bluuebunny/crossref_metadata_2025 huggingface dataset:
Size-Dependent Biodistribution of Fluorescent Furano-Allocolchicinoid-Chitosan Formulations in Mice
Authors: Gracheva Iuliia , Konovalova Maria , Aronov Dmitrii , Moiseeva Ekaterina , Fedorov Alexey , Svirshchevskaya Elena
Published: June 2021

The aim of this study was to compare the biodistribution in mice of functionalized rhodamine B (Rh) labeled colchicine derivative furano-allocolchicinoid (AC, 6) either conjugated to 40 kDa chitosan (AC-Chi, 8) or encapsulated into chitosan nanoparticles (AC-NPs). AC-NPs were formed by ionotropic gelation and were 400–450 nm in diameter as estimated in mice by dynamic light scattering and confocal microscopy. AC-Chi and AC-NPs preserved the specific colchicine activity in vitro. AC preparations were once IV injected into C75BL/6 mice; muscles, spleen, kidney, liver, lungs, blood cells and serum were collected at 30 min, 2, 5, 10, and 20 h post injection. To analyze the distribution of the furano-allocolchicinoid preparations in body liquids and tissues, Rh was measured directly in sera or extracted by acidic ethanol from tissue homogenates. Preliminary Rh extraction rate was estimated in vitro in tissue homogenates and was around 25–30% from total quantity added. After in vivo injection, AC-NPs were accumulated more in liver and spleen, while less in kidney and lungs in comparison with free AC and AC-Chi. Therefore, incorporation of colchicine derivatives as well as other hydrophobic substances into nano/micro sized carriers may help redistribute the drug to different organs and, possibly, improve antitumor accumulation.
Christianity and Slavic Folk Culture: The Mechanisms of Their Interaction
Authors: Tolstaya Svetlana M.
Published: June 2021

In Slavic folk culture, Christianity is a foreign, borrowed cultural model, while the oral tradition is native and familiar. The different areas of folk culture were influenced to varying degrees by the Christian tradition. The most dependent area of Slavic folk culture on Christianity was the calendar. In many cases, it only superficially accepted the Christian content of calendar elements and reinterpreted it in accordance with the traditional mythological notions. The same can be said about the folk cult of saints. The Christian saints replaced pagan gods and over time were included in the system of folk ideas, beliefs and rituals. The mechanism for regulating the balance between man and the world is a system of prohibitions, the violation of which is recognized as sin and is punished by natural disasters, death, disease and human misfortunes. The Slavic folk tradition adapted not only the individual elements, structures and semantic categories of Christianity, but also the whole texts, plots, motifs, and themes developed in various folklore genres. Therefore, the pre-Christian folk tradition of the Slavs was able to assimilate many Christian concepts, symbols, and texts, translate them into its own language and fill them with its own content.
Building Structure Mapping on Level Terrains and Sea Surfaces in Vietnam
Authors: Ngo Khanh , Nghiem Son , Lechner Alex , Vu Tuong
Published: June 2021

Mapping building structures is crucial for environmental change and impact assessment, and is especially important to accurately estimate fossil fuel CO2 emissions from human settlements. In this regard, the objective of this study is to develop novel and robust methods using time-series data acquired from Sentinel-1 synthetic aperture radar (SAR) to identify and map persistent building structures from coastal plains to high plateaus, as well as on the sea surface. From annual composites of SAR data in the two-dimensional VV-VH polarization space, we determined the VV-VH domain for detecting building structures, whose persistence was defined based on the number of times that a pixel was identified as a building in time-series data. Moreover, the algorithm accounted for misclassified buildings due to water-tree interactions in radar signatures and due to topography effects in complex mountainous landforms. The methods were tested in five cities (Bạc Liêu, Cà Mau, Sóc Trăng, Tân An, and Phan Thiết) in Vietnam located in different socio-environmental regions with a range of urban configurations. Using in-situ data and field observations, we validated the methods and found that the results were accurate, with an average false negative rate of 10.9% and average false positive rate of 6.4% for building detection. The algorithm could also detect small houses in rural settlements and in small islands such as in Hòn Sơn and Hòn Tre. Over sea surfaces, the algorithm effectively identified lines of power poles connecting islands to the mainland, guard shacks in marine blood clam farms in Kiên Giang, individual wind towers in the off-shore wind farm in Bạc Liêu, and oilrigs in the Vũng Tàu oil fields. The new approach was developed to be robust against variations in SAR incidence and azimuth angles. The results demonstrated the potential use of satellite dual-polarization SAR to identify persistent building structures annually across rural–urban landscapes and on sea surfaces with different environmental conditions.
Cross-Domain Submesoscale Eddy Detection Neural Network for HF Radar
Authors: Liu Fangyuan , Zhou Hao , Huang Weimin , Tian Yingwei , Wen Biyang
Published: June 2021

