bluuebunny/crossref_metadata_2025 huggingface dataset:
Vegetation Coverage in the Desert Area of the Junggar Basin of Xinjiang, China, Based on Unmanned Aerial Vehicle Technology and Multisource Data
Authors: Miao Yuhao , Zhang Renping , Guo Jing , Yi Shuhua , Meng Baoping , Liu Jiaqing
Published: October 2022

Vegetation coverage information is an important indicator of desert ecological environments. Accurately grasping vegetation coverage changes in desert areas can help in assessing the quality of ecosystems and maintaining their functions. Improving remote sensing methods to detect the vegetation coverage in areas of low vegetation coverage is an important challenge for the remote sensing of vegetation in deserts. In this study, based on the fusion of MOD09GA and MOD09GQ data, 2019–2021 low-altitude unmanned aerial vehicle (UAV) remote sensing data, and other factors (such as geographical, topographic, and meteorological factors), three types of inversion models for vegetation coverage were constructed: a multivariate parametric regression model, a support vector machine (SVM) regression model, and a back-propagation neural network (BPNN) regression model. The optimal model was then used to map the spatial distribution of vegetation coverage and its dynamic change in the Junggar Basin of Xinjiang, China, over 22 years (from 2000 to 2021). The results show that: (1) The correlation between enhanced vegetation index (EVI) obtained from image fusion and vegetation coverage in desert areas is the highest (r = 0.72). (2) Among the geographical and topographic factors, only longitude and latitude were significantly correlated with vegetation coverage (p < 0.05). The average monthly temperature and precipitation from the previous six months were correlated with the vegetation coverage (p < 0.05), but the vegetation coverage of the current month had the highest correlation with the average temperature (r = −0.27) and precipitation (r = 0.33) of the previous month. (3) Among the multivariate parametric models established by selecting the five aforementioned factors, the multiple linear regression model performed the best (R2 = 0.64). (4) The SVM regression model was superior to the other regression models (R2 = 0.80, mean squared error = 8.35%). (5) The average vegetation coverage in the desert area of the Junggar Basin was 7.36%, and from 2000–2021, the vegetation coverage in 54.59% of the desert area increased.
Intra-Pulse Frequency Coding Design for a High-Resolution Radar against Smart Noise Jamming
Authors: Xi Rongyan , Ma Dingyou , Liu Xiang , Wang Lei , Liu Yimin
Published: October 2022

Smart noise jamming forms active jamming by intercepting, modulating, and forwarding radar signals into the radar receiver, which seriously affects the radar range recovery performance. In this paper, we propose a novel waveform design approach and an efficient range recovery method for high-resolution radar in the jamming scenario. Firstly, we propose an intra-pulse frequency-coded frequency-modulated continuous waveform (IPFC-FMCW), which contains multiple FMCW chips with different widths and frequencies, to combat the smart noise jamming. After the jamming suppression, the proposed waveform has a low sidelobe level, which is different from traditional FMCW signals for which the observations are periodically missing, resulting in high sidelobe levels. Then, to improve the range recovery performance of the waveform after jamming suppression, we optimize the range profile by designing the transmit waveform and then solve it by a simulated annealing algorithm. Next, based on the designed waveform, we derive the echo model after jamming suppression and propose a gridless compressed sensing (CS) method to recover the range of the targets. Compared with the existing waveforms and methods, the proposed waveform and the processing method achieve better range recovery performance in the jamming scenario. Numerical simulations are utilized to demonstrate the range recovery effectiveness of the proposed waveform and method in smart noise jamming.
Exploring the Impacts of Data Source, Model Types and Spatial Scales on the Soil Organic Carbon Prediction: A Case Study in the Red Soil Hilly Region of Southern China
Authors: Tan Qiuyuan , Geng Jing , Fang Huajun , Li Yuna , Guo Yifan
Published: October 2022

Rapid and accurate mapping of soil organic carbon (SOC) is of great significance to understanding the spatial patterns of soil fertility and conducting soil carbon cycle research. Previous studies have dedicated considerable efforts to the spatial prediction of SOC content, but few have systematically quantified the effects of environmental covariates selection, the spatial scales and the model types on SOC prediction accuracy. Here, we spatially predicted SOC content through digital soil mapping (DSM) based on 186 topsoil (0–20 cm) samples in a typical hilly red soil region of southern China. Specifically, we first determined an optimal covariate set from different combinations of multiple environmental variables, including multi-sensor remote sensing images (Sentinel-1 and Sentinel-2), climate variables and DEM derivatives. Furthermore, we evaluated the impacts of spatial resolution (10 m, 30 m, 90 m, 250 m and 1000 m) of covariates and the model types (three linear and three non-linear machine learning techniques) on the SOC prediction. The results of the performance analysis showed that a combination of Sentinel-1/2-derived variables, climate and topographic predictors generated the best predictive performance. Among all variables, remote sensing covariates, especially Sentinel-2-derived predictors, were identified as the most important explanatory variables controlling the variability of SOC content. Moreover, the prediction accuracy declined significantly with the increased spatial scales and achieved the highest using the XGBoost model at 10 m resolution. Notably, non-linear machine learners yielded superior predictive capability in contrast with linear models in predicting SOC. Overall, our findings revealed that the optimal combination of predictor variables, spatial resolution and modeling techniques could considerably improve the prediction accuracy of the SOC content. Particularly, freely accessible Sentinel series satellites showed great potential in high-resolution digital mapping of soil properties.
A Robust Algorithm for Multi-GNSS Precise Positioning and Performance Analysis in Urban Environments
Authors: Chai Dashuai , Ning Yipeng , Wang Shengli , Sang Wengang , Xing Jianping , Bi Jingxue
Published: October 2022

