bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Analysis of the Free-Falling Process of a Water Droplet at Different Temperatures
Authors: Song Yuchao , Zhang Yafei , Gao Hongtao
Published: January 2023

The collision behavior and ice formation of a water droplet are affected by its falling process. In this paper, the two-phase flow of air and a water droplet at a specific temperature is adopted to investigate the processes of falling and freezing of a single water droplet. To track the air–water droplet interface and the temperature distribution, the level-set method and the non-isothermal flow coupling method are used, and the freezing model is added into the water’s control equations. The numerical results indicated that with the initial temperature at 283.15 K and the spherical shape, the water droplet changes to the shape of a straw hat at 293.15 K and a drum at 293.15 K but an oval face in freezing temperatures at 0.10 s. There is an obvious drop in the downward velocity when the water droplet falls in mild temperatures at 0.09 s. The downward velocity of the water droplet in air at sub-zero temperatures has a continuous increase during the time span from 0 s to 0.10 s. There is also an obvious difference when the water droplet impinges on the solid bottom. Lastly, the freezing of sessile water droplets attached on the horizontal surface is helpful to reveal the unique phase change process of water droplets in cold air.
Study on Gas Migration Mechanism and Multi-Borehole Spacing Optimization in Coal under Negative Pressure Extraction
Authors: Du Feng , Cui Weilong , Wang Kai
Published: January 2023

In order to study the gas migration in gas-bearing coal, and reasonably arrange gas drainage boreholes to improve the efficiency of gas drainage, a gas-solid coupling model is established based on the pore-fracture dual medium porous model. The solid deformation of coal body, gas seepage and diffusion, and gas adsorption and desorption are considered in this model. The COMSOL software is used to simulate the gas change in the coal matrix and coal fracture under single borehole extraction. We analyze the effective extraction range and study the migration mechanism of gas between coal fracture and borehole, coal matrix and coal fracture, and coal matrix. The effective extraction area of multi-borehole negative pressure gas extraction varies with extraction time and borehole spacing. At 140 d, the effective extraction radius is r = 1.3 m, and the spacing of boreholes is 233 r=1.5 m, 2 r=2.6 m,4 m,5 m,and 6 m, respectively. The influence of the equilateral triangle shape of three boreholes on the gas extraction effect is studied. The simulation results show that when three boreholes are extracted for 140 days under different borehole spacing, different gas extraction effects will be affected by a superposition effect. Considering the change in gas pressure, the effect of gas extraction in the effective extraction area, and the safety and cost performance of gas extraction, it is concluded that the optimal hole spacing is 5 m around 140 d. This study aims to provide reference for underground gas drilling layout and reasonable hole spacing.
Nonlinearly Parametrized Modeling and Adaptive Control for a Generic Hypersonic Vehicle
Authors: Yang Shaohua , Li Xia
Published: January 2023

This paper constructs a nonlinearly parametrized hypersonic cruise vehicle model on the basis of the existing on-orbit flight data using a curve-fitting technique. The hypersonic cruise vehicle system is separated into two interconnected subsystem: an attitude subsystem with rotational dynamics and a velocity subsystem with engine dynamics. The continuous adaptive controllers are designed for two subsystems using a novel function bounding technique and appropriate coordinate transformations, respectively, which ensure the global boundedness of all signals and achieve the non-zero equilibrium point regulation of nonlinearly parametrized hypersonic vehicle systems. One of the implications of this result is that growing nonlinearities in the uncertain model of the hypersonic vehicle system may be allowed for global stabilization. A simulation result verifies the effectiveness of the proposed adaptive control scheme.
Acknowledgment to the Reviewers of Psychiatry International in 2022
Authors:   Psychiatry International Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Factors Associated with Changes in E-Cigarette Use and Tobacco Smoking by Adolescents and Young People in Nigeria during the COVID-19 Pandemic
Authors: Folayan Morenike Oluwatoyin , Alade Omolola T. , Sabbagh Heba , Oyapero Afolabi , Adeyemo Yewande I. , Popoola Bamidele Olubukola , Adeniyi Abiola A. , Eigbobo Jocelyn , Quritum Maryam , Nzomiwu Chioma , Chukwumah Nneka Maureen , El Tantawi Maha
Published: January 2023

