bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental study on the discharge characteristics of an air rotating gliding arc
Authors: Sun Su-Rong , Chen Fei , Zheng Yu-Hang , Wang Chao , Wang Hai-Xing 
Published: June 2024

Abstract
In this study, the discharge characteristics of an air rotating gliding arc (RGA) are investigated by synchronous measurements of a digital oscilloscope and a high-speed camera, and the emission spectrum measurement. The discharge evolution in one complete motion cycle exhibits a ‘breakdown-elongation-extinction’ process accompanied by a jump phenomenon of the arc root and a back-breakdown phenomenon. The discharge evolves from the unstable breakdown mode (U-B), to the transition mode and finally to the stable gliding mode (S-G) by increasing the input voltage or decreasing the tangential and axial gas flow rates. The U-B mode at an input voltage of 120 V is featured by the large reduced electric field and high electron temperature of 1.90 eV, but the arc length and existence time are very short. The S-G mode at an input voltage of 270 V has a relatively low breakdown frequency of 0.33 kHz and an average breakdown current of 1.31 A, implying that the arc steadily glides and rotates along the spiral electrode. The average electron temperature is 0.64 eV in the S-G mode, while the arc length and existence time are longer. The rotational and vibrational temperatures of the

N

2

(

C

3

∏
u

)

state are respectively measured at 2200 K and 4400 K in the U-B mode, and in the S-G mode are 2600 K and 4820 K. From the evolution of emission intensities of measured excited species, it is found that the

NO
γ

band emission intensity generally rises from the U-B mode to the S-G mode since the gas temperature and arc existence time rise, indicating that the S-G mode may be beneficial for the vibrationally promoted Zeldovich reactions. This study could deepen the understanding of arc characteristics in air RGA for selecting a suitable mode to achieve better plasma performance in practical applications.
High-accuracy data readout in multi-dimensional optical data storage using convolutional neural networks
Authors: Zhu Y P , Xie X Y , Gao J C , Yan Z , Cao Q , Zhang J Y 
Published: June 2024

Abstract
The escalating global volume of digital data poses a critical challenge for storage solutions. Optical data storage techniques have garnered lots of interests due to their excellent offline storage capabilities, including low energy consumption, high capacity, and long lifespan. However, despite the focus on data recording, minimal attention has been dedicated to the readout aspect. This study introduced femtosecond laser direct writing to perform multi-dimensional optical data storage and employed a specialized convolutional neural network to enhance voxel readout accuracy. The proposed network architecture achieved a remarkable voxel readout accuracy of 98.83%, surpassing support vector machine method (90.07%) and LeNet (96.85%). Furthermore, the proposed method yielded a substantial increase in actual user capacity, outperforming traditional approaches and presenting a novel solution for addressing readout challenges in multi-dimensional optical data storage.
On the analytical soliton approximations to fractional forced Korteweg–de Vries equation arising in fluids and plasmas using two novel techniques
Authors: Alhejaili Weaam , Az-Zo’bi Emad A , Shah Rasool , El-Tantawy S A 
Published: June 2024

Abstract
The current investigation examines the fractional forced Korteweg–de Vries (FF-KdV) equation, a critically significant evolution equation in various nonlinear branches of science. The equation in question and other associated equations are widely acknowledged for their broad applicability and potential for simulating a wide range of nonlinear phenomena in fluid physics, plasma physics, and various scientific domains. Consequently, the main goal of this study is to use the Yang homotopy perturbation method and the Yang transform decomposition method, along with the Caputo operator for analyzing the FF-KdV equation. The derived approximations are numerically examined and discussed. Our study will show that the two suggested methods are helpful, easy to use, and essential for looking at different nonlinear models that affect complex processes.
171Yb+ optical clock with

2.2
×

10

−
18

systematic uncertainty and absolute frequency measurements
Authors: Tofful A , Baynham C F A , Curtis E A , Parsons A O , Robertson B I , Schioppo M , Tunesi J , Margolis H S , Hendricks R J , Whale J , Thompson R C , Godun R M 
Published: June 2024

