diff --git a/latex/chapters/id/04_results.tex b/latex/chapters/id/04_results.tex
index 774dc7b..f42b98f 100644
--- a/latex/chapters/id/04_results.tex
+++ b/latex/chapters/id/04_results.tex
@@ -203,42 +203,16 @@ pada data validasi. Proses ini memakan waktu hingga 1 jam 38 detik untuk Sensor
 
 \begin{table}[H]
 \centering
-\begin{tabular}{rrrrr}
-\toprule
-$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} \\
-\midrule
-4 & 5 & -5 & 0.80764 & 11.22306 \\
-8 & 5 & -5 & 0.97076 & 10.88293 \\
-16 & 5 & -5 & 0.99116 & 10.53770 \\
-32 & 10 & -10 & 0.99394 & 10.45783 \\
-64 & 10 & -10 & 0.99631 & 13.46819 \\
-128 & 5 & -10 & 0.99728 & 13.43715 \\
-256 & 5 & -10 & 0.99756 & 17.84189 \\
-512 & 5 & -10 & 0.99763 & 31.24036 \\
-\bottomrule
-\end{tabular}
 \caption{Hasil ringkasan \textit{coarse grid-search} pada Sensor A ($\mathcal{D}_A$).}
 \label{tab:coarse_summary_A}
+\input{chapters/id/tables/coarse_summary_A}
 \end{table}
 
 \begin{table}[H]
 \centering
-\begin{tabular}{rrrrr}
-\toprule
-$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} \\
-\midrule
-4 & 5 & -5 & 0.87845 & 13.77282 \\
-8 & 0 & -5 & 0.98051 & 12.51643 \\
-16 & 5 & -5 & 0.99443 & 10.90890 \\
-32 & 5 & -10 & 0.99596 & 13.42619 \\
-64 & 5 & -10 & 0.99735 & 11.40759 \\
-128 & 5 & -10 & 0.99728 & 14.54694 \\
-256 & 5 & -10 & 0.99777 & 20.27980 \\
-512 & 5 & -10 & 0.99791 & 39.63068 \\
-\bottomrule
-\end{tabular}
 \caption{Hasil ringkasan \textit{coarse grid-search} pada Sensor B ($\mathcal{D}_B$).}
 \label{tab:coarse_summary_B}
+\input{chapters/id/tables/coarse_summary_B}
 \end{table}
 
 Tabel~\ref{tab:coarse_summary_A} dan~\ref{tab:coarse_summary_B} menunjukkan hasil ringkasan \textit{coarse grid-search} dengan nilai maksimum \textit{mean test score} untuk setiap konfigurasi $n_{\text{components}}$ pada Sensor A ($\mathcal{D}_A$) dan Sensor B ($\mathcal{D}_B$). Kolom \gls{not:S_i} menunjukkan akurasi tertinggi yang dicapai, sedangkan kolom \gls{not:T_i} mencatat waktu rata-rata (dalam detik) yang dibutuhkan untuk melatih model (\textit{mean fit time}) pada setiap kombinasi parameter.
@@ -333,42 +307,16 @@ model mampu mencapai akurasi tinggi dengan waktu pelatihan yang relatif singkat.
 
 \begin{table}[H]
 \centering
-\begin{tabular}{rrrrrr}
-\toprule
-$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} & \gls{not:E_i} $(\times10^{-3})$ \\
-\midrule
-4 & 5 & -5 & 0.80764 & 11.22306 & 71.96291 \\
-8 & 5 & -5 & 0.97076 & 10.88293 & 89.20027 \\
-16 & 5 & -5 & 0.99116 & 10.53770 & 94.05832 \\
-32 & 10 & -10 & 0.99394 & 10.45783 & 95.04296 \\
-64 & 10 & -10 & 0.99631 & 13.46819 & 73.97505 \\
-128 & 5 & -10 & 0.99728 & 13.43715 & 74.21849 \\
-256 & 5 & -10 & 0.99756 & 17.84189 & 55.91131 \\
-512 & 5 & -10 & 0.99763 & 31.24036 & 31.93410 \\
-\bottomrule
-\end{tabular}
 \caption{Hasil ringkasan nilai maksimum \textit{mean test score} untuk setiap konfigurasi $n_{\text{components}}$ pada Sensor A ($\mathcal{D}_A$).}
 \label{tab:efficiency_summary_A}
+\input{chapters/id/tables/efficiency_summary_A}
 \end{table}
 