This study aimed to assess the proportion of adolescents and young people (AYP) in Nigeria who changed their frequency of e-cigarette use and tobacco smoking during the COVID-19 pandemic; and factors associated with the increase, decrease or no change in e-cigarette use and tobacco smoking (including night smoking). This study was a cross-sectional study of AYP recruited from all geopolitical zones in the country. Multivariate logistic regression analyses were conducted to determine if respondents’ health HIV and COVID-19 status and anxiety levels were associated with changes in e-cigarette use and tobacco smoking frequency. There were 568 (59.5%) e-cigarette users, of which 188 (33.1%) increased and 70 (12.3%) decreased e-cigarette use and 389 (68.5%) increased night e-cigarette use. There were 787 (82.4%) current tobacco smokers, of which 305 (38.8%) increased and 102 (13.0%) decreased tobacco smoking and 534 (67.9%) increased night tobacco smoking. Having a medical condition was associated with lower odds of increased e-cigarette use (AOR:0.649; p = 0.031). High anxiety (AOR:0.437; p = 0.027) and having a medical condition (AOR:0.554; p = 0.044) were associated with lower odds of decreased e-cigarette use. Having COVID-19 symptoms (AOR:2.108; p < 0.001) and moderate anxiety (AOR:2.138; p = 0.006) were associated with higher odds of increased night e-cigarette use. We found complex relationships between having a medical condition, experiencing anxiety, changes in tobacco smoking and e-cigarette use among AYP in Nigeria during the COVID-19 pandemic that need to be studied further.
Uncertainty Relation and the Thermal Properties of an Isotropic Harmonic Oscillator (IHO) with the Inverse Quadratic (IQ) Potentials and the Pseudo-Harmonic (PH) with the Inverse Quadratic (IQ) Potentials
Authors: Onate Clement A. , Okon Ituen B. , Jude Gian. O. , Onyeaju Michael C. , Antia Akaninyene. D.
Published: January 2023

The solutions for a combination of the isotropic harmonic oscillator plus the inversely quadratic potentials and a combination of the pseudo-harmonic with inversely quadratic potentials has not been reported, though the individual potentials have been given attention. This study focuses on the solutions of the combination of the potentials, as stated above using the parametric Nikiforov–Uvarov (PNV) as the traditional technique to obtain the energy equations and their corresponding unnormalized radial wave functions. To deduce the application of these potentials, the expectation values, the uncertainty in the position and momentum, and the thermodynamic properties, such as the mean energy, entropy, heat capacity, and the free mean energy, are also calculated via the partition function. The result shows that the spectra for the PHIQ are higher than the spectra for the IHOIQ. It is also shown that the product of the uncertainties obeyed the Heisenberg uncertainty relation/principle. Finally, the thermal properties of the two potentials exhibit similar behaviours.
Does a Religious Atmosphere Impact Corporate Social Responsibility? A Comparative Study between Taoist and Buddhist Dominated Atmospheres
Authors: Shao Jing , Zhang Tianzi , Lee Young-Chan , Xu Yingbo
Published: January 2023

The impact of religion on business has attracted cross-academic attention in recent years. Does the religious atmosphere impact corporate social responsibility (CSR)? This study addressed this question using a sample of Chinese-listed companies from 2010 to 2018. Our findings reveal that firms in regions with a Taoist-dominated religious atmosphere are more charitable and less environmentally invested. In contrast, firms with a Buddhist-dominated religious atmosphere are more ecologically engaged and less charitable. This study extends the literature on the impact of the informal institutional environment on corporate social responsibility by distinguishing the heterogeneity of the impact of Buddhist- and Taoist-dominated religious atmospheres on CSR. It also provides a new perspective for enterprises to formulate corporate social responsibility strategies based on the regional cultural environment. And it also enriches the application of informal institutional theory to the fields of management and religion.
SiamHRnet-OCR: A Novel Deforestation Detection Model with High-Resolution Imagery and Deep Learning
Authors: Wang Zhipan , Liu Di , Liao Xiang , Pu Weihua , Wang Zhongwu , Zhang Qingling
Published: January 2023

Forests play a critical role in global carbon cycling, and continuous forest destruction together with other factors has accelerated global warming. Furthermore, continued decline of forest areas will critically hinder the accomplishment of carbon neutrality goals. Although the geographical location of deforestation can now be rapidly and accurately detected with remote sensing technology, current forest change products are still not fine-grained, especially from the perspective of carbon trading. Here, we used a deep learning method to detect deforestation in large regions based on 2 m high-resolution optical remote sensing images. Firstly, we proposed a new deforestation detection dataset, which was generated from 11 provincial regions in the Yangtze River Economic Zone of China, containing a total number of 8330 samples (the size of each sample being 512 × 512 pixels). Then, a new deforestation detection model, SiamHRnet-OCR, was designed, based on this dataset. Compared with other deep learning models, SiamHRnet-OCR achieves better results in terms of precision, F1-score, and OA indicator: 0.6482, 0.6892, and 0.9898, respectively. Finally, two large-scale scenarios of deforestation experiments in Southern China were further tested; the deforestation detection results demonstrate that SiamHRnet-OCR can not only detect deforestation effectively but also capture the accurate boundary of the changing area.
Contrastive Domain Adaptation-Based Sparse SAR Target Classification under Few-Shot Cases
Authors: Bi Hui , Liu Zehao , Deng Jiarui , Ji Zhongyuan , Zhang Jingjing
Published: January 2023

