bluuebunny/crossref_metadata_2025_split huggingface dataset:
Edge state behavior in a Su–Schrieffer–Heeger like model with periodically modulated hopping
Authors: Kar Satyaki 
Published: October 2023

Abstract
Su–Schrieffer–Heeger (SSH) model is one of the simplest models to show topological end/edge states and the existence of Majorana fermions. Here we consider a SSH like model both in one and two dimensions where a nearest neighbor hopping features spatially periodic modulations. In the 1D chain, we witness appearance of new in-gap end states apart from a pair of Majorana zero modes (MZMs) when the hopping periodicity go beyond two lattice spacings. The pair of MZMs, that appear in the topological regime, characterize the end modes each existing in either end of the chain. These, however, crossover to both-end end modes for small hopping modulation strength in a finite chain. Contrarily in a 2D SSH model with symmetric hopping that we consider, both non-zero and zero energy topological states appear in a finite square lattice even with a simple staggered hopping, though the zero energy modes disappear in a ribbon configuration. Apart from edge modes, the 2D system also features corner modes as well as modes with satellite peaks distributed non-randomly within the lattice. In both the dimensions, an increase in the periodicity of hopping modulation causes the zero energy Majorana modes to become available for either sign of the modulation. But interestingly with different periodicity for hopping modulations in the two directions, the zero energy modes in a 2D model become rarer and does not appear for all strength and sign of the modulation.
Effective methods for monitoring internal contamination among workers handling radioactive materials in various fields
Authors: Yoon Seokwon , Kim Jae Seok , Yoo Jaeroyng , Cho Minsu 
Published: October 2023

Abstract
This study aimed to develop effective methods for monitoring internal contamination among workers handling radioactive materials in various fields. A total of 160 workers from nuclear power plants, medical institutions, military units, and educational/research institutions were included in the study. The monitoring methods included urinalysis and whole-body counting (WBC) using a mobile radio bioassay laboratory (MRL). Gamma-emitting radionuclides were monitored using the MRL WBC system, and a separate pretreatment procedure was used for tritium measurement in urine samples. Gross beta-screening was performed using a liquid scintillation counting system. The results were evaluated on the basis of the established screening criteria and compared with the dose limits. Additionally, tritium concentrations in the bodies of workers in the vicinity of a heavy-water reactor was analysed to assess the association between tritium concentration and occupation. The results showed a wide distribution of tritium concentrations. Workers involved in fuel and maintenance tasks demonstrated the maximum exposure. Workers in medical facilities showed low levels of internal contamination, which was primarily related to tasks involving radioactive isotopes. Military personnel involved in equipment repair showed significant tritium contamination due to damage during repairs. Workers in educational and research institutions in general had low levels of internal contamination.
Incipient fault detection and condition assessment in DFIGs based on external leakage flux sensing and modified multiscale poincare plots analysis
Authors: Zhao Shouwang , Chen Yu , Liang Feng , Zhang Sichao , Shahbaz Nadeem , Wang Shuang , Zhao Yong , Deng Wei , Cheng Yonghong 
Published: October 2023

Abstract
Although doubly fed induction generators (DFIG) are widely used, difficulties in early fault detection and severity assessment for inter-turn short-circuit (ITSC) faults are highly prominent. In this manuscript, a novel incipient fault detection and state assessment method based on external leakage flux sensing and modified multiscale poincare plots (MMSPP) is proposed. An external leakage flux sensor is placed on the axial-end position of the generator to monitor the presence and evolution of ITSC faults. Multiscale poincare mapping is a novel nonlinear tool that is further developed and modified using the normal cumulative distribution function and multiscale computing methods to capture the behavior evolution and changes in the generator’s external leakage flux signals. The healthy indicator is based on the analysis of elliptical orbit features extracted from MMSPP, established by support vector data description with parameter optimization. The effectiveness of the proposed method was implemented and verified under an experimental environment on a 100 kW DFIG platform to detect incipient inter-turn short circuits (mainly considering the two-turn ITSC) and evaluate the performance degradation (three- to eight-turn ITSC) with 0%, 50%, and 100% different load conditions. The experimental results showed the viability of the approach and fault indicator for incipient fault detection and condition assessment of the wind generator’s low inter-turn insulation faults and for relative quantification of ITSC fault severity.
Designing architectured ceramics for transient thermal applications using finite element and deep learning
Authors: Kiyani Elham , Yazdani Sarvestani Hamidreza , Ravanbakhsh Hossein , Behbahani Razyeh , Ashrafi Behnam , Rahmat Meysam , Karttunen Mikko 
Published: October 2023

