bluuebunny/crossref_metadata_2025 huggingface dataset:
Research of Seismogenic Structures of the 2016 and 2022 Menyuan Earthquakes, in the Northeastern Tibetan Plateau
Authors: Wang Junyi , Ding Lin , He Jiankun , Cai Fulong , Wang Chao , Zhang Zongkun
Published: January 2023

On 8 January 2022, a Moment Magnitude (Mw) 6.7 earthquake occurred in Menyuan, China. The epicenter was located in the western segment of the Lenglongling fault of the Qilian-Haiyuan fault zone. In this area, the Mw 5.9 Menyuan earthquake on 26 August 1986 and the Mw 5.9 Menyuan earthquake on 21 January 2016 successively occurred. The seismogenic structures of the 1986 and 2016 earthquakes are on the Northern Lenglongling fault, which is a few kilometers away from the Lenglongling fault. After the 2022 Menyuan earthquake, we collected GF-7 and Sentinel-1 satellite images to measure the surface deformation of the earthquake sequence. Based on the elastic dislocation theory, the fault model and fault slip distribution of the 2016 and 2022 Mengyuan earthquakes were inverted using coseismic surface displacements. The results show that the 2016 event is a reverse event, with the maximum coseismic surface displacement on LOS reaching 8 cm. The strike, dip, and rake of the earthquake rupture were 139°, 41°, and 78°, with the maximum slip reaching 0.6 m at a depth of 8 km. The surface rupture of the 2022 Mw 6.7 earthquake ran in the WNW–ESE direction with a maximum displacement on LOS of 72 cm. The main seismogenic fault of the 2022 event was the western segment of the Lenglongling fault. The strike, dip, and rake of the rupture were 112°, 85°, and 3°, with the maximum slip reaching 4 m at a depth of 4 km. The Coulomb failure stress change shows that the earthquake sequence generated a considerable positive Coulomb failure stress of more than 2 bar. These observations suggest that the earthquake sequence around Menyuan is mainly governed by the activities of the Lenglongling fault around the northeastern Tibetan Plateau. In addition, their sequential occurrences could be related to earthquake-triggering mechanisms due to stress interaction on different deforming faults. Thus, the Lenglongling fault has received a great amount of attention regarding its potential earthquake hazards.
Exploring the Relationship between the Eco-Environmental Quality and Urbanization by Utilizing Sentinel and Landsat Data: A Case Study of the Yellow River Basin
Authors: Wang Xiaolei , Zhang Shiru , Zhao Xue , Shi Shouhai , Xu Lei
Published: January 2023

Yellow River Basin urban agglomeration (YRBU) is the main carrier of regional socio-economic development in the Yellow River Basin, and its eco-environmental quality, urbanization, and coupling coordination degree are facing higher demands. It is of great significance for the development of YRBU to understand the interactive coupling relationship between the eco-environment and urbanization development from the multi-scale perspective. This research intended to understand the spatio-temporal characteristics of eco-environmental quality, urbanization, and coupling coordination degree in the study area from 2013 to 2021. We proposed an Adjusted Remote Sensing Ecological Index (A-RSEI), integrated Sentinel-2A, Landsat 8, and other remote sensing data to evaluate the eco-environmental quality of the study area, from 2013 to 2021. Coupled coordination degree (CCD) model was used to obtain the CCD between eco-environmental quality and urbanization. In addition, spatio-temporal and multi-scale analysis was carried out from the perspectives of urban agglomeration, municipal, county, and pixel scales. Combined with spatial autocorrelation analysis and Tapio decoupling model, the CCD was further explored. The results show that the proposed A-RSEI model is more suitable for monitoring the eco-environmental quality of the Yellow River Basin. The coupling coordination degree of eco-environment and urbanization in most regions of the study area are rising in a relatively green development trend. The multi-scale analysis among eco-environmental quality, urbanization, and CCD can not only indicate the impact of the central city on its surrounding areas but also help to describe the details of CCD combined with the terrain. The comprehensive discrimination of urban agglomeration and county scale is helpful to express the relationship between urbanization and eco-environmental quality centered on a certain city. The results can provide scientific support for eco-environment protection and high-quality development of the Yellow River Basin.
Remote Sensing Applications to Climate Change
Authors: Wang Xander
Published: January 2023

Climate change research remains a challenging task, as it requires vast quantities of long-term data to investigate the past, present, and future scenarios of Earth’s climate system and other biophysical systems at global to local scales [...]
Editorial for the Special Issue “Disaster Monitoring Using Remote Sensing”
Authors: Choi Jinmu , Kim Duk-Jin
Published: January 2023

Human civilization is rapidly developing thanks to the spread of geospatial technology and artificial intelligence [...]
HYPSO-1 CubeSat: First Images and In-Orbit Characterization
Authors: Bakken Sivert , Henriksen Marie B. , Birkeland Roger , Langer Dennis D. , Oudijk Adriënne E. , Berg Simen , Pursley Yeshi , Garrett Joseph L. , Gran-Jansen Fredrik , Honoré-Livermore Evelyn , Grøtte Mariusz E. , Kristiansen Bjørn A. , Orlandic Milica , Gader Paul , Sørensen Asgeir J. , Sigernes Fred , Johnsen Geir , Johansen Tor A.
Published: January 2023

