bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compact modeling of short-channel effects in back-gated 2D negative capacitance (NC) FETs
Authors: Jiang Chunsheng , Lu Qing , Pan Liyang , Li Quanfu , Peng Huiling , Zhang Zhigang , Song Shuxiang , Xu Jun 
Published: July 2024

Abstract
The negative capacitance field-effect transistor with 2D channel material (2D NC-FET) holds significant promise for low-power applications owing to its remarkable resilience against short-channel effects (SCEs) and favorable noise characteristics. In this study, we establish a compact current–voltage (I–V) model for short-channel back-gated 2D NC-FETs with metal-ferroelectric-metal–insulator–semiconductor structure by self-consistently solving the two-dimensional Poisson, drift–diffusion and Landau–Khalatnikov equations. The proposed model is valid and continuous throughout the entire operating regime, including the fully-depleted region, partly-depleted region, and accumulation region. Furthermore, we derive analytical equations for the threshold voltage (

V

TH

) and subthreshold swing (

SS

) of back-gated 2D NC-FETs based on the developed I–V model. Lastly, we elucidate the influence mechanisms of various device parameters and voltage bias on the subthreshold characteristics of short-channel back-gated 2D NC-FETs using the proposed I–V model in conjunction with analytical expressions of

V

T
H

and

SS

. Our findings reveal that back-gated 2D NC-FETs shows unconventional degradation behavior in

V

TH

and SS, resulting from the competition between traditional SCEs and novel negative capacitance effects.
Switchable optical trapping and manipulation enabled by polarization-modulated multifunctional phase-change metasurfaces
Authors: Xu Yaning , Tian Ximin , Xu Junwei , Zhang Shenglan , Huang Yafeng , Li Liang , Liu Jielong , Xu Kun , Yu Zhanjun , Li Zhi-Yuan 
Published: July 2024

Abstract
Optical trapping, a cutting-edge methodology, is pivotal for contactlessly controlling and exploring microscopic objects. However, it encounters formidable challenges such as multiparticle trapping, flexible control, and seamless integration. Here, we employ a polarization-modulated multi-foci technique for versatile nanoparticle trapping using multifunctional metasurfaces relying on geometric phase. Numerical simulations demonstrate the generation of two focused spots with orthogonal polarization distributions through our metasurfaces when illuminated with linearly polarized light, with their polarization distributions be interchanged by orthogonally switching the incident polarizations. We extend this design to an array of multi-foci metasurface tweezers modulated by polarization, highlighting the versatility and robustness of our approach. Furthermore, we demonstrate the simultaneous generation of two distinct focusing cylindrical vector beams using a monolayer metasurface, showcasing the two vector beams possess the interchange ability of their polarization distributions. By leveraging the Maxwell stress tensor, we assess the distinct contributions of the focused beams to longitudinal and transverse optical forces on SiO2 spheres, validating diverse trapping and manipulation behaviors for nanoparticles with the proposed metasurface designs. By manipulating the phase states of Sb2S3 nanopillars, binary-switchable optical trapping and manipulation are facilitated for all proposed metasurface tweezers. Our work underscores the efficacy of polarization-modulation multifunctional metasurface tweezers in consolidating multiple trapping tasks into a single device, paving the way for innovative lab-on-a-chip optical trapping applications in biophysics, nanotechnology, and photonics.
MosViT: towards vision transformers for moving object segmentation based on Lidar point cloud
Authors: Ma Chunyun , Shi Xiaojun , Wang Yingxin , Song Shuai , Pan Zhen , Hu Jiaxiang 
Published: July 2024

