bluuebunny/crossref_metadata_2025 huggingface dataset:
Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China
Authors: Xie Yi , Huang Jianxi
Published: November 2021

Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher R2 values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates.
Identifying and Mapping the Responses of Ecosystem Services to Land Use Change in Rapidly Urbanizing Regions: A Case Study in Foshan City, China
Authors: Wu Zhuo , Zhou Rubo , Zeng Ziyao
Published: November 2021

Rapid urbanization has degraded some important ecosystem services and threatens socioeconomic sustainability. Although many studies have focused on the effect of urbanization on ecosystem services, the effect and its threshold have not been well-identified spatially. In this study, we propose a research framework by integrating nighttime light data, the InVEST (Integrated Valuation of Environmental Service and Tradeoffs) model, and a spatial response index to characterize the response of ecosystem services to rapid urbanization. We considered Foshan City as a case study to explore the effect of rapid urbanization on ecosystem services during 2000–2018. Our results showed that rapid urbanization resulted in a 49.13% reduction in agricultural production and a 10.13% reduction in habitat quality. The spatial response index of agricultural production, habitat quality, soil retention, water yield, and carbon sequestration were 14.25%, 2.94%, 0.04%, 0.78%, and 0.07%, respectively. We found that developing urban areas had the highest spatial response index, indicating that this area was the crucial area for future land management. We consider that our research framework can help identify the key areas affected by rapid urbanization. Visualizing the spatial response index and extracting the threshold for different levels of urbanization will be conducive to sustainable urban management and planning.
Evaluating the Performance of Ozone Products Derived from CrIS/NOAA20, AIRS/Aqua and ERA5 Reanalysis in the Polar Regions in 2020 Using Ground-Based Observations
Authors: Wang Hongmei , Wang Yapeng , Cai Kun , Zhu Songyan , Zhang Xinxin , Chen Liangfu
Published: November 2021

Quantifying spatiotemporal polar ozone changes can promote our understanding of global stratospheric ozone depletion, polar ozone-related chemical processes, and atmospheric dynamics. By means of ground-level measurements, satellite observations, and re-analyzed meteorology, the global spatial and temporal distribution characteristics of the total column ozone (TCO) and ozone profile can be quantitatively described. In this study, we evaluated the ozone datasets from CrIS/NOAA20, AIRS/Aqua, and ERA5/ECWMF for their performance in polar regions in 2020, along with the in situ observations of the Dobson, Brewer, and ozonesonde instruments, which are regarded as benchmarks. The results showed that the ERA5 reanalysis ozone field had good consistency with the ground observations (R > 0.95) and indicated whether the TCO or ozone profile was less affected by the site location. In contrast, both CrIS and AIRS could capture the ozone loss process resulting from the Antarctic/Arctic ozone hole at a monthly scale, but their ability to characterize the Arctic ozone hole was weaker than in the Antarctic. Specifically, the TCO values derived from AIRS were apparently higher in March 2020 than those of ERA5, which made it difficult to assess the area and depth of the ozone hole during this period. Moreover, the pattern of CrIS TCO was abnormal and tended to deviate from the pattern that characterized ERA5 and AIRS at the Alert site during the Arctic ozone loss process in 2020, which demonstrates that CrIS ozone products have limited applicability at this ground site. Furthermore, the validation of the ozone profile shows that AIRS and CrIS do not have good vertical representation in the polar regions and are not able to characterize the location and depth of ozone depletion. Overall, the results reveal the shortcomings of the ozone profiles derived from AIRS and CrIS observations and the reliability of the ERA5 reanalysis ozone field in polar applications. A more suitable prior method and detection sensitivity improvement on CrIS and AIRS ozone products would improve their reliability and applicability in polar regions.
Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection
Authors: Sun Long , Chen Jie , Feng Dazheng , Xing Mengdao
Published: November 2021

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.
National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
Authors: Htitiou Abdelaziz , Boudhar Abdelghani , Chehbouni Abdelghani , Benabdelouahab Tarik
Published: November 2021

Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as Google Earth Engine (GEE). Therefore, the present work is an attempt to automate the extraction of multi-year (2016–2020) cropland phenological metrics on GEE and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in Morocco. The comparison of our phenological retrievals against the MODIS phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification. The entire country is mapped using a large volume of reference samples collected and labelled with a visual interpretation of high-resolution imagery on Collect-Earth-Online, an online platform for systematically collecting geospatial data. The cropland classification product for the nominal year 2019–2020 showed an overall accuracy of 97.86% with a Kappa of 0.95. When compared to Morocco’s utilized agricultural land (SAU) areas, the cropland probabilities maps demonstrated the ability to accurately estimate sub-national SAU areas with an R-value of 0.9. Furthermore, analyzing cropland dynamics reveals a dramatic decrease in the 2019–2020 season by 2% since the 2018–2019 season and by 5% between 2016 and 2020, which is partly driven by climate conditions, but even more so by the novel coronavirus disease 2019 (COVID-19) that impacted the planting and managing of crops due to government measures taken at the national level, like complete lockdown. Such a result proves how much these methods and associated maps are critical for scientific studies and decision-making related to food security and agriculture.
Risk Assessment of Urban Floods Based on a SWMM-MIKE21-Coupled Model Using GF-2 Data
Authors: Zhao Lidong , Zhang Ting , Fu Jun , Li Jianzhu , Cao Zhengxiong , Feng Ping
Published: November 2021

Global climate change and rapid urbanization have caused increases in urban floods. Urban flood risk assessment is a vital method for preventing and controlling such disasters. This paper takes the central region of Cangzhou city in Hebei Province as an example. Detailed topographical information, such as the buildings and roads in the study area, was extracted from GF-2 data. By coupling the two models, the SWMM and MIKE21, the spatial distribution of the inundation region, and the water depth in the study area under different return periods, were simulated in detail. The results showed that, for the different return periods, the inundation region was generally consistent. However, there was a large increase in the mean inundation depth within a 10-to-30-year return period, and the increase in the maximum inundation depth and inundation area remained steady. The comprehensive runoff coefficient in all of the scenarios exceeded 0.8, indicating that the drainage system in the study area is insufficient and has a higher flood risk. The flood risk of the study area was evaluated based on the damage curve, which was obtained from field investigations. The results demonstrate that the loss per unit area was less than CNY 250/m2 in each return period in the majority of the damaged areas. Additionally, the total loss was mainly influenced by the damaged area, but, in commercial areas, the total loss was highly sensitive to the inundation depth.
Self-Supervised Despeckling Algorithm with an Enhanced U-Net for Synthetic Aperture Radar Images
Authors: Zhang Gang , Li Zhi , Li Xuewei , Liu Sitong
Published: November 2021

Self-supervised method has proven to be a suitable approach for despeckling on synthetic aperture radar (SAR) images. However, most self-supervised despeckling methods are trained by noisy-noisy image pairs, which are constructed by using natural images with simulated speckle noise, time-series real-world SAR images or generative adversarial network, limiting the practicability of these methods in real-world SAR images. Therefore, in this paper, a novel self-supervised despeckling algorithm with an enhanced U-Net is proposed for real-world SAR images. Firstly, unlike previous self-supervised despeckling works, the noisy-noisy image pairs are generated from real-word SAR images through a novel generation training pairs module, which makes it possible to train deep convolutional neural networks using real-world SAR images. Secondly, an enhanced U-Net is designed to improve the feature extraction and fusion capabilities of the network. Thirdly, a self-supervised training loss function with a regularization loss is proposed to address the difference of target pixel values between neighbors on the original SAR images. Finally, visual and quantitative experiments on simulated and real-world SAR images show that the proposed algorithm notably removes speckle noise with better preserving features, which exceed several state-of-the-art despeckling methods.
Effect of Permafrost Thawing on Discharge of the Kolyma River, Northeastern Siberia
Authors: Suzuki Kazuyoshi , Park Hotaek , Makarieva Olga , Kanamori Hironari , Hori Masahiro , Matsuo Koji , Matsumura Shinji , Nesterova Nataliia , Hiyama Tetsuya
Published: November 2021