 \begin{table}[H]
 \centering
-\begin{tabular}{rrrrrr}
-\toprule
-$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} & \gls{not:E_i} $(\times10^{-3})$ \\
-\midrule
-4 & 5 & -5 & 0.87845 & 13.77282 & 63.78107 \\
-8 & 0 & -5 & 0.98051 & 12.51643 & 78.33758 \\
-16 & 5 & -5 & 0.99443 & 10.90890 & 91.15776 \\
-32 & 5 & -10 & 0.99596 & 13.42619 & 74.18057 \\
-64 & 5 & -10 & 0.99735 & 11.40759 & 87.42906 \\
-128 & 5 & -10 & 0.99728 & 14.54694 & 68.55632 \\
-256 & 5 & -10 & 0.99777 & 20.27980 & 49.20029 \\
-512 & 5 & -10 & 0.99791 & 39.63068 & 25.18027 \\
-\bottomrule
-\end{tabular}
 \caption{Hasil ringkasan nilai maksimum \textit{mean test score} untuk setiap konfigurasi $n_{\text{components}}$ pada Sensor B ($\mathcal{D}_B$).}
 \label{tab:efficiency_summary_B}
+\input{chapters/id/tables/efficiency_summary_B}
 \end{table}
 
 Hasil pada Tabel~\ref{tab:efficiency_summary_A} dan Tabel~\ref{tab:efficiency_summary_B} menunjukkan bahwa,
@@ -400,44 +348,14 @@ Metrik klasifikasi model \textit{baseline} pada dataset pengujian disajikan pada
     \centering
     \caption{\textit{Classification report} model \textit{baseline} pada Sensor A}
     \label{tab:metrics-baseline_A}
-    \begin{tabular}{lrrrr}
-    \toprule
-    & precision & recall & f1-score & support \\
-    \midrule
-    0 & 0.99 & 0.98 & 0.99 & 2565.00 \\
-    1 & 0.99 & 1.00 & 1.00 & 2565.00 \\
-    2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-    3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-    4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-    5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-    6 & 0.99 & 1.00 & 0.99 & 2565.00 \\
-    accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
-    macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
-    weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
-    \bottomrule
-    \end{tabular}
+    \input{chapters/id/tables/metrics-baseline_A.tex}
 \end{table}
 
 \begin{table}[htbp]
     \centering
     \caption{\textit{Classification report} model \textit{baseline} pada Sensor B}
     \label{tab:metrics-baseline_B}
-    \begin{tabular}{lrrrr}
-    \toprule
-    & precision & recall & f1-score & support \\
-    \midrule
-    0 & 0.98 & 0.99 & 0.99 & 2565.00 \\
-    1 & 0.99 & 1.00 & 0.99 & 2565.00 \\
-    2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-    3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-    4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-    5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-    6 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-    accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
-    macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
-    weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
-    \bottomrule
-    \end{tabular}
+    \input{chapters/id/tables/metrics-baseline_B.tex}
 \end{table}
 
 Hasil ini menunjukkan bahwa model \textit{baseline} kedua sensor mencapai akurasi 99\%. Nilai \textit{recall} yang relatif tinggi (99.0\%) menunjukkan bahwa model lebih sensitif untuk mendeteksi kelas kerusakan, meskipun nilai \textit{precision} yang sedikit lebih rendah, menunjukkan bahwa ada beberapa \textit{false-positive} yang dihasilkan.
@@ -472,7 +390,7 @@ Optimasi model yang dilakukan yaitu dengan melakukan \textit{fine \gls{grid-sear
 \end{align*}. Pada proses ini, \textit{\gls{standard-scaler}} dan \textit{\gls{stratified-k-fold} \gls{cross-validation}} dengan $k=5$ tetap digunakan untuk menjaga konsistensi evaluasi model, sehingga total kombinasi parameter yang diuji adalah \(9\times9 = 81\) kandidat model dengan total 405 kali \textit{fitting}.
 