Due to the imaging mechanism of synthetic aperture radar (SAR), it is difficult and costly to acquire abundant labeled SAR images. Moreover, a typical matched filtering (MF) based image faces the problems of serious noise, sidelobes, and clutters, which will bring down the accuracy of SAR target classification. Different from the MF-based result, a sparse image shows better quality with less noise and higher image signal-to-noise ratio (SNR). Therefore, theoretically using it for target classification will achieve better performance. In this paper, a novel contrastive domain adaptation (CDA) based sparse SAR target classification method is proposed to solve the problem of insufficient samples. In the proposed method, we firstly construct a sparse SAR image dataset by using the complex image based iterative soft thresholding (BiIST) algorithm. Then, the simulated and real SAR datasets are simultaneously sent into an unsupervised domain adaptation framework to reduce the distribution difference and obtain the reconstructed simulated SAR images for subsequent target classification. Finally, the reconstructed simulated images are manually labeled and fed into a shallow convolutional neural network (CNN) for target classification along with a small number of real sparse SAR images. Since the current definition of the number of small samples is still vague and inconsistent, this paper defines few-shot as less than 20 per class. Experimental results based on MSTAR under standard operating conditions (SOC) and extended operating conditions (EOC) show that the reconstructed simulated SAR dataset makes up for the insufficient information from limited real data. Compared with other typical deep learning methods based on limited samples, our method is able to achieve higher accuracy especially under the conditions of few shots.
Seasonal and Diurnal Characteristics of the Vertical Profile of Aerosol Optical Properties in Urban Beijing, 2017–2021
Authors: Zhang Xinglu , Zheng Yu , Che Huizheng , Gui Ke , Li Lei , Zhao Hujia , Liang Yuanxin , Yao Wenrui , Zhang Xindan , Zhao Hengheng , Lu Yanting , Zhang Xiaoye
Published: January 2023

Seasonal and diurnal characteristics of the vertical profiles of aerosol properties are essential for detecting the regional transport and the climatic radiative effects of aerosol particles. We have studied the seasonal and diurnal characteristics of the vertical distribution of aerosols in urban Beijing from 2017 to 2021 based on long-term Raman–Mie LiDAR observations. The influence of the vertical distribution of aerosols, the meteorological conditions within the boundary layer, the optical–radiometric properties of aerosols, and their interconnections, were investigated during a heavy haze pollution event in Beijing from 8 to 15 February 2020 using both meteorological and sun photometer data. The aerosol extinction coefficient was highest in summer (0.4 km−1), followed by winter (0.35 km−1), and roughly equal in spring and autumn (0.3 km−1). The aerosol extinction coefficient showed clear daily variations and was different in different seasons as a result of the variation in the height of the boundary layer. During the haze pollution event, the particulate matter mainly consisted of scattered spherical fine particles and the accumulation time of pollutants measured via the AOD440nm and PM2.5 mass concentration was different as a result of the hygroscopic growth of the aerosol particles. This growth increased scattering and led to an increase in the aerosol optical depth. The vertical transport of particulate matter also contributed to the increase in the aerosol optical depth.
Implementation Method of Automotive Video SAR (ViSAR) Based on Sub-Aperture Spectrum Fusion
Authors: Guo Ping , Wu Fuen , Tang Shiyang , Jiang Chenghao , Liu Changjie
Published: January 2023

The automotive synthetic aperture radar (SAR) can obtain two-dimensional (2-D) high-resolution images and has good robustness compared with the other sensors. Generally, the 2-D high-resolution always conflicts with the real-time requirement in conventional SAR imaging. This article suggests an automotive video SAR (ViSAR) imaging technique based on sub-aperture spectrum fusion to address this issue. Firstly, the scene space variation problem caused by close observation distance in automotive SAR is analyzed. Moreover, the sub-aperture implementation method, frame rate and resolution of automotive ViSAR are also introduced. Then, the improved Range Doppler algorithm (RDA) is used to focus the sub-aperture data. Finally, a sub-aperture stitching strategy is proposed to obtain a high-resolution frame image. Compared with the available ViSAR imaging method, the proposed method is more efficient, performs better, and is more appropriate for automotive ViSAR. The simulation results and actual data of the automotive SAR validate the effectiveness of the proposed method.
A Standardized Procedure to Build a Spectral Library for Hazardous Chemicals Mixed in River Flow Using Hyperspectral Image
Authors: Gwon Yeonghwa , Kim Dongsu , You Hojun , Nam Su-Han , Kim Young Do
Published: January 2023

