bluuebunny/crossref_metadata_2025_split huggingface dataset:
An elevation stochastic model constrained by C/N0 for GNSS real-time kinematic positioning in harsh environments
Authors: Li Yuan , Zhang Zhetao , He Xiufeng , Yuan Haijun , Zang Nan 
Published: September 2022

Abstract
When the global navigation satellite system (GNSS) is used for positioning in harsh environments with high occlusion and strong reflection, the signals are easily reflected, diffracted, or even blocked, which can lead to multipath and non-line-of-sight (NLOS) errors. Existing stochastic models do not fully consider the significant unmodeled errors, such as multipath and NLOS, in specific scenarios such as canyons. This paper systematically establishes the carrier-to-noise ratio (C/N0) template functions of the low-cost receiver for the first time and proposes an elevation stochastic model constrained by C/N0 (elevation-C/N0 model) by combining the two indicators (elevation angle and C/N0) with the idea of robust estimation. Then, real-time kinematic positioning (RTK) experiments are conducted to verify the effectiveness of the new model, and both static monitoring and urban kinematic situations are included. The results showed that: (a) in the static data, the average ambiguity fixed rate of the elevation-C/N0 model is 95.79%, which is 10.05%, 15.74%, and 12.57% higher than that of the equal-weight, elevation-based, and C/N0-based stochastic models, respectively. At the same time, only the new model consistently meets centimeter-level accuracy requirements in harsh environments. (b) In the kinematic data, compared with the three traditional stochastic models, the ambiguity fixed rate of the elevation-C/N0 stochastic model is increased by 58.33%, 19.79%, and 28.13%, respectively. The motion trajectory calculated by the elevation-C/N0 model is the smoothest out of all the models. In conclusion, compared with the traditional stochastic models, the elevation-C/N0 stochastic model is more applicable to both static and kinematic measurements in harsh environments. It can effectively mitigate the adverse effects of errors such as multipath and NLOS, increase the ambiguity fixed rate, and improve the positioning reliability to a certain extent, hence the proposed method has a better positioning performance.
A blended method incorporating a multi-model for pulse wind tunnel aerodynamic identification considering large-scale aircraft
Authors: Li Shichao , Wang Qinchao , Ma Guilin , Gao Hongli , Guo Liang , Liu Zihao , Shao Kaibo 
Published: September 2022

Abstract
Pulse wind tunnel aerodynamic tests are an important part of hypersonic aircraft design and development. With the development of hypersonic aircraft technology, the aircraft model in pulsed wind tunnels has gradually become large-scale and heavy-loaded. During the force measurement test, due to the influence of the increased size of the aircraft model, the real aerodynamic signal of the force measurement system is submerged by the interference signal. During the effective test time of several hundred milliseconds, it is impossible to obtain high-precision aerodynamic signals of the force measuring system using traditional signal-processing methods. Therefore, for the in-depth study of aerodynamic identification of short-time hypersonic pulsed wind tunnels, there is an urgent need for an identification algorithm that can adapt to the force measurement system of large-scale aircraft. In this paper, a new aerodynamic identification algorithm that combines a traditional signal-processing method and a deep neural network is proposed and applied to pulse combustion wind tunnels. The algorithm is mainly divided into signal preprocessing and deep learning. First, the original signal is decomposed into different sub-signals via variational modal decomposition (VMD), and then the real aerodynamic signal is obtained via convolutional neural network (CNN)-long short-term memory (LSTM) training. For different interference signals in the pulsed wind tunnel test, this algorithm innovatively designs VMD preprocessing and optimizes hyperparameters, to extract signal features for subsequent deep learning and to filter out interference components. To ensure the consistency between the validation and the application, the algorithm was verified using the suspension test bench during training, and satisfactory results were obtained. Finally, the algorithm is applied to the suspension force measurement system of a pulse combustion wind tunnel, and the aerodynamic identification results present satisfactory accuracy.
Fabrication and characterization of a multimodal 3D printed mouse phantom for ionoacoustic quality assurance in image-guided pre-clinical proton radiation research
Authors: Lascaud Julie , Dash Pratik , Schnürle Katrin , Bortfeldt Jonathan , Niepel Katharina , Maas Jessica , Würl Matthias , Vidal Marie , Hérault Joël , Landry Guillaume , Savoia Alessandro Stuart , Lauber Kirsten , Parodi Katia 
Published: September 2022

