bluuebunny/crossref_metadata_2025 huggingface dataset:
Sound Absorption Properties of Perforated Recycled Polyurethane Foams Reinforced with Woven Fabric
Authors: Atiénzar-Navarro Roberto , del Rey Romina , Alba Jesús , Sánchez-Morcillo Víctor J. , Picó Rubén
Published: February 2020

The acoustic properties of recycled polyurethane foams are well known. Such foams are used as a part of acoustic solutions in different fields such as building or transport. This paper aims to seek improvements in the sound absorption of these recycled foams when they are combined with fabrics. For this aim, foams have been drilled with cylindrical perforations, and also combined with different fabrics. The effect on the sound absorption is evaluated based on the following key parameters: perforation rate (5% and 20%), aperture size (4 mm and 6 mm), and a complete perforation depth. Experimental measurements were performed by using an impedance tube for the characterization of its acoustic behavior. Sound absorption of perforated samples is also studied—numerically by finite element simulations, where the viscothermal losses were considered; and analytically by using models for the perforated foam and the fabric. Two textile fabrics were used in combination with perforated polyurethane samples. Results evidence a modification of the sound absorption at mid frequencies employing fabrics that have a membrane-type acoustic response.
Laser Welding of Transmitting High-Performance Engineering Thermoplastics
Authors: Fernandes Fábio A.O. , Pereira António B. , Guimarães Bernardo , Almeida Tiago
Published: February 2020

Laser processing is a rapidly growing key technology driven by several advantages such as cost and performance. Laser welding presents numerous advantages in comparison with other welding technologies, providing high reliability and cost-effective solutions. Significant interest in this technology, combined with the increasing demand for high-strength lightweight structures has led to an increasing interest in joining high-performance engineering thermoplastics by employing laser technologies. Laser transmission welding is the base method usually employed to successfully join two polymers, a transmitting one through which the laser penetrates, and another one responsible for absorbing the laser radiation, resulting in heat and melting of the two components. In this work, the weldability of solely transmitting high-performance engineering thermoplastic is analyzed. ERTALON® 6 SA, in its white version, is welded by a pulsed Nd:YAG laser. Tensile tests were performed in order to evaluate the quality of each joint by assessing its strength. A numerical model of the joint is also developed to support the theoretical approaches employed to justify the experimental observations.
Highly Dual Antifouling and Antibacterial Ultrafiltration Membranes Modified with Silane Coupling Agent and Capsaicin-Mimic Moieties
Authors: Zhang Lili , Tang Yuanyuan , Jiang Xiaohui , Yu Liangmin , Wang Changyun
Published: February 2020

Dual antifouling and antibacterial polysulfone(PSf)/polyethersulfone(PES) hybrid membranes were developed by the synergy of capsaicin-mimic N-(5-methyl acrylamide-2,3,4 hydroxy benzyl) acrylamide (AMTHBA) and vinyl triethylene (b-methoxy ethoxy) silane (VTMES). First, AMTHBA as a natural antimicrobial agent was incorporated into a casting solution via “microwave-assistance (MWA) in situ polymerization-blending” process to construct a hydroxyl-rich environment. Then, VTMES crosslinked to a hydroxyl-rich polymer matrix via hydrolytic condensation, and the influence of VTMES content on the hybrid membrane properties was systematically investigated. When the VTMES added amount was 1.0 wt %, the hybrid membrane achieved an optimal separation performance including a steady-state humic acid (HA) (5 mg/L) permeation flux of 326 L·m−2·h−1 and a rejection percentage of 97%. The antibacterial tests revealed that the hybrid membranes exhibited sustained bactericidal activity and effective inhibition of bacterial adhesion. Besides, the dual-functional membranes were clean as new after two-cycles filtration (with a cleaning efficiency of ~90%), indicating that the network silicone film on the surface benefits the foulant repellence. Hopefully, the dual-functional membranes constructed in this study can be applicable to the pretreatment stage of water treatment.
Chitosan Composites in Packaging Industry—Current Trends and Future Challenges
Authors: Souza Victor G. L. , Pires João R. A. , Rodrigues Carolina , Coelhoso Isabel M. , Fernando Ana Luísa
Published: February 2020

