bluuebunny/crossref_metadata_2025_split huggingface dataset:
A temperature compensation method for fiber Bragg gratings using negative thermal expansive β-eucryptite packaging
Authors: Qiao Yujing , Zhang Wen , Chen Guanghui , Wen Ke , Zhu Lianqing 
Published: December 2024

Abstract
Fiber optic sensing measurements hold significant potential for various applications, but temperature compensation of fiber Bragg grating (FBG) remains a formidable challenge. This paper introduces the utilization of β-eucryptite as an encapsulation material for FBG, exploiting its negative thermal expansion property to achieve temperature compensation. A two-point adhesive sealing technique was employed to affix FBG to the β-eucryptite substrate. While bare FBG and sensors encapsulated in 7075 aluminum alloy served as comparison. Experimental outcomes reveal that the FBG encapsulated in β-eucryptite exhibits temperature sensitivities of 1.67 pm °C−1 within the temperature ranges of −30 °C to 150 °C. In contrast, bare FBG and sensor encapsulated with 7075 aluminum alloy display temperature sensitivities of 10.46 pm °C−1 and 37.31 pm °C−1, respectively. The use of β-eucryptite for sensor encapsulation effectively reduces the temperature sensitivity of FBG. This temperature compensation method is straightforward yet promising and has great potential in aerospace and precision instrument measurement.
Dynamic behavior of permanent magnet synchronous motor rotor radial eccentricity
Authors: Huang Fengchao , Wang Zhengqi , Chen Changzheng , Wang Tong , Yang Mingzheng , Fu Hao 
Published: December 2024

Abstract
With the rapid advancement of new energy vehicles, permanent magnet synchronous motors are being increasingly utilized. Among the various faults that can occur, rotor eccentricity fault in permanent magnet synchronous motors is a common issue. This study utilizes the finite element method and the Timoshenko beam element theory to develop a dynamic model of the rotor. It also establishes models for the hybrid eccentric unbalanced magnetic pull (UMP) and the bearing restoring force. It was found that the rotation frequency component in the spectrum response diagram under fault conditions appears a ‘peak’ phenomenon in the subcritical speed region. By analyzing the dynamic characteristics of the system when the permanent magnet synchronous motor rotor experiences radial eccentricity fault, it is observed that the UMP leads to an increase in the bearing contact load. The system’s spectrum response is primarily influenced by the rotor rotation frequency and frequency multiplication components due to the coupling of UMP and bearing restoring force. This paper investigates the influence of permanent magnet residual magnetism on the vibration characteristics associated with rotor eccentric faults. The findings indicate that the magnitude of the residual magnetism is positively correlated with the amplitude of frequency doubling. Additionally, this study includes experimental research that corroborates the accuracy of the theoretical model. The conclusions presented in this article offer a theoretical foundation for the detection and diagnosis of radial eccentricity faults in permanent magnet synchronous motor rotors.
Origami-inspired triboelectric sensor (TES) for shape detection of underwater thruster
Authors: Pan Xueting , Li Cao , Yang Fei , Zhao Yong , Yue Honghao , Geng Zhongtai 
Published: December 2024

Abstract
The shape change of the thruster can reflect its motion state, and motion control of the thruster can be carried out more effectively by monitoring its motion state. The unique structure of the origami thruster increases the difficulty of sensor configuration, which significantly limits the application of origami structures (ORSs) in the thruster field. To solve the above problems, we propose a triboelectric sensor (TES) based on a triboelectric nanogenerator, which can be perfectly matched with an ORS and used to monitor the shape of an underwater thruster. The TES proposed can sense the folding angle of the structural unit of the origami thruster and then monitor its shape. The effects of different friction layer materials, different friction layer thicknesses, and multiple pairs of friction subs in series/parallel were analyzed on TES output characteristics. A scheme with thin copper sheets as electrodes, the polyethylene terephthalate resin as dielectric layers, and four pairs of friction pairs connected in parallel were selected for sensor performance testing. Finally, the origami thruster and TES are assembled for underwater testing. The shape of the origami thruster was monitored using the TES. The dihedral angle that TES can detect ranges from 22.5° to 63.5°, corresponding to the radius variation of the thruster ranging to 44.4%. The maximum error between the output result and the measured value was 4.19%, which proves that TES has a good detection effect on the shape change of the thruster and has a broad application prospect in robot motion monitoring.
Gold coating reduces thermal conductivity of oriented polypropylene fibers
Authors: Zhou Yupeng , Yin Hao , Hu Xue , Zhao Gutian , Yang Juekuan 
Published: December 2024

