Marvelocity Pdf Access

\subsection{Machine‑Learning Approaches} Bai et al. \cite{Bai2021} employed deep neural networks to predict fuel consumption from AIS and weather data, achieving a 5 \% error reduction. Chen and Li \cite{Chen2022} introduced a physics‑informed neural network (PINN) to enforce momentum balance, yet their dataset (≈ 200 k samples) limits generalisation.

\section{Methodology} \label{sec:method} \subsection{Data Acquisition} \begin{itemize} \item \textbf{AIS}: 2.3 M messages (2018–2023) from the Global Fishing Watch and MarineTraffic APIs. \item \textbf{Oceanographic Reanalysis}: ERA5 \cite{Hersbach2020} providing 10‑m wind vectors, significant wave height, and surface currents at 0.25° resolution. \item \textbf{Ship Catalog}: Technical specifications (length overall, beam, draft, block coefficient, engine power) extracted from the Lloyd’s Register database. \end{itemize} All timestamps are aligned to UTC and interpolated to a 10‑minute cadence. marvelocity pdf

The final **MarVelocity** prediction is: \begin{equation} V_{\text{MarV}} = V_{\text{HM}} + \hat{\Delta V}(\mathbf{x}). \end{equation} \subsection{Machine‑Learning Approaches} Bai et al

\section{Conclusion} \label{sec:conclusion} We presented **MarVelocity**, a hybrid metric that blends classical hydrodynamic resistance modelling with a universal machine‑ \end{itemize} All timestamps are aligned to UTC and

\begin{figure}[H] \centering \includegraphics[width=0.75\linewidth]{ablation.png} \caption{Ablation results: MAE increase when a feature group is omitted.} \label{fig:ablation} \end{figure}