bluuebunny/crossref_metadata_2025 huggingface dataset:
Thickness-Prediction Method Involving Tow Redistribution for the Dome of Composite Hydrogen Storage Vessels
Authors: Wang Hui , Fu Shuang , Chen Yizhe , Hua Lin
Published: February 2022

Traditional thickness-prediction methods underestimate the actual dome thickness at polar openings, leading to the inaccurate prediction of the load-bearing capacity of composite hydrogen storage vessels. A method of thickness prediction for the dome section of composite hydrogen storage vessels was proposed, which involved fiber slippage and tow redistribution. This method considered the blocking effect of the port on sliding fiber tows and introduced the thickness correlation to predict the dome thickness at polar openings. The arc length corresponding to the parallel circle radius was calculated, and then, the actual radius values corresponding to the bandwidth were obtained by the interpolation method. The predicted thickness values were compared with the actual measured thickness. The maximum relative error of the predicted thickness was 4.19%, and the mean absolute percentage error was 2.04%. The results show that the present method had a higher prediction accuracy. Eventually, this prediction method was used to perform progressive damage analysis on vessels. By comparing with the results of the cubic spline function method, the analysis results of the present method approached the actual case. This showed that the present method improved the accuracy of the design.
Influence Mechanism of Ultrasonic Vibration Substrate on Strengthening the Mechanical Properties of Fused Deposition Modeling
Authors: Wu Wenzheng , Li Jialin , Jiang Jili , Liu Qingping , Zheng Aodu , Zhang Zheng , Zhao Ji , Ren Luquan , Li Guiwei
Published: February 2022

Fused deposition modeling is the most widely used 3D-printing technology, with the advantage of being an accessible forming process. However, the poor mechanical properties of the formed parts limit its application in engineering. Herein, a new ultrasonic-assisted fused deposition modeling 3D-printing method was proposed to improve the mechanical properties of the formed parts. The effects of ultrasonic vibration substrate process parameters and printing process parameters on the tensile and bending properties of formed samples were studied. The tensile strength and bending strength of the samples printed with a 12 μm ultrasonic amplitude can be increased by 13.2% and 12.6%, respectively, compared with those printed without ultrasonic vibration. The influence mechanism of ultrasonic vibration on mechanical properties was studied through microscopic characterization and in situ infrared monitoring experiments. During the printing process, increasing the ultrasonic vibration and temperature employed via the ultrasonic substrate can reduce the pore defects inside the sample. The mechanical properties of FDM-formed samples can be controlled by adjusting ultrasonic-assisted process parameters, which can broaden the application of 3D printing.
Experimental and Numerical Study of Downward Flame Spread over Glass-Fiber-Reinforced Epoxy Resin
Authors: Korobeinichev Oleg , Karpov Alexander , Shaklein Artem , Paletsky Alexander , Chernov Anatoliy , Trubachev Stanislav , Glaznev Roman , Shmakov Andrey , Barbot’ko Sergey
Published: February 2022

For the first time, a comprehensive study of downward flame spread over glass-fiber-reinforced epoxy resin (GFRER) slabs in oxidizer flow has been carried out experimentally and numerically. Microthermocouples were used to measure the temperature profiles on the solid fuel’s surface and in the flame, and a video camera was used to measure the rate of flame spread (ROS). The ROS was found to be linearly dependent on the oxygen concentration, to be inversely proportional to the slab thickness and not to depend on the direction of the flame spread over the slab. The absence of the influence of the forced oxidizing flow velocity and the weak influence of the GFRER pyrolysis kinetics on the ROS were observed. For the first time, a numerical model of flame spread over reinforced material with thermal conductivity anisotropy was developed on the basis of a coupled ‘gas–solid’ heat and mass transfer model, using modifications of the OpenFOAM open-source code. The sensitivity analysis of the model showed that the thermal conductivity in the normal direction to the GFRER surface had a much greater effect on the ROS than the thermal conductivity along the direction of flame propagation. The numerical results show good agreement with the experimental data on the dependences of the ROS on oxygen concentration, slab thickness and the N2/O2 mixture flow velocity, as well as temperature distributions on the fuel surface, the maximum flame temperatures and the flame zone length.
Mechanical Characteristics and Micro-Mechanism of Modified Dredged Sludge Based on Calcium-Containing Solid Waste Used as Landfill Cover Materials
Authors: Shang Kejian
Published: February 2022