Abstract
Topologically interlocking architectures have demonstrated the potential to create durable ceramics with desirable thermo-mechanical properties. However, designing such materials poses challenges due to the intricate design space, rendering traditional modeling approaches ineffective and impractical. This paper presents a novel approach to designing high-performance architectured ceramics by integrating machine learning (ML) techniques and finite element analysis (FEA) data. The design space of interlocked architectured ceramics encompasses tiles with varying angles and sizes. The study considers three configurations

3
×
3

,

5
×
5

, and

7
×
7

arrays of tiles with five sets of interlocking angles

(

5
∘

,

10
∘

,

15
∘

,

20
∘

,

a
n
d

25
∘

)

. By training ML models, specifically convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) using FEA simulation data, we establish correlations between architectural parameters and thermo-mechanical characteristics. A grid comprising all possible designs was generated to predict high-performance architectured ceramics. This grid was then fed into the networks that were trained using results from the FEA simulation. The predicted results for all possible interpolated designs are utilized to determine the optimal structure among the configurations. The goal is to identify the optimal interlocked ceramics that minimize the out-of-plane deformation for thermal shielding and maximize heat absorption for heat sink applications. To validate the performance of the outcomes, FEA simulations were conducted on the best predictions obtained from both the MLP and CNN algorithms. Despite the limited amount of available simulation data, our networks demonstrate effectiveness in predicting the transient thermo-mechanical responses of potential panel designs. Notably, the optimal design predicted by CNN led to

≈
30
%

improvement in edge temperature.
Discretely-supported transfer nanoimprint anti-reflection nanostructures on complex uneven surface of Fresnel lenses
Authors: Wang Chunhui , Sun Jiaxing , Fan Yu , Shao Jinyou 
Published: October 2023

Abstract
Nanopatterning complex uneven surface of numerous functional devices to improve their performance is significantly appealing; however, it is extremely challenging. This study proposes a discretely-supported transfer nanoimprint technique to fabricate nanostructures on complex device surfaces containing multi-spatial frequencies. First, a discretely-supported nanoimprint template was designed based on the built energy criterion. A contact fidelity of over 99% was achieved between the designed template and the targeted complex uneven substrate surface. Next, the prefilled nanostructures on the template were transferred to the target surface after contact. By precisely controlling the amount of micro-droplet jetting on the template on-demand, the accumulation of the polymer in the micro-valley sites on the complex substrate was avoided, thus maintaining the morphology and generating function of the devices. Finally, high-quality Fresnel lenses with broadband wide-directional antireflection and excellent imaging performance were developed by imprinting subwavelength-tapered nanostructures on the relief surface.
Epsilon-near-zero material-based bi-layer metamaterials for selective mid-infrared radiation
Authors: Chen Ju , Liu Xianghui , Wang Pan , Xiao Chengyu , Chen Shaowen , Zhou Han 
Published: October 2023

Abstract
Selective mid-infrared (MIR) radiation is highly desirable in many applications. However, there are still great challenges to simultaneously achieve MIR camouflage and radiative cooling utilizing simple structure. This work theoretically and experimentally proposes a bi-layer metamaterial composed of aluminum doped zinc oxide (AZO) nanoparticles embedded in Al2O3 matrix on the aluminum film. The bi-layer metamaterial exhibits high performance in MIR camouflage with radiative cooling, a low emissivity (

