bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating the Effect of Temperature History on Crystal Morphology of Thermoplastic Composites Using In Situ Polarized Light Microscopy and Probabilistic Machine Learning
Authors: Wynn Mathew , Zobeiry Navid
Published: December 2022

Processing parameters including temperature history affect the morphology of semicrystalline thermoplastic composites, and in turn their performance. In addition, the competition between spherulite growth in resin-rich areas, and transcrystallinity growth from fiber surfaces, determines the final morphology. In this study, growth of crystals in low volume fraction PEEK-carbon fiber composites was studied in situ, using a polarized microscope equipped with a heating and cooling controlled stage and a probabilistic machine learning approach, Gaussian Process Regression (GPR). GPR showed that for spherulites, growth kinetics follows the established Lauritzen-Hoffman equation, while transcrystallinity growth deviates from the theory. Combined GPR model and Lauritzen-Hoffman equation were used to deconvolute the underlying competition between diffusion and secondary nucleation at growth front of spherulites and transcrystalline regions.
Fabrication of Superhydrophobic Coating Based on Waterborne Silicone-Modified Polyurethane Dispersion and Silica Nanoparticles
Authors: Liu Haidong , Xiong Hengsen , Chang Yongming , Xu Jianhui , Xu Chuanlai , Liu Yaolu
Published: December 2022

In this work, eco-friendly superhydrophobic coatings were prepared by dispersing hydrophobic silica nanoparticles and a waterborne silicone-modified polyurethane dispersion into an ethanol solution, which was free of fluorine and volatile toxic solvents. The effects of the silica content on the hydrophobicity and scratch resistance of the hydrophobic surfaces were investigated by WCA measurements and a sandpaper abrasion test, respectively. The experimental results indicated that when the silica content exceeded 30% by mass, the silica/silicone-modified polyurethane coatings had superhydrophobicity. Meanwhile, the superhydrophobic coatings with a silica content of 30% by mass simultaneously had the optimal mechanical stability. We studied the morphology and roughness of the hydrophobic surfaces with different silica content and attempted to briefly explain the influence mechanism of silica content. Furthermore, anti-icing and oil–water separation experiments were carried out on the superhydrophobic coatings, which exhibited good anti-icing performance and high separation efficiency. The eco-friendly superhydrophobic coating is expected to be applied in the fields of oil–water separation, anti-icing, and self-cleaning, etc.
Highly Biocompatible Apigenin-Loaded Silk Fibroin Nanospheres: Preparation, Characterization, and Anti-Breast-Cancer Activity
Authors: Qu Weikun , Ji Peng , Han Xibin , Wang Xianglong , Li Yang , Liu Jin
Published: December 2022

Breast cancer is among the most common fatal diseases among women. Low-toxicity apigenin (AGN) is of interest due to its good antitumor activity, but its clinical application is severely limited due to its poor water solubility and low bioavailability. An effective strategy to enhance the anti-breast-cancer activity of AGN is to develop it as a nanodelivery system. Silk fibroin (SF) is an ideal drug carrier with good biocompatibility, biodegradability, and a simple extraction process. This paper develops a novel and efficient apigenin-loaded silk fibroin nanodelivery system (SF-AGN) by nanoprecipitation with SF as a carrier. The system was characterized in terms of morphology, zeta potential, particle size, ultraviolet (UV), infrared (IR), and synchronous thermal analyses (TG-DSC), and the in vitro cytotoxicity and in vivo pharmacokinetics were examined. Finally, the chronic toxicity of SF-AGN in mice was studied. The SF-AGN nanodelivery system has good dispersibility, a hydrated particle size of 163.35 nm, a zeta potential of −18.5 mV, an average drug loading of 6.20%, and good thermal stability. MTT studies showed that SF-AGN significantly enhanced the inhibitory effect of AGN on 4T1 and MDA-MB-231 cells. Pharmacokinetic studies have demonstrated that SF-AGN can dramatically improve the bioavailability of AGN. The results of toxicity experiments showed that SF-AGN is biocompatible and does not alter normal tissues or organs. In sum, the SF-AGN nanodelivery system is a promising drug-delivery system for the clinical treatment of breast cancer.
Strength of Compressed Reinforced Concrete Elements Reinforced with CFRP at Different Load Application Eccentricity
Authors: Polskoy Petr P. , Mailyan Dmitry , Beskopylny Alexey N. , Meskhi Besarion , Shilov Aleksandr V. , Umarov Artur
Published: December 2022