Chitosan-based composites play an important role in food packaging applications and can be used either as films or as edible coatings. Due to their high costs and lower performance (i.e., lower barrier against water vapor, thermal, and mechanical properties) when compared to the traditional petroleum-based plastics, the use of such biopolymers in large-scale is still limited. Several approaches of chitosan composites in the packaging industry are emerging to overcome some of the disadvantages of pristine polymers. Thus, this work intends to present the current trends and the future challenges towards production and application of chitosan composites in the food packaging industry.
Theoretical and Experimental Studies of a Digital Flow Booster Operating at High Pressures and Flow Rates
Authors: Yuan Chenggang , Mao Lung Vinrea Lim , Plummer Andrew , Pan Min
Published: February 2020

The switched inertance hydraulic converter (SIHC) is a new technology providing an alternative to conventional proportional or servo-valve-controlled systems in the area of fluid power. SIHCs can adjust or control flow and pressure by means of using digital control signals that do not rely on throttling the flow and dissipation of power, and provide hydraulic systems with high-energy efficiency, flexible control, and insensitivity to contamination. In this article, the analytical models of an SIHC in a three-port flow-booster configuration were used and validated at high operating pressure, with the low- and high-pressure supplies of 30 and 90 bar and a high delivery flow rate of 21 L/min. The system dynamics, flow responses, and power consumption were investigated and theoretically and experimentally validated. Results were compared to previous results achieved using low operating pressures, where low- and high-pressure supplies were 20 and 30 bar, and the delivery flow rate was 7 L/min. We concluded that the analytical models could effectively predict SIHC performance, and higher operating pressures and flow rates could result in system uncertainties that need to be understood well. As high operating pressure or flow rate is a common requirement in hydraulic systems, this constitutes an important contribution to the development of newly switched inertance hydraulic converters and the improvement of fluid-power energy efficiency.
Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network
Authors: Mansor Mohd. Asyraf , Mohd Jamaludin Siti Zulaikha , Mohd Kasihmuddin Mohd Shareduwan , Alzaeemi Shehab Abdulhabib , Md Basir Md Faisal , Sathasivam Saratha
Published: February 2020

Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered from significant computational error and difficulties in approximating the optimal hidden neuron, especially when dealing with Boolean Satisfiability logical rule. In this paper, we present a comprehensive investigation of the potential effect of systematic Satisfiability programming as a logical rule, namely 2 Satisfiability (2SAT) to optimize the output weights and parameters in RBFNN. The 2SAT logical rule has extensively applied in various disciplines, ranging from industrial automation to the complex management system. The core impetus of this study is to investigate the effectiveness of 2SAT logical rule in reducing the computational burden for RBFNN by obtaining the parameters in RBFNN. The comparison is made between RBFNN and the existing method, based on the Hopfield Neural Network (HNN) in searching for the optimal neuron state by utilizing different numbers of neurons. The comparison was made with the HNN as a benchmark to validate the final output of our proposed RBFNN with 2SAT logical rule. Note that the final output in HNN is represented in terms of the quality of the final states produced at the end of the simulation. The simulation dynamic was carried out by using the simulated data, randomly generated by the program. In terms of 2SAT logical rule, simulation revealed that RBFNN has two advantages over HNN model: RBFNN can obtain the correct final neuron state with the lowest error and does not require any approximation for the number of hidden layers. Furthermore, this study provides a new paradigm in the field feed-forward neural network by implementing a more systematic propositional logic rule.
Shannon Entropy in Confined He-Like Ions within a Density Functional Formalism
Authors: Majumdar Sangita , Roy Amlan K.
Published: February 2020

Shannon entropy in position ( S r ) and momentum ( S p ) spaces, along with their sum ( S t ) are presented for unit-normalized densities of He, Li + and Be 2 + ions, spatially confined at the center of an impenetrable spherical enclosure defined by a radius r c . Both ground, as well as some selected low-lying singly excited states, viz., 1sns (n = 2–4) 3S, 1snp (n = 2–3) 3P, 1s3d 3D, are considered within a density functional methodology that makes use of a work function-based exchange potential along with two correlation potentials (local Wigner-type parametrized functional, as well as the more involved non-linear gradient- and Laplacian-dependent Lee-Yang-Parr functional). The radial Kohn-Sham (KS) equation is solved using an optimal spatial discretization scheme via the generalized pseudospectral (GPS) method. A detailed systematic analysis of the confined system (relative to the corresponding free system) is performed for these quantities with respect to r c in tabular and graphical forms, with and without electron correlation. Due to compression, the pattern of entropy in the aforementioned states becomes characterized by various crossovers at intermediate and lower r c regions. The impact of electron correlation is more pronounced in the weaker confinement limit and appears to decay with the rise in confinement strength. The exchange-only results are quite good to provide a decent qualitative discussion. The lower bounds provided by the entropic uncertainty relation hold well in all cases. Several other new interesting features are observed.
Hydration of Nucleobase as Probed by Electron Emission of Uridine-5′-Mono-Phosphate (UMP) in Aqueous Solution Induced by Nitrogen K-Shell Ionization
Authors: Takeda Yasuaki , Shimada Hiroyuki , Miura Ryosuke , Ukai Masatoshi , Fujii Kentaro , Fukuda Yoshihiro , Saitoh Yuji
Published: February 2020

