bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structure and scaling of Kitaev chain across a quantum critical point in real space
Authors: He Yan , Chien Chih-Chun 
Published: July 2024

Abstract
The spatial Kibble–Zurek mechanism is applied to the Kitaev chain with inhomogeneous pairing interactions that vanish in half of the lattice and result in a quantum critical point separating the superfluid and normal-gas phases in real space. The weakly-interacting BCS theory predicts scaling behavior of the penetration of the pair wavefunction into the normal-gas region different from conventional power-law results due to the non-analytic dependence of the BCS order parameter on the interaction. The Bogoliubov–de Gennes (BdG) equation produces numerical results confirming the scaling behavior and hints complications in the strong-interaction regime. The limiting case of the step-function quench reveals the dominance of the BCS coherence length in absence of additional length scale. Furthermore, the energy spectrum and wavefunctions from the BdG equation show abundant in-gap states from the normal-gas region in addition to the topological edge states.
Transfer functions for QA
/QB
international regulatory limits for the safe transport of radioactive materials
Authors: Frosio Thomas , Thomas Samuel , Endres Janis , Eberhardt Holger , Louis Baptiste , Cabianca Tiberio , Brown Iain , Foster Matthew , Menaa Nabil , Bertreix Philippe 
Published: July 2024

Abstract
This paper presents a proposed revision of the International Atomic Energy Agency transport regulations, related to the A
1 and A
2 limit values used to determine the radioactive transport classification. Based on the ‘Q system’, a novel methodology was introduced to derive QA
and QB
values related to scenarios involving external exposure from a distant source. These values are key parameters that respectively represent the total effective dose and total equivalent dose to the skin, from all primary and secondary particles contributing to radiation exposure. The International Working Group (WG A
1/A
2) is established and associated with the TRANSSC Technical Expert Group on Radiation Protection. A review of the A
1 and A
2 values is performed in response to identified limitations within the existing Q system. The followed approach is based on Monte Carlo simulations that enabled the development of transfer functions aimed at reducing computational time and increasing the flexibility of dose evaluations for any radionuclide with known particle emission spectra. This method allows updating the QA
and QB
values to account for future data evolutions (decay data, fluence-to-dose conversion coefficients) and standardizing the calculation of regulation limits across all referenced radionuclides and scenarios related to external exposure. The transfer functions are established using three Monte Carlo simulation codes—FLUKA, Geant4, and MCNP—and address the previous limitations of the ‘Q system’, reflecting the latest International Commission for Radiation Protection recommendations and improvements in calculation techniques. The results of the WG show consistent agreement across the codes, with minor discrepancies observed at low primary energies due to statistical uncertainties and different handling of stopping power for electrons/positrons in the codes. This revised approach aligns with current standards and recommendations, ensuring that the radiological consequences of transport accidents are acceptable for the new A
1 and A
2 limits from a radiological protection perspective.
A dynamic object removing 3D reconstruction system based on multi-sensor fusion
Authors: Zhao Chenxi , Liu Zeliang , Pan Zihao , Yu Lei 
Published: July 2024

Abstract
Currently, one of the key technologies for autonomous navigation of unmanned mobile robots is SLAM, which faces many challenges in practical applications. These challenges include a lack of texture, deterioration in sensor performance, and interference from moving objects in dynamic outdoor environments, all of which have an impact on the mapping system. To address these issues, this paper proposes a framework for lidar, vision camera, and inertial navigation data, resulting in fusion and dynamic object removing. The system consists of three sub-modules: the Lidar-Inertial Module (LIM), the Visual-Inertial Module (VIM), and the Dynamic-Object-Removing Module (DORM). LIM and VIM assist each other, with lidar point clouds providing three-dimensional information for the global voxel map and the camera providing pixel-level color information. At the same time, the DORM performs synchronous dynamic object detection to remove dynamic objects from the global map. The system constructs a multi-sensor factor graph using the state and observation models, and the optimal solution is obtained using least squares. Furthermore, this paper employs triangle descriptors and bundle adjustment methods for loop closure detection in order to reduce accumulated errors and maintain consistency. Experimental results demonstrate that the system can perform clean state estimation, dynamic removing and scene reconstruction in a variety of complex scenarios.
Removing spikes in TianQin-1 inertial sensor data using machine learning method
Authors: Wang H M , Cai L , Shen Z Y , Zhou Z B 
Published: July 2024

