bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Lead-Free Radiation Shielding Material Utilizing Barium Sulfate and Magnesium Oxide as Fillers in Addition Cure Liquid Silicone Rubber
Authors: Souza Everton G. , Kruger Kaiser , Nascimento Chiara D. , Aguzzoli Cesar , Hoff Gabriela , Moraes Ana Cristina B. K. , Lund Rafael G. , Nascente Patrícia S. , Cuevas-Suárez Carlos E. , Piva Evandro , Carreno Neftali L. V.
Published: November 2023

The radiological protection has the purpose of safeguarding the physical well-being of the user, preventing exposure to detrimental levels of ionizing radiation. This study introduces a novel, cost-effective category of lead-free elastomeric material designed for radiation shielding. The filler compounds utilized are notably lighter than conventional lead-based materials, enhancing user ergonomics during application. They comprise of a blend of barium sulfate combined or not with magnesium oxide with addition-cure liquid silicone rubber. To ensure the effectiveness of the radiation shielding, X-ray transmission measurements were performed for the different thicknesses of the materials and the results compared with Monte Carlo simulations. Additionally, the physical properties of the new materials, such as density, homogeneity, tensile strength, viscosity, and wettability, were also evaluated. The findings indicate that both materials fulfill the requirement for application in radiation protection garments.
Properties of Eco-Friendly Composites Based on Post-Consumer Recycled Resin Filled with Walnut Shell Powder
Authors: Pączkowski Przemysław
Published: November 2023

Increased demand for environmentally friendly materials resulted in a worldwide interest in manufacturing composite materials from agricultural wastes. Thus, this paper presents the results of research on the synthesis of eco-friendly composites and their properties. For their preparation, unsaturated polyester resin based on post-consumer recycled poly (ethylene terephthalate) was filled with walnut (Júglans régia L.) shell powder. After the filler incorporation, the deterioration of gloss and mechanical properties were observed. The flexural strength and modulus are significantly affected by the filler amount. Distilled water, 1% sodium hydroxide, toluene, and acetone were used as solvents in the chemical resistance test. Changes to the structure and properties of composites after 49 days of immersion in solvents were investigated. The immersion in water has no significant effect on the pure resin, but for its composites, the plasticizing effect of water was observed. The results show that all specimens show resistance toward toluene. In acetone, the resin and its composite shrink and fall into pieces, but the most destructive is an alkaline environment. After the immersion test, a huge increase in mass and a deterioration of gloss and mechanical properties were observed.
Multifunctional Nanoparticles with Superparamagnetic Mn(II) Ferrite and Luminescent Gold Nanoclusters for Multimodal Imaging
Authors: Casteleiro Bárbara , Rocha Mariana , Sousa Ana R. , Pereira André M. , Martinho José M. G. , Pereira Clara , Farinha José P. S.
Published: November 2023

Gold nanoclusters (AuNCs) with fluorescence in the Near Infrared (NIR) by both one- and two-photon electronic excitation were incorporated in mesoporous silica nanoparticles (MSNs) using a novel one-pot synthesis procedure where the condensation polymerization of alkoxysilane monomers in the presence of the AuNCs and a surfactant produced hybrid MSNs of 49 nm diameter. This method was further developed to prepare 30 nm diameter nanocomposite particles with simultaneous NIR fluorescence and superparamagnetic properties, with a core composed of superparamagnetic manganese (II) ferrite nanoparticles (MnFe2O4) coated with a thin silica layer, and a shell of mesoporous silica decorated with AuNCs. The nanocomposite particles feature NIR-photoluminescence with 0.6% quantum yield and large Stokes shift (290 nm), and superparamagnetic response at 300 K, with a saturation magnetization of 13.4 emu g−1. The conjugation of NIR photoluminescence and superparamagnetic properties in the biocompatible nanocomposite has high potential for application in multimodal bioimaging.
Study on the Isotherms, Kinetics, and Thermodynamics of Adsorption of Crystal Violet Dye Using Ag-NPs-Loaded Cellulose Derived from Peanut-Husk Agro-Waste
Authors: Aljeddani Ghalia Saleem , Alghanmi Reem Mohammad , Hamouda Ragaa A.
Published: November 2023