In this paper, we propose a partial ambiguity method of global navigation satellite system (GNSS) reliable positioning based on a robust estimation model to address the problems of the low reliability and availability of GNSS positioning in urban complex environments. First, the high-precision observations selected on the basis of the signal-to-noise ratio (SNR) were used to solve ambiguities. Then, the fixed ambiguities were used as constraints to solve the ambiguities of low-quality observations. The robust estimation method was used to reduce the impact of outliers for the ambiguity solutions. The robust estimation was also used to solve the position parameters to reduce the influence of the residual errors and uncorrected ambiguities for GNSS high-accuracy positioning. Static and dynamic data were used to evaluate the proposed algorithm. These experiments show that the proposed algorithm with the robust estimation can reduce the fixed time of ambiguity initialization, compared with the conventional algorithm without the robust estimation. The positioning accuracy and solution rate are similar regardless of whether the robust estimation is used in the GNSS unblocked environment. In blocked environments, the solution rate improves to more than 99%, and the three-dimensional (3D) position accuracy improves by more than 70% when the robust estimation is used. When the observation number of simulated small gross error accounts for 40.91% of total observations, the centimeter-level positioning accuracy can still be obtained via several robust estimation models. In the urban blocked environment, the IGG (Institute of Geodesy and Geophysics) III scheme has a better performance than other robust schemes discussed in this paper with regard to the positioning performance and computational efficiency.
L2AMF-Net: An L2-Normed Attention and Multi-Scale Fusion Network for Lunar Image Patch Matching
Authors: Zhong Wenhao , Jiang Jie , Ma Yan
Published: October 2022

The terrain-relative navigation (TRN) method is often used in entry, descent and landing (EDL) systems for position estimation and navigation of spacecraft. In contrast to the crater detection method, the image patch matching method does not depend on the integrity of the database and the saliency of the crater features. However, there are four difficulties associated with lunar images: illumination transformation, perspective transformation, resolution mismatch, and the lack of texture. Deep learning offers possible solutions. In this paper, an L2-normed attention and multi-scale fusion network (L2AMF-Net) was proposed for patch descriptor learning to effectively overcome the above four difficulties and achieve lunar image patch matching accurately and robustly. On the one hand, an L2-Attention unit (LAU) was proposed to generate attention score maps in spatial and channel dimensions and enhance feature extraction. On the other hand, a multi-scale feature self and fusion enhance structure (SFES) was proposed to fuse multi-scale features and enhance the feature representations. L2AMF-Net achieved a 95.57% matching accuracy and excellent performance compared with several other methods in lunar image patch dataset generated in this paper. Experiments verified the illumination, perspective and texture robustness of L2AMF-Net and the validity of the attention module and feature fusion structure.
Study on the Parameters of Ice Clouds Based on 1.5 µm Micropulse Polarization Lidar
Authors: Li Yudie , Wang Chong , Xue Xianghui , Wang Yu , Shang Xiang , Jia Mingjiao , Chen Tingdi
Published: October 2022

Dust aerosols can participate in the heterogeneous nucleation process as effective ice nucleation particles, thus changing the physical properties of clouds. In this paper, we used an eye-safe 1550 nm micropulse polarization single photon lidar combined with meteorological stations, HYSPLIT backward trajectory analysis, ERA5 reanalysis data, CALIPSO, Himawari-8 and Terra-MODIS satellite data to compare the difference in cloud characteristics between dust and clean cirrus cases in Jinan from 26–29 March 2022. The study found that the aerosol affected the cloud effective radius, and the cloud top temperature impacted the properties of depolarization of dust ice clouds. According to the statistical results of the upper and lower quartiles, the depolarization ratio (DPR) range of dust cirrus on 26 March was 0.46–0.49, a similar range to the clean cirrus, while that of dust cirrus on 27 March was 0.54–0.59, which seemed much larger. Different height and temperature conditions lead to differences in the habits of ice crystals in clouds, thus changing the DPR. However, the range of the DPR between clean cirrus and dust cirrus showed no obvious difference, as the former was 0.43–0.53 and the latter was 0.46–0.59. Under the condition of higher aerosol loading, the lidar range-corrected signal (RCS) of cirrus clouds was stronger, and the cloud effective radius was 48 μm, larger than that of clean cirrus (32 μm). This may be the effect of dust on the microphysical properties of clouds. This study discusses the indirect effects of dust aerosols on cirrus clouds and the underlying mechanisms from the perspectives of microphysics and optics, which can provide more references for urban air pollution processes and aerosol-cloud interactions.
Interpreting Mangrove Habitat and Coastal Land Cover Change in the Greater Bay Area, Southern China, from 1924 to 2020 Using Historical Aerial Photos and Multiple Sources of Satellite Data
Authors: Liu Mingfeng , Leung Felix , Lee Shing-Yip
Published: October 2022