Abstract
The spikes in the inertial sensor data have been found to impact on the retrieval of the non-gravitational signals and the evaluation of the inertial sensor performance. Removing the spikes in the inertial sensor is critical for studies of gravitational reference sensors in space-based gravitational wave detection missions and accelerometers in gravity satellite missions. Thanks to a long period of inertial sensor data without thruster spikes, we can conduct machine learning based on this data to remove spikes. In this paper, a machine learning model called bi-directional long short-term memory (Bi-LSTM) neural network was built based on the inertial sensor data of TianQin-1 (TQ-1) mission. We use the machine learning method to remove the spikes in the inertial sensor data. After removing the spikes in the inertial sensor data, acceleration noise is suppressed form 2.0 × 10

−
7

m s

−
2

Hz

−
1
/
2

to the 2.8 × 10

−
10

m s

−
2

Hz

−
1
/
2

at 0.1 Hz, which is far better than the existing methods, including the linear interpolation, data substitution and mean value of adjacent data.
A novel RUL prediction method for rolling bearings based on dynamic control chart and adaptive incremental filtering
Authors: Li Junxing , Wang Zhihua , Shen Lijuan 
Published: July 2024

Abstract
Degradation of rolling bearings typically consists of two stages: a stable stage (Stage I) characterized by stable fluctuations in the health indicator (HI), and a degradation stage (Stage II) where early damage leads to HI degradation, eventually reaching the failure threshold. Therefore, to achieve remaining useful life prediction for bearings, three aspects should be studied: (1) degradation modeling; (2) inter stage change point identification; (3) degradation state updating. Firstly, a two-stage degradation model is constructed by simultaneously considering inherent randomness, individual differences, and measurement errors. Then, a dynamic statistical process control (SPC) method is proposed to identify the change point from Stage I to Stage II. The SPC is designed to dynamically control limits based on the bearing’s condition monitoring data to prevent false alarms. An adaptive incremental filtering is proposed to update the degradation states by simultaneously considering the state increment and the dynamics of the system noise and measurement noise. The effectiveness of the proposed method is validated on 16 004 bearing test data and XJTU-SY bearing data. Results show that the proposed method can accuracy identify the change point and improve the accuracy of the prediction result during stage II.
Simulation data-driven fault diagnosis method for metro traction motor bearings under small samples and missing fault samples
Authors: Bi Kailin , Liao Aihua , Hu Dingyu , Shi Wei , Liu Rongming , Sun Changjiang 
Published: July 2024

Abstract
Traction motor bearings are crucial for guaranteeing the safe operation of metro vehicles. However, in the metro traction motor bearing fault diagnosis, there are usually problems of small samples and missing fault samples, leading to inaccurate results. Therefore, a novel bearing fault diagnosis method utilizing a track-vehicle-bearing coupled dynamic model and the improved deep convolutional generative adversarial network-multiscale convolutional neural network with mixed attention (IDCG-MAMCNN) model is proposed in this paper. The IDCG-MAMCNN model combines an improved deep convolutional generative adversarial network (IDCGAN) with a multi-scale convolutional neural network with mixed attention (MA-MCNN). Specifically, simulation data is first provided by the coupled dynamic model to supplement missing fault samples. Secondly, the IDCGAN, along with a training method that involves pre-training models with simulation samples and fine-tuning models with experimental samples, is introduced to generate high-quality samples and augment experimental samples under small samples. Lastly, the MA-MCNN serves as the classification model, trained with the augmented dataset comprising experimental, simulation, and generated samples. The fault diagnosis performance of the proposed method is evaluated on the experimental samples of two bearing datasets under small samples and various conditions of missing fault samples. It has been demonstrated by the experimental results that the proposed method exhibits robust fault diagnosis performance and generates high-quality samples under small samples and missing fault samples. Furthermore, the proposed method showcases its adaptability to different operation speeds.
ERT image reconstruction using marker region segmentation method
Authors: Li Bo , Yang Daoye 
Published: July 2024

