Electromagnetic (EM) exploration is one of the most important geophysical methods for monitoring hydraulic fracturing. However, recently published research has focused on the effects of steel infrastructures while neglecting the contribution of the cement layer that interfaces between the casing and the surrounding geological formation. Additionally, they have ignored the effects of the anisotropic nature of subsurface structures, arising from both hydraulic fractures and natural fissures. In this article, we first analyze the transient EM (TEM) responses in various conductive backgrounds with cement-coated steel casing boreholes. We then investigate the effects of the anisotropic nature of subsurface structures caused by hydraulic fractures and natural fissures. To balance computational efficiency with accuracy, we model the hollow steel casing as a rectangular prism. Numerical results demonstrate that the presence of cement-coated steel casing not only alters the electrostatic field within the casing but also affects the distribution of geometric currents in the surrounding geological formation. Moreover, electrical anisotropy significantly influences the TEM responses. If processed with an isotropic model, potential artifacts could be introduced into the target images. These findings could enhance the accuracy of geophysical surveys in hydraulic fracturing operations, leading to more reliable detection and characterization of subsurface fractures and improving the management of extraction processes.

Relational databases are widely applied in various industries, such as government departments, medical institutions and enterprises, for data storage and relationship management. It is convenience to data maintenance, however, the data in it maybe vulnerable to be forged or tampered with. Therefore, protecting the copyright of relational databases is a critical issue. Fortunately, watermarking technology can be used to prove copyright ownership. Since the digital watermarking technology can solve this problem effectively, based on semantic, we propose two watermarking approaches with high security and strong robustness for numeric and non-numeric data of relational databases in this paper. On the one hand, we propose a reversible numeric watermarking approach. It performs the replacement operation at the semantic level and retain the statistical characteristics of the data. On the other hand, based on word segmentation and word embedding, the non-numerical attributes of the relational database with natural language are chosen to embed watermark. It is noteworthy that the proposed mechanism can be applied to both Chinese and English with minimum distortion. Additionally, we also propose the virtual splitting of attribute column and the principle of modification minimum to guarantee the capacity of watermark and reduce the data modification rate. Additionally, the BCH (31,16,7) error control code is added to the binary watermark string to improve the detection rate of watermark. Based on the above innovations, the security of watermarking algorithm is improved successfully by double encryption (chaos encryption and hash encryption) with two keys (the user's private key and the attribute column key). The simulation results demonstrate that the proposed two algorithms in this paper have stronger robustness on defensing malicious attacks than previous works.

Recently, most existing single image dehazing methods adopt the physical scattering model to generate clear images. The model variables are often estimated by trainable neural networks. However, estimating the variables heavily rely on dataset that usually does not take into account of the physical process induced by the scattering model. In this scheme, error accumulation cannot be avoided when applying the end-to-end methods. In this paper, we propose a physical constraint generative adversarial network (PHC-GAN) for single image dehazing. The PHC-GAN is a physics aware model that leveraging the physical scattering process as an additional constraint. To the best of our knowledge, we are the first introducing physical constraint in learning an end-to-end image dehazing model. Our proposed model not only effectively reduce the error accumulation, but can be well adapted in complex and realistic natural scenes compared to the existing methods. In detail, we realize the physical constraint in terms of a double discriminator architecture. The self-attention module is also utilized to guarantee fast convergence. In experiments, quantitative and qualitative results on both synthetic and natural images demonstrate that PHC-GAN is superior to state-of-the-art dehazing methods.