The HYPSO-1 satellite, a 6U CubeSat carrying a hyperspectral imager, was launched on 13 January 2022, with the Goal of imaging ocean color in support of marine research. This article describes the development and current status of the mission and payload operations, including examples of agile planning, captures with low revisit time and time series acquired during a campaign. The in-orbit performance of the hyperspectral instrument is also characterized. The usable spectral range of the instrument is in the range of 430 nm to 800 nm over 120 bands after binning during nominal captures. The spatial resolvability is found empirically to be below 2.2 pixels in terms of Full-Width at Half-Maximum (FWHM) at 565 nm. This measure corresponds to an inherent ground resolvable resolution of 142 m across-track for close to nadir capture. In the across-track direction, there are 1216 pixels available, which gives a swath width of 70 km. However, the 684 center pixels are used for nominal captures. With the nominal pixels used in the across-track direction, the nadir swath-width is 40 km. The spectral resolution in terms of FWHM is estimated to be close to 5 nm at the center wavelength of 600 nm, and the Signal-to-Noise Ratio (SNR) is evaluated to be greater than 300 at 450 nm to 500 nm for Top-of-Atmosphere (ToA) signals. Examples of images from the first months of operations are also shown.
G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
Authors: Hou Liping , Lu Ke , Yang Xue , Li Yuqiu , Xue Jian
Published: January 2023

Typical representations for arbitrary-oriented object detection tasks include the oriented bounding box (OBB), the quadrilateral bounding box (QBB), and the point set (PointSet). Each representation encounters problems that correspond to its characteristics, such as boundary discontinuity, square-like problems, representation ambiguity, and isolated points, which lead to inaccurate detection. Although many effective strategies have been proposed for various representations, there is still no unified solution. Current detection methods based on Gaussian modeling have demonstrated the possibility of resolving this dilemma; however, they remain limited to OBB. To go further, in this paper, we propose a unified Gaussian representation called G-Rep to construct Gaussian distributions for OBB, QBB, and PointSet, which achieves a unified solution to various representations and problems. Specifically, PointSet- or QBB-based object representations are converted into Gaussian distributions and their parameters are optimized using the maximum likelihood estimation algorithm. Then, three optional Gaussian metrics are explored to optimize the regression loss of the detector because of their excellent parameter optimization mechanisms. Furthermore, we also use Gaussian metrics for sampling to align label assignment and regression loss. Experimental results obtained on several publicly available datasets, such as DOTA, HRSC2016, UCAS-AOD, and ICDAR2015, show the excellent performance of the proposed method for arbitrary-oriented object detection.
An Efficient and Robust Hybrid SfM Method for Large-Scale Scenes
Authors: Liu Zhendong , Qv Wenhu , Cai Haolin , Guan Hongliang , Zhang Shuaizhe
Published: January 2023

The structure from motion (SfM) method has achieved great success in 3D sparse reconstruction, but it still faces serious challenges in large-scale scenes. Existing hybrid SfM methods usually do not fully consider the compactness between images and the connectivity between subclusters, resulting in a loose spatial distribution of images within subclusters, unbalanced connectivity between subclusters, and poor robustness in the merging stage. In this paper, an efficient and robust hybrid SfM method is proposed. First, the multifactor joint scene partition measure and the preassignment balanced image expansion algorithm among subclusters are constructed, which effectively solves the loose spatial distribution of images in subclusters problem and improves the degree of connection among subclusters. Second, the global GlobalACSfM method is used to complete the local sparse reconstruction of the subclusters under the cluster parallel framework. Then, a decentralized dynamic merging rule considering the connectivity of subclusters is proposed to realize robust merging among subclusters. Finally, public datasets and oblique photography datasets are used for experimental verification. The results show that the method proposed in this paper is superior to the state-of-the-art methods in terms of accuracy and robustness and has good feasibility and advancement prospects.
Robust Single-Image Tree Diameter Estimation with Mobile Phones
Authors: Holcomb Amelia , Tong Linzhe , Keshav Srinivasan
Published: January 2023

