Added section

report_pre_thesis/src/sections/related_work.tex

@@ -16,7 +16,7 @@ \section{Related work}\label{sec:related_works}
 
 \citeauthor{bai_application_2023} explored elastic net regression for analyzing Mars-analog LIBS data - specifically, Earth-based samples crafted to simulate Martian geological conditions - and demonstrated that it efficiently balances feature selection with model stability.
 This technique was proficient in detecting relevant spectral lines, which is crucial for interpreting multivariate data.
-The elastic net model was trained with three normalization methods: Norm 1 and Norm 3 which are described in \ref{sec:background} and Standard Normal Variate. Their findings suggest Norm 3 as the optimal normalization technique for this context.
+The elastic net model was trained with three normalization methods: Norm 1 and Norm 3 which are described in section \ref{sec:background} and Standard Normal Variate. Their findings suggest Norm 3 as the optimal normalization technique for this context.
 The elastic net provided reliable SiO2 estimates in Martian soil analogs, often aligning closely with or surpassing traditional ChemCam team models.
 Across a vast dataset of over 23,000 Mars spectra, the elastic net showed strong correlations with established models, though some challenges in iron oxide predictions were noted\cite{bai_application_2023}.
 The examination of elastic net regression for Mars-analog LIBS data offers valuable perspectives on feature selection and model stability, essential for interpreting multivariate Martian data. Their findings, particularly on optimal normalization techniques and model performance across a large dataset, provide a comparative framework for our experiments.