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Abstract

The development of optical clocks has experienced significant acceleration in recent years, positioning them as one of the most promising quantum optical sensors for next-generation gravimetric missions (NGGMs). This study investigates the feasibility of retrieving the temporal gravity field via improved optical clocks through a closed-loop simulation. It evaluates optical clock capabilities in temporal gravity field inversion by considering the clock noise characteristics, designing satellite formations, and simulating the performance of optical clocks. The results indicate that optical clocks exhibit higher sensitivity to low-degree gravity field signals. However, when the optical clock noise level drops below 1 × 10−19 \(/\sqrt{\uptau }\) (τ being the averaging time in seconds) in the satellite-to-ground (SG) mode or below 1 × 10−20 \(/\sqrt{\uptau }\) in the satellite-to-satellite (SS) mode, atmospheric and oceanic (AO) errors become the dominant source of error. At this noise level, optical clocks can detect time-variable gravity signals up to approximately degree 30. Compared to existing gravity measurement missions such as GRACE-FO, optical clocks exhibit consistent results in detecting signals below degree 20. If the orbital altitude is reduced to 250 km, the performance of optical clocks across all degrees aligns with the results of GRACE-FO. Furthermore, the study reveals that lowering the orbital altitude of satellite-based optical clocks from 485 to 250 km improves results by an average of 33%. Switching from the SS mode to the SG mode results in an average improvement of 51%, while each order-of-magnitude improvement in clock precision enhances results by an average of 60%. In summary, these findings highlight the tremendous potential of optical clock technology in determining Earth’s temporal gravity field and provide crucial technological support for NGGMs.

Abstract

The accuracy of satellite clock bias (SCB) directly affects the precision and reliability of positioning in Global Navigation Satellite System. Through precise clock bias prediction, positioning errors can be effectively reduced, and the overall reliability of the system can be improved. This paper proposes a deep learning model for SCB prediction based on the fusion of the Beluga Whale Optimization (BWO), Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism. The CNN is utilized to extract the spatiotemporal characteristic information from the clock bias sequence, while the BiGRU fully extracts relevant features through forward and backward propagation. The introduction of an attention mechanism aims to preserve essential features within the clock bias sequence to enhance feature extraction by both CNN and BiGRU networks. Additionally, the BWO is employed to optimize parameter selection in order to improve model accuracy. Experimental verification demonstrates that for the BeiDou Navigation Satellite System’s hydrogen-maser atomic clocks, the predicted clock bias for 6 h, 3 days, and 15 days are 0.078 ns, 0.475 ns, and 2.130 ns respectively, superior to the CNN-BiGRU-Attention, CNN-BiGRU, BiGRU, GRU, LSTM, BP, Kalman filter and ARIMA models.

Author(s): Takayoshi Sano, Shogo Isayama, Kenta Takahashi, and Shuichi Matsukiyo

The interaction between a thin foil target and a circularly polarized laser light injected along an external magnetic field is investigated numerically by particle-in-cell simulations. A standing wave appears at the front surface of the target, overlapping the injected and partially reflected waves.…

[Phys. Rev. E 110, 065212] Published Mon Dec 30, 2024

Author(s): Trishul Dhalia, Rohit Juneja, and Amita Das

There has been of significant interest lately in the study of electromagnetic (EM) waves interacting with magnetized plasmas. The variety of resonances and the existence of several pass and stop bands in the dispersion curve for different orientations of the magnetic field offer new mechanisms of EM…

[Phys. Rev. E 110, 065213] Published Mon Dec 30, 2024

Author(s): Heng Zhang, Zhi-Lin Zhu, Malcolm-Wray Dunlop, Wen-Shan Duan, and Qing-He Zhang

Rogue waves, presented in numerous fields of science, are attracting significant attention. We study the excitation of electromagnetic rogue waves in magnetized plasmas caused by the thermal electron anisotropic loss cone distribution. The Krylov-Bogoliubov-Mitropolsky method is used to derive the n…

[Phys. Rev. E 110, 065214] Published Mon Dec 30, 2024

On January 1st, 2024, a moment magnitude (Mw) 7.5 earthquake occurred on an active reverse fault in the northern part of Noto Peninsula, being one of the largest intraplate events recorded in Japan. In previous s...

