A Flexible Hybrid Attention Mechanism for Multi-Architecture Segmentation of Small Maritime Targets in South East Region of Iran 2 2 Maritime vessel detection in satellite imagery is essential for coastal monitoring, traffic regulation, and maritime security. Vessels along the southeastern coast of Iran exhibit unique structural and geometric characteristics; they are small and overlapped, differing substantially from international benchmarks. Detecting small and overlapping vessels presents additional challenges due to the loss of fine-grained features and ambiguous object boundaries in conventional deep learning pipelines. Consequently, existing pre-trained models, trained primarily on global datasets, often fail to generalize effectively to this region. To address this, in our study, we provide the first systematic investigation of ship detection for southeastern Iran, supported by a curated dataset of high-resolution satellite imagery from its major ports. We, then, propose a flexible Hybrid Attention Fusion (HAF) module that can be seamlessly integrated into multiple segmentation architectures, including FPN, Mask R-CNN, U-Net, and DeepLab. The module sequentially applies channel and spatial attention mechanisms to adaptively recalibrate multi-scale features, enhancing the representation of subtle and occluded instances. Experimental results demonstrate that HAF-augmented models significantly outperform their baseline counterparts across all architectures. For semantic segmentation, U-Net+HAF and DeepLabv3+HAF achieve mean IoU improvements of 4.5% and 4.4%, respectively, reaching 83.8% and 85.5% mIoU. For instance segmentation, Mask R-CNN+HAF demonstrates the most substantial improvement in small object detection, with Average Precision for small objects (APs) increasing from 42.3% to 50.6%—an 8.3-point improvement. Qualitative analysis confirms superior capability in detecting missed small instances, separating overlapping vessels, and producing more precise boundaries compared to baseline models. 1 11 2025/09/22 1404/6/31 2026/03/28 1405/1/8 Zobeir Raisi Zobeir Raisi Chabahar Maritime University, Chabahar, Iran ایران zobeir.raisi@cmu.ac.ir Esmaeil Sarani Esmaeil Sarani Chabahar Maritime University, Chabahar, Iran ایران sarani@cmu.ac.ir Rasoul Damani Rasoul Damani Chabahar Maritime University, Chabahar, Iran ایران damani@cmu.ac.ir Valimohammad Nazarzehi Had Valimohammad Nazarzehi Had Chabahar Maritime University, Chabahar, Iran ایران v.nazarzehi@cmu.ac.ir Satellite images ship detection and segmentation deep learning South East Iran Attention Mechanism T. Zhao et al.,(2024), "Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances," Remote Sens., vol. 16, no. 7, p. 1145, Mar. 2024, doi: 10.3390/rs16071145.##Z. 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Risk Assessment of Dropped Objects on Corroded Submarine Pipelines Using Machine Learning Algorithms 2 2 This paper proposes a probabilistic model based on machine learning algorithms to estimate the risk associated with different levels of damage (per DNV-RP-F101) due to a dropped-object impact on subsea pipelines. The model is generalized by considering a wide range of pipeline geometric and mechanical specifications, corrosion conditions, and various possible impact scenarios. Multiple machine learning algorithms—including Linear Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, and Gradient Boosting—were evaluated, with Random Forest demonstrating the highest accuracy. The analysis of how pipeline characteristics influence the probability of different damage levels provides a basis for decision-making on implementing preventive