Currently, many studies are devoted to the use of polymer composite materials to increase the strength and stability of concrete elements. In compressed reinforced concrete elements, the bearing capacity depends on the eccentricity of the external application of the external force and the corresponding stress-strain state, as well as the location and number of composite materials glued to the surface of the structure. The choice of a scheme for placing composite materials depending on the stress state of the structure is an urgent scientific problem. At the same time, the issue of central compression and the compression of columns with large eccentricities has been well studied. However, studies conducted in the range of average eccentricities often have conflicting results, which is the problem area of this study. The primary aim of this study was to increase the strength and stiffness of compressed reinforced concrete elements reinforced with composite materials, as well as a comparative analysis of the bearing capacity of ten different combinations of external longitudinal, transverse, and combined reinforcement. The results of testing 16 compressed columns under the action of various eccentricities of external load application (e0/h = 0; 0.16; 0.32) are presented. It is shown that the use of composite materials in strengthening structures increases the bearing capacity up to 41%, and the stiffness of the sections increases up to 30%. Based on the results of the study, recommendations are proposed for improving the calculation method for inflexible columns reinforced in the transverse direction, which take the work of concrete under the conditions of a three-dimensional stress state into consideration.
Effect of Disparity in Self Dispersion Interactions on Phase Behaviors of Molten A-b-B Diblock Copolymers
Authors: Zhang Xinyue , Zhao Mingge , Cho Junhan
Published: December 2022

Phase behaviors of molten A-b-B diblock copolymers with disparity in self dispersion interactions are revisited here. A free energy functional is obtained for the corresponding Gaussian copolymers under the influence of effective interactions originating in the localized excess equation of state. The Landau free energy expansion is then formulated as a series in powers of A and B density fluctuations up to 4th order. An alternative and equivalent Landau energy is also provided through the transformation of the order parameters to the fluctuations in block density difference and free volume fraction. The effective Flory χ is elicited from its quadratic term as the sum of the conventional enthalpic χH and the entropic χS that is related to energetic asymmetry mediated by copolymer bulk modulus. It is shown that the cubic term is balanced with Gaussian cubic vertex coefficients in corporation with energetics to yield a critical point at a composition rich in a component with stronger self interactions. The full phase diagrams with classical mesophases are given for the copolymers exhibiting ordering upon cooling and also for others revealing ordering reversely upon heating. These contrasting temperature responses, along with the skewness of phase boundaries, are discussed in relation to χH and χS. The pressure dependence of their ordering transitions is either barotropic or baroplastic; or anomalously exhibits anomalously both at different stages. These actions are all explained by the opposite responses of χH and χS to pressure.
Robust Intelligent Tracking Control Technique for Single-Phase SPWM Inverters
Authors: Chang En-Chih , Wu Rong-Ching , Chang Heidi H. , Cheng Chun-An
Published: December 2022

This paper presents a robust intelligent tracking-control technique which is subsequently applied to single-phase SPWM inverters. The proposed technique mixes advanced sliding mode control (ASMC) with grey-Markov model (GMM). The advanced sliding mode control allows the system state to converge quickly to the origin in a limited amount of time. Unfortunately, a chattering problem frequently occurs when the inverter suffers drastically from highly nonlinear or internal parameters changes. The large power losses and high harmonic distortion emerge in the inverter output. The role that the grey-Markov model plays is to reduce the chattering of the ASMC during system uncertainty overestimation, or to reduce the steady-state error caused by underestimation of system uncertainty. In conjunction with the GMM and the ASMC, fast transient response, low distortion of the sinusoidal output-voltage and reduced chattering can be obtained. Simulation results of the proposed single-phase SPWM inverter are carried out in MATLAB/Simulink. The experimental results have been validated through digital signal processors, enabling the single-phase SPWM inverter output to effectively improve the transient tracking speed and steady-state performance.
Use of Small Internal Combustion Engines for LNG Boil-Off Gas Conversion: An Experimental and Numerical Analysis
Authors: De Simio Luigi , Iannaccone Sabato , Gambino Piersabato , Catanese Dario
Published: December 2022

LNG technologies have long been used but only recently found widespread employment on medium and small scales compared to the traditional cycle of liquefaction, transport by ship, regasification and injection into the gas network. This has increased the direct use of LNG with the problem of limiting greenhouse gas emissions, linked to gas released principally in the event of prolonged absence of fuel drawing from the cryogenic tank. This study analyzes the energetic exploitation of BOG in small internal combustion engines. The effect on CO2 equivalent emissions was evaluated, making a comparison with the BOG emission into the atmosphere directly or after burning. A 1 kW gasoline engine was selected for a 500-litre LNG tank and converted to gas fueling. The measured consumption and emissions resulted in compliance with a lower environmental impact compared to direct BOG release into the atmosphere despite simplified technical solutions, such a cheap and light 2-stroke engine. In contrast, only a 4-stroke engine has performance such as achieving a reduction in GHG emissions, up to zero, even compared to the case of BOG combustion before releasing it into the atmosphere.
Weibull Reliability and Mechanical Properties of Chemical Strengthening Lightweight Glass Containers Using Spray Coating
Authors: Min Kyung Won , Choi Jae Ho , Jung YoonSung , Byun Young Min , Im Won Bin , Kim Hyeong-Jun
Published: December 2022