In order to prepare a new type of landfill covering material for closure, we used industrial calcium-containing waste (construction rubbish, slag, desulfurized gypsum and fly ash) to modify the dredged urban sludge. Shrink, unconfined compression, shear and infiltration tests were performed to obtain the volume shrinkage, compressive strength, shear strength and permeability coefficient of the modified sludge, as well as the permeability coefficient under the action of wet and dry cycles. Comprehensive characterization of the modified sludge using X-ray diffraction, Fourier-transform infrared spectroscopy, and scanning electron microscopy with energy dispersive spectroscopy detection methods, resulted in the hydration products, molecular groups and microstructure characteristics of the modified sludge and revealed the modification mechanism of calcium-containing waste to sludge. After natural curing for 28 d, the volume shrinkage rate of the modified sludge sample was 2.6~8.3%, the unconfined compressive strength was 7.9~14.5 MPa, the cohesion force c was 179~329 kPa, and the internal friction angle φ was 42.59~53.60°. After six wet and dry cycles, there were no cracks in the modified sludge; the permeability coefficient of the modified sludge reached stability at 0.84–11.1 × 10−7 cm/s; and the permeability coefficient of MS7 sample was less than 1 × 10−7 cm/s, which met the engineering anti-seepage requirements of the landfill closure cover. The industrial calcium-containing waste by alkali formed C–S–H and C–A–S–H gelled geopolymer, which filled the gaps between soil particles to form a strong soil cement skeleton. Therefore, the mix ratio of sludge:construction waste:slag:fly ash:desulfurized gypsum was 50:22:15:8:5. Calcium-containing solid waste modified sludge can be used as a cover material for landfill closure.
Aluminium-Assisted Alloying of Carbon Steel in Submerged Arc Welding: Application of Al-Cr-Ti-Cu Unconstrained Metal Powders
Authors: Coetsee Theresa , De Bruin Frederik
Published: February 2022

Al assisted alloying of carbon steel in Submerged Arc Welding (SAW) by Al-Cr-Ti-Cu unconstrained metal powders is applied. A base case without metal powder additions is compared to two metal powder addition schedules, Al-Cu-Ti and Al-Cu-Ti-Cr. Al powder is used as a deoxidiser element to control the oxygen partial pressure at the weld pool–molten flux interface to ensure that most of the Ti and Cr metal powder is transferred into the weld pool and that the weld metal ppm O is controlled within acceptable limits of 200 to 500 ppm O. The likely sequence of alloy melt formation is deduced from the relevant alloy phase diagrams. The effect of Fe addition into the initial Al-Cu-Ti and Al-Cu-Ti-Cr alloy melt is illustrated in thermochemical calculations. Increased metal deposition productivity with metal powder addition in SAW is confirmed. The metal deposition rates increased by 19% and 40% when Al-Cu-Ti and Al-Cu-Ti-Cr powders were applied at the same weld heat input used in the absence of metal powder additions.
Adsorbent Minimization for Removal of Ibuprofen from Water in a Two-Stage Batch Process
Authors: Farzaneh Hajar , Saththasivam Jayaprakash , McKay Gordon , Parthasarathy Prakash
Published: February 2022

Pharmaceutical products in water, also known as personal pharmaceutical products or PCPPs, are developing contaminants that have the potential to impair human health and the environment in a variety of ecosystems. In this work, waste date stones, a waste product obtained from the seedless dates manufacturing industry, were used to make acid-activated carbon. This material has been utilized to extract the medicinal component ibuprofen from water, with a high adsorption capacity of 126 mg ibuprofen per g of waste date stone-generated activated carbon. A design study was conducted to minimize the amount of activated carbon required, utilizing a two-stage batch adsorption system to optimize the usage of the activated carbon. To test the model and compare the quantities of adsorbent required in the two-stage and single-stage systems under various conditions, several variables were entered into the design model.
Investigating the Trade-Off between Design and Operational Flexibility in Continuous Manufacturing of Pharmaceutical Tablets: A Case Study of the Fluid Bed Dryer
Authors: Jiang Sheng-Long , Papageorgiou Lazaros G. , Bogle Ian David L. , Charitopoulos Vassilis M.
Published: February 2022

Market globalisation, shortened patent lifetimes and the ongoing shift towards personalised medicines exert unprecedented pressure on the pharmaceutical industry. In the push for continuous pharmaceutical manufacturing, processes need to be shown to be agile and robust enough to handle variations with respect to product demands and operating conditions. In this paper we examine the use of operational envelopes to study the trade-off between the design and operational flexibility of the fluid bed dryer at the heart of a tablet manufacturing process. The operating flexibility of this unit is key to the flexibility of the full process and its supply chain. The methodology shows that for the fluid bed dryer case study there is significant effect on flexibility of the process at different drying times with the optimal obtained at 700 s. The flexibility is not affected by the change in volumetric flowrate, but only by the change in temperature. Here the method used a black box model to show how it could be done without access to the full model equation set, as this often needs to be the case in commercial settings.
Design and Experimental Research of a New Film-Picking Mulch Film Recovery Machine with Impurity Separation Function
Authors: Dong Jianhao , Li Shuzhuo , Bi Xinsheng , Wang Guangheng , Wang Jikui , Wang Wensheng , Tong Ningze
Published: February 2022