measures to reduce damage probability during the pipeline design stage 12 28 2025/09/222025/08/23 1404/6/1 2026/03/282026/04/11 1405/1/22 Pedram Edalat Pedram Edalat Mechanical Engineering Department, Petroleum University of Technology, Abadan, Iran ایران edalat@put.ac.ir Erfan Rezaei Erfan Rezaei Mechanical Engineering Department, Petroleum University of Technology, Abadan, Iran ایران erfanrezaei1380@gmail.com Alireza Abyari Bidgoli Alireza Abyari Bidgoli Mechanical Engineering Department, Petroleum University of Technology, Abadan, Iran ایران abyari1381@gmail.com Submarine pipeline dropped object Machine Learning (ML) Monte Carlo Simulation (MCS) Risk assessment pitting corrosion F. Jiang and S. Dong, (2020), Collision failure risk analysis of falling object on subsea pipelines based on machine learning scheme, Eng. Fail. 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Aanesland, (1987), Numerical and Experimental Investigation of Accidentally Falling Drilling Pipes, doi: 10.4043/5497-MS.##M. R. U. Kawsar, S. A. Youssef, M. Faisal, A. Kumar, J. K. Seo, and J. K. Paik, (2015), Assessment of dropped object risk on corroded subsea pipeline, Ocean Eng., vol. 106, pp. 329-340, doi: 10.1016/j.oceaneng.2015.06.056.##DNV-RP-F107, Risk assessment of pipeline protection.##S. T. Edmollaii, P. Edalat, and M. Dyanati, (2019), Reliability sensitivity analysis of dropped object on submarine pipelines, Ocean Syst. Eng., vol. 9, no. 2, doi: 10.12989/ose.2019.9.2.135.##Y. Zhou and S. Zhang, (2022), Perforation analysis and limit prediction of submarine pipelines subjected to extreme impact loadings, Ocean Eng., vol. 246, p. 110651, doi: 10.1016/J.OCEANENG.2022.110651.##F. Jiang and S. 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Optimizing Hydrophone Array Configurations for 6-DoF Motion-Induced Phase Error Correction in Synthetic Aperture Sonar Imaging 2 2 Synthetic aperture sonar (SAS) achieves high-resolution imaging by coherently integrating echoes along the platform trajectory, making it inherently sensitive to six-degree-of-freedom (6-DOF) motion. Sub-wavelength perturbations distort phase, broaden the main lobe, and raise sidelobes. This paper introduces a geometry-centric array optimization framework that: (i) operationalizes a Phase Sensitivity Index (PSI) with translational and rotational components to quantify array-dependent motion sensitivity; and (ii) calibrates PSLR/ISLR to image signatures (main-lobe width and sidelobe proximity) against a back-projection (BP) reference, ensuring physics-consistent visual interpretation. Three receiver arrays—2D planar, 3D cubic, and 3D hemispherical (36 elements each)—are evaluated under six motions (surge, sway, heave, roll, pitch, yaw) and three imaging states (ideal, motion-degraded, corrected), yielding 54 scenarios. Motion parameters reflect realistic autonomous underwater vehicle conditions: translational RMS 2.0–3.2 mm, rotational RMS 0.25°–0.35°, platform speed 2.5 m/s, and dominant 0.2–2 Hz content. The hemispherical array attains the lowest normalized PSI and consistently superior sidelobe metrics, outperforming the planar array by approximately 6.5 dB in PSLR and 5.6 dB in ISLR on updated corrected global means, and the cubic array by approximately 2.9 dB in both PSLR and ISLR. Yaw and Heave cause the greatest degradation; Roll is least harmful. Motion correction improves PSLR by ≥4 dB and ISLR by ≥3 dB relative to degraded images, while the residual gap to ideal remains ≤4 dB, by design. The proposed framework enables quantitative array selection and supports robust SAS imaging in embedded AUVs without external navigation sensors. 