In this study, the effects of heat-treatment temperature and hot-end coating (HEC) by spray coating on the mechanical properties and reliability of lightweight glass bottles were investigated. When the chemical strengthening occurred at Tg, the hardness and impact strength increased by 163% and 198%, respectively. All specimens exhibited improved mechanical properties with chemical strengthening, regardless of the HEC. The strengthening effect was relatively large in the absence of HEC. However, the distribution of the impact strength was smaller when HEC was applied. Compared to non-HEC bottles, HEC glass bottles had higher Weibull modulus values after chemical strengthening, which increased their reliability. Therefore, it is possible to chemically strengthen a lightweight glass bottle by spray-coating. Chemically strengthened lightweight glass bottles with excellent mechanical properties and high reliability can be produced when both HEC and chemical strengthening are applied.
In Silico Infrared Spectroscopy as a Benchmark for Identifying Seized Samples Suspected of Being N-Ethylpentylone
Authors: Rodrigues Caio H. P. , de O. Mascarenhas Ricardo , Bruni Aline T.
Published: December 2022

New psychoactive substances (NPSs) have concerned authorities worldwide, and monitoring them has become increasingly complex. In addition to the frequent emergence of new chemical structures, the composition of adulterants has changed rapidly. Reliable reference data on NPS are not always available, and identifying them has become an operational problem. In this study, we evaluated the infrared spectral data of 68 seized samples suspected of containing a synthetic cathinone (N-ethylpentylone). We used quantum chemistry tools to simulate infrared spectra as a benchmark and obtained infrared spectra for different cathinones, structurally analogous amphetamines, and possible adulterants. We employed these in silico data to construct different chemometric models and investigated the internal and external validation and classification requirements of the models. We applied the best models to predict the classification of the experimental data, which showed that the seized samples did not have a well-defined profile. Infrared spectra alone did not allow N-ethylpentylone to be distinguished from other substances. This study enabled us to evaluate whether experimental, in silico, and applied statistical techniques help to promote forensic analysis for decision-making. The seized samples required in-depth treatment and evaluation so that they could be correctly analyzed for forensic purposes.
Blocky Diagonalized Scattering Matrices in Chaotic Scattering with Direct Processes
Authors: Castañeda-Ramírez Felipe , Martínez-Mares Moisés
Published: December 2022

Scattering matrices that can be diagonalized by a rotation through an angle θ in 2×2 blocks of independent scattering matrices of rank N, are considered. Assuming that the independent scattering matrices are chosen from one of the circular ensembles, or from the Poisson kernel, the 2N×2N scattering matrix may describe the scattering through chaotic cavities with reduced symmetry in the absence, or presence, of direct processes, respectively. To illustrate the effect of such symmetry, the statistical distribution of the dimensionless conductance through a ballistic chaotic cavity in the presence of direct processes is analyzed for N=1 using analytical calculations. We make a conjecture for N=2 in the absence of direct processes, which is verified by numerical random-matrix theory simulations, and the first two moments are calculated analytically for arbitrary N.
Text, Method, or Goal? On What Really Matters in Biblical Thomism
Authors: Roszak Piotr
Published: December 2022

This article presents the history and main assumptions of biblical Thomism, which began with an attempt to restore interest in the biblical commentaries of Thomas Aquinas, but has managed to develop its own methodological procedure. The key to understanding it may be the idea of integration. Biblical Thomismism is thomistic in a mode that allows for, and encourages, direct engagement with theological and exegetical resources. Its aim is bringing together dogmatic, metaphysical, and exegetical modes into a contemporary theology that is Thomistic, ecumenical, and grounded in Scripture and the Fathers. This paper is divided into three parts. In the first part, the essence of the Biblical Thomism project is explained. Next, the general lines along which Biblical Thomism has been developing in recent times are detailed. Finally, an example of a proposed approach (quies Dei) is analyzed. In the conclusion, there is an outline of the emerging prospects for further research.
Educational Effectiveness of Catholic Schools in Poland Based on the Results of External Exams
Authors: Cichosz Wojciech
Published: December 2022

Church education boasts a rich history of achievements. European church education (referred to as Catholic) was already present at the turn of the 9th and 10th centuries and in Poland at the end of the 11th (schools educating future members of the clergy). In Poland, the collapse of church education was marked by the communist system (1945–1989), and a dynamic revival was possible thanks to the democratic change in 1989. At present, Catholic schools, i.e., schools run by church legal entities and schools run by other legal or natural persons recognized as Catholic by decree of the diocesan bishop, entertain the same possibilities with respect to setup and operations on equal rights. Their number and proportion of the overall student population remain relatively stable. As the results published by District Examination Boards and rankings of Catholic schools show, the teaching efficiency of Catholic elementary schools is higher than average. High schools reach a very good level of education as well, although in their case, the dominance of Catholic schools is not in place. Teaching efficiency is one of many factors that influence the well-established position of Catholic schools.
ECLIPSE: Holistic AI System for Preparing Insurer Policy Data
Authors: Sriram Varun , Fan Zijie , Liu Ni
Published: December 2022