ε

3
−
5
μ
m

= 0.11,

ε

8
−
14
μ
m

= 0.20) in atmospheric windows and a high emissivity (

ε

5
−
8
μ
m

= 0.81) in non-atmospheric windows. The interaction of the epsilon-near-zero (ENZ) mode and localized surface plasmon resonance (LSPR) mode is responsible for the perfect emission over the wavelength range of 5–8

μ

m. Additionally, the proposed selective MIR emitter supports large-angle incidence and has great polarization insensitivity. This demonstrates that epsilon-near-zero material-based bi-layer metamaterial is highly promising for the development of selective mid-infrared radiation.
Anti-motion imaging photoplethysmography via self-adaptive multi-ROI tracking and selection
Authors: Duan Yaran , He Chao , Zhou Mei 
Published: October 2023

Abstract

Objective. The imaging photoplethysmography (IPPG) technique allows people to measure heart rate (HR) from face videos. However, motion artifacts caused by rigid head movements and nonrigid facial muscular movements are one of the key challenges. Approach. This paper proposes a self-adaptive region of interest (ROI) pre-tracking and signal selection method to resist motion artifacts. Based on robust facial landmark detection, we split the whole facial skin (including the forehead, cheeks, and chin) symmetrically into small circular regions. And two symmetric sub-regions constitute a complete ROI. These ROIs are tracked and the motion state is simultaneously assessed to automatically determine the visibility of these ROIs. The obscured or invisible sub-regions will be discarded while the corresponding symmetric sub-regions will be retained as available ROIs to ensure the continuity of the IPPG signal. In addition, based on the frequency spectrum features of IPPG signals extracted from different ROIs, a self-adaptive selection module is constructed to select the optimum IPPG signal for HR calculation. All these operations are updated per frame dynamically for the real-time monitor. Results. Experimental results on the four public databases show that the IPPG signal derived by our proposed method exhibits higher quality for more accurate HR estimation. Compared with the previous method, metrics of the evaluated HR value on our approach demonstrates superior or comparable performance on PURE, VIPL-HR, UBFC-RPPG and MAHNOB-HCI datasets. For instance, the RMSEs on PURE, VIPL-HR, and UBFC-RPPG datasets decrease from 4.29, 7.62, and 3.80 to 4.15, 3.87, and 3.35, respectively. Significance. Our proposed method can help enhance the robustness of IPPG in real applications, especially given motion disturbances.
The spatial density distribution of H2O2 in the effluent of the COST-Jet and the kINPen-sci operated with a humidified helium feed gas
Authors: Harris B , Krös L , C Nave A S , Wagenaars E , van Helden J H 
Published: October 2023

Abstract
Cold atmospheric plasma jets operated with a helium feed gas containing small admixtures of water vapour are excellent sources of H2O2 for direct biomedical applications. However, H2O2 is typically distributed non-uniformly throughout the effluent region, meaning the dosage received by a patient or substrate is dependent on their positioning relative to the plasma source. This study presents the spatial distribution of absolute H2O2 number densities in the effluent of two popular plasma jets, the COST-Jet and the kINPen-sci plasma jet, when operated with a humidified helium feed gas. The measurements were performed using continuous wave cavity ring-down spectroscopy with a tunable, mid-infrared laser. The H2O2 number density measured close to the jet nozzle is 2.3

