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Abstract During space weather events, geomagnetic field variations produce geoelectric fields (GEFs) that drive geomagnetically induced currents (GIC), which may affect operation of power systems, pipelines, railway signaling, and submarine cables. It is often assumed that GEFs and GICs are proportional to the rate of change of the magnetic field,∂B/∂t. However, the GEFs drive electric currents in the Earth that themselves create magnetic fields and this complicates the relationship between the GEFs (and associated GICs) and geomagnetic field variations. Previous theoretical studies have shown this can lead to GEFs that are correlated with eitherB(t)or∂B/∂tdepending on the Earth conductivity structure. In this paper we extend this work to determine the electric field/magnetic field relationships for two limiting cases of the conductivity structure; and use these to calculate the GIC produced in a pipeline and a power system. The results show that GICs follow the variations inB(t)when the system is in a region with a conductive surface layer on top of a resistive substratum; but follow∂B/∂twhen the system is in a region with a resistive surface layer on top of conductive substratum. This illustrates the range of responses that can be obtained with different Earth conductivity structures and shows the importance of including Earth conductivity models in the calculations of GIC. It also shows that neitherB(t)nor∂B/∂trepresents a reliable proxy for GIC and that a better proxy is consistent with empirical proxy proposed by Marshall et al. (2010),https://doi.org/10.1029/2009sw000553. 

AI-generated images have become pervasive, raising critical concerns around content authenticity, intellectual property, and the spread of misinformation. Invisible watermarks offer a promising solution for identifying AI-generated images, preserving content provenance without degrading visual quality. However, their real-world robustness remains uncertain due to the lack of standardized evaluation protocols and large-scale stress testing. To bridge this gap, we organized “Erasing the Invisible,” a NeurIPS 2024 competition and newly established benchmark designed to systematically stress testing the resilience of watermarking techniques. The competition introduced two attack tracks—Black-box and Beige-box—that simulate practical scenarios with varying levels of attacker knowledge on watermarks, providing a comprehensive assessment of watermark robustness. The competition attracted significant global participation, with 2,722 submissions from 298 teams. Through a rigorous evaluation pipeline featuring real-time feedback and human-verified final rankings, participants developed and demonstrated new attack strategies that revealed critical vulnerabilities in state-of-the-art watermarking methods. On average, the top-5 teams in both tracks could remove watermarks from $$\geq$$ 89% of the images while preserving high visual quality, setting strong baselines for future research on watermark attacks and defenses. To support continued progress in this field, we summarize the insights and lessons learned from this competition in this paper, and release the benchmark dataset, evaluation toolkit, and competition results. “Erasing the Invisible” establishes a valuable open resource for advancing more robust watermarking techniques and strengthening content provenance in the era of generative AI. 

Abstract Modern submarine communication cables, though fiber‐optic in nature, remain vulnerable to space weather hazards due to their internal conductive cables used for powering repeaters. During geomagnetic storms, variations in the geomagnetic field induce geoelectric fields that drive geomagnetically induced voltages along these cables. This study validates the Submarine Cable Upset By Auroral Streams (SCUBAS) model framework by analyzing the induced voltages on two transatlantic submarine cables—TAT‐1 and TAT‐8—during the 11 February 1958 and 13 March 1989 superstorms, respectively. SCUBAS models the cable as a segmented conductor placed atop oceanic and subsea conductivity structures and calculates voltages from both cable‐parallel electric fields and coastal Earth potential at cable terminals. Model outputs are compared against digitized observations from historical literature. SCUBAS successfully captures both large‐scale voltage patterns and event‐specific dynamics, reproducing peak voltages within a median percentage error of <30%. High skill scores (>0.85) further confirm the reliability of the model across events and spatial locations. The results highlight that both subsea induction and coastal Earth potential shifts contribute significantly to geomagnetically induced voltages, emphasizing the need for comprehensive modeling in space weather risk assessments. This validation positions SCUBAS as a robust tool for evaluating the vulnerability of submarine cables to geomagnetic disturbances, with relevance for space weather forecasting, improving infrastructure resilience, and future mitigation strategies. 

Abstract Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification of stochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary. The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization, causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo (MC) simulations. In this work, we present a machine learning (ML) approach, as a data-driven surrogate to MD simulations, which only needs a few MD simulations. This efficient yet high-fidelity ML approach enables MC simulations for full-scale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate. Unlike conventional ML models, the proposed hybrid polynomial correlated function expansion here, being a Bayesian ML approach, is data efficient. Further, it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations, which in principle addresses the issue of repeatability in stochastic simulations with low variance. Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus, yield strength and ultimate strength, illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency. 

Extreme waves, also known as ‘rogue waves’, have posed considerable challenges to maritime traffic over some time. Efforts have been directed at investigating the mechanisms governing these extreme energy localizations in oceanic environments. Modulational instability, also known as sideband instability, is one such mechanism that has been proposed to explain the occurrence of such phenomena in the framework of non-linear theory. The current work is aimed at better understanding the effects of sideband modulations on the propagation of unidirectional waves. To achieve this, a numerical wave tank (NWT) has been constructed using Weakly Compressible Smoothed Particle Hydrodynamics (WCSPH) to investigate the different parameters associated with the generation and propagation of plane, modulated waves. General Process Graphics Computing Unit (GPGPU) computing has been utilized to accelerate the computational process and improve the computational efficiency. The chosen numerical scheme has been validated by carrying out irregular waves focusing simulations to compare with available experimental data. Additionally, a Peregrine-type breather experiment has also been performed as part of the validation studies to look at energy localization within the NWT. The effects of the different parameters associated with the modulations to a plane propagating wave have been investigated using a blend of surface elevation data, eigenvalue, and frequency spectra. The effect of water depth on the perturbations to plane waves has been also investigated. The observations from these experiments can help shed light into the effects of modulations in the propagation of plane waves and help in the study of oceanic energy localization studies in future. 

Abstract Mountain waves are known sources of fluctuations in the upper atmosphere. However, their effects over the Continental United States (CONUS) are considered modest as compared to hot spots such as the Southern Andes. Here, we present an observation‐guided case study examining the dynamics of gravity waves (GWs) and their impacts on the ionosphere over the CONUS prior to the cold air outbreak in December 2022, which resulted from a significant distortion of the tropospheric polar vortex. The investigation relies on MERRA‐2 and ERA5 reanalysis data sets for the climatological contextualization, analysis of GWs based on National Aeronautics and Space Administration Aqua satellite's Atmospheric Infrared Sounder, 557.7 and 630.0 nm airglow emission observations, and the measurements of ionospheric disturbances retrieved from Global Navigation Satellite System signal‐based total electron content (TEC) and Super Dual Auroral Radar Network observations. We demonstrate that the tropospheric polar jet stream shifted toward the Rocky Mountains, generated large amplitude GWs (up to 11 K of brightness temperature), which, aided by winter‐time winds over mid‐latitudes, could propagate to mesospheric heights. The breaking of GWs plausibly led to the generation of a plethora of secondary acoustic and GWs that eventually emerged as the sources of extensive ionospheric fluctuations of ∼3–30 min periods and up to 0.7 TECu, observed across the entire CONUS for several days. This case offers a valuable demonstration of the interplay between tropospheric circulation and the ionosphere over CONUS, pointing to the need for a better understanding of wave‐driven deep‐atmosphere coupled dynamics.