When the film is under no stress, it does not show any stiffness or capability to withstand loads. Therefore, with the help of ANSYS motion, the author applied pre-stress, controlled point coordinates and the equation of equivalent stress surface and used form-finding conditions to explore the equilibrium state of the films in different depths. The author established curve fitting equations and designed a new membrane-removing mulch film recovery machine. The author also analyzed key components and separately established regression equations between recovery rate, damage rate, release rate and the working angle of film picking mechanism, as well as the regression equation between the depth of picking mechanism into the soil and the working angle of clamping conveyor. Then the author conducted the variance analysis on these regression equations. By using Design Expert 8.0.6 to optimize the solution, when the working angle of the pick-up mechanism is 27.31°, the depth of the pick-up mechanism is 73.0 mm. The working angle of the clamping and conveying device is 38.33°, the recovery rate is 93.9% and the damage rate is 89.3%. The test result shows that when parameters are set as above values, the recovery rate of residual film is 92.5%, the release rate is 87.6% and the error values of damage rate and release rate are both lower than 1.5%, which have verified the feasibility of the optimization solution. The experimental result can work as a reference for the optimum working parameters of the new membrane-removing mulch film recovery machine.
Electroconductive Polyaniline–Ag-ZnO Green Nanocomposite Material
Authors: Kyomuhimbo Hilda Dinah , Michira Immaculate Nyambura , Iwuoha Emmanuel Iheanyichukwu , Feleni Usisipho
Published: February 2022

Metal-conducting polyaniline (PANI)-based nanocomposite materials have attracted attention in various applications due to their synergism of electrical, mechanical, and optical properties of the initial components. Herein, metal-PANI nanocomposites, including silver nanoparticle-polyaniline (AgNP-PANI), zinc oxide nanoparticle-polyaniline (ZnONP-PANI), and silver-zinc oxide nanoparticle-polyaniline (Ag–ZnONP-PANI), were prepared using the two processes. Nanocomposite-based electrode platforms were prepared by depositing AgNPs, ZnONPs, or Ag–ZnONPs on a PANI modified glass carbon electrode (GCE) in the presence of 1-ethyl-3-(3-dimethyl aminopropyl) carbodiimide/N-Hydroxysuccinimide (EDC/NHS, 1:2) as coupling agents. The incorporation of AgNPs, ZnONPs, and Ag–ZnONPs onto PANI was confirmed by UV-Vis spectrophotometry, which showed five absorbance bands at 216 nm, 412 nm, 464 nm, 550 nm, and 831 nm (i.e., transition of π-π*, π-polaron band transition, polaron-π* electronic transition, and AgNPs). The FTIR characteristic signatures of the nanocomposite materials exhibited stretching arising from C–H aromatic, C–O, and C–N stretching mode for benzenoid rings, and =C–H plane bending vibration formed during protonation. The CV voltammograms of the nanocomposite materials showed a quasi-reversible behavior with increased redox current response. Notably, AgNP–PANI–GCE electrode showed the highest conductivity, which was attributed the high conductivity of silver. The increase in peak currents exhibited by the composites shows that AgNPs and ZnONPs improve the electrical properties of PANI, and they could be potential candidates for electrochemical applications.
Pierre Claverie: Decolonising Mission
Authors: Crișan Alexandru-Marius
Published: February 2022

In the early 1980s, the Catholic Church in Algeria was experiencing upheaval, having been depopulated almost overnight when the great majority of Catholic Christians had left the country and resettled in France or elsewhere after the Algerian proclamation of independence two decades earlier. The remaining Christians were regarded not only as a reduced minority but mostly as a reminding symbol of an era of political and religious colonization. The Church in Algeria was in need of a deep reflection to find a new reason for its presence in this totally new context. A man of faith, capable of illuminating this reflection was Pierre Lucien Claverie, a Dominican friar and bishop of Oran between 1981–1996, who tried to lead the perception of the Church in Algeria from notions such as colonisation or proselytism, to notions such as otherness and plurality in an attempt to liberate the act of mission from any aggressive tendency or colonial roots. Claverie’s writings show that he did not shape the concept of mission only on a theological or social level but responded to the needs of his Church through his spiritual experiences and monastic background.
A Femto-Satellite Localization Method Based on TDOA and AOA Using Two CubeSats
Authors: Vidal-Valladares Matías G. , Díaz Marcos A.
Published: February 2022

This article presents a feasibility analysis to remotely estimate the geo-location of a femto-satellite only using two station-CubeSats and the communication link between the femto-satellite and each CubeSat. The presented approach combines the Time Difference Of Arrival (TDOA) and Angle Of Arrival (AOA) methods. We present the motivation, the envisioned solution together with the constraints for reaching it, and the best potential sensitivity of the location precision for different (1) deployment scenarios of the femto-satellite, (2) precisions in the location of the CubeSats, and (3) precisions in each CubeSat’s Attitude Determination and Control Systems (ADCS). We implemented a simulation tool to evaluate the average performance for different random scenarios in space. For the evaluated cases, we found that the Cramér-Rao Bound (CRB) for Gaussian noise over the small error region of the solution is highly dependent on the deployment direction, with differences in the location precision close to three orders of magnitude between the best and worst deployment directions. For the best deployment case, we also studied the best location estimation that might be achieved with the current Global Navigation Satellite System (GNSS) and ADCS commercially available for CubeSats. We found that the mean-square error (MSE) matrix of the proposed solution under the small error condition can attain the CRB for the simulated time, achieving a precision below 30 m when the femto-satellite is separated by around 800 m from the mother-CubeSat.
Study of Atmospheric Carbon Dioxide Retrieval Method Based on Normalized Sensitivity
Authors: Zhao Liang , Chen Shengbo , Xue Yong , Cui Tengfei
Published: February 2022