Reinsurers possess high volumes of policy listings data from insurers, which they use to provide insurers with analytical insights and modeling that guide reinsurance treaties. These insurers often act on the same data for their own internal modeling and analytics needs. The problem is this data is messy and needs significant preparation in order to extract meaningful insights. Traditionally, this has required intensive manual labor from actuaries. However, a host of modern AI techniques and ML system architectures introduced in the past decade can be applied to the problem of insurance data preparation. In this paper, we explore a novel application of AI/ML on policy listings data that poses its own unique challenges, by outlining the holistic AI-based platform we developed, ECLIPSE (Elegant Cleaning and Labeling of Insurance Policies while Standardizing Entities). With ECLIPSE, actuaries not only save time on data preparation but can build more effective loss models and provide crisper insights.
A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data
Authors: Li Yanxi , He Binbin
Published: December 2022

Forest fuel load is the key factor for fire risk assessment, firefighting, and carbon emissions estimation. Remote sensing technology has distinct advantages in fuel load estimation due to its sensitivity to biomass and adequate spatiotemporal observations for large scales. Many related works applied empirical methods with individual satellite observation data to estimate fuel load, which is highly conditioned on local data and limited by saturation problems. Here, we combined optical data (i.e., Landsat 7 ETM+) and spaceborne Synthetic Aperture Radar (SAR) data (i.e., ALOS PALSAR) in a proposed semi-empirical retrieval model to estimate above-ground live forest fuel loads (FLAGL). Specifically, optical data was introduced into water cloud model (WCM) to compensate for vegetation coverage information. For comparison, we also evaluated the performance of single spaceborne L-band SAR data (i.e., ALOS PALSAR) in fuel load estimation with common WCM. The above two comparison experiments were both validated by field measurements (i.e., BioSAR-2008) and leave-one-out cross-validation (LOOCV) method. WCM with single SAR data could achieve reasonable performance (R2 = 0.64 or higher and RMSEr = 35.3% or lower) but occurred an underestimation problem especially in dense forests. The proposed method performed better with R2 increased by 0.05–0.13 and RMSEr decreased by 5.8–12.9%. We also found that the underestimation problem (i.e., saturation problem) was alleviated even when vegetation coverage reached 65% or the total FLAGL reached about 183 Tons/ha. We demonstrated our FLAGL estimation method by validation in an open-access dataset in Sweden.
An Empirical Bayesian Approach to Quantify Multi-Scale Spatial Structural Diversity in Remote Sensing Data
Authors: Schuh Leila A. , Santos Maria J. , Schaepman Michael E. , Furrer Reinhard
Published: December 2022

Landscape structure is as much a driver as a product of environmental and biological interactions and it manifests as scale-specific, but also as multi-scale patterns. Multi-scale structure affects processes on smaller and larger scales and its detection requires information from different scales to be combined. Herein, we propose a novel method to quantify multi-scale spatial structural diversity in continuous remote sensing data. We combined information from different extents with an empirical Bayesian model and we applied a new entropy metric and a value co-occurrence approach to capture heterogeneity. We tested this method on Normalized Difference Vegetation Index data in northern Eurasia and on simulated data and we also tested the effect of coarser pixel resolution. We find that multi-scale structural diversity can reveal itself as patches and linear landscape features, which persist or become apparent across spatial scales. Multi-scale line features reveal the transition zones between spatial regimes and multi-scale patches reveal those areas within transition zones where values are most different from each other. Additionally, spatial regimes themselves can be distinguished. We also find the choice of scale need not be informed by typical length-scales, which makes the method easy to implement. The proposed multi-scale approach can be applied to other contexts, following the roadmap we pave out in this study and using the tools available in the accompanying R package StrucDiv.
Planar Constraint Assisted LiDAR SLAM Algorithm Based on Manhattan World Assumption
Authors: Wu Haiyang , Wu Wei , Qi Xingyu , Wu Chaohong , An Lina , Zhong Ruofei
Published: December 2022

Simultaneous localization and mapping (SLAM) technology based on light detection and ranging (LiDAR) sensors has been widely used in various environmental sensing tasks indoors and outdoors. However, it still lacks effective constraints in structured environments such as corridors and parking lots, and its accuracy needs improvement. Based on this, a planar constraint-assisted LiDAR SLAM algorithm based on the Manhattan World (MW) assumption is proposed in this paper. The algorithm extracts planes from the environment point cloud submap, classifies the planes according to the ground and vertical planes, and calculates the main direction angles of the ground and vertical plane, respectively, to construct constraints. To enhance the stability and robustness of the system, a two-step main direction angle calculation and update strategy are designed, and a hysteresis update is used to avoid the introduction of errors by unoptimized planes. This paper uses a backpack laser scanning system to collect experimental data in various scenes. These data are used to compare our method with three open-source LiDAR SLAM algorithms, that are currently more widely used and perform better. Qualitative and quantitative experiments are conducted to verify the effectiveness of our method. The experimental results show that the absolute accuracy of the point clouds obtained by our method is improved by 77.46% on average compared with the other three algorithms in the environment, conforming to the MW assumption, which verifies the effectiveness of the algorithm.
Off-Road Drivable Area Detection: A Learning-Based Approach Exploiting LiDAR Reflection Texture Information
Authors: Zhong Chuanchuan , Li Bowen , Wu Tao
Published: December 2022