29 47 2025/09/222025/08/232025/08/12 1404/5/21 2026/03/282026/04/112026/04/18 1405/1/29 Hamid Hajirahimi Kashani Hamid Hajirahimi Kashani Faculty of Engineering, Ferdowsi University of Mashhad ایران hajirahimi.hamid@mail.um.ac.ir Seyed Alireza Seyedin Seyed Alireza Seyedin Faculty of Engineering, Ferdowsi University of Mashhad ایران seyedin@um.ac.ir Synthetic Aperture Sonar (SAS) 6-DoF motion Phase Sensitivity Index (PSI) Hydrophone Array Optimization Back-Projection Zeng, S., Fan, W. and Du, X., (2022), Three-Dimensional Imaging of Circular-Array Synthetic Aperture Sonar for Unmanned Surface Vehicle, Sensors, Vol.22(10), p.3797. DOI: 10.3390/s22103797.##Gill, J. and Rama Rao, V.V.S., (2014), Motion Compensation of Airborne Synthetic Aperture Radar, IFAC Proceedings Volumes (Proc. 19th IFAC World Congress). DOI: 10.3182/20140313-3-IN-3024.00065.##Zhang, J., Cheng, G., Tang, J., Wu, H. and Tian, Z., (2023), A Subaperture Motion Compensation Algorithm for Wide-Beam, Multiple-Receiver SAS Systems, Journal of Marine Science and Engineering, Vol.11(8), p.1627. DOI: 10.3390/jmse11081627.##Zhang, X., Huang, P., Sun, H., Ying, W. and Yang, P., (2022), Wide-Bandwidth Signal-Based Multireceiver SAS Imagery Using Extended Chirp Scaling Algorithm, IET Radar, Sonar & Navigation, Vol.16(3), p.531-541. DOI: 10.1049/rsn2.12200.##Zhang, X., Yang, P. and Zhou, M., (2023), Multireceiver SAS Imagery with Generalized PCA, IEEE Geoscience and Remote Sensing Letters, Vol.20, p.1-5 (Art. 1502205). DOI: 10.1109/LGRS.2023.3286180.##Baron, V., Finez, A., Bouley, S., Fayet, F., Mars, J.I. and Nicolas, B., (2021), Hydrophone Array Optimization, Conception, and Validation for Localization of Acoustic Sources in Deep-Sea Mining, IEEE Journal of Oceanic Engineering, Vol.46(2), p.555-563. DOI: 10.1109/JOE.2020.3004018.##Król, J. and Błażejewski, A., (2020), Fibonacci Array-Based Focused Acoustic Camera for Broad-Band Beamforming, Journal of Sound and Vibration, Vol.478, p.115351. DOI: 10.1016/j.jsv.2020.115351.##Zhang, L., Liu, H., Liu, Y., et al., (2019), Direction-of-Arrival Estimation with Structured Array Designs, IET Microwaves, Antennas & Propagation. DOI: 10.1049/iet-map.2019.0518.##de Bree, H.E., Druyvesteyn, W.F. and co-authors, (2008), Moving Microphone Arrays to Reduce Spatial Aliasing in the Beamforming Technique: Theoretical Background and Numerical Investigation, Journal of the Acoustical Society of America, Vol.124(6), p.3648-3658. DOI: 10.1121/1.2998778.##Zhong, H., Zhou, Z., Zhang, P., et al., (2022), An Efficient Multireceiver SAS Imaging Algorithm for Large Data in Heterogeneous Environment, Research Square, Preprint. DOI: 10.21203/rs.3.rs-1624407/v.## ##

Operational Forecasting for an Offshore Wind Turbine: Benchmarking Data-Driven against Physics-Informed Machine Learning 2 2 Accurate forecasting of operational parameters is essential for predictive maintenance and digital twinning of offshore wind turbines. Using a unique dataset from the Levenmouth 7MW demonstration turbine, we compare a purely data-driven stacked ensemble model (StackedRidge) with a novel physics-informed neural network (GET-PINN) that incorporates the Energy Gradient (K) parameter from Energy Gradient Theory (GET). The StackedRidge model achieves superior predictive accuracy (RMSE = 0.2976, R² = 0.9731) for barometric pressure signals. In contrast, the GET-PINN provides valuable physics-aware diagnostics by jointly estimating the flow instability parameter K, supporting the detection of phenomena such as vortex-induced vibrations (VIV), albeit with higher forecasting error. These results highlight the complementary strengths of the two approaches: the stacked ensemble for high-fidelity point forecasting and the GET-PINN for interpretable, physics-guided maintenance decision support in operational wind farm digital twins. 