bluuebunny/crossref_metadata_2025 huggingface dataset:
The Visual Representation of the Crown of Thorns Motif in Irish Stained Glass: A Symbol of Universal Suffering for the Catholic Revival in France and Ireland in the Early Twentieth Century
Authors: Hayes Myra
Published: June 2022

The fire that broke out on 15 April 2019 in Notre Dame Cathedral, Paris was devastating for the French nation, as the cathedral had long been regarded as a symbol of French religious and cultural patrimony, described by the Washington Post as “the spiritual heart of France”. The heroic rescue of the relic of the crown of thorns by the chaplain of the Paris Fire Department, Père Jean-Marc Fournier, offered some consolation in a night of national trauma. World attention was focused on the relic, its history, and what its loss would signify to the people of France, and to the world. This paper will examine the importance of the relic to the people of France, and its enduring legacy as a symbol of universal suffering. It will demonstrate how the motif of the crown of thorns, as attested to in three of the canonical gospels, has acted as a powerful source of inspiration for the proponents of the French Catholic Revival of the early twentieth century. It will also examine the appearance of the motif in the earliest stained-glass windows produced for Loughrea Cathedral, Co. Galway at the beginning of the twentieth century, during a period of extensive building of churches and cathedrals following Catholic Emancipation in Ireland in 1829. Finally, it will reveal the largely undocumented link between the French Catholic Revival (le renouveau Catholique) and the sacrificial politics of early-twentieth-century Ireland.
Epistles from Jerusalem: The Paratexts of Syriac 2 Baruch and the Peshitta Jeremiah Corpus
Authors: Lied Liv Ingeborg
Published: June 2022

This article explores the paratexts of the epistles attributed to Baruch, the scribe of Jeremiah, in Syriac, Peshitta Old Testament manuscripts. In early manuscripts, the epistles ascribed to the figure Baruch are, most commonly, either included in the Jeremiah corpus or embedded in the work known as 2 Baruch. This article argues that 2 Baruch has had a larger influence on the Syriac Christian corpus-building and literary imagination than has been hitherto acknowledged. This hypothesis would explain both the inclusion of two epistles of Baruch in the Peshitta Jeremiah corpus and the appearance of unprecedented readings in the titles and introductory addresses of the epistles attributed to Baruch in this corpus.
A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode
Authors: Reid Jeffrey S. , Gumber Amanda , Zhang Jianglong , Holz Robert E. , Rubin Juli I. , Xian Peng , Smirnov Alexander , Eck Thomas F. , O’Neill Norman T. , Levy Robert C. , Reid Elizabeth A. , Colarco Peter R. , Benedetti Angela , Tanaka Taichu
Published: June 2022

Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (e.g., coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD550, fine mode AOD (FAOD550; also known as FM AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (η550) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOD/MYD04 dark target aerosol retrievals and the International Cooperative for Aerosol Prediction (ICAP) core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total MODIS AOD550. Differences were adjudicated by the global shipboard Maritime Aerosol Network (MAN) and selected island AERONET sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the C4C and its members, while having good correlations overall, have a persistent +0.04 to +0.02 biases relative to MAN and AERONET for typical AOD550 values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD550 and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved Ångström Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s−1, the C4C and MODIS AOD550s also overestimate CAOD550 and FAOD550, respectively, for wind speeds above 12 m/s. Finally, sampling bias inherent in MAN, as well as other circumstantial evidence, suggests biases in MODIS are likely even larger than what was diagnosed here. We conclude with a discussion on how MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research.
Curvilinear Flight Synthetic Aperture Radar (CF-SAR): Principles, Methods, Applications, Challenges and Trends
Authors: Chen Zhanye , Tang Shiyang , Ren Yi , Guo Ping , Zhou Yu , Huang Yan , Wan Jun , Zhang Linrang
Published: June 2022

The research into curvilinear flight synthetic aperture radar (CF-SAR) is the inevitable result of the comprehensive practicality of SAR. The flight path of the SAR platform in real applications, which is highly nonlinear or curvy due to three-dimensional velocity and acceleration, cannot be described by the traditional uniform linear motion model. New mathematical models, signal characteristics, imaging algorithms, and system design criteria must be proposed and investigated for CF-SAR. This paper provides a comprehensive overview of CF-SAR. Firstly, the basic concept, unified model, and general signal characteristics of CF-SAR are defined, derived, and analyzed, respectively. Additionally, the advantages and drawbacks of current methodologies are reviewed. Discussions on the CF-SAR’s applications are presented from the perspective of typical platforms, new configurations, and advanced technologies, which are suitable means to fulfill the increasing user requirements. Finally, the challenges faced by CF-SAR are summarized, and some future trends for the study of CF-SAR are explored. Hopefully, this paper will serve as a reference for SAR researchers/engineers and stimulate the future development and actual application of CF-SAR.
Fast and Accurate Registration of Terrestrial Point Clouds Using a Planar Approximation of Roof Features
Authors: Alicandro Maria , Di Angelo Luca , Di Stefano Paolo , Dominici Donatella , Guardiani Emanuele , Zollini Sara
Published: June 2022

