCR, the applications have been limited by low accuracy, high memory footprint, and slow speed, especially for dealing with a large number of point cloud data. To solve these problems, a novel local descriptor is proposed for efficient PCR. We formed a comprehensive description of local geometries with their statistical properties on a normal angle, dot product of query point normal and vector from the point to its neighborhood point, the distance between the query point and its neighborhood point, and curvature variation. Sub-features in descriptors were low-dimensional and computationally efficient. Moreover, we applied the optimized sample consensus (OSAC) algorithm to iteratively estimate the optimum transformation from point correspondences. OSAC is robust and practical for matching highly self-similar features. Experiments and comparisons with the commonly used descriptor were conducted on several synthetic datasets and our real scanned bridge data. The result of the simulation experiments showed that the rotation angle error was below 0.025° and the translation error was below 0.0035 m. The real dataset was terrestrial laser scanning (TLS) data of Sujiaba Bridge in Chongqing, China. The results showed the proposed descriptor successfully registered the practical TLS data with the smallest errors. The experiments demonstrate that the proposed method is fast with high alignment accuracy and achieves a better performance than previous commonly used methods.
The Regional Disparity of Urban Spatial Expansion Is Greater than That of Urban Socioeconomic Expansion in China: A New Perspective from Nighttime Light Remotely Sensed Data and Urban Land Datasets
Authors: Chang Zhijian , Liu Shirao , Wu Yizhen , Shi Kaifang
Published: September 2022

The regional disparity of urban expansion varies significantly in China’s different regions, hindering sustainable socioeconomic development. However, most studies to date have focused on a single aspect of urban expansion, e.g., urban spatial expansion (USS) disparity. This study attempts to define urban expansion from USS and urban socioeconomic expansion (USE) based on nighttime light remotely sensed (NTL) data and urban land datasets. Then, taking China’s 241 prefecture-level cities within different provinces as experimental subjects, the Dagum Gini (DG) coefficient and stochastic convergence test are employed to assess the disparity of urban expansion from two different dimensions. The results show that, on the national scale, the regional disparity of USS is always greater than that of USE and has a converging trend. Additionally, regional disparity is the main factor causing the difference between USS and USE, with average contribution rates of 55% and 45%, respectively. The average difference between USS and USE in the eastern region (ER) is greater than 10%, while it is the lowest in the northeastern region (NER) and shows a significant expansion trend in performance convergence with a regression coefficient of 0.0022, followed by the central (CR), eastern, and western (WR) regions. Through the panel unit root test, we found that urban expansion in China in terms of USS and USE has internal random convergence in certain regions under the premise of global random divergence, and there may be differentiation and formation of one or more convergence clubs in the future. Using this novel perspective to define urban expansion, this study quantifies the contributions of USS and USE to regional disparity and provides a scientific basis for governments to implement appropriate approaches to sustainable urban development in different regions.
Elevation Multi-Channel Imbalance Calibration Method of Digital Beamforming Synthetic Aperture Radar
Authors: Chen Hao , Ming Feng , Li Liang , Liu Guikun
Published: September 2022

The digital beamforming synthetic aperture radar (DBF-SAR) is proposed by scholars as a promising solution to overcome the constraint of the minimum antenna area of the traditional single-channel SAR to achieve high resolution and wide swath (HRWS) by scan-on-receive (SCORE) in the elevation multiple channel. However, the inevitable channel imbalance between the elevation channels of DBF-SAR will degrade the DBF-SAR image quality. In this paper, we present a method to estimate the sampling time delay error, amplitude error and phase error based on the external calibration data. For the sampling time delay deviation, we adopt to calculate the statistical average of the position deviation of several external calibration points in the reference channel image with that of the error channel image. To avoid noise interference, we image the DBF-SAR original echo-carrying amplitude information to obtain the amplitude error between channels by dividing the absolute values of the complex image data of the error channel. Due to the phase error between channels, the image contrast will decrease. Therefore, the problem of estimating the phase error can be transformed into the problem of maximizing the image contrast. So, in this paper, we use the gradient descent method to optimally estimate the phase error. Finally, the effectiveness of the method is verified by the simulation of airborne measured data and simulation data.
Elevator Car Vibration Signal Denoising Method Based on CEEMD and Bilateral Filtering
Authors: Niu Dapeng , Wang Jiaqi
Published: September 2022

