bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel error model for the line scan imaging system
Authors: Chen Xiangling , Qiu Zhongjun , Fan Haipeng 
Published: December 2023

Abstract
The factors influencing the accuracy of the line scan imaging system (LSIS) are highly complex, and their error propagation mechanisms are poorly clarified. To reasonably and effectively mitigate the impact of mechanical factors on imaging accuracy, a novel comprehensive error model for LSIS is proposed in this paper, revealing the relationship between imaging errors and various mechanical error sources from manufacturing, assembly, and movement. Based on the analysis of the generation and propagation relationship of mechanical errors in LSIS, the integration of the mechanical system and the imaging system is accomplished to form a comprehensive multibody system, thus establishing a complete linear transmission mechanism for mechanical errors in the imaging process within the model. The experiments demonstrate that this model can provide a reference for the localization of mechanical error sources by utilizing the changes in extrinsic parameters during the calibration process. Furthermore, when the sensitivities of error sources are not exceptionally low, and certain cases with systematic errors are excluded, the model can make accurate estimations of the sensitivities of imaging errors to those errors generated by various mechanical sources. The estimated relative errors do not exceed 6%. The results confirm the effectiveness of this model, which can be utilized to ascertain the range of error sources and assess the extent to which errors from various sources impact imaging accuracy. This model provides a basis for the precision optimization of LSIS.
Characteristics of tin droplet target system for EUV source research
Authors: Sun Qin , Wang Xinbing , Zuo Duluo 
Published: December 2023

Abstract
Droplet-based laser-produced plasma source shows enormous significance in extreme ultraviolet lithography, which places high demands on the stability of tin droplets. This paper presents a tin droplet target system including the tin droplet generator (DG), droplet diagnosis, and spatiotemporal synchronization of tin droplets and laser. Shadowgraph technology is used to determine the stability of tin droplets. The characteristics of the DG were analyzed, and the operation parameter maps are provided. By varying operating frequencies from 18.4 kHz to 49.3 kHz, the diameter and spacing of droplets can be adjusted in the ranges of 120–200 μm and 200–1100 μm respectively. Both theoretical calculations and experimental results show that tin droplets keep high stability when the operation parameters locate at the optimal range. The long-term lateral stability is also proven under a high degree of vacuum. Additionally, the application feasibility of the DG system is verified by the experiments of laser impact tin droplets.
Automatic flaw detection of carbon fiber prepreg using a CFP-SSD model during preparation
Authors: Liu Xiangyu , Gan Xuehui , Ping An 
Published: December 2023

Abstract
As an intermediate material for carbon fiber composites, surface flaws inevitably occur during carbon fiber prepreg preparation, which will seriously affect the quality of carbon fiber composite products. The current approaches for identifying flaws on carbon fiber prepreg have the drawbacks of being labor-intensive and inefficient. This research puts forward a novel model for identifying surface flaws on carbon fiber prepregs using an improved single-shot multibox detector (SSD), called CFP-SSD model. A machine vision-based platform for surface flaws identification on carbon fiber prepreg is created. Additionally, the modified-Resnet50 backbone employed in the proposed CFP-SSD model can enhance the effectiveness of network feature extraction. Then, the multi-scale fusion remote context feature extraction module is designed to efficiently fuse the information from the shallow and deep layers. The findings of performance comparison experiments and ablation experiments indicate that the proposed CFP-SSD model achieves 86.63% mean average precision and a detection speed of 47 frames per second, which is sufficient for real-time automatic identification of carbon fiber prepreg surface flaws.
Ray and caustic structure of Ince-Gauss beams
Authors: Gutiérrez-Cuevas R , Dennis M R , Alonso M A 
Published: December 2023

Abstract
The Ince-Gauss beams, separable in elliptic coordinates, are studied through a ray-optical approach. Their ray structure can be represented over a Poincaré sphere by generalized Viviani curves (intersections of a cylinder and a sphere). This representation shows two topologically different regimes, in which the curve is composed of one or two loops. The overall beam shape is described by the ray caustics that delimit the beams’ bright regions. These caustics are inferred from the generalized Viviani curve through a geometric procedure that reveals connections with other physical systems and geometrical constructions. Depending on the regime, the caustics are composed either of two confocal ellipses or of segments of an ellipse and a hyperbola that are confocal. The weighting of the rays is shown to follow the two-mode meanfield Gross–Pitaevskii equations, which can be mapped to the equation of a simple pendulum. Finally, it is shown that the wave field can be accurately estimated from the ray description.
A semi-analytical approach via yang transform on fractional-order navier-stokes equation
Authors: Kapoor Mamta , Kour Simran 
Published: December 2023