The occurrence of natural disasters as a consequence of accidental hazardous chemical spills remains a concern. The inadequate, or delayed, initial response may fail to mitigate their impact; hence, imminent monitoring of responses in the initial stage is critical. Classical contact-type measurement methods, however, sometimes miss solvent chemicals and invoke risks for operators during field operation. Remote sensing methods are an alternative method as non-contact, spatially distributable, efficient and continuously operatable features. Herein, we tackle challenges posed by the increasingly available UAV-based hyperspect ral images in riverine environments to identify the presence of hazardous chemical solvents in rivers, which are less investigated in the absence of direct measurement strategies. We propose a referable standard procedure for a unique spectral library based on pre-scanning hyperspectral sensors with respect to representative hazardous chemicals registered on the national hazardous chemical list. We utilized the hyperspectral images to identify 18 types of hazardous chemicals injected into the river in an outdoor environment, where a dedicated hyperspectral ground imaging system mounted with a hyperspectral camera was designed and applied. Finally, we tested the efficiency of the library to recognize unknown chemicals, which showed >70% success rate.
Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities
Authors: Zhang Wangle , Wang Jiwen , Lin Hate , Cong Ming , Wan Yue , Zhang Jingxiong
Published: January 2023

There are a variety of land cover products generated from remote-sensing images. However, misclassification errors in individual products and inconsistency among them undermine their utilities for research and other applications. While it is worth developing advanced pattern classifiers and utilizing the images of finer spatial, temporal, and/or spectral resolution for increased classification accuracy, it is also sensible to increase map classification accuracy through effective map fusion by exploiting complementarity among multi-source products over a study area. This paper presents a novel fusion method that works by weighting multiple source products based on their map-reference cover type transition probabilities, which are predicted using random forest for individual map pixels. The proposed method was tested and compared with three alternatives: consensus-based weighting, random forest, and locally modified Dempster–Shafer evidential reasoning, in a case study, over Shaanxi province, China. For this case study, three types of land cover products (GlobeLand30, FROM-GLC, and GLC_FCS30) of two nominal years (2010 and 2020) were used as the base maps for fusion. Reference sample data for model training and testing were collected following a robust stratified random sampling design that allows for augmenting reference data flexibly. Accuracy assessments show that overall accuracies (OAs) of fused land cover maps have been improved (1~9% in OAs), with the proposed method outperforming other methods by 2~8% in OAs. The proposed method does not need to have the base products’ classification systems harmonized beforehand, thus being robust and highly recommendable for fusing land cover products.
Comparative Analysis of Machine Learning Algorithms for Soil Erosion Modelling Based on Remotely Sensed Data
Authors: Fernández Daniel , Adermann Eromanga , Pizzolato Marco , Pechenkin Roman , Rodríguez Christina G. , Taravat Alireza
Published: January 2023

Recent years have seen an increase in the use of remote-sensing based methods to assess soil erosion, mainly due to the availability of freely accessible satellite data, with successful results on a consistent basis. There would be valuable benefits from applying these techniques to the Arctic areas, where ground local studies are typically difficult to perform due to hardly accessible roads and lands. At the same time, however, the application of remote-sensing methods comes with its own set of challenges when it comes to the peculiar features of the Arctic: short growing periods, winter storms, wind, and frequent cloud and snow cover. In this study we perform a comparative analysis of three commonly used classification algorithms: Support Vector Machine (SVM), Random Forest (RF) and Multilayer Perceptron (MLP), in combination with ground truth samples from regions all over Iceland, provided by Iceland’s Soil Conservation Service department. The process can be automated to predict soil erosion risk for larger, less accessible areas from Sentinel-2 images. The analysis performed on validation data sets supports the effectiveness of both approaches for modeling soil erosion, albeit differences are highlighted.
Identification of Sea Surface Temperature and Sea Surface Salinity Fronts along the California Coast: Application Using Saildrone and Satellite Derived Products
Authors: Vazquez-Cuervo Jorge , García-Reyes Marisol , Gómez-Valdés José
Published: January 2023

Coastal upwelling regions are one of the most dynamic areas of the world’s oceans. The California and Baja California Coasts are impacted by both coastal upwelling and the California Current, leading to frontal activity that is captured by gradients in both Sea Surface Temperature (SST) and Sea Surface Salinity (SSS). Satellite data are a great source of spatial data to study fronts. However, biases near coastal areas and coarse resolutions can impair its usefulness in upwelling areas. In this work gradients in SST from NASA Multi-Scale Ultra-High Resolution (MUR) and in two SSS products derived from the Soil Moisture Active Passive (SMAP) NASA mission are compared directly with gradients derived from the Saildrone uncrewed vehicles to validate the gradients as well as to assess their ability to detect known frontal features. The three remotely sensed data sets (MURSST/JPL, SMAP SSS/RSS, SMAP SSS) were co-located with the Saildrone data prior to the calculation of the gradients. Wavelet analysis is used to determine how well the satellite derived SST and SSS products are reproducing the Saildrone derived gradients. Overall results indicate the remote sensing products are reproducing features of known areas of coastal upwelling. Differences between the SST and SSS gradients are mainly associated with the limitations of the microwave derived SSS coverage near land and its reduced spatial resolution. The results are promising for using remote sensing data sets to monitor frontal structure along the California Coast and the application to long term changes in coastal upwelling and dynamics.
Tight Integration Kinematic PPP-AR Using GPS/Galileo/QZSS Overlapping Frequency Signals and Its Performance in High-Shade Environments
Authors: Wang Siyao , Tu Rui , Li Bofeng , Zhang Rui , Fan Lihong , Han Junqiang , Lu Xiaochun
Published: January 2023

