Dectot--Le Monnier de Gouville Esteban, Mohammad Hamdaqa, Moataz Chouchen
YARA has established itself as the de facto standard for "Detection as Code," enabling analysts and DevSecOps practitioners to define signatures for malware identification across the software supply chain. Despite its pervasive use, the open-source YARA ecosystem remains characterized by ad-hoc sharing and opaque quality. Practitioners currently rely on public repositories without empirical evidence regarding the ecosystem's structural characteristics, maintenance and diffusion dynamics, or operational reliability. We conducted a large-scale mixed-method study of 8.4 million rules mined from 1,853 GitHub repositories. Our pipeline integrates repository mining to map supply chain dynamics, static analysis to assess syntactic quality, and dynamic benchmarking against 4,026 malware and 2,000 goodware samples to measure operational effectiveness. We reveal a highly centralized structure where 10 authors drive 80% of rule adoption. The ecosystem functions as a "static supply chain": repositories show a median inactivity of 782 days and a median technical lag of 4.2 years. While static quality scores appear high (mean = 99.4/100), operational benchmarking uncovers significant noise (false positives) and low recall. Furthermore, coverage is heavily biased toward legacy threats (Ransomware), leaving modern initial access vectors (Loaders, Stealers) severely underrepresented. These findings expose a systemic "double penalty": defenders incur high performance overhead for decayed intelligence. We argue that public repositories function as raw data dumps rather than curated feeds, necessitating a paradigm shift from ad-hoc collection to rigorous rule engineering. We release our dataset and pipeline to support future data-driven curation tools.
Matthieu Chatelain, Isambard Goodbody, Nived Rajeev Saritha
et al.
Transport and mixing of tracers in the ocean is thought to be preferentially along neutral planes defined by the potential temperature and salinity fields. This gives rise to a conceptual model of ocean transport in which water parcel trajectories are everywhere neutral, that is, tangent to the neutral planes. Because the distribution of neutral planes is not integrable, neutral transport, while locally two dimensional, is globally three dimensional. We describe this form of transport, building on its connection with contact and sub-Riemannian geometry. We discuss a Lie-bracket interpretation of local dianeutral transport, the quantitative meaning of helicity and the implications of the accessibility theorem. We compute sub-Riemnanian geodesics for climatological neutral planes and put forward the use of the associated Carnot--Carathéodory distance as a diagnostic of the strong anisotropy of neutral transport. We propose a stochastic toy model of neutral transport which represents motion along neutral planes by a Brownian motion. The corresponding diffusion process is degenerate and not (strongly) elliptic. The non-integrability of the neutral planes however ensures that the diffusion is hypoelliptic. As a result, trajectories are not confined to surfaces but visit the entire three-dimensional ocean. The short-time behaviour is qualitatively different from that obtained with a non-degenerate highly anisotropic diffusion. We examine both short- and long-time behaviours using Monte Carlo simulations. The simulations provide an estimate for the time scale of ocean vertical transport implied by the constraint of neutrality.
Aiming at the shortcomings of the gazelle optimization algorithm, such as the imbalance between exploration and exploitation and the insufficient information exchange within the population, this paper proposes a multi-strategy improved gazelle optimization algorithm (MSIGOA). To address these issues, MSIGOA proposes an iteration-based updating framework that switches between exploitation and exploration according to the optimization process, which effectively enhances the balance between local exploitation and global exploration in the optimization process and improves the convergence speed. Two adaptive parameter tuning strategies improve the applicability of the algorithm and promote a smoother optimization process. The dominant population-based restart strategy enhances the algorithms ability to escape from local optima and avoid its premature convergence. These enhancements significantly improve the exploration and exploitation capabilities of MSIGOA, bringing superior convergence and efficiency in dealing with complex problems. In this paper, the parameter sensitivity, strategy effectiveness, convergence and stability of the proposed method are evaluated on two benchmark test sets including CEC2017 and CEC2022. Test results and statistical tests show that MSIGOA outperforms basic GOA and other advanced algorithms. On the CEC2017 and CEC2022 test sets, the proportion of functions where MSIGOA is not worse than GOA is 92.2% and 83.3%, respectively, and the proportion of functions where MSIGOA is not worse than other algorithms is 88.57% and 87.5%, respectively. Finally, the extensibility of MSIGAO is further verified by several engineering design optimization problems.