With permafrost warming, the observed discharge of the Kolyma River in northeastern Siberia decreased between 1930s and 2000; however, the underlying mechanism is not well understood. To understand the hydrological changes in the Kolyma River, it is important to analyze the long-term hydrometeorological features, along with the changes in the active layer thickness. A coupled hydrological and biogeochemical model was used to analyze the hydrological changes due to permafrost warming during 1979–2012, and the simulated results were validated with satellite-based products and in situ observational records. The increase in the active layer thickness by permafrost warming suppressed the summer discharge contrary to the increased summer precipitation. This suggests that the increased terrestrial water storage anomaly (TWSA) contributed to increased evapotranspiration, which likely reduced soil water stress to plants. As soil freeze–thaw processes in permafrost areas serve as factors of climate memory, we identified a two-year lag between precipitation and evapotranspiration via TWSA. The present results will expand our understanding of future Arctic changes and can be applied to Arctic adaptation measures.
Learned Hyperspectral Compression Using a Student’s T Hyperprior
Authors: Guo Yuanyuan , Chong Yanwen , Ding Yun , Pan Shaoming , Gu Xiaolin
Published: November 2021

Hyperspectral compression is one of the most common techniques in hyperspectral image processing. Most recent learned image compression methods have exhibited excellent rate-distortion performance for natural images, but they have not been fully explored for hyperspectral compression tasks. In this paper, we propose a trainable network architecture for hyperspectral compression tasks, which not only considers the anisotropic characteristic of hyperspectral images but also embeds an accurate entropy model using the non-Gaussian prior knowledge of hyperspectral images and nonlinear transform. Specifically, we first design a spatial-spectral block, involving a spatial net and a spectral net as the base components of the core autoencoder, which is more consistent with the anisotropic hyperspectral cubes than the existing compression methods based on deep learning. Then, we design a Student’s T hyperprior that merges the statistics of the latents and the side information concepts into a unified neural network to provide an accurate entropy model used for entropy coding. This not only remarkably enhances the flexibility of the entropy model by adjusting various values of the degree of freedom, but also leads to a superior rate-distortion performance. The results illustrate that the proposed compression scheme supersedes the Gaussian hyperprior universally for virtually all learned natural image codecs and the optimal linear transform coding methods for hyperspectral compression. Specifically, the proposed method provides a 1.51% to 59.95% average increase in peak signal-to-noise ratio, a 0.17% to 18.17% average increase in the structural similarity index metric and a 6.15% to 64.60% average reduction in spectral angle mapping over three public hyperspectral datasets compared to the Gaussian hyperprior and the optimal linear transform coding methods.
Building Extraction from Terrestrial Laser Scanning Data with Density of Projected Points on Polar Grid and Adaptive Threshold
Authors: Chen Maolin , Liu Xiangjiang , Zhang Xinyi , Wang Mingwei , Zhao Lidu
Published: November 2021

The extraction of building information with terrestrial laser scanning (TLS) has a number of important applications. As the density of projected points (DoPP) of facades is commonly greater than for other types of objects, building points can be extracted based on projection features. However, such methods usually suffer from density variation and parameter setting, as illustrated in previous studies. In this paper, we present a building extraction method for single-scan TLS data, mainly focusing on those problems. To adapt to the large density variation in TLS data, a filter using DoPP is applied on a polar grid, instead of a commonly used rectangular grid, to detect facade points. In DoPP filtering, the threshold to distinguish facades from other objects is generated adaptively for each cell by calculating the point number when placing the lowest building in it. Then, the DoPP filtering result is further refined by an object-oriented decision tree mainly based on grid features, such as compactness and horizontal hollow ratio. Finally, roof points are extracted by region growing on the non-facade points, using the highest point in each facade cell as a seed point. The experiments are conducted on two datasets with more than 1.7 billion points and with point density varying from millimeter to decimeter levels. The completeness and correctness of the first dataset containing more than 50 million points are 91.8% and 99.8%, with a running time of approximately 970 s. The second dataset is Semantic3D, of which the point number, completeness and correctness are about 1.65 billion, 90.2% and 94.5%, with a running time of about 14,464 s. The test shows that the proposed method achieves a better performance than previous grid-based methods and a similar level of accuracy to the point-based classification method and with much higher efficiency.
Scale and Landscape Features Matter for Understanding Waterbird Habitat Selection
Authors: Li Jinya , Zhang Yang , Zhao Lina , Deng Wanquan , Qian Fawen , Ma Keming
Published: November 2021