The satellite-based remote-sensing detection of CO2 can provide long-term and continuous observations at the global scale, and is the primary observational approach to monitoring CO2 changes. Surface albedo and aerosol are two significant challenges for retrieving CO2 concentrations from near-infrared remote-sensing data. This research addressed the dependence of radiance sensitivities on CO2 concentration, aerosol, surface albedo, and water vapor. The band ratio method was used to retrieve CO2 with band selection of the high- and low-sensitivity channels to restrain the influence of surface albedo and aerosol. Results showed that the band ratio method had better efficiency at reducing the impact of aerosol and surface albedo than that of the optical estimation method. The retrieval error of aerosol was reduced by 2.5% overall. We validated the band ratio method retrieval results with two TCCON sites and the GOSAT L3 product. Pearson’s correlation coefficient, mean bias, mean absolute bias, and root mean squared error of the monthly retrieval data of the band ratio method showed high agreement between the TCCON in Park Falls and Wollongong. These results indicate that the band ratio method based on normalized sensitivity can effectively reduce the influence of surface albedo and aerosol.
An Error Overbounding Method Based on a Gaussian Mixture Model with Uncertainty Estimation for a Dual-Frequency Ground-Based Augmentation System
Authors: Gao Zhen , Fang Kun , Wang Zhipeng , Guo Kai , Liu Yuan
Published: February 2022

To ensure the integrity of a ground-based augmentation system (GBAS), an ionosphere-free (Ifree) filtering algorithm with dual-frequency measurements is employed to make the GBAS free of the first-order ionospheric influence. However, the Ifree algorithm outputs the errors of two frequencies. The protection level obtained via the traditional Gaussian overbound is overconservative. This conservatism may cause false alarms and diminish availability. An overbounding framework based on a Gaussian mixture model (GMM) is proposed to handle samples drawn from Ifree-based GBAS range errors. The GMM is employed to model the single-frequency errors that concern the uncertainty estimation. A Monte Carlo simulation is performed to determine the accuracy of the estimated GMM confidence level obtained by using the general estimation approach. Then, the final GMM used to overbound the Ifree error distribution is analyzed. Based on the convolution invariance property, vertical protection levels in the position domain are explicitly derived without introducing complex numerical calculations. A performance evaluation based on a real-world road test shows that the Ifree-based vertical protection levels are tightened with a small computational cost.
Monitoring of Radial Deformations of a Gravity Dam Using Sentinel-1 Persistent Scatterer Interferometry
Authors: Jänichen Jannik , Schmullius Christiane , Baade Jussi , Last Katja , Bettzieche Volker , Dubois Clémence
Published: February 2022

Dams have many important socio-economic functions, fulfilling roles ranging from storing water to power generation, but also serving as leisure areas. Monitoring of their deformation is usually performed using time-consuming traditional terr estrial techniques, leading to a yearly monitoring cycle. To increase the monitoring cycle, new methods are needed. Persistent Scatterer Interferometry (PSI) is a well-established technique for monitoring millimeter deformation of the Earth’s surface. The availability of free and open SAR data with a repeat cycle of 6 to 12 days from the Copernicus mission Sentinel-1, allows PSI to be used complementary to traditional surveying techniques. This present study investigates deformation dynamics at the Moehne gravity dam in North Rhine-Westphalia, Germany. The applicability of the PSI technique to the deformation monitoring of dams is evaluated, in relation to the necessary accuracy requirements. For this purpose, Sentinel-1 data from January 2015 to November 2020 are analyzed and the deformation estimates are assessed with in situ information. Using a precise dam model, the radial deformation of the dam could be extracted and compared to trigonometric and plumb measurements. The first results show that the movements of the Moehne dam follow a seasonal pattern, reaching a maximum radial deformation of up to 4 mm in Spring, following a decline to −4 mm in the late summer. RMSE between 1.1 mm and 1.5 mm were observed between the PSI observations and the in situ data, showing that the PSI technique achieves the necessary accuracy requirements for gravity dam monitoring from space.
No-Reference Quality Assessment of Pan-Sharpening Images with Multi-Level Deep Image Representations
Authors: Stępień Igor , Oszust Mariusz
Published: February 2022