48 59 2025/09/222025/08/232025/08/122025/11/20 1404/8/29 2026/03/282026/04/112026/04/182026/04/21 1405/2/1 Kimia Nazarizadeh Kimia Nazarizadeh Babol Noshirvani University of Technology, Babol, Iran ایران k.nazarizadeh@outlook.com Hashem Nowruzi Hashem Nowruzi Babol Noshirvani University of Technology, Babol, Iran ایران h.nowruzi@nit.ac.ir Floating Offshore Wind Turbine (FWOT) Physics-Informed Neural Networks (PINN) Gradient Energy Theory (GET) Digital Twin Hybrid ML D. Zhang, Y. Si, H. Liu, L. Wang, H. Huang, Y. Sun, and J. Li, "A coupled numerical framework for hybrid floating offshore wind turbine and oscillating water column wave energy converters," Energy Conversion and Management, vol. 267, 2022.##S. Feng, L. Song, J. Zhou, Z. 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A Comprehensive Fatigue Damage Assessment and Life Extension Strategy for Jacket-Type Offshore Wind Turbines Using a Semi-Active Control Damper 2 2 Fatigue damage is the predominant failure mechanism for offshore wind turbine support structures, with tubular joints of jacket foundations being particularly vulnerable due to stress concentrations under myriad cyclic loadings. This paper presents a rigorous and detailed methodology for assessing and enhancing the fatigue life of a 5 MW jacket-supported offshore wind turbine (JOWT) through the implementation of an advanced vibration control system. A Semi-Active Liquid Column Gas Damper (SALCGD) is employed to mitigate dynamic responses, thereby reducing stress ranges at critical hotspots. The study employs a multi-step, integrated numerical framework: a high-fidelity finite element model of the JOWT is developed; dynamic analyses are conducted under 29 distinct North Sea wave and wind conditions for three structural configurations—uncontrolled, with a passive damper (TLCGD), and with the semi-active damper (SALCGD); stress time histories at all tubular joints are extracted; critical members are identified based on stress severity; and finally, cumulative fatigue damage is calculated using the Rainflow counting method, relevant S-N curves for welded details, and Palmgren-Miner's rule. The results demonstrate a transformative improvement in fatigue performance. The SALCGD dramatically reduces stress ranges at the hotspots, leading to a remarkable increase in the fatigue life of the most critical structural joints by a factor of 2.5 to 3.2 compared to the uncontrolled case. Furthermore, it outperforms the passive TLCGD, providing up to 1.5 times greater life extension. The analysis reveals that in the uncontrolled scenario, several critical joints, particularly in the wave-dominant lower stories and wind-dominant upper stories, possess a fatigue life shorter than the 20-year design life. The SALCGD successfully elevates the life of all these joints well beyond this critical threshold. This study quantitatively establishes that semi-active vibration control is not merely a serviceability tool but a powerful and essential strategy for significantly extending the operational lifespan, ensuring structural integrity, and improving the economic sustainability of offshore wind infrastructure. 