Clarifying species-environment relationships is crucial for the development of efficient conservation and restoration strategies. However, this work is often complicated by a lack of detailed information on species distribution and habitat features and tends to ignore the impact of scale and landscape features. Here, we tracked 11 Oriental White Storks (Ciconia boyciana) with GPS loggers during their wintering period at Poyang Lake and divided the tracking data into two parts (foraging and roosting states) according to the distribution of activity over the course of a day. Then, a three-step multiscale and multistate approach was employed to model habitat selection characteristics: (1) first, we minimized the search range of the scale for these two states based on daily movement characteristics; (2) second, we identified the optimized scale of each candidate variable; and (3) third, we fit a multiscale, multivariable habitat selection model in relation to natural features, human disturbance and especially landscape composition and configuration. Our findings reveal that habitat selection of the storks varied with spatial scale and that these scaling relationships were not consistent across different habitat requirements (foraging or roosting) and environmental features. Landscape configuration was a more powerful predictor for storks’ foraging habitat selection, while roosting was more sensitive to landscape composition. Incorporating high-precision spatiotemporal satellite tracking data and landscape features derived from satellite images from the same periods into a multiscale habitat selection model can greatly improve the understanding of species-environmental relationships and guide efficient recovery planning and legislation.
Reproducibility of Pansharpening Methods and Quality Indexes versus Data Formats
Authors: Arienzo Alberto , Aiazzi Bruno , Alparone Luciano , Garzelli Andrea
Published: November 2021

In this work, we investigate whether the performance of pansharpening methods depends on their input data format; in the case of spectral radiance, either in its original floating-point format or in an integer-packed fixed-point format. It is theoretically proven and experimentally demonstrated that methods based on multiresolution analysis are unaffected by the data format. Conversely, the format is crucial for methods based on component substitution, unless the intensity component is calculated by means of a multivariate linear regression between the upsampled bands and the lowpass-filtered Pan. Another concern related to data formats is whether quality measurements, carried out by means of normalized indexes depend on the format of the data on which they are calculated. We will focus on some of the most widely used with-reference indexes to provide a novel insight into their behaviors. Both theoretical analyses and computer simulations, carried out on GeoEye-1 and WorldView-2 datasets with the products of nine pansharpening methods, show that their performance does not depend on the data format for purely radiometric indexes, while it significantly depends on the data format, either floating-point or fixed-point, for a purely spectral index, like the spectral angle mapper. The dependence on the data format is weak for indexes that balance the spectral and radiometric similarity, like the family of indexes, Q2n, based on hypercomplex algebra.
Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers
Authors: Ebrahimi Samira , Fleuret Julien R. , Klein Matthieu , Théroux Louis-Daniel , Ibarra-Castanedo Clemente , Maldague Xavier P. V.
Published: November 2021

Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.
A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model
Authors: Li Zheming , He Wei
Published: November 2021

Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.
Handheld Multispectral Fluorescence Imaging System to Detect and Disinfect Surface Contamination
Authors: Sueker Mitchell , Stromsodt Kristen , Gorji Hamed Taheri , Vasefi Fartash , Khan Nadeem , Schmit Taylor , Varma Rangati , Mackinnon Nicholas , Sokolov Stanislav , Akhbardeh Alireza , Liang Bo , Qin Jianwei , Chan Diane E. , Baek Insuck , Kim Moon S. , Tavakolian Kouhyar
Published: November 2021