The Pan-Sharpening (PS) techniques provide a better visualization of a multi-band image using the high-resolution single-band image. To support their development and evaluation, in this paper, a novel, accurate, and automatic No-Reference (NR) PS Image Quality Assessment (IQA) method is proposed. In the method, responses of two complementary network architectures in a form of extracted multi-level representations of PS images are employed as quality-aware information. Specifically, high-dimensional data are separately extracted from the layers of the networks and further processed with the Kernel Principal Component Analysis (KPCA) to obtain features used to create a PS quality model. Extensive experimental comparison of the method on the large database of PS images against the state-of-the-art techniques, including popular NR methods adapted in this study to the PS IQA, indicates its superiority in terms of typical criteria.
Predicting Individual Tree Diameter of Larch (Larix olgensis) from UAV-LiDAR Data Using Six Different Algorithms
Authors: Sun Yusen , Jin Xingji , Pukkala Timo , Li Fengri
Published: February 2022

Individual tree detection is an increasing trend in LiDAR-based forest inventories. The locations, heights, and crown areas of the detected trees can be estimated rather directly from the LiDAR data by using the LiDAR-based canopy height model and segmentation methods to delineate the tree crowns. However, the most important tree variable is the diameter of the tree stem at the breast height (DBH) which can seldom be interpreted directly from the LiDAR data. Therefore, the use of individually detected trees in forest planning calculations requires predictions for the DBH. This study tested six methods for predicting the DBH from laser scanning data collected by an unmanned aerial vehicle from Larix olgensis plantations located in northeast China. The tested methods were the linear regression model (LM), a linear model with ridge regularization (LMR), support vector regression (SVR), random forest (RF), artificial neural network (ANN), and the k-nearest neighbors (KNN) method. Both tree-level and stand-level metrics derived from the LiDAR point cloud data (for instance percentiles of the height distribution of the echoes) were used as potential predictors of DBH. Compared to the LM, all other methods improved the accuracy of the predictions. On the other hand, all methods tended to underestimate the DBH of the largest trees, which could be due to the inability of the methods to sufficiently describe nonlinear relationships unless different transformations of the LiDAR metrics are used as predictors. The support vector regression was evaluated to be the best method for predicting individual tree diameters from LiDAR data. The benefits of the methods tested in this study can be expected to be the highest in the case of little prior knowledge on the relationships between the predicted variable and predictors, a high number of potential predictors, and strong mutual correlations among the potential predictors.
Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion
Authors: Alevizos Evangelos , Oikonomou Dimitrios , Argyriou Athanasios V. , Alexakis Dimitrios D.
Published: February 2022

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum be available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets with centimeter resolution. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery as an alternative to costly hyperspectral sensors for drones. Combining drone-based RGB and multispectral imagery into a single cube dataset provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require the input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types with a maximum depth of six meters. The use of suitable end-member spectra, which are representative of the seafloor types of the study area, are important parameters in model tuning. The results of this study are promising, showing good correlation (R2 > 0.75 and Lin’s coefficient > 0.80) and less than half a meter average error when they are compared with sonar depth measurements. Consequently, the integration of imagery from various drone-based sensors (visible range) assists in producing detailed bathymetry maps for small-scale shallow areas based on optical modelling.
A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic
Authors: Liu Jun , Xie Huan , Guo Yalei , Tong Xiaohua , Li Peinan
Published: February 2022

NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018. The sole instrument onboard ICESat-2 is ATLAS, a highly precise laser that now provides routine, very-high-resolution, surface height measurements across the globe, including over the Arctic. To further improve the detection accuracy of the sea ice concentration (SIC), we demonstrate a new processing chain that can be used to convert the along-track sea ice freeboard products (ATL10) obtained by ICESat-2 into the SIC, with our initial efforts being focused on the Arctic. For this conversion, we primarily make use of the classification results from the type (sea ice or lead) and segment length data gathered from ATL10. The along-track SIC is the ratio of the area that is covered by sea ice segments to the area of all of the along-track segments. We generated a monthly gridded SIC product with a 25 km resolution and compared this to the NSIDC Climate Data Record (CDR) sea ice concentration. The highest correlation was determined to be 0.7690 in September at high latitudes and the lowest correlation was found to be 0.8595 in June at mid-latitudes. The regions with large standard deviations in summer and autumn are mainly distributed in the thin-ice areas at mid-latitudes. In the Laptev Sea and Kara Sea of east Siberia, the differences in the standard deviation were large; the maximum bias was −0.1566, in November, and the minimum bias was −0.0216, in June. ICESat-2 shows great potential for the accurate estimation of the SIC.
Exploiting Resistive Matrix Technology to Build a Stretchable Sensorised Sock for Gait Analysis in Daily Life
Authors: Carbonaro Nicola , Arcarisi Lucia , Marinai Carlotta , Laurino Marco , Di Rienzo Francesco , Vallati Carlo , Tognetti Alessandro
Published: February 2022