Abstract

Objective. Image guidance and precise irradiation are fundamental to ensure the reliability of small animal oncology studies. Accurate positioning of the animal and the in-beam monitoring of the delivered radio-therapeutic treatment necessitate several imaging modalities. In the particular context of proton therapy with a pulsed beam, information on the delivered dose can be retrieved by monitoring the thermoacoustic waves resulting from the brief and local energy deposition induced by a proton beam (ionoacoustics). The objective of this work was to fabricate a multimodal phantom (x-ray, proton, ultrasound, and ionoacoustics) allowing for sufficient imaging contrast for all the modalities. Approach. The phantom anatomical parts were extracted from mouse computed tomography scans and printed using polylactic acid (organs) and a granite/polylactic acid composite (skeleton). The anatomical pieces were encapsulated in silicone rubber to ensure long term stability. The phantom was imaged using x-ray cone-beam computed tomography, proton radiography, ultrasound imaging, and monitoring of a 20 MeV pulsed proton beam using ionoacoustics. Main results. The anatomical parts could be visualized in all the imaging modalities validating the phantom capability to be used for multimodal imaging. Ultrasound images were simulated from the x-ray cone-beam computed tomography and co-registered with ultrasound images obtained before the phantom irradiation and low-resolution ultrasound images of the mouse phantom in the irradiation position, co-registered with ionoacoustic measurements. The latter confirmed the irradiation of a tumor surrogate for which the reconstructed range was found to be in reasonable agreement with the expectation. Significance. This study reports on a realistic small animal phantom which can be used to investigate ionoacoustic range (or dose) verification together with ultrasound, x-ray, and proton imaging. The co-registration between ionoacoustic reconstructions of the impinging proton beam and x-ray imaging is assessed for the first time in a pre-clinical scenario.
Study of Stark broadening of krypton helium-β lines and estimation of electron density and temperature in NIF compressed capsules
Authors: Hill K W , Gao L , Kraus B F , Bitter M , Efthimion P C , Pablant N , Schneider M B , Thorn D B , Chen H , Kauffman R L , Liedahl D A , MacDonald M J , MacPhee A G , Scott H A , Stoupin S , Doron R , Stambulchik E , Maron Y , Lahmann B 
Published: September 2022

Abstract
The National Ignition Facility (NIF) diagnostic instrument manipulator (DIM) - based high resolution (dHIRES) x-ray spectrometer was used to measure the time evolution of the electron density (n
e) and temperature (T
e) in the hot spot of four NIF compressed capsules with 25 ps time resolution during the ‘stagnation’ phase. The electron density was inferred by comparing the measured Stark broadening of the krypton (Kr) Heβ spectral complex with theoretical calculations that include ion dynamic effects, and the electron temperature was inferred by comparing the measured ratio of the intensity of a dielectronically excited Li-like Kr line to the intensity of the Kr Heβ resonance line with calculations using the spectroscopic collisional radiative atomic model (SCRAM) and CRETIN collisional-radiative models. The inferred, time averaged n
e values mainly agree with n
e values from neutron diagnostics within uncertainties, but the neutron time-of-flight values of T
ion are consistently higher than dHIRES T
e values by 200–700 eV. The dHIRES measurements and measurement techniques, method of uncertainty analysis, and discussion of comparisons with measurements from neutron diagnostics are presented.
Numerical study of m = 2/n = 1 neoclassical tearing mode stabilized by the Ohkawa-mechanism-dominated current drive of electron cyclotron waves
Authors: Shi M L , Zheng P W , Yin L , He L H , Huang Q H , Zhong Y J , Deng S , Gong X Y 
Published: September 2022