A huge amount of textile dyes are released as industrial waste into the environment each year, which alters the water’s natural appearance and causes toxicity and carcinogenicity in the human body. Peanut husk is considered an agro-waste and contains many valuable compounds, such as cellulose. Different concentrations of cellulose were extracted from peanut husk and then loaded with bio-silver nanoparticles, which were fabricated using neem leaves (Azadirachta indica) as a reducing agent to form Ag-cellulose nanocomposites (Ag-Cell-NCMs). Different devices were used to characterize Ag-Cell-NCMs. The TEM images displayed that the size of Ag-Cell-NCMs ranged between 13.4 and 17.4 nm after dye adsorption. The Ag-Cell-NCMs were used to adsorb toxic dyes such as crystal violet (CV). Different parameters were applied, such as the ratio of cellulose to Ag-NPs, pH, contact time, adsorbent dose, dye concentration, and the temperature required to reach the optimization conditions to remove CV dye from the aqueous solution. Different kinetics and isotherm models were applied to the experimental data to explain the mechanism of the adsorption process. The adsorption of CV on Ag-Cell-NCMs follows the pseudo-second order, and the best-fit isotherm was the Langmuir isotherm. The new composite was tested for the possibility of dye desorption and ability to be reused several times, and we found that the new nanocomposite can be reused for multiple adsorptions and there is a possibility of dye desorption.
Separation of an Industrial Mixture of Decalin or Naphthalene Fluorination Products: Cis-Perfluorodecalin, Trans-Perfluorodecalin and Perfluoro(butylcyclohexane): Physicochemical, Thermophysical, and Spectral Data
Authors: Lupachev Egor V. , Voshkin Andrey A. , Kisel’ Alexey V. , Kulov Nikolai N. , Zakhodyaeva Yulia A. , Polkovnichenko Andrei V.
Published: November 2023

New physicochemical data for trans-perfluorodecalin (trans-PDF) and cis-perfluorodecalin (cis-PFD) are presented. Based on the differential scanning calorimetry, the temperature and heat of the solid−liquid phase transition are determined. The coefficients of Antoine’s equation are calculated based on the experimental temperature–pressure dependence data. This article also presents data on the rheological properties («zero» shear viscosity and apparent activation energy for the viscous flow) of the studied compounds. The dependencies of refractive index and excess volume (density) on temperature are studied. Gas chromatography–mass spectrometry data and FTIR, 13C NMR, and 19F NMR spectra are provided. The dependencies are given for the perfluoro(butylcyclohexane) (BCH)–trans-PFD, BCH–cis-PFD, and trans-PFD–cis-PFD binary systems and BCH–trans-PFD–cis-PFD ternary system: refractive index and density (liquid molar volume and excess molar volume) of composition and temperature. The dependences of the excess molar volume on the composition and temperature of the mixtures are correlated with Redlich-Kister and Kohler equations.
Kinetic Investigation of the Oxidative Thermal Decomposition of Levonorgestrel
Authors: Ridichie Amalia , Ledeţi Adriana , Peter Francisc , Ledeţi Ionuţ , Muntean Cornelia , Rădulescu Matilda
Published: November 2023

In this study, we have focused on studying the heterogenous degradation kinetics regarding the decomposition of the emergency contraceptive agent levonorgestrel (LNG), which is a second-generation synthetic progestogen that is the active component of the racemic mixture of norgestrel. The degradation processes of the active pharmaceutical ingredient (API) were compared with the ones obtained from a model system containing the API along with the excipients that are found in a commercialized pharmaceutical formulation in a mass ratio of 1:1 (LNGMIX), in order to observe if the excipients have a stabilizing or destabilizing effect on the degradation of this progestogen. To achieve this, the following investigational methods were used: FTIR (Fourier transform infrared) spectroscopy and thermal analysis (TG/DTG/DSC analysis). For the kinetic analysis, the data obtained from two main decomposition processes observed on the DTG curves were used and processed with a preliminary method, namely ASTM E698, and two isoconversional methods: Friedman and Flynn–Wall–Ozawa. The isoconversional study revealed that the decomposition mechanisms of both LNG and LNGMIX are complex, and the excipients have a stabilizing effect on the decomposition of the API in tablet.
A Novel Prediction Model of the Drag Coefficient of Irregular Particles in Power-Law Fluids
Authors: Hou Zhaokai , Jiang Enyuan , Chen Ye , Wang Huaishan , Feng Jinyu , Tao Xutao
Published: November 2023

The settlement drag coefficient of irregular particles in power-law fluids is a crucial parameter in the field of petroleum engineering. However, the irregular shape of the particle and the non-Newtonian rheological properties of the fluid make it challenging to predict the settlement drag coefficient. In this study, the spherical and irregular particle sedimentation processes in power-law fluids have been analyzed using a visual device and a high-speed camera system. A mechanical model dependent on the force balance of settlement particles was adopted to conduct a detailed statistical analysis of 114 spherical particle experimental results, and a prediction model of the drag coefficient of spherical particles in the power-law fluid was established with a mean relative error of 3.85%. On this basis, considering the influence of geometric shape on the law of particle sedimentation, a new irregular particle sedimentation resistance coefficient model in power-law fluid is established via the incorporation of the parameter circularity of 2D shape description c into the spherical particle sedimentation resistance coefficient predictive model. The parameters in the new irregular particle sedimentation resistance coefficient predictive model can be obtained via nonlinear data fitting of the 211 groups of irregular particles using experimental results in the power-law fluid. The model has high prediction accuracy for the drag coefficient of irregular particles in power-law fluid, with a mean relative error of 4.47, and expands the scope of engineering applications, which is of great significance for fracturing scheme design and wellbore cleaning.
A Real-Time Remaining Fatigue Life Prediction Approach Based on a Hybrid Deep Learning Network
Authors: Zhu Yifeng , Zhang Jianzhao , Luo Jiaxiang , Guo Xinyan , Liu Ziyu , Zhang Ruonan
Published: November 2023