The detection of drivable areas in off-road scenes is a challenging problem due to the presence of unstructured class boundaries, irregular features, and dust noise. Three-dimensional LiDAR data can effectively describe the terrain features, and a bird’s eye view (BEV) not only shows these features, but also retains the relative size of the environment compared to the forward viewing. In this paper, a method called LRTI, which is used for detecting drivable areas based on the texture information of LiDAR reflection data, is proposed. By using an instance segmentation network to learn the texture information, the drivable areas are obtained. Furthermore, a multi-frame fusion strategy is applied to improve the reliability of the output, and a shelter’s mask of a dynamic object is added to the neural network to reduce the perceptual delay caused by multi-frame fusion. Through TensorRT quantization, LRTI achieves real-time processing on the unmanned ground vehicle (UGV). The experiments on our dataset show the robustness and adaptability of LRTI to sand dust and occluded scenes.
Inversion of Deflection of the Vertical in the South China Sea Using ICESat-2 Sea Surface Height Data
Authors: Liu Xin , Hui Guihua , Guo Jinyun , Zhang Tinghui , Song Menghao
Published: December 2022

The traditional altimetry satellites based on pulse-limited radar altimeter only calculate along-track deflection of the vertical (DOV), which results in poorer precision of the prime vertical component than that of the meridian component and limits the precision of the marine gravity field inversion. We expect an improvement in the higher precision prime vertical component using the Ice, Cloud and land Elevation Satellite 2 (ICESat-2) sea surface height (SSH) data. In this paper, the 2′ × 2′ gridded DOVs derived from along-beam DOVs, cross-beam DOVs, and joint along-cross beam DOVs in the South China Sea (SCS; 0°–23°N, 103°–120°E) are calculated with the weighted least squares method, respectively. The inverse Vening–Meinesz (IVM) formula is applied to derive 2′ × 2′ gravity anomalies over the SCS from ICESat-2-derived gridded DOVs. In addition, the XGM2019e_2159-DOV and SIO V31.1-DOV models are used to assess the precision of the gridded DOVs. The XGM2019e_2159-GRA, SIO V31.1-GRA models, and ship-borne gravity anomalies are also adopted to evaluate the quality of gravity anomalies. The results show that the gridded DOVs calculated by the joint along-cross beam DOVs have the highest precision among the three gridded DOVs determined by ICESat-2. The precision of difference between gravity anomalies derived from the joint along-cross beam DOV and the above verification data are higher than those derived from the along-beam and cross-beam DOVs. We conclude that the joint along-cross beam DOV can effectively improve the precision of the gridded DOV, which is conducive to the inversion of a high-precision marine gravity field.
Reconstruction of Annual Glacier Mass Balance from Remote Sensing-Derived Average Glacier-Wide Albedo
Authors: Zhang Zhimin , Jiang Liming , Sun Yafei , Sirguey Pascal , Dumont Marie , Liu Lin , Gao Ning , Gao Songfeng
Published: December 2022

Annual mass balance is an important reflection of glacier status that is also very sensitive to climate fluctuations. However, there is no effective and universal albedo-based method for the reconstruction of annual mass balance due to the scarcity of field observations. Here, we present an improved albedo–mass balance (IAMB) method to estimate annual glacier surface mass balance series using remote sensing techniques. The averaged glacier-wide albedo derived with the MODImLab algorithm during the summer season provides an effective proxy of the annual mass change. Defined as the variation in the albedo as a function of elevation change, the altitude–albedo gradient (∂z/∂α) can be obtained from a glacier digital elevation model (DEM) and optical images. The Chhota Shigri glacier situated in the western Himalayas was selected to test and assess the accuracy of this method over the period from 2003 to 2014. Reconstructed annual mass budgets correlated well with those from the observed records, with an average difference and root mean square error (RMSE) of −0.75 mm w.e. a−1 and 274.91 mm w.e. a−1, respectively, indicating that the IAMB method holds promise for glacier mass change monitoring. This study provides a new technique for annual mass balance estimation that can be applied to glaciers with no or few mass balance observations.
Performance of Haiyang-2 Derived Gravity Field Products in Bathymetry Inversion
Authors: Wan Xiaoyun , Wang Huaibing , Jia Yongjun , Ma Wenjie
Published: December 2022

