bluuebunny/crossref_metadata_2025 huggingface dataset:
Three-Dimensional Numerical Simulations and Antifouling Mechanism of Microorganisms on Microstructured Surfaces
Authors: Yang Hongyue , Wang Songling , Li Chunxi , Li Hengfan
Published: February 2021

As marine biofouling seriously affects the development and utilization of oceans, the antifouling technology of microstructured surface has become a research hotspot due to its green and environmentally friendly advantages. In the present research, the motion models of microorganisms on the surfaces of five rectangular micropits, in co-current and counter-current flow direction, were established. Dynamic mesh technology was used to simulate the movements of microorganisms with different radii in the near-wall area, and the fluid kinematics and shear stress distributions in different-sized micropits were compared. Furthermore, moving microorganisms were included in the three-dimensional microstructure model to achieve the real situation of biofouling. Simulation results revealed that the vortex flow velocity in the micropits increased with the increase of the inlet flow velocity and the existence of the vortex flow effectively reduced the formation of conditioning layers in the micropits. In the downstream and countercurrent directions, the average shear stresses on the wall decreased with the increase of the micropit depth and width, and the shear stress on the inner wall of the Mp1 micropit (a patterned surface arranged with cubes of 2 µm × 2 µm × 2 µm) was found to be the largest. A low shear stress region with a low flow velocity was formed around microorganisms in the process of approaching the microstructured surface. The shear stress gradient of micro-ridge steps increased with the approach of microorganisms, indicating that microridge edges had a better effect on reducing microbial attachment.
Skin Moisturizing Effects of a Microneedle Patch Containing Hyaluronic Acid and Lonicerae flos
Authors: Han Sang-Kuk , Lee Seung-Jun , Ha Hun-Yong
Published: February 2021

The utilization of cosmetic raw materials for medicinal purposes in a microneedle patch containing hyaluronic acid (HA) and Lonicerae flos ethanol extract (LEE) has increased based on their eco-friendly characteristics. The moisturizing effects of this microneedle patch were assessed on the forearms of 20 female participants, and our results showed an increase between 29.60% and 88.54% in skin moisture content at the same time points relative to the untreated group. Apart from confirming the safety of HA and Lonicerae flos, our findings may also promote the use of HA and medicinal herbs in relevant industries, indicating their potential as raw ingredients for the development of cosmetic products and topical dermatological drugs. The results suggested that HA, Lonicerae flos, and other combinations of chemical and natural products based on traditional oriental medicine could beneficially affect skin health.
Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results
Authors: Polimeni Simone , Nespoli Alfredo , Leva Sonia , Valenti Gianluca , Manzolini Giampaolo
Published: February 2021

Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.
Effortless Expressions: Dōgen’s Non-Thinking about ‘Words and Letters’
Authors: Markowski Joe
Published: February 2021

What is the relationship between Zen experience and language? Is Zen awakening/enlightenment ineffable? In this article, I will address this general question by providing a panoramic treatment of Dōgen’s (道元) philosophy of language which Hee-Jin Kim characterizes as “realizational”. Building on the research of Kim, Victor Sōgen Hori and Dale S. Wright, I maintain that the idea of ineffable experiences in Dōgen’s Zen is embedded within language, not transcendent from it. My focus begins by reviewing Dōgen’s critical reflections on the idea of ineffability in Zen, and then proceeds to make sense of such in the context of zazen, and the practice of non-thinking, hi-shiryo (非思量). Based upon this inquiry, I then move into an examination of how Dōgen’s “realizational” philosophy of language, in the context of non-thinking, conditions a ‘practice of words and letters’ that is effortless, vis-à-vis non-action, wu-wei (無為). From there we shall then inquire into Dōgen’s use of kōan for developing his “realizational” perspective. In doing such, I shall orient my treatment around Hori’s research into kōan (公案), specifically the logic of nonduality. This inquiry shall in turn provide a clearing for highlighting the non-anthropocentric perspectivism that is salient to Dōgen’s “realizational” philosophy of language. Finally, I bring closure to this inquiry by showing how Dōgen’s “realizational” perspective of language sets the stage for expressing a range of value judgments and normative prescriptions, both on and off the cushion, despite his commitment to the philosophy of emptiness, śūnyatā, whereby all things, including good and evil, lack an inherent self essence, svabhāva.
Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System
Authors: Zhao Guixin , Wang Xuesong , Kong Yi , Cheng Yuhu
Published: February 2021