With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of detecting eddies from one experiment is not directly inherited from the detection model for other experiments. Therefore, a cross-domain submesoscale eddy detection neural network (CDEDNet) based on the high-frequency radar (HFR) data of the Nansan and Xuwen region is proposed in this paper. Firstly, a fundamental deep eddy detection architecture CDEDNet-0 is constructed with a fully convolutional network (FCN). Secondly, for solving the problem of insufficient labeled eddy data, an instance-based domain adaption method is adopted in CDEDNet-1 to increase training samples. Thirdly, for tackling the problem of unable to inherit previous detection experience, parameter-based transfer learning is incorporated in CDEDNet-2 for multi-scene eddy detection. The experiment results demonstrate CDEDNet-1 and CDEDNet-2 perform better than CDEDNet-0 in terms of accuracy. Meanwhile, eddy characteristics including eddy type, radius, occurring time, merger, and dynamic trajectory are analyzed for the Nansan and Xuwen regions.
A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content
Authors: Lv Jichao , Zhang Rui , Tu Jinsheng , Liao Mingjie , Pang Jiatai , Yu Bin , Li Kui , Xiang Wei , Fu Yin , Liu Guoxiang
Published: June 2021

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.
Spatiotemporal Variability of Chlorophyll-a and Sea Surface Temperature, and Their Relationship with Bathymetry over the Coasts of UAE
Authors: Hussein Khalid A. , Al Abdouli Khameis , Ghebreyesus Dawit T. , Petchprayoon Pakorn , Al Hosani Naeema , O. Sharif Hatim
Published: June 2021

The catastrophic implication of harmful algal bloom (HAB) events in the Arabian Gulf is a strong indication that the study of the spatiotemporal distribution of chlorophyll-a and its relationship with other variables is critical. This study analyzes the relationship between chlorophyll-a (Chl-a) and sea surface temperature (SST) and their trends in the Arabian Gulf and the Gulf of Oman along the United Arab Emirates coast. Additionally, the relationship between bathymetry and Chl-a and SST was examined. The MODIS Aqua product with a resolution of 1 × 1 km2 was employed for both chlorophyll-a and SST covering a timeframe from 2003 to 2019. The highest concentration of chlorophyll-a was seen in the Strait of Hormuz with an average of 2.8 mg m−3, which is 1.1 mg m−3 higher than the average for the entire study area. Three-quarters of the study area showed a significant correlation between the Chl-a and SST. The shallow (deep) areas showed a strong positive (negative) correlation between the Chl-a and SST. The results indicate the presence of trends for both variables across most of the study area. SST significantly increased in more than two-thirds of the study area in the summer with no significant trends detected in the winter.
Insights from the P Wave Travel Time Tomography in the Upper Mantle Beneath the Central Philippines
Authors: Shi Huiyan , Li Tonglin , Sun Rui , Zhang Gongbo , Zhang Rongzhe , Kang Xinze
Published: June 2021

In this paper, we present a high resolution 3-D tomographic model of the upper mantle obtained from a large number of teleseismic travel time data from the ISC in the central Philippines. There are 2921 teleseismic events and 32,224 useful relative travel time residuals picked to compute the velocity structure in the upper mantle, which was recorded by 87 receivers and satisfied the requirements of teleseismic tomography. Crustal correction was conducted to these data before inversion. The fast-marching method (FMM) and a subspace method were adopted in the forward step and inversion step, respectively. The present tomographic model clearly images steeply subducting high velocity anomalies along the Manila trench in the South China Sea (SCS), which reveals a gradual changing of the subduction angle and a gradual shallowing of the subduction depth from the north to the south. It is speculated that the change in its subduction depth and angle indicates the cessation of the SCS spreading from the north to the south, which also implies that the northern part of the SCS opened earlier than the southern part. Subduction of the Philippine Sea (PS) plate is exhibited between 14° N and 9° N, with its subduction direction changing from westward to eastward near 13° N. In the range of 11° N–9° N, the subduction of the Sulu Sea (SS) lies on the west side of PS plate. It is notable that obvious high velocity anomalies are imaged in the mantle transition zone (MTZ) between 14° N and 9° N, which are identified as the proto-SCS (PSCS) slabs and paleo-Pacific (PP) plate. It extends the location of the paleo-suture of PSCS-PP eastward from Borneo to the Philippines, which should be considered in studying the mechanism of the SCS and the tectonic evolution in SE Asia.
Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review
Authors: Maxwell Aaron E. , Warner Timothy A. , Guillén Luis Andrés
Published: June 2021

Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently developed image classification approach. With origins in the computer vision and image processing communities, the accuracy assessment methods developed for CNN-based DL use a wide range of metrics that may be unfamiliar to the remote sensing (RS) community. To explore the differences between traditional RS and DL RS methods, we surveyed a random selection of 100 papers from the RS DL literature. The results show that RS DL studies have largely abandoned traditional RS accuracy assessment terminology, though some of the accuracy measures typically used in DL papers, most notably precision and recall, have direct equivalents in traditional RS terminology. Some of the DL accuracy terms have multiple names, or are equivalent to another measure. In our sample, DL studies only rarely reported a complete confusion matrix, and when they did so, it was even more rare that the confusion matrix estimated population properties. On the other hand, some DL studies are increasingly paying attention to the role of class prevalence in designing accuracy assessment approaches. DL studies that evaluate the decision boundary threshold over a range of values tend to use the precision-recall (P-R) curve, the associated area under the curve (AUC) measures of average precision (AP) and mean average precision (mAP), rather than the traditional receiver operating characteristic (ROC) curve and its AUC. DL studies are also notable for testing the generalization of their models on entirely new datasets, including data from new areas, new acquisition times, or even new sensors.
A 3D Cascaded Spectral–Spatial Element Attention Network for Hyperspectral Image Classification
Authors: Yan Huaiping , Wang Jun , Tang Lei , Zhang Erlei , Yan Kun , Yu Kai , Peng Jinye
Published: June 2021

Most traditional hyperspectral image (HSI) classification methods relied on hand-crafted or shallow-based descriptors, which limits their applicability and performance. Recently, deep learning has gradually become the mainstream method of HSI classification, because it can automatically extract deep abstract features for classification. However, it remains a challenge to learn more meaningful features for HSI classification from a small training sample set. In this paper, a 3D cascaded spectral–spatial element attention network (3D-CSSEAN) is proposed to solve this issue. The 3D-CSSEAN integrates the spectral–spatial feature extraction and attention area extraction for HSI classification. Two element attention modules in the 3D-CSSEAN enable the deep network to focus on primary spectral features and meaningful spatial features. All attention modules are implemented though several simple activation operations and elementwise multiplication operations. In this way, the training parameters of the network are not added too much, which also makes the network structure suitable for small sample learning. The adopted module cascading pattern not only reduces the computational burden in the deep network but can also be easily operated via plug–expand–play. Experimental results on three public data sets show that the proposed 3D-CSSEAN achieved comparable performance with the state-of-the-art methods.
A Study of Two Impactful Heavy Rainfall Events in the Southern Appalachian Mountains during Early 2020, Part I; Societal Impacts, Synoptic Overview, and Historical Context
Authors: Miller Douglas , Forsythe John , Kusselson Sheldon , Straka III William , Yin Jifu , Zhan Xiwu , Ferraro Ralph
Published: June 2021

Two heavy rainfall events occurring in early 2020 brought flooding, flash flooding, strong winds and tornadoes to the southern Appalachian Mountains. The atmospheric river-influenced events qualified as extreme (top 2.5%) rain events in the archives of two research-grade rain gauge networks located in two different river basins. The earlier event of 5–7 February 2020 was an event of longer duration that caused significant flooding in close proximity to the mountains and had the higher total accumulation observed by the two gauge networks, compared to the later event of 12–13 April 2020. However, its associated downstream flooding response and number of landslides (two) were muted compared to the April event (21). The purpose of this study is to understand differences in the surface response of the two events, primarily by examining the large-scale weather pattern and available space-based observations. Both storms were preceded by anticyclonic Rossby wave breaking events that led to a highly amplified 500 hPa wave during the February storm (a broad continent-wide 500 hPa cyclone during the April storm) in which the accompanying low-level cyclone moved slowly (rapidly). Model analyses and space-based water vapor observations of the two events indicated a deep sub-tropical moisture source during the February storm (converging sub-tropical low-level moisture streams and a dry mid-tropospheric layer during the April storm). Systematic differences of environmental stability were reflected in differences of storm-averaged rain rate intensity, with large-scale atmospheric structures favoring higher intensities during the April storm. Space-based observations of post-storm surface conditions suggested antecedent soil moisture conditioned by rainfall of the February event made the widespread triggering of landslides possible during the higher intensity rains of the April event, a period exceeding the 30 day lag explored in Miller et al. (2019).
Observing and Predicting Coastal Erosion at the Langue de Barbarie Sand Spit around Saint Louis (Senegal, West Africa) through Satellite-Derived Digital Elevation Model and Shoreline
Authors: Taveneau Adélaïde , Almar Rafaël , Bergsma Erwin W. J. , Sy Boubou Aldiouma , Ndour Abdoulaye , Sadio Mamadou , Garlan Thierry
Published: June 2021