Abstract
This paper reports a numerical study of the 2/1 neoclassical tearing mode (NTM) stabilized by the Ohkawa-mechanism-dominated current drive (OKCD) of electron cyclotron (EC) waves, and the results are compared with those of the traditional Fisch–Boozer mechanism dominated EC current drive (ECCD). The peak values, radial positions and radial widths of the driven current profiles by EC waves are passed to the modified Rutherford equation to study the effect of OKCD/ECCD on the 2/1 NTM. Well-localized current density profiles and large driven current can be achieved for 2/1 NTM stabilization in a low-aspect-ratio tokamak (R/a ∼ 2.7) by using OKCD. The optimal minimum EC powers are calculated for both OKCD and ECCD to fully stabilize the 2/1 NTM. We compare our results with those of ECCD to stabilize the 2/1 NTM, when the choices of magnetic field strength and gyrotron frequency are such that off-axis deposition on the high-field side is not practical to generate localized current effectively, so that the electron trapping effect is large and important. In this paper, we show that it is better to use lower gyrotron frequencies optimized for the Ohkawa mechanism to obtain a higher current drive efficiency for 2/1 NTM stabilization.
Origin of negative and excess capacitances in carrier selective contact-based silicon heterojunction solar cells: role of back contact in conductivity modulation
Authors: Nayak Mrutyunjay , Pandey Ashutosh , Mandal Sourav , Komarala Vamsi K 
Published: September 2022

Abstract
We have investigated the effect of back contact on the evolution of capacitance–voltage (C–V) characteristics in molybdenum oxide (MoO
x
) based Ag/ITO/MoO
x
/n-Si/LiF
x
/Al carrier selective contact (CSC) solar cells. From MoO
x
cell without i-a-Si:H back surface passivation layer, the negative capacitance (NC) is observed from low to high (100 Hz–500 kHz) frequency in forward bias C–V characteristics, whereas, with the i-a-Si:H layer, the positive excess capacitance is observed from 100 Hz to 50 kHz. The NC from a device is explained based on the bulk conductivity modulation in the c-Si wafer and mismatch of capture/emission dynamics of injected charge carriers at the back contact. With an additional back i-a-Si:H layer: (a) injection/extraction of majority/minority carriers is restricted, and the conductivity modulation is decreased, and (b) slowing down the movement of charge carriers increases the probability of carrier capture in the defect centers, which is reflected with the excess capacitance from a device. The carrier dynamics of the CSC cells were also analyzed under the light and the bias voltage to understand the C–V characteristics.
Disorder induced absorption enhancement of light in GaAs nanowire array
Authors: Shahnewaz Md , Iqbal Shahrin , Zunaid Baten Md , Ishfak Tahmid Md 
Published: September 2022

Abstract
Light absorption characteristics of vertically aligned GaAs nanowire (NW) arrays with disordered diameters and heights are investigated in this work using finite difference time domain (FDTD) analysis technique. By varying the random height ranges, an optimum variation range of 1000–2000 nm is obtained that provides the maximum average absorbance at different fill-factors of the arrays. An array having random heights of the NWs within the optimized range is found to have better absorbance for both normal and oblique incidence of light compared to the uniform height structure. Even for 45° incidence angle of light, average absorbance is obtained to be 2% higher for the random-height array, compared to the case of absorbance obtained for the uniform height structure. The proposed arrays having random diameters provide up to 12.8% improvement in short circuit current density whereas the random-height structure enhances the short circuit current density by 1.1% compared to the arrays having uniform diameter and height. The present work also provides an effective medium theory based theoretical model taking into account the random height variation of the arrays. The theoretically calculated values are found to be in good agreement with FDTD simulation results, thereby providing further guidelines for designing random array based high performance photonic devices.
Learning-based landmark detection in pelvis x-rays with attention mechanism: data from the osteoarthritis initiative
Authors: Pei Yun , Mu Lin , Xu Chuanxin , Li Qiang , Sen Gan , Sun Bin , Li Xiuying , Li Xueyan 
Published: September 2022