Leonard Lupin-Jimenez, Moein Darman, Subhashis Hazarika
et al.
Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico. Regional ocean emulation presents unique challenges owing to the complex bathymetry and lateral boundary conditions as well as from fundamental biases in deep learning-based frameworks, such as instability and hallucinations. In this paper, we develop a deep learning-based framework to autoregressively integrate ocean-surface variables over the Gulf of Mexico at $8$ Km spatial resolution without unphysical drifts over decadal time scales and simulataneously downscale and bias-correct it to $4$ Km resolution using a physics-constrained generative model. The framework shows both short-term skills as well as accurate long-term statistics in terms of mean and variability.
High-resolution remote sensing image change captioning (RSICC) aims to understand the change content in bitemporal high-resolution remote sensing images and generate corresponding descriptive captions. By presenting change information in the form of natural language, it makes the information more intuitive and easier to communicate, which has garnered widespread attention. However, there are still two challenges in RSICC: First, most existing methods adopt a unidirectional interaction from images to text, resulting in insufficient semantic alignment between images and text, which limits method's performance. Second, in remote sensing images, there are interfering factors such as illumination and climate, leading to overall differences between bitemporal images, which affect the recognition of change information. To address the aforementioned challenges, this article proposes a bidirectional image–text interaction method for high-resolution RSICC (BITCC). BITCC first introduces the image-to-text interaction component based on reconstruction. This approach along with the caption generation component, forms a bidirectional interaction to enhance the semantic correlation between the local change information of the images and the textual information. To address the issue of global discrepancies between bitemporal images, a noise-based change extractor is designed, which reduces the model's focus on irrelevant factors by adding noise. Finally, the images-and-text interaction component constrains the global representations of both modalities through contrastive alignment, enhancing the global semantic consistency between the image and text in the high-level representation. Experiments on two public datasets show that our method outperforms the current state-of-the-art methods.
Inverse synthetic aperture radar (ISAR) images have become one of the most important pieces of information for airborne and maritime target identification. In general, ISAR images with higher resolution and lower background noise provide more precise target information, thus improving target identification accuracy. However, upgrading the resolution of the ISAR system is costly. Super-resolution algorithms that can utilize low-resolution echoes to obtain high-resolution imaging results have become an important means of improving ISAR imaging resolution. The traditional ISAR super-resolution imaging technique suffers from high side lobes and wide main lobes. In addition, denoising algorithms based on filtering operators tend to lead to image blurring. This work proposes a deep network for super-resolution and de-noising of ISAR images called SR-DNnet. Specifically, we view super-resolution and de-noising as a series of up-sampling, two-dimensional filtering, and threshold shrinkage. These operations are exactly what deep networks are good at. SR-DNnet has 15 layers, enabling 4x super-resolution and de-noising of ISAR images. The parameter scale of SR-DNnet is much smaller than most deep networks, which makes it efficient to train. The SR-DNnet we built features complex-value inputs, residual learning, multipath learning, and progressive up-sampling. A series of simulated and measured dataset experiments prove that the SR-DNnet is efficient and well-performed on super-resolution and de-noising.
IntroductionArtificial intelligence (AI) has been widely used to detect faults and failures in photovoltaic (PV) systems, particularly those that conventional protection devices fail to identify. However, previous AI-based approaches still face major limitations, including neglecting critical detection conditions, relying on large and complex datasets, and lacking simultaneous and accurate multi-fault detection and classification.MethodsTo address these challenges, a novel PV fault detection framework is proposed by combining a fuzzy logic (FL) system with a particle swarm optimization (PSO) algorithm. An initial dataset is generated from the current–voltage (I–V) curve of a PV array. Manhattan distance (MD) and Chebyshev distance (CD) features are extracted from the I–V characteristics. A wide set of machine-learning classifiers is evaluated, and the FL system nominates the most reliable models based on mean accuracy, F1-score, and standard deviation. PSO is then used to determine the optimal subset of classifiers and to assign optimized weights for ensemble prediction. Several output-combining techniques are also examined to obtain the most accurate final classification.ResultsModel verification is performed using a dataset that includes normal operation as well as line-to-line (LL), open-circuit (OC), and degradation (DEG) faults under various environmental (irradiance, temperature) and electrical (mismatch, impedance) conditions. The proposed FL+PSO-based model achieves outstanding accuracy in detecting and classifying multiple PV faults and outperforms recent state-of-the-art approaches.DiscussionThe integration of distance-based feature extraction, fuzzy-driven classifier selection, and PSO-optimized weighting significantly enhances robustness and reduces sensitivity to environmental variations. These improvements enable reliable multi-fault detection even when fault signatures closely resemble normal conditions.ConclusionThe proposed FL and PSO-based ensemble provides a highly accurate and reliable solution for multi-fault detection in PV arrays. Its performance surpasses existing approaches, making it a strong candidate for practical implementation in real PV monitoring systems.
