bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the visual measurement algorithm of cam base circle radius based on virtual structured light plane
Authors: Hu Zhengyi , He Jinglin 
Published: November 2024

Abstract
Using line structured light vision measurement technology, it is possible to perform non-contact measurement of the cam’s base circle radius. To address the issue of the structured light plane not being perpendicular to the axis of the cam being measured, this paper obtains the axis equation of the cam being measured through calibration, and based on the axis equation, it obtains the spatial equation of a virtual light plane that is perpendicular to the axis. The point cloud on the surface of the cam is projected onto the virtual plane, and the base circle radius of the cam is obtained using the coordinates of the projected points. To improve the measurement accuracy of the cam’s base circle radius, this paper designs a base revision model to address measurement errors caused by the non-coincidence of bases. The measurement results of the base circle radii of four cams on the engine camshaft show that the line structured light vision measurement technology meets the measurement requirements and is feasible for non-contact measurement. The innovation presented herein lies in the development of a cloud acquisition model predicated on line structured light vision technology, which effectively converts a complex spatial curve fitting challenge into a simpler planar curve fitting problem. This transformation not only streamlines the measurement process for the cam base circle radius but also significantly enhances the measurement precision of engine camshafts.
Study of magnetic field error suppression methods based on atomic parameters of cell in atomic co-magnetometer rotation measurement
Authors: Liu Ye , Fan Wenfeng , Liu Yao , Ma Longyan , Quan Jiale , Zhou Saixin , Pang Haoying , Quan Wei 
Published: November 2024

Abstract
We propose a new model for magnetic field errors based on the rotation measurement of the K-Rb-

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co-magnetometer, considering the density ratio and spin collisions. The spin polarization characteristics of different alkali metal density ratios were analyzed by creating five cells with different density ratios, and the correctness of the magnetic field error model was verified experimentally. Moreover, the effects of five different alkali metal density ratios of cells on the sensitivity, long-term stability and magnetic field error suppression of the K-Rb-

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co-magnetometer were investigated experimentally. When the density ratio is 1:107, there can be the optimal magnetic field error suppression effect, sensitivity and long-term stability. According to the derived magnetic field error equation, the magnetic noise error of the K-Rb-

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co-magnetometer with an optimized density ratio is reduced by nearly 5 times from 260°/h/nT to 56.5°/h/nT, the sensitivity is improved from 2.9×10−5 °/s/Hz

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to 7.0×10−6 °/s/Hz

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at 1 Hz, and the long-term stability Allan variance over two hours is improved from 1.9×10−2 °/h to 8.9×10−3 °/h. This result provides a theoretical basis for designing the parameters of the K-Rb-

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co-magnetometer cell.
A novel method for anomaly detection and correction of GNSS time series
Authors: Li Haiyang , Xie Yilin , Meng Xiaolin , Wu Shuguang , Xu Jianping , Wang Xiang 
Published: November 2024

Abstract
Global navigation satellite systems (GNSS) provides a novel means for deformation monitoring, which is an important guarantee for structures. Accurately separating its linear and nonlinear signals, and noise in GNSS time series is the foundation for analyzing deformation mechanisms and the prerequisite for assessing the status. However, extracting deformation signals is a challenging obstacle to applying GNSS for deformation monitoring. Aimed at that, a new method is proposed in this study. Fine-tuning the window size and threshold of the Hampel filter (HF) through grid search allows for initial anomaly detection and correction. Optimizing the K parameter of the K-nearest neighbors algorithm via rigorous K-fold cross-validation ensures further smoothing of the filtered data, which overcomes the limitations of the HF in handling continuous anomalies. Experimental results demonstrate that the proposed method improves performance by approximately 62% compared to traditional methods and by around 40% relative to interquartile range (IQR) and other methods. This study presents an effective approach for detecting and eliminating outliers in GNSS deformation monitoring, offering noteworthy theoretical and practical implications.
Fault detection of RV reducer cycloidal gear under robot working conditions based on optimization improved envelope spectrum sideband energy ratio
Authors: Shen Yankang , Guo Yu , Fan Jiawei , Wu Xianliang 
Published: November 2024