×

1014 cm−3 for the kINPen-sci plasma jet and 1.4

×

1014 cm−3 for the COST-Jet. The average number density of H2O2 in the effluent of the kINPen-sci plasma jet is a factor of two higher than in the effluent of the COST-Jet. The distribution of H2O2 in the COST-Jet effluent is initially highly uniform and suggests negligible mixing of H2O2 with the ambient air up to 15 mm from the jet nozzle, although it is rapidly diluted at further distances. In the case of the kINPen-sci plasma jet, the number density of H2O2 has a more pronounced radial distribution close to the nozzle, while the mixing with the ambient air is more gradual at further distances from the nozzle. It is evident that a detailed understanding of the H2O2 production in the plasma source, as well as of the transport of H2O2 to the substrate through the effluent, is required in order to optimise the intended effects. This work serves to highlight the difference of the distinct spatial distribution of H2O2 in the effluent of both types of plasma jets when considering their direct application in biomedicine.
Variable stiffness methods for robots: a review
Authors: Li Zhang , Chu Xiaoyu , Hu Xinye , Zhang Zhiyi , Li Nanpei , Li Junfeng 
Published: October 2023

Abstract
Traditional robots with constant stiffness demonstrate reliable output power and positioning precision, which may conversely reduce their flexibility and adaptability or even incur greater damage for accidental collisions with the environment or humans. Here, we review state-of-the-art robots with a variable stiffness mechanism, which is a key design concept that is widely used to improve robot reliability and impart new functionalities. To determine the similarities and differences between variable stiffness methods, we discuss the existing principles for variable stiffness of both rigid and soft robots, such as coupled and uncouple structures, thermal stimuli and magneto-rheological approaches. We hope this paper can help readers better understand these methods with regard to interesting applications. In addition, we also outline challenges and perspectives, where a simpler structure, larger band and faster response of stiffness modulation are required for robots in the future.
A superradiant two-level laser with intrinsic light force generated gain
Authors: Bychek Anna , Ritsch Helmut 
Published: October 2023

Abstract
The implementation of a superradiant laser as an active frequency standard is predicted to provide better short-term stability and robustness to thermal and mechanical fluctuations when compared to standard passive optical clocks. However, despite significant recent progress, the experimental realization of continuous wave superradiant lasing still remains an open challenge as it requires continuous loading, cooling, and pumping of active atoms within an optical resonator. Here we propose a new scenario for creating continuous gain by using optical forces acting on the states of a two-level atom via bichromatic coherent pumping of a cold atomic gas trapped inside a single-mode cavity. Analogous to atomic maser setups, tailored state-dependent forces are used to gather and concentrate excited-state atoms in regions of strong atom-cavity coupling while ground-state atoms are repelled. To facilitate numerical simulations of a sufficiently large atomic ensemble, we rely on a second-order cumulant expansion and describe the atomic motion in a semi-classical point-particle approximation subject to position-dependent light shifts which induce optical gradient forces along the cavity axis. We study minimal conditions on pump laser intensities and detunings required for collective superradiant emission. Balancing Doppler cooling and gain-induced heating we identify a parameter regime of a continuous narrow-band laser operation close to the bare atomic frequency.
Phase-locking an interferometer with single-photon detections
Authors: Hacker Bastian , Günthner Kevin , Rößler Conrad , Marquardt Christoph 
Published: October 2023

Abstract
We report on a novel phase-locking technique for fiber-based Mach–Zehnder interferometers based on discrete single-photon detections, and demonstrate this in a setup. Our interferometer decodes relative-phase-encoded optical pulse pairs for quantum key distribution applications and requires no locking laser in addition to the weak received signal. Our new simple locking scheme is shown to produce an Ornstein–Uhlenbeck dynamic and achieve optimal phase noise for a given count rate. In case of wavelength drifts that arise during the reception of Doppler-shifted satellite signals, the arm-length difference gets continuously readjusted to keep the interferometer phase stable.
Constraints on the ghost-free parity-violating gravity from laser-ranged satellites
Authors: Qiao Jin , Li Zhao , Ji Ran , Zhu Tao , Li Guoliang , Zhao Wen , Chen Jiming 
Published: October 2023