Anurag Gupta, Debojyoti Ganguly, Mini Raman
et al.
In ocean colour remote sensing, radiance at the sensor level can be modeled using molecular scattering and particle scattering based on existing mathematical models and gaseous absorption in the atmosphere. The modulation of light field by optical constituents within the seawater waters results in the spectral variation of water leaving radiances that can be related to phytoplankton pigment concentration, total suspended matter, vertical diffuse attenuation coefficients etc. Atmospheric correction works very well over open ocean using NIR channels of ocean colour sensors to retrieve geophysical products with reasonable accuracy while it fails over sediment laden and/or optically complex waters. To resolve this issue, a combination of SWIR channels or NIR-SWIR channels are configured in some ocean colour sensors such as Sentinel- OLCI, EOS- 06 OCM etc. Ocean Colour Monitor (OCM)-3 on board EOS -06 was launched on Nov 26, 2022. It has 13 bands in VNIR (400-1010 nm range) with ~1500 km swath for ocean colour monitoring. Arabian Sea near Gujarat coast is chosen as our study site to showcase the geophysical products derived using OCM-3 onboard EOS-06.
The operational state of piston rings in marine diesel engines significantly influences the overall performance of the machinery. However, traditional data-driven diagnosis methods have problems with relying on manual feature extraction and failing to adequately leverage the temporal characteristics inherent in fault vibration signals. Therefor a fault diagnosis method based on long short-term memory neural network (LSTM) optimized by the improved beluga whale optimization algorithm (IBWO) is proposed in this paper. The LSTM process vibration signals, leveraging their gating mechanism for temporal feature extraction before classification via softmax. Setting optimal combinations of hidden layers and learning rates is difficult due to complexity and lengthy training times, making parameter optimization a significant challenge. The beluga whale optimization (BWO) algorithm for parameter optimization is employed to address this. Additionally, to reduce the risk of convergence to local optima, the balance factor is improved by replacing the linear function with a nonlinear function in the original algorithm. Finally, IBWO-LSTM is compared with BWO-LSTM, FOA-LSTM, PSO-LSTM and LSTM. Experimental validation shows that IBWO-LSTM outperforms BWO-LSTM, FOA-LSTM, PSO-LSTM, and standard LSTM, with an average accuracy higher than 90 %. Therefore, the IBWO-LSTM demonstrates better fault identification accuracy, providing a more precise solution for marine diesel engine piston ring fault diagnosis.
Among ship maneuvers, berthing/unberthing maneuvers are one of the most challenging and stressful phases for captains. Concerning burden reduction on ship operators and preventing accidents, several researches have been conducted on trajectory planning to automate berthing/unberthing. However, few studies have aimed at assisting captains in berthing/unberthing. The trajectory to be presented to the captain should be a maneuver that reproduces human captain's control characteristics. The previously proposed methods cannot explicitly reflect the motion and navigation, which human captains pay particular attention to reduce the mental burden in the trajectory planning. Herein, mild constraints to the trajectory planning method are introduced. The constraints impose certain states (position, bow heading angle, ship speed, and yaw angular velocity), to be taken approximately at any given time. The introduction of this new constraint allows imposing careful trajectory planning (e.g., in-situ turns at zero speed or a pause for safety before going astern), as if performed by a human during berthing/unberthing. The algorithm proposed herein was used to optimize the berthing/unberthing trajectories for a large car ferry. The results show that this method can generate the quantitatively equivalent trajectory recorded in the actual berthing/unberthing maneuver performed by a human captain.
Ocean and climate research benefits from global ocean observation initiatives such as Argo, GLOSS, and EMSO. The Argo network, dedicated to ocean profiling, generates a vast volume of observatory data. However, data quality issues from sensor malfunctions and transmission errors necessitate stringent quality assessment. Existing methods, including machine learning, fall short due to limited labeled data and imbalanced datasets. To address these challenges, we propose an ODEAL framework for ocean data quality assessment, employing AL to reduce human experts' workload in the quality assessment workflow and leveraging outlier detection algorithms for effective model initialization. We also conduct extensive experiments on five large-scale realistic Argo datasets to gain insights into our proposed method, including the effectiveness of AL query strategies and the initial set construction approach. The results suggest that our framework enhances quality assessment efficiency by up to 465.5% with the uncertainty-based query strategy compared to random sampling and minimizes overall annotation costs by up to 76.9% using the initial set built with outlier detectors.
Stuart C. Painter, Yuri Artioli, Fathimath Hana Amir
et al.
Nitrogen pollution is a widespread and growing problem in the coastal waters of South Asia yet the ecological impacts on the region’s coral ecosystems are currently poorly known and understood. South Asia hosts just under 7% of global coral reef coverage but has experienced significant and widespread coral loss in recent decades. The extent to which this coral ecosystem decline at the regional scale can be attributed to the multiple threats posed by nitrogen pollution has been largely overlooked in the literature. Here, we assess the evidence for nitrogen pollution impacts on corals in the central Indian Ocean waters of India, Sri Lanka and the Maldives. We find that there is currently limited evidence with which to clearly demonstrate widespread impacts on coral reefs from nitrogen pollution, including from its interactions with other stressors such as seawater warming. However, this does not prove there are no significant impacts, but rather it reflects the paucity of appropriate observations and related understanding of the range of potential impacts of nitrogen pollution at individual, species and ecosystem levels. This situation presents significant research, management and conservation challenges given the wide acceptance that such pollution is problematic. Following from this, we recommend more systematic collection and sharing of robust observations, modelling and experimentation to provide the baseline on which to base prescient pollution control action.
Science, General. Including nature conservation, geographical distribution
To solve linear and nonlinear eigenvalue problems, we develop a simple method by directly solving a nonhomogeneous system obtained by supplementing a normalization condition on the eigen-equation for the uniqueness of the eigenvector. The novelty of the present paper is that we transform the original homogeneous eigen-equation to a nonhomogeneous eigen-equation by a normalization technique and the introduction of a simple merit function, the minimum of which leads to a precise eigenvalue. For complex eigenvalue problems, two normalization equations are derived utilizing two different normalization conditions. The golden section search algorithms are employed to minimize the merit functions to locate real and complex eigenvalues, and simultaneously, we can obtain precise eigenvectors to satisfy the eigen-equation. Two regularized normalization methods can accelerate the convergence speed for two extensions of the simple method, and a derivative-free fixed-point Newton iterative scheme is developed to compute real eigenvalues, the convergence speed of which is ten times faster than the golden section search algorithm. Newton methods are developed for solving two systems of nonlinear regularized equations, and the efficiency and accuracy are significantly improved. Over ten examples demonstrate the high performance of the proposed methods. Among them, the two regularization methods are better than the simple method.
Parvathi Kooloth, Leslie M. Smith, Samuel N. Stechmann
One of the most important conservation laws in atmospheric and oceanic science is conservation of potential vorticity. The original derivation is approximately a century old, in the work of Rossby and Ertel, and it is related to the celebrated circulation theorems of Kelvin and Bjerknes. However, the laws apply to idealized fluids, and extensions to more realistic scenarios have been problematic. Here, these laws are extended to hold with additional fundamental complexities, including salinity in the ocean, or moisture and clouds in the atmosphere. In the absence of these additional complexities, it is known that potential vorticity is conserved following each fluid parcel; here, for a salty ocean or cloudy atmosphere, the general conserved quantity is potential vorticity integrated over certain pancake-shaped volumes. Furthermore, the conservation laws are also related to a symmetry in the Lagrangian, which brings a connection to the symmetry-conservation relationships seen in other areas of physics.