Several global navigation satellite systems (GNSS) broadcast overlapping frequencies to enhance interoperability, allowing tight integration with only one reference satellite for every system with the same frequency. The key to realizing tight integration is estimating and utilizing differential intersystem biases, which allows the integer characteristic of the differenced ambiguity of two satellites from different systems to be retrieved. In this study, a detailed algorithm flow of a tight integration kinematic uncalibrated phase delay (UPD)-based PPP ambiguity resolution (PPP-AR) method, which includes multiple parts, is introduced. Subsequently, PPP-AR numerical experiments were conducted in a high-shade observation environment to determine the performance. In comparison with traditional methods, our results indicate that the average success fix rate improves from 83% to 100% after using the tight integration method in an environment where only three satellites are observed for each system. Moreover, for fewer than nine satellites, the tight integration method can still consistently maintain a fixed state. However, for comparison, the traditional loose integration method could no longer be implemented.
Consistency and Discrepancy between Visibility and PM2.5 Measurements: Potential Application of Visibility Observation to Air Quality Study
Authors: Fei Ye , Liao Jie , Zhang Zhisen
Published: January 2023

High-quality measurements of air quality are the highest priority for understanding widespread air pollution. Visibility has been widely suggested to be a good alternative to PM2.5 concentration as a measure. In this study, the similarities and differences between visibility and PM2.5 measurements in China are checked and the results reveal the potential application of visibility observation to the study of air quality. Based on the quality-controlled PM2.5 and visibility data from 2016 to 2018, the nonparametric Spearman correlation coefficient (ρ) values between stations for PM2.5 and visibility-derived surface extinction coefficient (bext) decrease as the station distance (R) increases. Some relatively low ρ values (<0.4) occur in regions characterized by the lowest (background) levels of PM2.5 and bext values, for example, the Tibetan and Yungui Plateau. The relatively lower ρ for bext compared to PM2.5 is probably caused by the predefined maximum threshold of visibility measurements (generally 30 km). A significant correlation between PM2.5 and bext is derived in most stations and relatively larger ρ values are evident in eastern China (Northeast China excluded) and in winter (the national median ρ is 0.67). The abrupt changes in specific mass extinction efficiency (αext) imply a potentially large influence of alternation of visibility sensors or recalibrations on visibility measurements. The bext data are thereafter corrected by comparison to the reference measurements at the adjacent stations, which leads to a three-year quality assured of visibility and bext datasets.
The Effectiveness of Zero-Day Attacks Data Samples Generated via GANs on Deep Learning Classifiers
Authors: Peppes Nikolaos , Alexakis Theodoros , Adamopoulou Evgenia , Demestichas Konstantinos
Published: January 2023

Digitization of most of the services that people use in their everyday life has, among others, led to increased needs for cybersecurity. As digital tools increase day by day and new software and hardware launch out-of-the box, detection of known existing vulnerabilities, or zero-day as they are commonly known, becomes one of the most challenging situations for cybersecurity experts. Zero-day vulnerabilities, which can be found in almost every new launched software and/or hardware, can be exploited instantly by malicious actors with different motives, posing threats for end-users. In this context, this study proposes and describes a holistic methodology starting from the generation of zero-day-type, yet realistic, data in tabular format and concluding to the evaluation of a Neural Network zero-day attacks’ detector which is trained with and without synthetic data. This methodology involves the design and employment of Generative Adversarial Networks (GANs) for synthetically generating a new and larger dataset of zero-day attacks data. The newly generated, by the Zero-Day GAN (ZDGAN), dataset is then used to train and evaluate a Neural Network classifier for zero-day attacks. The results show that the generation of zero-day attacks data in tabular format reaches an equilibrium after about 5000 iterations and produces data that are almost identical to the original data samples. Last but not least, it should be mentioned that the Neural Network model that was trained with the dataset containing the ZDGAN generated samples outperformed the same model when the later was trained with only the original dataset and achieved results of high validation accuracy and minimal validation loss.
Inertial Tracking System for Monitoring Dual Mobility Hip Implants In Vitro
Authors: Shuttleworth Matthew Peter , Vickers Oliver , Smeeton Mackenzie , Board Tim , Isaac Graham , Culmer Peter , Williams Sophie , Kay Robert William
Published: January 2023