3D reconstructed models are becoming more diffused daily, especially in the Cultural Heritage field. These geometric models are typically obtained from elaborating a 3D point cloud. A significant limit in using these methods is the realignment of different point clouds acquired from different acquisitions, particularly for those whose dimensions are millions of points. Although several methodologies have tried to propose a solution for this necessity, none of these seems to solve definitively the problems related to the realignment of large point clouds. This paper presents a new and innovative procedure for the fine registration of large point clouds. The method performs an alignment by using planar approximations of roof features, taking the roof’s extension into account. It looks particularly suitable for the alignment of large point clouds acquired in urban and archaeological environments. The proposed methodology is compared in terms of accuracy and time with a standard photogrammetric reconstruction based on Ground Control Points (GCPs) and other ones, aligned by the Iterative Closest Point method (ICP) and markers. The results evidence the excellent performance of the methodology, which could represent an alternative for aligning extensive photogrammetric reconstructions without the use of GCPs.
Monitoring Regional-Scale Surface Deformation of the Continuous Permafrost in the Qinghai–Tibet Plateau with Time-Series InSAR Analysis
Authors: Xu Zhida , Jiang Liming , Niu Fujun , Guo Rui , Huang Ronggang , Zhou Zhiwei , Jiao Zhiping
Published: June 2022

As an important indicator of permafrost degradation, surface deformation is often used to monitor the thawing and freezing process in the permafrost active layer. However, due to the large area of the continuous permafrost of the Qinghai–Tibet Plateau (QTP) and the large amount of data processed by conventional time-series InSAR, previous studies have mostly focused on local area investigations, and regional characteristics of surface deformation of the continuous permafrost area on the QTP are still unclear. In this paper, we characterized surface deformation in space and time over the main continuous permafrost area on the QTP, by analyzing 11 ascending and 8 descending orbits of Sentinel-1 SAR data acquired between 2018 and 2021 with the time-series InSAR processing system LiCSAR. The reliability of the InSAR deformation results was verified by a combination of leveling measurement data, the intercomparison of overlapping area results, and field verification. The results show that the permafrost regions of the central QTP exhibited the most significant linear subsidence trend. The subsidence trend of permafrost on the QTP was mainly related to the thermal stability of permafrost, and the regions with larger subsidence rates were concentrated in sub-stable, transitional and unstable permafrost areas. We also found that, according to analysis of time-series displacement, the beginning and ending times of permafrost thawing were highly spatially heterogeneous, with the time of maximum thawing depth varying between mid-October and mid-November, which was probably attributed to the active layer thickness (ALT), water content in the active layer, and vegetation cover in these regions. This study is of great significance for understanding the changing trend of permafrost on the QTP under the background of climate change. In addition, this study also demonstrates that combination of Sentinel-1 SAR images with the LiCSAR system has significant potential for detecting permafrost deformation with high accuracy and high efficiency at regional and global scales.
Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study
Authors: Kalinić Hrvoje , Ćatipović Leon , Matić Frano
Published: June 2022

In this paper, we discuss different approaches to optimal sensor placement and propose that an optimal sensor location can be selected using unsupervised learning methods such as self-organising maps, neural gas or the K-means algorithm. We show how each of the algorithms can be used for this purpose and that additional constraints such as distance from shore, which is presumed to be related to deployment and maintenance costs, can be considered. The study uses wind data over the Mediterranean Sea and uses the reconstruction error to evaluate sensor location selection. The reconstruction error shows that results deteriorate when additional constraints are added to the equation. However, it is also shown that a small fraction of the data is sufficient to reconstruct wind data over a larger geographic area with an error comparable to that of a meteorological model. The results are confirmed by several experiments and are consistent with the results of previous studies.
Precipitation Estimation from the NASA TROPICS Mission: Initial Retrievals and Validation
Authors: Kidd Chris , Matsui Toshi , Blackwell William , Braun Scott , Leslie Robert , Griffith Zach
Published: June 2022