Moritz Korte-Stapff, Drew Yarger, Stilian Stoev
et al.
Leveraging available measurements of our environment can help us understand complex processes. One example is Argo Biogeochemical data, which aims to collect measurements of oxygen, nitrate, pH, and other variables at varying depths in the ocean. We focus on the oxygen data in the Southern Ocean, which has implications for ocean biology and the Earth's carbon cycle. Systematic monitoring of such data has only recently begun to be established, and the data is sparse. In contrast, Argo measurements of temperature and salinity are much more abundant. In this work, we introduce and estimate a functional regression model describing dependence in oxygen, temperature, and salinity data at all depths covered by the Argo data simultaneously. Our model elucidates important aspects of the joint distribution of temperature, salinity, and oxygen. Due to fronts that establish distinct spatial zones in the Southern Ocean, we augment this functional regression model with a mixture component. By modelling spatial dependence in the mixture component and in the data itself, we provide predictions onto a grid and improve location estimates of fronts. Our approach is scalable to the size of the Argo data, and we demonstrate its success in cross-validation and a comprehensive interpretation of the model.
Analyses of both altimetric data and in-situ measurements reveal patterns of meridionally-alternating, nearly zonal, coherent jet-like structures in many of the world ocean basins. In this context, recent Ocean General Circulation Model (OGCM) simulations that resolve such zonal jet-like features show that they also propagate, largely in the meridional direction. To investigate such propagation, a feature that has received scant attention, we consider the behavior of such jets in a two-layer quasi-geostrophic model on a closed mid-latitude basin and forced by a double-gyre wind forcing. In this setup, we find that jets are evident in the baroclinic mode of instantaneous fields, that they propagate largely in the meridional direction, and that the propagation is related to an advective mechanism.
Idealized and comprehensive ocean models at low resolutions cannot reproduce nominally-resolved flow structures similar to those presented in the high-resolution solution. Although there are various underlying physical reasons for this, from the dynamical system point of view all these reasons manifest themselves as a low-resolution trajectory avoiding the phase space occupied by the reference solution (the high-resolution solution projected onto the coarse grid). In order to solve this problem, a set of hyper-parameterization methods has recently been proposed and successfully tested on idealized ocean models. In this work, for the first time we apply one of hyper-parameterization methods (Advection of the image point) to a comprehensive, rather than idealized, general circulation model of the North Atlantic. The results show that the hyper-parameterization method significantly improves a non-eddy-resolving solution towards the reference eddy-resolving solution by reproducing both the large- and small-scale features of the Gulf Stream flow. The proposed method is much faster than even a single run of the coarse-grid ocean model, requires no modification of the model, and is easy to implement. Moreover, the method can take not only the reference solution as input data but also real measurements from different sources (drifters, weather stations, etc.), or combination of both. All this offers a great flexibility to ocean modellers working with mathematical models and/or measurements.
Romain Hugonnet, Fanny Brun, Etienne Berthier
et al.
The monitoring of Earth’s and planetary surface elevations at larger and finer scales is rapidly progressing through the increasing availability and resolution of digital elevation models (DEMs). Surface elevation observations are being used across an expanding range of fields to study topographical attributes and their changes over time, notably in glaciology, hydrology, volcanology, seismology, forestry, and geomorphology. However, DEMs frequently contain large-scale instrument noise and varying vertical precision that lead to complex patterns of errors. Here, we present a validated statistical workflow to estimate, model, and propagate uncertainties in DEMs. We review the state-of-the-art of DEM accuracy and precision analyses, and define a conceptual framework to consistently address those. We show how to characterize DEM precision by quantifying the heteroscedasticity of elevation measurements, i.e., varying vertical precision with terrain- or sensor-dependent variables, and the spatial correlation of errors that can occur across multiple spatial scales. With the increasing availability of high-precision observations, our workflow based on independent elevation data acquired on stable terrain can be applied almost anywhere on Earth. We illustrate how to propagate uncertainties for both pixel-scale and spatial elevation derivatives, using terrain slope and glacier volume changes as examples. We find that uncertainties in DEMs are largely underestimated in the literature, and advocate that new metrics of DEM precision are essential to ensure the reliability of future land elevation assessments.