Coastal habitat dynamics and ecosystem function in response to human-induced disturbance, especially urbanization, are of increasing concern. However, how changes in landscape composition as well as habitat quantity and quality may affect the long-term sustainability of rapidly urbanizing coasts remains unclear. This study aimed to quantify the extent, change rate, patterns, change process and interrelationships of mangrove habitats, impervious surfaces, and other land cover types in Deep Bay in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), China, the world’s largest megalopolis, from 1924 to 2020. We processed historical aerial photos (1924–2020) and multiple sources of satellite data (1973–2020) for different types of land cover mapping. Post-classification analysis, including correlation analysis and change detection analysis, was conducted based on the long time-series land cover classification results. Mangrove habitats increased in Deep Bay from 1924 to 2020, except for a large area decrease from 1954 to 1964 due to the construction of tidal aquaculture ponds. Mudflat areas contributed most to the expansion of mangrove habitats of about 275 ha from 1987 to 2020. During this period, reclamation and urbanization for the construction of the megacity of Shenzhen turned large areas of water and mudflat (about 4000 ha) on the northern shore into impervious surface and urban vegetation. Overall, the landscape pattern of mangrove habitats in Deep Bay showed increasing connectivity and decreasing degree of fragmentation from 1987 to 2020. These changes have significant implications for the ecosystem services, e.g., supporting migratory waterbirds, supported by these wetlands.
Integrating Multi-Scale Remote-Sensing Data to Monitor Severe Forest Infestation in Response to Pine Wilt Disease
Authors: Li Xiujuan , Liu Yongxin , Huang Pingping , Tong Tong , Li Linyuan , Chen Yuejuan , Hou Ting , Su Yun , Lv Xiaoqi , Fu Wenxue , Huang Xiaojun
Published: October 2022

Pine wilt disease (PWD) is one of the most destructive forest diseases that has led to rapid wilting and mortality in susceptible host pine trees. Spatially explicit detection of pine wood nematode (PWN)-induced infestation is important for forest management, policy making, and practices. Previous studies have mapped forest disturbances in response to various forest diseases and/or insects over large areas using remote-sensing techniques, but these efforts were often constrained by the limited availability of ground truth information needed for the calibration and validation of moderate-resolution satellite algorithms in the process of linking plot-scale measurements to satellite data. In this study, we proposed a two-level up-sampling strategy by integrating unmanned aerial vehicle (UAV) surveys and high-resolution Radarsat-2 satellite imagery for expanding the number of training samples at the 30-m resampled Sentinel-1 resolution. Random forest algorithms were separately used in the prediction of the Radarsat-2 and Sentinel-1 infestation map induced by PWN. After data acquisition in Muping District during August and September 2021, we first verified the ability of a deep-learning-based object detection algorithm (i.e., YOLOv5 model) in the detection of infested trees from coregistered UAV-based RGB images (Average Precision (AP) of larger than 70% and R2 of 0.94). A random forest algorithm trained using the up-sampling UAV infestation map reference and corresponding Radarsat-2 pixel values was then used to produce the Radarsat-2 infestation map, resulting in an overall accuracy of 72.57%. Another random forest algorithm trained using the Radarsat-2 infestation pixels with moderate and high severity (i.e., an infestation severity of larger than 0.25, where the value was empirically set based on a trade-off between classification accuracy and infection detectability) and corresponding Sentinel-1 pixel values was subsequently used to predict the Sentinel-1 infestation map, resulting in an overall accuracy of 87.63%, where the validation data are Radarsat-2 references rather than UAV references. The Sentinel-1 map was also validated by independent UAV surveys, with an overall accuracy of 76.30% and a Kappa coefficient of 0.45. We found that the expanded training samples by the integration of UAV and Radarsat-2 strengthened the medium-resolution Sentinel-1-based prediction model of PWD. This study demonstrates that the proposed method enables effective PWN infestation mapping over multiple scales.
Spatial Validation of Spectral Unmixing Results: A Case Study of Venice City
Authors: Cavalli Rosa Maria
Published: October 2022