Abstract
In this current, the applications of the Yang transformation technique are taken under consideration to deal with the non-linear fractional Navier–Stokes equation and fractional coupled Navier–Stokes equation. The suggested method produces approximate-analytical solutions in the form of a series that are correspondingly dependent on fractional-order derivative values and have modest, comprehensible mechanics and easy implementation. The Caputo fractional derivative is employed, and the numerical scheme’s stability and convergence are examined. Numerical examples demonstrate the analytical solution of the technique and it is examined that the proposed techniques are robust, efficient and reduce the number of numerical computations. The current technique’s results are compatible with the theoretical analysis, and the suggested technique can be extended to solve numerous higher-order non-linear dynamics.
Physics based analysis of a high-performance dual line tunneling TFET with reduced corner effects
Authors: Ashok Tammisetti , Pandey Chandan Kumar 
Published: December 2023

Abstract
To improve the DC and analog/HF performance, a novel dual line tunneling based TFET (DLT-ES-TFET) with elevated source and L-shaped pocket is proposed in this manuscript. In DLT-ES-TFET, the elevated top (G1) and extended back (G2) gates overlapping the source region enhance the line tunneling of charge carriers in both vertical and horizontal directions across the source-pocket interface. TCAD-based simulation results reveal that DLT-ES-TFET offers an improvement of ∼47% and ∼54% in average subthreshold swing when it is compared with E-VTSFET and L-TFET, respectively. Furthermore, ON-current in DLT-ES-TFET is also found to be improved by an order of ∼1 as compared to other two devices. In fact, the L-shaped pocket reduces the corner effects caused by the electric filed crowding across source-channel (S-C) interface, which eventually suppresses the OFF-state leakage in the proposed DLT-ES-TFET. Moreover, enhancement in the charge carriers tunneling across S-C interface leads to a huge increment in the transconductance (∼157μs/μm) of DLT-ES-TFET, which further helps in achieving a high cut-off frequency of 12.3 GHz. Next, transient response of DLT-ES-TFET-based resistive load inverter suggests a notable improvement in peak over- and under-shoots along with propagation delay as compared to E-VTSFET and L-TFET. Lastly, interface traps and temperature analysis is also found to be in favor of the proposed DLT-ES-TFET.
Generalized time-delay reverse synchronization with error feedback coefficients
Authors: Shi Qiqin , Zhao Yuzhuo , Zeng Jian , Ding Qun 
Published: December 2023

Abstract
To enhance the application of discrete chaotic systems in secure communication, we introduce a novel generalized time-delay inverse synchronization method with error feedback coefficient. This paper outlines the derivation of an inverse master-slave time-delay generalized synchronization system using the Lyapunov theory with error feedback coefficient design, which is proposed for the first time. To verify the efficacy of the synchronization method, we constructed a new 4D discrete hyperchaotic system. The dynamic properties of the novel system, such as the phase diagram and Lyapunov exponents, are investigated. Empirical outcomes reveal that generalized discrete time-delay synchronization can be accomplished under certain conditions for the error feedback coefficient. The study utilizes the chaotic masking technique to encrypt and decrypt messages in the secure communication system. The experimental findings reveal that by integrating the synchronization approach with the time-delay context of secure communication, the applicable design of the synchronization procedure is adaptable and trustworthy, catering to the pertinent requirements of engineering, and has the capacity to attain instantaneous chaotic synchronization requisites.
Spatial and temporal coherence via polarization mutual coherence function
Authors: Luis Alfredo 
Published: December 2023

Abstract
We address polarization coherence in terms of correlations of Stokes variables. We develop an scalar polarization mutual coherence function that allows us to define a polarization coherence time. We find a suitable spectral polarization density allowing a polarization version of the Wiener-Khintchine theorem. With these tools we also address the polarization version of the van Cittert-Zernike theorem.
The Large Array Survey Telescope—Pipeline. I. Basic Image Reduction and Visit Coaddition
Authors: Ofek E. O. , Shvartzvald Y. , Sharon A. , Tishler C. , Elhanati D. , Segev N. , Ben-Ami S. , Nir G. , Segre E. , Sofer-Rimalt Y. , Blumenzweig A. , Strotjohann N. L. , Polishook D. , Krassilchtchikov A. , Zenin A. , Fallah Ramazani V. , Weimann S. , Garrappa S. , Shanni Y. , Chen P. , Zimmerman E. 
Published: December 2023