Abstract
Self-heating methods are widely used in thermal conductivity measurements of polymer fibers. In these methods, a thin metallic film needs to be coated on the fibers if the fibers are electrical insulators. In this paper, the thermal conductivities of oriented polypropylene (PP) fiber samples before and after gold coating were measured. The results show that the gold-coating process can reduce the thermal conductivity of oriented PP fibers by about 50%. Therefore, the self-heating methods are not appropriate for thermal conductivity measurements of insulative polymers. This finding is important to the research of thermal conductive polymers.
Traceability uncertainty evaluation of electronic transformer calibrators based on DREAM-integrating Sparrow Search
Authors: Chen Mianzhou , Li Hongbin , Zhang Chuanji , Du Yufei 
Published: December 2024

Abstract
Accurate traceability uncertainty evaluation guarantees the effectiveness and reliability of electronic transformer calibrators. The traceability uncertainty evaluation of electronic transformer calibrators using the guide to uncertainty in measurement (GUM) method ignores the type and width differences of the probability distribution of the inputs as well as the correlation among the inputs. To solve this problem, which impacts the accuracy and reliability of the evaluation results, a Bayesian uncertainty evaluation model based on DREAM-integrating Sparrow Search (SDREAM) is proposed. The proposed model organically combines Bayesian statistical inference with the Monte Carlo method (MCM) sampling idea, imitating the posterior probability density of multidimensional parameters with the multi-chain sampling of Markov chain Monte Carlo. Based on the multi-chain sampling of DREAM, the proposed model integrates the idea of sparrow finder search, improves the mutation strategy, and dynamically updates the crossover probability factor to balance the global and local search capabilities, which enhances the search efficiency and accuracy of the posterior probability density space of the indirectly measured multidimensional parameters. Moreover, a multidimensional posterior probability density sampling simulation example is presented based on the traceability of the digital differential method of electronic transformer calibrators. The simulation results show that SDREAM outperforms DREAM. The proposed model is also used to evaluate the traceability uncertainty of the digital differential method of electronic transformer calibrators. The obtained results are consistent with the results of the GUM and MCM methods but exhibit higher stability and good robustness.
Assessing the precision and accuracy of cloud removal and satellite angle correction techniques for SST retrieval in the South Sea with MODIS
Authors: Fan Bowen , Qin Zhiliang , Li Hui 
Published: December 2024

Abstract
Cloud cover and satellite angle significantly impact sea surface temperature (SST) retrievals from remote sensing imagery, yet traditional methods often overlook these factors. This study takes Moderate Resolution Imaging Spectroradiometer remote sensing imagery from the northern South China Sea in winter as a case study to investigate the effects of cloud cover and satellite angle on SST retrieval. By employing adaptive cloud detection, cloud masking, and angle correction techniques, a SST retrieval model based on the split-window algorithm was established and compared with microwave and in-situ data. The research results indicate that: (1) The established cloud removal and satellite angle correction model for SST retrieval demonstrates high precision and accuracy, with an average error of less than 0.5 °C. (2) Combination a (cloud removal + angle correction), Combination b (non-cloud removal + angle correction), and Combination c (non-cloud removal+ non-angle correction) have an average error of −0.916 °C, −0.311 °C, −1.047 °C respectively. It is evident that the inversion results of the proposed model (Combination a) exhibit a higher level of agreement with the measured data compared to t Combination b and Combination c. (3) The average error of the microwave inversion data (combination x) is 0.560 °C; Furthermore, combination x shows a different temperature curve trend compared to the measured data, whereas combination a aligns more closely with the temperature curve trend of the measured data. Accurate retrieval of SST is of great significance for understanding large-scale oceanic circulation systems, boundary currents, eddies, and ocean currents, as well as studying seasonal and interannual variations in marine ecosystems.
Effects of viscous damping on the bandgaps of multi-frequency periodic structure
Authors: Wang Jun , Li Guizhong , Huang Wenhao , Zhang Binbin 
Published: December 2024