Ground-based forest inventories are reliable methods for forest carbon monitoring, reporting, and verification schemes and the cornerstone of forest ecology research. Recent work using LiDAR-equipped mobile phones to automate parts of the forest inventory process assumes that tree trunks are well-spaced and visually unoccluded, or else require manual intervention or offline processing to identify and measure tree trunks. In this paper, we designed an algorithm that exploits a low-cost smartphone LiDAR sensor to estimate the trunk diameter automatically from a single image in complex and realistic field conditions. We implemented our design and built it into an app on a Huawei P30 Pro smartphone, demonstrating that the algorithm has low enough computational costs to run on this commodity platform in near real-time. We evaluated our app in 3 different forests across 3 seasons and found that in a corpus of 97 sample tree images, our app estimated the trunk diameter with a RMSE of 3.7 cm (R2 = 0.97; 8.0% mean absolute error) compared to manual DBH measurement. It achieved a 100% tree detection rate while reducing the surveyor time by up to a factor of 4.6. Our work contributes to the search for a low-cost, low-expertise alternative to terrestrial laser scanning that is nonetheless robust and efficient enough to compete with manual methods. We highlight the challenges that low-end mobile depth scanners face in occluded conditions and offer a lightweight, fully automatic approach for segmenting depth images and estimating the trunk diameter despite these challenges. Our approach lowers the barriers to in situ forest measurements outside of an urban or plantation context, maintaining a tree detection and accuracy rate comparable to previous mobile phone methods even in complex forest conditions.
Dictionary Learning for Few-Shot Remote Sensing Scene Classification
Authors: Ma Yuteng , Meng Junmin , Liu Baodi , Sun Lina , Zhang Hao , Ren Peng
Published: January 2023

With deep learning-based methods growing (even with scarce data in some fields), few-shot remote sensing scene classification (FSRSSC) has received a lot of attention. One mainstream approach uses base data to train a feature extractor (FE) in the pre-training phase and employs novel data to design the classifier and complete the classification task in the meta-test phase. Due to the scarcity of remote sensing data, obtaining a suitable feature extractor for remote sensing data and designing a robust classifier have become two major challenges. In this paper, we propose a novel dictionary learning (DL) algorithm for few-shot remote sensing scene classification to address these two difficulties. First, we use natural image datasets with sufficient data to obtain a pre-trained feature extractor. We fine-tune the parameters with the remote sensing dataset to make the feature extractor suitable for remote sensing data. Second, we design the kernel space classifier to map the features to a high-dimensional space and embed the label information into the dictionary learning to improve the discrimination of features for classification. Extensive experiments on four popular remote sensing scene classification datasets demonstrate the effectiveness of our proposed dictionary learning method.
Quantifying the Interaction Effects of Climatic Factors on Vegetation Growth in Southwest China
Authors: Wang Meng , An Zhengfeng
Published: January 2023

Due to the complex and variable climate structure in Southwest China (SW), the impacts of climate variables on vegetation change and the interactions between climate factors remain controversial, considering the uncertainty and complexity in the relationships between climate factors and vegetation in this region. In this study, the CRU TS v. 4.02 from 1982 to 2017 and the annual maximum (P100), upper quarter quantile (P75), median (P50), lower quarter quantile (P25), minimum (P5), and mean (Mean) of GIMMS NDVI were utilized to reveal the main and interaction effects of significant climate variables on vegetation development at the level of SW and the core areas (CAs) of typical climate type (including T+ *–P+ *, T+ *–P–, T+ *–P+, and NSC) using the simple moving average method, a multivariate linear model, the slope method, and the Johnson–Neyman method. The obtained regression relationships between NDVI, temperature, and precipitation were verified successfully by constructing multiple linear models with interaction terms. Within the T+ *–P– CA, precipitation had the main impact; meanwhile, in the SW and other CAs, the temperature had the main effect. In general, most of the significant moderating effects of temperature (precipitation) on vegetation growth predominantly increased with the increase in precipitation (temperature). Nevertheless, the significant moderating effect varied in different regions and directions. In the SW area, when the temperature/precipitation was in the range of [4.73 °C, 5.13 °C]/[730.00 mm, 753.95 mm], the impact of temperature/precipitation on NDVI had a significant positive regulating effect with respect to the precipitation/temperature. Meanwhile, in the NSC/T+ *–P+ * areas, when the temperature/precipitation was in the range of [15.99 °C, 16.03 °C]/[725.17 mm, 752.82 mm], the impact of temperature/precipitation on NDVI has a significant negative moderating role with respect to the precipitation/temperature. Overall, our study provides a modern context for clearly uncovering the complexity of the effect of climate alteration on vegetation development, allowing for clarification of the alterations in vegetation development due to climate change.
Estimating Fractional Snow Cover in the Pan-Arctic Region Using Added Vegetation Extraction Algorithm
Authors: Ma Yuan , Shao Donghang , Wang Jian , Li Haojie , Zhao Hongyu , Ji Wenzheng
Published: January 2023