Coastal erosion at Saint Louis in Senegal is affecting the local population that consists of primarily fishermen communities in their housing and their access to the sea. This paper aims at quantifying urban beach erosion at Saint Louis, Senegal, West Africa which is located on the northern end of the 13 km long Langue de Barbarie sand spit. The coastal evolution is examined quantitatively over a yearly period using Pleiades sub-metric satellite imagery that allows for stereogrammetry to derive Digital Elevation Models (DEMs). The comparison with ground truth data shows sub-metric differences to the satellite DEMs. Despite its interest in remote areas and developing countries that cannot count on regular surveys, the accuracy of the satellite-derived topography is in the same order as the coastal change itself, which emphasizes its current limitations. These 3D data are combined with decades-long regular Landsat and Sentinel-2 imagery derived shorelines. These observations reveal that the sand spit is stretching, narrowing at its Northern part while it is lengthening downdrift Southward, independently from climatological changes in the wave regime. A parametric model based on a stochastic cyclic sand spit behaviour allows for predicting the next northern opening of a breach and the urban erosion at Saint Louis.
Angular Calibration of Visible and Infrared Binocular All-Sky-View Cameras Using Sun Positions
Authors: Xie Wanyi , Wang Yiren , Xia Yingwei , Gao Zhenyu , Liu Dong
Published: June 2021

Visible and infrared binocular all-sky-view cameras can provide continuous and complementary ground-based cloud observations. Accurate angular calibration for every pixel is an essential premise to further cloud analysis and georeferencing. However, most current calibration methods mainly rely on calibration plates, which still remains difficult for simultaneously calibrating visible and infrared binocular cameras, especially with different imaging resolutions. Thus, in this study, we present a simple and convenient angular calibration method for wide field-of-view visible and infrared binocular cameras. Without any extra instruments, the proposed method only utilizes the relation between the angular information of direct sun lights and the projected sun pixel coordinates to compute the geometric imaging parameters of the two cameras. According to the obtained parameters, the pixel-view-angle for the visible and infrared all-sky images is efficiently computed via back projection. Meanwhile, the projected pixel coordinates for the incident lights at any angle can also be computed via reprojection. Experimental results show the effectiveness and accuracy of the proposed angular calibration through the error estimation of reprojection and back projection. As a novel application, we successfully achieve visible and infrared binocular image registration at the pixel level after finishing angular calibration, which not only verifies the accuracy of calibration results, but also contributes to further cloud parameter analysis under these two different imaging features. The registration results, to our knowledge, also provide a reference for the current blank in visible and infrared binocular cloud image registration.
The Dynamical Structure of a Warm Core Ring as Inferred from Glider Observations and Along-Track Altimetry
Authors: Meunier Thomas , Pallás Sanz Enric , de Marez Charly , Pérez Juan , Tenreiro Miguel , Ruiz Angulo Angel , Bower Amy
Published: June 2021

This study investigates the vertical structure of the dynamical properties of a warm-core ring in the Gulf of Mexico (Loop Current ring) using glider observations. We introduce a new method to correct the glider’s along-track coordinate, which is, in general, biased by the unsteady relative movements of the glider and the eddy, yielding large errors on horizontal derivatives. Here, we take advantage of the synopticity of satellite along-track altimetry to apply corrections on the glider’s position by matching in situ steric height with satellite-measured sea surface height. This relocation method allows recovering the eddy’s azimuthal symmetry, precisely estimating the rotation axis position, and computing reliable horizontal derivatives. It is shown to be particularly appropriate to compute the eddy’s cyclo-geostrophic velocity, relative vorticity, and shear strain, which are otherwise out of reach when using the glider’s raw traveled distance as a horizontal coordinate. The Ertel potential vorticity (PV) structure of the warm core ring is studied in details, and we show that the PV anomaly is entirely controlled by vortex stretching. Sign reversal of the PV gradient across the water column suggests that the ring might be baroclinically unstable. The PV gradient is also largely controlled by gradients of the vortex stretching term. We also show that the ring’s total energy partition is strongly skewed, with available potential energy being 3 times larger than kinetic energy. The possible impact of this energy partition on the Loop Current rings longevity is also discussed.
A Convolutional Neural Network Based on Grouping Structure for Scene Classification
Authors: Wu Xuan , Zhang Zhijie , Zhang Wanchang , Yi Yaning , Zhang Chuanrong , Xu Qiang
Published: June 2021

