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What are the main findings? * We construct a large-scale, high-resolution optical–SAR registration dataset, pairing 3-m SAR imagery from the HongTu-1 satellite with Google Earth optical imagery at zoom level 17, covering the major geographical regions of China, and we release the standardized pipeline—including full-scene pairing, DEM-based terrain correction, geometric refinement, standardized 512 × 512 slicing and multi-stage quality filtering—that was used to build it. * Our proposed Log-domain reformulation of the Total Variation (Log-TV) filter substantially improves SAR image preprocessing by converting the multiplicative speckle noise model into an additive one, thereby enabling effective suppression of speckle while preserving edge structures and providing a much cleaner foundation for subsequent keypoint detection. * Combining a machine learning-based edge filter (Structured Random Forest, SRF) with the hand-crafted phase congruency filter yields a strong synergistic effect for cross-modal optical–SAR edge filtering, producing more stable and consistent shared structural responses than either component alone. We construct a large-scale, high-resolution optical–SAR registration dataset, pairing 3-m SAR imagery from the HongTu-1 satellite with Google Earth optical imagery at zoom level 17, covering the major geographical regions of China, and we release the standardized pipeline—including full-scene pairing, DEM-based terrain correction, geometric refinement, standardized 512 × 512 slicing and multi-stage quality filtering—that was used to build it. Our proposed Log-domain reformulation of the Total Variation (Log-TV) filter substantially improves SAR image preprocessing by converting the multiplicative speckle noise model into an additive one, thereby enabling effective suppression of speckle while preserving edge structures and providing a much cleaner foundation for subsequent keypoint detection. Combining a machine learning-based edge filter (Structured Random Forest, SRF) with the hand-crafted phase congruency filter yields a strong synergistic effect for cross-modal optical–SAR edge filtering, producing more stable and consistent shared structural responses than either component alone. What are the implications of the main findings? * Large-scale, high-resolution optical–SAR datasets are both essential and scarce for registration and other downstream tasks. Only on larger and more complex benchmarks do the robustness and the true relative performance of competing algorithms become evident, making such datasets a necessary foundation for future research in this area. * Different imaging modalities require different filtering strategies: for heavily speckled data such as SAR imagery, regularisation in the logarithmic domain is more appropriate than directly applying denoisers designed for additive noise, highlighting the importance of modality-aware preprocessing in cross-modal registration. * Hybrid pipelines that integrate learning-based components with hand-crafted filters are a promising direction: beyond edge filtering, similar combinations of deep features and classical hand-crafted operators may also benefit cross-modal feature description and matching stages. Large-scale, high-resolution optical–SAR datasets are both essential and scarce for registration and other downstream tasks. Only on larger and more complex benchmarks do the robustness and the true relative performance of competing algorithms become evident, making such datasets a necessary foundation for future research in this area. Different imaging modalities require different filtering strategies: for heavily speckled data such as SAR imagery, regularisation in the logarithmic domain is more appropriate than directly applying denoisers designed for additive noise, highlighting the importance of modality-aware preprocessing in cross-modal registration. Hybrid pipelines that integrate learning-based components with hand-crafted filters are a promising direction: beyond edge filtering, similar combinations of deep features and classical hand-crafted operators may also benefit cross-modal feature description and matching stages. Optical and Synthetic Aperture Radar (SAR) image registration is a fundamental task in remote sensing information fusion, yet it remains challenging due to significant differences in imaging mechanisms, radiation characteristics, and noise properties between the two modalities. Existing public datasets suffer from limited resolution, small scale, and insufficient scene diversity, and these limitations have hindered algorithm development. This paper constructs a large-scale, high-resolution optical–SAR registration dataset based on the HongTu-1 satellite 3-m SAR imagery and Google Earth optical imagery at zoom level 17, covering diverse scenes across China with a standardized pipeline including terrain correction, geometric alignment, standardized slicing, and quality filtering. Building upon this dataset, a hand-crafted keypoint-based cross-modal registration method is proposed, incorporating multi-level edge filtering and hybrid feature detection. Unlike conventional hand-crafted methods such as RIFT, SRIF, and LNIFT, which mainly refine keypoint detection, description, or matching within a SIFT-style pipeline, the core novelty of this work lies in SAR-specific preprocessing and multi-level hybrid filtering. These components are designed to suppress speckle while extracting more stable and discriminative shared edge responses for cross-modal registration. An improved Log-domain Total Variation (Log-TV) denoising model is introduced for SAR preprocessing. A hybrid edge filtering framework combining phase congruency analysis and Structured Random Forest (SRF) edge detection is constructed within a Gaussian scale space. A dual-branch feature detection scheme integrating blob and corner features is designed with a robust orientation assignment strategy. Feature description uses the Gradient Location–Orientation Histogram (GLOH) descriptor with Principal Component Analysis (PCA) reduction, while geometric estimation employs the Fast Sample Consensus (FSC) algorithm. Experiments on the self-constructed HT dataset and on the public OSdataset and SAR2Opt benchmarks show that the proposed method consistently achieves low RMSE and high success rates. It also maintains competitive efficiency among hand-crafted methods while retaining strong robustness to scale and rotation variations.