60 72 2025/09/222025/08/232025/08/122025/11/202025/11/3 1404/8/12 2026/03/282026/04/112026/04/182026/04/212026/04/27 1405/2/7 Sina Mohammadi Sina Mohammadi MSc Student, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran. ایران sinamohammadi7456@gmail.com Reza Dezvareh Reza Dezvareh Associate Professor, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran. ایران rdezvareh@nit.ac.ir Offshore Wind Turbine Jacket Structure Fatigue Damage Tubular Joints S-N Curve Rainflow Counting Semi-Active Damper Life Extension Structural Integrity. Esteban, M. D., López-Gutiérrez, J. S., Diez, J. J., & Negro, V. (2011). Offshore wind farms: foundations and influence on the littoral processes. Journal of Coastal Research, 656-660.##Lu, F., Long, K., Diaeldin, Y., Saeed, A., Zhang, J., & Tao, T. (2023). A time-domain fatigue damage assessment approach for the tripod structure of offshore wind turbines. Sustainable Energy Technologies and Assessments, 60, 103450.##Velarde, J., & Bachynski, E. E. (2017). Design and fatigue analysis of monopile foundations to support the DTU 10 MW offshore wind turbine. Energy Procedia, 137, 3-13.##Muskulus, M. (2015). Simplified rotor load models and fatigue damage estimates for offshore wind turbines. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2035), 20140347.##Marshall, P. W. (2013). Design of welded tubular connections: Basis and use of AWS code provisions (Vol. 37). Elsevier.##DNVGL-RP, D. N. V. (2016). C203: Fatigue design of offshore steel structures. 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(2019). Structural control of a fixed offshore structure using a new developed tuned liquid column ball gas damper (TLCBGD). Ocean Engineering, 192, 106551.##Dezvareh, R. (2020). Upgrading the seismic capacity of pile-supported wharfs using semi-active liquid column gas damper. Journal of Applied and Computational Mechanics, 6(1), 112-124.##Dezvareh, R., & Nazokkar, A. (2025). Enhancing Dynamic Performance of OC4-DeepCwind Semi-Submersible Floating Wind Turbine Utilizing Multi-Level Semi-Active Dampers. Arabian Journal for Science and Engineering, 1-23.## ##

Numerical Investigation of Sea Wave and Oil Slick Interactions Using ANSYS AQWA Software 2 2 Oil spills represent a critical source of marine pollution with profound environmental, economic, and social ramifications. This numerical study investigates the interaction between sea waves and oil slicks using ANSYS AQWA software to elucidate the dynamics of oil pollution distribution under wave action. The research examines three pollution mass models with varying dimensions under different wave approach angles (0°, 30°, and 60°) utilizing second-order Stokes wave theory. The investigation reveals that displacement of the pollution mass center decreases with increasing wave angle. Maximum displacement and mobility increase proportionally with expanding oil slick dimensions at constant mass. Wave-induced forces on pollution masses exhibit inverse correlation with wave angle. Forces from irregular waves exceed those from regular Stokes waves by approximately 20% for larger pollution masses. For expanded oil slicks, irregular wave forces can reach up to three times the magnitude of regular wave forces. These findings provide crucial insights for oil spill response planning, containment system design, and environmental impact assessment in marine environments. 73 82 2025/09/222025/08/232025/08/122025/11/202025/11/32025/09/4 1404/6/13 2026/03/282026/04/112026/04/182026/04/212026/04/272026/05/30 1405/3/9 roshanak khosrojerdi roshanak khosrojerdi PhD Student, Water and Hydraulic Structures, Faculty of Civil Engineering-University of Tehran ایران khosrojerdi.rosha@ut.ac.ir farhoud kalateh farhoud kalateh Professor at University of Tabriz ,Ph.D, P.E., M.ASCEVisiting Associate Professor at University of Warwick, Associate ایران fkalateh@tabrizu.ac.ir oil spill modeling wave-pollution interaction numerical simulation ANSYS AQWA marine pollution dynamics hydrodynamic analysis [1] Chao, X., Shankar, N.J., & Cheong, H.F. (2001). Two-and three-dimensional oil spill model for coastal waters. Ocean Engineering, 28(12), 1557-1573.