Field uniformity is essential in guaranteeing the validity of test results for electromagnetic field facilities. And, its evaluation has attracted a great deal of attention in various electromagnetic compatibility test standards and researches. Thus, this leads to many different rules to assess the important performance, which inevitably prone to bring some inconvenience in understanding, application, calculation or evaluation, in particular, not much relative words could not be found in the context. In this article, the relationship and rationale of these criteria are clarified and elucidated by statistical analysis and comparison. The discussions and conclusions are advantageous to help evaluate the field uniformity more clearly and accurately.

Deep learning-based data-driven methods have gained significant attention in underwater acoustic source localization. However, their performance is often constrained by environmental disturbances and the scarcity of real-world underwater acoustic data. To address these issues, this paper presents a novel network termed MTCL-Net, a multi-task learning network that incorporates contrastive learning as an auxiliary task for underwater acoustic source ranging. A standard dataset and a perturbed dataset to simulate real underwater interferences are constructed based on known environmental parameters in this method. A Siamese dual-branch architecture is employed, where a contrastive learning task enables the automatic extraction of position-related features. The network jointly optimizes three tasks: source localization in perturbed environments, localization on the standard dataset, and position similarity discrimination, which improves the robustness and generalization ability. The experimental results on simulated and sea trial data demonstrate that MTCL-Net outperforms traditional matched field processing (MFP), single-task learning (STL), and multi-task learning based on depth–range (MTL-DR) methods in terms of mean absolute error (MAE) and probability of credible localization (PCL-10%). Specifically, on SWellEx-96 sea trial data, MTCL-Net achieves an MAE of 0.17 km and a PCL-10% of 90.36%. Moreover, the proposed method only needs a few samples for fine-tuning and shows strong practicality in uncertain marine environments.

A large number of seabed depressions, covering an area of 2500 km² in the Xisha Massif of the South China Sea, are investigated using newly collected high-resolution acoustic data. By analyzing the morphological features and seismic attributes of the focused fluid flow system, five geological structures are recognized and described in detail, including pockmarks, volcanic mounds, pipes, faults, and forced folds. Pockmarks and volcanic mounds occur as clustered groups and their distributions are related to two large-scale volcanic zones with chaotic seismic reflections. Pipes, characterized by disordered seismic reflections, mainly occur within the focused fluid flow zone (FFFZ) and directly link with the large-scale deep volcano and its surrounding areas. Faults and fractures mainly occur along pipes and extend to the seafloor, commonly presenting lateral walls of mega-pockmarks. Forced folds are primarily clustered above volcanic zones and commonly restricted between faults or pipes, characterized by sediment deformations as indicated in seismic profiles. By comprehensive analysis of the above observations and a simplified simulation model, the volcanism-induced hydrothermal fluid activities are argued herein to contribute to these focused fluid flow structures. In addition, traces of suspected submarine instability disasters such as landslides have been found in this sea area, and more observational data will be needed to determine whether seafloor fluid flow zones can be used as a predictor of seafloor instability in the future.