bluuebunny/crossref_metadata_2025 huggingface dataset:
Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution
Authors: Li Zhongbin , Zhang Hankui K. , Roy David P. , Yan Lin , Huang Haiyan
Published: July 2020

Combination of near daily 3 m red, green, blue, and near infrared (NIR) Planetscope reflectance with lower temporal resolution 10 m and 20 m red, green, blue, NIR, red-edge, and shortwave infrared (SWIR) Sentinel-2 reflectance provides potential for improved global monitoring. Sharpening the Sentinel-2 reflectance with the Planetscope reflectance may enable near-daily 3 m monitoring in the visible, red-edge, NIR, and SWIR. However, there are two major issues, namely the different and spectrally nonoverlapping bands between the two sensors and surface changes that may occur in the period between the different sensor acquisitions. They are examined in this study that considers Sentinel-2 and Planetscope imagery acquired one day apart over three sites where land surface changes due to biomass burning occurred. Two well-established sharpening methods, high pass modulation (HPM) and Model 3 (M3), were used as they are multiresolution analysis methods that preserve the spectral properties of the low spatial resolution Sentinel-2 imagery (that are better radiometrically calibrated than Planetscope) and are relatively computationally efficient so that they can be applied at large scale. The Sentinel-2 point spread function (PSF) needed for the sharpening was derived analytically from published modulation transfer function (MTF) values. Synthetic Planetscope red-edge and SWIR bands were derived by linear regression of the Planetscope visible and NIR bands with the Sentinel-2 red-edge and SWIR bands. The HPM and M3 sharpening results were evaluated visually and quantitatively using the Q2n metric that quantifies spectral and spatial distortion. The HPM and M3 sharpening methods provided visually coherent and spatially detailed visible and NIR wavelength sharpened results with low distortion (Q2n values > 0.91). The sharpened red-edge and SWIR results were also coherent but had greater distortion (Q2n values > 0.76). Detailed examination at locations where surface changes between the Sentinel-2 and the Planetscope acquisitions occurred revealed that the HPM method, unlike the M3 method, could reliably sharpen the bands affected by the change. This is because HPM sharpening uses a per-pixel reflectance ratio in the spatial detail modulation which is relatively stable to reflectance changes. The paper concludes with a discussion of the implications of this research and the recommendation that the HPM sharpening be used considering its better performance when there are surface changes.
Sentinel-1 Change Detection Analysis for Cyclone Damage Assessment in Urban Environments
Authors: Malmgren-Hansen David , Sohnesen Thomas , Fisker Peter , Baez Javier
Published: July 2020

For disaster emergency response, timely information is critical and satellite data is a potential source for such information. High-resolution optical satellite images are often the most informative, but these are only available on cloud-free days. For some extreme weather disasters, like cyclones, access to cloud-free images is unlikely for days both before and after the main impact. In this situation, Synthetic Aperture Radar (SAR) data is a unique first source of information, as it works irrespective of weather and sunlight conditions. This paper shows, in the context of the cyclone Idai that hit Mozambique in March 2019, that Change Detection between pairs of SAR data is a perfect match with weather data, and therefore captures impact from the severe cyclone. For emergency operations, the filtering of Change Detections by external data on the location of houses prior to an event allows assessment of the impact on houses as opposed to impact on the surrounding natural environment. The free availability of SAR data from Sentinel-1, with further automated processing of it, means that this analysis is a cost-effective and quick potential first indication of cyclone destruction.
Performance of the Landsat 8 Provisional Aquatic Reflectance Product for Inland Waters
Authors: Ogashawara Igor , Jechow Andreas , Kiel Christine , Kohnert Katrin , Berger Stella A. , Wollrab Sabine
Published: July 2020

Recently, the United States Geological Service (USGS) released a new provisional product which estimates aquatic reflectance from Landsat 8 Operational Land Imager (OLI), called Landsat 8 Provisional Aquatic Reflectance (L8PAR). However, as indicated in the product guide, the use of this product for inland waters needs further verification and improvements. The goal of this study was to determine how the novel L8PAR product performs for different small turbid and eutrophic lakes in Northern Germany compared to in situ measurements of above water remote sensing reflectance (Rrs). For several recent scenes during our monitoring, the L8PAR product failed to produce full data for the lakes of our interest. For the best scene with in situ spectra, L8PAR was not able to retrieve any information for band 1 and not all information for bands 2, 3 and 4. The pixels with valid values for reflectance showed a weak relationship for band 2 (R2 of 0.24) and a medium relationship for bands 3 and 4 (R2 of 0.68 and 0.72, respectively). Compared to other atmospheric correction routines (ACOLITE, C2RCC, C2X, iCOR and L8SR), L8PAR was the only product which was not able to retrieve Rrs for all match up samples. This work provides an evaluation of the L8PAR product for inland waterbodies. Although more analysis and validation need to be conducted, our study suggests that the L8PAR product cannot be used for small inland lakes in its current state and has to be used with care for inland waters in general.
Construction of High Spatial-Temporal Water Body Dataset in China Based on Sentinel-1 Archives and GEE
Authors: Li Yang , Niu Zhenguo , Xu Zeyu , Yan Xin
Published: July 2020