 \subsection{Diagram \textit{Fine Grid-Search Heatmap}}
-Gambar~\ref{fig:svm_fine_heatmap} menunjukkan diagram \textit{heatmap} terhadap parameter \textit{fine grid-search} \gls{not:C}dan~\gls{not:gamma} untuk masing-masing sensor. Akurasi tertinggi pada Sensor A diperoleh pada $C= \{\,2^{8}, \,2^{8.5}, \,2^{9}, \,2^{9.5}, \,2^{10}, \,2^{10.5},\,2^{11}, \,2^{11.5}, \,2^{12} \,\}$ dan $\gamma=2^{-9.5}$ dengan akurasi meningkat 0.15\% menjadi 99.54\%, sedangkan pada Sensor B diperoleh pada $C = \{\,2^{5},\,2^{5.5} \,\}$ dan $\gamma= \{\, 2^{-3},\, 2^{-3.5},\, 2^{-4}\,\}$ dengan akurasi meningkat 0.05\% menjadi 99.49\%. Hasil ini menunjukkan bahwa optimasi \textit{hyperparameter} lebih lanjut dapat meningkatkan performa model meskipun peningkatannya relatif kecil dibandingkan dengan model \textit{baseline}.
+Gambar~\ref{fig:svm_fine_heatmap} menunjukkan diagram \textit{heatmap} terhadap parameter \textit{fine grid-search} \gls{not:C}dan~\gls{not:gamma} untuk masing-masing sensor. Akurasi tertinggi pada Sensor A diperoleh pada $C= \{\,2^{8}, \,2^{8.5}, \,2^{9}, \,2^{9.5}, \,2^{10}, \,2^{10.5},\,2^{11}, \,2^{11.5}, \,2^{12} \,\}$ dan $\gamma=2^{-8}$ dengan akurasi meningkat 0.15\% menjadi 99.54\%, sedangkan pada Sensor B diperoleh pada $\gamma = \{\,2^{-5},\,2^{-5.5} \,\}$ dan $C= \{\, 2^{3},\, 2^{3.5},\, 2^{4}\,\}$ dengan akurasi meningkat 0.05\% menjadi 99.49\%. Hasil ini menunjukkan bahwa optimasi \textit{hyperparameter} lebih lanjut dapat meningkatkan performa model meskipun peningkatannya relatif kecil dibandingkan dengan model \textit{baseline}.
 \begin{figure}[H]
     \centering
     \subfloat[Sensor A (PCA 32)]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_fine_pca32.png}}
@@ -491,44 +409,14 @@ Hasil performa model \textit{fine \gls{grid-search}} pada data uji disajikan pad
 \centering
 \caption{\textit{Classification report} model Sensor A}
 \label{tab:metrics-fine-a}
-\begin{tabular}{lrrrr}
-\toprule
- & precision & recall & f1-score & support \\
-\midrule
-0 & 0.99 & 0.99 & 0.99 & 2565.00 \\
-1 & 0.99 & 1.00 & 0.99 & 2565.00 \\
-2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-6 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
-macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
-weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
-\bottomrule
-\end{tabular}
+\input{chapters/id/tables/metrics-fine-a.tex}
 \end{table}
 
 \begin{table}
 \centering
 \caption{\textit{Classification report} model Sensor B}
 \label{tab:metrics-fine-b}
-\begin{tabular}{lrrrr}
-\toprule
- & precision & recall & f1-score & support \\
-\midrule
-0 & 0.98 & 0.97 & 0.98 & 2565.00 \\
-1 & 0.99 & 1.00 & 1.00 & 2565.00 \\
-2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-4 & 0.99 & 1.00 & 1.00 & 2565.00 \\
-5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
-6 & 0.98 & 0.99 & 0.99 & 2565.00 \\
-accuracy & 0.99 & 0.99 & 0.99 & 0.99 \\
-macro avg & 0.99 & 0.99 & 0.99 & 17955.00 \\
-weighted avg & 0.99 & 0.99 & 0.99 & 17955.00 \\
-\bottomrule
-\end{tabular}
+\input{chapters/id/tables/metrics-fine-b.tex}
 \end{table}
 