Convolutional neural network (CNN) is capable of automatically extracting image features and has been widely used in remote sensing image classifications. Feature extraction is an important and difficult problem in current research. In this paper, data augmentation for avoiding over fitting was attempted to enrich features of samples to improve the performance of a newly proposed convolutional neural network with UC-Merced and RSI-CB datasets for remotely sensed scene classifications. A multiple grouped convolutional neural network (MGCNN) for self-learning that is capable of promoting the efficiency of CNN was proposed, and the method of grouping multiple convolutional layers capable of being applied elsewhere as a plug-in model was developed. Meanwhile, a hyper-parameter C in MGCNN is introduced to probe into the influence of different grouping strategies for feature extraction. Experiments on the two selected datasets, the RSI-CB dataset and UC-Merced dataset, were carried out to verify the effectiveness of this newly proposed convolutional neural network, the accuracy obtained by MGCNN was 2% higher than the ResNet-50. An algorithm of attention mechanism was thus adopted and incorporated into grouping processes and a multiple grouped attention convolutional neural network (MGCNN-A) was therefore constructed to enhance the generalization capability of MGCNN. The additional experiments indicate that the incorporation of the attention mechanism to MGCNN slightly improved the accuracy of scene classification, but the robustness of the proposed network was enhanced considerably in remote sensing image classifications.
A New Method to Determine the Optimal Thin Layer Ionospheric Height and Its Application in the Polar Regions
Authors: Jiang Hu , Jin Shuanggen , Hernández-Pajares Manuel , Xi Hui , An Jiachun , Wang Zemin , Xu Xueyong , Yan Houxuan
Published: June 2021

The conversion between the line-of-sight slant total electron content (STEC) and the vertical total electron content (VTEC) depends on the mapping function (MF) under the widely used thin layer ionospheric model. The thin layer ionospheric height (TLIH) is an essential parameter of the MF, which affects the accuracy of the conversion between the STEC and VTEC. Due to the influence of temporal and spatial variations of the ionosphere, the optimal TLIH is not constant over the globe, particularly in the polar regions. In this paper, a new method for determining the optimal TLIH is proposed, which compares the mapping function values (MFVs) from the MF at different given TLIHs with the “truth” mapping values from the UQRG global ionospheric maps (GIMs) and the differential TEC (dSTEC) method, namely the dSTEC- and GIM-based thin layer ionospheric height (dG-TLIH) techniques. The optimal TLIH is determined using the dG-TLIH method based on GNSS data over the Antarctic and Arctic. Furthermore, we analyze the relationship between the optimal TLIH derived from the dG-TLIH method and the height of maximum density of the F2 layer (hmF2) based on COSMIC data in the polar regions. According to the dG-TLIH method, the optimal TLIH is mainly distributed between 370 and 500 km over the Arctic and between 400 and 500 km over the Antarctic in a solar cycle. In the Arctic, the correlation coefficient between the hmF2 and optimal TLIH is 0.7, and the deviation between them is 162 km. Meanwhile, in the Antarctic, the correlation coefficient is 0.60, with a phase lag of ~3 months, with the hmF2 leading the optimal TLIH, and the deviation between them is 177 km.
Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
Authors: Ribeiro Laura Michaella Batista , Müller Ivan , Buss Becker Leandro
Published: June 2021

Exchanging messages with stable connections in missions composed of multiple unmanned aerial vehicles (UAV) remains a challenge. The variations in UAV distances from each other, considering their individual trajectories, and the medium dynamic factors are important points to be addressed.In this context, to increase the stability of UAV-to-UAV (U2U) communication with link quality, this paper presents an interface manager (IM) that is capable of improving communication in multi-UAV networks.Given a predefined set of available individual wireless interfaces, the proposed IM dynamically defines the best interface for sending messages based on on-flight conditions sensed and calculated dynamically from the wireless medium. Different simulation scenarios are generated using a complex and realistic experimental setup composed of traditional simulators such as NS-3, Gazebo, and GzUAV. IEEE 802.11n 2.4 GHz and 802.11p 5 GHz interfaces are used for the IM selection. The IM performance is evaluated in terms of metrics from the medium-access-control (MAC) and physical layers, which aim to improve and maintain the connectivity between the UAVs during the mission, and from the application layer, which targets the reliability in the delivery of messages. The obtained results show that compared with the cases where a single interface is used, the proposed IM is able to increase the network throughput and presents the best proportion of transmitted and received packets, reception power (−60 dBm to −75 dBm), and loss (−80 dB to −85 dB), resulting in a more efficient and stable network connections.
A Simple Sensor System for Onsite Monitoring of O2 in Vacuum-Packed Meats during the Shelf Life
Authors: Santovito Elisa , Elisseeva Sophia , Cruz-Romero Malco C. , Duffy Geraldine , Kerry Joseph P. , Papkovsky Dmitri B.
Published: June 2021