At present many researchers pay attention to a combination of spectral features and spatial features to enhance hyperspectral image (HSI) classification accuracy. However, the spatial features in some methods are utilized insufficiently. In order to further improve the performance of HSI classification, the spectral-spatial joint classification of HSI based on the broad learning system (BLS) (SSBLS) method was proposed in this paper; it consists of three parts. Firstly, the Gaussian filter is adopted to smooth each band of the original spectra based on the spatial information to remove the noise. Secondly, the test sample’s labels can be obtained using the optimal BLS classification model trained with the spectral features smoothed by the Gaussian filter. At last, the guided filter is performed to correct the BLS classification results based on the spatial contextual information for improving the classification accuracy. Experiment results on the three real HSI datasets demonstrate that the mean overall accuracies (OAs) of ten experiments are 99.83% on the Indian Pines dataset, 99.96% on the Salinas dataset, and 99.49% on the Pavia University dataset. Compared with other methods, the proposed method in the paper has the best performance.
Sea Ice Image Classification Based on Heterogeneous Data Fusion and Deep Learning
Authors: Han Yanling , Liu Yekun , Hong Zhonghua , Zhang Yun , Yang Shuhu , Wang Jing
Published: February 2021

Sea ice is one of the typical causes of marine disasters. Sea ice image classification is an important component of sea ice detection. Optical data contain rich spectral information, but they do not allow one to easily distinguish between ground objects with a similar spectrum and foreign objects with the same spectrum. Synthetic aperture radar (SAR) data contain rich texture information, but the data usually have a single source. The limitation of single-source data is that they do not allow for further improvements of the accuracy of remote sensing sea ice classification. In this paper, we propose a method for sea ice image classification based on deep learning and heterogeneous data fusion. Utilizing the advantages of convolutional neural networks (CNNs) in terms of depth feature extraction, we designed a deep learning network structure for SAR and optical images and achieve sea ice image classification through feature extraction and a feature-level fusion of heterogeneous data. For the SAR images, the improved spatial pyramid pooling (SPP) network was used and texture information on sea ice at different scales was extracted by depth. For the optical data, multi-level feature information on sea ice such as spatial and spectral information on different types of sea ice was extracted through a path aggregation network (PANet), which enabled low-level features to be fully utilized due to the gradual feature extraction of the convolution neural network. In order to verify the effectiveness of the method, two sets of heterogeneous sentinel satellite data were used for sea ice classification in the Hudson Bay area. The experimental results show that compared with the typical image classification methods and other heterogeneous data fusion methods, the method proposed in this paper fully integrates multi-scale and multi-level texture and spectral information from heterogeneous data and achieves a better classification effect (96.61%, 95.69%).
DSM Generation from Single and Cross-Sensor Multi-View Satellite Images Using the New Agisoft Metashape: The Case Studies of Trento and Matera (Italy)
Authors: Lastilla Lorenzo , Belloni Valeria , Ravanelli Roberta , Crespi Mattia
Published: February 2021

DSM generation from satellite imagery is a long-lasting issue and it has been addressed in several ways over the years; however, expert and users are continuously searching for simpler but accurate and reliable software solutions. One of the latest ones is provided by the commercial software Agisoft Metashape (since version 1.6), previously known as Photoscan, which joins other already available open-source and commercial software tools. The present work aims to quantify the potential of the new Agisoft Metashape satellite processing module, considering that to the best knowledge of the authors, only two papers have been published, but none considering cross-sensor imagery. Here we investigated two different case studies to evaluate the accuracy of the generated DSMs. The first dataset consists of a triplet of Pléiades images acquired over the area of Trento and the Adige valley (Northern Italy), which is characterized by a great variety in terms of geomorphology, land uses and land covers. The second consists of a triplet composed of a WorldView-3 stereo pair and a GeoEye-1 image, acquired over the city of Matera (Southern Italy), one of the oldest settlements in the world, with the worldwide famous area of Sassi and a very rugged morphology in the surroundings. First, we carried out the accuracy assessment using the RPCs supplied by the satellite companies as part of the image metadata. Then, we refined the RPCs with an original independent terrain technique able to supply a new set of RPCs, using a set of GCPs adequately distributed across the regions of interest. The DSMs were generated both in a stereo and multi-view (triplet) configuration. We assessed the accuracy and completeness of these DSMs through a comparison with proper references, i.e., DSMs obtained through LiDAR technology. The impact of the RPC refinement on the DSM accuracy is high, ranging from 20 to 40% in terms of LE90. After the RPC refinement, we achieved an average overall LE90 <5.0 m (Trento) and <4.0 m (Matera) for the stereo configuration, and <5.5 m (Trento) and <4.5 m (Matera) for the multi-view (triplet) configuration, with an increase of completeness in the range 5–15% with respect to stereo pairs. Finally, we analyzed the impact of land cover on the accuracy of the generated DSMs; results for three classes (urban, agricultural, forest and semi-natural areas) are also supplied.
Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation
Authors: Wasonga Daniel O. , Yaw Afrane , Kleemola Jouko , Alakukku Laura , Mäkelä Pirjo S.A.
Published: February 2021