Since remote sensing images offer unique access to the distribution of land cover on earth, many countries are investing in this technique to monitor urban sprawl. For this purpose, the most widely used methodology is spectral unmixing which, after identifying the spectra of the mixed-pixel constituents, determines their fractional abundances in the pixel. However, the literature highlights shortcomings in spatial validation due to the lack of detailed ground truth knowledge and proposes five key requirements for accurate reference fractional abundance maps: they should cover most of the area, their spatial resolution should be higher than that of the results, they should be validated using other ground truth data, the full range of abundances should be validated, and errors in co-localization and spatial resampling should be minimized. However, most proposed reference maps met two or three requirements and none met all five. In situ and remote data acquired in Venice were exploited to meet all five requirements. Moreover, to obtain more information about the validation procedure, not only reference spectra, synthetic image, and fractional abundance models (FAMs) that met all the requirements, but also other data, that no previous work exploited, were employed: reference fractional abundance maps that met four out of five requirements, and fractional abundance maps retrieved from the synthetic image. Briefly summarizing the main results obtained from MIVIS data, the average of spectral accuracies in root mean square error was equal to 0.025; using FAMs, the average of spatial accuracies in mean absolute error (MAEk-Totals) was equal to 1.32 and more than 78% of these values were related to sensor characteristics; using reference fractional abundance maps, the average MAEk-Totals value increased to 1.97 because errors in co-localization and spatial-resampling affected about 29% of these values. In conclusion, meeting all requirements and the exploitation of different reference data increase the spatial accuracy, upgrade the validation procedure, and improve the knowledge of accuracy.
Nonlinear Unmixing via Deep Autoencoder Networks for Generalized Bilinear Model
Authors: Zhang Jinhua , Zhang Xiaohua , Meng Hongyun , Sun Caihao , Wang Li , Cao Xianghai
Published: October 2022

Hyperspectral unmixing decomposes the observed mixed spectra into a collection of constituent pure material signatures and the associated fractional abundances. Because of the universal modeling ability of neural networks, deep learning (DL) techniques are gaining prominence in solving hyperspectral analysis tasks. The autoencoder (AE) network has been extensively investigated in linear blind source unmixing. However, the linear mixing model (LMM) may fail to provide good unmixing performance when the nonlinear mixing effects are nonnegligible in complex scenarios. Considering the limitations of LMM, we propose an unsupervised nonlinear spectral unmixing method, based on autoencoder architecture. Firstly, a deep neural network is employed as the encoder to extract the low-dimension feature of the mixed pixel. Then, the generalized bilinear model (GBM) is used to design the decoder, which has a linear mixing part and a nonlinear mixing one. The coefficient of the bilinear mixing part can be adjusted by a set of learnable parameters, which makes the method perform well on both nonlinear and linear data. Finally, some regular terms are imposed on the loss function and an alternating update strategy is utilized to train the network. Experimental results on synthetic and real datasets verify the effectiveness of the proposed model and show very competitive performance compared with several existing algorithms.
Energy-Based Adversarial Example Detection for SAR Images
Authors: Zhang Zhiwei , Gao Xunzhang , Liu Shuowei , Peng Bowen , Wang Yufei
Published: October 2022

Adversarial examples (AEs) bring increasing concern on the security of deep-learning-based synthetic aperture radar (SAR) target recognition systems. SAR AEs with perturbation constrained to the vicinity of the target have been recently in the spotlight due to the physical realization prospects. However, current adversarial detection methods generally suffer severe performance degradation against SAR AEs with region-constrained perturbation. To solve this problem, we treated SAR AEs as low-probability samples incompatible with the clean dataset. With the help of energy-based models, we captured an inherent energy gap between SAR AEs and clean samples that is robust to the changes of the perturbation region. Inspired by this discovery, we propose an energy-based adversarial detector, which requires no modification to a pretrained model. To better distinguish the clean samples and AEs, energy regularization was adopted to fine-tune the pretrained model. Experiments demonstrated that the proposed method significantly boosts the detection performance against SAR AEs with region-constrained perturbation.
Discrimination of Rock Units in Karst Terrains Using Sentinel-2A Imagery
Authors: Gizdavec Nikola , Gašparović Mateo , Miko Slobodan , Lužar-Oberiter Borna , Ilijanić Nikolina , Peh Zoran
Published: October 2022

We explored the potential incorporation of Sentinel-2A imagery for rock unit determination in the Croatian karst region dominated by carbonate rocks. The various lithological units are potential sources of both stone aggregates and dimension stone, and their spatial distribution is of high importance for mineral resource management. The presented approach included the preprocessing and processing of existing analog data (geological maps), Sentinel-2A satellite images and the United States Geological Survey spectral indices, all in combination with ground truth data. Geological mapping and digital processing of legacy maps using the K-means and random forest algorithm reduced the spatial error of the geometry of geological boundaries from 100 m and 300 m to below 100 m. The possibility of discriminating individual lithological units based on spectral analysis and discriminant function analysis was also examined, providing a tool for evaluating the geological potential for mineral resources. Despite the challenges posed by the lithological homogeneity of karst terrain, the results of this study show that the use of spectral signature data derived from Sentinel-2A satellite images can be successfully implemented in such terrains for the enhancement of existing geological maps and mineral resources exploration.
Building Extraction and Floor Area Estimation at the Village Level in Rural China Via a Comprehensive Method Integrating UAV Photogrammetry and the Novel EDSANet
Authors: Zhou Jie , Liu Yaohui , Nie Gaozhong , Cheng Hao , Yang Xinyue , Chen Xiaoxian , Gross Lutz
Published: October 2022

