Ivik corrections

report_thesis/src/index.tex

@@ -9,14 +9,14 @@
 By integrating machine learning techniques and ensemble regression models, the study addresses challenges like high dimensionality, multicollinearity, and limited data availability.
 Key innovations include the use of stacked generalization for improved model performance and an automated hyperparameter optimization framework.
 The research contributes a comprehensive catalog of models and preprocessing techniques, and integrates findings into the \gls{pyhat} by the \gls{usgs}, enhancing its scientific capabilities.
-This work lays a robust foundation for future advancements in geochemical analysis and planetary exploration using \gls{libs} data.
+This work aims to establish a robust foundation for future advancements in geochemical analysis and planetary exploration using \gls{libs} data.
 \end{abstract}
 
 \maketitle
 
-\subsubsection*{Acknowledgements:} We would like to thank our supervisors Daniele Dell'Aglio and Juan Manuel Rodriguez for their guidance and support throughout this project.
-We also thank our external supervisor Jens Frydenvang for his guidance as a domain expert from the ChemCam team, as well as volunteering his time to provide feedback on our work.
-Furthermore, we extend our gratitude to Ryan B. Anderson and Travis S. Gabriel for their invaluable discussions regarding calibration and quantification based on \gls{libs} data. We also thank them for the opportunity to contribute to \gls{pyhat}.
+\subsubsection*{Acknowledgements:} We would like to thank our supervisors Dr. Daniele Dell'Aglio and Dr. Juan Manuel Rodriguez for their guidance and support throughout this project.
+We also thank our external supervisor Dr. Jens Frydenvang for his guidance as a domain expert from the ChemCam team, as well as volunteering his time to provide feedback on our work.
+Furthermore, we extend our gratitude to Dr. Ryan B. Anderson and Dr. Travis S.J. Gabriel for their invaluable discussions regarding calibration and quantification based on \gls{libs} data. We also thank them for the opportunity to contribute to \gls{pyhat}.
 
 \input{sections/introduction.tex}
 \input{sections/related_work.tex}

report_thesis/src/sections/introduction.tex

@@ -1,5 +1,5 @@
 \section{Introduction}\label{sec:introduction}
-\gls{nasa} has been studying the Martian environment for decades through a series of missions, including the Viking missions~\cite{marsnasagov_vikings}, the \gls{mer} mission~\cite{marsnasagov_observer, marsnasagov_spirit_opportunity}, and the \gls{msl} mission~\cite{marsnasagov_msl}, each building on the knowledge gained from the previous ones.
+The \gls{nasa} has been studying the Martian environment for decades through a series of missions, including the Viking missions~\cite{marsnasagov_vikings}, the \gls{mer} mission~\cite{marsnasagov_observer, marsnasagov_spirit_opportunity}, and the \gls{msl} mission~\cite{marsnasagov_msl}, each building on the knowledge gained from the previous ones.
 Today, the rovers exploring Mars are equipped with sophisticated instruments for analyzing the chemical composition of Martian soil in search of past life and habitable environments.
 
 Part of this research is facilitated through interpretation of spectral data gathered by \gls{libs} instruments, which fire a high-powered laser at soil samples to create a plasma.
@@ -15,7 +15,7 @@ \section{Introduction}\label{sec:introduction}
 Tailored approaches have also been developed, where different models are selected based on their performance with specific spectral characteristics~\cite{rezaei_dimensionality_reduction, andersonPostlandingMajorElement2022}.
 Moreover, models incorporating physical principles have demonstrated improved accuracy by handling residuals that traditional models fail to explain~\cite{song_DF-K-ELM}.
 However, predicting oxide compositions remains challenging due to the complex, nonlinear nature of \gls{libs} data.
-This underscores the need for continued research into more adaptive and robust machine learning strategies to tackle these issues effectively.
+This underscores the need for continued research into more accurate and robust machine learning strategies to tackle these issues effectively.
 
 This thesis aims to improve upon previous work in the field of \gls{libs} data analysis.
 Our goal is to develop a machine learning pipeline that is tailored to the unique characteristics of \gls{libs} data, with the goal of achieving higher prediction accuracy and robustness.
@@ -49,6 +49,7 @@ \section{Introduction}\label{sec:introduction}
 Section~\ref{sec:proposed_approach} presents our proposed approach for optimizing pipeline configurations, detailing the selection of models and preprocessing techniques, our approach to data partitioning, validation and testing procedures, and the implementation of the hyperparameter optimization framework.
 Section~\ref{sec:methodology} presents the design and results of our experiments, as well as the analysis of the results.
 Our experiments include initial model selection, hyperparameter optimization, and the final evaluation of our proposed stacking ensemble.
+Section~\ref{sec:pyhat_contribution} discusses our contribution to \gls{pyhat} and how our work has been integrated into the toolset.
 Finally, Section~\ref{sec:conclusion} summarizes our key findings and contributions, while Section~\ref{sec:future_work} discusses potential future research directions and improvements.
 
 Due to the overlapping nature of terminology used in \gls{libs} data analysis and machine learning, we provide a list of terms in Table~\ref{tab:terms} to clarify their meaning.

report_thesis/src/sections/summary.tex

@@ -5,7 +5,7 @@ \section*{Summary}
 
 \vspace{0.5em}
 
-For decades, \gls{nasa} has deployed rovers equipped with advanced instruments to analyze the Martian environment.
+For decades, the \gls{nasa} has deployed rovers equipped with advanced instruments to analyze the Martian environment.
 The two most recent rovers, Curiosity and Perseverance, are equipped with the \gls{chemcam} and SuperCam \gls{libs} instruments, respectively.
 \gls{libs} is a powerful technique for analyzing the chemical composition of Martian soil, offering valuable insights into the planet's geology and potential for past habitability.
 This technique involves firing high-powered lasers at soil samples to create plasma, which emits light that is captured by spectrometers aboard the rovers.