Fatigue failure is a typical failure mode of welded structures. It is of great engineering significance to predict the remaining fatigue life of structures after a certain period of service. In this paper, a two-stage hybrid deep learning approach is proposed only using the response of structures under fatigue loading to predict the remaining fatigue life. In the first stage, a combination of convolutional neural network (CNN), squeeze-and-excitation (SE) block, and long short-term memory (LSTM) network is employed to calculate health indicator values based on the current measured data sequence. In the second stage, a particle filtering-based algorithm is utilized to predict the remaining fatigue life using the previously calculated health indicators. Experimental results on different welded specimens under the same loading conditions demonstrate that the hybrid deep learning approach achieves superior prediction accuracy and generalization ability compared to CNN, LSTM, or CNN-LSTM models in the first stage. Moreover, the average relative deviation between the predicted and actual fatigue life is less than 6% during the final quarter of the crack propagation and fracture stage.
Presenting a Protocol for Dental Implant Restorations
Authors: Iannello Gaetano , Fiorillo Luca , D’Amico Cesare , Minervini Giuseppe , Terranova Antonella , Cervino Gabriele
Published: November 2023

This case report focuses on applying a protocol for dental implant restoration in aesthetic zones, specifically the anterior sectors. The protocol aims to achieve optimal results with minimal complexity, reducing the number of sessions and impressions required for transferring clinical information to the laboratory. The historical development of dental implantology and the evolution of dental implant restoration workflows are discussed, highlighting the significance of osseointegration and advancements in materials, surgical techniques, and prosthetic components. The case report describes rehabilitating congenitally missing lateral incisors using fibre-reinforced composite Maryland bridges for provisional restoration and Osstem TSIII (Osstem®, Seoul, South Korea) implants, zirconia abutments, and disilicate lithium crowns for definitive repair. The discussion covers essential aspects of aesthetic dental implant restoration, including materials, types of dental implants, surgical techniques for soft tissue enhancement, and prosthetic and impression techniques. The study emphasises the importance of selecting appropriate materials, employing advanced surgical procedures, and utilising accurate prosthetic and impression techniques to enhance the aesthetic outcomes of dental implant restorations. A multidisciplinary approach and patient-centred care are essential for successful aesthetic dental implant restorations. The methodology presented in this manuscript demonstrates its effectiveness in achieving optimal outcomes while minimising stress on soft tissues and improving patient comfort and satisfaction. This case report contributes to dental implant restoration, providing evidence-based techniques for achieving aesthetic and functional success.
Identifying the Spin-Incoherent Contribution to Quasielastic Neutron Scattering with a Cold Triple-Axis Spectrometer
Authors: Manning Andrew G. , Yano Shinichiro , Kim Sojeong , Lee Won Bo , Choi Soo-Hyung , de Souza Nicolas R.
Published: November 2023

Polarisation analysis for neutron scattering experiments is a powerful tool suitable for a wide variety of studies, including soft-matter samples which have no bulk magnetic behaviour and/or a significant hydrogen content. Here, we describe a method to leverage the versatility and spin-polarisation capabilities of a cold triple-axis spectrometer to perform a measurement to separate coherent and incoherent neutron scattering for a non-magnetic sample in the quasielastic neutron scattering (QENS) regime. Such measurements are complementary to unpolarised QENS measurements, which may typically be performed on a backscattering or time-of-flight spectrometer instrument where polarisation analysis can be significantly more difficult to achieve, and utilise the strengths of each type of instrument.
Generations and Life Worlds: The Case of Braga in Portugal
Authors: Duque Eduardo , Durán Vázquez José F.
Published: November 2023

The objective of this research is to present the primary results of a study on generations and generational relationships in Braga, Portugal, specifically in the spheres of family, school, work, leisure and consumption, and religion. For this purpose, three generational cohorts were differentiated, belonging to those born in the decades 1940–1950, 1965–1975, and 1990–2000. This work will focus on the first of these generations, 1940–1950. To carry out this research, each generation was structured based on gender and social class, and qualitative (Life Stories) and quantitative (questionnaires) techniques were used. We started from the hypotheses suggested by previous research (blinded for review) and confirmed by the one being developed that it is possible to observe certain generational connections, despite the divisions related to gender and social class that exist within them (a division that gains greater visibility in the qualitative dimension), and that relationships between generations cannot be approached from theoretical models based on mere reproduction or opposition. Instead, we must grasp these relationships through more complex and dynamic processes, through which they will reconfigure and rework what is transmitted and what is received by each generation, resulting in a unique outcome that will be explored in this work. The religious dimension, which was previously a naturally assumed and lived tradition, ceased to be recognised as an identity determinant by the younger generations. As a result, the cultural heritage is no longer passed down as tradition. In this sense, there is a whole “integral ecology” (LS 159), in its broadest sense, recovering that is capable of cementing principles and values that unite generations and give meaning to life. To carry out this research, a theoretical and analytical framework has been established based on the following dimensions: transmissions (concerning the narratives and the people who appear as their referents); temporalities that articulate both the relationships within each generation and between generations; spaces, structures, and rituals that make up and give meaning to the transmissions; recognised debts, obligations, and interdependencies; expectations and achievements; and tensions between the expected and the achieved.
The Understanding and Translation of De 德 in the English Translation of the Daodejing 道德經
Authors: Cui Xiaojiao
Published: November 2023