To identify the precise early radiation processes of DNA lesions, we measure electron kinetic energy spectra emitted from uridine-5′ monophosphate (UMP) in aqueous solution for the photoionization of the N 1s orbital electron and for the following Auger effect using a monochromatic soft X-ray synchrotron radiation at energies above the nitrogen K-shell ionization threshold. The change of photoelectron spectra for UMP in aqueous solutions at different proton concentrations (pH = 7.5 and 11.3) is ascribed to the chemical shift of the N3 nitrogen atom in uracil moiety of canonical and deprotonated forms. The lowest double ionization potentials for aqueous UMP at different pH obtained from the Auger electron spectra following the N 1s photoionization values show the electrostatic aqueous interaction of uracil moiety of canonical (neutral) and deprotonated (negatively charged) forms with hydrated water molecules.
Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China
Authors: Wang Yuchen , Zhang Yu , Ding Nan , Qin Kai , Yang Xiaoyan
Published: February 2020

As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m−2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.
A Spatio-Temporal Analysis of Rainfall and Drought Monitoring in the Tharparkar Region of Pakistan
Authors: Usman Muhammad , Nichol Janet E.
Published: February 2020

The Tharpakar desert region of Pakistan supports a population approaching two million, dependent on rain-fed agriculture as the main livelihood. The almost doubling of population in the last two decades, coupled with low and variable rainfall, makes this one of the world’s most food-insecure regions. This paper examines satellite-based rainfall estimates and biomass data as a means to supplement sparsely distributed rainfall stations and to provide timely estimates of seasonal growth indicators in farmlands. Satellite dekadal and monthly rainfall estimates gave good correlations with ground station data, ranging from R = 0.75 to R = 0.97 over a 19-year period, with tendency for overestimation from the Tropical Rainfall Monitoring Mission (TRMM) and underestimation from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) datasets. CHIRPS was selected for further modeling, as overestimation from TRMM implies the risk of under-predicting drought. The use of satellite rainfall products from CHIRPS was also essential for derivation of spatial estimates of phenological variables and rainfall criteria for comparison with normalized difference vegetation index (NDVI)-based biomass productivity. This is because, in this arid region where drought is common and rainfall unpredictable, determination of phenological thresholds based on vegetation indices proved unreliable. Mapped rainfall distributions across Tharparkar were found to differ substantially from those of maximum biomass (NDVImax), often showing low NDVImax in zones of higher annual rainfall, and vice versa. This mismatch occurs in both wet and dry years. Maps of rainfall intensity suggest that low yields often occur in areas with intense rain causing damage to ripening crops, and that total rainfall in a season is less important than sustained water supply. Correlations between rainfall variables and NDVImax indicate the difficulty of predicting drought early in the growing season in this region of extreme climatic variability. Mapped rainfall and biomass distributions can be used to recommend settlement in areas of more consistent rainfall.
Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques
Authors: Delgado Blasco José Manuel , Cian Fabio , Hanssen Ramon F. , Verstraeten Gert
Published: February 2020

Population growth in rural areas of Egypt is rapidly transforming the landscape. New cities are appearing in desert areas while existing cities and villages within the Nile floodplain are growing and pushing agricultural areas into the desert. To enable control and planning of the urban transformation, these rapid changes need to be mapped with high precision and frequency. Urban detection in rural areas in optical remote sensing is problematic when urban structures are built using the same materials as their surroundings. To overcome this limitation, we propose a multi-temporal classification approach based on satellite data fusion and artificial neural networks. We applied the proposed methodology to data of the Egyptian regions of El-Minya and part of Asyut governorates collected from 1998 until 2015. The produced multi-temporal land cover maps capture the evolution of the area and improve the urban detection of the European Space Agency (ESA) Climate Change Initiative Sentinel-2 Prototype Land Cover 20 m map of Africa and the Global Human Settlements Layer from the Joint Research Center (JRC). The extension of urban and agricultural areas increased over 65 km2 and 200 km2, respectively, during the entire period, with an accelerated increase analysed during the last period (2010–2015). Finally, we identified the trends in urban population density as well as the relationship between farmed and built-up land.
An Assessment of the GOCE High-Level Processing Facility (HPF) Released Global Geopotential Models with Regional Test Results in Turkey
Authors: Erol Bihter , Işık Mustafa Serkan , Erol Serdar
Published: February 2020