Contamination inspection is an ongoing concern for food distributors, restaurant owners, caterers, and others who handle food. Food contamination must be prevented, and zero tolerance legal requirements and damage to the reputation of institutions or restaurants can be very costly. This paper introduces a new handheld fluorescence-based imaging system that can rapidly detect, disinfect, and document invisible organic residues and biofilms which may host pathogens. The contamination, sanitization inspection, and disinfection (CSI-D) system uses light at two fluorescence excitation wavelengths, ultraviolet C (UVC) at 275 nm and violet at 405 nm, for the detection of organic residues, including saliva and respiratory droplets. The 275 nm light is also utilized to disinfect pathogens commonly found within the contaminated residues. Efficacy testing of the neutralizing effects of the ultraviolet light was conducted for Aspergillus fumigatus, Streptococcus pneumoniae, and the influenza A virus (a fungus, a bacterium, and a virus, respectively, each commonly found in saliva and respiratory droplets). After the exposure to UVC light from the CSI-D, all three pathogens experienced deactivation (> 99.99%) in under ten seconds. Up to five-log reductions have also been shown within 10 s of UVC irradiation from the CSI-D system.
A CSI-Based Human Activity Recognition Using Deep Learning
Authors: Fard Moshiri Parisa , Shahbazian Reza , Nabati Mohammad , Ghorashi Seyed Ali
Published: November 2021

The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.
A Provably Secure IBE Transformation Model for PKC Using Conformable Chebyshev Chaotic Maps under Human-Centered IoT Environments
Authors: Meshram Chandrashekhar , Imoize Agbotiname Lucky , Aljaedi Amer , Alharbi Adel R. , Jamal Sajjad Shaukat , Barve Sharad Kumar
Published: November 2021

The place of public key cryptography (PKC) in guaranteeing the security of wireless networks under human-centered IoT environments cannot be overemphasized. PKC uses the idea of paired keys that are mathematically dependent but independent in practice. In PKC, each communicating party needs the public key and the authorized digital certificate of the other party to achieve encryption and decryption. In this circumstance, a directory is required to store the public keys of the participating parties. However, the design of such a directory can be cost-prohibitive and time-consuming. Recently, identity-based encryption (IBE) schemes have been introduced to address the vast limitations of PKC schemes. In a typical IBE system, a third-party server can distribute the public credentials to all parties involved in the system. Thus, the private key can be harvested from the arbitrary public key. As a result, the sender could use the public key of the receiver to encrypt the message, and the receiver could use the extracted private key to decrypt the message. In order to improve systems security, new IBE schemes are solely desired. However, the complexity and cost of designing an entirely new IBE technique remain. In order to address this problem, this paper presents a provably secure IBE transformation model for PKC using conformable Chebyshev chaotic maps under the human-centered IoT environment. In particular, we offer a robust and secure IBE transformation model and provide extensive performance analysis and security proofs of the model. Finally, we demonstrate the superiority of the proposed IBE transformation model over the existing IBE schemes. Overall, results indicate that the proposed scheme posed excellent security capabilities compared to the preliminary IBE-based schemes.
Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment
Authors: Auguste Amaury , Kaddah Wissam , Elbouz Marwa , Oudinet Ghislain , Alfalou Ayman
Published: November 2021

In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).
Thermographic Fault Diagnosis of Ventilation in BLDC Motors
Authors: Glowacz Adam
Published: November 2021

Thermographic fault diagnosis of ventilation in BLDC (brushless DC) motors is described. The following states of BLDC motors were analyzed: a healthy BLDC motor running at 1450 rpm, a healthy BLDC motor at 2100 rpm, blocked ventilation of the BLDC motor at 1450 rpm, blocked ventilation of the BLDC motor at 2100 rpm, healthy clipper, and blocked ventilation of the clipper. A feature extraction method called the Common Part of Arithmetic Mean of Thermographic Images (CPoAMoTI) was proposed. Test thermal images were analyzed successfully. The developed method, CPoAMoTI is useful for industry and society. Electric cars, trains, fans, clippers, computers, cordless power tools can be diagnosed using the developed method.
A Generic Sequential Stimulation Adapter for Reducing Muscle Fatigue during Functional Electrical Stimulation
Authors: Ye Gongkai , Ali Saima S. , Bergquist Austin J. , Popovic Milos R. , Masani Kei
Published: November 2021

