fine...

report_thesis/src/sections/background/data_overview.tex

@@ -1,5 +1,5 @@
 \subsection{Data Overview}\label{sec:data-overview}
-Similarly to our previous work (\citet{p9_paper}), we used the publicly available \gls{ccs} data from the \gls{nasa}'s \gls{pds}~\cite{PDSGeoscienceNode}.
+Similarly to our previous work (\citet{p9_paper}), we used the publicly available \gls{ccs} data from \gls{nasa}'s \gls{pds}~\cite{PDSGeoscienceNode}.
 \gls{ccs} refers to \gls{libs} data that has been through a series of preprocessing steps such as subtracting the ambient light background, noise removal and removing the electron continuum to derive data that is more suitable for quantitative analysis.
 A comprehensive description of this preprocessing procedure is available in \citet{wiensPreflightCalibrationInitial2013}.
 

report_thesis/src/sections/experiments/data_preparation.tex

@@ -1,6 +1,6 @@
 \subsection{Data Preparation}\label{sec:data-preparation}
 The first step in our methodology is to prepare the datasets for model training and evaluation.
-As mentioned in Section~\ref{sec:data-overview}, the data used in this study was obtained from the \gls{nasa}'s \gls{pds} and consists of \gls{ccs} data and major oxide compositions for various samples.
+As mentioned in Section~\ref{sec:data-overview}, the data used in this study was obtained from \gls{nasa}'s \gls{pds} and consists of \gls{ccs} data and major oxide compositions for various samples.
 
 The initial five shots from each sample are excluded because they are usually contaminated by dust covering the sample, which is cleared away by the shock waves produced by the laser \cite{cleggRecalibrationMarsScience2017}.
 The remaining 45 shots from each location are then averaged, yielding a single spectrum $s$ per location $l$ in the Averaged Intensity Tensor\ref{matrix:averaged_intensity}, resulting in a total of five spectra for each sample.

report_thesis/src/sections/introduction.tex

@@ -3,7 +3,7 @@ \section{Introduction}\label{sec:introduction}
 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.
-The emitted light is captured by spectrometers and analyzed using machine learning models to assess the presence and concentration of certain major oxides, informing the \gls{nasa}'s understanding of Mars' geology~\cite{cleggRecalibrationMarsScience2017}.
+The emitted light is captured by spectrometers and analyzed using machine learning models to assess the presence and concentration of certain major oxides, informing \gls{nasa}'s understanding of Mars' geology~\cite{cleggRecalibrationMarsScience2017}.
 
 However, predicting major oxide compositions from \gls{libs} data still presents significant computational challenges.
 These include the high dimensionality and non-linearity of the data, compounded by issues of multicollinearity and matrix effects~\cite{andersonImprovedAccuracyQuantitative2017}.

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.