The launch of dedicated satellite missions at the beginning of the 2000s led to significant improvement in the determination of Earth gravity field models. As a consequence of this progress, both the accuracies and the spatial resolutions of the global geopotential models increased. However, the spectral behaviors and the accuracies of the released models vary mainly depending on their computation strategies. These strategies are briefly explained in this article. Comprehensive quality assessment of the gravity field models by means of spectral and statistical analyses provides a comparison of the gravity field mapping accuracies of these models, as well as providing an understanding of their progress. The practical benefit of these assessments by means of choosing an optimal model with the highest accuracy and best resolution for a specific application is obvious for a broad range of geoscience applications, including geodesy and geophysics, that employ Earth gravity field parameters in their studies. From this perspective, this study aims to evaluate the GOCE High-Level Processing Facility geopotential models including recently published sixth releases using different validation methods recommended in the literature, and investigate their performances comparatively and in addition to some other models, such as GOCO05S, GOGRA04S and EGM2008. In addition to the validation statistics from various countries, the study specifically emphasizes the numerical test results in Turkey. It is concluded that the performance improves from the first generation RL01 models toward the final RL05 models, which were based on the entire mission data. This outcome was confirmed when the releases of different computation approaches were considered. The accuracies of the RL05 models were found to be similar to GOCO05S, GOGRA04S and even to RL06 versions but better than EGM2008, in their maximum expansion degrees. Regarding the results obtained from these tests using the GPS/leveling observations in Turkey, the contribution of the GOCE data to the models was significant, especially between the expansion degrees of 100 and 250. In the study, the tested geopotential models were also considered for detailed geoid modeling using the remove-compute-restore method. It was found that the best-fitting geopotential model with its optimal expansion degree (please see the definition of optimal degree in the article) improved the high-frequency regional geoid model accuracy by almost 15%.
Identification of Active Gully Erosion Sites in the Loess Plateau of China Using MF-DFA
Authors: Cao Jianjun , Tang Guoan , Fang Xuan , Liu Yongjuan , Zhu Ying , Li Jinlian , Wagner Wolfgang
Published: February 2020

Gullies of different scales and types have developed in the Loess Plateau, China. Differences in the amount of gully erosion influence the development, evolution, morphology, and spatial distribution of these gullies. The strengths of headward erosion on the gully shoulder line are used to dictate soil and water conservation measures. In this study, six typical loess landforms in the Loess Plateau were selected as sampling sites: Shenmu, Suide, Ganquan, Yanchuan, Yijun, and Chunhua, which respectively represent loess–aeolian and dune transition zones, loess hills, loess ridge hills, loess ridges, loess long-ridge fragmented tablelands, and loess tablelands. Using 5 m resolution digital elevation model data from the National Basic Geographic Information Database, a small representative watershed was selected from each sampling site to obtain elevation data on the terrain profiles of gully shoulder lines. Multifractal detrended fluctuation analysis (MF-DFA) was used to conduct statistical and comparative analysis of the elevation fluctuation characteristics of these profiles. The results show that MF-DFA is capable of detecting active gully erosion sites. Sites of active gully erosion are concentrated in Shenmu and Suide but more widely distributed in the other five sites. The results provide a scientific basis for small watershed management planning and the design of soil and water conservation measures.
Sliding Time Master Digital Image Correlation Analyses of CubeSat Images for landslide Monitoring: The Rattlesnake Hills Landslide (USA)
Authors: Mazzanti Paolo , Caporossi Paolo , Muzi Riccardo
Published: February 2020