This article investigates the translation of De 德 in the English translation of the Daodejing, compares and analyzes several representative translations, and tries to present the complexity and richness of the meaning of De in the thought of the Daodejing. The article is divided into three parts. First, it briefly traces the concept of De back to the Shang 商 and Zhou 周 periods, thus laying the foundation for subsequent study. Second, taking Chapter 51 of the Daodejing as an example, it explores the meaning of “virtue”, which is the most important and common translation of De, in the context of the Daodejing and examines related terms such as “potency” and “inner power”. Finally, two representative translations of “Xuan De” 玄德 are examined and discussed.
New Classes of Distortion Risk Measures and Their Estimation
Authors: Sepanski Jungsywan H. , Wang Xiwen
Published: November 2023

In this paper, we present a new method to construct new classes of distortion functions. A distortion function maps the unit interval to the unit interval and has the characteristics of a cumulative distribution function. The method is based on the transformation of an existing non-negative random variable whose distribution function, named the generating distribution, may contain more than one parameter. The coherency of the resulting risk measures is ensured by restricting the parameter space on which the distortion function is concave. We studied cases when the generating distributions are exponentiated exponential and Gompertz distributions. Closed-form expressions for risk measures were derived for uniform, exponential, and Lomax losses. Numerical and graphical results are presented to examine the effects of the parameter values on the risk measures. We then propose a simple plug-in estimate of risk measures and conduct simulation studies to compare and demonstrate the performance of the proposed estimates. The plug-in estimates appear to perform slightly better than the well-known L-estimates, but also suffer from biases when applied to heavy-tailed losses.
Discovering Intraday Tail Dependence Patterns via a Full-Range Tail Dependence Copula
Authors: Hua Lei
Published: November 2023

In this research, we employ a full-range tail dependence copula to capture the intraday dynamic tail dependence patterns of 30 s log returns among stocks in the US market in the year of 2020, when the market experienced a significant sell-off and a rally thereafter. We also introduce a model-based unified tail dependence measure to directly model and compare various tail dependence patterns. Using regression analysis of the upper and lower tail dependence simultaneously, we have identified some interesting intraday tail dependence patterns, such as interactions between the upper and lower tail dependence over time among growth and value stocks and in different market regimes. Our results indicate that tail dependence tends to peak towards the end of the regular trading hours, and, counter-intuitively, upper tail dependence tends to be stronger than lower tail dependence for short-term returns during a market sell-off. Furthermore, we investigate how the Fama–French five factors affect the intraday tail dependence patterns and provide plausible explanations for the occurrence of these phenomena. Among these five factors, the market excess return plays the most important role, and our study suggests that when there is a moderate positive excess return, both the upper and lower tails tend to reach their lowest dependence levels.
Prediction of Sea Surface Reflectivity under Different Sea Conditions Based on the Clustering of Marine Environmental Parameters
Authors: Li Yalan , Ma Liwen , Zhang Yushi , Wu Tao , Zhang Jinpeng , Li Haiying
Published: November 2023

The high-precision prediction of sea clutter reflectivity is helpful in improving the performance of marine radar and sea surface remote sensing capabilities. Under the same sea state, when the significant wave height, wave period, wind speed, and other marine environmental parameters are different, the backward reflectivity of the sea clutter corresponding to the wave structure is not the same. Due to the complex and variable nature of sea clutter characteristics across various wave structures, a meticulous classification of wave structures by integrating multiple marine environmental parameters enables the achievement of the high-precision prediction of sea clutter reflectivity. In this study, utilizing measured data of diverse marine environmental parameters in the Yellow Sea, China, we applied the Affinity Propagation algorithm to data clustering. Based on the clustering outcomes, we accomplished a refined classification of wave structures and developed a discriminant model to precisely classify the refined wave structure, facilitating the categorization of new data. In order to achieve more accurate predictions of sea clutter reflectivity, this paper proposes a deep neural network model named GIT-HYB-DNN, which combines the empirical models GIT and HYB. The GIT-HYB-DNN model is applied to predict the reflectivity for each wave structure category separately. The results demonstrate that the root mean square errors of sea clutter reflectivity predictions for different wave structure categories in this study range from 0.62 dB to 0.84 dB. The prediction errors are significantly reduced compared to the root mean square error of 1.08 dB, which was obtained without refined wave structure classification. This study holds theoretical significance and practical value for the investigation of sea clutter characteristics and the selection of radar parameters.
Simulation-Based Self-Supervised Line Extraction for LiDAR Odometry in Urban Road Scenes
Authors: Wang Peng , Zhou Ruqin , Dai Chenguang , Wang Hanyun , Jiang Wanshou , Zhang Yongsheng
Published: November 2023