This paper describes the initial results of precipitation estimates from the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Millimeter-wave Sounder (TMS) using the Precipitation Retrieval and Profiling Scheme (PRPS). The TROPICS mission consists of a Pathfinder CubeSat and a constellation of six CubeSats, providing a low-cost solution to the frequent sampling of precipitation systems across the Tropics. The TMS instrument is a 12-channel cross-track scanning radiometer operating at frequencies of 91.655 to 204.8 GHz, providing similar resolutions to current passive microwave sounding instruments. These retrievals showcase the potential of the TMS instrument for precipitation retrievals. The PRPS has been modified for use with the TMS using a database based upon observations from current sounding sensors. The results shown here represent the initial postlaunch version of the retrieval scheme, as analyzed for the Pathfinder CubeSat launched on 30 June 2021. In terms of monthly precipitation estimates, the results fall within the mission specifications and are similar in performance to retrievals from other sounding instruments. At the instantaneous scale, the results are very promising.
ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data
Authors: Li Yingbo , Chai Guoqi , Wang Yueting , Lei Lingting , Zhang Xiaoli
Published: June 2022

Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine-scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been conducted on the delineation of individual tree crown and species classification using drone high-resolution red, green and blue (RGB) images, and Light Detection and Ranging (LiDAR) data, performing the above tasks simultaneously has rarely been explored, especially in complex forest environments. In this study, we improve upon the state of the Mask region-based convolution neural network (Mask R-CNN) with our proposed attention complementary network (ACNet) and edge detection R-CNN (ACE R-CNN) for individual tree species identification in high-density and complex forest environments. First, we propose ACNet as the feature extraction backbone network to fuse the weighted features extracted from RGB images and canopy height model (CHM) data through an attention complementary module, which is able to selectively fuse weighted features extracted from RGB and CHM data at different scales, and enables the network to focus on more effective information. Second, edge loss is added to the loss function to improve the edge accuracy of the segmentation, which is calculated through the edge detection filter introduced in the Mask branch of Mask R-CNN. We demonstrate the performance of ACE R-CNN for individual tree species identification in three experimental areas of different tree species in southern China with precision (P), recall (R), F1-score, and average precision (AP) above 0.9. Our proposed ACNet–the backbone network for feature extraction–has better performance in individual tree species identification compared with the ResNet50-FPN (feature pyramid network). The addition of the edge loss obtained by the Sobel filter further improves the identification accuracy of individual tree species and accelerates the convergence speed of the model training. This work demonstrates the improved performance of ACE R-CNN for individual tree species identification and provides a new solution for tree-level species identification in complex forest environments, which can support carbon stock estimation and biodiversity assessment.
Remote Sensing of Marine Phytoplankton Sizes and Groups Based on the Generalized Addictive Model (GAM)
Authors: Wang Yuchao , Liu Fenfen
Published: June 2022

Marine phytoplankton are the basis of the whole marine ecosystem, and different groups of phytoplankton play different roles in the biogeochemical cycle. Satellite remote sensing is widely used in the retrieval of marine phytoplankton over a wide range and long time series, but not yet for taxonomical composition. In this study, we used coincident in situ measurement data from high-performance liquid chromatography (HPLC) and remote sensing reflectance (Rrs) to investigate the empirical relationships between phytoplankton groups and satellite measurements. A nonparametric model, generalized additive model (GAM), is introduced to establish inversion models of various marine phytoplankton groups. Seven inversion models (two sizes classes among the microphytoplankton and nanophytoplankton and four groups among the diatoms, dinoflagellates, chrysophytes, and cryptophytes) are applied to the South China Sea (SCS) for 2020, and satellite images of phytoplankton sizes and groups are presented. Microphytoplankton prevails in the coastal and continental shelf, and nanophytoplankton prevails in oligotrophic oceans. Among them, the dominant contribution of microphytoplankton comes from diatoms, and nanophytoplankton comes from chrysophytes. Diatoms (nearshore) and chrysophytes (outside the continental shelf) are the dominant groups in the SCS throughout the year. Dinoflagellates only become dominant in some coastal areas, while cryptophytes rarely become dominant.
Temporal Coherence Estimators for GBSAR
Authors: Beni Alessandra , Miccinesi Lapo , Michelini Alberto , Pieraccini Massimiliano
Published: June 2022

Many Ground-Based Synthetic Aperture Radar (GBSAR) applications demand preliminary analysis to select areas with high-quality signal. That is, areas in which the phase can be processed to extract the desired information. The interferometric coherence and the amplitude dispersion index are important tools widely used in the literature to assess the quality of GBSAR images. So far, no direct relation has been found between the two. Indeed, they are parameters of different natures: amplitude dispersion index is calculated with only amplitude values, while coherence provides information also on the signal phase. The purpose of this article is to find a relation between the two parameters. Indeed, the amplitude dispersion index provides some practical advantages if compared to coherence estimators, especially to perform fast preliminary analysis. In this article, a theoretical relation between amplitude dispersion index and coherence is retrieved. GBSAR measurements acquired in different scenarios, at different working frequencies are presented and used to validate such a relation.
A Novel Approach for Automatic Urban Surface Water Mapping with Land Surface Temperature (AUSWM)
Authors: Cui Yaoping , Fu Yiming , Li Nan , Liu Xiaoyan , Shi Zhifang , Dong Jinwei , Zhou Yan
Published: June 2022