Landslide monitoring is a global challenge that can take strong advantage from opportunities offered by Earth Observation (EO). The increasing availability of constellations of small satellites (e.g., CubeSats) is allowing the collection of satellite images at an incredible revisit time (daily) and good spatial resolution. Furthermore, this trend is expected to grow rapidly in the next few years. In order to explore the potential of using a long stack of images for improving the measurement of ground displacement, we developed a new procedure called STMDA (Slide Time Master Digital image correlation Analyses) that we applied to one year long stack of PlanetScope images for back analyzing the displacement pattern of the Rattlesnake Hills landslide occurred between the 2017 and 2018 in the Washington State (USA). Displacement maps and time-series of displacement of different portions of the landslide was derived, measuring velocity up to 0.5 m/week, i.e., very similar to velocities available in literature. Furthermore, STMDA showed also a good potential in denoising the time-series of displacement at the whole scale with respect to the application of standard DIC methods, thus providing displacement precision up to 0.01 pixels.
Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index
Authors: Wu Jinhui , Liang Shunlin
Published: February 2020

Quantitative approaches to measuring and assessing terrestrial ecosystem resilience, which expresses the ability of an ecosystem to recover from disturbances without shifting to an alternative state or losing function and services, is critical and essential to forecasting how terrestrial ecosystems will respond to global change. However, global and continuous terrestrial resilience measurement is fraught with difficulty, and the corresponding attribution of resilience dynamics is lacking in the literature. In this study, we assessed global terrestrial ecosystem resilience based on the long time-series GLASS LAI product and GIMMS AVHRR LAI 3g product, and validated the results using drought and fire events as the main disturbance indicators. We also analyzed the spatial and temporal variations of global terrestrial ecosystem resilience and attributed their dynamics to climate change and environmental factors. The results showed that arid and semiarid areas exhibited low resilience. We found that evergreen broadleaf forest exhibited the highest resilience (mean resilience value (from GLASS LAI): 0.6). On a global scale, the increase of mean annual precipitation had a positive impact on terrestrial resilience enhancement, while we found no consistent relationships between mean annual temperature and terrestrial resilience. For terrestrial resilience dynamics, we observed three dramatic raises of disturbance frequency in 1989, 1995, and 2001, respectively, along with three significant drops in resilience correspondingly. Our study mapped continuous spatiotemporal variation and captured interannual variations in terrestrial ecosystem resilience. This study demonstrates that remote sensing data are effective for monitoring terrestrial resilience for global ecosystem assessment.
Insect Monitoring Radar: Maximizing Performance and Utility
Authors: Drake V. Alistair , Hatty Shane , Symons Colin , Wang Haikou
Published: February 2020

Autonomously-operating radars employing the ‘ZLC configuration’ have been providing long-term datasets of insect flight activity to heights of about 1 km since the late 1990s. A unit of this type operating in Australia has recently received a major upgrade. The aim of the project was to maximize the utility of the radar to entomologists and aeroecologists by providing larger and more continuous datasets and extending observations to 2.5 km. The upgrade was achieved primarily by incorporating modern digital technology, which has enabled much improved data-acquisition, control performance, and data-archiving capacity; by implementing a more comprehensive observing protocol; and by replacing fixed electronic signal-acquisition gates with specially developed software that identifies insect echoes and applies a narrow moving gate that follows them. The upgraded version provides an approximately five-fold increase in hourly sample sizes, a doubling of the duration of observations (from 12 to 24 h per day) and a doubling of the height range over which observations are made. The design considerations (incentives and constraints) that informed the various subsystem implementations are identified, and the necessary compromises are discussed. Observations of the development of a layer echo during a migration by two different insect types are presented as a demonstration of the upgraded unit’s capabilities.
Monitoring Grass Phenology and Hydrological Dynamics of an Oak–Grass Savanna Ecosystem Using Sentinel-2 and Terrestrial Photography
Authors: Gómez-Giráldez Pedro J. , Pérez-Palazón María J. , Polo María J. , González-Dugo María P.
Published: February 2020

