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Water quality monitoring is essential for protecting aquatic ecosystems and detecting environmental pollution. This paper presents the design and experimental validation of a bio-inspired miniature submarine for low-cost water quality monitoring. Inspired by the jet propulsion mechanism of squids, the proposed system employs pump-driven water jets for propulsion and steering, combined with a pump-based buoyancy control mechanism that enables both depth regulation and water sampling. The vehicle integrates low-cost, commercially available components including an ESP32 microcontroller, IMU, pressure sensor, GPS receiver, and LoRa communication module. The complete system can be constructed at a hardware cost of approximately $122.5, making it suitable for educational and environmental monitoring applications. Experimental validation was conducted through pool tests and field trials in a lake. During a 360 degrees rotation test, roll and pitch deviations remained within +/-2 degrees and +/-1.5 degrees, respectively, demonstrating stable attitude control. Steering experiments showed a heading step response with approximately 2 s rise time and 5 s settling time. Depth control experiment

This article includes a discussion of the ``lost submarine problem", following Morey \emph{et al} (2016). As the title of that paper suggests (\emph{The fallacy of placing confidence in confidence intervals}), the example is intended to illustrate the futility of relying on the confidence interval as a formal inference statement. In the view of this author, the misgivings expressed in Morey \emph{et al} (2016) can be resolved using a decision theoretic approach. While it is true that a variety of statistical methods lead to a variety of confidence intervals, once we precisely define their purpose, a single optimal choice emerges. Furthermore, distinct purposes lead to distinct optimal choices. Therefore, that a variety of procedures exist is an advantage rather than a liability.

This paper addresses a critical gap in hydroacoustics through a systematic wind tunnel investigation of wall-pressure fluctuations on the fully appended DARPA SUBOFF model at operationally relevant Reynolds numbers ranging from $5.6 \times 10^{6}$ to $1.4 \times 10^{7}$. The experimental campaign encompasses baseline straight-ahead flow, complex maneuvering (yaw and pitch) conditions, and a first-of-its-kind assessment of a novel vortex control baffle (VCB). To ensure benchmark-quality spectral data, rigorous signal processing techniques were applied, specifically Wiener filtering for background noise suppression and dynamic transfer function correction for pinhole sensors. Key findings indicate that while spectral self-similarity holds across Reynolds numbers, the primary finding is the critical role of appendages in noise amplification. Unstable horseshoe vortex dynamics at the sail-hull junction drive localized pressure fluctuations of up to 300%, establishing this feature as a major coherent noise source. To address this, the study provides the pioneering experimental validation of the VCB. By physically suppressing horseshoe vortex formation at the sail-hull junction, the VCB

Submarine power and telecommunication cables constitute critical global infrastructure, yet they remain vulnerable to mechanical damage caused by maritime activities and intentional tampering. Continuous monitoring of these assets is therefore essential for early detection of anomalous events. This paper proposes a model-based framework for real-time anomaly detection in submarine cables using spatially distributed deformation measurements along the cable. The cable is modeled as a tensioned structure governed by a damped wave equation with fixed boundary conditions. A finite-dimensional state-space representation is obtained through spatial discretization, enabling the use of a Kalman filter to estimate the cable's dynamic state under stochastic environmental disturbances. Anomaly detection is then formulated as a statistical hypothesis test applied to the innovation sequence of the filter. Compared with purely data-driven alarms, the proposed framework provides an interpretable residual signal whose threshold can be related to a prescribed false-alarm probability. Numerical simulations demonstrate that the proposed framework can reliably identify localized disturbances while rema

This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic Sensing (DAS), which is one of the distributed fiber-optic sensing technologies. To address environmental variability and limited training data in offshore environments, a regression-based feature extraction method was introduced to derive low-dimensional latent representations that retain exposure length-dependent vibration characteristics while suppressing environmental influences. The extracted features were used for one-class Support Vector Machine (SVM)-based anomaly detection. The proposed framework was evaluated through wave-tank experiments with exposure lengths ranging from 2 to 10 m. Experimental results showed that anomaly scores decreased approximately monotonically with increasing exposure-length change, exhibiting a strong correlation ($r = -0.83$). The binary classification achieved an F1 score of 0.82 despite training with only small-sample datasets. These findings demonstrate that exposure-length variations can be reliably detected under severe data limitations, supporting the potential of DAS-based cable condition mo

Nuclear-powered submarines are difficult to track with conventional methods in congested waterways. We revisit antineutrino-based detection as a barrier concept, analogous to a neutrino-enabled SOSUS-style fence in strategic straits. Using analytic scaling relations and numerical estimates, we show that detectability depends primarily on closest approach, detector depth, and deployed mass. For representative assumptions, a 20\,kt detector in the Strait of Gibraltar reaches a local benchmark score $Z_A\simeq2.54$ for an assumed 100\,MW thermal-power sensitivity-study case in a conservative worst-case transit (with Poisson operating point $(P_\mathrm{FA},P_\mathrm{det})\simeq(5.5\times10^{-3},0.51)$ at threshold $k=2$), while a three-detector line raises the mapped score to $Z_A\simeq4.66$. For broad ocean passages such as GIUK, required detector counts are substantially larger; in the baseline maximum passing distance $\mathrm{PDD}_{\max}=5$\,km geometry, about 80 detectors yield only $Z_A\sim1.6$. The paper outlines detector technology choices, statistical assumptions, and deployment constraints for a first-generation feasibility assessment.

Submarine cables play a critical role in global internet connectivity, energy transmission, and communication but remain vulnerable to accidental damage and sabotage. Recent incidents in the Baltic Sea highlighted the need for enhanced monitoring to protect this vital infrastructure. Traditional vessel detection methods, such as synthetic aperture radar, video surveillance, and multispectral satellite imagery, face limitations in real-time processing, adverse weather conditions, and coverage range. This paper explores Distributed Acoustic Sensing (DAS) as an alternative by repurposing submarine telecommunication cables as large-scale acoustic sensor arrays. DAS offers continuous real-time monitoring, operates independently of cooperative systems like the "Automatic Identification System" (AIS), being largely unaffected by lighting or weather conditions. However, existing research on DAS for vessel tracking is limited in scale and lacks validation under real-world conditions. To address these gaps, a general and systematic methodology is presented for vessel detection and distance estimation using DAS. Advanced machine learning models are applied to improve detection and localizatio

When coping with the urgent challenge of locating and rescuing a deep-sea submersible in the event of communication or power failure, environmental uncertainty in the ocean can not be ignored. However, classic physical models are limited to deterministic scenarios. Therefore, we present a hybrid algorithm framework combined with dynamic analysis for target submarine, Monte Carlo and Bayesian method for conducting a probabilistic prediction to improve the search efficiency. Herein, the Monte Carlo is performed to overcome the environmental variability to improve the accuracy in location prediction. According to the trajectory prediction, we integrated the Bayesian based grid research and probabilistic updating. For more complex situations, we introduced the Bayesian filtering. Aiming to maximize the rate of successful rescue and costs, the economic optimization is performed utilizing the cost-benefit analysis based on entropy weight method and the CER is applied for evaluation.

The recent progress in low-loss hollow-core fibers allows to speculate on the possibility of building a transatlantic submarine cable that can achieve the goal of 1 Pb/s per direction, leveraging bidirectional transmission, and at the same time drastically increase span length, theoretically to 200km. In this version, we add the analysis of the impact of Rayleigh backscattering.

We present a multichannel hybrid quantum cryptography approach intended for submarine quantum optical communications between Alice and Bob separated a distance beyond the current QKD possibilities, each located on a coastline. It is based on the difficult of a simultaneous access to $M$ optical submarine channels. The optical lines from the coastline and ideally to the end of the continental platform are governed by the quantum properties of the light under an autocompensating high-dimensional discrete-modulation continuous variable QKD protocol. The hybrid approach consists of combining several secret keys of the $M$ channels and introducing extra layers of security, passive and/or active, on the non quantum optical lines located beyond the continental platform.

Submarines are vital for maritime defense, requiring optimized hydrodynamic performance to minimize resistance. Advancements in Computational Fluid Dynamics (CFD) enable accurate predictions of submarine hydrodynamics for optimal design. This study compared the meshing capabilities of OpenFOAM and commercial software as well as the performance of High-Performance Computing (HPC) and standard PC resources upon hydrodynamic characteristics. The RANS turbulence model with was employed to analyze the resistances of the MARIN's BB2-class submarine. CFD simulations were conducted at a model scale (1:35.1) at a speed of 1.8235 m/s ( of 21 knots) upon various mesh densities from 1 to 97 million cells. Empirical equations were initialized for turbulence parameters. Mesh sensitivity and iteration convergence ensured validated results. The findings showed that the results were validated with errors ranging from 0.3% to 10% across different mesh densities. The lowest error (0.3%) was achieved with 97 million cells generated by the commercial meshing tool with HPC, while 13 million cells by OpenFOAM with a standard PC resulted in a 3.4% error. Accuracy improved with precise initialization of tu

Submarine hydrodynamics presents unique challenges in accurately predicting flow separation, wake structure, and resistance due to complex geometry and turbulent behaviour at high Reynolds (Re) numbers. Traditional Reynolds-Averaged Navier-Stokes (RANS) approaches are often limited in resolving unsteady flow structures and turbulence in the near and far regions. To address these limitations, hybrid RANS-LES models such as Detached Eddy Simulation (DES) and Large Eddy Simulation (LES) offer improved performance in capturing near-wall vortical structures. The capturing of turbulent vortices and wake structures significantly contributes to conduct hydrodynamic noise analysis. Detailed resolution and understanding of these coherent structures help minimize hydroacoustic signatures, essential for submarines' stealth characteristics. Based on prior studies, Breuer et al. (2003) reported that RANS failed to capture unsteady vortex shedding, producing only steady results even in 3D simulations. In contrast, DES and LES successfully resolved asymmetric shedding across different grid resolutions. Spalart (2009) reported that DES is more effective than RANS or LES for high Re flows, although

This study employs high-fidelity numerical simulations to investigate the influence of appendages on the turbulent flow dynamics and far-field acoustic radiation of the SUBOFF submarine model at a Reynolds number of Re = 1.2*10^7. Utilizing a third-order numerical scheme combined with wall-modeled large eddy simulation (WMLES) and the Ffowcs Williams-Hawkings (FW-H) acoustic analogy, the hydrodynamic and acoustic behaviors of an appended SUBOFF configuration are compared to those of a bare hull. A computational grid of 103 million cells resolves the intricate flow interactions, while 648 hydrophones positioned 500 diameters from the model capture far-field acoustic signatures. Key results reveal that appendages significantly amplify hydrodynamic and acoustic disturbances. Flow separations and vortex shedding at appendage junctions elevate pressure-induced drag contributions, contrasting the viscous-dominated drag of the bare hull. The sail-hull interaction intensifies local surface pressure fluctuations, increasing power spectral density (PSD) amplitudes by up to an order of magnitude. In the far field, the appended SUBOFF generates sound pressure levels approximately 20 dB higher

This research explores several critical factors affecting CFD-based prediction accuracy of submarine hydrodynamics and builds upon previous work on preliminary mesh and solver benchmarking. A scaled submarine model is analyzed numerically using the Reynolds-Averaged Navier-Stokes (RANS) turbulence model at a Reynold number (Re) of 3.6x106 with wall-resolved meshes targeting y+ <5. Based on prior work using a 13x106 cells mesh generated with SnappyHexMesh, a mesh convergence study was performed with increasing resolution of fine (15x106 cells), finer (18x106 cells), and finest (22x106 cells). Subsequently, five turbulence closure models which are k-$ω$ SST, k-$ω$ 2006, Lien Cubic, k-$ε$ Launder-Sharma, and Spalart-Allmaras were assessed for predictive performance and computational efficiency. Boundary layer fidelity was examined by comparing velocity profiles extracted from Cadence and SnappyHexMesh meshes against the theoretical law of the wall. In the final phase, the influence of appendages on local and global resistance was analyzed. The results presented that the finest mesh (22x106 cells) achieved a near mesh-independent solution with an error of 1.16% and extrapolated to l

In this paper, we establish the theoretical framework for understanding the sensing capabilities of optical fibers. We show the distinct advantage of polarization over phase in detecting subhertz environmental processes. Subsequently, we propose a scheme capable of extracting the spectrum of perturbations affecting a specific section at any position along an optical fiber by detecting the state of polarization of the backreflected light. We discuss two examples of earthquake detection and the detection of sea swells and ocean tides through the analysis of the state of polarization of an optical signal reconstructed by the receiver of a transoceanic cable, obtained from an online database.\cite{zhongwenzhan_2020} Finally, we provide the analytical expression for the cross-correlation of the polarization perturbations of two wavelength division multiplexed channels, and show that the analysis of the polarization correlations between adjacent channels can provide valuable insights into the localization of earthquakes.

We present direct covariance estimates of temperature flux from a conventional taut wire mooring placed in a narrow submarine canyon on the continental slope west of Ireland. Estimates of stratification from both moored sensors and vertical profiling instrumentation are used to facilitate the interpretation of these temperature transport estimates in terms of diathermal upwelling. At depths of 50-125 meters above bottom, the temperature flux is up-gradient, rather than down the mean gradient, and the corresponding vertical divergence of the diathermal temperature flux implies a diapycnal velocity of approximately 1 mm/s, consistent with diathermal migration estimated from a dye release study. Cospectra document that the temperature flux is related to a wave breaking process under-pinned by semi-diurnal frequencies. This up-gradient flux also results in a highly non-local temperature variance budget. We demonstrate that nonlinear temperature variance production at the bottom boundary is the root cause, with redistribution aloft determining the temperature flux profile.

This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a \gls{LAUV}, operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first annotated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly available online at https://github.com/remaro-network/SubPipe-dataset

Submarine cables constitute the backbone of the Internet. However, these critical infrastructure components are vulnerable to several natural and man-made threats, and during failures, are difficult to repair in their remote oceanic environments. In spite of their crucial role, we have a limited understanding of the impact of submarine cable failures on global connectivity, particularly on the higher layers of the Internet. In this paper, we present Nautilus, a framework for cross-layer cartography of submarine cables and IP links. Using a corpus of public datasets and Internet cartographic techniques, Nautilus identifies IP links that are likely traversing submarine cables and maps them to one or more potential cables. Nautilus also gives each IP to cable assignment a prediction score that reflects the confidence in the mapping. Nautilus generates a mapping for 3.05 million and 1.43 million IPv4 and IPv6 links respectively, covering 91% of all active cables. In the absence of ground truth data, we validate Nautilus mapping using three techniques: analyzing past cable failures, using targeted traceroute measurements, and comparing with public network maps of two operators.

This work introduces two multi-level control strategies to address the problem of guidance and control of underwater vehicles. An outer-loop path-following algorithm and an outer-loop trajectory tracking algorithm are presented. Both outer-loop algorithms provide reference commands that enable the generic submarine to adhere to a three-dimensional path, and both use an inner-loop adaptive controller to determine the required actuation commands. Further, a reduced order model of a generic submarine is presented. Computational fluid dynamics (CFD) results are used to create and validate a model that includes depth dependence and the effect of waves on the craft. %The model and the procedure to obtain its coefficients are discussed, and examples of the data used to obtain the model coefficients are presented. An example of operation following a complex path is presented and Results from the reduced order model for each control strategy are compared.