Abstract
Moving object segmentation is fundamental for various downstream tasks in robotics and autonomous driving, providing crucial information for them. Effectively extracting spatial-temporal information from consecutive frames and addressing the scarcity of dataset is important for learning-based 3D LiDAR moving object segmentation (LIDAR-MOS). In this work, we propose a novel deep neural network based on vision transformers (ViTs) to tackle this problem. We first validate the feasibility of transformer networks for this task, offering an alternative to CNNs. Specifically, we utilize a dual-branch structure using range (residual) image as input to extract spatial-temporal information from consecutive frames and fuse it using a motion-guided attention mechanism. Furthermore, we employ the ViT as the backbone, keeping its architecture unchanged from what is used for RGB images. This enables us to leverage pre-trained models on RGB images to improve results, addressing the issue of limited LiDAR point cloud data, which is cheaper compared to acquiring and annotating point cloud data. We validate the effectiveness of our approach on the LIDAR-MOS benchmark of SemanticKitti and achieve comparable results to methods that use CNNs on range image data. The source code and trained models will be available at https://github.com/mafangniu/MOSViT.git.
Semi-supervised adaptive anti-noise meta-learning for few-shot industrial gearbox fault diagnosis
Authors: Hu Junwei , Xie Chao 
Published: July 2024

Abstract
Real-time and accurate predictive maintenance of industrial equipment is fundamental for ensuring the safety and stability of advanced manufacturing processes. Current fault diagnosis methods based on data mining rely on a large number of labeled samples, and obtaining sufficient labeled data for diagnosing industrial equipment faults is challenging. Meta-learning can achieve the diagnosis of few-shot samples to a certain extent, but the effect is not ideal. Semi-supervision can effectively leverage a large number of unlabeled samples, which is of great practical significance for handling scenarios involving limited labeled samples. However, noise interference can occur when unlabeled samples appear that do not belong to known categories. Therefore, this study proposes adaptive semi-supervised meta-learning networks (ASMNs) for noisy few-shot gearbox fault diagnosis. Firstly, a residual network with a Morlet Wavelet layer is used to extract signal features. Next, sample-level attention is defined to select unlabeled samples that are more similar to labeled sample prototypes, thereby reducing the influence of noisy samples. The adaptive metric is used to obtain the relational distance functions of labeled samples and unlabeled samples. Adaptive semi-supervised ASMNs uses unlabeled data to refine prototypes for better fault diagnosis. The effectiveness and anti-noise performance of the proposed method are verified by using two gearbox datasets with various few-shot noise scenarios.
An adaptive feature mode decomposition-guided phase space feature extraction method for rolling bearing fault diagnosis
Authors: Xin Jiayi , Jiang Hongkai , Jiang Wenxin , Li Lintao 
Published: July 2024

Abstract
The extraction of fault features from rolling bearings is a challenging and highly important task. Since they have complex operating conditions and are usually under a strong noise background. In this study, a novel approach termed phase space feature extraction guided by an adaptive feature mode decomposition (AFMDPSFE) is proposed to detect subtle faults in rolling bearings. Initially, a new method using Kullback–Leiber divergence is introduced to automatically select the optimal mode number and filter length for the decomposition of vibration signals, facilitating the automatic extraction of optimal components and ensuring efficient screening. This eliminates the need for manual configuration of feature mode decomposition parameters. Furthermore, a criterion that could determine two crucial parameters to capture system dynamics characteristics in phase space reconstruction is embedded into AFMDPSFE algorithm. Subsequently, a series of high-dimensional independent components is derived. The envelope spectrum of the principal component exhibiting the highest kurtosis value is computed to achieve fault identification, consequently enhancing the separation of signal from noise. Both simulations and experimental results confirm the effectiveness of AFMDPSFE approach. A comparison analysis shows the excellent performance of AFMDPSFE in extracting fault features from significant noise interference.
Research on remote reference denoising method based on non-coaxial and non-coplanar tunnel NMR detection
Authors: Sun Yong , Yi Xiaofeng , Li Cong , Yang Zhiqin , Lin Jun 
Published: July 2024

Abstract
The limited space within the tunnel constrains the size of the antenna for NMR detection, thereby significantly impacting the signal-to-noise ratio (SNR) of NMR signals. Insufficient SNR data poses substantial challenges to obtaining reliable NMR signals. The paper presents a novel approach to address the challenge of strong background noise in tunnel environments and low SNR data by incorporating the ground multi-channel remote reference denoising method into tunnel NMR advance detection. Specifically designed for narrow tunnels, a multi-channel non-coaxial and non-coplanar remote reference denoising method is proposed. Firstly, the effectiveness of the non-coaxial, non-coplanar remote reference denoising method is verified in the laboratory environment. Secondly, the correlation between the detector antenna and the reference antenna is calculated theoretically to ensure the significant correlation between the detector antenna and the reference antenna. Finally, two processing methods of reference denoising and non-reference denoising are carried out respectively by combining the tunnel detection data. By comparing the inversion results and the engineering construction results, the effectiveness of non-coaxial and non-coplanar remote reference denoising methods in tunnel NMR detection is proved, which provides relevant research support for expanding the application of tunnel NMR detection technology.
Accelerated relaxation enhancing flows cause total dissipation
Authors: Rowan Keefer 
Published: July 2024