Surface water is the most important resource and environmental factor in maintaining human survival and ecosystem stability; therefore, timely accurate information on dynamic surface water is urgently needed. However, the existing water datasets fall short of the current needs of the various organizations and disciplines due to the limitations of optical sensors in dynamic water mapping. The advancement of the cloud-based Google Earth Engine (GEE) platform and free-sharing Sentinel-1 imagery makes it possible to map the dynamics of a surface water body with high spatial-temporal resolution on a large scale. This study first establishes a water extraction method oriented towards Sentinel-1 Synthetic Aperture Radar (SAR) data based on the statistics of a large number of samples of land-cover types. An unprecedented high spatial-temporal water body dataset in China (HSWDC) with monthly temporal and 10-m spatial resolution using the Sentinel-1 data from 2016 to 2018 is developed in this study. The HSWDC is validated by 14,070 random samples across China. A high classification accuracy (overall accuracy = 0.93, kappa coefficient = 0.86) is achieved. The HSWDC is highly consistent with the Global Surface Water Explorer dataset and water levels from satellite altimetry. In addition to the good performance of detecting frozen water and small water bodies, the HSWDC can also classify various water cover/uses, which are obtained from its high spatial-temporal resolution. The HSWDC dataset can provide more detailed information on surface water bodies in China and has good application potential for developing high-resolution wetland maps.
Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China
Authors: Bai Xiao , Zhang Lanhui , He Chansheng , Zhu Yi
Published: July 2020

Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (>5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.
Greening and Browning Trends across Peru’s Diverse Environments
Authors: Polk Molly H. , Mishra Niti B. , Young Kenneth R. , Mainali Kumar
Published: July 2020

If he were living today, Alexander von Humboldt would be using current technology to evaluate change in the Andes. Inspired by von Humboldt’s scientific legacy and the 2019 celebrations of his influence, we utilize a Moderate Resolution Imaging Spectroradiometer (MODIS)time-series vegetation index to ask questions of landscape change. Specifically, we use an 18-year record of Normalized Difference Vegetation Index (NDVI) data as a proxy to evaluate landscape change in Peru, which is well known for its high biological and ecological diversity. Continent-level evaluations of Latin America have shown sites with a positive trend in NDVI, or “greening” and “browning”, a negative trend in NDVI that suggests biophysical or human-caused reductions in vegetation. Our overall hypothesis was that the major biomes in Peru would show similar NDVI change patterns. To test our expectations, we analyzed the NDVI time-series with Thiel-Sen regression and evaluated Peru overall, by protected area status, by biome, and by biome and elevation. Across Peru overall, there was a general greening trend. By protected area status, surprisingly, the majority of greening occurred outside protected areas. The trends were different by biome, but there were hotspots of greening in the Amazon, Andean Highlands, and Drylands where greening dominated. In the Tropical Subtropical Dry Broadleaf Forest biome, greening and browning signals were mixed. Greening trends varied across the elevation gradient, switching from greening, to browning, and then back to greening as elevation increased. By biome and elevation, the results were variable. We further explored biome-specific drivers of greening and browning drawing on high-resolution imagery, the literature, and field expertise, much as we imagine von Humboldt might have approached similar questions of landscape dynamism.
Unprecedented Temporary Reduction in Global Air Pollution Associated with COVID-19 Forced Confinement: A Continental and City Scale Analysis
Authors: Zhang Zhijie , Arshad Arfan , Zhang Chuanrong , Hussain Saddam , Li Weidong
Published: July 2020