Haiyang-2A (HY-2A), China’s first altimetry satellite mission, was launched more than ten years ago, and its follow-up satellites, HY-2B, HY-2C, and HY-2D, have also been launched. More attention has been paid to the evaluation of these satellite observations in marine gravity field inversion. However, this is not the case for bathymetry inversion. This study is aimed at evaluating the performance of HY-2 gravity field products in bathymetry recovery. Not only gravity anomaly, but also deflection of the vertical from the HY-2 series’ observations is also used. The results show that the bathymetry derived from the deflection of the vertical from HY-2A has a precision of around 128~130 m, and the north-south component performs better than the east-west component. Three versions of the gravity anomaly are used in bathymetry inversion, i.e., HY2ONLY_GRA, WHU16_GRA, and NSOASS22_GRA, and three bathymetry models are derived correspondingly, named as HY2ONLY_BAT, NSOASS22_BAT, and WHU16_BAT, respectively. The results show that HY2ONLY_BAT has a precision of 82.93 m, which is a little poorer than WHU16_BAT; NSOAS22_BAT has the best performance in bathymetry inversion among the three versions of the gravity anomaly. It indicates that HY-2 observations can also contribute to bathymetry inversion compared to current altimetry datasets, since the main difference between WHU16_GRA and NSOASS22_GRA is the use of HY-2 observations. According to spatial analysis results, considerable improvements appear in the west of the Pacific and Indian oceans, and most of the improvements are within 20 m. Meanwhile, the improvements are stronger in the regions with depths ranging between 2600~5500 m. Correlation analysis demonstrates that NSOASS22_BAT is very close to SIO V19.1 and DTU21BAT, which once again indicates the excellent performance of NSOASS22_BAT.
CLISAR-Net: A Deformation-Robust ISAR Image Classification Network Using Contrastive Learning
Authors: Ni Peishuang , Liu Yanyang , Pei Hao , Du Haoze , Li Haolin , Xu Gang
Published: December 2022

The inherent unknown deformations of inverse synthetic aperture radar (ISAR) images, such as translation, scaling, and rotation, pose great challenges to space target classification. To achieve high-precision classification for ISAR images, a deformation-robust ISAR image classification network using contrastive learning (CL), i.e., CLISAR-Net, is proposed for deformation ISAR image classification. Unlike traditional supervised learning methods, CLISAR-Net develops a new unsupervised pretraining phase, which means that the method uses a two-phase training strategy to achieve classification. In the unsupervised pretraining phase, combined with data augmentation, positive and negative sample pairs are constructed using unlabeled ISAR images, and then the encoder is trained to learn discriminative deep representations of deformation ISAR images by means of CL. In the fine-tuning phase, based on the deep representations obtained from pretraining, a classifier is fine-tuned using a small number of labeled ISAR images, and finally, the deformation ISAR image classification is realized. In the experimental analysis, CLISAR-Net achieves higher classification accuracy than supervised learning methods for unknown scaled, rotated, and combined deformations. It implies that CLISAR-Net learned more robust deep features of deformation ISAR images through CL, which ensures the performance of the subsequent classification.
UIR-Net: A Simple and Effective Baseline for Underwater Image Restoration and Enhancement
Authors: Mei Xinkui , Ye Xiufen , Zhang Xiaofeng , Liu Yusong , Wang Junting , Hou Jun , Wang Xuli
Published: December 2022

Because of the unique physical and chemical properties of water, obtaining high-quality underwater images directly is not an easy thing. Hence, recovery and enhancement are indispensable steps in underwater image processing and have therefore become research hotspots. Nevertheless, existing image-processing methods generally have high complexity and are difficult to deploy on underwater platforms with limited computing resources. To tackle this issue, this paper proposes a simple and effective baseline named UIR-Net that can recover and enhance underwater images simultaneously. This network uses a channel residual prior to extract the channel of the image to be recovered as a prior, combined with a gradient strategy to reduce parameters and training time to make the operation more lightweight. This method can improve the color performance while maintaining the style and spatial texture of the contents. Through experiments on three datasets (MSRB, MSIRB and UIEBD-Snow), we confirm that UIR-Net can recover clear underwater images from original images with large particle impurities and ocean light spots. Compared to other state-of-the-art methods, UIR-Net can recover underwater images at a similar or higher quality with a significantly lower number of parameters, which is valuable in real-world applications.
Clustering Denoising of 2D LiDAR Scanning in Indoor Environment Based on Keyframe Extraction
Authors: Hu Weiwei , Zhang Keke , Shao Lihuan , Lin Qinglei , Hua Yongzhu , Qin Jin
Published: December 2022

In the indoor laser simulation localization and mapping (SLAM) system, the signal emitted by the LiDAR sensor is easily affected by lights and objects with low reflectivity during the transmission process, resulting in more noise points in the laser scan. To solve the above problem, this paper proposes a clustering noise reduction method based on keyframe extraction. First, the dimension of a scan is reduced to a histogram, and the histogram is used to extract the keyframes. The scans that do not contain new environmental information are dropped. Secondly, the laser points in the keyframe are divided into different regions by the region segmentation method. Next, the points are separately clustered in different regions and it is attempted to merge the point sets from adjacent regions. This greatly reduces the dimension of clustering. Finally, the obtained clusters are filtered. The sets with the number of laser points lower than the threshold will be dropped as abnormal clusters. Different from the traditional clustering noise reduction method, the technique not only drops some unnecessary scans but also uses a region segmentation method to accelerate clustering. Therefore, it has better real-time performance and denoising effect. Experiments on the MIT dataset show that the method can improve the trajectory accuracy based on dropping a part of the scans and save a lot of time for the SLAM system. It is very friendly to mobile robots with limited computing resources.
Multi-Layered QCA Content-Addressable Memory Cell Using Low-Power Electronic Interaction for AI-Based Data Learning and Retrieval in Quantum Computing Environment
Authors: Jeon Jun-Cheol , Almatrood Amjad , Kim Hyun-Il
Published: December 2022

