esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Fatty Acid Profiles of Regular Raw Milk and DHA-Biorortified Raw Milk  
The composition and contents of fatty acids in regular raw milk and DHA-biortified raw milk from cows at different stages of lactation are presented in Tables 1 and 2. The DHA-biortified raw milk had a higher DHA content than the regular raw milk, which is consistent with previous research findings. The fat content of the milk from cows fed a DHA-enriched diet was considerably lower than that of the milk without supplementation. The possible reason for this is that DHA has the function of lowering serum triglycerides, the mammary glands need to provide fatty acids through the blood to synthesize milk fat, and the reduction in fatty acids in the blood reduces the milk fat content of milk. Another study attributed the decrease in milk fat percentage mainly to the production of the fatty acids trans 10, cis 12-C18:2, and trans 10-C18:1 in the rumen hydrogenation pathway of polyunsaturated fatty acids from algae that have the ability to strongly inhibit milk fat synthesis [21]. However, the content of total PUFAs and n-3 PUFA in DHA-biortified raw milk was significantly higher than that in regular raw milk across all lactation stages, and the n-6/n-3 fatty acid ratio of the DHA-biortified raw milk was lower than that of the regular raw milk, which is consistent with the findings of a previous study [8]. PUFAs, particularly the n-6 and n-3 series, constitute crucial nutrients in the diet. As a result of the absence of n-3 desaturase in the body, the conversion of these two series of fatty acids into one another is impossible [22], and they must therefore be sourced from the diet. Balancing the ratio of n-6/n-3 is crucial throughout the life process for maintaining internal environment stability, promoting normal growth and development, preserving health, and preventing and treating chronic diseases. Among the 30 fatty acids identified in regular raw milk, 17 were saturated and 13 were unsaturated fatty acids. In DHA-biortified raw milk, 32 fatty acids were identified, with 17 being saturated and 15 being unsaturated fatty acids. Notably, C15:1, C17:1, C18:2, C22:1, and C24:1 were absent in both types of raw milk, while C20:3 and C22:6 were exclusively found in DHA-biortified raw milk. The physiological function of C20:3 has not been extensively studied, but it is uniquely detected in DHA-biortified raw milk. This may indicate that C20:3 is a distinctive component of DHA raw milk. The saturated fatty acid content of the regular raw milk decreased gradually as the days of lactation increased, whereas the unsaturated fatty acid content increased. Specifically, the monounsaturated fatty acid content increased gradually, and the polyunsaturated fatty acid content initially decreased and subsequently increased with the progression of lactation duration. The saturated fatty acid content of the DHA-bioforortified raw milk initially increased and then decreased with the progression of lactation duration.
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 4 Details of the Implementation
### Path Length Algorithm  
We calculate the length of a path by traveling along it and counting one unit of length when moving from one pixel to the next one if they are on the same horizontal row or vertical column, and the length of \(\sqrt{2}\) if they lie on a diagonal.