Abstract
Patients with developmental dysplasia of the hip can have this problem throughout their lifetime. The problem is difficult to detect by radiologists throughout x-ray because of an abrasion of anatomical structures. Thus, the landmarks should be automatically and precisely located. In this paper, we propose an attention mechanism of combining multi-dimension information on the basis of separating spatial dimension. The proposed attention mechanism decouples spatial dimension and forms width-channel dimension and height-channel dimension by 1D pooling operations in the height and width of spatial dimension. Then non-local means operations are performed to capture the correlation between long-range pixels in width-channel dimension, as well as that in height-channel dimension at different resolutions. The proposed attention mechanism modules are inserted into the skipped connections of U-Net to form a novel landmark detection structure. This landmark detection method was trained and evaluated through five-fold cross-validation on an open-source dataset, including 524 pelvis x-ray, each containing eight landmarks in pelvis, and achieved excellent performance compared to other landmark detection models. The average point-to-point errors of U-Net, HR-Net, CE-Net, and the proposed network were 3.5651 mm, 3.6118 mm, 3.3914 mm and 3.1350 mm, respectively. The results indicate that the proposed method has the highest detection accuracy. Furthermore, an open-source pelvis dataset is annotated and released for open research.
Reliability test of fully printed and flexible organic electrolyte-based supercapacitor
Authors: Rokaya Chakra , Keskinen Jari , Lahokallio Sanna , Lupo Donald 
Published: September 2022

AbstractAs the demand for supercapacitors in various flexible and wearable energy sectors grows, reliability becomes a key aspect to consider. We report the fabrication and reliability study of printed, flexible organic electrolyte-based supercapacitors. The supercapacitor can be operated over a wide temperature range from −40 °C to 100 °C with excellent repeatability and stability. Thermal shock tests led to a defect in the electrode layer’s microstructure, which reduces the supercapacitor performance. Cyclic bending experiments show that the device has excellent robustness, mechanical flexibility, long-term electrical stability, and 100% capacitance retention up to 10 000 bending cycles with a bending radius of 0.41 cm. Thus, the device is suitable for wearable and flexible energy storage applications over a wide temperature range.
Layer-structured NiFe nanosheets on CoNi nanowires for enhanced oxygen evolution reaction
Authors: Guo Yichuan , Gong Zizhen , Yu Hang , Liu Guihua , Zhang Zisheng , Wu Changcheng , Li Jingde 
Published: September 2022

Abstract
Efficient electrocatalysts are critical for the oxygen evolution reaction (OER) that occurs during water electrolysis. Herein, a simple and low-cost strategy of assembling CoNi nanowire arrays with NiFe nanosheets on flexible carbon cloth (CC) support as an efficient OER catalyst is developed. This unique ‘nanosheets on nanowires’ structure design increases its specific surface area, enabling access to more active sites. The resulting NiFe@H-CoNi/CC catalyst exhibits excellent OER activity (280 mV overpotential at 100 mA cm−2) with a Tafel slope of 36 mV dec−1 and also has outstanding durability at high current operation conditions (over 100 h at 100 mA cm−2). Moreover, in-situ Raman analysis suggests that the NiOOH is the realistic OER active phase. This ‘nanosheet on nanowire’ design gives a means for fabricating OER catalysts that are both high-performance and long-lasting.
Electric field – velocity relation for strongly coupled Fröhlich polaron in emerging 2D materials
Authors: Singh Ramji , Dutta Mitra , Stroscio Michael A 
Published: September 2022

Abstract
In this paper we evaluate the electric field—velocity relation for strong electron—LO phonon coupled 2D materials namely

Hf

S

2

,

HfS

e

2

,

Zr

S

2

and

ZrS

e

2

by applying Thornber—Feynman (TF) formalism applied to the case of the 2D Fröhlich polaron. We demonstrate that the generally-accepted Fermi’s Golden Rule based approach breaks down for these strongly coupled materials. We predict

ZrS

e

2

has highest mobility of 449

c

m

2

/
V

-s at room temperature followed by

HfS

e

2

with 240

c

m

2

/
V

-s whereas other materials have below 100

c

m

2

/
V

-s.
Low apparent temperature increases the number of epilepsy-related clinic visits in a humid subtropical region: a time-series study
Authors: Sun Haoxiang , Zhang Xiaoyu , Wang Linlin , Tao Min , Wang XiaoSong , Li Kun , Zhao Shuang , Hu Yue , Hu Huaqing 
Published: September 2022