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers.
3D Airborne EM Forward Modeling Based on Time-Domain Spectral Element Method
Authors: Yin Changchun , Gao Zonghui , Su Yang , Liu Yunhe , Huang Xin , Ren Xiuyan , Xiong Bin
Published: February 2021

Airborne electromagnetic (AEM) method uses aircraft as a carrier to tow EM instruments for geophysical survey. Because of its huge amount of data, the traditional AEM data inversions take one-dimensional (1D) models. However, the underground earth is very complicated, the inversions based on 1D models can frequently deliver wrong results, so that the modeling and inversion for three-dimensional (3D) models are more practical. In order to obtain precise underground electrical structures, it is important to have a highly effective and efficient 3D forward modeling algorithm as it is the basis for EM inversions. In this paper, we use time-domain spectral element (SETD) method based on Gauss-Lobatto-Chebyshev (GLC) polynomials to develop a 3D forward algorithm for modeling the time-domain AEM responses. The spectral element method combines the flexibility of finite-element method in model discretization and the high accuracy of spectral method. Starting from the Maxwell's equations in time-domain, we derive the vector Helmholtz equation for the secondary electric field. We use the high-order GLC vector interpolation functions to perform spectral expansion of the EM field and use the Galerkin weighted residual method and the backward Euler scheme to do the space- and time-discretization to the controlling equations. After integrating the equations for all elements into a large linear equations system, we solve it by the multifrontal massively parallel solver (MUMPS) direct solver and calculate the magnetic field responses by the Faraday's law. By comparing with 1D semi-analytical solutions for a layered earth model, we validate our SETD method and analyze the influence of the mesh size and the order of interpolation functions on the accuracy of our 3D forward modeling. The numerical experiments for typical models show that applying SETD method to 3D time-domain AEM forward modeling we can achieve high accuracy by either refining the mesh or increasing the order of interpolation functions.
Spatial Interconnections of Land Surface Temperatures with Land Cover/Use: A Case Study of Tokyo
Authors: Liu Fei , Hou Hao , Murayama Yuji
Published: February 2021

As one of the most populated metropolitan areas in the world, the Tokyo Metropolitan Area (TMA) has experienced severe climatic modifications and pressure due to densified human activities and urban expansion. The surface urban heat island (SUHI) phenomenon particularly constitutes a significant threat to human comfort and geo-environmental health in TMA. This study aimed to profile the spatial interconnections between land surface temperature (LST) and land cover/use in TMA from 2001 to 2015 using multi-source spatial data. To this end, the thermal gradients between the urban and non-urban fabric areas in TMA were examined by joint analysis of land cover/use and LST. The spatiotemporal aggregation patterns, variations, and movement trajectories of SUHI intensity in TMA were identified and delineated. The spatial relationship between SUHI and the potential driving forces in TMA was clarified using geographically weighted regression (GWR) analysis. The results show that the thermal environment of TMA exhibited a polynucleated spatial structure with multiple thermal island cores. Overall, the magnitude and extent of SUHI in TMA increased and expanded from 2001 to 2015. During that time, SUHIs clustered in the compact residential quarters and redevelopment/renovation areas rather than downtown. The GWR models showed better performance than ordinary least squares (OLS) models, with Adj R2 > 0.9, indicating that the magnitude of SUHI significantly depended on its neighboring geographical setting, including land cover composition and configuration, population size, and terrain. We suggest that UHI mitigation in Tokyo should be focused on alleviating the magnitude of persistent thermal cores and controlling unstable SUHI occurrence based on partitioned or location-specific landscape design. This study’s findings have immense implications for SUHI mitigation in metropolitan areas situated in bay regions.
Polish Cadastre Modernization with Remotely Extracted Buildings from High-Resolution Aerial Orthoimagery and Airborne LiDAR
Authors: Wierzbicki Damian , Matuk Olga , Bielecka Elzbieta
Published: February 2021