Vacuum packaging (VP) is used to reduce exposure of retail meat samples to ambient oxygen (O2) and preserve their quality. A simple sensor system produced from commercial components is described, which allows for non-destructive monitoring of the O2 concentration in VP raw meat samples. Disposable O2 sensor inserts were produced by spotting small aliquots of the cocktail of the Pt–benzoporphyrin dye and polystyrene in ethyl acetate onto pieces of a PVDF membrane and allowing them to air-dry. These sensor dots were placed on top of the beef cuts and vacuum-packed. A handheld reader, FirestinGO2, was used to read nondestructively the sensor phase shift signals (dphi°) and relate them to the O2 levels in packs (kPa or %). The system was validated under industrial settings at a meat processing plant to monitor O2 in VP meat over nine weeks of shelf life storage. The dphi° readings from individual batch-calibrated sensors were converted into the O2 concentration by applying the following calibration equation: O2 (%) = 0.034 * dphi°2 − 3.413 * dphi° + 85.02. In the VP meat samples, the O2 levels were seen to range between 0.12% and 0.27%, with the sensor dphi signals ranging from 44.03° to 56.02°. The DIY sensor system demonstrated ease of use on-site, fast measurement time, high sample throughput, low cost and flexibility.
Deep Eutectic Solvents (DESs) and Their Application in Biosensor Development
Authors: Svigelj Rossella , Dossi Nicolò , Grazioli Cristian , Toniolo Rosanna
Published: June 2021

Deep Eutectic Solvents (DESs) are a new class of solvents characterized by a remarkable decrease in melting point compared to those of the starting components. The eutectic mixtures can be simply prepared by mixing a Hydrogen Bond Acceptor (HBA) with a Hydrogen Bond Donor (HBD) at a temperature of about 80 °C. They have found applications in different research fields; for instance, they have been employed in organic synthesis, electrochemistry, and bio-catalysis, showing improved biodegradability and lower toxicity compared to other solvents. Herein, we review the use of DESs in biosensor development. We consider the emerging interest in different fields of this green class of solvents and the possibility of their use for the improvement of biosensor performance. We point out some promising examples of approaches for the assembly of biosensors exploiting their compelling characteristics. Furthermore, the extensive ability of DESs to solubilize a wide range of molecules provides the possibility to set up new devices, even for analytes that are usually insoluble and difficult to quantify.
Low-Cost Beamforming Concept for the Control of Radiation Patterns of Antenna Arrays Installed onto UAVs
Authors: dos Santos Leonardo C. , Schlosser Edson R. , Heckler Marcos V. T.
Published: June 2021

This paper presents a low-cost architecture that allows for beamforming with antenna arrays installed onto unmanned aerial vehicles (UAVs). Beam switching is proposed to improve the antenna gain towards the ground station with two three-element arrays installed below the wings of the UAV. The electromagnetic modeling of the complete structure (UAV and integrated antennas) was performed with commercial electromagnetic simulator Ansys HFSS. The radiation patterns were synthesized with particle swarm optimization (PSO). By employing lumped surface-mount device (SMD) components and switches, the design of the feeder to deliver proper excitation coefficients to the antennas is presented, and its performance was assessed by simulations. The proposed approach is demonstrated to be very effective with low-cost production.
Development of a Transient Magnetic Field Sensor Based on Digital Integration and Frequency Equalization
Authors: Ouyang Hongzhi , Yao Xueling , Chen Jingliang
Published: June 2021

Transient magnetic field sensors are used in various electromagnetic environment measurement scenarios. In this paper, a novel magnetic field sensor based on a digital integrator was developed. The antenna was a small B-DOT loop. It was designed optimally for the simulation. The magnetic field signal was digitally integrated with the improved Al-Alaoui algorithm, resulting in less integration error. To compensate for the bandwidth loss of the optical fiber system, we specially designed an FIR (finite impulse response) filter for frequency compensation. The circuit was described, and the transimpedance amplifier was specially designed to ensure the low noise characteristic of the receiver. The sensitivity of the sensor was calibrated at 68.2 A·m−1/mV, the dynamic range was 50 dB (1–300 kA/m), the linear correlation coefficient was 0.96, and the bandwidth was greater than 100 MHz. It was tested and verified under the action of an A-type lightning current. The sensor exhibited high-precision performance and flat amplitude-frequency characteristics. Therefore, it is suitable for lightning positioning, partial discharge testing, electromagnetic compatibility management, and other applications.
Influence of Geometric Properties of Capacitive Sensors on Slope Error and Nonlinearity of Displacement Measurements
Authors: Daul Lars , Jin Tao , Busch Ingo , Koenders Ludger
Published: June 2021