Abstract
This paper explores the evolutionary behavior of the Earth-satellite binary system within the framework of the ghost-free parity-violating gravity and the corresponding discussion on the parity-violating effect from the laser-ranged satellites. For this purpose, we start our study with the Parameterized Post-Newtonian (PPN) metric of this gravity theory to study the orbital evolution of the satellites in which the spatial-time sector of the spacetime is modified due to the parity violation. With this modified PPN metric, we calculate the effects of the parity-violating sector of metrics on the time evolution of the orbital elements for an Earth-satellite binary system. We find that among the five orbital elements, the parity violation has no effect on the semi-latus rectum, while the eccentricity and ascending node are affected only in a periodic manner. These three orbital elements are the same as the results of general relativity and are also consistent with the observations of the present experiment.
In particular, parity violation produces non-zero corrections to the eccentricity and pericenter, which will accumulate with the evolution of time, indicating that the parity violation of gravity produces observable secular effects. The observational constraint on the parity-violating effect is derived by confronting the theoretical prediction with the observation by the LAGEOS II pericenter advance, giving a constraint on the parity-violating parameter space from the satellite experiments.
A Galaxy Morphology Classification Model Based on Momentum Contrastive Learning
Authors: Shen Guoqiang , Zou Zhiqiang , Luo A-Li , Hong Shuxin , Kong Xiao 
Published: October 2023

Abstract
The taxonomy of galaxy morphology plays an important role in astrophysics and provides great help for the study of galaxy evolution. To integrate the advantages of unsupervised learning without labels and supervised learning with high classification accuracy, this paper proposes a galaxy morphology classification model based on a momentum contrastive learning algorithm named Momentum Contrastive Learning Galaxy (MCL-Galaxy), which mainly includes two parts (i) pre-training of the model, where the ResNet_50 backbone network acts as an encoder to learn the galaxy morphology image features, which are stored in the queue and their consistency is ensured by using the momentum contrastive learning algorithm; and (ii) transfer learning, where Mahalanobis distance can assist in improving classification accuracy in downstream tasks where both encoder and queue are transferred. To evaluate the performance of MCL-Galaxy, we use the data set of the Galaxy Zoo challenge project on Kaggle for comparative testing. The experimental results show that the classification accuracy of MCL-Galaxy can reach 90.12%, which is 8.12% higher than the unsupervised state-of-the-art results. Although it is 3.1% lower than the advanced supervised method, it has the advantage of no label and can achieve a higher accuracy rate at the first epoch of classification iteration. This suggests that the gap between unsupervised and supervised representation learning in the field of Galaxy Morphologies classification tasks is well bridged.
A Multimodal Transfer Learning Method for Classifying Images of Celestial Point Sources
Authors: Wang Bingjun , Hong Shuxin , Yuan Zhiyang , Luo A-Li , Kong Xiao , Zou Zhiqiang 
Published: October 2023

Abstract
A large fraction of celestial objects exhibit point shapes in CCD images, such as stars and QSOs, which contain less information due to their few pixels. Point source classification based solely on image data may lead to low accuracy. To address this challenge, this paper proposes a Multi-modal Transfer Learning-based classification method for celestial objects with point shape images. Considering that spectral data possess rich features and that there is a correlation between spectral data and image data, the proposed approach fully utilizes the knowledge gained from celestial spectral data and transfers it to the original image-based classification, enhancing the accuracy of classifying stars and QSOs. Initially, a one-dimensional residual network is employed to extract a 128-dimensional spectral feature vector from the original 3700-dimensional spectral data. This spectral feature vector captures important features of the celestial object. The Generative Adversarial Network is then utilized to generate a simulated spectral vector of 128 dimensions, which corresponds to the celestial object image. By generating simulated spectral vectors, data from two modals (spectral and image) for the same celestial object are available, enriching the input features of the model. In the upcoming multimodal classification model, we only require the images of celestial objects along with their corresponding simulated spectral data, and we no longer need real spectral data. With the assistance of spectral data, the proposed method alleviates the above disadvantages of the original image-based classification method. Remarkably, our method has improved the F1-score from 0.93 to 0.9777, while reducing the error rate in classification by 40%. These enhancements significantly increase the classification accuracy of stars and QSOs, providing strong support for the classification of celestial point sources.