Abstract
Under industrial robot operating conditions, the cycloidal gear in the rotary vector (RV) reducer in the robot joint runs in a compound rotation motion with a low-speed and incomplete cycle, which complicates the fault detection for the cycloidal gear. Until now, no effective detection method for the cycloidal gear tooth fault under industrial robot operating conditions has been reported. Consequently, a method based on optimization improved envelope spectrum (IES) sideband energy ratio is proposed in this paper. Firstly, encoder signals are acquired and used to estimate the instantaneous angular speed signals, which are then separated into two sets of data based on rotation directions. Then, the data corresponding to the approximately stable speed is truncated and connected to form a constructed signal. Next, the coherence of the constructed signal is calculated using the fast spectral coherence algorithm. Subsequently, the IES sideband energy ratio is used as the objective value, and a global optimization algorithm is employed to determine the integration frequency band of the IES. Finally, the IES of this frequency band is calculated to obtain the fault-related order characteristics. An RV reducer test rig was used for the experiment, and the analysis results from the proposed approach, cyclic spectral coherence, and IES via feature optimization-gram were contrasted. Experiment results show that the proposed method is valid for the detection of tooth faults in the cycloidal gear of the RV reducer in robot operating conditions.
Sparse representation-based noise reduction for BOTDR monitoring signals in foundation pit anchor cables
Authors: Li Zi-xiang , Cai Hai-bing , Hou Gong-yu 
Published: November 2024

Abstract
Brillouin optical time-domain reflectometer (BOTDR) systems are commonly challenged by low signal-to-noise ratio (SNR) in foundation pit monitoring. This study proposes a noise-suppression method for BOTDR signals utilizing sparse representation (SR). The method involves creating an initial dictionary from the eigenvectors of the normalized graph Laplace matrix. The K-singular value decomposition and orthogonal matching pursuit algorithms are combined to update the dictionary and coefficient matrix, facilitating the SR of the signal’s intrinsic features and the removal of random noise. This results in improved quality of the reconstructed signal. An experimental system for BOTDR temperature sensing was developed to assess the algorithm’s denoising capabilities. The algorithm showed significant improvements in SNR and reductions in sample entropy (SE) compared to techniques such as wavelet threshold denoising, empirical wavelet transform, and empirical mode decomposition. Specifically, the average SNR increase was 27.4%, 15.4%, 13.1%, and 17.9%, while the average SE decrease was 24.4%, 16.0%, 15.4%, and 47.9% for the respective comparisons. The proposed Laplace-based dictionary also outperformed the discrete cosine transform dictionary. Field tests were conducted at the Beijing Stomatological Hospital, where the algorithm applied to on-site anchor cable fiber optic monitoring signals, achieving an average SE decrease of 45.2%. The research provides an effective denoising method for the application of BOTDR technology in foundation pit monitoring, underscoring the approach’s novelty and practical application.
Multi-parameter correlation analysis and multi-step prediction of seawater quality based on graph spatio-temporal analysis network
Authors: Zhu Qiguang , Shen Zhen , Qiao Wenjing , Wu Zhen , Chen Ying 
Published: November 2024

Abstract
In recent years, with the increasing pollution of near-shore waters, the water quality pollution incidents have been aggravated, which seriously threatens many aspects of coastal economic development, ecological environment and living health. Therefore, there is an urgent need for an effective method to predict the water quality of near-shore waters. However, due to seasonal changes, ocean currents, biological activities and other factors, the marine environment has strong complexity and uncertainty, which leads to the monitoring data of seawater quality parameters are unstable, non-linear and other characteristics. At the same time, there are interactions between different parameters, so it is not easy to dig deeper into the information in the data, and the accuracy of the existing prediction methods for multi-parameter multi-step prediction of seawater quality is generally low. To solve the above problems, a new graph neural network model is proposed in this paper. The model can effectively extract the local time correlation, global time correlation and spatial correlation in non-Euclidean space of seawater quality parameter data from multiple dimensions. Finally, this paper evaluates the model performance using the seawater parameter data from the near-shore waters of Beibu Gulf, and compared with the five baseline models, the model proposed in this paper shows the best performance in all the defined evaluation indexes.
Augmented deep transfer learning for SRP condition monitoring via physically-informed WGAN-GP approach
Authors: Zuo Jiye , Lei Miaoxin , Wang Shuqiang , Dong Shimin , Li Weicheng 
Published: November 2024