Snow cover is an essential indicator of global climate change. The composition of the underlying surface in the Pan-Arctic region is complex; forest and other areas with high vegetation coverage have a significant influence on the retrieval accuracy of fractional snow cover (FSC). Therefore, to explore the impact of vegetation on the extraction of the FSC algorithm, this study developed the normalized difference vegetation index (NDVI)-based Bivariate Linear Regression Model (BV-BLRM) to calculate the FSC. Then, the overall accuracy of the model and its changes under different classification conditions were evaluated and the relationship between the accuracy improvement and different underlying surfaces and elevations was analyzed. The results show that the BV-BLRM model is more accurate than MODIS’s traditional univariate linear algorithm for FSC (MOD-FSC) in each underlying surface. Overall, regarding the accuracy of the BV-BLRM model, the RMSE is 0.2, MAE is 0.15, and accuracy is 28.6% higher than the MOD-FSC model. The newly developed BV-BLRM model has the most significant improvement in the accuracy of FSC retrieval when the underlying surface has high vegetation coverage. Under different classification accuracies, the accuracy of BV-BLRM model was higher than that of MOD-FSC model, with an average of 30.5%. The improvement of FSC extraction accuracy by the model is smaller when the underlying surface is perpetual snow zone, with an average of 12.2%. This study is applicable to the scale mapping of FSC in large areas and is helpful to improve the FSC accuracy in areas with high vegetation coverage.
High Signal-to-Noise Ratio MEMS Noise Listener for Ship Noise Detection
Authors: Zhu Shan , Zhang Guojun , Wu Daiyue , Jia Li , Zhang Yifan , Geng Yanan , Liu Yan , Ren Weirong , Zhang Wendong
Published: January 2023

Ship noise observation is of great significance to marine environment research and national defense security. Acoustic stealth technology makes a variety of ship noise significantly reduced, which is a new challenge for marine noise monitoring. However, there are few high spatial gain detection methods for low-noise ship monitoring. Therefore, a high Signal-to-Noise Ratio (SNR) MEMS noise listener for ship noise detection is developed in this paper. The listener achieves considerable gain by suppressing isotropic noise in the ocean. The working principle and posterior end signal processing method of the listener are introduced in detail. A gain of 10 dB over the sound pressure detector is obtained by detecting the standard sound source. In addition, the traffic vessel noise monitoring experiment verifies that the listener can detect the ship noise. The results show that the listener has a very broad application prospect in the field of low-noise ship observation.
Cross-Comparison of Radiation Response Characteristics between the FY-4B/AGRI and GK-2A/AMI in China
Authors: Xie Lianni , Wu Shuang , Wu Ronghua , Chen Jie , Xu Zuomin , Cao Lei
Published: January 2023

In this study, we compare the data of the advanced geostationary radiation imager (AGRI) on board the FY-4B and the advanced meteorological imager (AMI) on board the GK-2A, in terms of overall data, different reflectivity/brightness temperature intervals, different regions, and different underlying surfaces. The results show that the AGRI and AMI data are generally consistent; the mean biases for reflectivity channels show a range of 0.50% to 1.69%, with channel VIR004 being exceptionally good, while brightness temperature (TB) differences in the IR channels ranging from 0.11 to 0.57 K, with channel IR120 being the most accurate. The reflectivity of the AGRI is higher than that of the AMI in terms of mean bias. The dispersion of the reflectivity difference between the AGRI and AMI is smaller at the short-wavelength channels than that at the longer-wavelength channels. The TB data observed by the AGRI are higher than those of AMI at conditions above 310 K. In the case of observing the same target, the difference in infrared brightness temperature due to the random noise signal is small. The differences between the two sensors can be considerably reduced by revising mean biases. In the following studies of quantitative product algorithms, the characteristics of sensor data need to be further analyzed in detail.
Densifying and Optimizing the Water Level Series for Large Lakes from Multi-Orbit ICESat-2 Observations
Authors: Chen Tan , Song Chunqiao , Zhan Pengfei , Fan Chenyu
Published: January 2023

Satellite laser altimetry has been widely used for monitoring surface height changes in inland waters. However, constructing time series of water levels is partially limited in temporal resolution only based on the individual orbit of altimeter observations. To densify and optimize the time series of altimetry-based water levels is crucial to the scientific understanding of lake hydrologic dynamics. This paper focuses on synthesizing the multi-orbit on-lake observations from the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) to densify and refine the water level time series for large lakes. The approach of synthesizing water level time series has been validated through experiments applied to 18 large lakes worldwide, resulting in an average R of 0.93, RMSE of 0.14 m, MAE of 0.12 m, NSE of 0.67, and CV of 2.86, according to the hydrologic gauge stations. The evaluation results demonstrate that our approach can provide an effective solution for densifying the water level series of large lakes covered by multi-orbit ICESat-2 observations. Further, the approach can be extended to monitor the high-frequency variation of other lakes covered by the multiple ICESat-2 orbits. This approach provides the potential of generating higher-frequency estimates of water levels based on satellite altimetry, which could not only help to reveal the characteristics of the seasonal dynamics of lakes but also be used to investigate the abrupt water level changes due to hydrological extreme events (e.g., floods, droughts, etc.).
Wavelet Integrated Convolutional Neural Network for Thin Cloud Removal in Remote Sensing Images
Authors: Zi Yue , Ding Haidong , Xie Fengying , Jiang Zhiguo , Song Xuedong
Published: January 2023