Abstract

In this paper, we review the theoretical studies of the electromagnetic and other non-seismic phenomena accompanying earthquakes. This field of geophysical research is at the interception of several sciences: electrodynamics, solid-state physics, fracture mechanics, seismology, acoustic-gravity waves, magnetohydrodynamics, ionospheric plasma, etc. In order to make physics of these phenomena as transparent as possible, we use a simplified way of deriving some theoretical results and restrict our analysis to order-of-magnitude estimates. The main emphasis is on those theoretical models which give not only a qualitative, but also a quantitative, description of the observed phenomena. After some introductory material, the review is begun with an analysis of the causes of local changes in the rock conductivity occasionally observed before earthquake occurrence. The mechanisms of electrical conductivity in dry and wet rocks, including the electrokinetic effect, are discussed here. In the next section, the theories explaining the generation of low-frequency electromagnetic perturbations resulting from the rock fracture are covered. Two possible mechanisms of the coseismic electromagnetic response to the propagation of seismic waves are studied theoretically. Hereafter, we deal with atmospheric phenomena, which can be related to seismic events. Here we discuss models describing the effect of pre-seismic changes in radon activity on atmospheric conductivity and examine hypotheses explaining abnormal changes in the atmospheric electric field and in infrared radiation from the Earth, which are occasionally observed on Earth and from space over seismically active regions. In the next section, we review several physical mechanisms of ionospheric perturbations associated with seismic activity. Among them are acoustic-gravity waves resulting from the propagation of seismic waves and tsunamis and ionospheric perturbations caused by vertical acoustic resonance in the atmosphere. In the remainder of this paper, we discuss whether variations in radon activity and vertical seismogenic currents in the atmosphere can affect the ionosphere.

Author(s): Wen-Xuan Yuan and Rui Guo

The phenomenon of dispersive shock waves (DSWs) exerts a critical influence on nonlinear dynamics in various nonlinear fields, and simulating this complex physical process remains a significant challenge. In this paper, we dramatically enhance the ability of physics-informed neural networks (PINNs) …

[Phys. Rev. E 110, 065307] Published Fri Dec 27, 2024

Author(s): Ethan C. McGarrigle, Hector D. Ceniceros, and Glenn H. Fredrickson

We introduce a projected complex Langevin (CL) numerical sampling method—a fictitious Langevin dynamics scheme that uses numerical projection to sample a constrained stationary distribution with highly oscillatory character. Despite the complex-valued degrees of freedom and associated sign problem, …

[Phys. Rev. E 110, 065308] Published Fri Dec 27, 2024

Abstract

Plasma flows with enhanced dynamic pressure, known as magnetosheath jets, are often found downstream of collisionless shocks. As they propagate through the magnetosheath, they interact with the surrounding plasma, shaping its properties, and potentially becoming geoeffective upon reaching the magnetopause. In recent years (since 2016), new research has produced vital results that have significantly enhanced our understanding on many aspects of jets. In this review, we summarise and discuss these findings. Spacecraft and ground-based observations, as well as global and local simulations, have contributed greatly to our understanding of the causes and effects of magnetosheath jets. First, we discuss recent findings on jet occurrence and formation, including in other planetary environments. New insights into jet properties and evolution are then examined using observations and simulations. Finally, we review the impact of jets upon interaction with the magnetopause and subsequent consequences for the magnetosphere-ionosphere system. We conclude with an outlook and assessment on future challenges. This includes an overview on future space missions that may prove crucial in tackling the outstanding open questions on jets in the terrestrial magnetosheath as well as other planetary and shock environments.