Dual mobility (DM) implants are being increasingly used for total hip arthroplasties due to the additional range of motion and joint stability they afford over more traditional implant types. Currently, there are no reported methods for monitoring their motions under realistic operating conditions while in vitro and, therefore, it is challenging to predict how they will function under clinically relevant conditions and what failure modes may exist. This study reports the development, calibration, and validation of a novel inertial tracking system that directly mounts to the mobile liner of DM implants. The tracker was custom built and based on a miniaturized, off-the-shelf inertial measurement unit (IMU) and employed a gradient-decent sensor fusion algorithm for amalgamating nine degree-of-freedom IMU readings into three-axis orientation estimates. Additionally, a novel approach to magnetic interference mitigation using a fixed solenoid and magnetic field simulation was evaluated. The system produced orientation measurements to within 1.0° of the true value under ideal conditions and 3.9° with a negligible drift while in vitro, submerged in lubricant, and without a line of sight.
Microstrip Antenna Development for Radar Sensor
Authors: Nagy Lajos
Published: January 2023

Tank level measurement is an important research area in radar technology. Level measurement using radar technology is a safe solution even under extreme process conditions, such as high pressure, temperature, and vapors. Special antennas are required to meet electromagnetic requirements such as high gain, low sidelobe level, and high bandwidth. Another requirement is a small size and good manufacturability of the antenna. One promising solution is the use of microstrip antennas for tank-level measurements. This paper presents a special microstrip antenna, that meets the main requirements in addition to a circular antenna layout, which fills optimally the tank hatch. To do this, we created a special antenna layout with a suitable feeding network and added an optimized ring around the antenna system, which significantly reduces the sidelobe level. The center frequency of the antenna is 25 GHz with a 1 GHz bandwidth.
Design of a Flexible Weight Sensor Using Optical Fibre Macrobending
Authors: Funnell Adam C. , Thomas Peter James
Published: January 2023

A flexible weight sensor based on optical fibre macrobending loss, using 1550 nm wavelength light and small fibre bending path lengths is presented. An applied load depresses an impactor layer of cylindrical protrusions into a soft mat covered with optical fibre, causing the optical loss of the fibre to increase. An experimental study of two fibre types, two impactor materials, two impactor designs and a range of protrusion bend radii from 3 mm to 10 mm is shown. For weights greater than 2 kg, a linear response in optical loss (dB) is observed for an applied weight load in kg. The proportionality constant between loss and load, and thus the total amount of optical loss for up to 10 kg of weight load, can be tuned by changing the sensor physical parameters, shown here in ranges from 0.5 dB up to 25 dB.
Reconstruction of Gap-Free Land Surface Temperature at a 100 m Spatial Resolution from Multidimensional Data: A Case in Wuhan, China
Authors: Wu Zefeng , Teng Hongfen , Chen Haoxiang , Han Lingyu , Chen Liangliang
Published: January 2023

Land surface temperatures (LST) are vital parameters in land surface–atmosphere interactions. Constrained by technology and atmospheric interferences, LST retrievals from various satellite sensors usually return missing data, thus negatively impacting analyses. Reconstructing missing data is important for acquiring gap-free datasets. However, the current reconstruction methods are limited for maintaining spatial details and high accuracies. We developed a new gap-free algorithm termed the spatial feature-considered random forest regression (SFRFR) model; it builds stable nonlinear relationships to connect the LST with related parameters, including terrain elements, land coverage types, spectral indexes, surface reflectance data, and the spatial feature of the LST, to reconstruct the missing LST data. The SFRFR model reconstructed gap-free LST data retrieved from the Landsat 8 satellite on 27 July 2017 in Wuhan. The results show that the SFRFR model exhibits the best performance according to the various evaluation metrics among the SFRFR, random forest regression and spline interpolation, with a coefficient of determination (R2) reaching 0.96, root-mean-square error (RMSE) of 0.55, and mean absolute error (MAE) of 0.55. Then, we reconstructed gap-free LST data gathered in Wuhan from 2016 to 2021 to analyze urban thermal environment changes and found that 2020 presented the coolest temperatures. The SFRFR model still displayed satisfactory results, with an average R2 of 0.91 and an MAE of 0.63. We further discuss and discover the factors affecting the visual performance of SFRFR and identify the research priority to circumvent these disadvantages. Overall, this study provides a simple, practical method for acquiring gap-free LST data to help us better understand the spatiotemporal LST variation process.
On the Performance of Secure Sharing of Classified Threat Intelligence between Multiple Entities
Authors: Fernandes Ricardo , Bugla Sylwia , Pinto Pedro , Pinto António
Published: January 2023