Capacitive sensors are widely used in industrial applications, such as CNC machine tools, where reliable positioning in the micrometer range with nanometer accuracy is required. Hence, these sensors are operated in harsh industrial environments. The accuracy of these sensors is mainly limited by slope errors and nonlinearities. In practice, the required accuracy of these sensors is achieved by a calibration against a metrological high-quality reference such as interferometric displacement measurement systems. This usually involves the use of high-order polynomials as calibration functions based on empirical data. In metrology, this is only the second-best approach and has disadvantages in terms of stability over the measurement range of the instrument. In addition, the validity of these empirical calibrations over time is questionable, and the associated uncertainty can only be roughly estimated. This makes regular recalibration of such sensors at short intervals mandatory to ensure the reliability of the displacement measurement. In this paper, we report on our investigations of the different parameters that affect the accuracy of capacitive sensors. Since the capacitance of these sensors results from the electric fields that build up between the electrodes, these field lines are calculated using FEM simulation models for typical commercial sensors. In the following the influence of various geometric parameters such as edge radius, guard ring size and shape, or thickness of the electrodes are individually analyzed according to their impact on the accuracy of these sensors. Based on these simulations, the deviations of the capacitance as they arise for real detector geometries can then be compared with idealized, de facto unrealizable parallel plate capacitors. This methodology allows overall uncertainty of capacitive sensors to be decomposed into their individual components and sorted in terms of their contribution to the uncertainty budget. The individual FEM-based analysis then enables a systematic analysis of the sources of uncertainty and, thus, reveals possibilities to improve manufacturing processes for capacitive sensors, to put these sensors on a solid metrological basis, and to improve the performance of these displacement measurement systems in the long run, i.e., to provide better sensors for the application.
A 3D-Printed Soft Fingertip Sensor for Providing Information about Normal and Shear Components of Interaction Forces
Authors: Wolterink Gerjan , Sanders Remco , van Beijnum Bert-Jan , Veltink Peter , Krijnen Gijs
Published: June 2021

Sensing of the interaction forces at fingertips is of great value in assessment and rehabilitation therapy. Current force sensors are not compliant to the fingertip tissue and result in loss of touch sensation of the user. This work shows the development and characterization of a flexible fully-3D-printed piezoresistive shear and normal force sensor that uses the mechanical deformation of the finger tissue. Two prototypes of the sensing structure are evaluated using a finite element model and a measurement setup that applies normal and shear forces up to 10 N on a fingertip phantom placed inside the sensing structure, which is fixed to prevent slippage. Furthermore, the relation between strain (rate) and resistance of the conductive TPU, used for the strain gauges, is characterized. The applied normal and shear force components of the 3D-printed sensing structure can be partly separated. FEM analysis showed that the output of the sensor is largely related to the sensor geometry and location of the strain gauges. Furthermore, the conductive TPU that was used has a negative gauge factor for the strain range used in this study and might cause non-linear behaviors in the sensor output.
Advances in Non-Invasive Blood Pressure Monitoring
Authors: Quan Xina , Liu Junjun , Roxlo Thomas , Siddharth Siddharth , Leong Weyland , Muir Arthur , Cheong So-Min , Rao Anoop
Published: June 2021

This paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary studies on a range of patient populations and discuss the accuracy and limitations of this capacitive-based technology and its potential application in hospitals and communities.
Current Trends on Green Wireless Sensor Networks
Authors: López-Ardao J. Carlos , Rodríguez-Rubio Raúl F. , Suárez-González Andrés , Rodríguez-Pérez Miguel , Sousa-Vieira M. Estrella
Published: June 2021

The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included.
Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm
Authors: Peng Cheng-Yu , Raihany Ully , Kuo Shu-Wei , Chen Yen-Zuo
Published: June 2021

