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This paper presents a geometric stochastic channel model designed for analyzing maritime communication and navigation services between moving ships using the C-band or sub-6 GHz spectrum, which aligns with the focus of emerging 5G networks on land. The channel model is validated through channel measurements conducted both on the sea and land. A software tool has been developed to integrate and analyze these measurements, which is included with this publication. The main challenge in developing the channel model for maritime services lies in the dynamic nature of the sea surface, leading to constantly changing reflection conditions due to varying reflectors and scatterers on the water. Additionally, the motion conditions of the transmitter and receiver on ships change in all three dimensions, depending on the sea state. To address these complexities, data from several measurement campaigns in diverse areas were collected. The analysis involved examining the propagation conditions over the sea with variations in sea surface roughness, antenna heights, and used bandwidths. Moreover, additional propagation conditions over nearby land were also taken into account. The study demonstrates that the changing antenna height on the ship, influenced by sea conditions, significantly affects the reflection and scattering conditions. The research aims to develop reliable, high-data rate, and broadband marine communication systems. Therefore, a measurement bandwidth of 120MHz was employed to derive the propagation model. This model not only offers absolute timing information but can also be used for time-based ranging or positioning systems. The proposed geometric stochastic channel model provides valuable insights into the complex maritime communication and navigation environment. By accounting for the continuously evolving sea surface and its impact on antenna height, the model offers a robust framework for studying and optimizing marine communication systems. The availability of

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The recurrence of lethal ship-whale collisions ('ship strikes') has prompted management entities across the globe to seek effective ways for reducing collision risk. Here we describe 'active whale avoidance' defined as a mariner making operational decisions to reduce the chance of a collision with a sighted whale. We generated a conceptual model of active whale avoidance and, as a proof of concept, apply data to the model based on observations of humpback whales surfacing in the proximity of large cruise ships, and simulations run in a full-mission bridge simulator and commonly used pilotage software. Application of the model demonstrated that (1) the opportunities for detecting a surfacing whale are often limited and temporary, (2) the cumulative probability of detecting one of the available 'cues' of whale's presence (and direction of travel) decreases with increased ship-to-whale distances, and (3) following detection time delays occur related to avoidance operations. These delays were attributed to the mariner evaluating competing risks (e.g., risk of whale collision vs. risk to human life, the ship, or other aspects of the marine environment), deciding upon an appropriate avoidance action, and achieving a new operational state by the ship once a maneuver is commanded. We thus identify several options for enhancing whale avoidance including training Lookouts to focus search efforts on a 'Cone of Concern,' defined here as the area forward of the ship where whales are at risk of collision based on the whale and ship's transit/swimming speed and direction of travel. Standardizing protocols for rapid communication of relevant sighting information among bridge team members can also increase avoidance by sharing information on the whale that is of sufficient quality to be actionable. We also found that, for marine pilots in Alaska, a slight change in course tends to be preferable to slowing the ship in response to a single sighted whale, owing, in part, to the substan

Jan DE NUL Group joins forces with Seiche Ltd to start trials for the development of an automated marine mammal detection system on board of its vessels. To this purpose, Seiche's visual and thermal cameras have been installed on board the vessel Adhémar de Saint-Venant currently working in the Netherlands. A collaboration agreement to start this innovative pilot project with Seiche's new second-generation HD thermal cameras was signed in May 2021. The first camera trials will be used to optimize the A.I software for future use on projects.

In general, a single scalar hydrophone cannot determine the orientation of an underwater acoustic target. However, through a study of sea trial experimental data, the authors found that the sound field interference structures of inbound and outbound ships differ owing to changes in the topography of the shallow continental shelf. Based on this difference, four different convolutional neural networks (CNNs), AlexNet, visual geometry group, residual network (ResNet), and dense convolutional network (DenseNet), are trained to classify inbound and outbound ships using only a single scalar hydrophone. Two datasets, a simulation and a sea trial, are used in the CNNs. Each dataset is divided into a training set and a test set according to the proportion of 40% to 60%. The simulation dataset is generated using underwater acoustic propagation software, with surface ships of different parameters (tonnage, speed, draft) modeled as various acoustic sources. The experimental dataset is obtained using submersible buoys placed near Qingdao Port, including 321 target ships. The ships in the dataset are labeled inbound or outbound using ship automatic identification system data. The results showed that the accuracy of the four CNNs based on the sea trial dataset in judging vessels' inbound and outbound situations is above 90%, among which the accuracy of DenseNet is as high as 99.2%. This study also explains the physical principle of classifying inbound and outbound ships by analyzing the low-frequency analysis and recording diagram of the broadband noise radiated by the ships. This method can monitor ships entering and leaving ports illegally and with abnormal courses in specific sea areas.

The main target of DSP in respect of Contship Italia Group, is to address the IT strategies of the various companies to guarantee an harmonious development respecting the peculiarity and the autonomy of the single realities. In the last years DSP, using the acquired know-how in the IT problems of shipping, port management and intermodal transportation has enlarged is portfolio of activities offering to the market professional services in terminal operations processes and systems deployment and optimisiation. In 2007 DSP became partner of NAVIS (part of Cargotec Corporation) and certified its staff as SPARCS 3.7 and SPARCS N4 senior consultants, carrying out various international projects. DSP has recently developed for Contship Italia Group an innovative and flexible system for automatic invoicing (Fatteuro) for container and general cargo terminals interfaced with other systems in order to manage all the necessary information to calculate and register the invoices. It is currently is use at CICT (Cagliari), EGT(Tangier), LSCT(La Spezia) and at the General Cargo Terminal SPETER of La Spezia. DSP is also tightly linked with the new University of Applied Science of Southern Switzerland. In his team a professor of this university is leading analysis and design activities and most part of his personnel has a degree in Computer Science and where recruited there. This also gives the chance to DSP to participate to research projects on the transport and IT area and to remain always skilled with the newest technology