The principal difficulty in extracting urban surface water using remote-sensing techniques is the influence of noise from complex urban environments. Although various methods exist, there are still many sources of noise interference when extracting urban surface water, and automatic cartographic methods with long time series are especially scarce. Here, we construct an automatic urban surface water extraction method from the combination of traditional water index, urban shadow index (USI), and land surface temperature (LST) by using the Google Earth Engine cloud computing platform and Landsat imagery. The three principal findings derived from the application of the method were as follows. (i) In comparison with autumn and winter, LST in spring and summer could better distinguish water from high-reflection ground objects, shadows, and roads and roofs covered by asphalt. (ii) The overall accuracy of Automated Water Extraction Index (AWEIsh) in Zhengzhou was 77.5% and the Kappa coefficient was 0.55; with consideration of the USI and LST, the overall accuracy increased to 96.0% and the Kappa coefficient increased to 0.92. (iii) During 1990–2020, the area of urban surface water in Zhengzhou increased, with an evident trend in expansion from 11.51 km2 in 2008 to 49.28 km2 in 2020. Additionally, possible omissions attributable to using 30m-resolution imagery to extract urban water areas were also discussed. The method proposed in this study was proven effective in eliminating the influence of noise in urban areas, and it could be used as a general method for high-accuracy long-term mapping of urban surface water.
Deep Learning-Based Super-Resolution Reconstruction and Algorithm Acceleration of Mars Hyperspectral CRISM Data
Authors: Sun Mingbo , Chen Shengbo
Published: June 2022

In Mars exploration, hyper-spectrometry plays an important role due to its high spectral resolution. However, due to the technical difficulty and the data size, the spatial resolution or the coverage of hyperspectral data is often limited. This limitation can be alleviated by deep learning-based super-resolution (SR) reconstruction. But the spatial size and batch size of the input training data is limited due to the large number of spectral channels. To improve the efficiency of model training and SR reconstruction, a dataset based on CRISM hyperspectral data is created in this paper, and its redundancy is analyzed in both spectral and spatial spital dimensions. Compression algorithms based on data selection and PCA are used to reduce the size of the input training data. A network that can perform spatial SR and spectral enhancement is also proposed to make the network can be trained with the compressed data. With these compression algorithms and network, high-resolution data with 235 bands can be reconstructed from the low-resolution data with only 40 bands. Compared with the network trained on the original low-resolution data with 235 bands, the model training time and the SR reconstruction runtime can be reduced to 30% and 23% with practically no accuracy loss. The effectiveness of compression algorithms based on data selection also indicates that maybe not all the bands need to be transmitted from the Mars probes or be collected. Furthermore, it would, in principle, help improve the efficiency of satellite data transmission and simplify the design of the hyper-spectrometer. Additionally, a method for spatial dimension correlation evaluation is also proposed in this paper. The spatial compression shows that the proposed method can reflect the correlation of spatial texture between patches, and the model can be acceptably trained with only half of the original data.
An Adaptive Image Segmentation Method with Automatic Selection of Optimal Scale for Extracting Cropland Parcels in Smallholder Farming Systems
Authors: Cai Zhiwen , Hu Qiong , Zhang Xinyu , Yang Jingya , Wei Haodong , He Zhen , Song Qian , Wang Cong , Yin Gaofei , Xu Baodong
Published: June 2022