Cloud occlusion phenomena are widespread in optical remote sensing (RS) images, leading to information loss and image degradation and causing difficulties in subsequent applications such as land surface classification, object detection, and land change monitoring. Therefore, thin cloud removal is a key preprocessing procedure for optical RS images, and has great practical value. Recent deep learning-based thin cloud removal methods have achieved excellent results. However, these methods have a common problem in that they cannot obtain large receptive fields while preserving image detail. In this paper, we propose a novel wavelet-integrated convolutional neural network for thin cloud removal (WaveCNN-CR) in RS images that can obtain larger receptive fields without any information loss. WaveCNN-CR generates cloud-free images in an end-to-end manner based on an encoder–decoder-like architecture. In the encoding stage, WaveCNN-CR first extracts multi-scale and multi-frequency components via wavelet transform, then further performs feature extraction for each high-frequency component at different scales by multiple enhanced feature extraction modules (EFEM) separately. In the decoding stage, WaveCNN-CR recursively concatenates the processed low-frequency and high-frequency components at each scale, feeds them into EFEMs for feature extraction, then reconstructs the high-resolution low-frequency component by inverse wavelet transform. In addition, the designed EFEM consisting of an attentive residual block (ARB) and gated residual block (GRB) is used to emphasize the more informative features. ARB and GRB enhance features from the perspective of global and local context, respectively. Extensive experiments on the T-CLOUD, RICE1, and WHUS2-CR datasets demonstrate that our WaveCNN-CR significantly outperforms existing state-of-the-art methods.
Interannual Variability of Extreme Precipitation during the Boreal Summer over Northwest China
Authors: Ma Qianrong , Li Zhongwai , Lei Hongjia , Chen Zhiheng , Liu Jiang , Wang Shuting , Su Tao , Feng Guolin
Published: January 2023

Herein, we investigated the characteristics and mechanisms of interannual variability of extreme summer precipitation in northwest China (NWC). The four high-resolution precipitation predicting products under assessment indicated that both the accumulation of summer daily precipitation ≥95th percentile, and the summer maxima of daily precipitation generally decreased in a southeast—northwest direction, while relatively high values were observed in the Tienshan and Qilian Mountain areas. In turn, the Tropical Rainfall Measuring Mission (TRMM) satellite dataset underestimated extreme precipitation in mountainous areas, while Asian precipitation highly—resolved observational data integration towards evaluation (APHRODITE) and Climate Prediction Center (CPC) captured the characteristics of extreme precipitation in NWC. AMIP-type simulations of the interannual variability of extreme summer precipitation in NWC were quite unsuccessful. However, all of them can capture the increasing trends. Therefore, we further found that the interannual increase in extreme precipitation in NWC is strongly correlated with the weakened South Asian high, strengthened Western Pacific Subtropical high, the enhanced westerly jet, and the mid- to high-latitude Rossby wave trains, whose formation and sustenance can be traced back to sea surface temperature-anomalies in the western Pacific in May, June, and July. An increased sea surface temperature promotes convection and induces diabatic heating, which stimulates anticyclonic anomalies that disturb the enhanced westerly jet, resulting in a barotropic Rossby wave train via the Gill-type response. Additionally, it guides more water vapor convergence to NWC and enhances upward motion via anticyclonic anomalies over western Europe and Eastern Asia, and cyclonic anomalies over Central Asia.
GNSS Horizontal Motion Field in the Beijing Plain in View of The Variation Characteristics of The 3D Deformation Field
Authors: Wang Jun , Hu Shunqiang , Wang Tan , Liang Hong , Yang Zhenyu
Published: January 2023

In view of the fact that there is no unified understanding of the GNSS horizontal velocity field in the Beijing Plain and the serious land subsidence in this area, we collected GNSS data from 2011 to 2021 and Sentinel 1A data from 2017 to 2021 and conducted high-precision GNSS data processing and PS-InSAR verification in order to determine the reason for the differences in the GNSS horizontal velocity field in the Beijing Plain. The results show that, under the stable Eurasian framework, the horizontal velocity of GNSS stations in the Beijing Plain is significantly inconsistent. The velocity of all GNSS stations ranged from −1.32 to 10.41 mm/yr in the E component and from −8.83 to 3.00 mm/yr in the N component. From 2011 to 2021, there was significant uneven land subsidence in the Beijing Plain, and the maximum land subsidence rate from 2017 to 2021 reached 107 mm/yr. In analyzing the observation data of the GNSS and InSAR, we conclude that the land subsidence in the Beijing Plain will indeed affect the GNSS horizontal velocity field in the subsidence area. Under the EURA_I08 reference framework, the horizontal deformation field in the Beijing Plain is mainly caused by the tectonic activity-derived overall SEE-direction movement, accompanied by the velocity field anomaly caused by local land subsidence.
A GNSS/LiDAR/IMU Pose Estimation System Based on Collaborative Fusion of Factor Map and Filtering
Authors: Chen Honglin , Wu Wei , Zhang Si , Wu Chaohong , Zhong Ruofei
Published: January 2023