Annual grasslands are an essential component of oak savanna ecosystems as the primary source of fodder for livestock and wildlife. Drought resistance adaptation has led them to complete their life cycle before serious soil and plant water deficits develop, resulting in a close link between grass phenology and soil water dynamics. In this work, these links were explored using a combination of terrestrial photography, satellite imagery and hydrological ground measurements. We obtained key phenological parameters of the grass cycle from terrestrial camera data using the Green Chromatic Coordinate (GCCc) index. These parameters were compared with those provided by time-series of vegetation indices (VI) obtained from Sentinel-2 (S2) satellites and time-series of abiotic variables, which defined the hydrology of the system. The results showed that the phenological parameters estimated by the S2 Normalized Difference Vegetation Index (NDVI) (r = 0.83, p < 0.001) and soil moisture (SM) (r = 0.75, p < 0.001) presented the best agreement with ground-derived observations compared to those provided by other vegetation indices and abiotic variables. The study of NDVI and SM dynamics, that was extended over four growing seasons (July 2015–May 2019), showed that the seasonality of both variables was highly synchronized, with the best agreements at the beginning and at the end of the dry seasons. However, stage changes were estimated first by SM, followed by NDVI, with a delay of between 3 and 10 days. These results support the use of a multi-approach method to monitor the phenology and the influence of the soil moisture dynamic under the study conditions.
Harmonizing Multi-Source Sonar Backscatter Datasets for Seabed Mapping Using Bulk Shift Approaches
Authors: Misiuk Benjamin , Brown Craig J. , Robert Katleen , Lacharité Myriam
Published: February 2020

The development of multibeam echosounders (MBES) as a seabed mapping tool has resulted in the widespread uptake of backscatter intensity as an indicator of seabed substrate properties. Though increasingly common, the lack of standard calibration and the characteristics of individual sonars generally produce backscatter measurements that are relative to a given survey, presenting major challenges for seabed mapping in areas that comprise multiple MBES surveys. Here, we explore methods for backscatter dataset harmonization that leverage areas of mutual overlap between surveys for relative statistical calibration—referred to as “bulk shift” approaches. We use three multispectral MBES datasets to simulate the harmonization of backscatter collected over multiple years, and using multiple operating frequencies. Results suggest that relatively simple statistical models are adequate for bulk shift harmonization procedures, and that more flexible approaches may produce inconsistent results that risk statistical overfitting. While harmonizing datasets collected using the same operating frequency from separate surveys is generally feasible given reasonable temporal limitations, results suggest that the success at harmonizing datasets of different operating frequencies partly depends on the extent to which the frequencies differ. We recommend approaches and diagnostics for ensuring the quality of harmonized backscatter mosaics, and provide an R function for implementing the methods presented here.
Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine
Authors: Li Qingyu , Qiu Chunping , Ma Lei , Schmitt Michael , Zhu Xiao
Published: February 2020

The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone.
Skills of Thunderstorm Prediction by Convective Indices over a Metropolitan Area: Comparison of Microwave and Radiosonde Data
Authors: Kulikov Mikhail Yu. , Belikovich Mikhail V. , Skalyga Natalya K. , Shatalina Maria V. , Dementyeva Svetlana O. , Ryskin Vitaly G. , Shvetsov Alexander A. , Krasil’nikov Alexander A. , Serov Evgeny A. , Feigin Alexander M.
Published: February 2020

In this work, we compare the values of 15 convective indices obtained from radiosonde and microwave temperature and water vapor profiles simultaneously measured over Nizhny Novgorod (56.2°N, 44°E) during 5 convective seasons of 2014–2018. A good or moderate correlation (with coefficients of ~0.7–0.85) is found for most indices. We assess the thunderstorm prediction skills with a lead time of 12 h for each radiosonde and microwave index. It is revealed that the effectiveness of thunderstorm prediction by microwave indices is much better than by radiosonde ones. Moreover, a good correlation between radiosonde and microwave values of a certain index does not necessarily correspond to similar prediction skills. Eight indices (Showalter Index, Maximum Unstable Convective Available Potential Energy (CAPE), Total Totals index, TQ index, Jefferson Index, S index, K index, and Thompson index) are regarded to be the best predictors from both the true skill statistics (TSS) maximum and Heidke skill score (HSS) maximum points of view. In the case of radiosonde data, the best indices are the Jefferson Index, K index, S index, and Thompson index. Only TSS and HSS maxima for these indices are close to the microwave ones, whereas the prediction skills of other radiosonde indices are essentially worse than in the case of microwave data. The analysis suggests that the main possible reason of this discrepancy is an unexpectedly low quality of radiosonde data.
Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter
Authors: Chen Xinqiang , Chen Huixing , Wu Huafeng , Huang Yanguo , Yang Yongsheng , Zhang Wenhui , Xiong Pengwen
Published: February 2020

Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker’s performance on typical maritime traffic scenarios through four maritime surveillance videos.
Signal-to-Noise Ratio of Brillouin Grating Measurement with Micrometer-Resolution Optical Low Coherence Reflectometry
Authors: Takada Kazumasa , Satoh Shin-ichi , Kawakami Akiya
Published: February 2020