# A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO
## 4 Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Approach** & **Dataset** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **AUC Score** & **Author(s)** \\ \hline VGG19 & Kaggle & \multirow{2}{*}{95.2} & \multirow{2}{*}{94.8} & \multirow{2}{*}{95.5} & \multirow{2}{*}{95.1} & \multirow{2}{*}{0.95} & \multirow{2}{*}{[PERSON] et al. [32]} \\  & Dataset 2 & & & & & & \\ \hline EfficientNet-B7 & PlantVillage & 97.8 & 97.4 & 98 & 97.7 & 0.98 & [PERSON] et al. [25] \\ \hline MobileNetV2 & Kaggle & \multirow{2}{*}{95.9} & \multirow{2}{*}{95.5} & \multirow{2}{*}{96.1} & \multirow{2}{*}{95.8} & \multirow{2}{*}{0.96} & \multirow{2}{*}{[PERSON] and [PERSON] [33]} \\  & Dataset 1 & & & & & & \\ \hline ResNet-50 + SVM & Kaggle & \multirow{2}{*}{97} & \multirow{2}{*}{96.6} & \multirow{2}{*}{97.2} & \multirow{2}{*}{96.8} & \multirow{2}{*}{0.97} & \multirow{2}{*}{[PERSON] and [PERSON] [27]} \\  & Dataset 1 & & & & & & \\ \hline Vision & PlantVillage & 96.8 & 96.5 & 97 & 96.7 & 0.98 & [PERSON] et al. [28] \\ Transformer (ViT) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont_. three benchmark datasets: PlantVillage, Kaggle Dataset 1, and Kaggle Dataset 2. The results demonstrate that the PSO-based variant consistently achieves superior performance across all evaluation metrics, including accuracy, precision, recall, and F1-Score. On the PlantVillage dataset, BERT-ResNet-PSO achieves an accuracy of 99.0%, outperforming AdamW (98.5%) and SGD (98.3%). Similar trends are observed in Kaggle Dataset 1 and Kaggle Dataset 2, where PSO-based optimization yields better generalization and classification consistency. The consistent margin of improvement--ranging between 0.3% and 0.7% in F1-Score--highlights the effectiveness of Particle Swarm Optimization (PSO) in fine-tuning model parameters beyond what gradient-based methods can achieve alone. These gains are particularly important in plant disease classification tasks, where minor improvements in accuracy can significantly reduce misdiagnosis in practical applications. Thus, PSO enhances both model robustness and precision, validating its integration into the BERT-ResNet framework.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 3 Machine Learning Techniques
### LightGBM  
LightGBM (LGBM) is a gradient boosting framework that is designed to be efficient, scalable, and highly accurate. It is based on decision tree algorithms and uses a uniqueapproach called \"Gradient-based One-Side Sampling\" (GOSS) to reduce the number of data points needed for training, while still achieving high accuracy. LGBM uses a leaf-wise approach for growing trees, which enables it to handle large datasets with many features and avoid overfitting. In addition, it provides several advanced features such as early stopping, cross-validation, and regularization to further improve model performance.
## 4 Conclusions
However, as stated earlier, if discharge measurement data are accessible for the rivers within the specific watershed of interest, it is feasible to calibrate the model using this data, allowing for the integration of the IMERG dataset into flood discharge forecasting with sufficient reliability. The hydrological modeling using the radar-based gridded rainfall data developed in this study is planned to be implemented in an ongoing research project for estimating inflow to ungauged dam reservoirs. Through this application, further research aims to facilitate the integration of hydrological models into ungauged and data-limited dam reservoir waterbody monitoring studies, utilizing other satellite data (optical or SAR; Synthetic Aperture Radar imageries). This mutual integration will be used as a framework to complement the current research efforts and establish linkages with hydrological modeling for inflow estimation in ungauged watersheds. The integration will also involve analyzing and estimating water levels, surface areas, and storage capacities