##[2] Abascal, A.J., Castanedo, S., Gutierrez, A.D., Comerma, E., Medina, R., & Losada, I.J. (2007). TESEO: An operational system for simulating oil spill trajectories and fate processes. ISOPE International Ocean and Polar Engineering Conference.##[3] Chen, H.Z., Li, D.M., & Li, X. (2007). Mathematical modeling of oil spill on the sea and application of the modeling in Daya Bay. Journal of Hydrodynamics, 19(3), 282-291.##[4] Wang, S.D., Shen, Y.M., Guo, Y.K., & Tang, J. (2008). Three-dimensional numerical simulation for transport of oil spills in seas. Ocean Engineering, 35(5-6), 503-510.##[5] Guo, W.J., & Wang, Y.X. (2009). A numerical oil spill model based on a hybrid method. Marine Pollution Bulletin, 58(5), 726-734.##[6] Nagheeby, M., & Kolahdoozan, M. (2010). Numerical modeling of two-phase fluid flow and oil slick transport in estuarine water. International Journal of Environmental Science & Technology, 7, 771-784.##[7] Heydariha, J.Z., & Ghiassi, R. (2010). Oil spill simulation in the mouth of the Persian Gulf. Advances in Waste Management, 56-60.##[8] Mariano, A., et al. (2011). On the modeling of the 2010 Gulf of Mexico oil spill. Dynamics of Atmospheres and Oceans, 52(1-2), 322-340.##[9] Hou, T., Sun, H., Jiao, B., Wang, G., Lin, H., Liu, H., & Gao, B. (2024). Numerical and experimental study of oil boom motion response and oil-stopping effect under wave-current action. Ocean Engineering, 291, 116439.##[10] Feng, X., Liu, Y., Wei, Q., Su, J., Zhang, D., Zhou, Z., Wu, W., Xiong, C., & Peng, S. (2024). Numerical simulations on the oil plume evolutions and the two critical aspects of emergent oil containment for ship collision-incurred oil spills. Ocean Engineering, 305, 118030.##[11] Dąbrowska, E. (2024). Numerical Modelling and Prediction of Oil Slick Dispersion and Horizontal Movement at Bornholm Basin in Baltic Sea. Water, 16(8), 1088.##[12] Putrananda, M.B.S., Bahatmaka, A., Aryadi, W., Puteri, B.A., & Hutagalung, C.I. (2025). Numerical Analysis of Six Degrees of Freedom Motion Response of Trimaran Semi-Submersible Ship. Mekanika: Majalah Ilmiah Mekanika, 24(1), 61-72.##[13] Nguyen, T.T., Phan, T.L., & Le, T.H. (2025). Numerical Study on the Motion and Wave Loads of an Uncrewed Surface Vehicle Catamaran at Low Speed in Sea Waves. Journal of Applied Fluid Mechanics, 18(9), 2321-2331.##[14] Arjomand, M.A., Bagheri, M., & Mostafaei, Y. (2025). Performance Enhancement of Tuned Liquid Dampers in Fixed Offshore Platforms: A Coupled ANSYS Aqwa-Transient Structural Approach. Civil Engineering and Applied Solutions, 1(1), 77-88.##[15] Paiva, M.S., Mocellin, A.P.G., Oleinik, P.H., Santos, E.D., Rocha, L.A.O., Isoldi, L.A., & Machado, B.N. (2025). Geometrical Evaluation of an Overtopping Wave Energy Converter Device Subject to Realistic Irregular Waves and Representative Regular Waves of the Sea State That Occurred in Rio Grande-RS. Processes, 13(2), 335.##[16] Wang, P., Yu, W., Zhao, M., & Du, X. (2024). Effects of wind-wave-current-earthquake interaction on the wave height and hydrodynamic pressure based on CFD method. Ocean Engineering, 305, 117909.##[17] Constantin, A. (2016). Extrema of the dynamic pressure in an irrotational regular wave train. Physics of Fluids, 28(11), 113604.##[18] Chakrabarti, S. (2005). Handbook of Offshore Engineering (2-volume set). Elsevier.##[19] McGovern, D.J., & Bai, W. (2014). Experimental study on kinematics of sea ice floes in regular waves. Cold Regions Science and Technology, 103, 15-30.## ##