Abstract
Inspired by image region segmentation method, a marking region segmentation iterative method is proposed to reconstruct sparse binary images of electrical resistance tomography. The grayscale matrix of the iteration process is mapped to another linear space for segmentation processing, adding the watershed thresholding of marking regions. In the iteration process for estimating conductivity distribution, the target regions are separated to avoid excessive segmentation effects. By applying this method in conjunction with the Landweber iterative model to solve the inverse problem of resistivity tomography imaging, more accurate binary images can be obtained, and the method exhibits superior convergence properties in comparison to the Landweber algorithm. To verify the reconstruction effectiveness of LW-TDIS method, numerical simulations and static experiments are conducted for comparison with three other methods. The results demonstrate that the proposed method effectively reduces reconstruction artifacts, improves reconstruction quality, and achieves better reconstruction performance.
CMOS-compatible Hf0.5Zr0.5O2-based ferroelectric memory crosspoints fabricated with damascene process
Authors: Coffineau Dorian , Gariépy Nicolas , Manchon Benoit , Dawant Raphaël , Jaouad Abdelatif , Grondin Etienne , Ecoffey Serge , Alibart Fabien , Beilliard Yann , Ruediger Andreas , Drouin Dominique 
Published: July 2024

Abstract
We report the fabrication of Hf0.5Zr0.5O2 (HZO) based ferroelectric memory crosspoints using a complementary metal-oxide-semiconductor-compatible damascene process. In this work, we compared 12 and 56 µm2 crosspoint devices with the 0.02 mm2 round devices commonly used as a benchmark. For all devices, a 9 nm thick ferroelectric thin film was deposited by plasma-enhanced atomic layer deposition on planarized bottom electrodes. The wake-up appeared to be longer for the crosspoint memories compared to 0.02 mm2 benchmark, while all the devices reached a 2Pr value of ∼50 µC cm−2 after 105 cycles with 3 V/10 µs squared pulses. The crosspoints stand out for their superior endurance, which was increased by an order of magnitude. Nucleation limited switching experiments were performed, revealing a switching time <170 ns for our 12 and 56 µm2 devices, while it remained in the µs range for the larger round devices. The downscaled devices demonstrate notable advantages with a rise in endurance and switching speed.
Physics and common senses of sci-fi movies and anime as the source of informal learning
Authors: Risdianto Norma Sidik , Khofifaturrohmah Anis , Kurniawan Rifqi Arif , Pancaningrum Rischa , Mubarok Abu Khamid 
Published: July 2024

Abstract
In this paper, we discuss the depiction of physical phenomena in movies and anime and how they might impact informal learning. We found that, to some extent, the physics shown in these media is accurate, though there are some deviations and exaggerations. Additionally, we observed that the physics presented in these movies and anime is often quite advanced. This discussion can inspire curious viewers to learn more about physics after watching them, highlighting the importance of informal sources for education nowadays.
Dosimetry of microbeam radiotherapy by flexible hydrogenated amorphous silicon detectors
Authors: Large Matthew James , Kanxheri Keida , Posar Jessie , Aziz Saba , Bashiri Aishah , Calcagnile Lucio , Calvo Daniela , Caputo Domenico , Caricato Anna Paola , Catalano Roberto , Cirio Roberto , Cirrone Giuseppe Antonio Pablo , Croci Tommaso , Cuttone Giacomo , De Cesare Gianpiero , De Remigis Paolo , Dunand Sylvain , Fabi Michele , Frontini Luca , Grimani Catia , Guarrera Mariacristina , Ionica Maria , Lenta Francesca , Liberali Valentino , Lovecchio Nicola , Martino Maurizio , Maruccio Giuseppe , Mazza Giovanni , Menichelli Mauro , Monteduro Anna Grazia , Morozzi Arianna , Moscatelli Francesco , Nascetti Augusto , Pallotta Stefania , Passeri Daniele , Pedio Maddalena , Petringa Giada , Peverini Francesca , Placidi Pisana , Quarta Gianluca , Rizzato Silvia , Sabbatini Federico , Servoli Leonello , Stabile Alberto , Thomet Jonathan Emanuel , Tosti Luca , Villani Mattia , Wheadon Richard James , Wyrsch Nicolas , Zema Nicola , Petasecca Marco , Talamonti Cinzia 
Published: July 2024