Abstract
A full evaluation of the uncertainty budget for the ytterbium ion optical clock at the National Physical Laboratory (NPL) was performed on the electric octupole (E3)

2

S

1
/
2

→
2

F

7
/
2

transition. The total systematic frequency shift was measured with a fractional standard systematic uncertainty of

2.2
×

10

−
18

. Furthermore, the absolute frequency of the E3 transition of the 171Yb+ ion was measured between 2019 and 2023 via a link to International Atomic Time (TAI) and against the local caesium fountain NPL-CsF2. The absolute frequencies were measured with fractional standard uncertainties between

3.7
×

10

−
16

and

1.1
×

10

−
15

, and all were in agreement with the 2021 BIPM recommended frequency.
Blindly separated spontaneous network-level oscillations predict corticospinal excitability
Authors: Ermolova Maria , Metsomaa Johanna , Belardinelli Paolo , Zrenner Christoph , Ziemann Ulf 
Published: June 2024

Abstract

Objective. The corticospinal responses of the motor network to transcranial magnetic stimulation (TMS) are highly variable. While often regarded as noise, this variability provides a way of probing dynamic brain states related to excitability. We aimed to uncover spontaneously occurring cortical states that alter corticospinal excitability. Approach. Electroencephalography (EEG) recorded during TMS registers fast neural dynamics—unfortunately, at the cost of anatomical precision. We employed analytic Common Spatial Patterns technique to derive excitability-related cortical activity from pre-TMS EEG signals while overcoming spatial specificity issues. Main results. High corticospinal excitability was predicted by alpha-band activity, localized adjacent to the stimulated left motor cortex, and suggesting a travelling wave-like phenomenon towards frontal regions. Low excitability was predicted by alpha-band activity localized in the medial parietal–occipital and frontal cortical regions. Significance. We established a data-driven approach for uncovering network-level neural activity that modulates TMS effects. It requires no prior anatomical assumptions, while being physiologically interpretable, and can be employed in both exploratory investigation and brain state-dependent stimulation.
A diagonal masking self-attention-based multi-scale network for motor imagery classification
Authors: Yang Kaijun , Wang Jihong , Yang Liantao , Bian Lifeng , Luo Zijiang , Yang Chen 
Published: June 2024

Abstract

Objective. Electroencephalography (EEG)-based motor imagery (MI) is a promising paradigm for brain-computer interface (BCI), but the non-stationarity and low signal-to-noise ratio of EEG signals make it a challenging task. Approach. To achieve high-precision MI classification, we propose a Diagonal Masking Self-Attention-based Multi-Scale Network (DMSA-MSNet) to fully develop, extract, and emphasize features from different scales. First, for local features, a multi-scale temporal-spatial block is proposed to extract features from different receptive fields. Second, an adaptive branch fusion block is specifically designed to bridge the semantic gap between these coded features from different scales. Finally, in order to analyze global information over long ranges, a diagonal masking self-attention block is introduced, which highlights the most valuable features in the data. Main results. The proposed DMSA-MSNet outperforms state-of-the-art models on the BCI Competition IV 2a and the BCI Competition IV 2b datasets. Significance. Our study achieves rich information extraction from EEG signals and provides an effective solution for MI classification.
Irregular optogenetic stimulation waveforms can induce naturalistic patterns of hippocampal spectral activity
Authors: Cole Eric R , Eggers Thomas E , Weiss David A , Connolly Mark J , Gombolay Matthew C , Laxpati Nealen G , Gross Robert E 
Published: June 2024