Background: Clinical applications of conventional functional electrical stimulation (FES) administered via a single electrode are limited by rapid onset neuromuscular fatigue. “Sequential” (SEQ) stimulation, involving the rotation of pulses between multiple active electrodes, has been shown to reduce fatigue compared to conventional FES. However, there has been limited adoption of SEQ in research and clinical settings. Methods: The SEQ adapter is a small, battery-powered device that transforms the output of any commercially available electrical stimulator into SEQ stimulation. We examined the output of the adaptor across a range of clinically relevant stimulation pulse parameters to verify the signal integrity preservation ability of the SEQ adapter. Pulse frequency, amplitude, and duration were varied across discrete states between 4 and 200 Hz, 10 and100 mA, and 50 and 2000 μs, respectively. Results: A total of 420 trials were conducted, with 80 stimulation pulses per trial. The SEQ adapter demonstrated excellent preservation of signal integrity, matching the pulse characteristics of the originating stimulator within 1% error. The SEQ adapter operates as expected at pulse frequencies up to 160 Hz, failing at a frequency of 200 Hz. Conclusion: The SEQ adapter represents an effective and low-cost solution to increase the utilization of SEQ in existing rehabilitation paradigms.
Image Processing Technique for Improving the Sensitivity of Mechanical Register Water Meters to Very Small Leaks
Authors: Carratù Marco , Dello Iacono Salvatore , Di Leo Giuseppe , Liguori Consolatina , Pietrosanto Antonio
Published: November 2021

Discovering very small water leaks at the household level is one of the most challenging goals of smart metering. While many solutions for sudden leakage detection have been proposed to date, the small leaks are still giving researchers a hard time. Even if some devices can be found on the market, their capability to detect a water leakage barely reaches the sensitivity of the employed mechanical water meter, which was not designed for detecting small water leakages. This paper proposes a technique for improving the sensitivity of the mechanical register water meters. By implementing this technique in a suitable electronic add-on device, the improved sensitivity could detect very small leaks. This add-on device continuously acquires the mechanical register’s digital images and, thanks to suitable image processing techniques and metrics, allows very small flows to be detected even if lower than the meter starting flow rate. Experimental tests were performed on two types of mechanical water meters, multijet and piston, whose starting flow rates are 8 L/h and 1 L/h, respectively. Results were very interesting in the leakage range of [1.0, 10.0] L/h for the multijet and even in the range [0.25, 1.00] L/h for the piston meter.
Determination of Blasting Vibration Safety Criterion for HDPE Pipeline Using Vibration and Strain Data in a Coastal Metro Line
Authors: Jiang Nan , Zhang Yuqi , Wu Tingyao , Yao Yingkang , Luo Xuedong
Published: November 2021

A key aspect of urban blasting engineering is evaluating the safety of the blasting dynamic load on the adjacent high-density polyethylene water supply pipeline and controlling the negative impact of the blasting vibration load on the pipeline. According to the special characteristics of the soil layer in Shenzhen coastal city, a field blasting test of a full-scale pre-buried HDPE pipeline was carried out, and the distribution characteristics of the blasting vibration velocity and dynamic strain were analyzed. The finite element model was established by LSDYNA, and the reliability of the calculation model and parameters was verified by comparing with the field test data. At the same time, the dynamic response characteristics of pipelines with different buried depths, operating water conveyance pressures, and diameters under blasting vibration loads were studied. Combined with the circumferential allowable stress control criterion of the pipeline, the safety control standard of the blasting vibration velocity of the HDPE water supply pipeline under different working conditions was proposed. The results show that the circumferential compressive strain of the HDPE pipe is the most affected by blasting vibration, and the pipe with the shortest blasting center distance has the largest response. The vibration velocity and equivalent stress of the pipeline increase with the increase of buried depth, internal pressure, and diameter. The vibration velocity and equivalent stress of the explosion side at the same section of the pipeline are greater than those of the back explosion side. Based on the dimensionless analysis, the vibration velocity prediction model of the buried depth, operating pressure, and diameter of the pipeline is proposed. The safety control velocity of the pipeline is 25 cm/s, and the vibration velocity of the upper surface is 22.5 cm/s according to the Mises yield strength criterion.
Vibrational Transportation on a Platform Subjected to Sinusoidal Displacement Cycles Employing Dry Friction Control
Authors: Kilikevičius Sigitas , Fedaravičius Algimantas
Published: November 2021