Reliable cropland parcel data are vital for agricultural monitoring, yield estimation, and agricultural intensification assessments. However, the inherently high landscape fragmentation and irregularly shaped cropland associated with smallholder farming systems restrict the accuracy of cropland parcels extraction. In this study, we proposed an adaptive image segmentation method with the automated selection of optimal scale (MSAOS) to extract cropland parcels in heterogeneous agricultural landscapes. The MSAOS method includes three major components: (1) coarse segmentation to divide the whole images into homogenous and heterogeneous regions, (2) fine segmentation to determine the optimal segmentation scale based on average local variance function, and (3) region merging to merge and dissolve the over-segmented objects with small area. The potential cropland objects derived from MSAOS were combined with random forest to generate the final cropland parcels. The MSAOS method was evaluated over different agricultural regions in China, and derived results were assessed by benchmark cropland parcels interpreted from high-spatial resolution images. Results showed the texture features of Homogeneity and Entropy are the most important features for MSAOS to extract potential cropland parcels, with the highest separability index of 0.28 and 0.26, respectively. MSAOS-derived cropland parcels had high agreement with the reference dataset over eight tiles in Qichun county, with average F1 scores of 0.839 and 0.779 for the area-based classification evaluation (Fab) and object-based segmentation evaluation (Fob), respectively. The further evaluation of MSAOS on different tiles of four provinces exhibited the similar results (Fab = 0.857 and Fob = 0.775) with that on eight test tiles, suggesting the good transferability of the MSAOS over different agricultural regions. Furthermore, MSAOS outperformed other widely-used approaches in terms of the accuracy and integrity of the extracted cropland parcels. These results indicate the great potential of using MSAOS for image segmentation in conjunction with random forest classification to effectively extract cropland parcels in smallholder farming systems.
Elevational Gradient of Climate-Driving Effects on Cropland Ecosystem Net Primary Productivity in Alpine Region of the Southwest China
Authors: Tao Jian , Xie Yujie , Wang Wenfeng , Zhu Juntao , Zhang Yangjian , Zhang Xianzhou
Published: June 2022

Investigating elevational gradient of climate driving effects on cropland ecosystem net primary productivity (NPP) plays an important role in food security in alpine region. We simulated cropland NPP by coupling a remote sensing model with an ecosystem process model and explored elevational gradient of climate driving effects on it in an alpine region of the southwest China during 1981–2014. The results showed that cropland NPP increased significantly with a rate of 3.85 gC m−2 year−1 year−1 under significant increasing solar radiation and climate warming and drying, among which the increasing solar radiation was the main driving factor of the increasing NPP. The driving effect of climate warming on cropland NPP shifted from negative at low elevations to positive at high elevations, which was caused by the fragile ecosystem characteristics and frequent drought at low elevations and a higher temperature sensitivity of cropland ecosystem at high elevations. Different effects of climate warming on NPP change at different elevations caused different results when we analyzed the climate-driving effects on cropland NPP at different spatial scales. These results reminded us that we should take the elevational gradient of climate driving effects into account when we manage food security in the alpine region.
High-Sensitivity Flexible Piezoresistive Pressure Sensor Using PDMS/MWNTS Nanocomposite Membrane Reinforced with Isopropanol for Pulse Detection
Authors: Long Zhiming , Liu Xinggu , Xu Junjie , Huang Yubo , Wang Zhuqing
Published: June 2022

Flexible pressure sensors with high sensitivity and good linearity are in high demand to meet the long-term and accurate detection requirements for pulse detection. In this study, we propose a composite membrane pressure sensor using polydimethylsiloxane (PDMS) and multiwalled carbon nanotubes (MWNTS) reinforced with isopropanol prepared by solution blending and a self-made 3D-printed mold. The device doped with isopropanol had a higher sensitivity and linearity owning to the construction of additional conductive paths. The optimal conditions for realizing a high-performance pressure sensor are a multiwalled carbon nanotube mass ratio of 7% and a composite membrane thickness of 490 μm. The membrane achieves a high linear sensitivity of −57.07 kΩ∙kPa−1 and a linear fitting correlation coefficient of 98.78% in the 0.13~5.2 kPa pressure range corresponding to pulse detection. Clearly, this device has great potential for application in pulse detection.
Snow Parameters Inversion from Passive Microwave Remote Sensing Measurements by Deep Convolutional Neural Networks
Authors: Yao Heming , Zhang Yanming , Jiang Lijun , Ewe Hong , Ng Michael
Published: June 2022

This paper proposes a novel inverse method based on the deep convolutional neural network (ConvNet) to extract snow’s layer thickness and temperature via passive microwave remote sensing (PMRS). The proposed ConvNet is trained using simulated data obtained through conventional computational electromagnetic methods. Compared with the traditional inverse method, the trained ConvNet can predict the result with higher accuracy. Besides, the proposed method has a strong tolerance for noise. The proposed ConvNet composes three pairs of convolutional and activation layers with one additional fully connected layer to realize regression, i.e., the inversion of snow parameters. The feasibility of the proposed method in learning the inversion of snow parameters is validated by numerical examples. The inversion results indicate that the correlation coefficient (R2) ratio between the proposed ConvNet and conventional methods reaches 4.8, while the ratio for the root mean square error (RMSE) is only 0.18. Hence, the proposed method experiments with a novel path to improve the inversion of passive microwave remote sensing through deep learning approaches.
Dynamic Distance Measurement Based on a Fast Frequency-Swept Interferometry
Authors: Chen Yuru , Lei Xiaohua , Xiao Lin , Zhang Peng , Liu Xianming
Published: June 2022

