Search Results (801)

We study underwater optical communication under photon-counting (Poisson) detection with realistic attenuation, background radiance, directionality, and pointing uncertainty. Information is embedded in (i) a temporal dictionary of pseudo-random noise (PRN) intensity sequences and (ii) an optional spatio-temporal extension, denoted SIM–TS (spatial-index modulation with temporal signaling), that combines temporal coding with spatial indexing across multiple transmit/receive apertures. For a fixed optical energy-per-symbol (photon budget), these structured waveforms increase observation dimensionality and improve maximum-likelihood separability under Poisson statistics. We present a layered modeling framework, derive the corresponding Poisson detection metrics, and use Monte Carlo evaluation to extract maximum range at a target symbol error rate. The results show that dimensional signaling provides a modest but repeatable gain in clear-water photon-limited regimes: at 100 kbps, SIM–TS increases the clear-water range from 593.8 m to 617.2 m at 450 nm (3.95%) and from 457.8 m to 473.4 m at 420 nm (3.41%) under fixed total power. In coastal water the gain falls below 1%, while in the 1 Gbps benchmark SIM–TS under fixed total power remains within about 2% of on–off keying (OOK) and the larger improvement under power combining is attributable primarily to increased photon budget. These rate–range trade-offs clarify when dimensional signaling yields practical gains and when attenuation, background, and misalignment dominate the link budget. Full article

Underwater towed survey systems are widely used for marine observation, resource exploration, and target identification. While high-speed towing is increasingly required to improve operational efficiency, conventional single-stage towed systems face a critical trade-off: active heave compensation systems are complex and costly, whereas purely passive configurations lack sufficient disturbance rejection at higher speeds. To address this gap, this study proposes a two-stage towing system consisting of a vessel, heavy cable, depressor, light cable, and detection towed body, where the depressor functions as a mechanical low-pass filter. The depressor reduces vessel-induced heave motion transmission by approximately 79% compared with a conventional single-stage system. CFD simulations are conducted to evaluate hydrodynamic performance and extract key coefficients. A lumped-mass dynamic model is established for time-domain motion simulations. An integral sliding-mode controller with vessel heave feedforward compensation is designed to enhance depth-tracking capability. The active controller eliminates step response overshoot and provides robust depth regulation under wave disturbances. Sea trials under real ocean conditions validate the system’s motion stability, demonstrating satisfactory depth-keeping performance at high towing speeds. The simulation results show good agreement with experimental data, confirming the effectiveness of the proposed system and dynamic model. This work offers a practically validated towing platform solution for high-precision underwater survey operations. Full article

This work presents a novel one-step plasma–solution synthesis of Prussian Blue (PB) and copper hexacyanoferrate (Cu-PBA) nanoparticles via underwater pulsed DC discharge. For the first time, the direct plasma-assisted formation of these coordination polymers is reported. The obtained materials were examined by X-ray diffraction, Fourier-transform infrared spectroscopy, Raman spectroscopy, and scanning electron microscopy (SEM). These analyses confirmed that the desired phases had formed, along with small amounts of oxide byproducts (α-Fe₂O₃, CuO) arising from the erosion of the electrodes. Photocatalytic activity was evaluated through the degradation of organic dyes (Reactive Red 6C, Rhodamine B, and Methylene Blue) under UV-light irradiation. Both catalysts achieved complete dye degradation within 90 min of UV irradiation (after an initial 30 min dark adsorption step, total experiment time 120 min). Notably, selective performance was observed: PB exhibited higher activity toward the cationic dye Methylene Blue, while Cu-PBA was more effective for the anionic dye Reactive Red 6C. This selectivity is attributed to the specific oxide impurities forming heterojunctions that facilitate charge separation and generate distinct reactive oxygen species. The plasma–liquid method offers a rapid and environmentally benign route to functional PBA-based composites, with potentially scalable characteristics pending further engineering optimization. These findings highlight the potential of utilizing synthesis-induced impurities to tailor photocatalytic selectivity for water purification applications. Full article