Signal-dependent speckle-like noise was the dominant noise in a Brillouin grating measurement with micrometer-resolution optical low coherence reflectometry (OLCR). The noise was produced by the interaction of a Stokes signal with beat noise caused by a leaked pump light via square-law detection. The resultant signal-to-noise ratio (SNR) was calculated and found to be proportional to the square root of the dynamic range (DR) defined by the ratio of the Stokes signal magnitude to the variance of the beat noise. The calculation showed that even when we achieved a DR of 20 dB on a logarithmic scale, the SNR value was only 7 on a linear scale and the detected signal tended to fluctuate over ±14% with respect to the mean level. We achieved an SNR of 24 by attenuating the pump light power entering the balanced mixer by 55 dB, and this success enabled us to measure the Brillouin spectrum distributions of mated fiber connectors and a 3-dB fused fiber coupler with a micrometer resolution as examples of OLCR diagnosis.
Validation of a Body-Conducted Sound Sensor for Respiratory Sound Monitoring and a Comparison with Several Sensors
Authors: Joyashiki Takeshi , Wada Chikamune
Published: February 2020

The ideal respiratory sound sensor exhibits high sensitivity, wide-band frequency characteristics, and excellent anti-noise properties. We investigated the body-conducted sound sensor (BCS) and verified its usefulness in respiratory sound monitoring through comparison with an air-coupled microphone (ACM) and acceleration sensor (B & K: 8001). We conducted four experiments for comparison: (1) estimation by equivalent circuit model of sensors and measurement by a sensitivity evaluation system; (2) measurement of tissue-borne sensitivity-to-air-noise sensitivity ratio (SRTA); (3) respiratory sound measurement through a simulator; and (4) actual respiratory sound measurement using human subjects. For (1), the simulation and measured values of all the sensors showed good agreement; BCS demonstrated sensitivity ~10 dB higher than ACM and higher sensitivity in the high-frequency segments compared with 8001. In (2), BCS showed high SRTA in the 600–1000 and 1200–2000-Hz frequency segments. In (3), BCS detected wheezes in the high-frequency segments of the respiratory sound. Finally, in (4), the sensors showed similar characteristics and features in the high-frequency segments as the simulators, where typical breathing sound detection was possible. BCS displayed a higher sensitivity and anti-noise property in high-frequency segments compared with the other sensors and is a useful respiratory sound sensor.
Chiral Plasmonics and Their Potential for Point-of-Care Biosensing Applications
Authors: A. Paiva-Marques Willian , Reyes Gómez Faustino , N. Oliveira Osvaldo , Mejía-Salazar J. Ricardo
Published: February 2020

There has been growing interest in using strong field enhancement and light localization in plasmonic nanostructures to control the polarization properties of light. Various experimental techniques are now used to fabricate twisted metallic nanoparticles and metasurfaces, where strongly enhanced chiral near-fields are used to intensify circular dichroism (CD) signals. In this review, state-of-the-art strategies to develop such chiral plasmonic nanoparticles and metasurfaces are summarized, with emphasis on the most recent trends for the design and development of functionalizable surfaces. The major objective is to perform enantiomer selection which is relevant in pharmaceutical applications and for biosensing. Enhanced sensing capabilities are key for the design and manufacture of lab-on-a-chip devices, commonly named point-of-care biosensing devices, which are promising for next-generation healthcare systems.
Enhanced Heuristic Drift Elimination with Adaptive Zero-Velocity Detection and Heading Correction Algorithms for Pedestrian Navigation
Authors: Zhu Ruihui , Wang Yunjia , Yu Baoguo , Gan Xingli , Jia Haonan , Wang Boyuan
Published: February 2020