Shortly after the outbreak of the novel infectious disease (COVID-19) started at the end of 2019, it turned into a global pandemic, which caused the lockdown of many countries across the world. Various strict measures were adopted to reduce anthropogenic activities in order to prevent further spread and infection of the disease. In this study, we utilized continental scale remotely sensed data along with city scale in situ air quality observations for 2020 as well as data from the baseline period (2015–2019) to provide an early insight on air pollution changes in response to the COVID-19 pandemic lockdown, by combining both continental and city scales. For the continental scale analysis, data of NO2, SO2, and O3 were acquired from the ozone monitoring instrument (OMI) and data of aerosol optical depth (AOD) were collected from the moderate resolution imaging spectroradiometer (MODIS). For city scale analysis, data of NO2, CO, PM2.5, O3, and SO2 were derived from ground-based air quality observations. Results from satellite observations at the continental scale showed that concentrations of NO2, SO2, and AOD substantially dropped in 2020 during the lockdown period compared to their averages for the baseline period over all continents, with a maximum reduction of ~33% for NO2 in East Asia, ~41% for SO2 in East Asia, and ~37% for AOD in South Asia. In the case of O3, the maximum overall reduction was observed as ~11% in Europe, followed by ~10% in North America, while a slight increase was found in other study regions. These findings align with ground-based air quality observations, which showed that pollutants such as NO2, CO, PM2.5, and SO2 during the 2020 lockdown period decreased significantly except that O3 had varying patterns in different cities. Specifically, a maximum reduction of ~49% in NO2 was found in London, ~43% in CO in Wuhan, ~38% in PM2.5 in Chennai, and ~48% in SO2 in Beijing. In the case of urban O3, a maximum reduction of ~43% was found in Wuhan, but a significant increase of ~47% was observed in Chennai. It is obvious that restricted human activities during the lockdown have reduced the anthropogenic emissions and subsequently improved air quality, especially across the metropolitan cities.
Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis
Authors: Rafiezadeh Shahi Kasra , Khodadadzadeh Mahdi , Tusa Laura , Ghamisi Pedram , Tolosana-Delgado Raimon , Gloaguen Richard
Published: July 2020

Hyperspectral imaging techniques are becoming one of the most important tools to remotely acquire fine spectral information on different objects. However, hyperspectral images (HSIs) require dedicated processing for most applications. Therefore, several machine learning techniques were proposed in the last decades. Among the proposed machine learning techniques, unsupervised learning techniques have become popular as they do not need any prior knowledge. Specifically, sparse subspace-based clustering algorithms have drawn special attention to cluster the HSI into meaningful groups since such algorithms are able to handle high dimensional and highly mixed data, as is the case in real-world applications. Nonetheless, sparse subspace-based clustering algorithms usually tend to demand high computational power and can be time-consuming. In addition, the number of clusters is usually predefined. In this paper, we propose a new hierarchical sparse subspace-based clustering algorithm (HESSC), which handles the aforementioned problems in a robust and fast manner and estimates the number of clusters automatically. In the experiment, HESSC is applied to three real drill-core samples and one well-known rural benchmark (i.e., Trento) HSI datasets. In order to evaluate the performance of HESSC, the performance of the new proposed algorithm is quantitatively and qualitatively compared to the state-of-the-art sparse subspace-based algorithms. In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM. The obtained clustering results demonstrate that HESSC performs well when clustering HSIs compared to the other applied clustering algorithms.
A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data
Authors: Knopp Lisa , Wieland Marc , Rättich Michaela , Martinis Sandro
Published: July 2020

Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques—which have successfully been applied to other segmentation tasks—have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94.
A GNSS-Based Crowd-Sensing Strategy for Specific Geographical Areas
Authors: Lin Chuan-Bi , Hung Ruo-Wei , Hsu Chi-Yueh , Chen Jong-Shin
Published: July 2020

Infectious diseases, such as COVID-19, SARS, MERS, etc., have seriously endangered human safety, economy, and education. During the spread of epidemics, restricting the range of activities of personnel is one of the options for the prevention and treatment of infectious diseases. A global navigation satellite system (GNSS), it can provide accurate coordinates of latitude and longitude to targets with GNSS receivers. However, it is not common to use GNSS coordinates to represent positions in social life. For epidemic management, it is important to know the locations (and addresses) of targets, especially in social life. When there are many targets, it is not easy to efficiently map these coordinates to locations. Therefore, we propose a GNSS-based crowd-sensing strategy for specific geographical areas that can be used to calculate how many targets are in specific geographical areas or whether a target is in a specific area. This strategy is based on the coordinates of latitude and longitude provided by GNSS to find the locations of these coordinates. As simulated data, the data records containing latitude and longitude in a well-known social networking service platform are used. The strategy is also available for mining hot spots or hot areas.
Analysis of Phenolic Content in Grape Seeds and Skins by Means of a Bio-Electronic Tongue
Authors: Garcia-Cabezon Cristina , Gobbi Teixeira Guilherme , Dias Luís Guimaraes , Salvo-Comino Coral , García-Hernandez Celia , Rodriguez-Mendez Maria Luz , Martin-Pedrosa Fernando
Published: July 2020

