Time-varying Field Research Articles

A Contactless Low-Carbon Steel Magnetostrictive Torquemeter: Numerical Analysis and Experimental Validation

Torque measurement is a key task in several mechanical and structural engineering applications. Most commercial torquemeters require the shaft to be interrupted to place the sensors between the two portions of the shaft where a torque has to be measured. Contactless torquemeters based on the inverse magnetostrictive effect represent an effective alternative to conventional ones. Most known ferromagnetic materials have an inverse magnetostrictive behavior: applied stresses induce variations in their magnetic properties. This paper investigates the possibility of measuring torsional loads applied to a shaft made of ferromagnetic steel S235 through an inverse magnetostrictive torquemeter. It consists of an excitation coil that produces a time-varying electromagnetic field inside the shaft and an array of sensing coils suitably arranged around it, in which voltages are induced. First, the system is analyzed both in unloaded and loaded conditions by a Finite Element Method, investigating the influence of relative positions between the sensor and the shaft. Then, the numerical results are compared with the experimental measurements, confirming a linear characteristic of the sensor (sensitivity about 0.013 mV/Nm for the adopted experimental setup) and revealing the consistency of the model used. Since the system exploits the physical behavior of a large class of structural steel and does not require the introduction of special materials, this torquemeter may represent a reliable, economical, and easy-to-install device.

Slow relaxation dynamics of superparamagnetic colloidal beads in time-varying fields

Slow relaxation dynamics of superparamagnetic colloidal beads in time-varying fields

Time-dependent Aharonov-Bohm type topological effects on dipoles

Exploring the time-dependent characteristics of AB-type effects holds significant importance in contemporary physics and its practical applications. Here, we delve into the investigation of time-dependent topological effects emerging in AB-type experimental setups. We first analyse the topological effects on magnetic dipoles moving in closed trajectories around the time-varying magnetic field source solenoid, then on electrical dipoles around a time-varying electric field source in 2+1 dimensions without any approximation. Last, we discuss the characteristics of the topological effects by considering the identity and dualities between phases from an integrated perspective.

Eolization: A project on spatial auralisation of wind turbine noise

The increase of onshore wind farm projects in Europe has heightened concerns regarding the noise emitted by wind turbines and the potential nuisance to nearby residents. To address this issue, advanced tools are required for accurately assessing and predicting the noise levels and perceived annoyance from wind turbines. Physics-based auralization emerges as a promising approach for recreating with fidelity wind turbine noise in controlled environments. To enhance the immersive experience, this study proposes to extend the current methodologies with the spatial representation of wind turbine noise using ambisonics. In this work, the sound emitted by each turbine is computed using an extended source model. The propagation is simulated with a parabolic equation method accounting for the flow surrounding the wind turbines. Finally, the signal is auralized and spatialized using spherical harmonic decomposition. This methodology provides a complete framework for predicting and reproducing the three-dimensional time-varying sound field around the listener. Overall, these improvements offer valuable insights into the spatial characteristics of wind turbine noise.

Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory

Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian machine learning framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Developed a Bayesian machine learning framework for inferring the non-linear potential function from observed cryptocurrency prices. (iii) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes in the literature. (iv) Analysis of cryptocurrency market during various Bitcoin crash durations shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (v) The proposed cryptocurrency market indicators (attractors and repellers) was used to improve the prediction performance of state-of-art deep learning price prediction models.

Mesoscale air motion and thermodynamics predict heavy hourly U.S. precipitation

Predicting heavy precipitation remains scientifically challenging. Here we combine Atmospheric Infrared Sounder (AIRS) temperature and moisture soundings and weather forecast winds to predict the formation of thermodynamic conditions favourable for convection in the hours following satellite overpasses. Here we treat AIRS retrievals as air parcels that are moved adiabatically to generate time-varying fields. Over much of the Central-Eastern Continental U.S. during the non-winter months of 2019–2020, our derived convective available potential energy alone predicts intense precipitation. For hourly precipitation above the all-hours 99.9th percentile, performance is marginally lower than forecasts from a convection permitting model, but similar to the ERA5 reanalysis and substantially better than using the original AIRS soundings. Our results illustrate how mesoscale advection is a major contributor to developing heavy precipitation in the region. Enhancing the full AIRS record as described here would provide an alternative approach to quantify multi-decade trends in heavy precipitation risk.