Abstract
The Large Array Survey Telescope (LAST) is a wide-field telescope designed to explore the variable and transient sky with a high cadence and to be a test-bed for cost-effective telescope design. A LAST node is composed of 48 (32 already deployed), 28 cm f/2.2 telescopes. A single telescope has a 7.4 deg2 field of view and reaches a 5σ limiting magnitude of 19.6 (21.0) in 20 (20 × 20) s (filter-less), while the entire system provides a 355 deg2 field of view. The basic strategy of LAST is to obtain multiple 20 s consecutive exposures of each field (a visit). Each telescope carries a 61 Mpix camera, and the system produces, on average, about 2.2 Gbit s−1. This high data rate is analyzed in near real-time at the observatory site, using limited computing resources (about 700 cores). Given this high data rate, we have developed a new, efficient data reduction and analysis pipeline. The LAST data pipeline includes two major parts: (i) Processing and calibration of single images, followed by a coaddition of the visit’s exposures. (ii) Building the reference images and performing image subtraction and transient detection. Here we describe in detail the first part of the pipeline. Among the products of this pipeline are photometrically and astrometrically calibrated single and coadded images, 32 bit mask images marking a wide variety of problems and states of each pixel, source catalogs built from individual and coadded images, Point-Spread Function photometry, merged source catalogs, proper motion and variability indicators, minor planets detection, calibrated light curves, and matching with external catalogs. The entire pipeline code is made public. Finally, we demonstrate the pipeline performance on real data taken by LAST.
Mathematical Modeling of Carbon Nanoparticle Distribution in Polymer Composites for High Electrical Conductivity: Implications for Green Technologies
Authors: Menseidov Dzhihan 
Published: December 2023

Abstract
A mathematical framework has been formulated to elucidate the conductivity behavior of polymer composites infused with carbon nanoparticles. This linear model, while simplistic, provides a foundational understanding of how factors like nanoparticle morphology, concentration, and surface properties, in conjunction with polymer matrix attributes such as viscosity and surface tension, influence the spatial distribution of these nanoparticles. The overarching aim is to harness the potential of electrically conductive composites in bolstering green technological advancements in sectors like road transport, solar energy, batteries, and environmental conservation. The model underscores the pivotal role of high electrical conductivity in ensuring the efficient, sustainable, and environmentally benign operation of these technologies. This mathematical construct emerges as an instrumental tool, paving the way for the strategic design and optimization of electrically conductive composites tailored for diverse green technology applications.
Furniture market demand forecasting using machine learning approaches
Authors: Yaneva P E , Kulina H N 
Published: December 2023

Abstract
Researching consumer demand in the market for goods is essential for any business. The purpose of this study is to forecast the demand for furniture from a large manufacturer in Bulgaria. Significant factors influencing customer flow, both in the company and online stores, are investigated. Daily observations for nearly two years are modelled using CART Ensemble with arcing. The constructed models describe the demand for furniture with high goodness-of-fit statistics: coefficient of determination up to 93% and determine the order of the factors influencing the demand.
Development of an imaging method for in vivo single-cell tracking under high-noise conditions: a proof of principle
Authors: Chang Yu-Fen , Struchalin Pavel , Næss Bodil , Adamsen Tom Christian Holm , Balakin Boris V. 
Published: December 2023

Abstract
This paper reports the development of a positron imaging method for in vivo single-cell tracking under high-noise conditions. Following biological processes spatially and temporally at a single-cell level in a living organism is desirable for inquiring into the relationships between the behaviours and properties of cells. Positron-emitting radionuclides enable detecting and following radioactivity-labelled substances deep inside living organisms. However, positron imaging has several challenges, such as the distribution of high noise in other areas close to the cell under investigation. In this work, an algorithm for locating a cell with millisecond resolution to combat the strong interference of nearby noise is developed. The feasibility of the method is verified by the demonstration of particle tracking and detection of behavioural changes in an environment with the signal-to-noise ratio of 1:9.
Assessing the Impact of Speed and Type of Road Surface on the Noise Level in a Passenger Car Cabin
Authors: Kovachev N Y , Mihova V A , Georgiev I R , Pavlov V T 
Published: December 2023

Abstract
This paper presents a study of noise pollution in the cabin of a passenger car in the context of various types of road. The purpose of the study is to understand how the type of road surface can influence the levels of noise inside the car. Sound pressure levels were measured on the A-scale of a sound level meter for 3 types of road surface - crushed stone pavement, asphalt, and cobblestone. The study was conducted at varying speeds of the car within the range of 10-60 km/h. Due to limitations of homogeneous road conditions, a duration of 10 seconds was chosen for each measurement. The sound level meter was placed at a selected measuring point, close to the driver’s head. On the data collected in the above-mentioned way, various regression models have been developed to assess the impact of speed and type of road surface on the noise level. It was found that the noise level increases with the roughness of the road surface, and the resulting risks were analysed.
Modeling the viscoplastic transient dynamic process
Authors: Zhelyazov Todor , Pshenichnov Sergey 
Published: December 2023

Abstract
A hollow viscoelastic right circular cylinder subjected to a time-dependent load is considered. The cylinder is with finite dimensions. A transient load acts upon the inner surface of the cylinder, whereas no loads are applied to outer surfaces. The end surfaces of the cylinder are restrained to move along the cylinder axis. The viscoelastic behaviour of the material is modelled using Boltzmann – Volterra equations. A yield criterion is implemented in the numerical analyses. By assumption, upon reaching the yield surface, the linear viscoelastic model switches to a plastic constitutive relation. The transient dynamic behaviour of the material is simulated for different intensities of the applied load and model parameters.
Design of a system for bioinformatics processing of metagenomic data
Authors: Kirilov A , Kirov B 
Published: December 2023