A bio-electronic tongue has been developed to evaluate the phenolic content of grape residues (seeds and skins) in a fast and easy way with industrial use in mind. A voltammetric electronic tongue has been designed based on carbon resin electrodes modified with tyrosinase combined with electron mediators. The presence of the phenoloxydase promotes the selectivity and specificity towards phenols. The results of multivariate analysis allowed discriminating seeds and skins according to their polyphenolic content. Partial least squares (PLS) has been used to establish regression models with parameters related to phenolic content measured by spectroscopic methods i.e., total poliphenol content (TPC) and Folin–Ciocalteu (FC) indexes. It has been shown that electronic tongue can be successfully used to predict parameters of interest with high correlation coefficients (higher than 0.99 in both calibration and prediction) and low residual errors. These values can even be improved using genetic algorithms for multivalent analysis. In this way, a fast and simple tool is available for the evaluation of these values. This advantage may be due to the fact that the electrochemical signals are directly related to the phenolic content.
A Panoramic Localizer Based on Coarse-to-Fine Descriptors for Navigation Assistance
Authors: Fang Yicheng , Yang Kailun , Cheng Ruiqi , Sun Lei , Wang Kaiwei
Published: July 2020

Visual Place Recognition (VPR) addresses visual instance retrieval tasks against discrepant scenes and gives precise localization. During a traverse, the captured images (query images) would be traced back to the already existing positions in the database images, rendering vehicles or pedestrian navigation devices distinguish ambient environments. Unfortunately, diverse appearance variations can bring about huge challenges for VPR, such as illumination changing, viewpoint varying, seasonal cycling, disparate traverses (forward and backward), and so on. In addition, the majority of current VPR algorithms are designed for forward-facing images, which can only provide with narrow Field of View (FoV) and come with severe viewpoint influences. In this paper, we propose a panoramic localizer, which is based on coarse-to-fine descriptors, leveraging panoramas for omnidirectional perception and sufficient FoV up to 360∘. We adopt NetVLAD descriptors in the coarse matching in a panorama-to-panorama way, for their robust performances in distinguishing different appearances, utilizing Geodesc keypoint descriptors in the fine stage in the meantime, for their capacity of detecting detailed information, formatting powerful coarse-to-fine descriptors. A comprehensive set of experiments is conducted on several datasets including both public benchmarks and our real-world campus scenes. Our system is proved to be with high recall and strong generalization capacity across various appearances. The proposed panoramic localizer can be integrated into mobile navigation devices, available for a variety of localization application scenarios.
Recognition of Blocking Categories for UWB Positioning in Complex Indoor Environment
Authors: Kong Yaguang , Li Chuang , Chen Zhangping , Zhao Xiaodong
Published: July 2020

The recognition of non-line-of-sight (NLOS) state is a prerequisite for alleviating NLOS errors and is crucial to ensure the accuracy of positioning. Recent studies only identify the line-of-sight (LOS) state and the NLOS state, but ignore the contribution of occlusion categories to spatial information perception. This paper proposes a bidirectional search algorithm based on maximum correlation, minimum redundancy, and minimum computational cost (BS-mRMRMC). The optimal channel impulse response (CIR) feature set, which can identify NLOS and LOS states well, as well as the blocking categories, are determined by setting the constraint thresholds of both the maximum evaluation index, and the computational cost. The identification of blocking categories provides more effective information for the indoor space perception of ultra-wide band (UWB). Based on the vector projection method, the hierarchical structure of decision tree support vector machine (DT-SVM) is designed to verify the recognition accuracy of each category. Experiments show that the proposed algorithm has an average recognition accuracy of 96.7% for each occlusion category, which is better than those of the other three algorithms based on the same number of CIR signal characteristics of UWB.
Printed Strain Sensors Based on an Intermittent Conductive Pattern Filled with Resistive Ink Droplets
Authors: Zymelka Daniel , Yamashita Takahiro , Sun Xiuru , Kobayashi Takeshi
Published: July 2020

In this study, we demonstrate a strain sensor fabricated as a hybrid structure of a conductive intermittent pattern with embedded single droplets of a functional resistive ink. The main feature of our proposed sensor design is that although the intermittent pattern comprises the majority of the entire sensor area, the strain sensitivity depends almost selectively on the resistive droplets. This opens up the possibility for fast and inexpensive evaluation of sensors manufactured from various functional materials. As the use of resistive ink was limited to single droplets deposition, the required ink amount needed to build a sensor can be considerably reduced. This makes the sensors cost-effective and simple for fabrication. In this study, our proposed sensor design was evaluated when a carbon-based ink was used as the resistive material incorporated into an intermittent structure made of silver. The developed strain sensors were tested during bending deformations demonstrating good strain sensitivity (gauge factor: 7.71) and no hysteresis within the investigated strain range.
Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling
Authors: Tang Jiabei , Xu Minpeng , Han Jin , Liu Miao , Dai Tingfei , Chen Shanguang , Ming Dong
Published: July 2020