One of the core issues of mobile measurement is the pose estimation of the carrier. The classic Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) integrated navigation scheme has high positioning accuracy but is vulnerable to Global Navigation Satellite System (GNSS) signal occlusion and multipath effect. Simultaneous Localization and Mapping (SLAM) is not affected by signal occlusion, but there are problems such as error accumulation and scene degradation. The multi-sensor fusion scheme combining the two technologies can effectively expand the scene coverage and improve the positioning accuracy and system robustness. However, the current scheme has some limitations. On the one hand, GNSS plays a less important role in most SLAM systems, only for initialization or as a closed-loop factor and other auxiliary work. On the other hand, in the fusion method, most of the current systems only use filtering or graph optimization, without taking into account the advantages of both. Aiming at pose estimation for mobile carriers, this paper combines the advantages of the global optimization of the factor graph and the local optimization of filtering and proposes a GNSS-IMU-LiDAR Constraint Kalman Filter (abbreviated as GIL-CKF), which has the characteristics of full coverage and effectively improving absolute accuracy and high output frequency. The scheme proposed in this paper consists of three parts. Firstly, an extensible factor map is used to fuse the positioning information from different sources, including GNSS, IMU, LiDAR, and a closed-loop map, to maintain a high-precision SLAM system, and the output is used as Multi-Sensor-Fusion-Odometry (MSFO). Then, the scene is divided according to the GNSS quality factor, and a Scene Optimizer (SO) is designed to filter GNSS pose and MSFO. Finally, the results are input into the Extended Kalman Filter (EKF) together with the original IMU data for further optimization and smoothing. The experimental results show that the integration of high-precision GNSS positioning information with IMU, LiDAR, a closed-loop map, and other information through the factor map can effectively suppress error accumulation and improve the overall accuracy of the SLAM system. The SO based on GNSS indicators can fully retain high-precision GNSS positioning information, effectively play their respective advantages of filtering and graph optimization, and alleviate the conflict between global and local optimization. SO with EKF filtering furthers optimization, can improve the output frequency, and smooth the trajectory. GIL-CKF can effectively improve the accuracy and robustness of pose estimation and has obvious advantages in multi-sensor scene complementarity, partial road section accuracy improvement, and high input frequency.
Leaf-Level Field Spectroscopy to Discriminate Invasive Species (Psidium guajava L. and Hovenia dulcis Thunb.) from Native Tree Species in the Southern Brazilian Atlantic Forest
Authors: Mallmann Caroline , Pereira Filho Waterloo , Dreyer Jaqueline , Tabaldi Luciane , Durgante Flavia
Published: January 2023

Invasive species are known to have potential advantages over the native community and can be expressed in their leaf functional traits. Thus, leaf-level traits with spectral reflectance can provide valuable insights for distinguishing invasive trees from native trees in complex forest environments. We conducted field spectroscopy measurements in a subtropical area, where we also collected trait data for 12 functional traits of invasive (Psidium guajava and Hovenia dulcis), and native species (Psidium cattleianum and Luehea divaricata). We found that photosynthetic pigments were responsible for the greatest interspecific variability, especially in the green region of the spectrum at 550 nm, therefore contributing to detection of invasive species. In addition, according to LDA and stepwise procedures, the most informative reflectance spectra were concentrated in the visible range that is closely related to pigment absorption features. Furthermore, we aimed to understand the leaf optical properties of the target invasive species by using a combination of narrow bands and linear regression models. P. guajava showed high correlations with specific leaf area, Car/Chl and relative water content. H. dulcis had a strong correlation with water content, specific leaf area and Chla/Chlb. Overall, this methodology proved to be appropriate for discriminating invasive trees, although parameterization by species is necessary.
Simulation-Based Cybersecurity Testing and Evaluation Method for Connected Car V2X Application Using Virtual Machine
Authors: Lee Dae-Hwi , Kim Chan-Min , Song Hyun-Seok , Lee Yong-Hee , Chung Won-Sun
Published: January 2023

In a connected car, the vehicle’s internal network is connected to the outside through communication technology. However, this can cause new security vulnerabilities. In particular, V2X communication, to provide the safety of connected cars, can directly threaten the lives of passengers if a security attack occurs. For V2X communication security, standards such as IEEE 1609.2 define the technical functions that digital signature and encryption to provide security of V2X messages. However, it is difficult to verify the security technology by applying it to the environment with real roads because it can be made up of other safety accidents. In addition, vehicle simulation R&D is steadily being carried out, but there is no simulation that evaluates security for the V2X application level. Therefore, in this paper, a virtual machine was used to implement a V2X communication simulation environment that satisfies the requirements for the security evaluation of connected cars. Then, we proposed scenarios for cybersecurity testing and evaluation, implemented and verified through CANoe Option.Car2X. Through this, it is possible to perform sufficient preliminary verification to minimize the variables before verifying security technology in a real road environment.
Electrochemical Sensing of Lead in Drinking Water Using Copper Foil Bonded with Polymer
Authors: Redhwan Taufique Z. , Ali Younus , Howlader Matiar M. R. , Haddara Yaser M.
Published: January 2023

