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It is important to appropriately model underwater sound speed structures to detect seafloor displacements accurately using GNSS-acoustic seafloor geodetic observations. In recent years, various sea surface platforms (e.g., wave gliders) have been developed for GNSS-acoustic observations. Sub-mesoscale oceanic phenomena can be detected by simultaneously employing multiple sea surface platforms. However, the use of a single sea surface platform with slow navigation speeds may degrade the modeling accuracy of underwater sound speed structures, even when compared to conventional ship-based observations. Therefore, the development of a GNSS-acoustic positioning technique that expresses a complex underwater sound speed structure and simultaneously provides constraints on sound speed parameters, if necessary. This study arranges the observation equation by considering multiple-layered sound speed gradients and develops a GNSS-acoustic positioning scheme using a Bayesian framework. The performance of the proposed GNSS-acoustic positioning method was investigated using synthetic datasets. The proposed method successfully modeled a complex underwater sound speed structure (e.g., temporal variations in sound speed gradients) using a dataset collected by dual sea surface platforms, which is highly sensitive to the underwater sound speed structure. It also provides robust solutions, even for a dataset with low sensitivity, by appropriately introducing constraints on the sound speed parameters. Moreover, the proposed method was applicable to an actual observational dataset, and it was confirmed that the GNSS-acoustic positioning method under special conditions (assumption of a temporally constant single-layered sound speed gradient) in a previous study can be reproduced by the constraints in the proposed method. Thus, the proposed method enabled us to flexibly model the underwater sound speed structure and accurately detect seafloor displacements for various types of observation datasets. The proposed method was implemented in the open-source GNSS-acoustic positioning software “SeaGap.” Graphical abstract

Reconstructing the topography of shallow underwater environments using Structure-from-Motion—Multi View Stereo (SfM-MVS) techniques applied to aerial imagery from Unmanned Aerial Vehicles (UAVs) is challenging, as it involves nonlinear distortions caused by water refraction. This study presents an experiment with aerial photographs collected with a consumer-grade UAV on the shallow-water reef of Fuvahmulah, the Maldives. Under conditions of rising tide, we surveyed the same portion of the reef in ten successive flights. For each flight, we used SfM-MVS to reconstruct the Digital Elevation Model (DEM) of the reef and used the flight at low tide (where the reef is almost entirely dry) to compare the performance of DEM reconstruction under increasing water levels. Our results show that differences with the reference DEM increase with increasing depth, but are substantially larger if no underwater ground control points are taken into account in the processing. Correcting our imagery with algorithms that account for refraction did not improve the overall accuracy of reconstruction. We conclude that reconstructing shallow-water reefs (less than 1 m depth) with consumer-grade UAVs and SfM-MVS is possible, but its precision is limited and strongly correlated with water depth. In our case, the best results are achieved when ground control points were placed underwater and no refraction correction is used.

In order to study the dynamic characteristics of an innovative underwater structure, Submerged Floating Tunnel (SFT), a model test study was carried out in a calibrated experimental tank based on the Qiongzhou Strait cross-sea passage project and considering the effects of wave and current loads. The size of the test tank is 36m (length) x 31m (width) x 3m (depth), which can simulate the effect of wave and current simultaneously. Under the conditions of pure current, pure wave and wave-current coupling, the pressure change of the test pipe is monitored, and the influence of the wave-current load on the surface pressure of the test pipe is analyzed. The experimental results show that the pressure at the inlet side of the test pipe increases with the increase of the current velocity, the wave height and period, regardless of whether the test pipe section is subjected to pure current, pure wave or wave-current coupling.

In certain precision work scenarios, underwater robots require the ability to adhere to surfaces in order to perform tasks effectively. An efficient and stable suction device plays a pivotal role in the functionality of such underwater robots. The vortex suction cup, distinguished by its uncomplicated design, high suction efficiency, and capability for non-contact adhesion, holds significant promise for integration into underwater robotic systems. This paper presents a novel design for a vortex suction cup and investigates its suction force and torque when encountering surfaces with varying curvature radii using Computational Fluid Dynamics (CFD) simulations and experimental testing. These findings offer valuable insights for the development of robots capable of adapting to underwater structures of different dimensions. Results from both experiments and simulations indicate that reducing the curvature radius of the adhered surface results in a decrease in suction force and an increase in torque exerted on the suction cup. As the adhered surface transitions from flat to a curvature radius of 150 mm, the adhesion force of our proposed vortex suction cup decreases by approximately 10%, while the torque increases by approximately 20% to 30%. Consequently, the adhesion efficiency of the suction cup decreases by about 25% to 30%.