Dynamic monitoring of building environments is essential for observing rural land changes and socio-economic development, especially in agricultural countries, such as China. Rapid and accurate building extraction and floor area estimation at the village level are vital for the overall planning of rural development and intensive land use and the “beautiful countryside” construction policy in China. Traditional in situ field surveys are an effective way to collect building information but are time-consuming and labor-intensive. Moreover, rural buildings are usually covered by vegetation and trees, leading to incomplete boundaries. This paper proposes a comprehensive method to perform village-level homestead area estimation by combining unmanned aerial vehicle (UAV) photogrammetry and deep learning technology. First, to tackle the problem of complex surface feature scenes in remote sensing images, we proposed a novel Efficient Deep-wise Spatial Attention Network (EDSANet), which uses dual attention extraction and attention feature refinement to aggregate multi-level semantics and enhance the accuracy of building extraction, especially for high-spatial-resolution imagery. Qualitative and quantitative experiments were conducted with the newly built dataset (named the rural Weinan building dataset) with different deep learning networks to examine the performance of the EDSANet model in the task of rural building extraction. Then, the number of floors of each building was estimated using the normalized digital surface model (nDSM) generated from UAV oblique photogrammetry. The floor area of the entire village was rapidly calculated by multiplying the area of each building in the village by the number of floors. The case study was conducted in Helan village, Shannxi province, China. The results show that the overall accuracy of the building extraction from UAV images with the EDSANet model was 0.939 and that the precision reached 0.949. The buildings in Helan village primarily have two stories, and their total floor area is 3.1 × 105 m2. The field survey results verified that the accuracy of the nDSM model was 0.94; the RMSE was 0.243. The proposed workflow and experimental results highlight the potential of UAV oblique photogrammetry and deep learning for rapid and efficient village-level building extraction and floor area estimation in China, as well as worldwide.
A Defect Detection Method Based on BC-YOLO for Transmission Line Components in UAV Remote Sensing Images
Authors: Bao Wenxia , Du Xiang , Wang Nian , Yuan Mu , Yang Xianjun
Published: October 2022

Vibration dampers and insulators are important components of transmission lines, and it is therefore important for the normal operation of transmission lines to detect defects in these components in a timely manner. In this paper, we provide an automatic detection method for component defects through patrolling inspection by an unmanned aerial vehicle (UAV). We constructed a dataset of vibration dampers and insulators (DVDI) on transmission lines in images obtained by the UAV. It is difficult to detect defects in vibration dampers and insulators from UAV images, as these components and their defective parts are very small parts of the images, and the components vary greatly in terms of their shape and color and are easily confused with the background. In view of this, we use the end-to-end coordinate attention and bidirectional feature pyramid network “you only look once” (BC-YOLO) to detect component defects. To make the network focus on the features of vibration dampers and insulators rather than the complex backgrounds, we added the coordinate attention (CA) module to YOLOv5. CA encodes each channel separately along the vertical and horizontal directions, which allows the attention module to simultaneously capture remote spatial interactions with precise location information and helps the network locate targets of interest more accurately. In the multiscale feature fusion stage, different input features have different resolutions, and their contributions to the fused output features are usually unequal. However, PANet treats each input feature equally and simply sums them up without distinction. In this paper, we replace the original PANet feature fusion framework in YOLOv5 with a bidirectional feature pyramid network (BiFPN). BiFPN introduces learnable weights to learn the importance of different features, which can make the network focus more on the feature mapping that contributes more to the output features. To verify the effectiveness of our method, we conducted a test in DVDI, and its mAP@0.5 reached 89.1%, a value 2.7% higher than for YOLOv5.
Low-Delay and Energy-Efficient Opportunistic Routing for Maritime Search and Rescue Wireless Sensor Networks
Authors: Xian Jiangfeng , Wu Huafeng , Mei Xiaojun , Chen Xinqiang , Yang Yongsheng
Published: October 2022

After the occurrence of a maritime disaster, to save human life and search for important property equipment in the first time, it is indispensable to efficiently transmit search and rescue sea area data to the maritime search and rescue command center (MSRCC) in real-time, so that the MSRCC can make timely and accurate decisions. The key to determining the efficiency of data forwarding is the quality of the routing protocol. Due to the high dynamics of the marine environment and the limited energy of the marine node, the coverage hole and routing path failure problems occur frequently when using the existing routing algorithm for marine data forwarding. Based on the above background, in this work, we study a low-latency and energy-efficient opportunistic routing protocol for maritime search and rescue wireless sensor networks (MSR-WSNs). Considering the adverse impact of wave shadowing on signal transmission, an effective link reliability prediction method is first investigated to quantify the link connectivity among nodes. To mitigate the end-to-end time delay, an optimal expected packet advancement is then derived by combining link con-nectivity with geographic progress threshold θ. After that, based on the link connectivity between marine nodes, the optimal expected packet advancement prediction, the distance from the sensing nodes to the sink, and the remaining energy distribution of the nodes, the priority of candidate nodes is calculated and sorted in descending order. Finally, timer-based coordination algorithm is adopted to perform the marine data packet forwarding so as to avoid packet conflict. Computer simulation results demonstrate that compared with benchmark algorithms, the data packet delivery ratio, the delay performance and the average node energy consumption (the average node speed is 20 m/s) of the proposed opportunistic routing protocol are improved by more than 21.4%, 39.2% and 18.1%, respectively.
Analysis of the Spatial and Temporal Evolution Patterns of Grassland Health and Its Driving Factors in Xilingol
Authors: Wang Kaimin , Cao Chunxiang , Xie Bo , Xu Min , Yang Xinwei , Guo Heyi , Duerler Robert Shea
Published: October 2022