The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.
Disturbance Recognition and Collision Detection of Manipulator Based on Momentum Observer
Authors: Zhang Xiaojun , Zhao Jian , Zhang Minglu , Liu Xiaoyu
Published: July 2020

Increasing requirements for the safety of human-robot interaction and the cost-effectiveness of collision detection rapidly promote the development of collision detection technology without torque sensors. To address nonlinear disturbance factors in collision detection that may cause unstable or even incorrect detection, this paper proposed a research strategy that considered the friction as the disturbance term in manipulator motion for the collision detection. The manipulator joint disturbance model was established based on the LuGre dynamic friction model, and the external torque observer was designed based on the generalized momentum. Then, the friction measurement was realized using the external torque observer, and the model parameters were identified through the genetic algorithm. The collision detection can be reduced errors after the friction model by compensating the disturbance and can be applicable to variable working conditions. Finally, the accuracy of the constructed disturbance model and the performance of the proposed collision detection method were validated by the experimental studies.
FPGA-Based Implementation of Stochastic Configuration Networks for Regression Prediction
Authors: Gao Yunqi , Luan Feng , Pan Jiaqi , Li Xu , He Yaodong
Published: July 2020

The implementation of neural network regression prediction based on digital circuits is one of the challenging problems in the field of machine learning and cognitive recognition, and it is also an effective way to relieve the pressure of the Internet in the era of intelligence. As a nonlinear network, the stochastic configuration network (SCN) is considered to be an effective method for regression prediction due to its good performance in learning and generalization. Therefore, in this paper, we adapt the SCN to regression analysis, and design and verify the field programmable gate array (FPGA) framework to implement SCN model for the first time. In addition, in order to improve the performance of the SCN model based on the FPGA, the implementation of the nonlinear activation function on the FPGA is optimized, which effectively improves the prediction accuracy while considering the utilization rate of hardware resources. Experimental results based on the simulation data set and the real data set prove that the proposed FPGA framework successfully implements the SCN regression prediction model, and the improved SCN model has higher accuracy and a more stable performance. Compared with the extreme learning machine (ELM), the prediction performance of the proposed SCN implementation model based on the FPGA for the simulation data set and the real data set is improved by 56.37% and 17.35%, respectively.
State and Force Estimation on a Rotating Helicopter Blade through a Kalman-Based Approach
Authors: Cumbo Roberta , Tamarozzi Tommaso , Jiranek Pavel , Desmet Wim , Masarati Pierangelo
Published: July 2020

The interaction between the rotating blades and the external fluid in non-axial flow conditions is the main source of vibratory loads on the main rotor of helicopters. The knowledge or prediction of the produced aerodynamic loads and of the dynamic behavior of the components could represent an advantage in preventing failures of the entire rotorcraft. Some techniques have been explored in the literature, but in this field of application, high accuracy can be reached if a large amount of sensor data and/or a high-fidelity numerical model is available. This paper applies the Kalman filtering technique to rotor load estimation. The nature of the filter allows the usage of a minimum set of sensors. The compensation of a low-fidelity model is also possible by accounting for sensors and model uncertainties. The efficiency of the filter for state and load estimation on a rotating blade is tested in this contribution, considering two different sources of uncertainties on a coupled multibody-aerodynamic model. Numerical results show an accurate state reconstruction with respect to the selected sensor layout. The aerodynamic loads are accurately evaluated in post-processing.
Assessing the Effects of Biochar on the Immobilization of Trace Elements and Plant Development in a Naturally Contaminated Soil
Authors: Campos Paloma , De la Rosa José María
Published: July 2020

