\texttt{GNU ddrescue}\footnote{GNU ddrescue: \url{https://www.gnu.org/software/ddrescue/}, hereinafter referred to as \texttt{ddrescue}, not to be confused with the older \texttt{dd\_rescue}} is a data recovery utility
that allows the transfer of data from one file or block device (such as hard disks, CD-ROMs, etc.) to another.
Unlike the eponymous tool \texttt{dd}, it focuses on the retrieval of undamaged parts in the event of read errors.
The basic operation of ddrescue is fully automated, eliminating the need for manual error handling or program restart.

The \textbf{mapfile}, a unique feature of \texttt{ddrescue}, is a core feature for its efficiency.
The mapfile allows data to be retrieved efficiently by reading only the necessary blocks.
It also allows to interrupt the rescue process at any point and resume later at the same point.

\texttt{ddrescueview}\footnote{ddrescueview: \url{https://sourceforge.net/projects/ddrescueview/}} is a graphical interface for \texttt{ddrescue} mapfiles.
It visualizes \texttt{ddrescue}'s mapfiles via a user-friendly GUI\@.
The main view shows a grid, with the colour of each cell indicating the rescue status of the blocks it represents.

Both tools are used for data recovery, both in private and professional environments, for example in IT forensics\cite[p. 130]{tatort2017}.

However, these tools are not always executed on a system with a graphical user interface.
In forensic applications, for example, it is common to work mainly under Windows,
as professional software is sometimes only supported there.
However, as \texttt{ddrescue} requires a unix-like operating system,
a small separate Linux is often used for this purpose,
which in some cases does not offer a graphical user interface.

\texttt{ddrescue} itself is a command line application, so there is mainly a need for another application
that visualises mapfiles on the terminal and thus provides more precise information on the progress of a backup.
The implementation of this other application is described in the following sections,
starting with the parsing of the mapfiles.

\subsection{Writing process}\label{subsec:writing-process}
In the writing process of this report, artificial neural networks were used for selected and individual purposes.

The online translation service DeepL, which according to the company is based on machine-learning methods
such as Transformers and Convolutional Neural Networks, was used as a formulation aid.

LLMs, specifically ChatGPT with GPT-4, were inter alia used to simplify the manual creation of latex code
such as tables and bibliography entries and for generating the abstract,
however everything was still corrected and adjusted by hand.