To improve the precision of dynamic distance measurement based on the frequency-swept interferometry (FSI) system, a Doppler-induced error compensation model based on a scheme increasing the frequency sweeping rate is proposed. A distance demodulation method based on a Fourier transformation is investigated when the defined quasi-stationary coefficient approaches a constant. Simulations and experiments based on dynamic distance with a sinusoidal change demonstrate that the proposed method has a standard deviation of 0.09 μm within a distance range of 4 μm at a sweeping rate of 60 KHz.
A Novel LiDAR–IMU–Odometer Coupling Framework for Two-Wheeled Inverted Pendulum (TWIP) Robot Localization and Mapping with Nonholonomic Constraint Factors
Authors: Zhai Yanwu , Zhang Songyuan
Published: June 2022

This paper proposes a method to solve the problem of localization and mapping of a two-wheeled inverted pendulum (TWIP) robot on approximately flat ground using a Lidar–IMU–Odometer system. When TWIP is in motion, it is constrained by the ground and suffers from motion disturbances caused by rough terrain or motion shaking. Combining the motion characteristics of TWIP, this paper proposes a framework for localization consisting of a Lidar-IMU-Odometer system. This system formulates a factor graph with five types of factors, thereby coupling relative and absolute measurements from different sensors (including ground constraints) into the system. Moreover, we analyze the constraint dimension of each factor according to the motion characteristics of TWIP and propose a new nonholonomic constraint factor for the odometry pre-integration constraint and ground constraint factor in order to add them naturally to the factor graph with the robot state node on SE(3). Meanwhile, we calculate the uncertainty of each constraint. Utilizing such a nonholonomic constraint factor, a complete lidar–IMU–odometry-based motion estimation system for TWIP is developed via smoothing and mapping. Indoor and outdoor experiments show that our method has better accuracy for two-wheeled inverted pendulum robots.
Voltammetric Detection of Glucose—The Electrochemical Behavior of the Copper Oxide Materials with Well-Defined Facets
Authors: Kusior Anna
Published: June 2022

Cu2O nanomaterials with well-defined facets and uniform size were synthesized by a wet-chemical method. Regardless of the additive composition, powders crystallize mostly in cuprite form. To compare their electrochemical behavior, the obtained materials were deposited on carbon glassy electrodes. The response to glucose from the materials with different exposed facets was recorded with a delay at the anodic curve. The chronoamperometric analyses (AMP) exhibited a lower signal in contrast to the cyclic voltammetry data (CV), indicating that the number of active sites involved in glucose oxidation processes resulting from the structure of the material is insufficient. For samples with dominant (100) or (111) planes, a typical characteristic was observed, however, with an additional peak at the anodic curve. The location of the peaks is approximately the same and no significant differences from the AMP and CV analysis were observed. The sample enclosed by the (111) facets exhibited higher activity; however, as a result of the redox reaction with glucose molecules, the surface state is changing. Cu2O materials enclosed by (100) planes exhibited optimal sensitivity as well as a large detective range. Samples with differential facet exposition present various current–potential profiles, as the effect of binder–particle interaction with Nafion.
Raspberry Shake-Based Rapid Structural Identification of Existing Buildings Subject to Earthquake Ground Motion: The Case Study of Bucharest
Authors: Özcebe Ali Güney , Tiganescu Alexandru , Ozer Ekin , Negulescu Caterina , Galiana-Merino Juan Jose , Tubaldi Enrico , Toma-Danila Dragos , Molina Sergio , Kharazian Alireza , Bozzoni Francesca , Borzi Barbara , Balan Stefan Florin
Published: June 2022

The Internet of things concept empowered by low-cost sensor technologies and headless computers has upscaled the applicability of vibration monitoring systems in recent years. Raspberry Shake devices are among those systems, constituting a crowdsourcing framework and forming a worldwide seismic network of over a thousand nodes. While Raspberry Shake devices have been proven to densify seismograph arrays efficiently, their potential for structural health monitoring (SHM) is still unknown and is open to discovery. This paper presents recent findings from existing buildings located in Bucharest (Romania) equipped with Raspberry Shake 4D (RS4D) devices, whose signal recorded under multiple seismic events has been analyzed using different modal identification algorithms. The obtained results show that RS4D modules can capture the building vibration behavior despite the short-duration and low-amplitude excitation sources. Based on 15 RS4D device readings from five different multistorey buildings, the results do not indicate damage in terms of modal frequency decay. The findings of this research propose a baseline for future seismic events that can track the changes in vibration characteristics as a consequence of future strong earthquakes. In summary, this research presents multi-device, multi-testbed, and multi-algorithm evidence on the feasibility of RS4D modules as SHM instruments, which are yet to be explored in earthquake engineering.
Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion
Authors: Wang Pangwei , Liu Cheng , Wang Yunfeng , Yu Hongsheng
Published: June 2022