Currently used vibrational transportation methods are usually based on asymmetries of geometric, kinematic, wave, or time types. This paper investigates the vibrational transportation of objects on a platform that is subjected to sinusoidal displacement cycles, employing periodic dynamic dry friction control. This manner of dry friction control creates an asymmetry, which is necessary to move the object. The theoretical investigation on functional capabilities and transportation regimes was carried out using a developed parametric mathematical model, and the control parameters that determine the transportation characteristics such as velocity and direction were defined. To test the functional capabilities of the proposed method, an experimental setup was developed, and experiments were carried out. The results of the presented research indicate that the proposed method ensures smooth control of the transportation velocity in a wide range and allows it to change the direction of motion. Moreover, the proposed method offers other new functional capabilities, such as a capability to move individual objects on the same platform in opposite directions and at different velocities at the same time by imposing different friction control parameters on different regions of the platform or on different objects. In addition, objects can be subjected to translation and rotation at the same time by imposing different friction control parameters on different regions of the platform. The presented research extends the classical theory of vibrational transportation and has a practical value for industries that operate manufacturing systems performing tasks such as handling and transportation, positioning, feeding, sorting, aligning, or assembling.
Interdigitated Electrode for Electrical Characterization of Commercial Pseudo-Binary Biodiesel–Diesel Blends
Authors: Santos-Neto Inocêncio Sanches dos , Carvalho Christian Diniz , Filho Gilberto Balby Araújo , Andrade Cassio Daniel Salomão Silva , Santos Giselle Cutrim de Oliveira , Barros Allan Kardec , Neto João Viana da Fonseca , Casas Vicente Leonardo Paucar , Alencar Luciana Magalhães Rebelo , Lopes Alberto Jorge Oliveira , Silva Fernando Carvalho , Sinfrônio Francisco Sávio Mendes
Published: November 2021

Non-standard diesel blends can be harmful to the environment and human health. In this context, a simple analytical method to estimate the biodiesel mixture ratio in diesel was developed based on impedance spectroscopy (IS) associated with interdigitated sensors. In this article, four different interdigitated sensors with varied comb spacing (G) were simulated using the COMSOL Multiphysics software. Based on finite element simulations, four interdigitated electrode architectures were manufactured and evaluated. The best geometry was chosen according to theoretical data simulations, and its interdigitated electrodes were manufactured for the compositional evaluation of pseudo-binary biodiesel–diesel mixtures. According to the X-ray powder diffraction technique, the deposition of the conductive layer (Au0) over the surface of the dielectric substrate (SiO2) did not alter its phase composition. In the analysis of AFM and SEM, it was possible to observe irregular edges on the electrodes, possibly related to the manufacturing process of the thin layers and mechanical stability. Another characteristic observed in the AFM images was the height of the step of the gold layer of the sensor. Several cross sections were obtained, and the mean step value was 225.71 ± 0.0032 nm. Although there were differences in the roughness, the whole sensor had nanometric roughness. Based on the finite element method simulation performed, it can be assumed that the geometric parameters more suitable for the manufacturing of the electrode are W = 20 µm, L = 1000 µm, G = 50 µm, and N = 40 digits. The electrical characterization performed by impedance spectroscopy showed that we could differentiate between biodiesel and diesel fuels and their pseudo-binary mixtures in the low-frequency region.
The COVID-19 Pandemic: Are There Any Impacts on Sustainability?
Authors: Begum Halima , Alam A. S. A. Ferdous , Leal Filho Walter , Awang Abd Hair , Ghani Ahmad Bashawir Abdul
Published: November 2021