Abstract
We show that by ‘accelerating’ relaxation enhancing flows, one can construct a flow that is smooth on

[
0
,
1
)
×

T

d

but highly singular at t = 1 so that for any positive diffusivity, the advection–diffusion equation associated to the accelerated flow totally dissipates solutions, taking arbitrary initial data to the constant function at t = 1.
Quantifying the effect of pulse-like ground motions on the seismic design of SMA restrained isolation bridges using probabilistic repair cost method
Authors: Zhong Jian , Shu Yiwei , Wang Hao , Xing Chenxi 
Published: July 2024

Abstract
The shape memory alloy (SMA) restrainer serves as an effective but expensive bridge restraining device. However, the impact of pulse effect on SMA design approach under pulse-like ground motions (PLGM) has not been fully quantified in previous studies, which hinders its application in near-fault regions. Therefore, based on the risk probability assessment method throughout the entire life cycle, by accounting for the comprehensive repair cost of each component damage, this paper introduces a parameter design method for SMA restrainer of near-fault bridges that considers structural parameters, pulse parameters, and economic indicators. Firstly, the repair cost ratio (RCR) of bridge system, which means the expenses for repairs expressed as a proportion of bridge replacement costs, was regarded as the life-time optimization goal and overall performance indicator. Secondly, by accounting for near-fault effects, the relationship between RCR and SMA design parameters was established by convolution algorithm of probabilistic seismic hazard analysis, demand analysis and capacity analysis. A novel probabilistic seismic demand model was utilized to quickly determine the RCR of the bridge system under PLGM. Finally, the influence of pulse period on the rational design parameter of SMA restrainer was comprehensively investigated by RCR-based method. A seismic isolation arch bridge was selected as the illustrated case in this article. The results indicate that the rational design parameters of SMA exhibit a pattern of initially rising, then falling as the pulse period increases, reaching the peak value when the pulse period approaches bridge fundamental period. Moreover, the price parameter of SMA significantly affect the optimal design parameters, and the effective range is also recommended.
Energy stabilization of high-charge bunches from laser plasma accelerators
Authors: Shi Xueyan , Xu Haisheng , Li Dazhang , Wang Jia , Zeng Ming 
Published: July 2024

Abstract
Laser plasma accelerators (LPAs) have become one of the frontiers of the accelerator community, mainly because they promise orders of magnitude improvement in the accelerating gradient. However, the energy stability and spread of the high-charge bunched beams (e.g. several hundred pC per bunch) from LPAs still strongly limit their application. In this work, we propose a novel method utilizing magnetic chicanes combined with both active and passive plasma dechirpers to simultaneously reduce the central energy deviation and the energy spread of high-charge bunched beams from LPAs. Start-to-end simulations demonstrate that the central energy deviation and the energy spread of approximately 500 pC bunches can be simultaneously reduced from approximately 2% and 1.2% to 0.1% and 0.5%, respectively, while maintaining almost perfect transmission efficiency (above 97%).
A theoretical perspective on the almost dark galaxy Nube: exploring the fuzzy dark matter model
Authors: Yang Yu-Ming , Bi Xiao-Jun , Yin Peng-Fei 
Published: July 2024