The combination of natural environment changes and human activities affects the growth of grasslands. In order to quantitatively assess the causes of spatial and temporal variation of grasslands in Xilingol, this study assessed the spatial and temporal evolution patterns of grassland health based on MOD13A1 long time series Normalized Difference Vegetation Index (NDVI) data from 2000–2019 using trend analysis. The geodetector model was used to explore the dominant drivers of spatial variation in grassland NDVI, combined with 34 factors covering natural environmental changes and human disturbances over the same period. The results show that the grasslands of Xilingol showed an overall recovery trend from 2000 to 2019, with an average annual NDVI growth rate of 0.0028/a, a monthly increasing rate of 0.0005/month, and 68.06% of the grassland at an average recovery level. Moisture-dominated natural climate change factors, such as Growing Season Precipitation (Prep2), Annual Mean Water Vapor Pressure (WVP), and Annual Mean Relative Humidity (RH), were the underlying cause of grassland health changes during the study period, with the highest explanatory factor being growing season precipitation (q value of 0.59 on a multi-year average). The influence of primary production value among human activities was greater, and the explanatory factor of tertiary production value showed an increasing trend. The interactions among natural and anthropogenic factors significantly enhances their explanatory credibility for NDVI, with the type of interaction dominated by the two-factor enhancement. Risk detection of the top 10 dominant drivers in terms of q statistic were carried out to obtain the threshold range of each driver in the high zone of grassland NDVI, which can provide a scientific reference for the sustainable restoration of grassland.
Flood Detection in Dual-Polarization SAR Images Based on Multi-Scale Deeplab Model
Authors: Wu Han , Song Huina , Huang Jianhua , Zhong Hua , Zhan Ronghui , Teng Xuyang , Qiu Zhaoyang , He Meilin , Cao Jiayi
Published: October 2022

The proliferation of massive polarimetric Synthetic Aperture Radar (SAR) data helps promote the development of SAR image interpretation. Due to the advantages of powerful feature extraction capability and strong adaptability for different tasks, deep learning has been adopted in the work of SAR image interpretation and has achieved good results. However, most deep learning methods only employ single-polarization SAR images and ignore the water features embedded in multi-polarization SAR images. To fully exploit the dual-polarization SAR data and multi-scale features of SAR images, an effective flood detection method for SAR images is proposed in this paper. In the proposed flood detection method, a powerful Multi-Scale Deeplab (MS-Deeplab) model is constructed based on the dual-channel MobileNetV2 backbone and the classic DeeplabV3+ architecture to improve the ability of water feature extraction in SAR images. Firstly, the dual-channel feature extraction backbone based on the lightweight MobileNetV2 separately trains the dual-polarization SAR images, and the obtained training parameters are merged with the linear weighting to fuse dual-polarization water features. Given the multi-scale space information in SAR images, then, a multi-scale feature fusion module is introduced to effectively utilize multi-layer features and contextual information, which enhances the representation of water features. Finally, a joint loss function is constructed based on cross-entropy and a dice coefficient to deal with the imbalanced categorical distribution in the training dataset. The experimental results on the time series of Sentinel-1A SAR images show that the proposed method for flood detection has a strong ability to locate water boundaries and tiny water bodies in complex scenes. In terms of quantitative assessment, MS-Deeplab can achieve a better performance compared with other mainstream semantic segmentation models, including PSPNet, Unet and the original DeeplabV3+ model, with a 3.27% intersection over union (IoU) and 1.69% pixel accuracy (PA) improvement than the original DeeplabV3+ model.
WTM: The Site-Wise Empirical Wuhan University Tropospheric Model
Authors: Zhou Yaozong , Lou Yidong , Zhang Weixing , Wu Peida , Bai Jingna , Zhang Zhenyi
Published: October 2022