Automatic building extraction from remote sensing data is a hot but challenging research topic for cadastre verification, modernization and updating. Deep learning algorithms are perceived as more promising in overcoming the difficulties of extracting semantic features from complex scenes and large differences in buildings’ appearance. This paper explores the modified fully convolutional network U-Shape Network (U-Net) for high resolution aerial orthoimagery segmentation and dense LiDAR data to extract building outlines automatically. The three-step end-to-end computational procedure allows for automated building extraction with an 89.5% overall accuracy and an 80.7% completeness, which made it very promising for cadastre modernization in Poland. The applied algorithms work well both in densely and poorly built-up areas, typical for peripheral areas of cities, where uncontrolled development had recently been observed. Discussing the possibilities and limitations, the authors also provide some important information that could help local authorities decide on the use of remote sensing data in land administration.
Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi
Authors: Nie Tong , Dong Guotao , Jiang Xiaohui , Lei Yuxin
Published: February 2021

As an important indicator of terrestrial ecosystems, vegetation plays an important role in the study of global or regional ecological environmental changes. Northern Shaanxi is located in the ecologically fragile area of the Loess Plateau, which is affected by interactions between natural and human factors. Here, we used the Normalized Difference Vegetation Index (NDVI) as an indicator to study the temporal and spatial variations of vegetation in Northern Shaanxi from 2000 to 2018. Based on the geographic detector method which can detect spatial differentiation, we analyzed the spatial differentiation characteristics and driving forces of vegetation in Northern Shaanxi, and revealed the most appropriate range or type of influencing factors for promoting vegetation growth. The results showed that the overall vegetation coverage improved in the study area, and NDVI showed an increasing trend with a growth rate of 0.10/10 years from 2000 to 2018. Natural and human factors are crucial driving forces of NDVI change, among which gross domestic product, land-use type, slope, and temperature have the greatest influence. The interaction between natural and human factors on NDVI was dominated by nonlinear and mutual enhancement effects, and the influence of interactions among all factors was significantly higher than that of a single factor. The range or types of factors suitable for vegetation growth were analyzed in the study area, and the joint action of natural and human factors had a more significant impact on vegetation. These findings provide a scientific basis for local governments to intervene in vegetation changes and ecological restoration through natural and human factors within the favorable scope.
Assessing Sea-State Effects on Sea-Salt Aerosol Modeling in the Lower Atmosphere Using Lidar and In-Situ Measurements
Authors: Varlas George , Marinou Eleni , Gialitaki Anna , Siomos Nikolaos , Tsarpalis Konstantinos , Kalivitis Nikolaos , Solomos Stavros , Tsekeri Alexandra , Spyrou Christos , Tsichla Maria , Kampouri Anna , Vervatis Vassilis , Giannakaki Elina , Amiridis Vassilis , Mihalopoulos Nikolaos , Papadopoulos Anastasios , Katsafados Petros
Published: February 2021

Atmospheric-chemical coupled models usually parameterize sea-salt aerosol (SSA) emissions using whitecap fraction estimated considering only wind speed and ignoring sea state. This approach may introduce inaccuracies in SSA simulation. This study aims to assess the impact of sea state on SSA modeling, applying a new parameterization for whitecap fraction estimation based on wave age, calculated by the ratio between wave phase velocity and wind speed. To this end, the new parameterization was incorporated in the coupled Chemical Hydrological Atmospheric Ocean wave modeling System (CHAOS). CHAOS encompasses the wave model (WAM) two-way coupled through the OASIS3-MCT coupler with the Advanced Weather Research and Forecasting model coupled with Chemistry (WRF-ARW-Chem) and, thus, enabling the concurrent simulation of SSAs, wind speed and wave phase velocity. The simulation results were evaluated against in-situ and lidar measurements at 2 stations in Greece (Finokalia on 4 and 15 July 2014 and Antikythera-PANGEA on 15 September 2018). The results reveal significant differences between the parameterizations with the new one offering a more realistic representation of SSA levels in some layers of the lower atmosphere. This is attributed to the enhancement of the bubble-bursting mechanism representation with air-sea processes controlling whitecap fraction. Our findings also highlight the contribution of fresh wind-generated waves to SSA modeling.
Effects of Motion Compensation Residual Error and Polarization Distortion on UAV-Borne PolInSAR
Authors: Lv Zexin , Li Fangfang , Qiu Xiaolan , Ding Chibiao
Published: February 2021

Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) can improve interferometric coherence and phase quality, which has good application potential. With the development of the Mini-SAR system, Unmanned Aerial Vehicle borne (UAV-borne) PolInSAR systems became a reality. However, UAV-borne PolInSAR is easily affected by air currents and other factors, which may cause large motion errors and polarization distortion inevitably exists. However, there are few pieces of research which are about motion compensation residual error (MCRE) and polarization distortion effects on PolInSAR. Though the effects of MCRE on Interferometric SAR (InSAR) and polarization distortion on PolInSAR were studied, respectively, these two parts are independently modeled and analyzed. In this paper, a model that simultaneously considers the effects of these two kinds of errors is proposed, and simulation results are given to validate the model. Then, a quantitative analysis based on a real Quadcopter UAV PolInSAR system is performed according to the model, which is valuable for system design and practical application of the UAV-borne PolInSAR system.
L5IN: Overview of an Indoor Navigation Pilot Project
Authors: Schuldt Caroline , Shoushtari Hossein , Hellweg Nils , Sternberg Harald
Published: February 2021

While outdoor navigation systems are already represented everywhere, the enclosed space is much less developed. The project Level 5 Indoor Navigation (L5IN) presents a new approach with mobile phone standard 5G as the orientation signal and without additional infrastructure for navigation in indoor environments. The aim of this project is to use the new available 5G technology to show how navigation systems, which have thus far only been available in the outdoor segment, can now be integrated into existing smartphone systems for indoor navigation. This paper gives an overview of the project and presents the different work packages leading to a holistic approach towards the development of an indoor navigation application for pedestrians. By using a specific app with open interfaces, it is planned to make navigation possible in all buildings modeled according to certain standards. The challenge involved is that, unlike outdoor maps, there is no map basis for buildings. For this reason, different approaches to map generation were examined. In a building information model (BIM), all information will be collected and made available via a database for positioning and visualization. The focus is furthermore on positioning, achieved through smartphone sensors and 5G, so that users can orientate themselves in buildings without having to connect to singular systems. It shall be shown that positioning with a standard deviation of 2–3 m and a confidence interval of 68 % is possible. Another advantage of 5G, the ability to send real-time data in higher data packages, will be used for data transmission. The basic idea of 5G-based indoor navigation will be enabled with radio cells of the providers, which will be set up on the HafenCity University campus. The complex university building will be used as a prototype environment.
A New Measurement Method for High Voltages Applied to an Ion Trap Generated by an RF Resonator
Authors: Park Yunjae , Jung Changhyun , Seong Myeongseok , Lee Minjae , Cho Dongil Dan , Kim Taehyun
Published: February 2021

A new method is proposed to measure unknown amplitudes of radio frequency (RF) voltages applied to ion traps, using a pre-calibrated voltage divider with RF shielding. In contrast to previous approaches that estimate the applied voltage by comparing the measured secular frequencies with a numerical simulation, we propose using a pre-calibrated voltage divider to determine the absolute amplitude of large RF voltages amplified by a helical resonator. The proposed method does not require measurement of secular frequencies and completely removes uncertainty caused by limitations of numerical simulations. To experimentally demonstrate our method, we first obtained a functional relation between measured secular frequencies and large amplitudes of RF voltages using the calibrated voltage divider. A comparison of measured relations and simulation results without any fitting parameters confirmed the validity of the proposed method. Our method can be applied to most ion trap experiments. In particular, it will be an essential tool for surface ion traps which are extremely vulnerable to unknown large RF voltages and for improving the accuracy of numerical simulations for ion trap experiments.
Gauging Gait Disorders with a Method Inspired by Motor Control Theories: A Pilot Study in Friedreich’s Ataxia
Authors: Gouelle Arnaud , Norman Samantha , Sharot Bryanna , Salabarria Stephanie , Subramony Sub , Corti Manuela
Published: February 2021

