Borehole radar, due to its high resolution and extensive radial detection capability, has become an important geophysical tool for detecting complex subsurface fractures. The key to evaluating fracture detectability lies in accurate fracture modeling and numerical simulation. To overcome the limitations of conventional fracture modeling approaches, including geometric oversimplification, inadequate representation of aperture and fracture surface correlation, and incomplete characterization of roughness, we propose a multi-factor three-dimensional (3D) rough fracture modeling method. This method integrates two-dimensional (2D) image reconstruction with the Weierstrass-Mandelbrot (W-M) fractal function, which enables a comprehensive description of fracture geometry, surface roughness, aperture, and correlation between the surfaces of a fracture. Based on the developed models, full-wave electromagnetic simulations of borehole radar are conducted using the finite-difference time-domain (FDTD) method, and the effects of fracture attitudes on radar responses are systematically investigated. The simulation results indicate that variations in dip angle and dip direction significantly influence the characteristics of the borehole radar signals. Fracture surface roughness is also found to introduce perturbations in echo details. Furthermore, the radar migration imaging results are more conducive to the evaluation of fracture attitude, as systematic simulation analysis demonstrates a high morphological consistency between the radar migration imaging results and the geometric projection of the fracture onto the Borehole-Fracture Coupling Plane (BFCP). This is further confirmed by the centroid offset distance and the Intersection over Union (IoU). In addition, the “dip direction ambiguity” in omnidirectional borehole radar detection is revealed, where fractures symmetric about the BFCP generate highly similar radar responses, thereby increasing the difficulty of interpretation. The presented fracture modeling method and observed response patterns from fractures support the accurate detection of complex fractures using borehole radar.

Network traffic classification is critical in network management, quality of service optimization, and security monitoring. However, most existing methods for encrypted traffic classification rely heavily on supervised learning, requiring large amounts of labeled data, and struggle to perform effectively in complex and dynamic network environments. To address these limitations, we propose a novel unsupervised method for encrypted traffic classification, which analyzes byte rate variations to capture traffic behavior patterns. Our approach does not require prior knowledge or large volumes of labeled data, enabling adaptive processing of encrypted traffic in complex network conditions. Specifically, we introduce a noise-resilient shape-line extraction method that preserves core behavioral characteristics of traffic; we design a multidimensional feature extraction strategy that analyzes both uplink and downlink features; and we propose an unsupervised classification algorithm that combines shape-based density clustering with a feature assignment strategy. This algorithm overcomes the limitations of traditional methods, such as the need for predefined cluster numbers, and can classify unknown traffic patterns. We validate our method on five real-world traffic datasets with differing levels of openness, demonstrating its remarkable robustness and accuracy in encrypted traffic classification tasks, thereby greatly enhancing the precision and stability of service classification.

The Vehicular Social Networks (VSNs) provide passengers, drivers and vehicles with multiple services, such as safe driving, data sharing and traffic management. However, transmitting data in VSNs can expose information such as the user’s identity and location. Malicious users who tamper with shared data can even cause serious traffic accidents. Considering the privacy protection and secure transmission of shared data in VSNs, we propose a blockchain-based and fog computing-assisted data access control scheme (BFDAC). We combine the multi-authority CP-ABE algorithm with the consortium blockchain to avoid the security and trust issues in the form of centralized key management, and outsource part of the decryption calculations to roadside units (RSUs) as fog nodes to realize lightweight calculation for users. Our BFDAC scheme also supports user tracking and revocation, while users can also revoke shared data saved in the cloud. Security analysis shows that the BFDAC scheme can effectively protect the shared data. Experiments show that our BFDAC scheme reduces 32.8% in storage cost, 9.5% in encryption cost, and 57.1% in outsourced decryption cost compared to previous ones.

Denoising is a crucial step in ground penetrating radar (GPR) data processing. Conventional denoising algorithms for GPR typically require selecting optimal processing parameters, which can be challenging to achieve in practical applications, resulting in unsatisfactory processing outcomes. In recent years, in order to address the issue of low accuracy in conventional GPR denoising algorithms, denoising neural networks have been applied in the field of GPR. Although conventional denoising neural networks have shown improvements in signal-to-noise ratio (SNR) in some cases, their performance is often inadequate when facing real GPR data with complex random noise, due to the training methods of the networks. To address the challenges in denoising of GPR data, a two-stage denoising method based on deep learning (DL) has been proposed. Initially, conventional GPR data processing is conducted, followed by training a denoising network model using both the processed and unprocessed signals. Leveraging the powerful nonlinear fitting capability of convolutional neural networks (CNNs), an end-to-end mapping relationship is established to obtain the final denoising network model, completing the two-stage denoising process. Finally, this letter validates the proposed two-stage denoising method using synthetic and field data. The radar data obtained through this two-stage denoising method not only improve mean squared error (mse) by 0.17 compared to conventional methods but also increase peak SNR (PSNR) by 8.1. Furthermore, there is a significant enhancement in the integrity of the waveform and the recovery of weak signals.

In the traditional blockchain system, data is public and cannot be redacted. With the development of blockchain technology, the problem that the data cannot be altered will be more serious once it is written on the chain. Recently, some redactable blockchain schemes have been proposed. However, most of the schemes are based on the public blockchain, and the users’ identities and transaction data may be disclosed. To solve the problem of privacy disclosure, we propose a privacy-preserving transaction-level redactable blockchain. In the proposed scheme, symmetric encryption and ring signature are used to protect transaction data and the users’ identities, respectively. In order to prove the legality of data redaction, the transaction sender can reveal the invalid users’ identities and transaction data in an anonymous environment. To construct a transaction-level redactable blockchain, the users only need to replace a single transaction to complete the data redaction instead of replacing the entire block. The experimental results show that the proposed scheme saves 20% of the redaction time compared to the previous privacy-preserving blockchains, so the redaction efficiency is higher.

In this paper, we propose a novel variational generator framework for conditional GANs to catch semantic details for improving the generation quality and diversity. Traditional generators in conditional GANs simply concatenate the conditional vector with the noise as the input representation, which is directly employed for upsampling operations. However, the hidden condition information is not fully exploited, especially when the input is a class label. Therefore, we introduce a variational inference into the generator to infer the posterior of latent variable only from the conditional input, which helps achieve a variable augmented representation for image generation. Qualitative and quantitative experimental results show that the proposed method outperforms the state-of-the-art approaches and achieves the realistic controllable images.