Underwater-transportation systems have significant potential for both military and commercial applications. Neural Network (NN)-based control offers enhanced robustness for actuators to manage the states of autonomous underwater-transportation systems which include Rigid-Connection Transportation Systems (RCTSs), Flexible-Connection Transportation Systems (FCTSs) and Leader–Follower-Formation Control Transportation Systems (LFFCTSs). In this study, NN-Augmented Control (NNAC) is applied to the aforementioned three transportation systems to enable accurate path tracking by the actuators installed onboard these systems under both ideal operating conditions and in the presence of sensor and process noise. The Extended Kalman Filter (EKF) is employed to estimate the system states under noisy conditions. The results demonstrate that NNAC provides robust and adaptive control of actuators, achieving efficient trajectory tracking via the transportation systems despite the influence of sensor and process noise disturbances. NNAC predominance was also observed in comparison with the conventional PID controller. Among the transportation configurations under the NNAC strategy, the RCTS exhibited the highest tracking accuracy with the lowest power consumption by the actuators. The power consumption of actuators installed on the LFFCTS was marginally higher than that of the RCTS. However, the translational motion accuracy of the follower vehicle in the LFFCTS was the lowest due to indirect actuation control through the formation controller. In contrast, actuators in the FCTS showed the highest power consumption while motion accuracy was comparatively lowest, attributed to the increased complexity of its dynamic positioning requirements. Full article

Effective operation and maintenance (O&M) of deep-sea observation networks are challenged by complex environments and energy limitations. While digital twin (DT) technology offers promising solutions, existing frameworks struggle with high-fidelity, multi-platform orchestration and predictions of electrical energy state. This study proposes a DT framework for a deep-sea observation network (DSON-DT), encompassing telemetry acquisition, predictive analytics, and feedback control to realize a closed-loop workflow for monitoring and managing platform states within virtual scenes. Powered by real-time Internet of underwater things (IoUT) data, a high-fidelity virtual environment is constructed in the Unreal Engine 5 game engine, accurately mapping ambient marine environments and reconstructing platform dynamic behaviors via data-driven approaches and geometric constraints. An improved auto-regressive long short-term memory (AR-LSTM) network is proposed to forecast the battery state of charge (SoC). Experimental results show that this algorithm effectively mitigates the impacts of severe deep-sea noise and the flat open-circuit voltage plateau, suppressing state oscillations to provide reliable references for proactive endurance management. The Vue.js-based web prototype, deployed via pixel streaming, offers seamless interfaces for interactive visualization, analysis, and remote operation. This research achieves comprehensive situational awareness for deep-sea platforms, providing validated technical support for the holistic evaluation and intelligent O&M of heterogeneous marine infrastructures. Full article

Cabled underwater information networks (CUINs) are a focal point and priority in the field of global marine science and technology. Reliability and economic viability are among the primary constraints on the large-scale deployment of such networks. The remote power supply system for grid-like CUINs is the component with the highest technical risk, exerting a significant impact on both network reliability and economic feasibility. This paper designs and constructs a minimal model and a basic model of a constant-current remote power supply system (CCRPSS) for grid-like CUINs. Through simulation modeling and analysis, the system’s capability to handle faults in a single underwater unit or multiple underwater units in different power supply link segments (PSLSs) is validated, and the impact of underwater unit faults on the system’s operational state is analyzed. Based on this, a descriptive method for determining the power supply reliability (PSR) of observation equipment (OE) is proposed, and the variation patterns of this reliability across different power supply links (PSLs) are derived. Building on this foundation, a constrained engineering design method for the grid-like CCRPSS is proposed. This method aims to deploy a larger number of secondary nodes (SNs) at a lower cost. By integrating constraints including the PSR of OE for each PSL, the open-circuit and short-circuit fault rates of underwater units, and the allowable number of SNs per PSLS, it optimizes the system engineering design problem. This approach yields an optimal solution for the number of longitudinally and transversely deployed SNs as well as the reliability requirements for each underwater unit. Case simulation results validate the descriptive method for the PSR of OE and the variation patterns of such reliability, thereby confirming the feasibility of the constrained engineering design approach. The research findings presented in this paper can provide theoretical references for the reliability analysis, scale design, and long-term planning of CUINs and their remote power supply systems. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