Abstract
Locally resonant elastic metamaterials (EMMs) have the ability to mitigate or even completely suppress the propagation of vibration within bandgaps, holding promising potential in the field of vibration control. However, achieving broadband vibration suppression remains a significant challenge for EMMs based on single resonance. To address this issue, we propose a scheme employing multiple resonators and introduce a damping mechanism to attenuate vibrations outside the bandgap frequency domain, aiming at wideband vibration control. The effectiveness of this approach is verified through a combination of analytical modeling and numerical analysis. By adjusting the damping characteristics of the internal resonators, we successfully extend the frequency range of vibration attenuation beyond the limitations of traditional designs. Using a mass–spring model coupled with a Kelvin–Voigt type oscillator damping model, an analytical framework is established for the dissipative multi-resonator unit and validated against published experimental data. Further parametric studies reveal the influence of additional loss coefficients on vibration attenuation performance. By optimizing the damping coefficients, the vibration attenuations are enhanced within 0–7500 Hz while maintaining a high attenuation rate near the resonance frequencies. This research provides new theoretical insights and practical guidance for the design and application of dissipative multi-resonator EMMs, holding promise for advancing broadband vibration and elastic wave suppression technologies.
A double neural network model predictive control method for series elastic actuator’s motor thermal protection
Authors: Zhang Anlong , Lin Zhiyun , Wang Bo , Han Zhimin 
Published: December 2024

Abstract
Traditional motor protection methods are conservative, neglecting actual thermal conditions and underutilizing the motor’s performance. This paper proposes a novel double neural network model predictive control (DNMPC)-based protection method to maximize motor performance. First, we analyze the system’s thermal properties and propose a thermal lemma to demonstrate that solving the protection problem involves determining the maximum allowable control input boundary. Second, instead of directly measuring the motor temperature, the neural network (NN) method is proposed to establish the system’s thermal model for estimating the motor temperatures. Third, leveraging the NN model, we propose a model predictive control strategy designed to determine the maximum allowable control input boundaries, which we refer to as the neural network model predictive control (NMPC) method. Consequently, this approach has led to the development of an advanced motor protection system. To further reduce the computational burden, we incorporate an additional NN to directly approximate the NMPC controller, thereby forming our proposed DNMPC approach. Finally, comparative experimental validation demonstrates the effectiveness and superiority of the proposed method. The results indicate that the NN model is highly accurate, eliminating the need for direct temperature measurements and enabling the prediction of future motor temperatures. Additionally, the DNMPC-based protection method significantly improves the joint’s peak torque output by 64.3

%

compared to conservative continuous current limited protection methods. The computational efficiency is also notable, with the NMPC achieving a runtime of approximately 20 ms, while the DNMPC achieves only 0.2 ms.
Improved singular spectrum decomposition method for resonance recognition of air-coupled ultrasonic signals in through-transmission steel plate detection
Authors: Li Rongxue , Yang Lijian , Zhang Jia , Lian Zheng 
Published: December 2024

Abstract
Air-coupled ultrasonic resonance detection is an emerging technique that eliminates the need for coupling agents in conventional ultrasonic testing and has a wide range of applications. However, this technique faces challenges, such as weak signal strength and susceptibility to noise, primarily due to the mismatch of acoustic impedance at the air–solid interface and sound attenuation in the air. This study proposed an improved signal processing method based on singular spectrum decomposition (SSD), optimizing the frequency components of each iteration using singular spectrum analysis. Additionally, a weighted Hurst exponent was introduced as a selection criterion to identify useful components and reconstruct the signal. Simulation results demonstrated the effectiveness of this method in denoising components and extracting weak resonance frequency features, especially in low signal-to-noise ratios (SNRs). Compared with conventional SSD and empirical mode decomposition, the proposed method improved the SNR by 39.4% and 301.33%. Furthermore, repeated experimental studies on steel plates with varying air lift-off distances and thicknesses confirmed that the proposed method could effectively reduce noise, thereby enabling accurate measurement of steel plate thickness.
Spatiotemporal-varying tropospheric stochastic model for GNSS meteorology enhancement
Authors: Xiao Yingchao , Zhan Xingqun , Zhai Yawei 
Published: December 2024