Soil contamination with trace elements is an important and global environmental concern. This study examined the potential of biochars derived from rice husk (RHB), olive pit (OPB), and a certified biochar produced from wood chips (CWB) to immobilize copper (Cu2+) and lead (Pb2+) in aqueous solution to avoid its leaching and in a pot experiment with acidic Xerofluvent soils multicontaminated with trace elements. After assessing the adsorption potential of Cu2+ and Pb2+ from an aqueous solution of the three studied biochars, the development of Brassica rapa pekinensis plants was monitored on polluted soils amended with the same biochars, to determine their capability to boost plant growth in a soil contaminated with several trace elements. RHB and CWB removed the maximum amounts of Cu2+ and Pb2+ from aqueous solution in the adsorption experiment. The adsorption capacity increased with initial metal concentrations for all biochars. The efficiency in the adsorption of cationic metals by biochars was clearly affected by biochar chemical properties, whereas total specific surface area seemed to not correlate with the adsorption capacity. Among the isotherm models, the Langmuir model was in the best agreement with the experimental data for both cations for CWB and RHB. The maximum adsorption capacity of Cu2+ was 30.77 and 58.82 mg g−1 for RHB and CWB, respectively, and of Pb2+ was 19.34 and 77.52 mg g−1 for RHB and CWB, respectively. The application of 5% of RHB and CWB to the acidic polluted soils improved soil physico-chemical properties, which permitted the development of Brassica rapa pekinensis plants. RHB and CWB have been shown to be effective for the removal of Cu2+ and Pb2+, and the results obtained regarding plant development in the soils contaminated with trace elements indicated that the soil amendments have promising potential for the recovery of land polluted with heavy metals.
Evaluating Active Traffic Management (ATM) Strategies under Non-Recurring Congestion: Simulation-Based with Benefit Cost Analysis Case Study
Authors: Farrag Siham G. , Outay Fatma , Yasar Ansar Ul-Haque , El-Hansali Moulay Youssef
Published: July 2020

Dynamic hard shoulder running and ramp closure are two active traffic management (ATM) strategies that are used to alleviate highway traffic congestion. This study aims to evaluate the effects of these two strategies on congested freeways under non-recurring congestion. The study’s efforts can be considered in two parts. First, we performed a detailed microsimulation analysis to quantify the potential benefits of these two ATM strategies in terms of safety, traffic operation, and environmental impact. Second, we evaluated the implementation feasibility of these two strategies. The simulation results indicated that the implementation of the hard shoulder showed a 50%–57% reduction in delay, a 41%–44% reduction in fuel consumption and emissions, and a 15%–18% increase in bottleneck throughput. By contrast, the implementation of ramp closure showed a 20%–34% decrease in travel time, a 6%–9% increase in bottleneck throughput, and an 18%–32% reduction in fuel consumption and emissions. Eventually, both strategies were found to be economically feasible.
Temperature Reduction Effects of Rooftop Garden Arrangements: A Case Study of Seoul National University
Authors: Kim Jaekyoung , Lee Sang Yeob , Kang Junsuk
Published: July 2020

Increasing urbanization has highlighted the need for more green spaces in built-up areas, with considerable attention of vertical installations such as green walls and rooftop gardens. This study hypothesizes that the rooftop-garden-induced temperature reduction effects vary depending on the type of arrangements. Therefore, the objective of this study is to find the most efficient arrangement of the roof gardens for temperature reduction. This paper presents the results of a quantitative analysis of the temperature reduction effect of rooftop gardens installed on structures and sites on the campus of Seoul National University. An ENVI-Met simulation is utilized to analyze the effects of roads, buildings, green areas, and vacant land on temperature and humidity. The effects of the following five rooftop garden configurations were compared: extreme, linear (longitudinal), linear (transverse), checkerboard, and unrealized rooftop gardens. The extreme and linear (longitudinal) gardens achieved the maximum temperature reduction, −0.3 °C, while the lowest maximum reduction of −0.2 °C was achieved by the checkerboard pattern. Over larger areas, the greatest impact has been recorded in the mornings rather than in the afternoons. The results of this study will be useful for those planning and installing rooftop gardens at the district and city levels.
Towards Sustainable Private Labels—What is the Consumer Behavior Relating to Private Labels in the UK and Poland?
Authors: Czeczotko Maksymilian , Górska-Warsewicz Hanna , Laskowski Wacław
Published: July 2020

Consumer behavior towards private labels (PLs) is constantly changing, accompanied by the development from generic products, offered at very low prices, towards sustainable PLs. Our study aimed to analyze the behavior of British and Polish consumers towards PLs of the retail chains. To achieve this, special attention was given to the following issues: frequency of purchasing PLs by food categories, motives for purchasing PL products, opinions of the current development of PLs, and length of the period of purchasing products under PLs. We also presented the socioeconomic features of the Polish and British consumers purchasing PL products using a correspondence graph. Our research was conducted using a sample of 500 adults from Poland and 500 adults from the UK and the Computer-Assisted Web Interviewing method (CAWI) was used. The questionnaire was addressed only to adults who declared that they purchase PL products. For a detailed analysis of consumer choices and service quality assessment, we used Pearson’s chi-square test, as well as the Kohonen’s neural network and multi-dimensional cluster analysis. We have divided the sample population into 4 clusters based on 6 factors, characterizing households: education level, income, household residence, age, gender, and period of buying PL products. Our study indicates that Polish consumers are more likely to pay attention to lower prices for PLs, while British consumers point quality compared to manufacturers’ brands. In the opinion of Polish consumers, an improvement in quality is only just beginning. This means that PLs available on the British market are characterized by a higher stage of development towards sustainable PLs.
Citizen Science in Germany as Research and Sustainability Education: Analysis of the Main Forms and Foci and Its Relation to the Sustainable Development Goals
Authors: Schleicher Katharina , Schmidt Constanze
Published: July 2020