An Improved Average Acceleration Approach of Modelling Earth Gravity Field Based on K-Band Range-Rate Observations

The conventional average acceleration approach relies on K-band range observation, containing an unknown bias, which leads to possible degradation of the precision of Earth’s gravity field modelling. It also suffers from correlated errors caused by three-point numerical differentiation. In this study, an improved approach is proposed that makes use of K-band range-rate observations instead and overcoming the influence of correlated errors by introducing a whitening filter. GRACE-Follow On data spanning the period from January 2019 to December 2022 were processed by the proposed approach and a series of time-varying gravity field models was derived, referred to as SSM-AAA-GFO in this paper. This model series is compared comprehensively with three official model series. Results demonstrate that all model series are highly coincident below degree 30 and reflect similar time-varying gravity field signals in both large and small basins. After filtering, SSM-AAA-GFO shows uncertainty, in the form of equivalent water height below 2.5 cm, which is comparable with three official model series. The comparison results confirm the effectiveness of the proposed approach for precisely modelling a time-varying gravity field based on K-band range-rate observations.

Simulation analysis of recovering time-varying gravity fields based on Starlink-like constellation

Simulation analysis of recovering time-varying gravity fields based on Starlink-like constellation

Distributed Estimation of Fields Using a Sensor Network with Quantized Measurements.

In this paper, the problem of estimating a scalar field (e.g., the spatial distribution of contaminants in an area) using a sensor network is considered. The sensors are assumed to have quantized measurements. We consider distributed estimation algorithms where each sensor forms its own estimate of the field, with sensors able to share information locally with its neighbours. Two schemes are proposed, called, respectively, measurement diffusion and estimate diffusion. In the measurement diffusion scheme, each sensor broadcasts to its neighbours the latest received measurements of every sensor in the network, while in the estimate diffusion scheme, each sensor will broadcast local estimates and Hessians to its neighbours. Information received from its neighbours will then be iteratively combined at each sensor to form the field estimates. Time-varying scalar fields can also be estimated using both the measurement diffusion and estimate diffusion schemes. Numerical studies illustrate the performance of the proposed algorithms, in particular demonstrating steady state performance close to that of centralized estimation.

Chipless RFID Sensor for Measuring Time-Varying Electric Fields Using a Contactless Air-Filled Substrate-Integrated Waveguide Resonator.

This paper presents a wireless chipless resonator-based sensor for measuring the absolute value of an external time-varying electric field. The sensor is developed using contactless air-filled substrate-integrated waveguide (CLAF-SIW) technology. The sensor employs a low-impedance electromagnetic band gap structure to confine the electric field within the sensor's air cavity. The air cavity is loaded with varactor diodes whose reverse bias voltage is modified by the to-be-measured external electric field. Variation in the external electric field results in a variation of the sensor's resonant frequency. The CLAF-SIW sensor offers a high unloaded quality factor, which is required for a long-distance ringback-based interrogation system. A prototype of the proposed sensor is fabricated and tested. It can measure a time-varying external electric field up to 6.9 kV/m, has a sensitivity of 1.86 (kHz)/(V/m), and can be interrogated from a distance of 80 cm. The feasible maximum bandwidth of the external electric field is 25 kHz. The proposed sensor offers a compact planar multilayer structure that can easily be incorporated with a planar antenna and its size can be reduced by selecting a higher operating frequency without an increase in dielectric loss.

Correction to: Jacobi set simplification for tracking topological features in time-varying scalar fields

Correction to: Jacobi set simplification for tracking topological features in time-varying scalar fields

Optimal control of a quantum sensor: A fast algorithm based on an analytic solution

Quantum sensors can show unprecedented sensitivities, provided they are controlled in a very specific, optimal way. Here, we consider a spin sensor of time-varying fields in the presence of dephasing noise, and we show that the problem of finding the pulsed control field that optimizes the sensitivity (i.e., the smallest detectable signal) can be mapped to the determination of the ground state of a spin chain. We find an approximate but analytic solution of this problem, which provides a lower bound for the sensitivity and a pulsed control very close to optimal, which we further use as initial guess for realizing a fast simulated annealing algorithm. We experimentally demonstrate the sensitivity improvement for a spin-qubit magnetometer based on a nitrogen-vacancy center in diamond.

CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video

The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes. Prior methods, such as DynamicNeRF, have shown impressive performance by leveraging time-varying dynamic radiation fields. However, these methods have limitations when it comes to accurately modeling the motion of complex objects, which can lead to inaccurate and blurry renderings of details. To address this limitation, we propose a novel approach that builds upon a recent generalization NeRF, which aggregates nearby views onto new viewpoints. However, such methods are typically only effective for static scenes. To overcome this challenge, we introduce a module that operates in both the time and frequency domains to aggregate the features of object motion. This allows us to learn the relationship between frames and generate higher-quality images. Our experiments demonstrate significant improvements over state-of-the-art methods on dynamic scene datasets. Specifically, our approach outperforms existing methods in terms of both the accuracy and visual quality of the synthesized views. Our code is available on https://github.com/xingy038/CTNeRF.

Joint analysis of JUICE and Europa Clipper tracking data to study the Jovian system ephemerides and dissipative parameters

Context. Jupiter and its moons form a complex dynamical system that includes several coupling dynamics at different frequencies. In particular the Laplace resonance is fundamental to maintaining the energy dissipation that sustain Io’s volcanic activity and Europa’s subsurface ocean; studying its stability is thus crucial for characterizing the potential habitability of these moons. The origin and evolution of the Laplace resonance is driven by the strong tidal interactions between Jupiter and its Galilean moons, and the future planetary exploration missions JUICE and Europa Clipper could bring new light to this unsolved mechanism. During the Jupiter tours of both missions and JUICE’s Ganymede orbital phase, two-way radiometric range and Doppler data will be acquired between Earth ground stations and the spacecraft, which will be processed to recover the static and time-varying gravity field of the moons. Moreover, range and Doppler data will improve the orbit accuracy of the moons, providing precise measurements of Jupiter’s tidal parameters. Aims. This work presents a covariance analysis of the joint orbit determination of JUICE and Europa Clipper, aimed at quantifying the expected uncertainties on the main parameters that characterize the dynamics of the Jupiter system. Methods. We simulated radio science data from JUICE and Clipper missions under conservative noise assumptions, using a multi-arc approach to estimate the ephemerides and dissipation in the system. Results. Even though JUICE and Europa Clipper will not perform flybys of Io, the strong coupling with Europa and Ganymede will allow an improvement of our knowledge of the Jupiter-Io dissipation parameters thanks to JUICE and Europa Clipper radiometric data. Moreover, the expected uncertainty in Jupiter’s dissipation at the frequency of Callisto could unveil a potential resonance locking mechanism between Jupiter and Callisto.

Observation of strong in-plane perpendicular electric field in a radio frequency plasma with a time-varying magnetic nozzle

The spatiotemporal evolution of the electron temperature and plasma potential in a 200-W radio frequency argon discharge with a time-varying (approximately 60 kHz) magnetic nozzle was measured. Unlike in conventional static magnetic nozzles, the two-dimensional profiles of the electron temperature and plasma potential changed in sync with the applied azimuthal electric field, not with the magnetic field. The temporally resolved electric field vectors demonstrated an enhancement of the perpendicular component, where the direction fairly matched that of electron Eθ×B drift, indicating a space charge separation. This observation suggests that the applied time-varying field actively enhanced cross field electron transport, resulting in a unique potential structure and charged particle acceleration.

Неотектоника зоны перехода океан-континент в районе Кот д’Ивуара (Западная Африка)

Structural and geophysical indicators of neotectonics in the area of the continental margin of Côte d'Ivoire are represented in three interacting zones: oceanic, continental and transitional. Together with the time-varying gravitational field, they form a system in which neotectonic processes are enhanced due to their equatorial position. Seismicity is represented by dense clusters of events in the areas of junction of the Strakhov, Sao Paulo and Romansh oceanic transform faults with the continent and their overland continuation. Seismicity and gravity variations show the relationship of deep density changes to stress discharge in the area and the effect of volumetric forces on lithospheric blocks. Free air and isostasy anomalies along the shelf are segmented by the effects of vertical motions. Pull-apart depressions near the passive parts of transform faults indicate basement deformations in the interfault blocks and activation of shear displacements of dextral kinematics near the bend of the passive parts of the faults to the northeast from main oceanic azimuth. Segments with isostasy maxima on the shelf correspond to areas with coarser grain size granulometric composition, indicating leaching of fine sedimentary fraction from the bottom material.