The Mediterranean Sea is a major biodiversity hotspot increasingly affected by biological invasions, climate warming, and habitat degradation. Among the most successful invaders are the rabbitfish species Siganus luridus and Siganus rivulatus, Lessepsian migrants from the Red Sea that are now widespread across the eastern Mediterranean. This study examined how these invasive herbivores influence native herbivore assemblages in shallow coastal habitats around Lipsi Island in the Aegean Sea, Greece. Using Underwater Visual Census (UVC) surveys and in situ feeding observations, we quantified the abundance and grazing activity of invasive rabbitfish relative to that of the native herbivores Sparisoma cretense and Sarpa salpa. Invasive rabbitfish represented approximately 35% of the herbivore assemblages and showed clear habitat and dietary preferences. Significant negative correlations were observed between invasive foraging activity and the feeding rate of the native S. cretense, while no such effect was found for S. salpa. High habitat overlap between S. luridus and S. cretense suggests that this native species may be more susceptible to competition on rocky substrates. Evidence of partial resource partitioning was observed, including increased use of seagrass habitats by S. salpa. These findings highlight how invasive herbivores can restructure native herbivore communities and alter grazing dynamics in eastern Aegean coastal ecosystems. Given the ongoing sea warming and widespread decline of seagrass habitats across the Mediterranean, understanding these competitive interactions is therefore essential for assessing future biodiversity trajectories and informing management strategies. Full article

This paper considers the problem of controlling the motion of an autonomous underwater vehicle (AUV) following a leader in a leader–follower scheme based on visual information about the leader’s position. It is assumed that the leader is equipped with a system of light markers with known geometry, and the follower determines its relative position based on data from an onboard camera without using a hydroacoustic communication channel or direct exchange of navigation information. To synthesize the control law, a reinforcement learning method based on the Proximal Policy Optimization algorithm is used. Policy learning is performed in a simulation environment, taking into account the dynamic model of the agent in the horizontal plane and observation noise. A structure of state space, actions, and reward function is proposed, aimed at minimizing the error in relative position and orientation. Additionally, Bayesian optimization of the weight coefficients of the reward function is performed. Bayesian optimization of the reward function weights reduces the RMS tracking error from 0.24 m to 0.09 m and demonstrates that heading regulation has a significantly stronger impact on stability than position penalties. The results of modeling, testing in the Webots environment, and experiments on MiddleAUV class devices confirm the feasibility and scalability of the approach. It is shown that a single trained policy ensures stable formation maintenance when the number of follower agents and initial conditions change without additional retraining. Full article

The quality of underwater laser welds is strongly dependent on the flow rates of the shielding and drainage gases. This study investigated the effect of argon and drainage gas flow rates on the formation, microstructure and mechanical properties of 304NG stainless steel using local dry underwater laser welding. At a water depth of 100 mm, with a laser power of 3.0 kW and a welding speed of 8 mm/s, the optimal conditions within the tested range were a shielding gas flow rate of 30 L/min and a drainage gas flow rate of 80 L/min. These conditions produced a continuous weld bead with an attractive surface and yielded the highest average maximum tensile load of 4.31 kN. Metallographic observations revealed that the weld metal primarily consisted of austenite alongside skeletal and lamellar ferrite, while the hardness along the weld depth remained relatively consistent at around 180 HV. These results demonstrate that matching the flow rates of the shielding and drainage gases properly is essential for stabilising the local dry cavity and improving weld quality and joint performance. Full article

Cross-interface architectures are increasingly central to large-scale ocean observation systems, where underwater sensor nodes transmit data to spatially distributed buoys that relay information to terrestrial networks. In these deployments, the inherent broadcast nature of underwater acoustic (UWA) propagation enables a single node’s signals to be captured by multiple buoys. However, substantial and dynamic propagation delays lead to inherent reception asynchrony and severe multi-user interference. Conventional detection relies on large hydrophone arrays on single platforms and assumes strict synchronization, hindering scalability and elevating costs. This study proposes a distributed asynchronous reception framework for buoy-assisted UWA networks. Under a cloud software-defined acoustic (C-SDA) architecture, spatially separated buoys are treated as a virtual distributed multiple-input multiple-output (MIMO) receiver. We introduce a minimum-delay-based equivalent reconstruction to regularize the asynchronous structure, followed by blind channel identification and pilot-assisted synchronization for robust multi-user detection. By leveraging long-delay broadcast propagation as a source of spatial diversity, the framework facilitates scalable and cost-effective multi-user access. The results demonstrate that the architecture provides a practical paradigm for the underwater Internet of Things and long-term ocean observation. Full article