At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.
Lamb Wave-Based Damage Localization and Quantification in Composites Using Probabilistic Imaging Algorithm and Statistical Method
Authors: Guo Jiahui , Zeng Xianping , Liu Qijian , Qing Xinlin
Published: June 2022

Quantitatively and accurately monitoring the damage to composites is critical for estimating the remaining life of structures and determining whether maintenance is essential. This paper proposed an active sensing method for damage localization and quantification in composite plates. The probabilistic imaging algorithm and the statistical method were introduced to reduce the impact of composite anisotropy on the accuracy of damage detection. The matching pursuit decomposition (MPD) algorithm was utilized to extract the precise TOF for damage detection. The damage localization was realized by comprehensively evaluating the damage probability evaluation results of all sensing paths in the monitoring area. Meanwhile, the scattering source was recognized on the elliptical trajectory obtained through the TOF of each sensing path to estimate the damage size. Damage size was characterized by the Gaussian kernel probability density distribution of scattering sources. The algorithm was validated by through-thickness hole damages of various locations and sizes in composite plates. The experimental results demonstrated that the localization and quantification absolute error are within 11 mm and 2.2 mm, respectively, with a sensor spacing of 100 mm. The algorithm proposed in this paper can accurately locate and quantify damage in composite plate-like structures.
Efficient Feature Learning Approach for Raw Industrial Vibration Data Using Two-Stage Learning Framework
Authors: Tnani Mohamed-Ali , Subarnaduti Paul , Diepold Klaus
Published: June 2022

In the last decades, data-driven methods have gained great popularity in the industry, supported by state-of-the-art advancements in machine learning. These methods require a large quantity of labeled data, which is difficult to obtain and mostly costly and challenging. To address these challenges, researchers have turned their attention to unsupervised and few-shot learning methods, which produced encouraging results, particularly in the areas of computer vision and natural language processing. With the lack of pretrained models, time series feature learning is still considered as an open area of research. This paper presents an efficient two-stage feature learning approach for anomaly detection in machine processes, based on a prototype few-shot learning technique that requires a limited number of labeled samples. The work is evaluated on a real-world scenario using the publicly available CNC Machining dataset. The proposed method outperforms the conventional prototypical network and the feature analysis shows a high generalization ability achieving an F1-score of 90.3%. The comparison with handcrafted features proves the robustness of the deep features and their invariance to data shifts across machines and time periods, which makes it a reliable method for sensory industrial applications.
Gait Synergy Analysis and Modeling on Amputees and Stroke Patients for Lower Limb Assistive Devices
Authors: Liang Feng-Yan , Gao Fei , Cao Junyi , Law Sheung-Wai , Liao Wei-Hsin
Published: June 2022

The concept of synergy has drawn attention and been applied to lower limb assistive devices such as exoskeletons and prostheses for improving human–machine interaction. A better understanding of the influence of gait kinematics on synergies and a better synergy-modeling method are important for device design and improvement. To this end, gait data from healthy, amputee, and stroke subjects were collected. First, continuous relative phase (CRP) was used to quantify their synergies and explore the influence of kinematics. Second, long short-term memory (LSTM) and principal component analysis (PCA) were adopted to model interlimb synergy and intralimb synergy, respectively. The results indicate that the limited hip and knee range of motions (RoMs) in stroke patients and amputees significantly influence their synergies in different ways. In interlimb synergy modeling, LSTM (RMSE: 0.798° (hip) and 1.963° (knee)) has lower errors than PCA (RMSE: 5.050° (hip) and 10.353° (knee)), which is frequently used in the literature. Further, in intralimb synergy modeling, LSTM (RMSE: 3.894°) enables better synergy modeling than PCA (RMSE: 10.312°). In conclusion, stroke patients and amputees perform different compensatory mechanisms to adapt to new interlimb and intralimb synergies different from healthy people. LSTM has better synergy modeling and shows a promise for generating trajectories in line with the wearer’s motion for lower limb assistive devices.
Research on Calculation Method of Radiation Response Eigenvalue of a Single-Chip Active Pixel Sensor
Authors: Qin Zhiwei , Xu Shoulong , Dong Hanfeng , Han Yongchao
Published: June 2022