We describe the development and preliminary evaluation of an innovative low-cost wearable device for gait analysis. We have developed a sensorized sock equipped with 32 piezoresistive textile-based sensors integrated in the heel and metatarsal areas for the detection of signals associated with the contact pressures generated during walking phases. To build the sock, we applied a sensing patch on a commercially available sock. The sensing patch is a stretchable circuit based on the resistive matrix method, in which conductive stripes, based on conductive inks, are coupled with piezoresistive fabrics to form sensing elements. In our sensorized sock, we introduced many relevant improvements to overcome the limitations of the classical resistive matrix method. We preliminary evaluated the sensorized sock on five healthy subjects by performing a total of 80 walking tasks at different speeds for a known distance. Comparison of step count and step-to-step frequency versus reference measurements showed a high correlation between the estimated measure and the real one.
A Sensor for Characterisation of Liquid Materials with High Permittivity and High Dielectric Loss
Authors: Wang Chen , Liu Xiaoming , Huang Zhixiang , Yu Shuo , Yang Xiaofan , Shang Xiaobang
Published: February 2022

This paper reports on a sensor based on multi-element complementary split-ring resonator for the measurement of liquid materials. The resonator consists of three split rings for improved measurement sensitivity. A hole is fabricated at the centre of the rings to accommodate a hollow glass tube, through which the liquid sample can be injected. Electromagnetic simulations demonstrate that both the resonant frequency and quality factor of the sensor vary considerably with the dielectric constant and loss tangent of the liquid sample. The volume ratio between the liquid sample and glass tube is 0.36, yielding great sensitivity in the measured results for high loss liquids. Compared to the design based on rectangular split rings, the proposed ring structure offers 37% larger frequency shifts and 9.1% greater resonant dips. The relationship between dielectric constant, loss tangent, measured quality factor and resonant frequency is derived. Experimental verification is conducted using ethanol solution with different concentrations. The measurement accuracy is calculated to be within 2.8%, and this validates the proposed approach.
Reconstructing the Spatial Parameters of a Laser Beam Using the Transport-of-Intensity Equation
Authors: Kovalev Michael , Gritsenko Iliya , Stsepuro Nikita , Nosov Pavel , Krasin George , Kudryashov Sergey
Published: February 2022

A simple method for reconstructing the spatial parameters of a laser beam, based on the transport-of-intensity equation, is presented. Registration of cross-section intensity distributions in several planes was carried out using a single CMOS camera. The processing of the experimental measurements with the help of specialized software helped to reconstruct all of the spatial parameters, namely, the radius and position of the waist, Rayleigh length, angular divergence, quality parameter M2 The method was compared with measurements made according to the international standard ISO 11146 and showed that the difference in the spatial parameters is 10% or less, which shows good agreement.
Noise-Resistant Demosaicing with Deep Image Prior Network and Random RGBW Color Filter Array
Authors: Kurniawan Edwin , Park Yunjin , Lee Sukho
Published: February 2022

In this paper, we propose a deep-image-prior-based demosaicing method for a random RGBW color filter array (CFA). The color reconstruction from the random RGBW CFA is performed by the deep image prior network, which uses only the RGBW CFA image as the training data. To our knowledge, this work is a first attempt to reconstruct the color image with a neural network using only a single RGBW CFA in the training. Due to the White pixels in the RGBW CFA, more light is transmitted through the CFA than in the case with the conventional RGB CFA. As the image sensor can detect more light, the signal-to-noise-ratio (SNR) increases and the proposed demosaicing method can reconstruct the color image with a higher visual quality than other existing demosaicking methods, especially in the presence of noise. We propose a loss function that can train the deep image prior (DIP) network to reconstruct the colors from the White pixels as well as from the red, green, and blue pixels in the RGBW CFA. Apart from using the DIP network, no additional complex reconstruction algorithms are required for the demosaicing. The proposed demosaicing method becomes useful in situations when the noise becomes a major problem, for example, in low light conditions. Experimental results show the validity of the proposed method for joint demosaicing and denoising.
Hough Transform Proposal and Simulations for Particle Track Recognition for LHC Phase-II Upgrade
Authors: Gabrielli Alessandro , Alfonsi Fabrizio , Del Corso Francesca
Published: February 2022