LiDAR odometry is a fundamental task for high-precision map construction and real-time and accurate localization in autonomous driving. However, point clouds in urban road scenes acquired by vehicle-borne lasers are of large amounts, “near dense and far sparse” density, and contain different dynamic objects, leading to low efficiency and low accuracy of existing LiDAR odometry methods. To address the above issues, a simulation-based self-supervised line extraction in urban road scene is proposed, as a pre-processing for LiDAR odometry to reduce the amount of input and the interference from dynamic objects. A simulated dataset is first constructed according to the characteristics of point clouds in urban road scenes; and then, an EdgeConv-based network, named LO-LineNet, is used for pre-training; finally, a model transferring strategy is adopted to transfer the pre-trained model from a simulated dataset to real-world scenes without ground-truth labels. Experimental results on the KITTI Odometry Dataset and the Apollo SouthBay Dataset indicate that the proposed method can accurately extract reliable lines in urban road scenes in a self-supervised way, and the use of the extracted reliable lines as input for odometry can significantly improve its accuracy and efficiency in urban road scenes.
Early Identification of Cotton Fields Based on Gf-6 Images in Arid and Semiarid Regions (China)
Authors: Zou Chen , Chen Donghua , Chang Zhu , Fan Jingwei , Zheng Jian , Zhao Haiping , Wang Zuo , Li Hu
Published: November 2023

Accurately grasping the distribution and area of cotton for agricultural irrigation scheduling, intensive and efficient management of water resources, and yield estimation in arid and semiarid regions is of great significance. In this paper, taking the Xinjiang Shihezi oasis agriculture region as the study area, extracting the spectroscopic characterization (R, G, B, panchromatic), texture feature (entropy, mean, variance, contrast, homogeneity, angular second moment, correlation, and dissimilarity) and characteristics of vegetation index (normalized difference vegetation index/NDVI, ratio vegetation index/DVI, difference vegetation index/RVI) in the cotton flowering period before and after based on GF-6 image data, four models such as the random forests (RF) and deep learning approach (U-Net, DeepLabV3+ network, Deeplabv3+ model based on attention mechanism) were used to identify cotton and to compare their accuracies. The results show that the deep learning model is better than that of the random forest model. In all the deep learning models with three kinds of feature sets, the recognition accuracy and credibility of the DeepLabV3+ model based on the attention mechanism are the highest, the overall recognition accuracy of cotton is 98.23%, and the kappa coefficient is 96.11. Using the same Deeplabv3+ model based on an attention mechanism with different input feature sets (all features and only spectroscopic characterization), the identification accuracy of the former is much higher than that of the latter. GF-6 satellite image data in the field of crop type recognition has great application potential and prospects.
TransHSI: A Hybrid CNN-Transformer Method for Disjoint Sample-Based Hyperspectral Image Classification
Authors: Zhang Ping , Yu Haiyang , Li Pengao , Wang Ruili
Published: November 2023

Hyperspectral images’ (HSIs) classification research has seen significant progress with the use of convolutional neural networks (CNNs) and Transformer blocks. However, these studies primarily incorporated Transformer blocks at the end of their network architectures. Due to significant differences between the spectral and spatial features in HSIs, the extraction of both global and local spectral–spatial features remains incomplete. To address this challenge, this paper introduces a novel method called TransHSI. This method incorporates a new spectral–spatial feature extraction module that leverages 3D CNNs to fuse Transformer to extract the local and global spectral features of HSIs, then combining 2D CNNs and Transformer to capture the local and global spatial features of HSIs comprehensively. Furthermore, a fusion module is proposed, which not only integrates the learned shallow and deep features of HSIs but also applies a semantic tokenizer to transform the fused features, enhancing the discriminative power of the features. This paper conducts experiments on three public datasets: Indian Pines, Pavia University, and Data Fusion Contest 2018. The training and test sets are selected based on a disjoint sampling strategy. We perform a comparative analysis with 11 traditional and advanced HSI classification algorithms. The experimental results demonstrate that the proposed method, TransHSI algorithm, achieves the highest overall accuracies and kappa coefficients, indicating a competitive performance.
Effects of Topside Ionosphere Modeling Parameters on Differential Code Bias (DCB) Estimation Using LEO Satellite Observations
Authors: Wang Yifan , Liu Mingming , Yuan Yunbin , Wang Guofang , Geng Hao
Published: November 2023