Abstract

Lateral inhomogeneity in the Earth’s mantle affects the tidal response. The current study reformulates the expressions for estimating the lateral inhomogeneity effects on tidal gravity with respect to the unperturbed Earth and supplements some critical derivation process to enhance the methodology. The effects of lateral inhomogeneity are calculated using several real Earth models. By considering the collective contributions of seismic wave velocity disturbances and density disturbance, the global theoretical changes of semidiurnal gravimetric factor are obtained, which vary from − 0.22 to 0.22% compared to those in a layered Earth model, about 1/2 of the ellipticity’s effect. The gravity changes caused by lateral-inhomogeneous disturbances are also computed and turn out to be up to 0.16% compared to the changes caused by tide-generating potential. The current study compares the influences of lateral inhomogeneity with rotation and ocean tide loading. The results indicate that the rotation and ellipticity on tidal gravity are the most dominant factors, the ocean tide loading is the moderate one, and the lateral inhomogeneity in the mantle has the least significant influence. Moreover, an anti-correlation between the effective elastic thickness and gravimetric factor change caused by lateral inhomogeneity is found, implying that it is difficult to generate tidal response at regions with high rigidity. We argue that the gravimetric factor change can be used as an effective indicator for lithospheric strength.

Abstract

Currently, the susceptibility of Global Navigation Satellite System (GNSS) signals underscores the importance of accurate GNSS time spoofing detection as a critical research area. Traditional spoofing detection methods have limitations in applicability, while the current learning-based algorithms are only applicable to the judgment of collected data, which is difficult to apply to real-time detection. In this paper, a real-time spoofing detection framework based on feature processing is proposed. The approach involves feature integration and correlation coefficient screening on each epoch of multi-satellite data. Additionally, special standardization strategy is employed to enhance the feasibility of real-time application. In the experimental phase, apart from utilizing the open dataset, an experimental platform is developed to generate dual-system data for experimentation purposes. Compared with the traditional clock difference detection method, this algorithm improves the detection performance by about 25%. Furthermore, the framework proposed can improve the detection F1 score of basic machine learning models and greatly reduce the computation time by more than ten times. On most datasets, models incorporating the framework achieved F1 scores of more than 99% and average response times of less than 10 μs. In summary, this study provides an effective intelligent solution for the application of real-time receiver spoofing detection.

Author(s): Adil A. Gangat and Johnnie Gray

Obtaining the low-energy configurations of spin glasses that have rugged energy landscapes is of direct relevance to combinatorial optimization and fundamental science. Search-based heuristics have difficulty with this task due to the existence of many local minima that are far from optimal. The wor…

[Phys. Rev. E 110, 065306] Published Mon Dec 23, 2024

Author(s): Kenan Qu and Nathaniel J. Fisch

This study addresses the question: how bright can one make a beam with nontrivial quantum correlations such as entanglement or squeezing? The authors consider a nonlinear mechanism in plasmas, namely four wave mixing to make polarization-entangled photon pairs. The discovery of stable high intensity “quantum light” in plasmas opens the door to remarkable applications, including enhanced x-ray imaging, quantum (sub-Rayleigh) lithography, and greatly improved quantum communication.

[Phys. Rev. E 110, 065211] Published Fri Dec 20, 2024

Abstract

The regional sea level budget and interannual sea level changes around Taiwan and Philippines are studied using altimetry, GRACE, and in-situ hydrographic data during 1993‒2021. Results show that the average sea level trend around Taiwan and Philippines during 1993–2021 derived from the altimetric data is 3.6 ± 0.2 mm/yr. Over 2002–2021, the study shows closure of sea level budget in the eastern ocean of Taiwan and Philippines within the observed data uncertainties, and the ocean mass accounts for 88%–100% of the observed sea level rise. In contrast, the sea level budget is not closed in the western ocean of Taiwan and Philippines, probably due to the lack of complete coverage by in-situ ocean observing systems. In addition, both regional sea level anomalies and their steric component around Taiwan and Philippines exhibit pronounced interannual and decadal variabilities. The trade wind stress associated with El Niño–Southern Oscillation and Pacific Decadal Oscillation offers a compelling explanation for the interannual and decadal signals of sea level anomalies in the southern ocean of Taiwan, with negative correlations of − 0.78 to − 0.64, indicating that trade wind stress makes a negative contribution to interannual-to-decadal sea level variability. In the northwestern ocean of Taiwan, the sea level variation is strongly influenced by the local monsoon system and shallow bathymetry with an annual amplitude of 90.3 ± 2.9 mm, larger than those in other regions around Taiwan and Philippines, where ocean mass is dominant with a high correlation with the sea level (+ 0.75 to + 0.78).