The sharing of cyberthreat information within a community or group of entities is possible due to solutions such as the Malware Information Sharing Platform (MISP). However, the MISP was considered limited if its information was deemed as classified or shared only for a given period of time. A solution using searchable encryption techniques that better control the sharing of information was previously proposed by the same authors. This paper describes a prototype implementation for two key functionalities of the previous solution, considering multiple entities sharing information with each other: the symmetric key generation of a sharing group and the functionality to update a shared index. Moreover, these functionalities are evaluated regarding their performance, and enhancements are proposed to improve the performance of the implementation regarding its execution time. As the main result, the duration of the update process was shortened from around 2922 s to around 302 s, when considering a shared index with 100,000 elements. From the security analysis performed, the implementation can be considered secure, thus confirming the secrecy of the exchanged nonces. The limitations of the current implementation are depicted, and future work is pointed out.
A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization
Authors: Li Peijuan , Liu Yiting , Yan Tingwu , Yang Shutao , Li Rui
Published: January 2023

The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors.
A Robust Planar Marker-Based Visual SLAM
Authors: Wang Zhoubo , Zhang Zhenhai , Zhu Wei , Hu Xuehai , Deng Hongbin , He Guang , Kang Xiao
Published: January 2023

Many visual SLAM systems are generally solved using natural landmarks or optical flow. However, due to textureless areas, illumination change or motion blur, they often acquire poor camera poses or even fail to track. Additionally, they cannot obtain camera poses with a metric scale in the monocular case. In some cases (such as when calibrating the extrinsic parameters of camera-IMU), we prefer to sacrifice the flexibility of such methods to improve accuracy and robustness by using artificial landmarks. This paper proposes enhancements to the traditional SPM-SLAM, which is a system that aims to build a map of markers and simultaneously localize the camera pose. By placing the markers in the surrounding environment, the system can run stably and obtain accurate camera poses. To improve robustness and accuracy in the case of rotational movements, we improve the initialization, keyframes insertion and relocalization. Additionally, we propose a novel method to estimate marker poses from a set of images to solve the problem of planar-marker pose ambiguity. Compared with the state-of-art, the experiments show that our system achieves better accuracy in most public sequences and is more robust than SPM-SLAM under rotational movements. Finally, the open-source code is publicly available and can be found at GitHub.
Paroxysmal Slow-Wave Events Are Uncommon in Parkinson’s Disease
Authors: Milikovsky Dan Z. , Sharabi Yotam , Giladi Nir , Mirelman Anat , Sosnik Ronen , Fahoum Firas , Maidan Inbal
Published: January 2023

Background: Parkinson’s disease (PD) is currently considered to be a multisystem neurodegenerative disease that involves cognitive alterations. EEG slowing has been associated with cognitive decline in various neurological diseases, such as PD, Alzheimer’s disease (AD), and epilepsy, indicating cortical involvement. A novel method revealed that this EEG slowing is composed of paroxysmal slow-wave events (PSWE) in AD and epilepsy, but in PD it has not been tested yet. Therefore, this study aimed to examine the presence of PSWE in PD as a biomarker for cortical involvement. Methods: 31 PD patients, 28 healthy controls, and 18 juvenile myoclonic epilepsy (JME) patients (served as positive control), underwent four minutes of resting-state EEG. Spectral analyses were performed to identify PSWEs in nine brain regions. Mixed-model analysis was used to compare between groups and brain regions. The correlation between PSWEs and PD duration was examined using Spearman’s test. Results: No significant differences in the number of PSWEs were observed between PD patients and controls (p > 0.478) in all brain regions. In contrast, JME patients showed a higher number of PSWEs than healthy controls in specific brain regions (p < 0.023). Specifically in the PD group, we found that a higher number of PSWEs correlated with longer disease duration. Conclusions: This study is the first to examine the temporal characteristics of EEG slowing in PD by measuring the occurrence of PSWEs. Our findings indicate that PD patients who are cognitively intact do not have electrographic manifestations of cortical involvement. However, the correlation between PSWEs and disease duration may support future studies of repeated EEG recordings along the disease course to detect early signs of cortical involvement in PD.
Feasibility of Skin Water Content Imaging Using CMOS Sensors
Authors: Saiko Gennadi
Published: January 2023