Abstract
Advancements in acquiring, storing and processing information have led to a significant accumulation of large databases in the field of metagenomics—a branch of biology that deals with the study of genetic material directly recovered from environmental or clinical samples. The data collected from metagenomic experiments are massive and noisy, often containing fragmented information representing over 10,000 species. Processing and analyzing this data require software installed on supercomputers. In this paper, the authors present the design, development, and creation of a computational server system aimed at processing and analyzing bioinformatics data in metagenomics and 16S rRNA gene studies. They utilized the specialized software product Galaxy Europe for this purpose. The analysis was performed using the metagenomics-based method Targeted Amplicon Sequencing. The data was divided into three test collections in a special fasta file format. These collections were processed using the web-based Galaxy platform and two configuration variants on a high-performance cluster equipped with NVIDIA Tesla V100 32 GB GPU cards. The analysis process was optimized to reduce information processing time, and the parallel performance of the three different system configurations was investigated for the data used.
Flowpattern in hydrocyclones, numerical simulations with experimental verification
Authors: Hoffmann Alex C. , Fonnes Daniel , Chang Yu-Fen 
Published: December 2023

Abstract
This paper reports a detailed study of the flow in cyclone separators, with the use of most up to date computational fluid dynamics simulations, which are validated with positron emission particle tracking (PEPT) experiments tracing the movement of particles through the cyclone. The parameters varied were the viscosity of the carrier liquid, the flowrate and, in the numerical simulations, the inlet configurations of the cyclone, namely one and two inlets and, with the two inlets, a) both at right angles to the cyclone axis and b) angled downwards. The study reveals features of the flow, which have not been seen till now, but are necessary for the understanding and modelling of the separation and purification efficiency of cyclones. The results of the simulations and the close agreement with experiment are a testament to the reliability and accuracy of large eddy simulation (LES), even for flow features as difficult to simulate as the confined strongly swirling flows in cyclone separators. The results show that a contiguous, smooth surface of zero axial velocity does exist and has approximately the shape that has been assumed by modellers. The significant effects of fluid viscosity, underflow and modifications to the inlet are also shown.
Matched impedance amplifier design for shear-force acoustic near-field microscopy
Authors: Brockman Theodore , Supichayanggoon Kacharat , La Rosa Andres H 
Published: December 2023

Abstract
Efficient detection of the acoustic signal constitutes the most critical aspect in Shear-force Acoustic Near-field Microscopy, for reliably characterize the dynamic response of confined fluids under shear stress. The near-field acoustic emission from the fluid is monitored as the probe gradually approaches to, and subsequently retracts from, the substrate. Herein we report a 7dB improvement in signal-to-noise sensitivity in detecting the acoustic emission from the fluid trapped between a flat sample and the apex of a tapered probe (the latter attached to a quartz tuning fork of 32 kHz resonance frequency). The new design capitalizes on the inherent capacitance of the SANM acoustic sensor (comprising a pile of piezoelectric plates) and a proper matching inductor/capacitance combination to, altogether connected in a tank-circuit fashion, optimize the sensor’s response at 32 kHz. A detailed construction of the circuit amplifier, as well as detailed frequency response bandwidth and noise characterization, is included herein.
Deep neural network uncertainty quantification for LArTPC reconstruction
Authors: Koh D. , Mishra A. , Terao K. 
Published: December 2023

Abstract
We evaluate uncertainty quantification (UQ) methods for deep
learning applied to liquid argon time projection chamber (LArTPC)
physics analysis tasks. As deep learning applications enter
widespread usage among physics data analysis, neural networks with
reliable estimates of prediction uncertainty and robust performance
against overconfidence and out-of-distribution (OOD) samples are
critical for their full deployment in analyzing experimental
data. While numerous UQ methods have been tested on simple datasets,
performance evaluations for more complex tasks and datasets are
scarce. We assess the application of selected deep learning UQ
methods on the task of particle classification using the
PiLArNet [1] monte carlo 3D LArTPC point cloud
dataset. We observe that UQ methods not only allow for better
rejection of prediction mistakes and OOD detection, but also
generally achieve higher overall accuracy across different task
settings. We assess the precision of uncertainty quantification
using different evaluation metrics, such as distributional
separation of prediction entropy across correctly and incorrectly
identified samples, receiver operating characteristic curves (ROCs),
and expected calibration error from observed empirical accuracy. We
conclude that ensembling methods can obtain well calibrated
classification probabilities and generally perform better than other
existing methods in deep learning UQ literature.