Abstract

Objective. Therapeutic brain stimulation is conventionally delivered using constant-frequency stimulation pulses. Several recent clinical studies have explored how unconventional and irregular temporal stimulation patterns could enable better therapy. However, it is challenging to understand which irregular patterns are most effective for different therapeutic applications given the massively high-dimensional parameter space. Approach. Here we applied many irregular stimulation patterns in a single neural circuit to demonstrate how they can enable new dimensions of neural control compared to conventional stimulation, to guide future exploration of novel stimulation patterns in translational settings. We optogenetically excited the septohippocampal circuit with constant-frequency, nested pulse, sinusoidal, and randomized stimulation waveforms, systematically varying their amplitude and frequency parameters. Main results. We first found equal entrainment of hippocampal oscillations: all waveforms provided similar gamma-power increase, whereas no parameters increased theta-band power above baseline (despite the mechanistic role of the medial septum in driving hippocampal theta oscillations). We then compared each of the effects of each waveform on high-dimensional multi-band activity states using dimensionality reduction methods. Strikingly, we found that conventional stimulation drove predominantly ‘artificial’ (different from behavioral activity) effects, whereas all irregular waveforms induced activity patterns that more closely resembled behavioral activity. Significance. Our findings suggest that irregular stimulation patterns are not useful when the desired mechanism is to suppress or enhance a single frequency band. However, novel stimulation patterns may provide the greatest benefit for neural control applications where entraining a particular mixture of bands (e.g. if they are associated with different symptoms) or behaviorally-relevant activity is desired.
Optimisation of the poloidal field system for advanced divertor configurations in STEP
Authors: Hudoba A. , Bakes S. , Cunningham G. , Henderson S. , Eriksson F. , Marsden S. , Wilson T. ,   the STEP Team
Published: June 2024

Abstract
The power exhaust proves to be one of the most challenging and concept–defining aspects in the design of a commercial fusion power plant, while the magnetic coil system, capable of supporting advanced exhaust solutions, emerges as one of the main design and cost drivers. Consequently, much effort should be dedicated to the optimisation of a robust global magnetic configuration, which integrates both the plasma and edge scenarios, while ensuring engineering feasibility and compatibility with the available technology. Here we present a multidisciplinary framework employed to analyse, evaluate, and optimise the Spherical Tokamak for Energy Production (STEP) equilibrium configuration, coupled with a viable divertor solution, and a compatible poloidal field coil system. The complexity of this task leads to a multitude of potentially conflicting requirements and competing constraints. We identify interfaces and conflicts between the aspects of the design that were previously considered independently, and highlight the key benefits, trends, and trade–offs between alternative configurations. We demonstrate that advanced exhaust solutions, simultaneously applied to both inboard and outboard divertors, are accessible with feasible coil sets under conditions relevant for STEP. We show that the most promising inner–X geometry, paired with the outer super–X configuration, can significantly enhance divertor’s power handling capability, allowing access to stable detached regimes. The coil set feasibility is further assessed considering its compatibility with the assumed plasma initiation scenario, and with the most demanding plasma current density profiles utilising alternative heating and current drive schemes.
Calibration and validation of DEM parameters of food waste with high total solid content (≥20%) using physical experiments and EDEM simulations
Authors: Wang Yachao , Wu Xuan , Fan Xianhe , Zhang Zeyu , Shen Lin , Wei Jiaqi , Li Xueqin 
Published: June 2024

Abstract
Since discrete element method (DEM) parameters are important for the calculation accuracy of EDEM simulations, the DEM parameters of food waste with high total solid content (≥20%) are calibrated and validated by experiments and simulations in this paper. The physical properties of food waste are obtained by physical experiments, and then the optimal values of DEM model parameters recommended for food waste can be obtained from the GEMM database. Finally, the repose angle and the average torque obtained by EDEM simulations are compared with the experimental results to validate the calibrated DEM parameters of food waste. The average relative error of the repose angle between the experimental values and the simulation values is 3.23%, and the average relative error of the average torque value is 5.29%. That means the DEM parameters of food waste calibrated in this paper are correct and can be applied for simulating the mixing performance of food waste with high total solid content (≥20%).
Flexible SERS substrate In-situ monitoring of SPR-induced P-aminothiophenol dimerization
Authors: Liu Jinhua , Wang Yifei , Chen Jing 
Published: June 2024

