fix $lambda$ -> $\lambda$

report_thesis/src/sections/problem_definition.tex

@@ -55,7 +55,7 @@ \subsection{Challenges}\label{subsec:challenges}
 As mentioned, quantifying chemical compositions from \gls{libs} spectral data involves several significant challenges that must be addressed to ensure accurate and robust predictions.
 
 \subsubsection{Data Dimensionality}
-The large number of dimensions, as seen by having many wavelengths $lambda$ in the Intensity Tensor $\mathbf{I}[\chi, l, s, \lambda]$, can lead to challenges such as the inclusion of irrelevant or redundant features.
+The large number of dimensions, as seen by having many wavelengths $\lambda$ in the Intensity Tensor $\mathbf{I}[\chi, l, s, \lambda]$, can lead to challenges such as the inclusion of irrelevant or redundant features.
 
 High-dimensional datasets, like \gls{libs} datasets, may include irrelevant or redundant features that obscure the true signal, complicating the process of accurately estimating the target variables. Effective dimensionality reduction techniques can help ensure the reliability of predictions.