Given the potential of low-earth orbit (LEO) satellites in terms of navigation enhancement, accurately estimating the differential code bias (DCB) of GNSS satellites and LEO satellites is an important research topic. In this study, to obtain accurate DCB estimates, the effects of vertical total electron content (VTEC) modeling parameters of the topside ionosphere on DCB estimation were investigated using LEO observations for the first time. Different modeling parameters were set in the DCB estimations, encompassing modeling spacing in the dynamic temporal mode and degree and order (D&O) in spherical harmonic modeling. The DCB precisions were then evaluated, and the impacts were analyzed. Thus, a number of crucial and beneficial conclusions are drawn: (1) The maximum differences in the GPS DCB estimates after adopting different modeling spacings and different D&Os exhibit that the different modeling spacings or D&Os both affect the GPS DCB estimates and their root-mean square (RMS), and the effects of the two are at the same level. (2) The maximum differences in receiver DCBs using different modeling spacings indicate that the modeling spacing has a significant impact on the receiver DCBs, compared with GPS DCBs. Whereas, the maximum differences in receiver DCBs with different modeling D&Os are inferior to the differences in the GPS DCBs. That is, the modeling spacing has a greater impact on the LEO DCBs than those of the modeling D&O. (3) The experimental results indicate that the GPS DCB estimates using a modeling spacing of 12H have higher precisions than the others, whereas LEO receiver DCBs using a spacing of 4H or 6H obtain optimal STD. In terms of modeling D&O, adopting 8D&O in the LEO-based VTEC modeling can attain superior estimates and precisions for both GPS and LEO DCBs. The research conclusions can provide references for LEO-augmented DCB estimation.
The Design and Performance Evaluation of a 1550 nm All-Fiber Dual-Polarization Coherent Doppler Lidar for Atmospheric Aerosol Measurements
Authors: Yu Ronghua , Wang Qichao , Dai Guangyao , Chen Xiangcheng , Ren Chao , Liu Jintao , Li Dongrui , Wang Xitao , Cao Haishuai , Qin Shengguang , Wu Songhua
Published: November 2023

A 1550 nm all-fiber dual-polarization coherent Doppler lidar (DPCDL) was constructed to measure the depolarization ratio of atmospheric aerosols. In lidar systems, the polarization state of the laser source is typically required to be that of linearly parallel polarization. However, due to the influence of the fiber-optical transmission and the large-mode field output of the telescope, the laser polarization state changes. Hence, a polarizer was mounted to the emitting channel of the telescope to eliminate the depolarization effect. A fiber-optical polarization beam splitter divided the backscattered light into components with parallel and perpendicular polarization. The DPCDL used two coherent channels to receive each of these two polarization components. A calibration procedure was designed for the depolarization ratio to determine the differences in gain and non-responsiveness in the two polarization channels. The calibration factor was found to be 1.13. Additionally, the systematic error and the measured random error of the DPCDL were estimated to evaluate the performance of the system. The DPCDL’s systematic error was found to be about 0.0024, and the standard deviation was lower than 0.0048. The Allan deviations of a 1-min averaging window with a low SNR of 19 dB and a high SNR of 27 dB were 0.0104 and 0.0031, respectively. The random errors at different measured heights were mainly distributed below 0.015. To confirm the authenticity of the atmospheric depolarization ratio measured with the DPCDL, two field observations were conducted with the use of a co-located DPCDL and micro-pulse polarization lidar to perform a comparison. The results showed that the correlation coefficients of the aerosol depolarization ratios were 0.73 and 0.77, respectively. Moreover, the two continuous observations demonstrated the robustness and stability of the DPCDL. The depolarization ratios were detected in different weather conditions.
GNSS Spoofing Detection Using Q Channel Energy
Authors: Wang Jiaqi , Tang Xiaomei , Ma Pengcheng , Wu Jian , Ma Chunjiang , Sun Guangfu
Published: November 2023

Spoofing interference poses a significant challenge to the Global Navigation Satellite System (GNSS). To effectively combat intermediate spoofing signals, this paper presents an enhanced spoofing detection method based on abnormal energy of the quadrature (Q) channel correlators. The detailed principle of this detection method is introduced based on the received signal model under spoofing attack. The normalization parameter used in this method was the estimation of the noise floor. The performance of the proposed Q energy detector is validated through simulations, the Texas Spoofing Test Battery dataset and field tests. The results demonstrate that the proposed detector significantly enhances detection performance compared to signal quality monitoring methods, particularly in overpowered scenarios and dynamic scenarios. By increasing the detection probability in the presence of spoofing signals and decreasing the false alarm probability in the absence of spoofing signals, the proposed detector can better meet the requirements of practical applications.
Evaluation of Temperature and Humidity Profiles Retrieved from Fengyun-4B and Implications for Typhoon Assimilation and Forecasting
Authors: Yang Weiyu , Chen Yaodeng , Bai Wenguang , Sun Xin , Zheng Hong , Qin Luyao
Published: November 2023