The tropospheric model is the key model in space geodetic techniques such as Global Navigation Satellite Systems (GNSS) and Very Long Baseline Interferometry (VLBI). In this paper, we established the site-wise empirical Wuhan University Tropospheric Model (WTM) by using 10-year (2011–2020) monthly mean and 5-year (2016–2020) hourly ERA5 reanalysis data, where the Zenith Path Delay (ZPD), mapping function, and horizontal gradient as well as meteorological parameters are provided at 1583 specific space geodetic stations with additionally considering the diurnal and semi-diurnal variations. The mapping function and horizontal gradient from the WTM model were evaluated at 524 globally distributed GNSS stations during the year 2020 and compared with the latest grid-wise (1° × 1°) Global Pressure and Temperature 3 (GPT3) model. The significant improvements of the WTM model to the GPT3 model were found at the stations with terrain relief, and the maximal mapping function and horizontal gradient accuracy improvements reached 12.8 and 14.71 mm. The ZPD and mapping functions from the two models were also validated at 31 Multi-GNSS Experiment (MGEX) stations spanning the year 2020 by BeiDou Navigation Satellite System (BDS) Precise Point Positioning (PPP). The significant vertical coordinate and ZTD difference biases between the PPP schemes adopted by the two models were also found, and the largest biases reached −1.78 and 0.87 mm.
Plasmonic Refractive Index and Temperature Sensor Based on Graphene and LiNbO3
Authors: Irfan Muhammad , Khan Yousuf , Rehman Atiq Ur , Butt Muhammad A. , Khonina Svetlana N. , Kazanskiy Nikolay L.
Published: October 2022

A high-efficiency dual-purpose plasmonic perfect absorber sensor based on LiNbO3 and graphene layers was investigated in this paper for the refractive index and thermal sensing. The sensor design was kept simple for easy fabrication, comprising a LiNbO3 substrate with a quartz layer, thin layer of graphene, four gold nanorods, and a nanocavity in each unit cell. The nanocavity is located in the middle of the cell to facilitate the penetration of EM energy to the subsurface layers. The proposed sensor design achieved an output response of 99.9% reflection, which was easy to detect without having any specialized conditions for operability. The performance of the device was numerically investigated for the biomedical refractive index range of 1.33 to 1.40, yielding a sensitivity value of 981 nm/RIU with a figure-of-merit of 61.31 RIU−1. By including an additional polydimethylsiloxane polymer functional layer on the top, the device was also tested as a thermal sensor, which yielded a sensitivity level of −0.23 nm/°C.
Explainable Machine-Learning-Based Characterization of Abnormal Cortical Activities for Working Memory of Restless Legs Syndrome Patients
Authors: Kim Minju , Kim Hyun , Seo Pukyeong , Jung Ki-Young , Kim Kyung Hwan
Published: October 2022

Restless legs syndrome (RLS) is a sensorimotor disorder accompanied by a strong urge to move the legs and an unpleasant sensation in the legs, and is known to accompany prefrontal dysfunction. Here, we aimed to clarify the neural mechanism of working memory deficits associated with RLS using machine-learning-based analysis of single-trial neural activities. A convolutional neural network classifier was developed to discriminate the cortical activities between RLS patients and normal controls. A layer-wise relevance propagation was applied to the trained classifier in order to determine the critical nodes in the input layer for the output decision, i.e., the time/location of cortical activities discriminating RLS patients and normal controls during working memory tasks. Our method provided high classification accuracy (~94%) from single-trial event-related potentials, which are known to suffer from high inter-trial/inter-subject variation and low signal-to-noise ratio, after strict separation of training/test/validation data according to leave-one-subject-out cross-validation. The determined critical areas overlapped with the cortical substrates of working memory, and the neural activities in these areas were correlated with some significant clinical scores of RLS.
Design of Synchronization Tracking Adaptive Control for Bilateral Teleoperation System with Time-Varying Delays
Authors: Chen Kesong , Zhang Haochen
Published: October 2022

The performances of position synchronization and force interaction of the teleoperation system provide a safe and efficient way for operators to perform tasks in remote, hazardous environments. In practice, however, communication delays and dynamic uncertainties can impair the performance of position synchronization controls. Under the above factors, it is necessary to study and design appropriate bilateral control methods to achieve stable and effective position synchronization control. In this paper, a new adaptive control architecture based on velocity feedback filter and radial basis function neural network is proposed. In the proposed control scheme, only the position signal is transmitted during the communication process, and the speed feedback filter and compensation method are designed and adopted to avoid the use of acceleration signals. In addition, a new auxiliary variable with a tracking error integral term is used to reduce the steady-state error of position tracking under nonzero external environmental forces. Using the Lyapunov–Krasovskii method, the stability of closed-loop remote operating systems is demonstrated. In the simulation and experiment sections, the algorithm was verified separately and compared with other algorithms. The results of a master–slave robot system verify the tracking performance of our proposed control scheme.
Influence of Composite Thickness on Ultrasonic Guided Wave Propagation for Damage Detection
Authors: Feng Tianyi , Sharif Khodaei Zahra , Aliabadi M. H. Ferri
Published: October 2022