In this study, we propose a quantum structure of an associative memory cell for effective data learning based on artificial intelligence. For effective learning of related data, content-based retrieval and storage rather than memory address is essential. A content-addressable memory (CAM), which is an efficient memory cell structure for this purpose, in a quantum computing environment, is designed based on quantum-dot cellular automata (QCA). A CAM cell is composed of a memory unit that stores information, a match unit that performs a search, and a structure, using an XOR gate or an XNOR gate in the match unit, that shows good performance. In this study, we designed an XNOR gate with a multilayer structure based on electron interactions and proposed a QCA-based CAM cell using it. The area and time efficiency are verified through a simulation using QCADesigner, and the quantum cost of the proposed XOR gate and CAM cell were reduced by at least 70% and 15%, respectively, when compared to the latest research. In addition, we physically proved the potential energy owing to the interaction between the electrons inside the QCA cell. We also proposed an additional CAM circuit targeting the reduction in energy dissipation that overcomes the best available designs. The simulation and calculation of power dissipation are performed by QCADesigner-E and it is confirmed that more than 27% is reduced.
Sensor-Driven Human-Robot Synergy: A Systems Engineering Approach
Authors: Tsolakis Naoum , Gasteratos Antonios
Published: December 2022

Knowledge-based synergistic automation is a potential intermediate option between the opposite extremes of manual and fully automated robotic labor in agriculture. Disruptive information and communication technologies (ICT) and sophisticated solutions for human-robot interaction (HRI) endow a skilled farmer with enhanced capabilities to perform agricultural tasks more efficiently and productively. This research aspires to apply systems engineering principles to assess the design of a conceptual human-robot synergistic platform enabled by a sensor-driven ICT sub-system. In particular, this paper firstly presents an overview of a use case, including a human-robot synergistic platform comprising a drone, a mobile platform, and wearable equipment. The technology framework constitutes a paradigm of human-centric worker-robot logistics synergy for high-value crops, which is applicable in operational environments of outdoor in-field harvesting and handling operations. Except for the physical sub-system, the ICT sub-system of the robotic framework consists of an extended sensor network for enabling data acquisition to extract the context (e.g., worker’s status, environment awareness) and plan and schedule the robotic agents of the framework. Secondly, this research explicitly presents the underpinning Design Structure Matrix (DSM) that systematically captures the interrelations between the sensors in the platform and data/information signals for enabling synergistic operations. The employed Systems Engineering approach provides a comprehensible analysis of the baseline structure existing in the examined human–robot synergy platform. In particular, the applied DSM allows for understanding and synthesizing a sensor sub-system’s architecture and enriching its efficacy by informing targeted interventions and reconfiguring the developed robotic solution modules depending on the required farming tasks at an orchard. Human-centric solutions for the agrarian sector demand careful study of the features that the particular agri-field possesses; thus, the insight DSM provides to system designers can turn out to be useful in the investigation of other similar data-driven applications.
Sensors for Road Vehicles of the Future
Authors: Jiménez Felipe
Published: December 2022

New vehicles include several systems that improve their safety, comfort, and performance [...]
Interrogation Method with Temperature Compensation Using Ultra-Short Fiber Bragg Gratings in Silica and Polymer Optical Fibers as Edge Filters
Authors: Pereira Luís , Min Rui , Woyessa Getinet , Bang Ole , Marques Carlos , Varum Humberto , Antunes Paulo
Published: December 2022

The use of simpler and less bulky equipment, with a reliable performance and at relative low cost is increasingly important when assembling sensing configurations for a wide variety of applications. Based on this concept, this paper proposes a simple, efficient and relative low-cost fiber Bragg grating (FBG) interrogation solution using ultra-short FBGs (USFBGs) as edge filters. USFBGs with different lengths and reflection bandwidths were produced in silica optical fiber and in poly(methyl methacrylate) (PMMA) microstructured polymer optical fiber (mPOF), and by adjusting specific inscription parameters and the diffraction pattern, these gratings can present self-apodization and unique spectral characteristics suitable for filtering operations. In addition to being a cost-effective edge filter solution, USFBGs and standard uniform FBGs in silica fiber have similar thermal sensitivities, which results in a straightforward operation without complex equipment or calculations. This FBG interrogation configuration is also quite promising for dynamic measurements, and due to its multiplexing capabilities multiple USFBGs can be inscribed in the same optical fiber, allowing to incorporate several filters with identical or different spectral characteristics at specific wavelength regions in the same fiber, thus showing great potential to create and develop new sensing configurations.
Fundamental Research on the Invention of Breast Support Underwear with a Focus on Women’s Comfort
Authors: Isono Minami , Tateoka Yumiko
Published: December 2022