Abstract

Objective. Detectors that can provide accurate dosimetry for microbeam radiation therapy (MRT) must possess intrinsic radiation hardness, a high dynamic range, and a micron-scale spatial resolution. In this work we characterize hydrogenated amorphous silicon detectors for MRT dosimetry, presenting a novel combination of flexible, ultra-thin and radiation-hard features. Approach. Two detectors are explored: an n-type/intrinsic/p-type planar diode (NIP) and an NIP with an additional charge selective layer (NIP + CSC). Results. The sensitivity of the NIP + CSC detector was greater than the NIP detector for all measurement conditions. At 1 V and 0 kGy under the 3T Cu–Cu synchrotron broadbeam, the NIP + CSC detector sensitivity of (7.76 ± 0.01) pC cGy−1 outperformed the NIP detector sensitivity of (3.55 ± 0.23) pC cGy−1 by 219%. The energy dependence of both detectors matches closely to the attenuation coefficient ratio of silicon against water. Radiation damage measurements of both detectors out to 40 kGy revealed a higher radiation tolerance in the NIP detector compared to the NIP + CSC (17.2% and 33.5% degradations, respectively). Percentage depth dose profiles matched the PTW microDiamond detector’s performance to within ±6% for all beam filtrations except in 3T Al–Al due to energy dependence. The 3T Cu–Cu microbeam field profile was reconstructed and returned microbeam width and peak-to-peak values of (51 ± 1) μm and (405 ± 5) μm, respectively. The peak-to-valley dose ratio was measured as a function of depth and agrees within error to the values obtained with the PTW microDiamond. X-ray beam induced charge mapping of the detector revealed minimal dose perturbations from extra-cameral materials. Significance. The detectors are comparable to commercially available dosimeters for quality assurance in MRT. With added benefits of being micron-sized and possessing a flexible water-equivalent substrate, these detectors are attractive candidates for quality assurance, in-vivo dosimetry and in-line beam monitoring for MRT and FLASH therapy.
Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence prediction
*
Authors: Jiao Changzhe , Lao Yi , Zhang Wenwen , Braunstein Steve , Salans Mia , Villanueva-Meyer Javier , Hervey-Jumper Shawn L , Yang Bo , Morin Olivier , Valdes Gilmer , Fan Zhaoyang , Shiroishi Mark , Zada Gabriel , Sheng Ke , Yang Wensha 
Published: July 2024

Abstract

Objective. We aim to develop a Multi-modal Fusion and Feature Enhancement U-Net (MFFE U-Net) coupling with stem cell niche proximity estimation to improve voxel-wise Glioblastoma (GBM) recurrence prediction. Approach. 57 patients with pre- and post-surgery magnetic resonance (MR) scans were retrospectively solicited from 4 databases. Post-surgery MR scans included two months before the clinical diagnosis of recurrence and the day of the radiologicaly confirmed recurrence. The recurrences were manually annotated on the T1ce. The high-risk recurrence region was first determined. Then, a sparse multi-modal feature fusion U-Net was developed. The 50 patients from 3 databases were divided into 70% training, 10% validation, and 20% testing. 7 patients from the 4th institution were used as external testing with transfer learning. Model performance was evaluated by recall, precision, F1-score, and Hausdorff Distance at the 95% percentile (HD95). The proposed MFFE U-Net was compared to the support vector machine (SVM) model and two state-of-the-art neural networks. An ablation study was performed. Main results. The MFFE U-Net achieved a precision of 0.79 ± 0.08, a recall of 0.85 ± 0.11, and an F1-score of 0.82 ± 0.09. Statistically significant improvement was observed when comparing MFFE U-Net with proximity estimation couple SVM (SVMPE), mU-Net, and Deeplabv3. The HD95 was 2.75 ± 0.44 mm and 3.91 ± 0.83 mm for the 10 patients used in the model construction and 7 patients used for external testing, respectively. The ablation test showed that all five MR sequences contributed to the performance of the final model, with T1ce contributing the most. Convergence analysis, time efficiency analysis, and visualization of the intermediate results further discovered the characteristics of the proposed method. Significance. We present an advanced MFFE learning framework, MFFE U-Net, for effective voxel-wise GBM recurrence prediction. MFFE U-Net performs significantly better than the state-of-the-art networks and can potentially guide early RT intervention of the disease recurrence.
Plasma plume enhancement of a dual-anode vacuum arc thruster with magnetic nozzle
Authors: Qi Jiayun , Zhang Zhongkai , Zhang Zun , Ren Junxue , Wang Yibai , Wang Weizong , Tang Haibin 
Published: July 2024