Elevator car vibration signals are important information to monitor and diagnose the operating status of elevators, but during the process of vibration signals acquisition, vibration signals are always inevitably disturbed by noise, which affects further research. Therefore, aiming at the vibration signal with noise, we propose a new vibration signal denoising method on the basis of complementary ensemble empirical mode decomposition (CEEMD) and bilateral filtering. Firstly, the collected original vibration signals are decomposed by the CEEMD into several inherent mode functions. Then, the false components are removed by determining the correlation coefficients of mode components, and then the noise-dominate components are denoised by bilateral filtering. Finally, the processed inherent mode functions are reconstructed to require the denoised signal. We test the method through simulation and practical application. The results indicate that the proposed method can efficaciously reduce the noise in the vibration signal of an elevator car.
Development of a Dual-Layer Structure for Cymbal Transducer Arrays to Achieve a Wider Bandwidth
Authors: Mudiyala Jahnavi , Shim Hayeong , Kim Donghyun , Roh Yongrae
Published: September 2022

Cymbal transducers are typically grouped and arranged in planar arrays. For projector arrays, a wide bandwidth on the transmitting voltage response (TVR) spectrum is required for better underwater communication and data transmission within a short time. The purpose of this study is to develop a wideband cymbal array by controlling the center-to-center (CTC) spacing between the cymbal transducers in the array. In the practical design of the array, due to the arrangement of elements in one layer, the minimum CTC spacing between the cymbals is constrained to the diameter of the cymbals in use. To overcome this limitation, we propose a new dual-layer array structure. Finite element analysis of the cymbal array showed that the bandwidth was generally inversely proportional to the CTC spacing. We explained the mechanism of this relationship using a theoretical analysis of the mutual radiation impedance between the cymbals in the array. Subsequently, we identified the optimum CTC spacing to achieve the widest possible bandwidth for the cymbal array. The validity of the wideband array design was verified through the fabrication and characterization of prototype arrays. We confirmed that the two-layered arrangement could significantly widen the bandwidth of the cymbal array while maintaining the TVR above a specified level.
Unsupervised Outlier Detection in IOT Using Deep VAE
Authors: Gouda Walaa , Tahir Sidra , Alanazi Saad , Almufareh Maram , Alwakid Ghadah
Published: September 2022

The Internet of Things (IoT) refers to a system of interconnected, internet-connected devices and sensors that allows the collection and dissemination of data. The data provided by these sensors may include outliers or exhibit anomalous behavior as a result of attack activities or device failure, for example. However, the majority of existing outlier detection algorithms rely on labeled data, which is frequently hard to obtain in the IoT domain. More crucially, the IoT’s data volume is continually increasing, necessitating the requirement for predicting and identifying the classes of future data. In this study, we propose an unsupervised technique based on a deep Variational Auto-Encoder (VAE) to detect outliers in IoT data by leveraging the characteristic of the reconstruction ability and the low-dimensional representation of the input data’s latent variables of the VAE. First, the input data are standardized. Then, we employ the VAE to find a reconstructed output representation from the low-dimensional representation of the latent variables of the input data. Finally, the reconstruction error between the original observation and the reconstructed one is used as an outlier score. Our model was trained only using normal data with no labels in an unsupervised manner and evaluated using Statlog (Landsat Satellite) dataset. The unsupervised model achieved promising and comparable results with the state-of-the-art outlier detection schemes with a precision of ≈90% and an F1 score of 79%.
A Hardware-Friendly Low-Bit Power-of-Two Quantization Method for CNNs and Its FPGA Implementation
Authors: Sui Xuefu , Lv Qunbo , Bai Yang , Zhu Baoyu , Zhi Liangjie , Yang Yuanbo , Tan Zheng
Published: September 2022

To address the problems of convolutional neural networks (CNNs) consuming more hardware resources (such as DSPs and RAMs on FPGAs) and their accuracy, efficiency, and resources being difficult to balance, meaning they cannot meet the requirements of industrial applications, we proposed an innovative low-bit power-of-two quantization method: the global sign-based network quantization (GSNQ). This method involves designing different quantization ranges according to the sign of the weights, which can provide a larger quantization-value range. Combined with the fine-grained and multi-scale global retraining method proposed in this paper, the accuracy loss of low-bit quantization can be effectively reduced. We also proposed a novel convolutional algorithm using shift operations to replace multiplication to help to deploy the GSNQ quantized models on FPGAs. Quantization comparison experiments performed on LeNet-5, AlexNet, VGG-Net, ResNet, and GoogLeNet showed that GSNQ has higher accuracy than most existing methods and achieves “lossless” quantization (i.e., the accuracy of the quantized CNN model is higher than the baseline) at low-bit quantization in most cases. FPGA comparison experiments showed that our convolutional algorithm does not occupy on-chip DSPs, and it also has a low comprehensive occupancy in terms of on-chip LUTs and FFs, which can effectively improve the computational parallelism, and this proves that GSNQ has good hardware-adaptation capability. This study provides theoretical and experimental support for the industrial application of CNNs.
Adaptive Points Sampling for Implicit Field Reconstruction of Industrial Digital Twin
Authors: Jin Jiongchao , Xu Huanqiang , Leng Biao
Published: September 2022