In this paper, we present a calculation method for the radiation response eigenvalue based on a monolithic active pixel sensor. By comparing the statistical eigenvalues of different regions of a pixel array in bright and dark environments, the linear relationship between the statistical eigenvalues obtained by different algorithms and the radiation dose rate was studied. Additionally, a dose rate characterization method based on the analysis of the eigenvalues of the MAPS response signal was proposed. The experimental results show that in the dark background environment, the eigenvalues had a good linear response in the region of any gray value in the range of 10–30. In the color images, due to the difference in the background gray values in adjacent color regions, the radiation response signal in dark regions was confused with the image information in bright regions, resulting in the loss of response signal and affecting the analysis results of the radiation response signal. For the low dose rate radiation field, as the radiation response signal was too weak and there was background dark noise, it was necessary to accumulate frame images to obtain a sufficient response signal. For the intense radiation field, the number of response events in a single image was very high, and only two consecutive frames of image data needed to be accumulated to meet the statistical requirements. The binarization method had a good characterization effect for the radiation at a low dose rate, and the binarization processing and the total gray value statistics of the response data at a high dose rate could better characterize the radiation dose rate. The calibration experiment results show that the binarization processing method can meet the requirements of using a MAPS for wide-range detection.
Estimation of the Influence of a Noisy Environment on the Binary Decision Strategy in a Quantum Illumination Radar
Authors: Borderieux Sylvain , Coatanhay Arnaud , Khenchaf Ali
Published: June 2022

A quantum illumination radar uses quantum entanglement to enhance photodetection sensitivity. The entanglement is quickly destroyed by the decoherence in an environment, although the sensitivity enhancement could survive thanks to quantum correlations beyond the entanglement. These quantum correlations are quantified by the quantum discord. Here, we use a toy model with an amplitude damping channel and Lloyd’s binary decision strategy to highlight the possible role of these correlations from the perspective of a quantum radar.
Addressed Combined Fiber-Optic Sensors as Key Element of Multisensor Greenhouse Gas Monitoring Systems
Authors: Morozov Oleg , Tunakova Yulia , Hussein Safaa M. R. H. , Shagidullin Artur , Agliullin Timur , Kuznetsov Artem , Valeev Bulat , Lipatnikov Konstantin , Anfinogentov Vladimir , Sakhabutdinov Airat
Published: June 2022

The design and usage of the addressed combined fiber-optic sensors (ACFOSs) and the multisensory control systems of the greenhouse gas concentration on their basis are investigated herein. The main development trend of the combined fiber-optic sensors (CFOSs), which consists of the fiber Bragg grating (FBG) and the Fabry–Perot resonator (FPR), which are successively formed at the optical fiber end, is highlighted. The use of the addressed fiber Bragg structures (AFBSs) instead of the FBG in the CFOSs not only leads to the significant cheapening of the sensor system due to microwave photonics interrogating methods, but also increasing its metrological characteristics. The structural scheme of the multisensory gas concentration monitoring system is suggested. The suggested scheme allows detecting four types of greenhouse gases (CO2, NO2, CH4 and Ox) depending on the material and thickness of the polymer film, which is the FPR sensitive element. The usage of the Karhunen–Loève transform (KLT), which allows separating each component contribution to the reflected spectrum according to its efficiency, is proposed. In the future, this allows determining the gas concentration at the AFBS address frequencies. The estimations show that the ACFOS design in the multisensory system allows measuring the environment temperature in the range of −60…+300 °C with an accuracy of 0.1–0.01 °C, and the gas concentration in the range of 10…90% with an accuracy of 0.1–0.5%.
Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM
Authors: Sarwat Samina , Ullah Naeem , Sadiq Saima , Saleem Robina , Umer Muhammad , Eshmawi Ala’ Abdulmajid , Mohamed Abdullah , Ashraf Imran
Published: June 2022

The availability of educational data obtained by technology-assisted learning platforms can potentially be used to mine student behavior in order to address their problems and enhance the learning process. Educational data mining provides insights for professionals to make appropriate decisions. Learning platforms complement traditional learning environments and provide an opportunity to analyze students’ performance, thus mitigating the probability of student failures. Predicting students’ academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems. This study proposes an improved conditional generative adversarial network (CGAN) in combination with a deep-layer-based support vector machine (SVM) to predict students’ performance through school and home tutoring. Students’ educational datasets are predominantly small in size; to handle this problem, synthetic data samples are generated by an improved CGAN. To prove its effectiveness, results are compared with and without applying CGAN. Results indicate that school and home tutoring combined have a positive impact on students’ performance when the model is trained after applying CGAN. For an extensive evaluation of deep SVM, multiple kernel-based approaches are investigated, including radial, linear, sigmoid, and polynomial functions, and their performance is analyzed. The proposed improved CGAN coupled with deep SVM outperforms in terms of sensitivity, specificity, and area under the curve when compared with solutions from the existing literature.