Abstract
In recent astronomical observations, an almost dark galaxy, designated as Nube, has unveiled an intriguing anomaly in its stellar distribution. Specifically, Nube exhibits an exceptionally low central brightness, with the 2D half-light radius of its stars far exceeding the typical values found in dwarf galaxies, and even surpassing those observed in ultra-diffuse galaxies (UDGs). This phenomenon is difficult to explain within the framework of cold dark matter (CDM). Meanwhile, due to its ultralight particle mass, fuzzy dark matter (FDM) exhibits a de Broglie wavelength on the order of kiloparsecs under the typical velocities of galaxies. The interference between different modes of the FDM wave gives rise to fluctuations in the gravitational field, which can lead to the dynamical heating of stars within galaxies, resulting in an expansion of their spatial distribution. In this paper, we aim to interpret the anomalous stellar distribution observed in Nube as a consequence of the dynamical heating effect induced by FDM. Our findings suggest that a FDM particle mass
around 1-2 × 10-23 eV can effectively account for this anomaly. And we propose that the FDM dynamical heating effect provides a new insight into understanding the formation of field UDGs.
Application of AI Models for Determination of Beam Parameters with an Array of Timepix3 Pixel Detectors
Authors: Ivanov Svetoslav ,   PADME Collaboration
Published: July 2024

Abstract
PADME Run III searched for a resonant production of the X17 particle. The precise knowledge of the beam position is critical for the measurements of X17 and an array of 2 × 6 Timepix3 hybrid pixel detectors were used to measure the parameters of the incident positron beam. During the operation of the Timepix detector, some of the pixels reported missing data which introduced a challenge for the accurate estimation of the center position. A Neural Network Regression algorithm was developed and compared with the conventional Gaussian fitting technique over a simulated data with masked pixels.
Multichannel SiPM test readout system for gamma ray measurements with monolithic inorganic CeBr3
Authors: Asova Veronika , Bistrev Galin , Buchakchiev Valentin , Kozhuharov Venelin 
Published: July 2024

Abstract
Energy resolution and the detection eﬃciency for gamma quanta are fundamental properties in the construction of detectors for ionizing radiation. In this study, a SiPM-based photodetector coupled to a monolithic inorganic CeBr3 crystal is exposed to gamma rays in order to study the performance of the CeBr3 crystal. Measurements are made using three different radioactive sources - 137Cs, 22Na and 60Co. For each source, the measurements are made using a few different values for the Bias voltage of the SiPM. Furthermore, two CeBr3 crystals with different thicknesses are used in order to study how detector eﬃciency is affected by crystal dimensions. A preliminary analysis of the data is presented.
Analysis of the S1 triplet component in the DarkSide-50 experiment
Authors: Sunny Clea ,   the DarkSide-50 collaboration
Published: July 2024

Abstract
The DarkSide-50 (DS-50) experiment aims at the direct detection of weakly interacting massive particles. It is a dual-phase liquid argon time projection chamber (LAr TPC) where Dark Matter (DM), which constitutes five sixths of all matter in the universe, is expected to interact with an argon nucleus resulting in a nuclear recoil. A scintillation signal (S1) is produced as a result of the ionising events from the DM-Ar interaction. The impurities in LAr, such as O,2, N2, H2O, etc., at the ppm level, quench the scintillation photons, leading to a reduction in the observed lifetime of the triplet state. In this contribution, the effect of impurities on the triplet lifetime is analyzed for individual events using DS-50 data, with a primary focus on nitrogen, one of the candidates for the impurities in LAr hypothesized to cause a suppression of triplet lifetime. This is done by determining the lifetime of the triplet component with a known purity, which can be used as a reference for the purity level of argon used in current and future dark matter searches.
Potential for improving nutrient use efficiencies of human food systems with a circular economy of organic wastes and fertilizer
Authors: Davis Sarah C , Maynard Finn G , Jenkins David , Herman Tess , Reza M Toufiq 
Published: July 2024