Nowadays, the digital twin (DT) plays an important role in Industry 4.0. It aims to model reality in the digital space for further industrial maintenance, management, and optimization. Previously, many AI technologies have been applied in this field and provide strong tools to connect physical and virtual spaces. However, we found that single-view 3D reconstruction (SVR) for DT has not been thoroughly studied. SVR can generate 3D digital models of real industrial products from just a single image. The application of SVR technology would bring convenience, cheapness, and robustness to modeling physical objects in digital space. However, the existing SVR methods cannot perform well in the reconstruction of details, which is indispensable and challenging in industrial products. In this paper, we propose a new detail-aware feature extraction network based on a feature pyramid network (FPN) for better detail reconstruction. Then, an extra network is designed to combine convolutional feature maps from different levels. Moreover, we also propose a novel adaptive points-sampling strategy to adaptively change the learning difficulty according to the training status. This can accelerate the training process and improve the fine-tuned network performance as well. Finally, we conduct comprehensive experiments on both the general objects dataset ShapeNet and a collected industrial dataset to prove the effectiveness of our methods and the practicability of the SVR technology for DT.
Optimization of Microwave-Assisted Extraction and Matrix Solid-Phase Dispersion for the Extraction of Polyphenolic Compounds from Grape Skin
Authors: Ašperger Danijela , Gavranić Marija , Prišlin Barbara , Rendulić Nera , Šikuten Iva , Marković Zvjezdana , Babić Bruna , Maletić Edi , Kontić Jasminka , Preiner Darko , Tomaz Ivana
Published: September 2022

Polyphenols are organic compounds that can be found in food, especially in fruits, vegetables, and their products. It was shown that their presence significantly affects the properties of food products and that the consumption of food rich in phenolic compounds has a beneficial effect on human health. The subjects of this research were polyphenols: anthocyanins, flavonols, and flavan-3-ols in the skin of grapevine variety Regent. Polyphenols from grape skins were extracted via microwave-assisted extraction (MAE) and matrix solid-phase dispersion (MSPD) as unconventional and green techniques. Therefore, the main aim of this work was to optimize the conditions for the extraction of polyphenolic compounds from grape skin using MAE and MSPD. The extracts were analyzed using high-performance liquid chromatography with a diode array detector and fluorescence detector. Analyses showed that MAE was a very effective method for extracting polyphenolic compounds from grape skin with 10 mL of 60% ethanol for 5 min at 40 °C. The best results for the MSPD extraction of polyphenolic compounds from grape skin were obtained with phenyl as an MSPD sorbent with 10 mL of acetonitrile:water 50:50 v/v as an elution solvent. This scientific research can be used for the better use of grapes as a basis for obtaining flavonoids for commercial purposes.
Perception of Community Environment, Satisfaction with Local Government, and Quality of Life: The Case of Gyeonggi, Korea
Authors: Lee Kyung-Young , Park Kwantae
Published: September 2022

This study examined the relationship between the perception of community environment, local government, and quality of life (QoL). Previous studies on QoL have not comprehensively analyzed factors related to community environments or local governments. Consequently, this study examined the influence of the perception of community environment on QoL and simultaneously analyzed the mediating effect of satisfaction with local government. Furthermore, this study divided the community environment into facility, safety, medical, and social environments, which enables comparisons to be made between the influences of each factor. This study analyzed the “2020 Residents’ Quality of Life Survey in Gyeonggi, Korea”. The survey included all 31 districts in Gyeonggi Province, and the total number of samples was 16,000. Moreover, a multilevel stratified sampling method was applied on the basis of the 31 districts and the number of households. Thus, the survey properly reflects the regional characteristics of Gyeonggi Province. The analysis results were as follow: First, perception of community environment had a positive influence on QoL. In particular, the safety environment had the greatest effect on QoL. Therefore, it is necessary to focus more on improving the safety environment in the community. Second, satisfaction with local government had a mediating effect between facility environment, medical environment, social environment, and QoL. This means that satisfaction with local government is an important factor in increasing residents’ QoL. Presently, developing countries such as China, the Philippines, and Myanmar are still pursuing “rapid urbanization” while focusing only on quantitative development. However, this can generate various problems regarding social sustainability. Thus, this study provides important implications for these developing countries.