Fengyun-4B (FY-4B) is the first operational satellite from China’s latest generation of geostationary meteorological satellites. It is equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), which is able to obtain highly accurate atmospheric temperature and humidity profiles through hyperspectral detection in long- and mid-wave infrared spectral bands. In this study, the accuracy of the FY-4B/GIIRS temperature and humidity profile retrievals over two months is evaluated using radiosonde observations and ERA5 reanalysis data. We go a step further to investigate the impact of the satellite retrievals on assimilation and forecasts for Typhoons Chaba and Ma-on in 2022. Results reveal that the root-mean-square difference (RMSD) for the FY-4B/GIIRS temperature and humidity profile retrievals were within 1 K and 1.5 g/kg, respectively, demonstrating high overall accuracy. Moreover, assimilating temperature and humidity profiles from FY-4B/GIIRS positively impacts model analysis and prediction, improving typhoon track and intensity forecasts. Additionally, improvements have been discovered in predicting precipitation, particularly with high-magnitude rainfall events.
BEMF-Net: Semantic Segmentation of Large-Scale Point Clouds via Bilateral Neighbor Enhancement and Multi-Scale Fusion
Authors: Ji Hao , Yang Sansheng , Jiang Zhipeng , Zhang Jianjun , Guo Shuhao , Li Gaorui , Zhong Saishang , Liu Zheng , Xie Zhong
Published: November 2023

The semantic segmentation of point clouds is a crucial undertaking in 3D reconstruction and holds great importance. However, achieving precise semantic segmentation represents a significant hurdle. In this paper, we present BEMF-Net, an efficient method for large-scale environments. It starts with an effective feature extraction method. Unlike images, 3D data comprise not only geometric relations but also texture information. To accurately depict the scene, it is crucial to take into account the impacts of texture and geometry on the task, and incorporate modifications to improve feature description. Additionally, we present a multi-scale feature fusion technique that effectively promotes the interaction between features at different resolutions. The approach mitigates the problem of the smoothing of detailed information caused by downsampling mechanisms, while ensuring the integrity of features across different layers, allowing a more comprehensive representation of the point cloud. We confirmed the effectiveness of this method by testing it on benchmark datasets such as S3DIS, SensatUrban, and Toronto3D.
Direction of Arrival Estimation with Nested Arrays in Presence of Impulsive Noise: A Correlation Entropy-Based Infinite Norm Strategy
Authors: Zhao Jun , Gui Renzhou , Dong Xudong , Sun Meng , Wang Yide
Published: November 2023

Direction of arrival (DOA) estimation with nested arrays has been widely investigated in the field of array signal processing, but most studies assume that the noise is Gaussian white noise. In practical situations, there may exist impulsive noise (a kind of heavy-tailed noise), wherein the performance of traditional subspace-based DOA estimation algorithms deteriorates significantly. In this paper, we propose a correlation entropy-based infinite norm preprocessing algorithm, which can be applicable to any type of impulsive noise. Each snapshot of the sensor array data is processed by an exponential kernel function with the infinite norm, which can effectively combat the outliers. Furthermore, we construct the equivalent second-order covariance matrix and perform DOA estimation using classical subspace methods. Simulation results demonstrate the effectiveness of the proposed method for both symmetric α-stable distribution and the Gaussian mixture model.
Multimodal Gait Abnormality Recognition Using a Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) Network Based on Multi-Sensor Data Fusion
Authors: Li Jing , Liang Weisheng , Yin Xiyan , Li Jun , Guan Weizheng
Published: November 2023

Global aging leads to a surge in neurological diseases. Quantitative gait analysis for the early detection of neurological diseases can effectively reduce the impact of the diseases. Recently, extensive research has focused on gait-abnormality-recognition algorithms using a single type of portable sensor. However, these studies are limited by the sensor’s type and the task specificity, constraining the widespread application of quantitative gait recognition. In this study, we propose a multimodal gait-abnormality-recognition framework based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network. The as-established framework effectively addresses the challenges arising from smooth data interference and lengthy time series by employing an adaptive sliding window technique. Then, we convert the time series into time–frequency plots to capture the characteristic variations in different abnormality gaits and achieve a unified representation of the multiple data types. This makes our signal processing method adaptable to several types of sensors. Additionally, we use a pre-trained Deep Convolutional Neural Network (DCNN) for feature extraction, and the consequently established CNN-BiLSTM network can achieve high-accuracy recognition by fusing and classifying the multi-sensor input data. To validate the proposed method, we conducted diversified experiments to recognize the gait abnormalities caused by different neuropathic diseases, such as amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). In the PDgait dataset, the framework achieved an accuracy of 98.89% in the classification of Parkinson’s disease severity, surpassing DCLSTM’s 96.71%. Moreover, the recognition accuracy of ALS, PD, and HD on the PDgait dataset was 100%, 96.97%, and 95.43% respectively, surpassing the majority of previously reported methods. These experimental results strongly demonstrate the potential of the proposed multimodal framework for gait abnormality identification. Due to the advantages of the framework, such as its suitability for different types of sensors and fewer training parameters, it is more suitable for gait monitoring in daily life and the customization of medical rehabilitation schedules, which will help more patients alleviate the harm caused by their diseases.
Limited Sampling Spatial Interpolation Evaluation for 3D Radio Environment Mapping
Authors: Ivanov Antoni , Tonchev Krasimir , Poulkov Vladimir , Manolova Agata , Vlahov Atanas
Published: November 2023