Levels of lead (Pb) in tap water that are well below established guidelines are now considered harmful, so the detection of sub-parts-per-billion (ppb) Pb levels is crucial. In this work, we developed a two-step, facile, and inexpensive fabrication approach that involves direct bonding of copper (Cu) and liquid crystal polymer (LCP) followed by polyester resin printing for masking onto Cu/LCP to fabricate Cu thin-film-based Pb sensors. The oxygen plasma-treated surfaces resulted in strongly bonded Cu/LCP with a high peel strength of 500 N/m due to the highly hydrophilic nature of both surfaces. The bonded specimen can withstand wet etching of the electrode and can address delamination of the electrode for prolonged use in application environments. The Cu-foil-based electrochemical sensor showed sensitivity of ~11 nA/ppb/cm2 and a limit of detection (LOD) of 0.2 ppb (0.2 µg/L) Pb ions in water. The sensor required only 30 s and a 100 µL sample to detect Pb. To date, this is the most rapid detection of Pb performed using an all-Cu-based sensor. The selectivity test of Cu to Pb with interferences from cadmium and zinc showed that their peaks were separated by a few hundred millivolts. This approach has strong potential towards realizing low-cost, highly reliable integrated water quality monitoring systems.
Rate-Responsive Cardiac Pacing: Technological Solutions and Their Applications
Authors: Świerżyńska Ewa , Oręziak Artur , Główczyńska Renata , Rossillo Antonio , Grabowski Marcin , Szumowski Łukasz , Caprioglio Francesco , Sterliński Maciej
Published: January 2023

Modern cardiac pacemakers are equipped with a function that allows the heart rate to adapt to the current needs of the patient in situations of increased demand related to exercise and stress ("rate-response" function). This function may be based on a variety of mechanisms, such as a built-in accelerometer responding to increased chest movement or algorithms sensing metabolic demand for oxygen, analysis of intrathoracic impedance, and analysis of the heart rhythm (Q-T interval). The latest technologies in the field of rate-response functionality relate to the use of an accelerometer in leadless endocavitary pacemakers; in these devices, the accelerometer enables mapping of the mechanical wave of the heart’s work cycle, enabling the pacemaker to correctly sense native impulses and stimulate the ventricles in synchrony with the cycles of atria and heart valves. Another modern system for synchronizing pacing rate with the patient’s real-time needs requires a closed-loop system that continuously monitors changes in the dynamics of heart contractions. This article discusses the technical details of various solutions for detecting and responding to situations related to increased oxygen demand (e.g., exercise or stress) in implantable pacemakers, and reviews the results of clinical trials regarding the use of these algorithms.
Research on Terminal Access Performance of Beam-Hopping Satellite in IoT Scenario
Authors: Nie Yulei , Zhang Gengxin
Published: January 2023

In recent years, low-orbit satellites have become an important development direction in satellite IoT systems. The number of terminals is large and data collisions occur frequently in the low-orbit satellite IoT scenario. How to design a reliable random access protocol to improve the tolerance of the system for collision is one of the research hotspots in this field. In this paper, the random access protocol, used in the Internet of Things (IoT), for low-orbit satellites is studied, and the access process of the IoT terminals in the scenario is constructed. The access performance of the SSA protocol is analyzed and an improved SSA random access strategy, called Retransmission-SSA (R-SSA), is proposed. The simulation results show that the designed R-SSA can effectively tolerate the signal conflicts between terminals in the beam-hopping LEO IoT scenario and improve the probability of the concurrent access of low-orbit sIoT terminals.
Overestimation of Oxygen Saturation Measured by Pulse Oximetry in Hypoxemia. Part 1: Effect of Optical Pathlengths-Ratio Increase
Authors: Elron Eyal , Bromiker Ruben , Gleisner Ori , Yosef-Hai Ohad , Goldberg Ori , Nitzan Itamar , Nitzan Meir
Published: January 2023

On average, arterial oxygen saturation measured by pulse oximetry (SpO2) is higher in hypoxemia than the true oxygen saturation measured invasively (SaO2), thereby increasing the risk of occult hypoxemia. In the current article, measurements of SpO2 on 17 cyanotic newborns were performed by means of a Nellcor pulse oximeter (POx), based on light with two wavelengths in the red and infrared regions (660 and 900 nm), and by means of a novel POx, based on two wavelengths in the infrared region (761 and 820 nm). The SpO2 readings from the two POxs showed higher values than the invasive SaO2 readings, and the disparity increased with decreasing SaO2. SpO2 measured using the two infrared wavelengths showed better correlation with SaO2 than SpO2 measured using the red and infrared wavelengths. After appropriate calibration, the standard deviation of the individual SpO2−SaO2 differences for the two-infrared POx was smaller (3.6%) than that for the red and infrared POx (6.5%, p < 0.05). The overestimation of SpO2 readings in hypoxemia was explained by the increase in hypoxemia of the optical pathlengths-ratio between the two wavelengths. The two-infrared POx can reduce the overestimation of SpO2 measurement in hypoxemia and the consequent risk of occult hypoxemia, owing to its smaller increase in pathlengths-ratio in hypoxemia.
Competition between Direct Detection Mechanisms in Planar Bow-Tie Microwave Diodes on the Base of InAlAs/InGaAs/InAlAs Heterostructures
Authors: Sužiedėlis Algirdas , Ašmontas Steponas , Gradauskas Jonas , Čerškus Aurimas , Požela Karolis , Anbinderis Maksimas
Published: January 2023