Abstract
Traditional dynamometer card sensors are costly and complex, making them unsuitable for real-time sucker rod pumping (SRP) well diagnostics. Recently, SRP diagnosis models using motor power curves offer an alternative, but irregular power curves and limited labeled data present challenges. To address this, we propose a deep transfer model enhanced by Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) data augmentation. First, a physics-driven model reconstructs polished rod torque from motor power, emphasizing fault features. Second, WGAN-GP generates faulty SRP torque-displacement samples to expand training data. Finally, a fully parameter-tuned deep transfer SRP diagnosis framework is established, which improves the automatic learning of advanced fault features and enhances diagnostic accuracy using the augmented dataset. Experiments confirm the model’s superior performance and generalization.
SDCSN: a hierarchical parallel localization method for pipeline leakage based on vibration signals
Authors: Zhang Xia , Li Juan 
Published: November 2024

Abstract
In applying deep learning methods to detecting and localising pipeline leaks, improving the fitness of deep learning methods to leak signals is an important task. We propose a novel detection model called stacked dilated convolutional shrinkage network. This model incorporates a stacked dilated convolution module specifically designed for vibration signals, enabling the extraction of rich multi-scale local features. Moreover, implementing the residual shrinkage building unit module for noise reduction in the network architecture. Building upon this foundation, we introduce a new concept centred around hierarchical leakage discrimination and parallel prediction positioning. This approach enables accurate assessment of leakage levels and precise identification of multiple leakage points. Finally, the performance of the proposed method is verified in real experiments and the optimal settings for the dilated rate are determined. The results demonstrate a maximum classification accuracy rate reaching 98.94%.
Inter-center comparison of proton range verification prototypes with an anthropomorphic head phantom
Authors: Hueso-González Fernando , Berthold Jonathan , Wohlfahrt Patrick , Bortfeld Thomas , Khamfongkhruea Chirasak , Tattenberg Sebastian , Zarifi Melek , Verburg Joost , Richter Christian 
Published: November 2024

Abstract

Objective. To compare in reproducible and equalized conditions the performance of two independent proton range verification systems based on prompt gamma-ray detectors from two different proton therapy centers. Approach. An anthropomorphic head phantom with calibrated stopping power, serving as ground truth, was irradiated with comparable treatment plans, spot positions and energies in both facilities. Clinical beam current, tumor contour and dose were used. The absolute range measurement was compared to the expected value according to the ground truth. The statistical precision was assessed by repeating each measurement ten times. Sensitivity to relative range shifts was evaluated by introducing 2 mm and 5 mm plastic slabs on half of the field. Main results. The resulting absolute range accuracy was within 2.4 mm in all cases. Relative range shifts were detected with deviations lower than 14%. Significance. The performance of both systems was deemed worthy of clinical application for the detection of range deviations. This study represents the first comparison of independent prompt gamma-ray-based proton range verification systems under equalized conditions with realistic treatment fields and beam currents.
Bimodal PET/MRI generative reconstruction based on VAE architectures
Authors: Gautier V , Bousse A , Sureau F , Comtat C , Maxim V , Sixou B 
Published: November 2024