Abstract

Background. At present, the effect of apparent temperature (AT) on epilepsy has not been confirmed. Therefore, we conducted this study in Hefei, China, a city in a humid subtropical region, to investigate the effects of AT on the daily number of epilepsy-related clinic visits. Methods. A time-series analysis of the number of daily epilepsy clinic visits during 2015–2020 was performed using a quasi-Poisson regression model combined with a distributed lag nonlinear model (DLNM). Time trends, days of the week, relative humidity, and PM2.5 concentration were adjusted for in the model. Subgroup analyses were performed by sex and age. Results. A total of 28,020 epilepsy-related clinic visits were reported by the hospital during the study period. Low AT showed significant negative and delayed effects on the number of epilepsy clinic visits, but no such effects were observed with high AT. The median apparent temperature (17 °C) was used as the reference, and the single-day lag effect of low AT (5th percentile, −1.5 °C) on the number of epilepsy clinic visits peaked on lag day 1, with a relative risk (RR) of 1.055 (95% CI: 1.015–1.097). The cumulative effect of low AT was most obvious on lag days 0–12, with a maximum RR of 1.451 (95% CI: 1.180–1.783). Males and young adults (0–14 years and 15–29 years) were more likely to be affected by low AT. Conclusions. We found that low AT led to an increase in the number of epilepsy-related clinic visits. This result provides an important scientific basis for the allocation of outpatient medical resources and the development of interventions.
Exploring local adaptation to small hydropower closure scenarios: evidence from a giant panda nature reserve in Sichuan, China
Authors: Zhang Yijing , Shen Jinyu , Song Zhenjiang , Duan Wei , Zhou Wei 
Published: September 2022

Abstract
Given China’s ecological priority principle of nature reserve development, reconciling rural livelihood and ecological conservation is challenging. Although the closure of small hydropower in the nature reserve is an absolute necessity, a minimum requirement for its success is the active participation of local communities. Due to the remote location and historical reasons, some communities in giant panda nature reserves still depend highly on small hydropower for living. There is little guidance on how these vulnerable communities experience the closure of small hydropower and how these grass-root voices could contribute to the local adaptation plan. Hence, a case study using exploratory participatory scenarios analysis in Wawushan giant panda nature reserve was conducted to explore local adaptation strategies. The scenario-building process brought out four possible but diverse future development options and reflected local stakeholders’ corresponding beliefs regarding their adaptation strategies. The study further provided insights into underlying policy options and highlighted the importance of engagement and knowledge exchange between stakeholders to achieve better collaboration and reciprocity for the sustainable development of nature reserves.
Data-centric machine learning in quantum information science
Authors: Lohani Sanjaya , Lukens Joseph M , Glasser Ryan T , Searles Thomas A , Kirby Brian T 
Published: September 2022

Abstract
We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a ‘toy model’ demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.
Fully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo
Authors: Jha Jayant , Hashemi Meysam , Vattikonda Anirudh Nihalani , Wang Huifang , Jirsa Viktor 
Published: September 2022

Abstract
Virtual brain models are data-driven patient-specific brain models integrating individual brain imaging data with neural mass modeling in a single computational framework, capable of autonomously generating brain activity and its associated brain imaging signals. Along the example of epilepsy, we develop an efficient and accurate Bayesian methodology estimating the parameters linked to the extent of the epileptogenic zone. State-of-the-art advances in Bayesian inference using Hamiltonian Monte Carlo (HMC) algorithms have remained elusive for large-scale differential-equations based models due to their slow convergence. We propose appropriate priors and a novel reparameterization to facilitate efficient exploration of the posterior distribution in terms of computational time and convergence diagnostics. The methodology is illustrated for in-silico dataset and then, applied to infer the personalized model parameters based on the empirical stereotactic electroencephalography recordings of retrospective patients. This improved methodology may pave the way to render HMC methods sufficiently easy and efficient to use, thus applicable in personalized medicine.