In the near future, LHC experiments will continue future upgrades by overcoming the technological obsolescence of the detectors and the readout capabilities. Therefore, after the conclusion of a data collection period, CERN will have to face a long shutdown to improve overall performance, by updating the experiments, and implementing more advanced technologies and infrastructures. In particular, the largest LHC experiment, i.e., ATLAS, will upgrade parts of the detector, the trigger, and the data acquisition system. In addition, the ATLAS experiment will complete the implementation of new strategies, algorithms for data handling, and transmission to the final storage apparatus. This paper presents an overview of an upgrade planned for the second half of this decade for the ATLAS experiment. In particular, we show a study of a novel pattern recognition algorithm used in the trigger system, which is a device designed to provide the information needed to select physical events from unnecessary background data. The idea is to use a well known mathematical transform, the Hough transform, as the algorithm for the detection of particle trajectories. The effectiveness of the algorithm has already been validated in the past, regardless of particle physics applications, to recognize generic shapes within images. On the contrary, here, we first propose a software emulation tool, and a subsequent hardware implementation of the Hough transform, for particle physics applications. Until now, the Hough transform has never been implemented on electronics in particle physics experiments, and since a hardware implementation would provide benefits in terms of overall Latency, we complete the studies by comparing the simulated data with a physical system implemented on a Xilinx hardware accelerator (FELIX-II card). In more detail, we have implemented a low-abstraction RTL design of the Hough transform on Xilinx UltraScale+ FPGAs as target devices for filtering applications.
SVIoT: A Secure Visual-IoT Framework for Smart Healthcare
Authors: Kaw Javaid A. , Gull Solihah , Parah Shabir A.
Published: February 2022

The advancement of the Internet of Things (IoT) has transfigured the overlay of the physical world by superimposing digital information in various sectors, including smart cities, industry, healthcare, etc. Among the various shared information, visual data are an insensible part of smart cities, especially in healthcare. As a result, visual-IoT research is gathering momentum. In visual-IoT, visual sensors, such as cameras, collect critical multimedia information about industries, healthcare, shopping, autonomous vehicles, crowd management, etc. In healthcare, patient-related data are captured and then transmitted via insecure transmission lines. The security of this data are of paramount importance. Besides the fact that visual data requires a large bandwidth, the gap between communication and computation is an additional challenge for visual IoT system development. In this paper, we present SVIoT, a Secure Visual-IoT framework, which addresses the issues of both data security and resource constraints in IoT-based healthcare. This was achieved by proposing a novel reversible data hiding (RDH) scheme based on One Dimensional Neighborhood Mean Interpolation (ODNMI). The use of ODNMI reduces the computational complexity and storage/bandwidth requirements by 50 percent. We upscaled the original image from M × N to M ± 2N, dissimilar to conventional interpolation methods, wherein images are upscaled to 2M × 2N. We made use of an innovative mechanism, Left Data Shifting (LDS), before embedding data in the cover image. Before embedding the data, we encrypted it using an AES-128 encryption algorithm to offer additional security. The use of LDS ensures better perceptual quality at a relatively high payload. We achieved an average PSNR of 43 dB for a payload of 1.5 bpp (bits per pixel). In addition, we embedded a fragile watermark in the cover image to ensure authentication of the received content.
Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables
Authors: Tunny Salma Sultana , Amanah Hanim Z. , Faqeerzada Mohammad Akbar , Wakholi Collins , Kim Moon S. , Baek Insuck , Cho Byoung-Kwan
Published: February 2022

Ensuring the quality of fresh-cut vegetables is the greatest challenge for the food industry and is equally as important to consumers (and their health). Several investigations have proven the necessity of advanced technology for detecting foreign materials (FMs) in fresh-cut vegetables. In this study, the possibility of using near infrared spectral analysis as a potential technique was investigated to identify various types of FMs in seven common fresh-cut vegetables by selecting important wavebands. Various waveband selection methods, such as the weighted regression coefficient (WRC), variable importance in projection (VIP), sequential feature selection (SFS), successive projection algorithm (SPA), and interval PLS (iPLS), were used to investigate the optimal multispectral wavebands to classify the FMs and vegetables. The application of selected wavebands was further tested using NIR imaging, and the results showed good potentiality by identifying 99 out of 107 FMs. The results indicate the high applicability of the multispectral NIR imaging technique to detect FMs in fresh-cut vegetables for industrial application.
A New In Situ Coaxial Capacitive Sensor Network for Debris Monitoring of Lubricating Oil
Authors: Wang Yishou , Lin Tingwei , Wu Diheng , Zhu Ling , Qing Xinlin , Xue Wendong
Published: February 2022

Wear debris monitoring of lubricant oil is an important method to determine the health and failure mode of key components such as bearings and gears in rotatory machines. The permittivity of lubricant oil can be changed when the wear debris enters the oil. Capacitive sensing methods showed potential in monitoring debris in lubricant due to the simple structure and good response. In order to improve the detection sensitivity and reliability, this study proposes a new coaxial capacitive sensor network featured with parallel curved electrodes and non-parallel plane electrodes. As a kind of through-flow sensor, the proposed capacitive sensor network can be in situ integrated into the oil pipeline. The theoretical models of sensing mechanisms were established to figure out the relationship between the two types of capacitive sensors in the sensor network. The intensity distributions of the electric field in the coaxial capacitive sensor network are simulated to verify the theoretical analysis, and the effects of different debris sizes and debris numbers on the capacitance values were also simulated. Finally, the theoretical model and simulation results were experimentally validated to verify the feasibility of the proposed sensor network.
Research on Distributed Energy Consensus Mechanism Based on Blockchain in Virtual Power Plant
Authors: Wang Dewen , Wang Zhao , Lian Xin
Published: February 2022