Abstract
Accurately estimating the zenith wet delay (ZWD) is critical in Global Navigation Satellite System (GNSS) meteorology. ZWD is typically modeled as a random walk process with spatiotemporal-invariant process noise in the Kalman filter using precise point positioning. However, this approach is not rigorous due to the notable geographical and seasonal variations in water vapor content. In response, we propose a spatiotemporal-varying ZWD stochastic model to improve the ZWD estimation accuracy, thereby enhancing the GNSS meteorology. First, 15 years of ZWD data are employed to derive the process noise. Next, the model is established using a trigonometric-based fitting function. Finally, hierarchical clustering is implemented for computational efficiency improvement. Experiments at 26 test stations indicate that the maximum ZWD accuracy is improved by 34.95% under dynamic mode and 22.67% under static mode. Moreover, the maximum data availability of GNSS meteorology is improved by 10.56% and 4.55% under dynamic and static modes, respectively.
Prediction of wheel-rail force and vehicle safety index using genetic algorithm–based backpropagation neural network with physics-based inversion model
Authors: Zhang Yanyan , Yang Xinwen , Sun Zhiang , Mao Ke , Xiang Kaiwen , Ye Zi , Qu Yingtao 
Published: December 2024

Abstract
Wheel-rail force is a crucial indicator for vehicle safety and stability in wheel-rail interactions. To predict and display the continuous wheel-rail force and vehicle safety index efficiently and in real time, a multiple-input, multiple-output backpropagation neural network (BPNN) for wheel-rail force and vehicle safety index prediction based on a physical inversion model is developed. The physics-based inversion model calculates wheel-rail forces by using the wheelset inertia force, the primary suspension displacement, and the Nadal derailment criterion. Vehicle safety indices such as wheel derailment coefficient and wheel unloading rate are estimated using the known wheel-rail forces. This physics-based model suggests a nonlinear inversion mapping from the input to the output for constructing the BPNN. Meanwhile, it is a low-cost method to collect training and test samples, and is also used as a training tool for the neural network. A genetic algorithm (GA) is introduced to optimize the initial weight and bias in the BPNN to enhance the network converge speed and prediction performance. The physics-based model is implemented in the field experimental tests conducted on a subway line in China to construct the sampled data. After the BPNN and GA-optimized BPNN (GA-BPNN) are trained, tested, and tuned based on the experimental data, it proves that the BPNN can predict the desired output reliably and that the GA-BPNN performs more accurately than BPNN. The wheel-rail force and vehicle safety index prediction model proposed in this paper can contribute to the development of vehicle intelligent diagnosis and fault warning platforms in the future.
Real-time undifferenced precision time transfer of multi-GNSS and multi-frequency
Authors: Xu Wei , Yan Chao , Zhang Pengfei , Chen Jian , Qiu Duobing , Liang Lei , Song Jia , Zhao Lewen , Tao Jing 
Published: December 2024