As pedestrian dead-reckoning (PDR), based on foot-mounted inertial sensors, suffers from accumulated error in velocity and heading, an improved heuristic drift elimination (iHDE) with a zero-velocity update (ZUPT) algorithm was proposed for simultaneously reducing the error in heading and velocity in complex paths, i.e., with pathways oriented at 45°, curved corridors, and wide areas. However, the iHDE algorithm does not consider the changes in pedestrian movement modes, and it can deteriorate when a pedestrian walks along a straight path without a pre-defined dominant direction. To solve these two problems, we propose enhanced heuristic drift elimination (eHDE) with an adaptive zero-velocity update (AZUPT) algorithm and novel heading correction algorithm. The relationships between the magnitude peaks of the y-axis angular rate and the detection thresholds were established only using the readings of the three-axis accelerometer and the three-axis gyroscopic, and a mechanism for constructing temporary dominant directions in real time was introduced. Real experiments were performed and the results showed that the proposed algorithm can improve the still-phase detection accuracy of a pedestrian at different movement motions and outperforms the iHDE algorithm in complex paths with many straight features.
A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis
Authors: Tahir Anas M. , Chowdhury Muhammad E. H. , Khandakar Amith , Al-Hamouz Sara , Abdalla Merna , Awadallah Sara , Reaz Mamun Bin Ibne , Al-Emadi Nasser
Published: February 2020

Gait analysis is a systematic study of human locomotion, which can be utilized in various applications, such as rehabilitation, clinical diagnostics and sports activities. The various limitations such as cost, non-portability, long setup time, post-processing time etc., of the current gait analysis techniques have made them unfeasible for individual use. This led to an increase in research interest in developing smart insoles where wearable sensors can be employed to detect vertical ground reaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortable for gait analysis, and can monitor plantar pressure frequently through embedded sensors that convert the applied pressure to an electrical signal that can be displayed and analyzed further. Several research teams are still working to improve the insoles’ features such as size, sensitivity of insoles sensors, durability, and the intelligence of insoles to monitor and control subjects’ gait by detecting various complications providing recommendation to enhance walking performance. Even though systematic sensor calibration approaches have been followed by different teams to calibrate insoles’ sensor, expensive calibration devices were used for calibration such as universal testing machines or infrared motion capture cameras equipped in motion analysis labs. This paper provides a systematic design and characterization procedure for three different pressure sensors: force-sensitive resistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that can be used for detecting vGRF using a smart insole. A simple calibration method based on a load cell is presented as an alternative to the expensive calibration techniques. In addition, to evaluate the performance of the different sensors as a component for the smart insole, the acquired vGRF from different insoles were used to compare them. The results showed that the FSR is the most effective sensor among the three sensors for smart insole applications, whereas the piezoelectric sensors can be utilized in detecting the start and end of the gait cycle. This study will be useful for any research group in replicating the design of a customized smart insole for gait analysis.
A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives
Authors: Garramiola Fernando , Poza Javier , Madina Patxi , del Olmo Jon , Ugalde Gaizka
Published: February 2020

Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order to move from preventive maintenance to condition-based maintenance. Most research works are limited to sensor fault detection and isolation, but only a few of them analyze the types of sensor faults, such as offset or gain, with the aim of reconfiguring the sensor in order to implement a fault tolerant system. This article is based on a fusion of model-based and data-driven techniques. First, an observer-based approach, using a Sliding Mode observer, is utilized for sensor fault reconstruction in real time. Then, once the fault is detected, a time window of sensor measurements and sensor fault reconstruction is sent to the remote maintenance center for fault evaluation. Finally, an offline processing is carried out to discriminate between gain and offset sensor faults, in order to get a maintenance decision-making to reconfigure the sensor during the next train stop. Fault classification is done by means of histograms and statistics. The technique here proposed is applied to the DC-link voltage sensor in a railway traction drive and is validated in a hardware-in-the-loop platform.
Empowering Children with ASD and Their Parents: Design of a Serious Game for Anxiety and Stress Reduction
Authors: Carlier Stéphanie , Van der Paelt Sara , Ongenae Femke , De Backere Femke , De Turck Filip
Published: February 2020

Autism Spectrum Disorder (ASD) is characterized by social interaction difficulties and communication difficulties. Moreover, children with ASD often suffer from other co-morbidities, such as anxiety and depression. Finding appropriate treatment can be difficult as symptoms of ASD and co-morbidities often overlap. Due to these challenges, parents of children with ASD often suffer from higher levels of stress. This research aims to investigate the feasibility of empowering children with ASD and their parents through the use of a serious game to reduce stress and anxiety and a supporting parent application. The New Horizon game and the SpaceControl application were developed together with therapists and according to guidelines for e-health patient empowerment. The game incorporates two mini-games with relaxation techniques. The performance of the game was analyzed and usability studies with three families were conducted. Parents and children were asked to fill in the Spence’s Children Anxiety Scale (SCAS) and Spence Children Anxiety Scale-Parents (SCAS-P) anxiety scale. The game shows potential for stress and anxiety reduction in children with ASD.