To date, it has been challenging for clinicians and researchers alike to use the multiple outcome measures available to create a meaningful clinical picture and perform effective longitudinal follow-up. It has been found that instrumented gait analysis can provide information associated with a patient’s performance and help to remedy the shortcomings of the currently available outcome measures. The goal of this methodological article is to set the background and justify a new outcome measure inspired by the motor control theories to analyze gait using spatiotemporal parameters. The method is applied in a population of individuals living with Friedreich’s ataxia (FRDA), a neurodegenerative disease. The sample population consisted of 19 subjects, 11 to 65 years of age with FRDA, who either ambulated independently, with a cane, or with a rollator. Three scores based on the distance from healthy normative data were used: Organization Score, Variability Score, and an overall measurement, the Global Ambulation Score. The scores were then compared to the Scale for Assessment and Rating of Ataxia (SARA) Gait Score (SARA-GS), a clinical scale currently being used for gait analysis in FRDA. Organization Scores demonstrated a longitudinal deterioration in the gait characteristics from independent ambulators to those who ambulated with a rollator. Variability Scores mostly reflected dynamic instability, which became greater as the requirement of an ambulation aid or the switch from a cane to a rollator was imminent. The global value given by the Global Ambulation Score, which takes into consideration both the Organization Score, the Variability Score, and the level of assistive device, demonstrated a logarithmic relationship with the SARA-GS. Overall, these results highlight that both components introduced should be analyzed concurrently and suggest that the Global Ambulation Score may be a valuable outcome measure for longitudinal disease progression.
A Method for Autonomous Navigation and Positioning of UAV Based on Electric Field Array Detection
Authors: Li Yincheng , Zhang Wenbin , Li Peng , Ning Youhuan , Suo Chunguang
Published: February 2021

At present, the method of using unmanned aerial vehicles (UAVs) with traditional navigation equipment for inspection of overhead transmission lines has the limitations of expensive sensors, difficult data processing, and vulnerable to weather and environmental factors, which cannot ensure the safety of UAV and power systems. Therefore, this paper establishes a mathematical model of spatial distribution of transmission lines to study the field strength distribution information around transmission lines. Based on this, research the navigation and positioning algorithm. The data collected by the positioning system are input into the mathematical model to complete the identification, positioning, and safety distance diagnosis of the field source. The detected data and processing results can provide reference for UAV obstacle avoidance navigation and safety warning. The experimental results show that the positioning effect of the positioning navigation algorithm is obvious, and the positioning error is within the range of use error and has good usability and application value.
An Optimum Deployment Algorithm of Camera Networks for Open-Pit Mine Slope Monitoring
Authors: Zhang Hua , Tao Pengjie , Meng Xiaoliang , Liu Mengbiao , Liu Xinxia
Published: February 2021

With the growth in demand for mineral resources and the increase in open-pit mine safety and production accidents, the intelligent monitoring of open-pit mine safety and production is becoming more and more important. In this paper, we elaborate on the idea of combining the technologies of photogrammetry and camera sensor networks to make full use of open-pit mine video camera resources. We propose the Optimum Camera Deployment algorithm for open-pit mine slope monitoring (OCD4M) to meet the requirements of a high overlap of photogrammetry and full coverage of monitoring. The OCD4M algorithm is validated and analyzed with the simulated conditions of quantity, view angle, and focal length of cameras, at different monitoring distances. To demonstrate the availability and effectiveness of the algorithm, we conducted field tests and developed the mine safety monitoring prototype system which can alert people to slope collapse risks. The simulation’s experimental results show that the algorithm can effectively calculate the optimum quantity of cameras and corresponding coordinates with an accuracy of 30 cm at 500 m (for a given camera). Additionally, the field tests show that the algorithm can effectively guide the deployment of mine cameras and carry out 3D inspection tasks.
Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter
Authors: Shaukat Nabil , Ali Ahmed , Javed Iqbal Muhammad , Moinuddin Muhammad , Otero Pablo
Published: February 2021

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.
Experimental Characterization of the FRCM-Concrete Interface Bond Behavior Assisted by Digital Image Correlation
Authors: De Domenico Dario , Quattrocchi Antonino , Alizzio Damiano , Montanini Roberto , Urso Santi , Ricciardi Giuseppe , Recupero Antonino
Published: February 2021