Abstract
Currently, the four major Global Navigation Satellite Systems (GNSS) are capable of supporting multi-frequency signals, and Real-time (RT) State Space Representation (SSR) correction products have been enriched and improved. Multi-GNSS, multi-frequency observations and multiple satellite RT SSR correction products present new opportunities to implement precise RT time transfer. This study extends the traditional post-processing dual-frequency undifferenced (UD) Precise Point Positioning (PPP) time transfer to RT multi-frequency scenarios, establishes models for dual-frequency, triple-frequency, quad-frequency and penta-frequency multi-GNSS PPP time transfer, and evaluates the product availability, orbit accuracy and clock offset accuracy of RT SSR products from BKG, CNE, DLR, ESA, GFZ, GMV, WHU, and IGS. A multi-frequency and multi-GNSS RT PPP time transfer experiment was conducted by selecting data from four stations, BRUX, PTBB, ONSA and SPT0, with precise atomic clocks mounted for one week. The accuracy of various SSR products in RT PPP time transfer was compared, and the performance of BDS/Galileo combination dual-frequency to penta-frequency RT UD PPP time transfer was evaluated. The experimental results show that the standard deviation and frequency stability difference of RT PPP time transfer among each SSR product is relatively small, especially after combining multi-systems. The frequency stability tends to be entirely consistent. In general, the WHU, GFZ, GMV, and WHU SSR products offer notable advantages in terms of accuracy and frequency stability for GPS, GLONASS, Galileo, and BDS RT PPP time transfer, respectively. Multi-GNSS and multi-frequency signals have both enhanced RT PPP time transfer performance, with a particularly noticeable improvement in terms of accuracy and short-term frequency stability. The effect of multi-GNSS on RT PPP time transfer is more pronounced than that of multi-frequency alone. Considering the higher costs associated with multi-frequency GNSS receivers, prioritizing multi-GNSS for RT PPP time transfer is recommended.
Research on the mechanism of emissivity enhancement by the structure of the blackbody cavity
Authors: Zhou Jingjing , Song Jian , Guo Guorui , Xu Chunyuan , Xu Xin , Hao Xiaopeng 
Published: December 2024

Abstract
The spaceborne blackbody with high emissivity can provide accurate infrared radiation to calibrate infrared payloads. To meet the need for improving the accuracy of infrared value traceability, this paper investigates the mechanism of enhancing emissivity through the structure of the blackbody cavity. The directional emissivity calculation based on Monte Carlo optical simulation was carried out. The normal and directional emissivity distributions of cylindrical cavity blackbody and trap-cavity blackbody (TCB) were simulated and compared. The emissivity of the 8–14 μm band was measured. The simulation and experimental results indicate that TCBs demonstrate better central normal emissivity and cylindrical cavity blackbodies exhibit higher variations in emissivity. The mechanism of high emissivity for two types of blackbodies by controlling the number and direction of light reflection is analyzed. Furthermore, we propose a blackbody design method with high-level emissivity by investigating the influence of cavity structure and reflection characteristics on blackbody emissivity, which has a high normal emissivity of 0.9997 and small variations in directional emissivity of 0.0002. The study provides theoretical and experimental support for the development of large aperture high emissivity spaceborne blackbody.
Air-stable, aluminium oxide encapsulated graphene phototransistors
Authors: Rojas Castiglione Tomás , Pucher Thomas , Dockx Kaj , Aburto Contreras Guillermo , Sanz Biava Diego , Briceño Elchiver Benjamín , Buscema Michele , Castellanos-Gomez Andres , van der Zant Herre , Dulić Diana 
Published: December 2024

Abstract
Graphene has garnered significant interest in optoelectronics due to its unique properties, including broad wavelength absorption and high mobility. However, its weak stability in ambient conditions requires encapsulation for practical applications. In this study, we investigate graphene CVD-grown field-effect transistors fabricated on

Si

/

SiO
2

wafers, encapsulated with aluminum oxide (

Al
2

O
3

) of different thicknesses. We measure and analyze their optoelectronic response across wavelengths from near-ultraviolet to near-infrared. We find that, while having a negligible role in the photogating process, the

Al
2

O
3

layer leads to stable and reproducible transferring curves operating in ambient conditions for over a month, with stable responsivities up to 1.5

A W

−
1

at the shortest wavelength. Moreover, the transferring curves are stable at elevated temperatures up to 107

°

C

. We also show that the sample performance can be tuned by changing the thickness of the

Si

O
2

and

Al
2

O
3

layer which brings further perspectives in developing robust sample technologies, especially in the ultraviolet region where the responsivity increases. Aluminum oxide encapsulated graphene-based photodetectors can thus be interesting for applications in air and at elevated temperatures.