The purpose of this study was to establish a system for measuring breast underling pressure, evaluate the degree of sustained underling pressure, and verify basic data on the breast lifting distance to improve it. We measured the lifting distance and force at which 24 adult women felt comfortable when their breasts were lifted with an uncovered cloth. The results revealed that the larger the breast size, the greater the pull-up distance and the wider the lifting distance perceived as comfortable. The validity of the measurement method in this study was determined to be useful for the measurement of breast ptosis pressure and breast ptosis position because the measurement was performed at least three times per participant, and the error was small. In the future, we would like to clarify the type of brassiere that supports the breast and gives women a sense of comfort.
Leak Localization on Cylinder Tank Bottom Using Acoustic Emission
Authors: Nguyen Tuan-Khai , Ahmad Zahoor , Kim Jong-Myon
Published: December 2022

In this study, a scheme for leak localization on a cylinder tank bottom using acoustic emission (AE) is proposed. This approach provides a means of early failure detection, thus reducing financial damage and hazards to the environment and users. The scheme starts with the hit detection process using a constant false alarm rate (CFAR) and a fixed thresholding method for a time of arrival (TOA) and an end-time determination. The detected hits are then investigated to group those originating from the same AE source together by enforcing an event definition and a similarity score. Afterwards, these newly grouped hits are processed by a time difference of arrival (TDOA) to find the locations of the events. Since the locations of the events alone do not pinpoint the leak location, a data density analysis using a Voronoi diagram is employed to find the area with the highest possibility of a leak’s existence. The proposed method was validated using the Hsu-Nielsen test on a cylinder tank bottom under a one-failed-sensor scenario, which returned a highly accurate result across multiple test locations.
Adsorbed Oxygen Ions and Oxygen Vacancies: Their Concentration and Distribution in Metal Oxide Chemical Sensors and Influencing Role in Sensitivity and Sensing Mechanisms
Authors: Ciftyurek Engin , Li Zheshen , Schierbaum Klaus
Published: December 2022

Oxidation reactions on semiconducting metal oxide (SMOs) surfaces have been extensively worked on in catalysis, fuel cells, and sensors. SMOs engage powerfully in energy-related applications such as batteries, supercapacitors, solid oxide fuel cells (SOFCs), and sensors. A deep understanding of SMO surface and oxygen interactions and defect engineering has become significant because all of the above-mentioned applications are based on the adsorption/absorption and consumption/transportation of adsorbed (physisorbed-chemisorbed) oxygen. More understanding of adsorbed oxygen and oxygen vacancies (VO•,VO••) is needed, as the former is the vital requirement for sensing chemical reactions, while the latter facilitates the replenishment of adsorbed oxygen ions on the surface. We determined the relation between sensor response (sensitivity) and the amounts of adsorbed oxygen ions (O2(ads)−, O(ads), −O2(ads)2−, O(ads)2−), water/hydroxide groups (H2O/OH−), oxygen vacancies (VO•, VO••), and ordinary lattice oxygen ions (Olattice2−) as a function of temperature. During hydrogen (H2) testing, the different oxidation states (W6+, W5+, and W4+) of WO3 were quantified and correlated with oxygen vacancy formation (VO•, VO••). We used a combined application of XPS, UPS, XPEEM-LEEM, and chemical, electrical, and sensory analysis for H2 sensing. The sensor response was extraordinarily high: 424 against H2 at a temperature of 250 °C was recorded and explained on the basis of defect engineering, including oxygen vacancies and chemisorbed oxygen ions and surface stoichiometry of WO3. We established a correlation between the H2 sensing mechanism of WO3, sensor signal magnitude, the amount of adsorbed oxygen ions, and sensor testing temperature. This paper also provides a review of the detection, quantification, and identification of different adsorbed oxygen species. The different surface and bulk-sensitive characterization techniques relevant to analyzing the SMOs-based sensor are tabulated, providing the sensor designer with the chemical, physical, and electronic information extracted from each technique.
A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset
Authors: Wang Fei , Dong Anqi , Zhang Kaiyu , Qian Dexing , Tian Yinsheng
Published: December 2022

Balance ability is one of the important factors in measuring human physical fitness and a common index for evaluating sports performance. Its quality directly affects the coordination ability of human movements and plays an important role in human productive activities. In the field of sports, balance ability is an important indicator of athletes’ selection and training. How to objectively analyze balance performance becomes a problem for every non-professional sports enthusiast. Therefore, in this paper, we used a dataset of lower limb collected by inertial sensors to extract the feature parameters, then designed a RUS Boost classifier for unbalanced data whose basic classifier was SVM model to predict three classifications of balance degree, and, finally, evaluated the performance of the new classifier by comparing it with two basic classifiers (KNN, SVM). The result showed that the new classifier could be used to evaluate the balanced ability of lower limb, and performed higher than basic ones (RUS Boost: 72%; KNN: 60%; SVM: 44%). The results meant the established classification model could be used for and quantitative assessment of balance ability in initial screening and targeted training.