Author(s): L. Brodoloni and S. Pilati

One major difficulty in applying quantum Monte Carlo to quantum spin glass models arises from the need to control the population of random walkers, which can lead to biases. Here, a projective quantum Monte Carlo method with neural-network-based guiding wave functions is used to eliminate population control bias. The study provides valuable insights into quantum spin glasses and demonstrates the effectiveness of neural network states in simulating frustrated quantum systems.

[Phys. Rev. E 110, 065305] Published Thu Dec 19, 2024

Abstract

The development of Global Navigation Satellite Systems (GNSS) results in large spatial geodetic networks with a distinct range of accuracy. Thus, classification of the GNSS stations is needed to determine which stations are appropriate for geodetic applications. Additionally, advanced Machine Learning (ML) techniques have been proposed. However, ML algorithms may sometimes be less sensitive due to a lack of samples or anomalies in input data. Therefore, this study introduces an approach in which human-based supervision is integrated into ML processes to improve the ML model’s performance in classifying the continuous GNSS stations. The human factor influences the ML processes through two sampling strategies: “suggest-decide” and “correct-retrain”, where the accuracy of ML models will be improved via human-based corrections. The idea is that the unsupervised ML-based clustering techniques are driven by human-based supervision to create samples for training the supervised ML-based classification models. In this study, we develop a MATLAB app to automate the clustering and labeling processes. Our finding demonstrates that applying these sampling strategies can enhance the accuracy of the ML-based classification models from under 50 % up to \(\sim\) 99 % after re-training. Also, this study categorizes almost 9000 continuous monitoring stations in the Nevada database, of which 1900 stations in Europe serve as samples for training the ML-based classification models. Furthermore, the methodologies developed in this study can be applied to warning systems, which utilize internal and external human resources to correct errors, address unusual situations, and provide timely feedback for better performance of ML-based forecasts.

Abstract

Displacement-based modal analysis has been proven to yield more robust and reliable modal parameter identification results compared to acceleration-based modal analysis. Global navigation satellite systems (GNSS) under real-time kinematic (RTK) mode is a widely used dynamic displacement monitoring technique. Notably, the monitoring accuracy of GNSS is limited due to the existence of multiple error sources such as multipath effect and satellite shielding effect. Particularly, blind source separation (BSS) can determine structural modal parameters from output-only responses. This method is advantageous compared with conventional modal analysis method because it does not require any prior knowledge of the structure. However, common BSS methodologies are susceptible to the local minima problem and are sensitive to low signal-to-noise ratio (SNR) signals. To address the aforementioned problems, this study first presents a combination filter strategy of Chebyshev and wavelet threshold (WT) to estimate the structural dynamic displacement based on GNSS RTK measurement. Then, a swarm-enhanced blind identification approach is proposed to determine structural modal parameters from the estimated displacement. The core of this approach is to develop a robust K-means clustering approach with swarm intelligence optimization to estimate the mixing matrix (i.e., mode shape matrix). Finally, the developed approach is verified in a four-degree-of-freedom numerical model and then implemented to a field test of a long-span cable-stayed bridge in engineering practice. The results illustrate that the designed combination filter can effectively weaken the influence of GNSS-RTK background noise while retaining the components related to structural dynamic vibration. Meanwhile, comparing with the conventional BSS approach (i.e., sparse component analysis), the developed swarm-enhanced blind identification approach exhibits higher robustness and convergence accuracy in determining structural modal parameters.

Abstract

The continued operation of missions such as Mars Express, Mars Reconnaissance Orbiter, and the ExoMars Trace Gas Orbiter has greatly enhanced our knowledge of seasonal processes on Mars. The most apparent evidence of the importance of seasons on Mars on the large scale is annual variation in the sizes of the Martian polar caps. However, high resolution imaging has also shown that seasonal forcing can lead to small-scale phenomena that are continuously changing the topography and the surface photometry. These phenomena often have no terrestrial analogue and involve complex interactions between seasonal ices, atmosphere, and substrate (surface and sub-surface). Although we now have better understanding of many of these processes (occasionally as a result of laboratory simulation), direct proof of some hypotheses remains elusive. We provide a brief review of the phenomena and list a series of open questions.