Abstract
Waste from the human food system includes a large quantity of nutrients that pose environmental and human health risks. If these nutrients can be captured and repurposed, they could potentially offset synthetic fertilizer demands. This study reviews several technologies—including anaerobic digestion, hydrothermal carbonization (HTC), and composting—that can be used to process wastes from the human food system. This study also assesses the quantity of nutrient resources that are available from wastes, including food waste, biosolids, manure, and yard waste. Three geographic scales were analyzed. At a national level in the United States, up to 27% of nitrogen and 33% of phosphorus demands for agriculture could be met with wastes from the human food system, primarily from food waste and biosolids. Some rural localities have a greater potential for circular economies of nutrients in the food system, with the potential to meet 100% of nitrogen and phosphorus fertilizer demands using waste nutrients, as in the case of Athens County, Ohio. Benefits of offsetting synthetic fertilizer use with waste nutrients include reduced greenhouse gas (GHG) emissions, with up to 64% reduction in GHG emissions per unit of nitrogen fertilizer produced with HTC.
Diverse impacts of the Indian summer monsoon on ENSO among CMIP6 models and its possible causes
Authors: Lin Shuheng , Dong Buwen , Yang Song , Zhang Tuantuan 
Published: July 2024

Abstract
This study examines the performance of 52 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in capturing the effects of the Indian summer monsoon on the evolution of El Niño–Southern Oscillation (ENSO). The ISM’s impacts on ENSO show a substantial diversity among the models. While some models simulate the strength of the impacts comparable to observations, others represent much weaker influences. Results indicate that the diversity is highly related to inter-model spread in interannual variability of ISM rainfall (ISMR) among the models. Models with a larger ISMR variability simulate stronger ISM-induced anomalies in precipitation and atmospheric circulation over the western North Pacific during the monsoon season. As a result, these models exhibit larger wind anomalies induced by monsoon on the south flank of the anomalous circulation in the western Pacific, thereby influencing subsequent ENSO evolution more significantly by causing stronger air-sea coupling processes over the tropical Pacific.
The unintended consequences of production bans: the case of the 2018 Kenya logging moratorium
Authors: Bartlett Anne , Alix-García Jennifer , Abarca Alejandro , Walker Sarah , Van Den Hoek Jamon , Murillo-Sandoval Paulo , Friedrich Hannah K 
Published: July 2024

Abstract
Production bans are a common way for governments to address issues of social concern. However, when consumer demand for banned items is insensitive to price changes, cross-border trade may undermine these efforts. We examine the effects of Kenya’s 2018 moratorium on the extraction of wood products, including logs and charcoal, from public and community forests. The data show an immediate 36% increase in the domestic charcoal price in Kenya, where over 80% of consumers use it as their primary energy source. Subsequently, we document an increase of 133% percent in charcoal imports from Uganda to Kenya during the first 6 months of the ban. Further, we estimate that avoided deforestation in Kenya was likely displaced to Uganda such that net carbon emissions increased. These findings demonstrate the ineffectiveness of the ban as a mechanism to decrease greenhouse gas emissions and biodiversity loss from deforestation.
Enhanced lake elevation mapping using a zone-based method
Authors: Fan Meiyi , Wang Yong , She Xiaojun , Liu Xin , Chen Ran , Gong Yulin , Xue Kun , Sun Fangdi , Li Yao 
Published: July 2024

Abstract
Inland lakes play a crucial role in monitoring global climate change and managing responses to extreme weather events, with lake elevation being critical for assessing their regulatory capacities. However, due to the limited temporal resolution of current altimetry satellites, obtaining high-frequency, high-precision elevation data for water bodies remains challenging. Consequently, most studies utilize elevation-area (E-A) models constructed from historical elevation and area records, integrated with area observations from high-temporal resolution optical satellites to infer precise water levels. Yet, the construction of the E-A model often assumes a uniform water level across the lake, thus overlooking potential segmentation during dry periods. To address this, our study implemented a zone-based approach, utilizing hydrological connectivity principles to ensure that elevation data within E-A models are confined to appropriate zonal regions. This method effectively minimized uncertainties by preventing errors from zonal discrepancies, significantly improving accuracy compared to traditional methods. It reduced root mean square errors (RMSE) by 0.71–1.73 m during the dry season, achieving RMSEs of 0.35, 0.64, and 0.37 m across three segments. Furthermore, this method ensures water level data are confined to specific zones, preventing the inconsistencies typically caused by averaging data across multiple stations or selecting data from varying elevations. This consistent domain definition reduces extrapolation errors during the model prediction and inversion. Moreover, the method compensates for time information losses often incurred by relying on multi-year percentile charts, thereby enabling more precise aquatic boundary delineation than traditional regional boundaries.