Seafloor massive sulfide (SMS) deposits formed by hydrothermal circulation generate measurable self-potential (SP) anomalies in seawater, providing an effective geophysical indicator of sulfide mineralization. In this study, a remotely operated vehicle (ROV)-borne SP survey was conducted at the Yuhuang hydrothermal field on the Southwest Indian Ridge to investigate the spatial distribution of SMS mineralization. The survey operated at a near-bottom altitude of approximately 10 m, substantially lower than that typically achieved by autonomous underwater vehicles (AUVs) or towed systems, enabling high-resolution data acquisition with improved signal quality. To efficiently discretize complex seafloor topography under irregular data coverage, an adaptive octree mesh was employed, enabling computationally efficient three-dimensional inversion over a large survey area and recovery of the subsurface source current density distribution. The inversion results resolve a main anomaly zone spatially correlated with known SMS mineralization, as well as an additional anomaly zone that was not resolved by previous surveys and suggests potential mineralization. Anomalies associated with known mineralization show good spatial agreement with independent near-bottom observations and drilling results. The results demonstrate that ROV-borne SP surveying combined with adaptive meshing and three-dimensional inversion provides a reliable approach for imaging SMS mineralization in deep-sea environments. Full article

Autonomous navigation in underwater environments is challenged by the absence of GPS, degraded visibility, and submerged obstacles. This article investigates these issues using the BlueROV2, an open platform for scientific experimentation. We propose a deep reinforcement learning approach based on the Proximal Policy Optimization (PPO) algorithm, using an observation space that combines target-oriented navigation information, a virtual occupancy grid, and raycasting along the boundaries of the operational area. This information is encoded into a high-dimensional observation space of 84 dimensions, providing the agent with comprehensive local and global situational awareness. The learned policy is compared against a reference deterministic kinematic planner, the Dynamic Window Approach (DWA), a robust baseline for obstacle avoidance. The evaluation is conducted in a realistic simulation environment and complemented by validation on a physical BlueROV2 supervised by a 3D digital twin of the test site, reducing risks associated with real-world experimentation. The results show that the PPO policy consistently outperforms DWA in highly cluttered environments, notably thanks to better local adaptation and reduced collisions. Finally, experiments demonstrate the transferability of the learned behavior from simulation to the real world, confirming the relevance of deep RL for autonomous navigation in underwater robotics. Full article

Wind-driven surface processes are a major source of underwater ambient sound and are therefore an important component of coastal soundscapes. Yet their frequency-dependent expression in shallow nearshore reef environments remains insufficiently characterized from field observations. This study investigates low-to-mid-frequency (20–1000 Hz) ambient acoustic variability at Faro’s natural reef (southern Portugal) using short-term passive acoustic monitoring combined with concurrent sea state measurements. The results show evidence of a relationship between frequency-dependent acoustic response and wind-driven surface processes. At frequencies of 20–100 Hz, ambient sound levels exhibit a weak relationship with wind-driven surface conditions, with elevated variability under low agitation. This is attributed to persistent background anthropogenic noise, particularly vessel traffic. In contrast, above 100 Hz, the ambient sound level increases consistently with wind-driven agitation, indicating that wind-driven surface processes dominate ambient sound in the 100–1000 Hz frequency range. Transient high-energy peaks increase in frequency and intensity with surface agitation, consistent with breaking-wave events, even though elevated background sound levels persist after peak removal. These findings demonstrate that wind-related ambient sound variability at Faro’s natural reef is robustly expressed above approximately 100 Hz. This highlights the importance of frequency-dependent interpretation in passive acoustic monitoring as a necessary baseline for assessing the nearshore reef environment’s influence on ambient sound levels and acoustic propagation under variable sea state conditions. Full article

The position of a non-cooperative underwater pulse signal source can be estimated by applying target motion analysis techniques to the direction of arrival (DOA) and frequency of arrival (FOA) measurements obtained from a hydrophone array. However, the harsh underwater acoustic environment, with its pronounced multipath propagation, high signal attenuation, and sparse detectable pulses, introduces considerable errors into the estimation of DOA and FOA. These errors can degrade the performance of conventional estimators such as the pseudolinear estimation (PLE) method, leading to significant bias and divergence issues. To address these issues, this paper proposes a method based on nonlinear batch processing for underwater non-cooperative target localization. A cost function is constructed based on a nonlinear observation model and the weighted least squares principle to ensure high modeling fidelity. Subsequently, a multi-start grid search combined with a trust region dogleg algorithm is employed for global iterative optimization of the cost function, enhancing the accuracy and stability of the final position estimate. Numerical simulation results demonstrate that the proposed method achieves high convergence speed and localization accuracy under adverse noise conditions and with a limited number of received pulses. Moreover, the sea trial results confirm that the algorithm attained a convergence rate of 93% with only 25 received pulses, and outperformed the conventional PLE method by approximately 80% in terms of positioning accuracy. Full article