Virtual power plant (VPP) composed of a large number of distributed energy resources (DERs) has become a regional multienergy aggregation model to realize the large-scale integration of renewable energy generation into the grid. Due to the characteristics of centralized management, the existing energy operation mode is difficult to simply apply to distributed energy resources transactions. The decentralization, transparency, contract execution automation and traceability of blockchain technology provide a new solution to the aggregation of decentralized resources and the opacity of transactions in VPP. In this paper, the existing problems of virtual power plants are analyzed, and the virtual power plant trading model is designed, which realizes the transparent benefit distribution and message transmission of virtual power plants. The virtual power plant blockchain network based on blockchain technology in this model solves the DERs coordination problem in VPP and the security and efficiency problems in information transmission. Combined with the actual situation of virtual power plant, the blockchain network collaboration mechanism (BNCM), which is convenient to reach agreement, is designed. Compared with the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm, this mechanism can make DERs reach a consensus quickly. Finally, simulation experiments on the consensus algorithm show that the algorithm can reduce the collaboration time between DERs under the premise of ensuring the same fault tolerance rate and is more suitable for VPP scenarios with a large number of DERs.
Development of an EEG Headband for Stress Measurement on Driving Simulators
Authors: Affanni Antonio , Aminosharieh Najafi Taraneh , Guerci Sonia
Published: February 2022

In this paper, we designed from scratch, realized, and characterized a six-channel EEG wearable headband for the measurement of stress-related brain activity during driving. The headband transmits data over WiFi to a laptop, and the rechargeable battery life is 10 h of continuous transmission. The characterization manifested a measurement error of 6 μV in reading EEG channels, and the bandwidth was in the range [0.8, 44] Hz, while the resolution was 50 nV exploiting the oversampling technique. Thanks to the full metrological characterization presented in this paper, we provide important information regarding the accuracy of the sensor because, in the literature, commercial EEG sensors are used even if their accuracy is not provided in the manuals. We set up an experiment using the driving simulator available in our laboratory at the University of Udine; the experiment involved ten volunteers who had to drive in three scenarios: manual, autonomous vehicle with a “gentle” approach, and autonomous vehicle with an “aggressive” approach. The aim of the experiment was to assess how autonomous driving algorithms impact EEG brain activity. To our knowledge, this is the first study to compare different autonomous driving algorithms in terms of drivers’ acceptability by means of EEG signals. The obtained results demonstrated that the estimated power of beta waves (related to stress) is higher in the manual with respect to autonomous driving algorithms, either “gentle” or “aggressive”.
Modifying Hata-Davidson Propagation Model for Remote Sensing in Complex Environments Using a Multifactional Drone
Authors: Almalki Faris A. , Soufiene Ben Othman
Published: February 2022

The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims’ chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters. A drone was coupled with a thermal FLIR lepton camera, a microcontroller, GPS, and weather station sensors. The proposed modified model utilized the least squares tuning algorithm to fit the data measured from the drone communication system. This enhanced the RF connectivity between the drone and the local authority, as well as leading to increased coverage footprint and, thus, the performance of wider search-and-rescue operations in a timely fashion using strip search patterns. The development of the proposed model considered both software simulation and hardware implementations. Since empirical propagation models are the most adjustable models, this study concludes with a comparison between the modified Hata-Davidson algorithm against other well-known modified empirical models for validation using root mean square error (RMSE). The experimental results show that the modified Hata-Davidson model outperforms the other empirical models, which in turn helps to identify missing persons and their locations using thermal imaging and a GPS sensor.
Recent State of Wearable IMU Sensors Use in People Living with Spasticity: A Systematic Review
Authors: Weizman Yehuda , Tirosh Oren , Fuss Franz Konstantin , Tan Adin Ming , Rutz Erich
Published: February 2022

Spasticity is a disabling characteristic of neurological disorders, described by a velocity-dependent increase in muscle tone during passive stretch. During the last few years, many studies have been carried out to assess spasticity using wearable IMU (inertial measurements unit) sensors. This review aims to provide an updated framework of the current research on IMUs wearable sensors in people living with spasticity in recent studies published between 2017 and 2021. A total of 322 articles were screened, then finally 10 articles were selected. Results show the lack of homogenization of study procedures and missing apparatus information in some studies. Still, most studies performed adequately on measures of reporting and found that IMUs wearable data was successful in their respective purposes and goals. As IMUs estimate translational and rotational body motions, we believe there is a strong potential for these applications to estimate velocity-dependent exaggeration of stretch reflexes and spasticity-related characteristics in spasticity. This review also proposes new directions of research that should be challenged by larger study groups and could be of interest to both researchers as well as clinicians. The use of IMUs to evaluate spasticity is a promising avenue to provide an objective measurement as compared to non-instrumented traditional assessments.