Abstract
Surface-enhanced Raman spectroscopy (SERS) is an efficient technique for in-situ molecular investigation that is highly sensitive and has the ability to provide extensive fingerprint information. Additionally, the surface plasmons on the SERS substrate can also simultaneously induce the catalytic reaction. P-aminothiophenol (PATP) is a crucial model molecule utilized in SERS catalytic reactions. Through dimerization, PATP induced by the plasmon effect can produce 4,4’-dimercaptoazobenzene (DMAB). Here, a flexible SERS substrate with a monolayer of Au nanoparticles on polydimethylsiloxane (PDMS) is prepared and used for monitoring the dimerization of PATP. In the experiment, the SERS substrate is inverted, the side with Au nanoparticles monolayer is kept in contact with the PATP solution directly, and the laser is irradiated from the backside of the flexible substrate. The effects of Ag+ and oxygen concentrations on the reaction are investigated. This work paves the way for SERS methodology in in-situ monitoring of other liquid-phase catalytic reactions.
Simulation of health assessment model for thermal management system of lithium-ion battery based on fuzzy clustering algorithm
Authors: Xie Lingdong , Zhou Honghui , Gao Ming , Yu Haohua , Mao Baiyi , Zeng Delong 
Published: June 2024

Abstract
Considering the inadequate assessment of the factor decline index in the thermal management system for lithium-ion batteries, which has a negative impact on their efficient utilization, a fuzzy clustering algorithm is suggested to create a simulation model for evaluating the health of the lithium-ion battery thermal management system. In the process of restoring characteristic data by the fuzzy clustering algorithm, according to the characteristic parameters of energy signal and power signal, the decay characteristic results of the lithium-ion battery thermal management system are extracted. Taking the minimum attenuation as a reference, the evaluation function is calculated, the health trend of the lithium-ion battery thermal management system is evaluated, the health grade is established, and the health evaluation model of the lithium-ion battery thermal management system is constructed. The experimental results show that the prediction of the remaining service life of five kinds of lithium-ion batteries by this model is consistent with the actual value, and the average absolute error is always within 0.02, which is superior in health assessment.
Preparation and performance of the non-platinum catalyst for oxygen reduction reaction in fuel cells
Authors: Ren Guohong , Chen Yuting , Sun Wei , Liu Chunyu , Song Kangle , Liu Xiaohong , Liu Zhongsu 
Published: June 2024

Abstract
The oxygen reduction reaction (ORR) of the fuel cells is a core of energy conversion, whereas its kinetics is slow due to the limitation of the activation energy barrier, which dramatically limits the commercial development of fuel cells. At present, the main electrocatalyst for the cathode oxygen reduction reaction of fuel cells is the platinum (Pt)-based catalyst. Still, as a precious metal, Pt has a high price, low reserves, and poor stability. These shortcomings seriously affect the mass production and commercialization of fuel cells. Therefore, there is an urgent need for the development of fuel cells to study a low price, sufficient production, high stability, and pollution-free non-Pt catalyst. The main research content of this paper is to use biomass sycamore ball as a carbon source, mixed with ferric chloride (III) hexahydrate and melamine, and prepare the carbon, nitrogen, and iron co-doped ORR catalyst by high-temperature calcination under a nitrogen atmosphere. The physical characterization and electrochemical testing of the obtained samples were conducted at room temperature. The results showed that the pretreatment of sycamore ball powder to iron ratio of 1:3 had the best catalytic performance.
Interpolation of wind lidar data supported by MLP
Authors: Jiang Dandan , Liu Zhixin , Wang Yiran , Zhang Daocheng , Hao Chunxiao , Wang Zuopeng 
Published: June 2024

Abstract
To address the issue of invalid values in the measured wind speed data of the wind lidar, a data interpolation experiment was conducted by using the wind speed data outputted by the wind lidar as the base data source. Based on data preprocessing, the interpolation model was constructed by integrating MLP and the mean wind speed shear index. The accuracy of the interpolated wind speed data was evaluated. The experimental results show that the proposed interpolation method for wind lidar data achieved high accuracy, with a root mean square error of less than 0.75 m/s and an average relative error of less than 3.5%. This approach enhances the reliability and integrity of wind lidar data, providing a reference for the use of wind lidar data in wind resource assessment.
Radiation hardness and quality validation of the on-detector electronics for the CMS Drift Tubes upgrade
Authors: Bellato M. , Bergnoli A. , Griggio A. , Toffano M. , Triossi A. ,   the CMS Drift Tubes collaboration
Published: June 2024