Abstract

Objective. In this study, we explore positron emission tomography (PET)/magnetic resonance imaging (MRI) joint reconstruction within a deep learning framework, introducing a novel synergistic method. Approach. We propose a new approach based on a variational autoencoder (VAE) constraint combined with the alternating direction method of multipliers (ADMM) optimization technique. We explore three VAE architectures, joint VAE, product of experts-VAE and multimodal JS divergence (MMJSD), to determine the optimal latent representation for the two modalities. We then trained and evaluated the architectures on a brain PET/MRI dataset. Main results. We showed that our approach takes advantage of each modality sharing information to each other, which results in improved peak signal-to-noise ratio and structural similarity as compared with traditional reconstruction, particularly for short acquisition times. We find that the one particular architecture, MMJSD, is the most effective for our methodology. Significance. The proposed method outperforms conventional approaches especially in noisy and undersampled conditions by making use of the two modalities together to compensate for the missing information.
Searching for Gravitational Wave Optical Counterparts with the Zwicky Transient Facility: Summary of O4a
Authors: Ahumada Tomás , Anand Shreya , Coughlin Michael W. , Gupta Vaidehi , Kasliwal Mansi M. , Karambelkar Viraj R. , Stein Robert D. , Waratkar Gaurav , Swain Vishwajeet , Jegou du Laz Theophile , Anumarlapudi Akash , Andreoni Igor , Bulla Mattia , Srinivasaragavan Gokul P. , Toivonen Andrew , Wold Avery , Bellm Eric C. , Cenko S. Bradley , Kaplan David L. , Sollerman Jesper , Bhalerao Varun , Perley Daniel , Salgundi Anirudh , Suresh Aswin , Hinds K-Ryan , Reusch Simeon , Necker Jannis , Cook David O. , Pletskova Natalya , Singer Leo P. , Banerjee Smaranika , Barna Tyler , Copperwheat Christopher M. , Healy Brian , Kiendrebeogo R. Weizmann , Kumar Harsh , Kumar Ravi , Pezzella Marianna , Sagués-Carracedo Ana , Sravan Niharika , Bloom Joshua S. , Chen Tracy X. , Graham Matthew , Helou George , Laher Russ R. , Mahabal Ashish A. , Purdum Josiah , Anupama G. C. , Barway Sudhanshu , Basu Judhajeet , Raman Dhananjay , Roychowdhury Tamojeet 
Published: November 2024

Abstract
During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network, the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron star–black hole (NSBH) merger candidates. Here, we present a comprehensive study of the five high-significance (False Alarm Rate less than 1 yr−1) BNS and NSBH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity observations and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have been developing, Fritz, an instance of SkyPortal, instrumental in coordinating and managing our telescope scheduling, candidate vetting, and follow-up observations through a user-friendly interface. ZTF covered a total of 2841 deg2 within the skymaps of the high-significance GW events, reaching a median depth of g ≈ 20.2 mag. We circulated 15 candidates, but found no viable KN counterpart to any of the GW events. Based on the ZTF non-detections of the high-significance events in O4a, we used a Bayesian approach, nimbus, to quantify the posterior probability of KN model parameters that are consistent with our non-detections. Our analysis favors KNe with initial absolute magnitude fainter than −16 mag. The joint posterior probability of a GW170817-like KN associated with all our O4a follow-ups was 64%. Additionally, we use a survey simulation software, simsurvey, to determine that our combined filtered efficiency to detect a GW170817-like KN is 36%, when considering the 5 confirmed astrophysical events in O3 (1 BNS and 4 NSBH events), along with our O4a follow-ups. Following Kasliwal et al., we derived joint constraints on the underlying KN luminosity function based on our O3 and O4a follow-ups, determining that no more than 76% of KNe fading at 1 mag day−1 can peak at a magnitude brighter than −17.5 mag.
Application of the Nirmala Index in Nanotechnology: Optimizing Molecular Structures for Advanced Nanomaterials
Authors: Prashanth K K , Annasagaram Gayathri , Parvathi M , Kumar Deepasree S , Shettar Anita , Uma S 
Published: November 2024

Abstract
Chemical graph theory has played a key role in advancing our understanding of molecular structure by producing degree-based topological indices that forecast crucial physical and chemical properties. In this paper we attempt to investigate this recently defined Nirmala index as an invariant from different topological angles and applications on multiple molecular graph structures such as triangular, double, and alternate quadrilateral snake structures. This index offers a more profound comprehension of the connections between molecular structures and attributes. The next generation of ground breaking developments in nanotechnology, where the creation of devices and functional nanomaterials hinges on molecular interaction at the nanoscale, may also be greatly influenced by this index. These indices have potential applications in drug delivery systems and in molecular structure optimization related to nanotechnological fields like nanophotonics and nanoelectronics. Consequently, the Nirmala index will pave the way for the development of more sophisticated tools for the production of high-performing nanoscale materials.
Structural, morphological and Magnetic studies of Soft magnetic Fe-Si-B Metallic Glass ribbons
Authors: Dhanalakshmi K. , Revathi V. 
Published: November 2024