Many citizen science projects are in the thematic area of species observation and natural environment monitoring but, in recent years, projects in other areas and disciplines have increasingly been using citizen science approaches. It is assumed that citizen science could potentially contribute to an increase in environmental awareness and to advancing knowledge about environmental change and sustainability issues. In this article, we present a review of 127 citizen science projects listed on the German platform, “Bürger schaffen Wissen”, with the aim of analysing whether the main focus of most projects is on the scientific results or on educational aspects and how citizen science projects are connected to the SDGs. The results show that many citizen science projects overlap with SDG 4 Quality Education. Of these projects, a larger proportion entail higher levels of involvement than those projects with a stronger focus on the scientific results, in which the participation of the citizen scientists is mainly standardised and at low levels. An even greater number of projects in the sample are linked to SDG 15 Life on Land and, thereby, are in line with the traditional focus of citizen science. Additionally, the analysis reveals that forms of education used in citizen science projects are much more diverse than those included in SDG 4.
Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea
Authors: Choi Boram , Suh Jong Hwan
Published: July 2020

In a weapon system, the accurate forecasting of the spare parts demand can help avoid the excess inventory, leading to the efficient use of budget. It can also help develop the combat readiness of the weapon system by improving weapon system utilization. Moreover, as performance-based logistics (PBL) projects have recently emerged, the accurate demand forecasting of spare parts has become an important issue for the PBL contractors as well. However, for the demand forecasting of spare parts, the time series methods, typically used in the military sector, have low prediction accuracies and the PBL contractors are mostly based on the judgment of practitioners. Meanwhile, most of the previous studies in the military sector have not considered the managerial characteristics of spare parts (e.g., reparability and the irregularity of maintenance). No previous work has considered any such features, which can indicate the reliability of spare parts (e.g., mean time between failures (MTBF)), although they can affect the spare parts demand. Therefore, to develop a more accurate forecasting of the spare parts demand of military aircraft, we designed and examined a systematic approach that uses data mining techniques. To fill up the research gaps of related works, our approach also considered the managerial characteristics of spare parts and included the new features that represent the reliability of spare parts. Consequently, given the case of South Korea and the full feature set, we found random forest gave better results than the other data mining techniques and the conventional time series methods. Using the best technique Random Forest, we identified the contribution of each managerial feature set to improving the prediction accuracy, and we found the reliability and operation environment are valuable feature sets in a significant way, so they should be collected, managed more carefully, and included for better prediction of spare parts demand of military aircraft.
Sustainable and Resilient Smart House Using the Internal Combustion Engine of Plug-in Hybrid Electric Vehicles
Authors: Abessi Ahad , Shirazi Elham , Jadid Shahram , Shafie-khah Miadreza
Published: July 2020

Nowadays, due to the increasing number of disasters, improving distribution system resiliency is a new challenging issue for researchers. One of the main methods for improving the resiliency in distribution systems is to supply critical loads after disasters during the power outage and before system restorations. In this paper, a “Sustainable and resilient smart house” is introduced for the first time by using plug-in hybrid electric vehicles (PHEVs). PHEVs have the ability to use their fuel for generating electricity in emergency situations as the Vehicle to Grid (V2G) scheme. This ability, besides smart house control management, provides an opportunity for distribution system operators to use their extra energy for supplying a critical load in the system. The proposed control strategy in this paper is dedicated to a short duration power outage, which includes a large percent of the events. Then, improvement of the resiliency of distribution systems is investigated through supplying smart residential customers and injecting extra power to the main grid. A novel formulation is proposed for increasing the injected power of the smart house to the main grid using PHEVs. The effectiveness of the proposed method in increasing power injection during power outages is shown in simulation results.
Leveraging Quasi-Experimental Methods to Estimate Model Structure: Understanding School Funding Changes in Response to Court Orders
Authors: Pierson Kawika , Thompson Jon C.
Published: July 2020