Pressure injuries (PI) result from pressure-induced damage to the skin and underlying tissues. Currently, Stage I PI are detected using visual skin assessments. However, this visual method is unable to detect skin color changes in persons with darkly pigmented skin, which results in a higher Stage II-IV PI incidence and PI-associated mortality in persons with a darker complexion. Thus, a more objective method of early-stage PI detection is of great importance. Optical spectroscopy is a promising modality for the noncontact diagnosis and monitoring of skin water content, capable of detecting edema and Stage I PI. The scope of the current study is to assess the feasibility of imaging the water content of the skin using Si-based sensors. We have considered two primary cases: the elevated bulk water content (edema) and localized water pool (e.g., blood vessels). These two cases were analyzed using analytical models. We found that detecting the watercontent contrast associated with edema in tissues is within the reach of Si-based sensors. However, although the effect is expected to be detectable even with consumer-grade cameras, with the current state of technologies, their use in real-world conditions faces numerous technical challenges, mainly due to the narrow dynamic range.
RF-Alphabet: Cross Domain Alphabet Recognition System Based on RFID Differential Threshold Similarity Calculation Model
Authors: Zhang Yajun , Yang Yan , Li Zijian , Yang Zhixiong , Liu Xu , Yuan Bo
Published: January 2023

Gesture recognition can help people with a speech impairment to communicate and promote the development of Human-Computer Interaction (HCI) technology. With the development of wireless technology, passive gesture recognition based on RFID has become a research hotspot. In this paper, we propose a low-cost, non-invasive and scalable gesture recognition technology, and successfully implement the RF-alphabet, a gesture recognition system for complex, fine-grained, domain-independent 26 English letters; the RF-alphabet has three major advantages: first, this paper achieves complete capture of complex, fine-grained gesture data by designing a dual-tag, dual-antenna layout. Secondly, to overcome the disadvantages of the large training sets and long training times of traditional deep learning. We design and combine the Difference threshold similarity calculation prediction model to extract digital signal features to achieve real-time feature analysis of gesture signals. Finally, the RF alphabet solves the problem of confusing the signal characteristics of letters. Confused letters are distinguished by comparing the phase values of feature points. The RF-alphabet ends up with an average accuracy of 90.28% and 89.7% in different domains for new users and new environments, respectively, by performing feature analysis on similar signals. The real-time, robustness, and scalability of the RF-alphabet are proven.
Reinforcement Learning Based Topology Control for UAV Networks
Authors: Yoo Taehoon , Lee Sangmin , Yoo Kyeonghyun , Kim Hwangnam
Published: January 2023

The recent development of unmanned aerial vehicle (UAV) technology has shown the possibility of using UAVs in many research and industrial fields. One of them is for UAVs moving in swarms to provide wireless networks in environments where there is no network infrastructure. Although this method has the advantage of being able to provide a network quickly and at a low cost, it may cause scalability problems in multi-hop connectivity and UAV control when trying to cover a large area. Therefore, as more UAVs are used to form drone networks, the problem of efficiently controlling the network topology must be solved. To solve this problem, we propose a topology control system for drone networks, which analyzes relative positions among UAVs within a swarm, then optimizes connectivity among them in perspective of both interference and energy consumption, and finally reshapes a logical structure of drone networks by choosing neighbors per UAV and mapping data flows over them. The most important function in the scheme is the connectivity optimization because it should be adaptively conducted according to the dynamically changing complex network conditions, which includes network characteristics such as user density and UAV characteristics such as power consumption. Since neither a simple mathematical framework nor a network simulation tool for optimization can be a solution, we need to resort to reinforcement learning, specifically DDPG, with which each UAV can adjust its connectivity to other drones. In addition, the proposed system minimizes the learning time by flexibly changing the number of steps used for parameter learning according to the deployment of new UAVs. The performance of the proposed system was verified through simulation experiments and theoretical analysis on various topologies consisting of multiple UAVs.
Prioritization-Driven Congestion Control in Networks for the Internet of Medical Things: A Cross-Layer Proposal
Authors: Buenrostro-Mariscal Raymundo , Santana-Mancilla Pedro C. , Montesinos-López Osval Antonio , Vazquez-Briseno Mabel , Nieto-Hipolito Juan Ivan
Published: January 2023

Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient quality of service (QoS) to transmit the collected data successfully. However, unsolved challenges in prioritization and congestion issues limit the functionality of IoT networks by increasing the likelihood of packet loss, latency, and high-power consumption in healthcare systems. This study proposes a priority-based cross-layer congestion control protocol called QCCP, which is managed by communication devices’ transport and medium access control (MAC) layers. Unlike existing methods, the novelty of QCCP is how it estimates and resolves wireless channel congestion because it does not generate control packets, operates in a distributed manner, and only has a one-bit overhead. Furthermore, at the same time, QCCP offers packet scheduling considering each packet’s network load and QoS. The results of the experiments demonstrated that with a 95% confidence level, QCCP achieves sufficient performance to support the QoS requirements for the transmission of health signals. Finally, the comparison study shows that QCCP outperforms other TCP protocols, with 64.31% higher throughput, 18.66% less packet loss, and 47.87% less latency.
Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform
Authors: Revert Calabuig Neus , Laarossi Ismail , Álvarez González Antonio , Pérez Nuñez Alejandro , González Pérez Laura , García-Minguillán Abraham Casas
Published: January 2023

A low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved.