Journal of Marine Science and Engineering

13 pages, 3305 KB

Open AccessArticle

Coastline Changes and Driving Forces Based on Remotely Sensed Data in Bohai Bay over the Past 20 Years

by Dong Wang, Jiayi Liu, Lei Cao and Dianjun Zhang

J. Mar. Sci. Eng. 2026, 14(11), 962; https://doi.org/10.3390/jmse14110962 (registering DOI) - 22 May 2026

As one of the three major bays in the Chinese Bohai Sea, Bohai Bay is located in a semi-encircled area consisting of three important provinces and cities with rich energy and fishery resources. The bay is not only a maritime gateway and transportation [...] Read more.

As one of the three major bays in the Chinese Bohai Sea, Bohai Bay is located in a semi-encircled area consisting of three important provinces and cities with rich energy and fishery resources. The bay is not only a maritime gateway and transportation hub but also an important industrial base, energy production base, and port. In this study, we combined Landsat remote sensing and Geographic Information System technologies to extract the coastline of Bohai Bay from 2001 to 2021 and obtained the variation in coastline length by refinement vector processing. Sediment as the natural driver was quantitatively analyzed based on sand transport in the Yellow River and Hai River. Moreover, port construction was qualitatively analyzed as the anthropogenic driver. The results demonstrated that the coastline of Bohai Bay showed an overall growth trend in this period, with a total increase of 881.05 km in shoreline length; the main increase was in the artificial shoreline. The two natural driving factors, sediment and hydrodynamic conditions, were weak, and the anthropogenic driving factor, i.e., various human activities, played a dominant role in the variation in the Bohai Bay shoreline in the past 20 years. The extracted shoreline information is important not only for the rational and effective development and utilization of the various natural resources in the coastal zone of Bohai Bay but also for the plan to develop this important region in the future. Full article

(This article belongs to the Section Coastal Engineering)

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20 pages, 2443 KB

Open AccessArticle

Acoustic Characteristics of Finless Porpoises (Neophocaena asiaeorientalis) and Their Relationships with Environmental Variables Revealed by Passive Acoustic Monitoring in Korean Coastal Aquaculture Waters

by Dongha Kang, Hansoo Kim, Young Geul Yoon, Jihoon Jung, Fredrich Simanungkalit, Hyun-Young Kim, Myounghee Kang and Donhyug Kang

J. Mar. Sci. Eng. 2026, 14(11), 961; https://doi.org/10.3390/jmse14110961 (registering DOI) - 22 May 2026

Abstract

The finless porpoise (Neophocaena asiaeorientalis) is a species frequently observed in Korean coastal waters that remains highly vulnerable to bycatch and habitat disturbance. To develop effective conservation strategies, it is essential to understand their acoustic behavior and environmental preferences. This study [...] Read more.

The finless porpoise (Neophocaena asiaeorientalis) is a species frequently observed in Korean coastal waters that remains highly vulnerable to bycatch and habitat disturbance. To develop effective conservation strategies, it is essential to understand their acoustic behavior and environmental preferences. This study utilized Passive Acoustic Monitoring to investigate the acoustic characteristics and activity patterns of finless porpoises in coastal waters near cage aquaculture farms from September to October 2021. A total of 372,707 clicks and 175,119 click trains were identified. Mean acoustic parameters were peak frequency 122.0 ± 11.1 kHz, 3 dB bandwidth 15.4 ± 12.0 kHz, 10 dB bandwidth 45.3 ± 16.1 kHz, and ICI 39.0 ± 34.8 ms. Click activity exhibited a distinct diel pattern, with significantly higher activity during the early morning and nighttime. Generalized Additive Model analysis revealed significant non-linear relationships between click activity and tide, temperature, salinity, and hour. Specifically, click activity decreased with rising temperatures and lower salinity, while the effect of tide was relatively limited. These findings provide critical baseline data for the development of acoustic deterrent devices tailored to Korean marine environments and contribute to the management of bycatch mitigation and coastal ecosystem conservation. Full article

(This article belongs to the Special Issue Marine Mammals: Spatio-Temporal Distributions and Habitat Preferences)

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18 pages, 24885 KB

Open AccessArticle

Numerical Simulation Study of a Triangular Numerical Wave Tank

by Juncheng Ruan, Ji Huang, Jiewei Liao, Bo Hu and Yulin Wang

J. Mar. Sci. Eng. 2026, 14(10), 960; https://doi.org/10.3390/jmse14100960 (registering DOI) - 21 May 2026

Abstract

This study establishes a triangular numerical wave tank based on the viscous incompressible Navier–Stokes equations. The model is implemented in STAR-CCM+, employing the Reynolds-Averaged Navier–Stokes equations and the Volume of Fluid method, combined with velocity boundary wave-making and momentum source wave-making techniques for [...] Read more.

This study establishes a triangular numerical wave tank based on the viscous incompressible Navier–Stokes equations. The model is implemented in STAR-CCM+, employing the Reynolds-Averaged Navier–Stokes equations and the Volume of Fluid method, combined with velocity boundary wave-making and momentum source wave-making techniques for wave generation. On this basis, systematic numerical simulations of oblique and head-on waves were conducted, along with simulation studies of wave interactions with both fixed and floating circular cylinders. The accuracy and reliability of the model were validated by comparing simulation results with theoretical solutions and existing literature data. The results demonstrate that the performance of this triangular wave tank is not affected by the wave incident direction. It can stably generate high-quality oblique and head-on waves, making it suitable for numerical simulation studies of wave–structure interactions. Full article

(This article belongs to the Section Ocean Engineering)

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Graphical abstract

28 pages, 7951 KB

Open AccessArticle

Task-Heterogeneous Formation Planning and Control for Unmanned Surface Vehicles Based on Hybrid Deep Reinforcement Learning

by Yawen Zhang, Wenkui Li, Chenyang Shan, Haoyu Bu and Bing Han

J. Mar. Sci. Eng. 2026, 14(10), 959; https://doi.org/10.3390/jmse14100959 (registering DOI) - 21 May 2026

Abstract

To address the control coupling challenges arising from task heterogeneity of unmanned surface vehicle (USV) formation, a distributed hybrid deep reinforcement learning (HDRL) framework is proposed. The framework decomposes the formation task into two subtasks: leader path planning using the single-agent deep reinforcement [...] Read more.

To address the control coupling challenges arising from task heterogeneity of unmanned surface vehicle (USV) formation, a distributed hybrid deep reinforcement learning (HDRL) framework is proposed. The framework decomposes the formation task into two subtasks: leader path planning using the single-agent deep reinforcement learning (SADRL) algorithm and follower formation tracking using the multi-agent deep reinforcement learning (MADRL) algorithm. By embedding the physical constraints of the real Otter USV into the training loop, the policy network outputs are mapped to propeller revolutions that conform to its dynamic characteristics. To optimize control performance, a dynamic gating mechanism triggered by formation position error is developed to mitigate multi-objective interference through temporal task scheduling. Concurrently, a mirror mapping mechanism leveraging the physical symmetry of the formation is designed to achieve policy sharing and data augmentation. Furthermore, the desired velocity calculated based on rigid-body kinematics is used to achieve kinematic-compensated formation tracking. The simulation results indicate that, compared to the SADRL algorithm, the planning success rate of HDRL is improved by 44.59%. Furthermore, compared to the MADRL algorithm, the integrated tracking performance is enhanced by 21.79–39.64%. Full article

(This article belongs to the Section Ocean Engineering)

17 pages, 2199 KB

Open AccessArticle

Robust Vessel Detection in Low-SNR DAS via Spatial Coherence Enhancement

by Zhongxiang Zheng, Peng Liu and Wei Huang

J. Mar. Sci. Eng. 2026, 14(10), 958; https://doi.org/10.3390/jmse14100958 (registering DOI) - 21 May 2026

Abstract

Robust vessel detection from low-Signal-to-Noise Ratio (SNR) Distributed Acoustic Sensing (DAS) data benefits from exploiting spatial correlations among adjacent channels. The Cross-Channel Attention Fusion Network (CASFNet) is presented, utilizing a Cross-Channel Attention Fusion (CASF) mechanism to dynamically model dependencies among adjacent channels. This [...] Read more.

Robust vessel detection from low-Signal-to-Noise Ratio (SNR) Distributed Acoustic Sensing (DAS) data benefits from exploiting spatial correlations among adjacent channels. The Cross-Channel Attention Fusion Network (CASFNet) is presented, utilizing a Cross-Channel Attention Fusion (CASF) mechanism to dynamically model dependencies among adjacent channels. This approach, based on a dual-component spectrogram representation, adaptively fuses local spatial context, enhancing signal coherence under low-SNR conditions. Experiments on real-world DAS data demonstrate superior accuracy and robustness compared to state-of-the-art methods, achieving a detection accuracy of 99.24% and an F1-score of 99.19%. Ablation results confirm the effectiveness of this spatial fusion strategy for vessel monitoring using submarine DAS data. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology and Application in Marine Science and Engineering)

28 pages, 1285 KB

Open AccessArticle

Embedded Mixture-Correntropy Spatial Smoothing for Robust DOA Estimation in Shallow-Water Underwater Acoustics

by Guanquan Da, Yang Sh and Fei-Yun Wu

J. Mar. Sci. Eng. 2026, 14(10), 957; https://doi.org/10.3390/jmse14100957 (registering DOI) - 21 May 2026

Abstract

Direction-of-arrival (DOA) estimation in shallow-water underwater acoustics is challenged by coherent multipath and impulsive disturbances, which jointly cause covariance rank deficiency and outlier-driven subspace distortion. This paper proposes an embedded robust covariance-construction mechanism for coherent-plus-impulsive DOA estimation. The mechanism is implemented as mixture-correntropy-weighted [...] Read more.

Direction-of-arrival (DOA) estimation in shallow-water underwater acoustics is challenged by coherent multipath and impulsive disturbances, which jointly cause covariance rank deficiency and outlier-driven subspace distortion. This paper proposes an embedded robust covariance-construction mechanism for coherent-plus-impulsive DOA estimation. The mechanism is implemented as mixture-correntropy-weighted simplified spatial smoothing (SS–MCC), in which snapshot reliability is enforced during subarray covariance accumulation rather than after decorrelation. A two-kernel residual-based weighting rule suppresses strongly contaminated snapshots while retaining moderately perturbed but informative snapshots. Under a controlled narrowband uniform linear array benchmark with fully coherent two-arrival multipath and Bernoulli–Gaussian impulsive noise, SS–MCC yields more stable DOA behavior than MUSIC, SS-MUSIC, and FLOM-MUSIC, especially in low-SNR, high-impulsiveness, and near-threshold regimes, although absolute strict recovery remains limited in the hardest cases. All-trial strict correct-two-peak statistics and ablation results show that the gain mainly comes from embedded covariance cleaning rather than post-processing or parameter tuning. A measured-noise-injected benchmark using NOAA–Navy SanctSound FK01 underwater recordings further confirms the same qualitative robustness trend under real noise waveforms, while remaining a semi-realistic noise-injection check rather than measured-array sea-trial validation. A simplified DOA- assisted MVDR benchmark indicates that improved covariance robustness can also support more favorable beamforming-oriented trends. The results provide controlled benchmark evidence that reliability-aware covariance construction can stabilize subspace extraction under joint coherent multipath and impulsive contamination; validation under wideband propagation, model mismatch, partial coherence, and measured array data remains future work. Full article

(This article belongs to the Topic Advances in Underwater Signal Processing and Communication: Challenges, Innovations, and Applications)

28 pages, 3469 KB

Open AccessArticle

Influence of Rotor–Nacelle Assembly Modeling Fidelity on Dynamic Behavior of 15 MW Monopile-Supported Offshore Wind Turbine

by Chuchen Wang, Haoyong Qian and Renqiang Xi

J. Mar. Sci. Eng. 2026, 14(10), 956; https://doi.org/10.3390/jmse14100956 (registering DOI) - 21 May 2026

Abstract

This paper investigates the impact of rotor–nacelle assembly (RNA) structural models on the dynamic response of a 15 MW monopile-supported offshore wind turbine (MOWT). Three RNA models, distributed parameter (DPM), multi-particle (MPM), and concentrated point mass (CPM), were established in ADINA. Model reliability [...] Read more.

This paper investigates the impact of rotor–nacelle assembly (RNA) structural models on the dynamic response of a 15 MW monopile-supported offshore wind turbine (MOWT). Three RNA models, distributed parameter (DPM), multi-particle (MPM), and concentrated point mass (CPM), were established in ADINA. Model reliability was confirmed through verification against BModes and OpenFAST, covering natural frequencies, mode shapes, and responses under normal environmental loads. The analyses reveal the following: (1) RNA modeling significantly impacts higher-order modal frequencies, with the MPM/CPM exhibiting substantial errors (up to −20.3% and 9.5% for second-order tower mode) and failing to capture blade deformation modes; (2) under low-frequency dominated wave loads, the MPM/CPM predict peak responses within ±10% tolerance; (3) for seismic loads, the discrepancy in three models is governed by input motion spectral characteristics, showing smaller errors under far-field motions (fundamental mode dominated) but significant errors under near-field motions (higher-mode excited). These findings collectively provide theoretical guidance for RNA model selection in MOWTs. Full article

(This article belongs to the Special Issue Wave Loads on Offshore Structure—2nd Edition)

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30 pages, 3444 KB

Open AccessArticle

Coral Species Strategies in the Gulf of Eilat (Aqaba)

by Alina Raphael and David Iluz

J. Mar. Sci. Eng. 2026, 14(10), 955; https://doi.org/10.3390/jmse14100955 (registering DOI) - 21 May 2026

Abstract

Coral reefs in the Gulf of Eilat maintain a high diversity of ~100 stony coral species. Despite intense competition for a limited substrate, this raises fundamental questions about spatial organization and mechanisms of coexistence. This study combines deep learning species classification with spatial [...] Read more.

Coral reefs in the Gulf of Eilat maintain a high diversity of ~100 stony coral species. Despite intense competition for a limited substrate, this raises fundamental questions about spatial organization and mechanisms of coexistence. This study combines deep learning species classification with spatial point-pattern analysis to quantify the frequency of intragenus versus intergenus competitive contacts among four dominant coral genera, Acropora, Favia, Platygyra, and Stylophora, across 12 standardized transects at four reef sites. The ResNet-50 convolutional neural network achieved 92.3% test accuracy for genus-level identification in field imagery of 1100 test images, enabling automated detection of 487 coral–coral competitive pairs exhibiting direct physical contact. Intragenus pairs comprised only 18.3% (89/487) of contacts, significantly below the 50% expected under spatial randomness (z = −14.0, p < 0.0001) with pair correlation functions g(r) > 1 at sub-meter scales indicating conspecific clustering. Genus-specific pair frequencies correlated strongly with relative abundance and spatial coverage (r = 1), with ecological traits explaining dominance patterns: fast-growing, competitive Acropora generated high contact rates, while stress-tolerant Favia and Platygyra prevailed through longevity and defensive competition. These findings demonstrate that intergeneric competition dominates despite local congeneric aggregation, maintaining diversity through niche partitioning rather than intransitive networks, even as coral cover declines amid rising temperatures above 0.05 °C yr⁻¹ and historical eutrophication. The deep learning workflow provides a scalable baseline for monitoring anthropogenic impacts on coral competition dynamics. Full article

(This article belongs to the Special Issue Combining Field Observations and Satellite Remote Sensing to Monitor Marine Ecosystem Dynamics)

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23 pages, 10844 KB

Open AccessArticle

Traction Response and Operational Risk of a Drag-Reduction System for HDD Submarine Cable Pulling Based on Local Full-Scale Experiments

by Chunri Sun, Chunhao Lu, Jingkui Jiang, Yan Luo, Renguo Gu, Xiaolong Li and Guanglong Cao

J. Mar. Sci. Eng. 2026, 14(10), 954; https://doi.org/10.3390/jmse14100954 (registering DOI) - 21 May 2026

Abstract

This study investigates the traction response and operational risk of a compact ball-frame and tensioned-steel-cable drag-reduction system for submarine cable pulling inside HDD steel casings, based on local full-scale experiments. Thirteen test cases were designed by considering pipe curvature, device spacing, terminal reaction-force [...] Read more.

This study investigates the traction response and operational risk of a compact ball-frame and tensioned-steel-cable drag-reduction system for submarine cable pulling inside HDD steel casings, based on local full-scale experiments. Thirteen test cases were designed by considering pipe curvature, device spacing, terminal reaction-force loading mode, and dry or sand–slurry in-casing conditions. In addition to the equivalent friction coefficient, three response descriptors, namely, the average traction force, peak coefficient, and fluctuation coefficient, were introduced to evaluate mean resistance, peak amplification, and process stability. The results show that pipe curvature significantly amplifies both traction peaks and response fluctuations, and should therefore be regarded as a key factor governing operational risk. The effect of device spacing is environment-dependent: under dry conditions, a moderate reduction in spacing improves rolling continuity, whereas under sand–slurry conditions, excessively dense deployment may aggravate local obstruction and response fluctuation. Stronger terminal reaction-force loading also increases peak amplification and instability. Based on these findings, a case-specific and experiment-oriented framework for operational-risk classification is proposed. The present results are intended to support traction-response characterization, device arrangement, and construction control under representative local conditions, rather than to replace full-scale field validation. Full article

(This article belongs to the Special Issue Marine Cable Technology: Cutting-Edge Research and Development Trends)

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18 pages, 4421 KB

Open AccessArticle

Water-AutoSAM: Dual-Domain Enhanced Auto-Prompting SAM for Underwater Segmentation

by Yingrui Sun, Yang Hong, Xiaowei Zhou and Junyu Dong

J. Mar. Sci. Eng. 2026, 14(10), 953; https://doi.org/10.3390/jmse14100953 (registering DOI) - 21 May 2026

Abstract

Foundation segmentation models exhibit strong generalization on natural images yet degrade substantially in underwater scenes due to color distortion, scattering, and low contrast, which collectively impair feature representation. Parameter-efficient fine-tuning strategies have been explored to adapt SAM to marine domains while preserving generalization, [...] Read more.

Foundation segmentation models exhibit strong generalization on natural images yet degrade substantially in underwater scenes due to color distortion, scattering, and low contrast, which collectively impair feature representation. Parameter-efficient fine-tuning strategies have been explored to adapt SAM to marine domains while preserving generalization, but degraded image quality still hampers feature extraction. Moreover, existing SAM-based underwater methods typically rely on ground-truth box prompts during inference. Since ground-truth boxes are inherently unavailable in real-world underwater scenarios, this dependence yields evaluation outcomes that fail to reflect actual deployment conditions, thereby limiting their practical applicability. To address these issues, Water-AutoSAM is introduced—a dual-domain enhanced auto-prompting framework tailored for underwater image segmentation. The auto-prompting mechanism decouples semantic and positional representations for generalized point generation, which are optimized via enhanced sharpness, correctness, and diversity losses under staged training. To counter the degrading effects typical of underwater imagery, a lightweight module designated SS-UIE is integrated as a frozen pre-enhancement stage. This module operates with spatial–frequency dual-branch processing and utilizes a fixed residual fusion coefficient to combine the two streams. Operating entirely without box prompts, Water-AutoSAM achieves competitive annotation-free performance, attaining 92.38% mIoU on SUIM and reducing the gap to the fully supervised upper bound to 2.08% on COD10K. Full article

(This article belongs to the Special Issue Artificial Intelligence Technology and Application in Marine Science and Engineering)

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21 pages, 15806 KB

Open AccessArticle

A Simple Method of Estimating Wave Height Based on Shadowing in X-Band Radar Images

by Chengming Zong, Guoteng Li, Yanbo Wei and Zhizhong Lu

J. Mar. Sci. Eng. 2026, 14(10), 952; https://doi.org/10.3390/jmse14100952 (registering DOI) - 21 May 2026

Abstract

X-band marine radar shadow features are widely applied to wave height estimation. Since the shadow fraction rises with the distance from the radar antenna, wave slope estimation is sensitive to the selected analysis region. To resolve this issue, a wave height estimation method [...] Read more.

X-band marine radar shadow features are widely applied to wave height estimation. Since the shadow fraction rises with the distance from the radar antenna, wave slope estimation is sensitive to the selected analysis region. To resolve this issue, a wave height estimation method is proposed by adopting the optimal shadowed fraction which is unrelated to the boundary selection of the analysis area. Within this paper, the shadow fraction is computed on the basis of the mechanism of radar image shadow imaging. Instead of adopting the widely used Smith fitting function, the wave slope with the non-shadow areas is achieved by using the obtained shadow fraction and the grazing angle. The collected marine radar images, totaling 450 h, are employed to demonstrate the performance of the proposed wave height retrieval method. Compared with fundamental shadow statistical approach, the root mean square error of the proposed method decreases by 0.19 m, and the correlation coefficient increases by 0.10. Meanwhile, the execution time of the presented algorithm has significantly decreased. Full article

(This article belongs to the Special Issue Applications of Sensors in Marine Observation)

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18 pages, 21503 KB

Open AccessArticle

GhostVision: Democratizing Derelict Gear Detection Using Low-Cost Sonar and Artificial Intelligence

by Cameron S. Bodine, Kleio Baxevani, Naveed Abbasi, Jared Wierzbicki, Ophelia Christoph, Catherine Hughes, Onur Bagoren, Olivia Hines, Julia Greco and Arthur Trembanis

J. Mar. Sci. Eng. 2026, 14(10), 951; https://doi.org/10.3390/jmse14100951 (registering DOI) - 20 May 2026

Abstract

Derelict crab pots (“ghost pots”) cause bycatch mortality, habitat degradation, and lost harvest in shallow coastal ecosystems. Existing detection and recovery programs rely on expert operators and high-cost sonar, limiting coverage and reproducibility. Here, we present GhostVision, an open-source framework that integrates low-cost [...] Read more.

Derelict crab pots (“ghost pots”) cause bycatch mortality, habitat degradation, and lost harvest in shallow coastal ecosystems. Existing detection and recovery programs rely on expert operators and high-cost sonar, limiting coverage and reproducibility. Here, we present GhostVision, an open-source framework that integrates low-cost consumer side-scan sonar with modern object-detection models to enable scalable, rapid post-processing and mapping of derelict gear. Mobile Mapping Units (MMUs) equipped with off-the-shelf fishfinders surveyed more than 1500 acres in Delaware’s Inland Bays between 2020 and 2022. Three architectures (YOLOv12, YOLOv26, RF-DETR) were trained on 3110 manually annotated sonar images and evaluated with both dataset-centric metrics and full pipeline implementation. YOLOv12 showed the strongest untuned operational performance (F1 = 0.512; recall = 0.922), while post-processing optimization produced comparable performance across all three models (F1 ≈ 0.71–0.73). Across 11 complete test recordings, end-to-end processing required only 8.87–9.79% of survey time (approximately 10–11× faster than real-time), supporting same-day analysis and recovery workflows. GhostVision can foster community engagement in derelict crab-pot removal by pairing low-cost sonar with AI to aid recovery efforts at management-relevant scales. By lowering financial and technical barriers, GhostVision provides a reproducible pathway for large-scale stewardship and supports future extensions to multi-class detection and autonomous platforms. Full article

(This article belongs to the Section Ocean Engineering)

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25 pages, 2217 KB

Open AccessArticle

A Standard-Compatible Forward Error Correction Extension for the Automatic Identification System

by Armin Dammann, Ronald Raulefs, Michael Walter and Markus Wirsing

J. Mar. Sci. Eng. 2026, 14(10), 950; https://doi.org/10.3390/jmse14100950 (registering DOI) - 20 May 2026

Abstract

The Automatic Identification System (AIS) is a maritime radio system that regularly broadcasts vessel data, such as the vessel’s identification, position, course and speed. For modulation, the AIS standard defines Gaussian minimum shift keying (GMSK) as an easy to implement modulation scheme with [...] Read more.

The Automatic Identification System (AIS) is a maritime radio system that regularly broadcasts vessel data, such as the vessel’s identification, position, course and speed. For modulation, the AIS standard defines Gaussian minimum shift keying (GMSK) as an easy to implement modulation scheme with constant envelope, meaning that a GMSK complex baseband signal carries information solely in its phase. AIS does not use any forward error correction (FEC) mechanism. In this paper we propose to extend GMSK with amplitude modulation, leading to multi-amplitude Gaussian minimum shift keying (MA-GMSK). The additional modulation of the amplitude increases the spectral efficiency so that additional information, i.e., additional bits can be transmitted. We use the increased spectral efficiency to implement FEC, where we transmit the redundancy bits of a systematic channel code via the additional amplitude modulation in the proposed MA-GMSK scheme. With this approach, the proposed MA-GMSK signal can be processed by off-the-shelf AIS receivers, thus demonstrating empirical standard compatibility with the tested receivers. Based on simulations and experimental results, we propose a suitable MA-GMSK modulation parameter setting and evaluate the packet error rate (PER) performance accordingly. To verify standard compatibility, we examine the performance of commercially available AIS receivers fed with MA-GMSK signals. Using the proposed modulation and coding scheme, an advanced MA-GMSK receiver including FEC provides performance improvements up to 3 dB in the required signal-to-noise ratio (SNR) compared to state-of-the art AIS using uncoded GMSK. Full article

(This article belongs to the Special Issue The Role of Maritime Automatic Identification System (AIS) and Communication in Achieving Net Zero GHG)

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34 pages, 1680 KB

Open AccessArticle

Ship Equipment Order Target Price Prediction: An Interpretable Model Based on Boruta–Lasso and CatBoost-SHAP

by Kai Li, Shengxiang Sun, Chen Zhu and Ying Zhang

J. Mar. Sci. Eng. 2026, 14(10), 949; https://doi.org/10.3390/jmse14100949 (registering DOI) - 20 May 2026

Abstract

The target price for naval equipment orders is driven by the coupling of multidimensional technical and economic factors, exhibiting typical characteristics such as high dimensionality, strong nonlinearity, multicollinearity, and small-sample fluctuations. Traditional cost estimation methods struggle to achieve high-precision fitting and interpretable decision [...] Read more.

The target price for naval equipment orders is driven by the coupling of multidimensional technical and economic factors, exhibiting typical characteristics such as high dimensionality, strong nonlinearity, multicollinearity, and small-sample fluctuations. Traditional cost estimation methods struggle to achieve high-precision fitting and interpretable decision support. To address these issues, this paper constructs an integrated prediction model that combines Boruta–Lasso two-stage feature selection, grid search-optimized CatBoost, and SHAP interpretability analysis. First, the Boruta algorithm is used for rough screening of feature significance, then Lasso regression is applied for sparse fine screening, effectively eliminating redundant features and significantly mitigating multicollinearity; grid search and five-fold repeated cross-validation are employed to optimize CatBoost hyperparameters, while 10 repeated experiments with random seeds are conducted to verify model generalization robustness. SHAP is used to quantify the marginal contribution of features, revealing nonlinear associations and statistical response transition points between core features and price. This study is based on 33 publicly available real data from main combat vessels, from which 198 modeling samples were generated through interpolation-based small-sample data augmentation. The interpolated samples were only used for data augmentation and were not considered independent empirical samples. All core conclusions were validated on the 33 original real samples, and there are no missing values in the dataset. Experimental results show that the proposed model achieved the best individual results on the test set, with a coefficient of determination of R² = 0.8949, root mean square error RMSE = 0.0554, and mean absolute error MAE = 0.0476. Across 10 repeated robustness experiments, the average results were R² = 0.8828, RMSE = 0.0586, and MAE = 0.0529, with overall performance better than comparison models such as XGBoost, random forest, and standard CatBoost. Ablation experiments validated the effectiveness of the two-stage Boruta–Lasso selection strategy in improving model accuracy and stability. SHAP attribution analysis shows that full-load displacement, number of vertical missile launch cells, number of phased array radars, and combat capability are core features highly correlated with price, all showing significant nonlinear positive correlations and clear statistical response transition points. The dataset in this study has no missing values, is entirely constructed based on publicly traceable data, and does not include confidential information such as internal shipyard costs. The findings reflect statistical associations rather than causal effects. However, the sample size and ship-type coverage are limited, so the model’s applicability is somewhat constrained, and its generalization ability needs to be further verified on larger-scale, multi-ship-type independent datasets. This model combines high prediction accuracy, strong robustness, and good interpretability, providing reliable technical support for ship equipment procurement pricing demonstration, full lifecycle cost management, and scientific procurement decision-making. Full article

(This article belongs to the Special Issue Machine Learning Methodologies and Ocean Science, Second Edition)

1 pages, 143 KB

Open AccessCorrection

Correction: Fetimi et al. Scissors Approach in Human and Equipment Reliability Vis-A-Vis the Use of Alternative Fuel in Ship Propulsion. J. Mar. Sci. Eng. 2025, 13, 1580

by Bebetebe Fetimi, Byongug Jeong, Yeongmin Park and Jaehoon Jee

J. Mar. Sci. Eng. 2026, 14(10), 948; https://doi.org/10.3390/jmse14100948 (registering DOI) - 20 May 2026

Abstract

In the original publication [...] Full article

26 pages, 3589 KB

Open AccessArticle

Multimode Reliability Analysis of an OFPV Mooring System with a Novel Parallel Structure of Elastic Ropes and Anchor Chains

by Wanhai Xu, Junling Hong, Shuai Li and Ziqi He

J. Mar. Sci. Eng. 2026, 14(10), 947; https://doi.org/10.3390/jmse14100947 (registering DOI) - 20 May 2026

Abstract

Offshore floating photovoltaic (OFPV) is an important renewable energy technology, and assessing the reliability of mooring systems is of great significance for promoting the large-scale commercial deployment of OFPV. However, owing to the complexity of the system structure, relevant reliability research has not [...] Read more.

Offshore floating photovoltaic (OFPV) is an important renewable energy technology, and assessing the reliability of mooring systems is of great significance for promoting the large-scale commercial deployment of OFPV. However, owing to the complexity of the system structure, relevant reliability research has not been extensively carried out. With this in view, this work focuses on the systematic reliability analysis of a novel parallel mooring system composed of elastic ropes and anchor chains under the ultimate limit state (ULS), accidental limit state (ALS) and fatigue limit state (FLS), considering both long-term cyclic and extreme environmental conditions. The first-order second moment (FOSM), first-order reliability method (FORM) and Monte Carlo simulation have been employed to calculate the failure probabilities. By applying the series-parallel model to integrate multimode failures, it is confirmed that the failure probability of the entire mooring system is significantly greater than that under any single limit state. The results indicate that anchor chain is the main fatigue-critical component, and the Monte Carlo simulation based on extensive random sampling data is more conservative in reliability estimation than FOSM and FORM which cannot fully capture all distribution characteristics. This work could provide essential theoretical support for the safe design of subsequent OFPV mooring systems. Full article

(This article belongs to the Section Ocean Engineering)

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25 pages, 551 KB

Open AccessReview

Advances in Harmful Algal Blooms (HABs) Monitoring: A Review of Sensor and Platform Technologies

by Ziyuan Yang, Aifeng Tao and Gang Wang

J. Mar. Sci. Eng. 2026, 14(10), 946; https://doi.org/10.3390/jmse14100946 (registering DOI) - 20 May 2026

Abstract

Against the backdrop of intensifying global climate change and water eutrophication, the increasing occurrence of Harmful Algal Blooms (HABs) poses a significant threat to aquatic ecosystems, human health, and socio-economic activities. The occurrence and development of HABs are complex processes governed by the [...] Read more.

Against the backdrop of intensifying global climate change and water eutrophication, the increasing occurrence of Harmful Algal Blooms (HABs) poses a significant threat to aquatic ecosystems, human health, and socio-economic activities. The occurrence and development of HABs are complex processes governed by the interaction of physical, chemical, and biological factors. Therefore, timely and accurate monitoring is essential for early warning and scientific research. This paper comprehensively reviews recent advances in HAB monitoring technologies, with a focus on two core components: sensors and monitoring platforms. First, organized around key environmental parameters, it summarizes the principles, applications, and limitations of in situ sensors, such as multi-parameter water quality sondes, Imaging Flow Cyto-bots (IFCB), and Environmental Sample Processors (ESP), as well as laboratory-based analytical techniques such as HPLC-MS for measuring physical, chemical, and biological indicators. Second, it compares the technical characteristics of three major monitoring platforms (including field surveys, remote sensing, and autonomous systems) and discusses their potential for synergistic application. Finally, this review proposes a future framework for an integrated “Space–Air–Ground–Sea” intelligent monitoring network and explores possible pathways to address current challenges through cross-platform data fusion, sensor miniaturization, intelligentization, and artificial intelligence-driven decision support. This review aims to provide a comprehensive reference for the optimization and innovation of HAB monitoring technologies and to promote the development of the field toward greater integration, intelligence, and real-time monitoring capability. Full article

(This article belongs to the Special Issue Novel Advances in Offshore Sensor Systems)

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21 pages, 4789 KB

Open AccessArticle

MPFT-UNet: A Boundary-Refined and Multi-Scale Dynamic Fusion Network for UAV-Based Port Ship Segmentation

by Mengna Shi, Xiulin Qiu, Ang Li, Yuwang Yang, Yaqi Ke and Yilan Chen

J. Mar. Sci. Eng. 2026, 14(10), 945; https://doi.org/10.3390/jmse14100945 (registering DOI) - 19 May 2026

Abstract

Ship semantic segmentation based on unmanned aerial vehicle (UAV) imagery has important application value in maritime scenarios such as marine surveillance, port management, and maritime safety. However, UAV images often contain large scale variations of ships, a high proportion of small targets, and [...] Read more.

Ship semantic segmentation based on unmanned aerial vehicle (UAV) imagery has important application value in maritime scenarios such as marine surveillance, port management, and maritime safety. However, UAV images often contain large scale variations of ships, a high proportion of small targets, and complex background interference, including sea surface reflections, waves, and clouds. These factors make accurate segmentation and boundary localization difficult. To address these issues, this paper proposes a UAV-based ship semantic segmentation network, termed MPFT-UNet. The network introduces a Multi-scale Dynamic Sparse Cross-gating (MDSC) module to improve the representation of small targets. A Boundary Supervision Refinement (BSR) module is used to enhance boundary delineation. In addition, a Transformer-based Feature Fusion (FFT) module is applied at the bottleneck layer to strengthen global semantic representation. Experimental results show that MPFT-UNet achieves better performance than existing methods across multiple evaluation metrics. The model obtains an IoU of 0.8365, Dice coefficient of 0.9028, Recall of 0.8881, and AP of 0.95731. These results indicate stable segmentation performance under complex maritime conditions. Compared with the baseline U-Net model, the IoU is improved by approximately 5.1%. Full article

(This article belongs to the Topic Advanced Technologies and Applications for Unmanned Systems)

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30 pages, 28887 KB

Open AccessArticle

A Data-Driven Framework for Detecting Unsafe Ship–Bridge Passages Based on AIS Trajectories

by Qiyang Li, Hongzhu Zhou, Jiao Liu, Yibing Wang, Manel Grifoll and Pengjun Zheng

J. Mar. Sci. Eng. 2026, 14(10), 944; https://doi.org/10.3390/jmse14100944 (registering DOI) - 19 May 2026

Abstract

Ship–bridge collisions in cross-sea bridge waterways are rare but potentially catastrophic events, making conventional accident-based risk assessment difficult to implement effectively. Existing AIS-based indicators capture vessel behaviors but insufficiently quantify bridge-passage safety margins, especially dynamic aspects such as crossing posture and readiness prior [...] Read more.

Ship–bridge collisions in cross-sea bridge waterways are rare but potentially catastrophic events, making conventional accident-based risk assessment difficult to implement effectively. Existing AIS-based indicators capture vessel behaviors but insufficiently quantify bridge-passage safety margins, especially dynamic aspects such as crossing posture and readiness prior to bridge transit. To address this limitation, this study proposes a data-driven framework for detecting unsafe ship–bridge passages using two bridge-passage-oriented surrogate safety measures (SSMs) and extreme value theory (EVT). The Bridge-passage Lateral Clearance Margin (BLCM) quantifies the effective lateral safety margin retained during the realized bridge-crossing stage, while the Bridge-passage Readiness Lead Time (BRLT) measures how early a vessel becomes stably prepared for bridge passage before crossing. The Peaks Over Threshold (POT) model is first used to characterize the marginal extremes of the two indicators, and a bivariate threshold exceedance model (BTE) is then established to examine their joint risk behavior. Case studies of the Jintang Bridge and Zhoudai Bridge waterways demonstrate that the proposed framework can effectively screen and identify trajectories with unsafe or margin-deficient bridge-passage characteristics. The results show that unsafe passages are typically associated with both reduced lateral clearance and insufficient preparation time, and that joint modeling of the two indicators improves risk identification performance. The findings suggest that ship–bridge risk is better interpreted from the perspective of passage quality deficiency rather than simple geometric proximity. The proposed framework provides an interpretable tool for retrospective unsafe passage screening, traffic monitoring support, and post-event safety analysis in complex bridge waterways. Full article

(This article belongs to the Section Ocean Engineering)

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Journal of Marine Science and Engineering

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