Abstract
A comparative study of two metallic glass ribbons produced by two different industries namely M/s Honeywell corporation, U.S.A., and M/s Vijay electricals. The potential of different experimental techniques like XRD, SEM and EDAX and VSM has been carried out for two soft metallic glass ribbons. XRD studies reveal that for sample A has two broad peaks and sample B shows the absence of any sharp peaks indicating crystalline secondary phases are <5%. Morphological studies show that sample A has homogeneous matrix whereas sample B has crystallites of varied shapes and sizes distributed in the range of 5-40 μm. EDAX for both the samples A and B was done to investigate the compositional elements. VSM measurements reveals sample B to exhibit a relatively larger anisotropy compared to sample A.
Investigation of the Dielectric Characteristics of Chromium-substituted Zinc and Cobalt Nanoferrite Systems
Authors: Lakshmi M. , Sreelalitha K. , Sarah P. , Samba Sivaiah B , Naveen D. 
Published: November 2024

Abstract
The series of Cr-substituted Zn and Co nanoferrite systems having the chemical composition CrxZnFe2–xO4 and CrxCoFe2–xO4 (0.0 ≤ x ≤0.5) were prepared by sol–gel technique. The present investigation aims to compare the impact of Cr3+ concentration on dielectric characteristics of Zn and Co-nanoferrite systems. The investigation of various dielectric characteristics was conducted using an LCR meter at 1kHz frequency and across the frequency range of 100Hz to 1MHz. The measurements were conducted at room temperature. In the case of both ferrite systems, the plots of frequency Vs dielectric constant (ε′) and AC conductivity (σac) show a common dielectric behavior of spinel ferrites. The plots of frequency Vs dissipation factor (D) of Cr- Cr-substituted zinc and Cobalt nanoferrite samples were observed to be unusual and display a relaxation peak at a particular frequency. In the current study, CrxZnFe2-xO4(x=0.2 and 0.3) and CrxCoFe2–xO4 (x=0.3) ferrite samples were found to exhibit very high values of dielectric constant. So these ferrites may be useful for a wide range of applications in electrical storage devices. According to LCR reports, all the Cr3+ substituted ferrite samples in both ferrite systems exhibit higher dielectric constant and AC conductivity values. Possible mechanisms that may be accountable for the outcomes are thoroughly scrutinized in this paper.
Accelerating solutions of the Korteweg–de Vries equation
Authors: Winkler Maricarmen A , Asenjo Felipe A 
Published: November 2024

Abstract
The Korteweg–de Vries equation is a fundamental nonlinear equation that describes solitons with constant velocity. On the contrary, here we show that this equation also presents accelerated wavepacket solutions. This behavior is achieved by putting the KdV equation in terms of the Painlevé I equation. The accelerated waveform solutions are explored numerically showing their accelerated behavior explicitly.
Ethnobotany of Plants Around Anaphalis javanica in the ‘Penanjakan Resort’ Area of Bromo Tengger Semeru National Park (BTSN)
Authors: Alifah Luthfi , Rahma Melati Julia , Batoro Jati 
Published: November 2024

Abstract
This research was conducted to reveal the potential of plants around Edelweiss Flowers (Anaphalis javanica) in the BTSN Penanjakan Resort Area and their benefits according to the local community. This study was carried out by plant inventory by use purposive sampling to choose the study site to 5 points with a plot size of 2×2 m2. Semi-structured interviews were carried out to find out the types of plants and their benefits. The results of the interviews were compiled to determine the significance of cultural interest index (ICS) and described based on ICS calculations. This study indicated that 17 different plant species were present around the growth site of Anaphalis javanica in the Penanjakan Resort Area of BTSN with the highest ICS value in Debregeasia longifolia or “mencogan” which has other benefits as a good water absorber.