This paper sets out to explain and describe the potential ways to control COVID-19′s impact on the environment and what controllable strategies and anticipations emerge from rethinking sustainable production. The rapid and devastating spread of this disease has made millions of people throughout the world cover themselves, wear gloves, and use hand sanitizers and other medical applications. However, it means that a huge amount of clinical waste is being dumped into landfills or the oceans, and such activity may simply worsen the infection’s transmission and the sustainability of the environment, the socio-economy, and sustainable productions. This disease has greatly changed the way people live and has caused considerable occupational job losses and misfortunes, sending sustainable businesses and other organizations to the wall. Virtually every country is trying to stop the infection transmission by testing patients and isolating people, but the environmental effects of the pandemic and sustainable business have not previously been analyzed. The study suggests that the current options for sustainable production must be measured and also further researched.
The Measures of Accuracy of Claim Frequency Credibility Predictor
Authors: Wolny-Dominiak Alicja , Żądło Tomasz
Published: November 2021

Nowadays, the sustainability risks and opportunities start to affect strongly insurance companies in regard to the resulting additional variability of future values of variables taken into account in the decision processes. This is important especially in the era of sustainable non-life insurance promoting, among others, the use of ecological car engines or ecological systems of building heating. The fundamental issue in non-life insurance is to predict future claims (e.g., the aggregate value of claims or the number of claims for a single policy) in a heterogeneous portfolio of policies taking account of claim experience. For this purpose, the so-called credibility theory is used, which was initiated by the fundamental Bühlmann model modified to the Bühlmann–Straub model. Several modifications of the model have been proposed in the literature. One of them is the development of the relationship between the credibility models and statistical mixed models (e.g., linear mixed models) for longitudinal data. The article proposes the use of the parametric bootstrap algorithm to estimate measures of accuracy of the credibility predictor of the number of claims for a single policy taking into account new risk factors resulting from the emergence of green technologies on the considered market. The predictor is obtained for the model which belongs to the class of Generalised Linear Mixed Models (GLMMs) and which is a generalization of the Bülmann–Straub model. Additionally, the possibility of predicting the number of claims and the problem of the assessment of the prediction accuracy are presented based on a policy characterized by new green risk factor (hybrid motorcycle engine) not previously present in the portfolio. The paper presents the proposed methodology in a case study using real insurance data from the Polish market.
Uncovering Trends and Spatial Biases of Research in a U.S. National Park
Authors: Agostini Ferrante Daniela , Vukomanovic Jelena , Smart Lindsey S.
Published: November 2021

National parks are vital public resources for the preservation of species and landscapes, and for decades have provided natural laboratories for studying environmental and cultural resources. Though significant scholarship has taken place in national parks, syntheses of research trends and biases are rarely available for needs assessments and decision making. In this paper, we demonstrate procedures to close this information gap using Congaree National Park (CNP) as an example of a protected area characterized by disparate research. We conducted a systematic review of research topics and funding sources of all peer-reviewed, published research conducted since its inception as a National Monument in 1976. We next paired our evaluation of research trends with a spatial analysis of study locations to uncover patterns and biases in research. A total of 49 peer-reviewed publications describing research conducted at CNP have been published between 1976–2018, with over 75% published since 2003. Quantitative studies accounted for nearly 90% of all studies, and vegetation was the most commonly studied discipline. Most studies were funded by federal agencies, with the National Park Service providing the most funding instances. Spatial analyses revealed statistically significant (p < 0.05) hotspots of studies near the park entrance, visitor center, roads, and hiking trails. In providing a comprehensive evaluation of research patterns and trends within a single park, we developed an approach that can be applied by managers in other parks or public lands to maximize the utility of past research, identify potentially valuable but understudied park resources, and prioritize research needs.