Digital Image Correlation (DIC) provides measurements without disturbing the specimen, which is a major advantage over contact methods. Additionally, DIC techniques provide full-field maps of response quantities like strains and displacements, unlike traditional methods that are limited to a local investigation. In this work, an experimental application of DIC is presented to investigate a problem of relevant interest in the civil engineering field, namely the interface behavior between externally bonded fabric reinforced cementitious mortar (FRCM) sheets and concrete substrate. This represents a widespread strengthening technique of existing reinforced concrete structures, but its effectiveness is strongly related to the bond behavior between composite fabric and underlying concrete. To investigate this phenomenon, a set of notched concrete beams are realized, reinforced with FRCM sheets on the bottom face, subsequently cured in different environmental conditions (humidity and temperature) and finally tested up to failure under three-point bending. Mechanical tests are carried out vis-à-vis DIC measurements using two distinct cameras simultaneously, one focused on the concrete front face and another focused on the FRCM-concrete interface. This experimental setup makes it possible to interpret the mechanical behavior and failure mode of the specimens not only from a traditional macroscopic viewpoint but also under a local perspective concerning the evolution of the strain distribution at the FRCM-concrete interface obtained by DIC in the pre- and postcracking phase.
A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems
Authors: Wu Tao , Hu Jun , Ye Lei , Ding Kai
Published: February 2021

Pedestrian detection plays an essential role in the navigation system of autonomous vehicles. Multisensor fusion-based approaches are usually used to improve detection performance. In this study, we aimed to develop a score fusion-based pedestrian detection algorithm by integrating the data of two light detection and ranging systems (LiDARs). We first evaluated a two-stage object-detection pipeline for each LiDAR, including object proposal and fine classification. The scores from these two different classifiers were then fused to generate the result using the Bayesian rule. To improve proposal performance, we applied two features: the central points density feature, which acts as a filter to speed up the process and reduce false alarms; and the location feature, including the density distribution and height difference distribution of the point cloud, which describes an object’s profile and location in a sliding window. Extensive experiments tested in KITTI and the self-built dataset show that our method could produce highly accurate pedestrian detection results in real-time. The proposed method not only considers the accuracy and efficiency but also the flexibility for different modalities.
Hybrid Nanocomposite Platform, Based on Carbon Nanotubes and Poly(Methylene Blue) Redox Polymer Synthesized in Ethaline Deep Eutectic Solvent for Electrochemical Determination of 5-Aminosalicylic Acid
Authors: Hosu Oana , Barsan Madalina M. , Săndulescu Robert , Cristea Cecilia , Brett Christopher M. A.
Published: February 2021

A novel hybrid composite of conductive poly(methylene blue) (PMB) and carbon nanotubes (CNT) was prepared for the detection of 5-aminosalicylic acid (5-ASA). Electrosynthesis of PMB with glassy carbon electrode (GCE) or with carbon nanotube modified GCE was done in ethaline deep eutectic solvent of choline chloride mixed with ethylene glycol and a 10% v/v aqueous solution. Different sensor architectures were evaluated in a broad range of pH values in a Britton-Robinson (BR) buffer using electrochemical techniques, chronoamperometry (CA), and differential pulse voltammetry (DPV), to determine the optimum sensor configuration for 5-ASA sensing. Under optimal conditions, the best analytical performance was obtained with CNT/PMBDES/GCE in 0.04 M BR buffer pH 7.0 in the range 5–100 µM 5-ASA using the DPV method, with an excellent sensitivity of 9.84 μA cm−2 μM−1 (4.9 % RSD, n = 5) and a detection limit (LOD) (3σ/slope) of 7.7 nM, outclassing most similar sensors found in the literature. The sensitivity of the same sensor obtained in CA (1.33 μA cm−2 μM−1) under optimal conditions (pH 7.0, Eapp = +0.40 V) was lower than that obtained by DPV. Simultaneous detection of 5-ASA and its analogue, acetaminophen (APAP), was successfully realized, showing a catalytic effect towards the electro-oxidation of both analytes, lowering their oxidation overpotential, and enhancing the oxidation peak currents and peak-to-peak separation as compared with the unmodified electrode. The proposed method is simple, sensitive, easy to apply, and economical for routine analysis.
Identification of Crop Type in Crowdsourced Road View Photos with Deep Convolutional Neural Network
Authors: Wu Fangming , Wu Bingfang , Zhang Miao , Zeng Hongwei , Tian Fuyou
Published: February 2021