Biomimetic superhydrophobic surfaces display many excellent underwater functionalities, which attribute to the slippery air mattress trapped in the structures on the surface. However, the air mattress is easy to collapse due to various disturbances, leading to the fully wetted Wenzel state, while the water filling the microstructures is difficult to be repelled to completely recover the air mattress even on superhydrophobic surfaces like lotus leaves. Beyond superhydrophobicity, here we find that the floating fern, Salvinia molesta, has the superrepellent capability to efficiently replace the water in the microstructures with air and robustly recover the continuous air mattress. The hierarchical structures on the leaf surface are demonstrated to be crucial to the recovery. The interconnected wedge-shaped grooves between epidermal cells are key to the spontaneous spreading of air over the entire leaf governed by a gas wicking effect to form a thin air film, which provides a base for the later growth of the air mattress in thickness synchronously along the hairy structures. Inspired by nature, biomimetic artificial Salvinia surfaces are fabricated using 3D printing technology, which successfully achieves a complete recovery of a continuous air mattress to exactly imitate the superrepellent capability of Salvinia leaves. This finding will benefit the design principles of water-repellent materials and expand their underwater applications, especially in extreme environments.

This work establishes a three-dimensional fluid-structure coupling calculation model for the response of SPH-RKPM underwater explosion structures and verifies its effectiveness. Using the established numerical model, the dynamic response of the water-containing liquid tank structure under underwater explosion shock wave load was modeled and calculated, and the response characteristics of the water-containing liquid tank under different detonation distances were discussed. The response law of the water-containing liquid tank structure under shock wave load is summarized.

Underwater object detection plays a crucial role in safeguarding and exploiting marine resources effectively. Addressing the prevalent issues of limited storage capacity and inadequate computational power in underwater robots, this study proposes FEB-YOLOv8, a novel lightweight detection model. FEB-YOLOv8, rooted in the YOLOv8 framework, enhances the backbone network by refining the C2f module and introducing the innovative P-C2f module as a replacement. To compensate for any potential reduction in detection accuracy resulting from these modifications, the EMA module is incorporated. This module augments the network’s focus on multi-scale information, thus boosting its feature extraction capabilities. Furthermore, inspired by Bi-FPN concepts, a new feature pyramid network structure is devised, achieving an optimal balance between model lightness and detection precision. The experimental results on the underwater datasets DUO and URPC2020 reveal that our FEB-YOLOv8 model enhances the mAP by 1.2% and 1.3% compared to the baseline model, respectively. Moreover, the model’s GFLOPs and parameters are lowered to 6.2G and 1.64M, respectively, marking a 24.39% and 45.51% decrease from the baseline model. These experiments validate that FEB-YOLOv8, by harmonizing lightness with accuracy, presents an advantageous solution for underwater object detection tasks.

Underwater quantum communication has recently been explored using polarization and orbital angular momentum (OAM). Here, we show that spatially structured modes, e.g., a coherent superposition of beams carrying both polarization and OAM, can also be used for underwater quantum cryptography. We also use the polarization degree of freedom to investigate the impact of the channel length on key rates for quantum communication applications. The underwater channel proves to be a difficult environment for establishing quantum communication as underwater optical turbulence results in significant beam wandering and distortions. However, the errors associated to the turbulence do not result in error rates above the threshold for establishing a positive key in a quantum communication link with both the polarization and spatially structured photons. The impact of the underwater channel on the spatially structured modes is also investigated at different distances using polarization tomography.

Aiming at the application scenarios of underwater structure gas leakage detection in marine oil and gas development process, the power spectrum characteristics of underwater structure gas leakage radiation noise are analyzed. An new target direction finding algorithm is proposed, and the gas leakage detection-finding software is designed. Then, the test platform is built to complete the experimental study of underwater structure gas leak detection. The test results show that the new target direction finding algorithm used in this paper is compared with the conventional beam forming algorithm. The difference between the maximum beam energy value and the sub-maximum value is bigger, indicates that the target's resolving power is strong, which effectively improves the detection capability of gas leakage targets in underwater structures.

Since its beginning, around the 50s decade, until present days, the area of unmanned underwater vehicles (UUV) has considerably grown through time; those have been used for many tasks and applications, from bomb searching and recovery to sea exploration. Initially, these robots were used mainly for military and scientific purposes. However, nowadays, they are very much extended into civils, and it is not hard to find them being used for recreation. In this context, the present research is an effort to make a walkthrough of evolution in this area, showing a diversity of structure designs, used materials, sensor and instrumentation technologies, kinds and the number of actuators employed, navigation control techniques, and what is new in development trends. The paper gives a clear starting point for those who are initializing into this research area; also, it brings some helpful knowledge for those who already have experience.