Computer numerical control (CNC) is a machine used in the manufacturing industry to produce components quickly for the engineering field or the desired shape. In the milling process carried out by CNC machines, sometimes vibrations occur that cause unwanted cracks or damage, which if left unchecked, will cause more severe damage. For this reason, this study describes how to monitor and analyze the sound produced by CNC during the milling process. This study uses six sound sample videos from YouTube, and there are two modes: (1) the operating mode is three different shapes with XY, XZ, and XYZ axes, and the second (2) is based on material differences. Namely, wood, Styrofoam, and plastic. The sound generated from all samples of the CNC milling processes will be detected using a sound detection program that has been designed in the LabVIEW using a simple microphone. The resulting sound frequency will be analyzed using the fast Fourier transform (FFT) process in spectral measurements, which will produce the amplitude and frequency of the detected sound in real time in the form of a graph. All frequency results that have been obtained from the sound detection monitoring tool in the CNC milling machine will be imported into the K-means clustering algorithm where the different frequencies between the resonant frequency and noise will be classified. Based on the experiments conducted, the sound detection program can detect sounds with a significant level of sensitivity.
Application of Eye Tracking Technology in Aviation, Maritime, and Construction Industries: A Systematic Review
Authors: Martinez-Marquez Daniel , Pingali Sravan , Panuwatwanich Kriengsak , Stewart Rodney A. , Mohamed Sherif
Published: June 2021

Most accidents in the aviation, maritime, and construction industries are caused by human error, which can be traced back to impaired mental performance and attention failure. In 1596, Du Laurens, a French anatomist and medical scientist, said that the eyes are the windows of the mind. Eye tracking research dates back almost 150 years and it has been widely used in different fields for several purposes. Overall, eye tracking technologies provide the means to capture in real time a variety of eye movements that reflect different human cognitive, emotional, and physiological states, which can be used to gain a wider understanding of the human mind in different scenarios. This systematic literature review explored the different applications of eye tracking research in three high-risk industries, namely aviation, maritime, and construction. The results of this research uncovered the demographic distribution and applications of eye tracking research, as well as the different technologies that have been integrated to study the visual, cognitive, and attentional aspects of human mental performance. Moreover, different research gaps and potential future research directions were highlighted in relation to the usage of additional technologies to support, validate, and enhance eye tracking research to better understand human mental performance.
Machine Learning Based Object Classification and Identification Scheme Using an Embedded Millimeter-Wave Radar Sensor
Authors: Arab Homa , Ghaffari Iman , Chioukh Lydia , Tatu Serioja , Dufour Steven
Published: June 2021

A target’s movements and radar cross sections are the key parameters to consider when designing a radar sensor for a given application. This paper shows the feasibility and effectiveness of using 24 GHz radar built-in low-noise microwave amplifiers for detecting an object. For this purpose a supervised machine learning model (SVM) is trained using the recorded data to classify the targets based on their cross sections into four categories. The trained classifiers were used to classify the objects with varying distances from the receiver. The SVM classification is also compared with three methods based on binary classification: a one-against-all classification, a one-against-one classification, and a directed acyclic graph SVM. The level of accuracy is approximately 96.6%, and an F1-score of 96.5% is achieved using the one-against-one SVM method with an RFB kernel. The proposed contactless radar in combination with an SVM algorithm can be used to detect and categorize a target in real time without a signal processing toolbox.
Embedded Brain Computer Interface: State-of-the-Art in Research
Authors: Belwafi Kais , Gannouni Sofien , Aboalsamh Hatim
Published: June 2021

There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.
Dedicated Algorithm for Unobtrusive Fetal Heart Rate Monitoring Using Multiple Dry Electrodes
Authors: Galli Alessandra , Peri Elisabetta , Zhang Yijing , Vullings Rik , van der Ven Myrthe , Giorgi Giada , Ouzounov Sotir , Harpe Pieter J. A. , Mischi Massimo
Published: June 2021

Multi-channel measurements from the maternal abdomen acquired by means of dry electrodes can be employed to promote long-term monitoring of fetal heart rate (fHR). The signals acquired with this type of electrode have a lower signal-to-noise ratio and different artifacts compared to signals acquired with conventional wet electrodes. Therefore, starting from the benchmark algorithm with the best performance for fHR estimation proposed by Varanini et al., we propose a new method specifically designed to remove artifacts typical of dry-electrode recordings. To test the algorithm, experimental textile electrodes were employed that produce artifacts typical of dry and capacitive electrodes. The proposed solution is based on a hybrid (hardware and software) pre-processing step designed specifically to remove the disturbing component typical of signals acquired with these electrodes (triboelectricity artifacts and amplitude modulations). The following main processing steps consist of the removal of the maternal ECG by blind source separation, the enhancement of the fetal ECG and identification of the fetal QRS complexes. Main processing is designed to be robust to the high-amplitude motion artifacts that corrupt the acquisition. The obtained denoising system was compared with the benchmark algorithm both on semi-simulated and on real data. The performance, quantified by means of sensitivity, F1-score and root-mean-square error metrics, outperforms the performance obtained with the original method available in the literature. This result proves that the design of a dedicated processing system based on the signal characteristics is necessary for reliable and accurate estimation of the fHR using dry, textile electrodes.