In this paper, the propagation properties of ultrasonic guided waves (UGWs) in different-thickness composites (i.e., 2, 4 and 9 mm) were critically assessed, and their effectiveness for damage detections and localisations under varying temperatures was demonstrated. A diagnostic film with phased-array lead zirconate titanate (PZT) transducers based on the ink-jet printing technique was used in the experiments. Initially, the dispersion curves for these composites were compared. Next, the effects of the composite thickness on the A0 and S0 mode amplitudes and the group velocity were investigated by active sensing. Next, the behaviours of UGWs under varying temperatures in different-thickness plates were also investigated. Finally, surface-mounted artificial damage and impact damage were detected and located in different composites.
Instrument Pointer Recognition Scheme Based on Improved CSL Algorithm
Authors: Liu Hailong , Wang Jielin , Ma Bo
Published: October 2022

The traditional pointer instrument recognition scheme is implemented in three steps, which is cumbersome and inefficient. So it is difficult to apply to the industrial production of real-time monitoring. Based on the improvement of the CSL coding method and the setting of the pre-cache mechanism, an intelligent reading recognition technology of the YOLOv5 pointer instrument is proposed in this paper, which realizes the rapid positioning and reading recognition of the pointer instrument. The problem of angle interaction in rotating target detection is eliminated, the complexity of image preprocessing is avoided, and the problems of poor adaptability of Hough detection are solved in this strategy. The experimental results show that compared with the traditional algorithm, the algorithm in this paper can effectively identify the angle of the pointer instrument, has high detection efficiency and strong adaptability, and has broad application prospects.
Optical Properties of a Tapered Optical Fiber Coated with Alkanes Doped with Fe3O4 Nanoparticles
Authors: Stasiewicz Karol A. , Jakubowska Iwona , Moś Joanna E. , Marć Paweł , Paczesny Jan , Zbonikowski Rafał , Jaroszewicz Leszek R.
Published: October 2022

The presented research shows the possibilities of creating in-line magnetic sensors based on the detection of changes of light propagation parameters, especially polarization, obtained by mixing Fe3O4 nanoparticles with hexadecane (higher alkane) surrounding a biconical optical fiber taper. The fiber optic taper allows to directly influence light parameters inside the taper without the necessity to lead the beam out of the structure. The mixture of hexadecane and Fe3O4 nanoparticles forms a special cladding surrounding a fiber taper which can be controlled by external factors such as the magnetic field. Described studies show changes of transmission (power, loss) and polarization properties like azimuth, and ellipticity, depending on the location of the mixture on sections of tapered optical fiber. The taper was made of a standard single-mode telecommunication fiber, stretched out to a length of 20.0 ± 0.5 mm and the diameter of the tapers is around 15.0 ± 0.3 μm, with the loss lower than 0.5 dB @ 1550 nm. Such a taper causes the beam to leak out of the waist structure and allows the addition of the external beam-controlling cladding material. The presented research can be used to build polarization switches or optical sensor. The results show that it can be a new way to control the propagation parameters of a light beam using tapered optical fiber and magnetic mixture.
Design Space Exploration of Clustered Sparsely Connected MPSoC Platforms
Authors: Frid Nikolina , Sruk Vlado , Jakobović Domagoj
Published: October 2022

Heterogeneous multiprocessor platforms are the foundation of systems that require high computational power combined with low energy consumption, like the IoT and mobile robotics. In this paper, we present five new algorithms for the design space exploration of platforms with elements grouped in clusters with very few connections in between, while these platforms have favorable electric properties and lower production costs, the limited interconnectivity and inability of heterogeneous platform elements to execute all types of tasks, significantly decrease the chance of finding a feasible mapping of application to the platform. We base the new algorithms on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) meta-heuristic and the previously published SDSE mapping algorithm designed for fully interconnected multiprocessor platforms. With the aim to improve the chance of finding feasible solutions for sparsely connected platforms, we have modified the parts of the search process concerning the penalization of infeasible solutions, chromosome decoding, and mapping strategy. Due to the lack of adequate existing benchmarks, we propose our own synthetic benchmark with multiple application and platform models, which we believe can be easily extended and reused by other researchers for further studying this type of platform. The experiments show that four proposed algorithms can find feasible solutions in 100% of test cases for platforms with dedicated clusters. In the case of tile-like platforms, the same four algorithms show an average success rate of 60%, with one algorithm going up to 84%.
2D Temperature Field Reconstruction Using Optical Frequency Domain Reflectometry and Machine-Learning Algorithms
Authors: Wolf Alexey , Shabalov Nikita , Kamynin Vladimir , Kokhanovskiy Alexey
Published: October 2022

We present experimental results on the reconstruction of the 2D temperature field on the surface of a 250 × 250 mm sensor panel based on the distributed frequency shift measured by an optical backscatter reflectometer. A linear regression and a feed-forward neural network algorithm, trained by varying the temperature field and capturing thermal images of the panel, are used for the reconstruction. In this approach, we do not use any information about the exact trajectory of the fiber, material properties of the sensor panel, and a temperature sensitivity coefficient of the fiber. Mean absolute errors of 0.118 °C and 0.086 °C are achieved in the case of linear regression and feed-forward neural network, respectively.