The coupled-motion enhanced wireless signal transmission with long distance based on Maxwell’s displacement current

Wireless signal transmission plays an increasingly imperative role in numerous aspects of modern society, but it still remains challenging to achieve it with low-cost and efficient way. Herein, we demonstrate a long-distance wireless signal transmission system, which mainly includes an electrodeless triboelectric nanogenerator coupled with linear motion and rotational motion (LR-TENG) as a transmitter and a receiver located at a distance. Based on the varying electric field originated from Maxwell's displacement current, the maximum transmission distance of LR-TENG can reach 86 cm under the external excitation of 1 Hz, creating the highest record among the relevant researches. In addition, the influence of obstacle type, thickness, size and position on signal transmission has been systematically investigated for the first time, and the distribution of time-varying electric field in space is also analyzed qualitatively. Furthermore, a Labview interface is developed for accurate positioning in complex scenes relying on the received signals from multiple receivers at different positions. This work illustrates a simple and feasible design method for extending the distance of wireless signal transmission based on TENG, and promotes its application in the field of wireless communication.

Vessel-targeted compensation of deformable motion in interventional cone-beam CT

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm−1 to 0.233 mm−1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm−1 before to 0.205 mm−1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.

Long-term analysis of Sentinel-6A orbit determination: Insights from three years of flight data

The Sentinel-6A mission extends the set of satellites dedicated to continuous ocean altimetry measurements, which started with TOPEX/Poseidon in 1992. To utilize these measurements, high-accuracy orbit solutions with radial position errors of less than 1.5cm (RMS) are required. For precise orbit determination (POD), a dedicated GPS/Galileo triple-frequency receiver (PODRIX) is available. Complementary to this, the TriG receiver provides GPS-only observations for POD and radio occultations. Previous research has shown that reduced-dynamic orbit solutions with PODRIX GPS/Galileo measurements meet the mission requirement for radial position accuracy of 1.5 cm (RMS). However, baseline estimation between the PODRIX and TriG antennas still reveals obvious inconsistencies in the along- and cross-track directions. In this study, we present a comprehensive reprocessing of orbit solutions using three years of Sentinel-6A flight data from both receivers covering January 2021 to December 2023. Cross-comparison between both receivers shows that inconsistencies can be removed by applying a yaw bias and a timing error correction. A comparative analysis of macro-models demonstrates strong correlation of cross-track empirical accelerations with the Sun elevation above the orbital plane when using nominal surface properties reported by the manufacturer. Adjusted macro-models improve performance at the expense of requiring increased surface areas or reflectivity coefficients. In addition to improved radiation pressure models, newly estimated antenna patterns and a time-varying gravity field were used in the analysis. The reprocessed data confirm good agreement of the results of both receivers except for a relative timing error of 1.3 μs, and the yaw bias correction of −0.43° removes previously-observed systematic differences in the estimated empirical accelerations. Satellite laser ranging results based on SLRF2020 station coordinates demonstrate a 5—6 mm (1D RMS) accuracy of the GNSS-based POD solutions across the IGb14/IGS20 reference frame transition in November 2022.

Assessing risk of acute respiratory infectious diseases in crowded indoor settings with digital twin and precision trajectory approach

Indoor environments can pose a substantial respiratory infection risk, especially in densely populated areas. It is therefore vital to evaluate and predict infection risks for mobile individuals engaging in indoor activities. However, previous studies have primarily focused on assessing viral transmission probabilities between relatively stationary individuals, disregarding the dynamic features of pedestrian trajectories. To address this issue, we propose an approach that uses precise trajectory data and digital twin technology to evaluate and forecast indoor infection risks for mobile individuals. Our approach involves the utilization of high-precision trajectory data to develop a time-varying virus density field map (VDFM), forming the basis for infection risk assessment. Additionally, we present a deep-learning model that employs the transformer method to predict future time-varying VDFMs and associated infection risks. Furthermore, we introduce digital twin to comprehend real-time interactions between the physical and digital realms within the structure. To validate our approach, we conducted a case study at the Wuhan International Exposition Center, serving as the control room. We performed a sensitivity analysis on various preventive measures, including mask-wearing, social distancing, and indoor infection control. The results highlighted the significant impact of these measures on individual infection risks associated with indoor activities. Our research contributes to the development of tailored prevention and control strategies, which play a critical role in mitigating the spread of respiratory infections. Thus, this study holds significant implications for technology-facilitated public health services.