The application of the unique properties of terahertz radiation is increasingly needed in sensors, especially in those operating at room temperature without an external bias voltage. Bow-tie microwave diodes on the base of InGaAs semiconductor structures meet these requirements. These diodes operate on the basis of free-carrier heating in microwave electric fields, which allows for the use of such sensors in millimeter- and submillimeter-wavelength ranges. However, there still exists some uncertainty concerning the origin of the voltage detected across these diodes. This work provides a more detailed analysis of the detection mechanisms in InAlAs/InGaAs selectively doped bow-tie-shaped semiconductor structures. The influence of the InAs inserts in the InGaAs layer is investigated under various illumination and temperature conditions. A study of the voltage–power characteristics, the voltage sensitivity dependence on frequency in the Ka range, temperature dependence of the detected voltage and its relaxation characteristics lead to the conclusion that a photo-gradient electromotive force arises in bow-tie diodes under simultaneous light illumination and microwave radiation.
Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data
Authors: Pyeon Hyeongoo , Kim Hanwul , Kim Rak Chul , Oh Geesung , Lim Sejoon
Published: January 2023

Driver’s hands on/off detection is very important in current autonomous vehicles for safety. Several studies have been conducted to create a precise algorithm. Although many studies have proposed various approaches, they have some limitations, such as robustness and reliability. Therefore, we propose a deep learning model that utilizes in-vehicle data. We also established a data collection system, which collects in-vehicle data that are auto-labeled for efficient and reliable data acquisition. For a robust system, we devised a confidence logic that prevents outliers’ sway. To evaluate our model in more detail, we suggested a new metric to explain the events, considering state transitions. In addition, we conducted an extensive experiment on the new drivers to demonstrate our model’s generalization ability. We verified that the proposed system achieved a better performance than in previous studies, by resolving their drawbacks. Our model detected hands on/off transitions in 0.37 s on average, with an accuracy of 95.7%.
Using the Electrocardiogram for Pain Classification under Emotional Contexts
Authors: Silva Pedro , Sebastião Raquel
Published: January 2023

The adequate characterization of pain is critical in diagnosis and therapy selection, and currently is subjectively assessed by patient communication and self-evaluation. Thus, pain recognition and assessment have been a target of study in past years due to the importance of objective measurement. The goal of this work is the analysis of the electrocardiogram (ECG) under emotional contexts and reasoning on the physiological classification of pain under neutral and fear conditions. Using data from both contexts for pain classification, a balanced accuracy of up to 97.4% was obtained. Using an emotionally independent approach and using data from one emotional context to learn pain and data from the other to evaluate the models, a balanced accuracy of up to 97.7% was reached. These similar results seem to support that the physiological response to pain was maintained despite the different emotional contexts. Attempting a participant-independent approach for pain classification and using a leave-one-out cross-validation strategy, data from the fear context were used to train pain classification models, and data from the neutral context were used to evaluate the performance, achieving a balanced accuracy of up to 94.9%. Moreover, across the different learning strategies, Random Forest outperformed the remaining models. These results show the feasibility of identifying pain through physiological characteristics of the ECG response despite the presence of autonomic nervous system perturbations.
A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring
Authors: Pujić Dea , Tomašević Nikola , Batić Marko
Published: January 2023

Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a 3% improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications.
Reliability of a Wearable Motion Tracking System for the Clinical Evaluation of a Dynamic Cervical Spine Function
Authors: Hani Hamed , Souchereau Reid , Kachlan Anas , Dufour Jonathan , Aurand Alexander , Mageswaran Prasath , Hyer Madison , Marras William
Published: January 2023

Neck pain is a common cause of disability worldwide. Lack of objective tools to quantify an individual’s functional disability results in the widespread use of subjective assessments to measure the limitations in spine function and the response to interventions. This study assessed the reliability of the quantifying neck function using a wearable cervical motion tracking system. Three novice raters recorded the neck motion assessments on 20 volunteers using the device. Kinematic features from the signals in all three anatomical planes were extracted and used as inputs to repeated measures and mixed-effects regression models to calculate the intraclass correlation coefficients (ICCs). Cervical spine-specific kinematic features indicated good and excellent inter-rater and intra-rater reliability for the most part. For intra-rater reliability, the ICC values varied from 0.85 to 0.95, and for inter-rater reliability, they ranged from 0.7 to 0.89. Overall, velocity measures proved to be more reliable compared to other kinematic features. This technique is a trustworthy tool for evaluating neck function objectively. This study showed the potential for cervical spine-specific kinematic measurements to deliver repeatable and reliable metrics to evaluate clinical performance at any time points.