In situ ground truth data are an important requirement for producing accurate cropland type map, and this is precisely what is lacking at vast scales. Although volunteered geographic information (VGI) has been proven as a possible solution for in situ data acquisition, processing and extracting valuable information from millions of pictures remains challenging. This paper targets the detection of specific crop types from crowdsourced road view photos. A first large, public, multiclass road view crop photo dataset named iCrop was established for the development of crop type detection with deep learning. Five state-of-the-art deep convolutional neural networks including InceptionV4, DenseNet121, ResNet50, MobileNetV2, and ShuffleNetV2 were employed to compare the baseline performance. ResNet50 outperformed the others according to the overall accuracy (87.9%), and ShuffleNetV2 outperformed the others according to the efficiency (13 FPS). The decision fusion schemes major voting was used to further improve crop identification accuracy. The results clearly demonstrate the superior accuracy of the proposed decision fusion over the other non-fusion-based methods in crop type detection of imbalanced road view photos dataset. The voting method achieved higher mean accuracy (90.6–91.1%) and can be leveraged to classify crop type in crowdsourced road view photos.
Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting
Authors: Zhang Wei , Gong Liang , Chen Suyue , Wang Wenjie , Miao Zhonghua , Liu Chengliang
Published: February 2021

In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.
Deep Learning-Based Diagnosing Structural Behavior in Dam Safety Monitoring System
Authors: Wang Longbao , Mao Yingchi , Cheng Yangkun , Liu Yi
Published: February 2021

Collecting a myriad of prototype data through various types of monitoring sensors plays a virtual important role in many aspects of dam safety such as real-time grasp of safety state, exposure of hidden dangers, and inspection design and construction. However, the current methods of prediction are weak in the long-term sequence of nodes with missing and abnormal error value. Moreover, the limitation caused by the apparatus, environmental factors, and network transmission can lead to the deviation and inconsistency of diagnosis and evaluation of local region. In this paper, we consider the correlation of data on nodes in the entire monitoring network. To avoid the deviation caused by noise and missing value in the single-node data sequence, we calculate the correlation between the multiple sequences. A single-node assessment model based on multiple relevant sequence (SAM) is proposed to improve the accuracy of single node assessment. Given the different nodes of a local region have varying impacts on the evaluation results, a local region evaluation algorithm based on node credibility (LREA) is presented to model the credibility of nodes in order to alleviate inconsistent evaluation results in the local region of dam. LREA can assess the dam’s operation state by considering the variations in credibility and multiple nodes coordination. The experimental results illustrate the LREA can reveal the trends of the monitoring values change in a timely and accurate way, which can elevate the accuracy of evaluation results of dam safety.
Wireless Sensor Networks for Environmental Monitoring
Authors: Lanzolla Anna , Spadavecchia Maurizio
Published: February 2021

In this editorial, an overview of the content of the Special Issue on “Wireless Sensor Networks for Environmental Monitoring” is provided [...]
A Highly Accurate Positioning Solution for C-V2X Systems
Authors: Liu Qi , Liang Peng , Xia Junjie , Wang Ti , Song Meng , Xu Xingrong , Zhang Jiachi , Fan Yuanyuan , Liu Liu
Published: February 2021

Cellular vehicle-to-everything (C-V2X) is essential in enabling safe, reliable, and efficient transportation services. It serves as serve as the foundation for vehicles to communicate with each other and everything around them. One fundamental element in C-V2X is positioning, namely extracting the vehicle’s absolute and relative positions concerning other objects such as buildings, pedestrians, traffic signs, and other vehicles. However, its feasibility in enabling vehicular positioning has not been fully explored yet. In this paper, key performance indicators (KPIs) for C-V2X positioning have been described firstly. Then positioning challenges and conventional positioning methods for C-V2X are reviewed. Afterward, two user equipment (UE)-based and UE-assisted C-V2X positioning architectures are proposed, and key technologies are also described. Lastly, testing and typical application cases are provided.
A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks
Authors: Del-Valle-Soto Carolina , Mex-Perera Carlos , Nolazco-Flores Juan Arturo , Rodríguez Alma , Rosas-Caro Julio C. , Martínez-Herrera Alberto F.
Published: February 2021

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.