Abstract
In view of the High Luminosity LHC upgrade, the so-called Phase 2 upgrade, the electronics
of the Drift Tubes (DT) subdetector of CMS will undergo a complete innovation. The requirements
in terms of trigger rate will exceed the capabilities of the present electronics. Thus, all the
on-detector electronics together with the associated back-end need to be replaced. Phase-2
on-detector electronics for DT consist of about 800 FPGAs (Field Programmable Gate Array) based
boards called OBDT (On-detector Board for Drift Tubes). These boards are sub-divided in two
different categories: 600 OBDT ϕ and 200 OBDT θ, targeting respectively the readout of
DT wires parallel and normal to the LHC beams. Each OBDT ϕ is able to time-digitize 240 channels with sub-nanosecond resolution and upstream to the back end using multiple high-speed
optical links running at 10.24 Gb/s. The choice of components known to have good resistance to
radiation was a requirement in the design of the OBDT. The main component, the FPGA, is a
flash-based PolarFire from Microsemi, already qualified in different facilities for radiation
hardness tests. As a validation step, a campaign of radiation tests was carried out at the
INFN-TIFPA Protontherapy Centre in Trento, Italy, using proton beams. The behavior of an OBDT
ϕ board was evaluated during radiation exposure with a total dose much higher than expected
to be integrated during 10 years of HL-LHC, which is 0.5 Gy.
Diffraction of light by a slit in anisotropic media with closed and open wave vector surface
Authors: Eritsyan H. , Lalayan A. , Margaryan A. , Arakelyan H. 
Published: June 2024

Abstract
Fraunhofer diffraction of an electromagnetic wave by a slit on an opaque screen located between vacuum and an anisotropic medium with closed and open wave vectors surface is considered. A formula for diffraction in a material medium is obtained, including the case of diffraction into a uniaxial anisotropic medium in the absence of absorption, and the formation of diffraction minima and maxima is studied.
Photon classification with Gradient Boosted Trees at CLAS12
Authors: Matousek G. , Vossen A. 
Published: June 2024

Abstract
Dihadron semi-inclusive deep inelastic scattering (SIDIS) of 10.6 GeV longitudinally polarized electrons off the proton has been measured using the CLAS12 detector at Jefferson Lab. Two separate channels, π^+π^0 and π^-π^0, were analyzed, requiring the reconstruction of diphoton pairs. In this analysis, we addressed the problem of false neutral particles being reconstructed by CLAS12's event builder, polluting the otherwise physical combinatorial background underneath the π^0 peak. A photon classifier using a Gradient Boosted Trees (GBTs) architecture was trained with Monte Carlo simulations to reduce the amount of background π^0's. We show that the nearest-neighbor features learned by the model lead to a substantial increase in signal vs. background discrimination compared to previous CLAS12 π^0 analyses. The machine learning approach recovers several times more dihadron statistics for the dataset.
The optimal use of segmentation for sampling calorimeters
Authors: Torales Acosta Fernando , Karki Bishnu , Karande Piyush , Angerami Aaron , Arratia Miguel , Barish Kenneth , Milton Ryan , Morán Sebastián , Nachman Benjamin , Sinha Anshuman 
Published: June 2024

Abstract
One of the key design choices of any sampling calorimeter is
how fine to make the longitudinal and transverse segmentation. To
inform this choice, we study the impact of calorimeter segmentation
on energy reconstruction. To ensure that the trends are due
entirely to hardware and not to a sub-optimal use of segmentation,
we deploy deep neural networks to perform the reconstruction. These
networks make use of all available information by representing the
calorimeter as a point cloud. To demonstrate our approach, we
simulate a detector similar to the forward calorimeter system
intended for use in the ePIC detector, which will operate at the
upcoming Electron Ion Collider. We find that for the energy
estimation of isolated charged pion showers, relatively fine
longitudinal segmentation is key to achieving an energy resolution
that is better than 10% across the full phase space. These results
provide a valuable benchmark for ongoing EIC detector optimizations
and may also inform future studies involving high-granularity
calorimeters in other experiments at various facilities.