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Since the establishment of Indonesia's new capital, Nusantara (IKN), hydroclimate extremes have emerged as a significant environmental concern. One of the most notable events was the devastating flash flood on March 15–16, 2022, which was triggered by 4–6 hours of prolonged heavy rainfall, causing severe damage and substantial economic loss.

Г. Лойхс

Author(s): Jarett LeVan and Scott D. Baalrud

The intrinsic bulk viscosity of the one-component plasma (OCP) is computed and analyzed using equilibrium molecular dynamics simulations and the Green-Kubo formalism. It is found that bulk viscosity exhibits a maximum at Γ≈1, corresponding to the condition that the average kinetic energy of particle…

[Phys. Rev. E 111, 015202] Published Tue Jan 07, 2025

Abstract

Vehicular attitude can be estimated using micro-electro-mechanical systems (MEMS) based magnetic, angular rate, and gravity (MARG) sensors or global navigation satellite systems (GNSS). In challenging environments external accelerations, magnetic distortions, and failure of GNSS will result in significant attitude estimation errors. We proposed a hybrid attitude estimation algorithm based on the low-cost dual-antenna GNSS/MEMS MARG sensor integration, in which the two GNSS antennas are connected to two separate low-cost receivers. Heading and pitch angles are obtained from the moving baseline spanned by the two antennas. An error state Kalman filter is built for data fusion, the filter shares the identical kinematic model but switches the measurement model according to the valid aiding sources. Six possible measurement update schemes are conditioned on the availability of GNSS-derived angles and the disturbances detected in the MARG sensor data. The accuracy degradation of attitude estimation caused by disturbances is alleviated by adjusting the measurement covariance matrix adaptively. A land vehicle-based dynamic experiment was performed to assess the proposed algorithm. Compared to the MARG sensor alone method, the root mean square errors of the proposed GNSS/MARG sensor integrated method were reduced by 38.9%, 65.8%, and 45.6% in the roll, pitch, and yaw angles, respectively.

Abstract

Atmospheric weighted mean temperature (Tm) is pivotal for converting zenith wet delay (ZWD) derived from global navigation satellite system (GNSS) signal to precipitable water vapor (PWV). Currently, most Tm models are developed based on radiosonde (RS) data or reanalysis data. These models are limited by the availability of measured surface temperature and the accuracy of input data used during modeling. Additionally, they face challenges in accounting for the diurnal effect on Tm. In this study, we innovatively use the latest AI weather model, Pangu-Weather, to estimate surface air temperature (Pangu-Ts) in 2016–2019. Compared with the measured surface temperature (RS-Ts) at RS stations, bias and root mean square error (RMSE) are − 0.75 K and 2.54 K, respectively. Subsequently, a Tm forecast model (RF-Tm) in China is developed based on this data using random forest (RF). The model only requires the input of time, 3D coordinates of stations, and predicted Pangu-Ts data to yield the forecasted Tm estimates. The validation results based on RS data show that bias of the RF-Tm is − 0.38 K and the RMSE is 2.47 K. Through comparison and validation with the Bevis model, GPT3, and the Tm forecast model (BP-Tm) developed using back propagation neural network (BPNN), RF-Tm demonstrates reductions in RMSE of 41.33%, 39.46%, and 2.76%, respectively. The mean values with theoretical RMSE and relative error of PWV derived from the RF-Tm are 0.171 mm and 0.90%. The RF-Tm proposed in this study can provide reliable and high-precision Tm estimation for real-time GNSS PWV retrieval.

Abstract

Jamming of Global Navigation Satellite System (GNSS) signals severely affects the security of critical infrastructures and applications. The localization of intentional jamming sources, jammers, is an important step in securing GNSS resilience as it provides the authorities with technical tools to prevent the jamming. However, jammers are difficult to localize in dense urban areas because the existence of multipath and non-line-of-sight propagation challenges conventional methods significantly. This challenge has not been comprehensively addressed in previous research. Motivated by this gap, a ray-tracing tool using 3-D city models is established to simulate jamming signal propagation with high precision and thereby augment the existing signal simulators, and measurements for localization are modeled by characterizing a commercial GNSS receiver under jamming conditions. Then, we propose a novel two-step strategy which consists of an ensemble subspace k-Nearest-Neighbor (KNN) as a raw-predictor and an improved gravitational searching algorithm (GSA) as a fine-optimizer. Based on this, two cloud-computing-based schemes using signal-matching and joint-localization in fine-optimizing stage are proposed. Finally, the proposed methods are evaluated in three typical urban areas, and their effectiveness and superiority over conventional least-squares method based on an empirical path-loss model are validated.

Abstract

Physics-informed neural networks (PINNs) provide a flexible and effective alternative for estimating seismic wavefield solutions due to their typical mesh-free and unsupervised features. However, their accuracy and training cost restrict their applicability. To address these issues, we propose a novel initialization for PINNs based on meta-learning to enhance their performance. In our framework, we first utilize meta-learning to train a common network initialization for a distribution of medium parameters (i.e., velocity models). This phase employs a unique training data container, comprising a support set and a query set. We use a dual-loop approach, optimizing network parameters through a bidirectional gradient update from the support set to the query set. Following this, we use the meta-trained PINN model as the initial model for a regular PINN training for a new velocity model, where the optimization of the network is jointly constrained by the physical and regularization losses. Numerical results demonstrate that, compared to the vanilla PINN with random initialization, our method achieves a much faster convergence speed, and also obtains a significant improvement in the results accuracy. Meanwhile, we showcase that our method can be integrated with existing optimal techniques to further enhance its performance.

Author(s): M. J. May, G. E. Kemp, J. D. Colvin, R. Benjamin, D. Liedahl, T. Fears, S. Kucheyev, P. L. Poole, K. Widmann, and B. E. Blue

Shooting a laser pulse at a porous silver target generates more intense x rays than previous targets, which will help studies of matter in extreme conditions.

[Phys. Rev. E 111, 015201] Published Fri Jan 03, 2025

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.