Abstract
Vacuum arc thruster (VAT) is a type of pulsed electric propulsion device that generates thrust based on vacuum arc discharges, it has great candidate for micro-newton force applications in orbit. To improve both the thrust and longevity of the VAT, a novel dual-anode structure, comprising a central anode and a ring anode, was developed. We conducted an investigation into the plasma discharge and acceleration process within the influence of a magnetic nozzle. The dual-anode architecture resulted in a reduction in the initial plasma impedance, thereby enhancing ion current and velocity. Analysis of surface parameters during discharge revealed a synergistic mechanism between the two insulator-conducting films, enabling a co-cyclic distribution of energy and resistance fluctuations within the discharge. Consequently, the dual-anode setup demonstrated a lifespan extension of at least twofold. Comparative analyses of arc energy, plasma velocity, ion current, and thrust variations with magnetic field strength were conducted between the dual-anode and single-anode configurations under magnetic nozzle influence. Results showed that the dual-anode structure increased ion current and velocity when subjected to magnetic nozzle influence, resulting in a thrust increase of up to 303%. Additionally, we developed a theoretical model for the diffusion coefficient to elucidate the adaptive splitting phenomenon of the arc within the dual-anode structure under magnetic field influence. This model suggests that the dual-anode structure can achieve a more significant enhancement in beam current from the magnetic nozzle compared to the single-anode configuration.
A multi-stable rotational energy harvester for arbitrary bi-directional horizontal excitation at ultra-low frequencies for self-powered sensing
Authors: Masabi Sayed N , Fu Hailing , Flint James , Theodossiades Stephanos 
Published: July 2024

Abstract
A rotational multi-stable energy harvester has been presented in this paper for harnessing broadband ultra-low frequency vibrations. The novel design adopts a toroidal-shaped housing to contain a rolling sphere magnet which absorbs mechanical energy from bidirectional base excitations and performs continuous rotational movement to transfer the energy using electromagnetic transduction. Eight alternating tethering magnets are placed underneath its rolling path to induce multi-stable nonlinearity in the system, to capture low-frequency broadband vibrations. Electromagnetic transduction mechanism has been employed by mounting eight series connected coils aligned with the stable regions in the rolling path of the sphere magnet, aiming to achieve greater power generation due to optimized rate of change of magnetic flux. A theoretical model has been established to explore the multi-stable dynamics under varying low-frequency excitation up to 5 Hz and 3 g acceleration amplitudes. An experimental prototype has been fabricated and tested under low frequency excitation conditions. The harvester is capable of operating in intra-well, cross-well, and continuous rotation mode depending on the input excitation, and the validated physical device can generate a peak power of 5.78 mW with 1.4 Hz and 0.8 g sinusoidal base excitation when connected to a 405 Ω external load. The physical prototype is also employed as a part of a self-powered sensing node and it can power a temperature sensor to get readings every 13 s on average from human motion, successfully demonstrating its effectiveness in practical wireless sensing applications.
Metrological detection of entanglement generated by non-Gaussian operations
Authors: Barral David , Isoard Mathieu , Sorelli Giacomo , Gessner Manuel , Treps Nicolas , Walschaers Mattia 
Published: July 2024

Abstract
Entanglement and non-Gaussianity are physical resources that are essential for a large number of quantum-optics protocols. Non-Gaussian entanglement is indispensable for quantum-computing advantage and outperforms its Gaussian counterparts in a number of quantum-information protocols. The characterization of non-Gaussian entanglement is a critical matter as it is in general highly demanding in terms of resources. We propose a simple protocol based on the Fisher information for witnessing entanglement in an important class of non-Gaussian entangled states: photon-subtracted states. We demonstrate that our protocol is relevant for the detection of non-Gaussian entanglement generated by multiple photon-subtraction and that it is experimentally feasible through homodyne detection.