The increasing densification and diversification of modern and upcoming wireless networks have become an important motivation for the development of agile spectrum sharing. Radio environment maps (REMs) are a basic tool for spectrum utilisation characterisation and adaptive resource allocation, but they need to be estimated through accurate interpolation methods. This work evaluated the performance of two established algorithms for spatial three-dimensional (3D) data collected in two real-world scenarios: indoors, through a mechanical measuring system, and outdoors, through an unmanned aerial vehicle (UAV) for measurement collection. The investigation was undertaken for the complete dataset on two-dimensional (2D) planes of different altitudes and for a subset of limited samples (representing the regions of interest or RoIs), which were combined together to describe the spatial 3D environment. A minimum error of −9.5 dB was achieved for a sampling ratio of 21%. The methods’ performance and the input data were analysed through the resulting Kriging error standard deviation (STD) and the STD of the distances between the measurement and the estimated points. Based on the results, several challenges for the interpolation performance and the analysis of the spatial RoIs are described. They facilitate the future development of 3D spectrum occupancy characterisation in indoor and UAV-based scenarios.
Phase Conductance of BiFeO3 Film
Authors: Wang Yufeng , Zhou Peng , Fetisov Leonid , Fetisov Yuri , Qi Yajun , Zhang Tianjin
Published: November 2023

In this work, the local conductance of the tetragonal-like (T-like) and rhombohedral-like (R-like) phases of epitaxial BiFeO3 film is systematically studied via conductive atomic force microscopy. At higher tip voltage, there is a mutual transition between the T-like and R-like phases, which could be attributed to the strain relaxation in the T-like phase induced by electric poling, as well as local polarization switching. The T-like phase exhibits a higher conductance, which is related to the lower interface potential barrier between the tip and film surface. Reversible low- and high-current states in the T-like phase can be tuned by polarization switching. These results will be helpful for designing novel nanoelectronic devices, such as voltage and strain sensors.
A Lightweight and Efficient Method of Structural Damage Detection Using Stochastic Configuration Network
Authors: Lu Yuanming , Wang Di , Liu Die , Yang Xianyi
Published: November 2023

With the advancement of neural networks, more and more neural networks are being applied to structural health monitoring systems (SHMSs). When an SHMS requires the integration of numerous neural networks, high-performance and low-latency networks are favored. This paper focuses on damage detection based on vibration signals. In contrast to traditional neural network approaches, this study utilizes a stochastic configuration network (SCN). An SCN is an incrementally learning network that randomly configures appropriate neurons based on data and errors. It is an emerging neural network that does not require predefined network structures and is not based on gradient descent. While SCNs dynamically define the network structure, they essentially function as fully connected neural networks that fail to capture the temporal properties of monitoring data effectively. Moreover, they suffer from inference time and computational cost issues. To enable faster and more accurate operation within the monitoring system, this paper introduces a stochastic convolutional feature extraction approach that does not rely on backpropagation. Additionally, a random node deletion algorithm is proposed to automatically prune redundant neurons in SCNs, addressing the issue of network node redundancy. Experimental results demonstrate that the feature extraction method improves accuracy by 30% compared to the original SCN, and the random node deletion algorithm removes approximately 10% of neurons.
Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease
Authors: Yu Tianze , Park Kye Won , McKeown Martin J. , Wang Z. Jane
Published: November 2023

The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson’s Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson’s Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future.
A Novel ST-YOLO Network for Steel-Surface-Defect Detection
Authors: Ma Hongtao , Zhang Zhisheng , Zhao Junai
Published: November 2023

Recent progress has been made in defect detection using methods based on deep learning, but there are still formidable obstacles. Defect images have rich semantic levels and diverse morphological features, and the model is dynamically changing due to ongoing learning. In response to these issues, this article proposes a shunt feature fusion model (ST-YOLO) for steel-defect detection, which uses a split feature network structure and a self-correcting transmission allocation method for training. The network structure is designed to specialize the process of classification and localization tasks for different computing needs. By using the self-correction criteria of adaptive sampling and dynamic label allocation, more sufficiently high-quality samples are utilized to adjust data distribution and optimize the training process. Our model achieved better performance on the NEU-DET datasets and the GC10-DET datasets and was validated to exhibit excellent performance.