Distributed Energy Resource (DER)-integrated power grids are vulnerable to cascading effects under concurrent cyber–physical attacks, where even minor disruptions in system states accumulate and amplify over time, leading to significant system failures. Traditional static risk assessment methods are insufficient for modeling these time-varying, dynamic scenarios, particularly in the context of concurrent attacks. This paper presents a dynamic risk assessment framework leveraging time-synchronized co-simulation, which integrates power system and communication network simulations within a unified time framework. Cyber-attack actions in the communication layer are mapped to corresponding physical disturbances in the distribution network, including voltage, frequency, and power variations. Using the resulting system state evolution trajectories, a Markov Decision Process (MDP)-based state transition tree captures the progression of system risk under concurrent attacks. This framework accounts for cumulative risk across different attack paths and identifies critical nodes and high-risk propagation paths within the network. By incorporating a concurrent event detector into the MDP model, the method quantifies evolving risk dynamics, overcoming the limitations of traditional static methods. Case studies on the IEEE 13-node test feeder and IEEE 14-bus system demonstrate that concurrent attacks result in a security risk metric 2.3 times higher than single-point attacks, validating the effectiveness of the proposed approach in identifying vulnerable nodes whose compromise could lead to cascading failures, supporting the risk-aware prioritization of defensive resources.

Consistency checking across independently developed implementations of the same industrial control protocol provides an effective signal for defect discovery because an implementation whose response deviates from the majority under identical inputs is more likely to contain faults or robustness issues. However, existing consistency checking methods remain difficult to apply to complex stateful protocols in practice, since sequence dependencies can cause error propagation, large test suites incur high execution cost across multiple implementations, and inconsistent outputs are costly to triage. This paper proposes ConDiffFuzz, a dependency-aware and dynamically adjusted hierarchical consistency checking method for industrial control protocol implementations. ConDiffFuzz analyzes dependencies among check sequences to optimize execution order and dynamically prunes and regenerates dependent sequences after failures to mitigate inconsistency error propagation. The checking process derives implementation-specific finite state machines and inconsistency records, which further support focused differential fuzzing, parallel execution across multiple implementations, and log-based anomaly triage. Experiments on five Modbus over Modbus/TCP implementations show that ConDiffFuzz achieves a test case acceptance rate of 86.00%, increases average path coverage to 74.46%, improves the average number of triggered anomalies by 12.28%, and reduces the false-positive rate by 20.94% compared with four representative baseline fuzzers (SPIKE, BooFuzz, PeachFuzzer, and Kitty).

As distributed terminals are increasingly integrated into modern power systems with high penetration of renewable energy and decentralized resources, access control mechanisms must support continuous and highly detailed trust assessment. Existing approaches based on machine learning primarily rely on network traffic features from a single source and analyze terminals in isolation, which limits their ability to capture complex device states and correlated attack behaviors. This paper presents a trust assessment framework for distributed power grid terminals that combines multidimensional behavioral modeling with dual domain graph neural networks. Behavioral features are collected from network traffic, runtime environment, and hardware or kernel events and are fused into compact representations through a variational autoencoder to mitigate redundancy and reduce computational overhead. Based on the fused features and observed communication relationships, two graphs are constructed in parallel: a feature domain graph reflecting behavioral similarity and a topological domain graph capturing communication structure between terminals. Graph convolution is performed in both domains to jointly model individual behavioral risk and correlation across terminals. A fusion mechanism based on attention is further introduced to adaptively integrate embeddings specific to each domain, together with a loss function that enforces both shared and complementary representations across domains. Experiments conducted on the CIC EV Charger Attack Dataset 2024 show that the proposed framework achieves a classification accuracy of 96.84%, while maintaining a recall rate above 95% for the low trust category. These results indicate that incorporating multidimensional behavior perception and dual domain relational modeling improves trust assessment performance for distributed power grid terminals under complex attack scenarios.

