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chhoumann · Jun 13, 2024 · a1b03d7 · a1b03d7
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diff --git a/report_thesis/src/sections/appendix/index.tex b/report_thesis/src/sections/appendix/index.tex
@@ -223,7 +223,7 @@ \subsection{Initial Experiment: Model Hyperparameters}\label{subsec:initial_expe
 
 \clearpage
 
-\subsection{Overview of best performing model configurations}\label{subsec:best_model_configurations}
+\subsection{Overview of Best Performing Model Configurations}\label{subsec:best_model_configurations}
 \input{sections/appendix/tables/SiO2_overview.tex}
 \input{sections/appendix/tables/TiO2_overview.tex}
 \input{sections/appendix/tables/Al2O3_overview.tex}
@@ -235,7 +235,7 @@ \subsection{Overview of best performing model configurations}\label{subsec:best_
 
 \FloatBarrier
 
-\subsection{PyHAT contribution certificate}\label{subsec:pyhat_contribution}
+\subsection{PyHAT Contribution Certificate}\label{subsec:pyhat_contribution}
 \begin{figure}
     \centering
     \includepdf[pages=-, pagecommand={}, width=\textwidth]{sections/PyHAT_contribution_letter.pdf}

diff --git a/report_thesis/src/sections/related_work.tex b/report_thesis/src/sections/related_work.tex
@@ -4,7 +4,7 @@ \section{Related Work}\label{sec:related-work}
 These approaches collectively aim to manage the complexities inherent in \gls{libs} data and improve predictive performance.
 We review existing and relevant work through a thematic taxonomy, highlighting their potential applications in our study.
 
-\subsection{Machine Learning Models in \gls{libs} Analysis}
+\subsection{Machine Learning Models in LIBS Analysis}
 Several studies have applied machine learning models to analyze \gls{libs} data, aiming to predict major oxide compositions with high accuracy.
 
 \citet{andersonPostlandingMajorElement2022} utilized machine learning models to quantify major oxides on Mars using the SuperCam instrument on the Perseverance rover.