We discuss an approach to using the event study, a common experimental design in the social sciences, to parameterize delays and develop other insights into system structure. We show a step-by-step process for undertaking a delay event study, discuss some of the conceptual reasons that this provides information about the delay, and illustrate the process for a typical example. We find evidence that school funding changes following court orders do not adjust quickly, and likely follow a higher-order, as opposed to a first-order, delay process. Our tests also suggest that school district budget makers appear to forecast revenue pessimistically, contributing an additional source of delay to the system.
Assessment of Metagenomic Sequencing and qPCR for Detection of Influenza D Virus in Bovine Respiratory Tract Samples
Authors: Zhang Maodong , Huang Yanyun , Godson Dale L. , Fernando Champika , Alexander Trevor W. , Hill Janet E.
Published: July 2020

High throughput sequencing is currently revolutionizing the genomics field and providing new approaches to the detection and characterization of microorganisms. The objective of this study was to assess the detection of influenza D virus (IDV) in bovine respiratory tract samples using two sequencing platforms (MiSeq and Nanopore (GridION)), and species-specific qPCR. An IDV-specific qPCR was performed on 232 samples (116 nasal swabs and 116 tracheal washes) that had been previously subject to virome sequencing using MiSeq. Nanopore sequencing was performed on 19 samples positive for IDV by either MiSeq or qPCR. Nanopore sequence data was analyzed by two bioinformatics methods: What’s In My Pot (WIMP, on the EPI2ME platform), and an in-house developed analysis pipeline. The agreement of IDV detection between qPCR and MiSeq was 82.3%, between qPCR and Nanopore was 57.9% (in-house) and 84.2% (WIMP), and between MiSeq and Nanopore was 89.5% (in-house) and 73.7% (WIMP). IDV was detected by MiSeq in 14 of 17 IDV qPCR-positive samples with Cq (cycle quantification) values below 31, despite multiplexing 50 samples for sequencing. When qPCR was regarded as the gold standard, the sensitivity and specificity of MiSeq sequence detection were 28.3% and 98.9%, respectively. We conclude that both MiSeq and Nanopore sequencing are capable of detecting IDV in clinical specimens with a range of Cq values. Sensitivity may be further improved by optimizing sequence data analysis, improving virus enrichment, or reducing the degree of multiplexing.
Hepatitis E Virus: How It Escapes Host Innate Immunity
Authors: Lhomme Sébastien , Migueres Marion , Abravanel Florence , Marion Olivier , Kamar Nassim , Izopet Jacques
Published: July 2020

Hepatitis E virus (HEV) is a leading cause of viral hepatitis in the world. It is usually responsible for acute hepatitis, but can lead to a chronic infection in immunocompromised patients. The host’s innate immune response is the first line of defense against a virus infection; there is growing evidence that HEV RNA is recognized by toll-like receptors (TLRs) and retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs), leading to interferon (IFN) production. The IFNs activate interferon-stimulated genes (ISGs) to limit HEV replication and spread. HEV has developed strategies to counteract this antiviral response, by limiting IFN induction and signaling. This review summarizes the advances in our knowledge of intracellular pathogen recognition, interferon and inflammatory response, and the role of virus protein in immune evasion.
Vaccine Design from the Ensemble of Surface Glycoprotein Epitopes of SARS-CoV-2: An Immunoinformatics Approach
Authors: Rahman Noor , Ali Fawad , Basharat Zarrin , Shehroz Muhammad , Khan Muhammad Kazim , Jeandet Philippe , Nepovimova Eugenie , Kuca Kamil , Khan Haroon
Published: July 2020

The present study aimed to work out a peptide-based multi-epitope vaccine against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We predicted different B-cell and T-cell epitopes by using the Immune Epitopes Database (IEDB). Homology modeling of the construct was done using SWISS-MODEL and then docked with different toll-like-receptors (TLR4, TLR7, and TLR8) using PatchDock, HADDOCK, and FireDock, respectively. From the overlapped epitopes, we designed five vaccine constructs C1–C5. Based on antigenicity, allergenicity, solubility, different physiochemical properties, and molecular docking scores, we selected the vaccine construct 1 (C1) for further processing. Docking of C1 with TLR4, TLR7, and TLR8 showed striking interactions with global binding energy of −43.48, −65.88, and −60.24 Kcal/mol, respectively. The docked complex was further simulated, which revealed that both molecules remain stable with minimum RMSF. Activation of TLRs induces downstream pathways to produce pro-inflammatory cytokines against viruses and immune system simulation shows enhanced antibody production after the booster dose. In conclusion, C1 was the best vaccine candidate among all designed constructs to elicit an immune response SARS-CoV-2 and combat the coronavirus disease (COVID-19).