 \subsection{\textit{Confusion Matrix}}
diff --git a/latex/chapters/id/tables/coarse_summary_A.tex b/latex/chapters/id/tables/coarse_summary_A.tex
new file mode 100644
index 0000000..435788d
--- /dev/null
+++ b/latex/chapters/id/tables/coarse_summary_A.tex
@@ -0,0 +1,15 @@
+\centering
+\begin{tabular}{rrrrr}
+\toprule
+$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} \\
+\midrule
+4 & 5 & -5 & 0.80764 & 11.22306 \\
+8 & 5 & -5 & 0.97076 & 10.88293 \\
+16 & 5 & -5 & 0.99116 & 10.53770 \\
+32 & 10 & -10 & 0.99394 & 10.45783 \\
+64 & 10 & -10 & 0.99631 & 13.46819 \\
+128 & 5 & -10 & 0.99728 & 13.43715 \\
+256 & 5 & -10 & 0.99756 & 17.84189 \\
+512 & 5 & -10 & 0.99763 & 31.24036 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/latex/chapters/id/tables/coarse_summary_B.tex b/latex/chapters/id/tables/coarse_summary_B.tex
new file mode 100644
index 0000000..8a78b4b
--- /dev/null
+++ b/latex/chapters/id/tables/coarse_summary_B.tex
@@ -0,0 +1,15 @@
+\centering
+\begin{tabular}{rrrrr}
+\toprule
+$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} \\
+\midrule
+4 & 5 & -5 & 0.87845 & 13.77282 \\
+8 & 0 & -5 & 0.98051 & 12.51643 \\
+16 & 5 & -5 & 0.99443 & 10.90890 \\
+32 & 5 & -10 & 0.99596 & 13.42619 \\
+64 & 5 & -10 & 0.99735 & 11.40759 \\
+128 & 5 & -10 & 0.99728 & 14.54694 \\
+256 & 5 & -10 & 0.99777 & 20.27980 \\
+512 & 5 & -10 & 0.99791 & 39.63068 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/latex/chapters/id/tables/efficiency_summary_A.tex b/latex/chapters/id/tables/efficiency_summary_A.tex
new file mode 100644
index 0000000..a81eef9
--- /dev/null
+++ b/latex/chapters/id/tables/efficiency_summary_A.tex
@@ -0,0 +1,15 @@
+\centering
+\begin{tabular}{rrrrrr}
+\toprule
+$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} & \gls{not:E_i} $(\times10^{-3})$ \\
+\midrule
+4 & 5 & -5 & 0.80764 & 11.22306 & 71.96291 \\
+8 & 5 & -5 & 0.97076 & 10.88293 & 89.20027 \\
+16 & 5 & -5 & 0.99116 & 10.53770 & 94.05832 \\
+32 & 10 & -10 & 0.99394 & 10.45783 & 95.04296 \\
+64 & 10 & -10 & 0.99631 & 13.46819 & 73.97505 \\
+128 & 5 & -10 & 0.99728 & 13.43715 & 74.21849 \\
+256 & 5 & -10 & 0.99756 & 17.84189 & 55.91131 \\
+512 & 5 & -10 & 0.99763 & 31.24036 & 31.93410 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/latex/chapters/id/tables/efficiency_summary_B.tex b/latex/chapters/id/tables/efficiency_summary_B.tex
new file mode 100644
index 0000000..9b8be07
--- /dev/null
+++ b/latex/chapters/id/tables/efficiency_summary_B.tex
@@ -0,0 +1,15 @@
+\centering
+\begin{tabular}{rrrrrr}
+\toprule
+$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} & \gls{not:E_i} $(\times10^{-3})$ \\
+\midrule
+4 & 5 & -5 & 0.87845 & 13.77282 & 63.78107 \\
+8 & 0 & -5 & 0.98051 & 12.51643 & 78.33758 \\
+16 & 5 & -5 & 0.99443 & 10.90890 & 91.15776 \\
+32 & 5 & -10 & 0.99596 & 13.42619 & 74.18057 \\
+64 & 5 & -10 & 0.99735 & 11.40759 & 87.42906 \\
+128 & 5 & -10 & 0.99728 & 14.54694 & 68.55632 \\
+256 & 5 & -10 & 0.99777 & 20.27980 & 49.20029 \\
+512 & 5 & -10 & 0.99791 & 39.63068 & 25.18027 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/latex/chapters/id/tables/metrics-baseline_A.tex b/latex/chapters/id/tables/metrics-baseline_A.tex
new file mode 100644
index 0000000..d770202
--- /dev/null