Network Intrusion Detection Systems(NIDSs) are crucial for resisting cyber threats. However, NIDSs equipped with supervised learning models do not generalize well to unknown attacks because the training samples for previously unseen new intrusions are usually not available in advance. Thus, a new framework based on a H ierarchical A ttention-based T riplet network with U nsupervised D omain A daptation(HAT-UDA) is proposed for this purpose. Concretely, a joint loss is introduced to force HAT-UDA to learn compact and discriminative embeddings for benign network traffic while being far from the representations of known attacks. Then, a One-class Support Vector Machine(OCSVM) model is trained on top of the benign embeddings for the unknown attack detection task. Furthermore, we propose an unsupervised domain adaptation module in an adversarial manner to reduce the false positives of HAT-UDA when applied to new network scenarios. HAT-UDA provides a novel approach for building a robust NIDS from benign traffic and available (known) attacks. This is particularly meaningful since collecting samples for benign traffic and known attacks is much easier than obtaining instances for unseen new attacks. Extensive experiments show that HAT-UDA outperforms other state-of-the-art methods and significantly improves the detection rate of unknown attacks.

Style-content dual-reference generation aims to synthesize an image that preserves the structure and semantics of a content reference while adopting the style of a separate style reference.Despite recent progress, this setting remains challenging because models must balance content fidelity, style alignment, and instruction following avoiding semantic leakage from the style reference.A key bottleneck is the lack of large-scale triplet data with clean content-style separation and broad long-tail style coverage.In this work, we propose FreeStyle, a scalable dual-reference generation framework based on community LoRA mining.We treat community LoRAs as compositional anchors for style and content, and design a rigorous generation and filtering pipeline to construct large-scale Style-Reference and Content-Reference triplets across multiple base models.To address content leakage, we adopt a two-stage curriculum with stage-specific disentanglement mechanisms: an attention-level enrichment constraint that suppresses style-reference leakage in the style-transfer stage, and a frequency-aware RoPE modulation strategy that targets positional-correspondence-based leakage in the harder dual-reference stage.We also introduce a benchmark covering both style-reference and dual-reference generation, with evaluations on style similarity, content preservation, aesthetics, instruction following, and leakage rejection. The benchmark incorporates a style-invariant Content Alignment Score (CAS) and introduces a calibrated VLM-based Rejection Score for evaluating generation reliability and leakage suppression.Extensive experiments show that our model achieves a strong balance among style alignment, content preservation, and leakage suppression.

Image restoration under real-world degradations is critical for downstream tasks such as autonomous driving and object detection. However, existing restoration models are often limited by the scale and distribution of their training data, resulting in poor generalization to real-world scenarios. Recently, large-scale image editing models have shown strong generalization ability in restoration tasks, especially for closed-source models like Nano Banana Pro, which can restore images while preserving consistency. Nevertheless, achieving such performance with those large universal models requires substantial data and computational costs. To address this issue, we construct a large-scale dataset covering nine common real-world degradation types and train a state-of-the-art open-source model to narrow the gap with closed-source alternatives. Furthermore, we introduce RealIR-Bench, which contains 464 real-world degraded images and tailored evaluation metrics focusing on degradation removal and consistency preservation. Extensive experiments demonstrate our model ranks first among open-source methods, achieving state-of-the-art performance.

Pre-trained video models learn powerful priors for generating high-quality, temporally coherent content. While these models excel at temporal coherence, their dynamics are often constrained by the continuous nature of their training data. We hypothesize that by injecting the rich and unconstrained content diversity from image data into this coherent temporal framework, we can generate image sets that feature both natural transitions and a far more expansive dynamic range. To this end, we introduce iMontage, a unified framework designed to repurpose a powerful video model into an all-in-one image generator. The framework consumes and produces variable-length image sets, unifying a wide array of image generation and editing tasks. To achieve this, we propose an elegant and minimally invasive adaptation strategy, complemented by a tailored data curation process and training paradigm. This approach allows the model to acquire broad image manipulation capabilities without corrupting its invaluable original motion priors. iMontage excels across several mainstream many-in-many-out tasks, not only maintaining strong cross-image contextual consistency but also generating scenes with extraordinary dynamics that surpass conventional scopes. Find our homepage at: https://kr1sjfu.github.io/iMontage-web/.

In this paper, we studied the limiting regimes α→n− and α→0+ in the affine Hardy–Littlewood–Sobolev (HLS) inequalities. Specifically, we established affine logarithmic HLS inequalities and affine Beckner-type logarithmic Sobolev inequalities for pairs of functions. The results extended classical logarithmic inequalities to the affine setting and recovered known sharp inequalities in the limiting cases.

In this paper, stronger versions of Stein–Weiss inequalities and reverse Stein–Weiss inequalities are established.