+++ b/latex/chapters/id/tables/metrics-baseline_A.tex
@@ -0,0 +1,16 @@
+    \begin{tabular}{lrrrr}
+    \toprule
+    & precision & recall & f1-score & support \\
+    \midrule
+    0 & 0.99 & 0.98 & 0.99 & 2565.00 \\
+    1 & 0.99 & 1.00 & 1.00 & 2565.00 \\
+    2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+    3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+    4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+    5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+    6 & 0.99 & 1.00 & 0.99 & 2565.00 \\
+    accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
+    macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
+    weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
+    \bottomrule
+    \end{tabular}
\ No newline at end of file
diff --git a/latex/chapters/id/tables/metrics-baseline_B.tex b/latex/chapters/id/tables/metrics-baseline_B.tex
new file mode 100644
index 0000000..40707a0
--- /dev/null
+++ b/latex/chapters/id/tables/metrics-baseline_B.tex
@@ -0,0 +1,17 @@
+\centering
+\begin{tabular}{lrrrr}
+\toprule
+& precision & recall & f1-score & support \\
+\midrule
+0 & 0.99 & 0.98 & 0.99 & 2565.00 \\
+1 & 0.99 & 1.00 & 1.00 & 2565.00 \\
+2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+6 & 0.99 & 1.00 & 0.99 & 2565.00 \\
+accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
+macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
+weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/latex/chapters/id/tables/metrics-fine-a.tex b/latex/chapters/id/tables/metrics-fine-a.tex
new file mode 100644
index 0000000..e923c7c
--- /dev/null
+++ b/latex/chapters/id/tables/metrics-fine-a.tex
@@ -0,0 +1,16 @@
+\begin{tabular}{lrrrr}
+\toprule
+ & precision & recall & f1-score & support \\
+\midrule
+0 & 0.99 & 0.99 & 0.99 & 2565.00 \\
+1 & 0.99 & 1.00 & 0.99 & 2565.00 \\
+2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+6 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
+macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
+weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/latex/chapters/id/tables/metrics-fine-b.tex b/latex/chapters/id/tables/metrics-fine-b.tex
new file mode 100644
index 0000000..58c3fed
--- /dev/null
+++ b/latex/chapters/id/tables/metrics-fine-b.tex
@@ -0,0 +1,17 @@
+\centering
+\begin{tabular}{lrrrr}
+\toprule
+& precision & recall & f1-score & support \\
+\midrule
+0 & 0.99 & 0.99 & 0.99 & 2565.00 \\
+1 & 0.99 & 1.00 & 0.99 & 2565.00 \\
+2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+6 & 1.00 & 1.00 & 1.00 & 2565.00 \\
+accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
+macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
+weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
+\bottomrule
+\end{tabular}
\ No newline at end of file
diff --git a/latex/chapters/img/sensor2/grid_fine_pca16.png b/latex/chapters/img/sensor2/grid_fine_pca16.png
index 7c4e654..a1dc576 100644
--- a/latex/chapters/img/sensor2/grid_fine_pca16.png
+++ b/latex/chapters/img/sensor2/grid_fine_pca16.png
@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:86890d5f72b0769398020df79a37a4b8204f204e32c2269c9f7eda40b37607f3
-size 240617
+oid sha256:8b7a81f453ea9c85db6ac50f39427d2eccee7065441b9b42dafb702d457af27d
+size 221726