obtained from satellite imagery data, thereby enhancing the accuracy and completeness of the analyses and estimations. This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-00155763). The data that support the findings of this study are available from the corresponding author upon reasonable request. Hydrometeorological data support from the Korea Water Resources Corporation (K-water) for this work is gratefully acknowledged. The author declares no conflict of interest. Figure 11: Hydrographs of flood simulation for the developed models at each flow gauge. The dashed line represents the aggregated values of individually measured discharges, accounting for 65.9% of the observed discharge at the outlet point (storm event #3). Figure 13: Hydrographs of flood simulation for the developed models at each flow gauge. The dashed line represents the aggregated values of individually measured discharges, accounting for 62.7% of the observed discharge at the outlet point (storm event #5).
# Integrated Thermal and Dynamic Analysis of Dry Automotive Clutch Linings
## Appendix A Inertia, damping, and stiffness matrices for use in Equation (12) are:  
\[I=\left[\begin{array}{cccccccc}I_{1}&0&0&0&0&0&0&0&0\\ 0&I_{2}&0&0&0&0&0&0&0\\ 0&0&I_{3}&0&0&0&0&0&0\\ 0&0&0&(I_{4}n_{1}^{2}+I_{5})&0&0&0&0&0\\ 0&0&0&0&(I_{6}n_{2}^{2}+I_{7})&0&0&0&0\\ 0&0&0&0&0&I_{8}&0&0&0\\ 0&0&0&0&0&0&I_{9}&0&0\\ 0&0&0&0&0&0&0&I_{10}&0\\ 0&0&0&0&0&0&0&I_{11}\end{array}\right] \tag{13}\]  
\[c=\left[\begin{array}{cccccccc}c_{1}&-c_{1}&0&0&0&0&0&0\\ -c_{1}&c_{1}&0&0&0&0&0&0&0\\ 0&0&c_{2}&-n_{1}c_{2}&0&0&0&0&0\\ 0&0&n_{1}c_{2}&n_{1}^{2}c_{2}+c_{3}&-n_{2}c_{3}&0&0&0&0\\ 0&0&0&-n_{2}c_{3}&n_{2}^{2}c_{3}+c_{4}+c_{6}&-c_{4}&0&-c_{6}&0\\ 0&0&0&0&-c_{4}&c_{4}+c_{5}&-c_{5}&0&0\\ 0&0&0&0&-c_{5}&c_{5}&0&0\\ 0&0&0&-c_{6}&0&0&c_{6}+c_{7}&-c_{7}\\ 0&0&0&0&0&0&-c_{7}&c_{7}\end{array}\right] \tag{14}\]  
\[k=\left[\begin{array}{cccccccc}k_{1}&-k_{1}&0&0&0&0&0&0&0\\ -k_{1}&k_{1}&0&0&0&0&0&0&0\\ 0&0&k_{2}&-n_{1}k_{2}&0&0&0&0&0\\ 0&0&n_{1}k_{2}&n_{1}^{2}k_{2}+k_{3}&-n_{2}k_{3}&0&0&0&0\\ 0&0&0&-n_{2}k_{3}&n_{2}^{2}k_{3}+k_{4}+k_{6}&-k_{4}&0&-k_{6}&0\\ 0&0&0&0&-k_{4}&k_{4}+k_{5}&-k_{5}&0&0\\ 0&0&0&0&0&-k_{5}&k_{5}&0&0\\ 0&0&0&0&-k_{6}&0&0&k_{6}+k_{7}&-k_{7}\\ 0&0&0&0&0&0&-k_{7}&k_{7}\end{array}\right] \tag{15}\]  
Inertia, damping, and stiffness matrices for use in Equations (13)-(19) are  
\[I=\left[\begin{array}{cccccccc}I_{1}&0&0&0&0&0&0\\ 0&I_{2/3}&0&0&0&0&0&0\\ 0&0&(I_{4}n_{1}^{2}+I_{5})&0&0&0&0&0\\ 0&0&0&(I_{6}n_{2}^{2}+I_{7})&0&0&0&0\\ 0&0&0&I_{8}&0&0&0\\ 0&0&0&0&0&I_{9}&0&0\\ 0&0&0&0&0&I_{10}&0\\ 0&0&0&0&0&0&I_{11}\end{array}\right] \tag{16}\]\[c=\left[\begin{array}{cccccccc}c_{1}&-c_{1}&0&0&0&0&0&0\\ -c_{1}&c_{1}+c_{2}&-c_{2}&0&0&0&0&0\\ 0&n_{1}c_{2}&n_{1}^{2}c_{2}+c_{3}&-n_{2}c_{3}&0&0&0&0\\ 0&0&-n_{2}c_{3}&n_{2}^{2}c_{3}+c_{4}+c_{6}&-c_{4}&0&-c_{6}&0\\ 0&0&0&-c_{4}&c_{4}+c_{5}&-c_{5}&0&0\\ 0&0&0&0&-c_{5}&c_{5}&0&0\\ 0&0&0&-c_{6}&0&0&c_{6}+c_{7}&-c_{7}\\ 0&0&0&0&0&-c_{7}&c_{7}\end{array}\right] \tag{10}\]  
\[k=\left[\begin{array}{cccccccc}k_{1}&-k_{1}&0&0&0&0&0&0\\ -k_{1}&k_{1}+k_{2}&-k_{2}&0&0&0&0&0\\ 0&n_{1}k_{2}&n_{1}^{2}k_{2}+k_{3}&-n_{2}k_{3}&0&0&0&0\\ 0&0&-n_{2}k_{3}&n_{2}^{2}k_{3}+k_{4}+k_{6}&-k_{4}&0&-k_{6}&0\\ 0&0&0&-k_{4}&k_{4}+k_{5}&-k_{5}&0&0\\ 0&0&0&-k_{5}&k_{5}&0&0\\ 0&0&0&-k_{6}&0&k_{6}+k_{7}&-k_{7}\\ 0&0&0&0&0&-k_{7}&k_{7}\end{array}\right] \tag{11}\]  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Covariant Pin-on-Disc Condition**} & \multicolumn{2}{c}{**Clutch Condition**} \\ \hline
**Test Time (s)** & **Speed (m/s)/(RPM)** & **Load (kg)** & **Clamp Load (N)** & **Flywheel Speed (m/s)** \\ \hline
30 & 10.1/2412 & 0.21 & 400 & 10.1 \\
37 & 8.3/1972 & 0.48 & 900 & 8.3 \\
55 & 5.5/1311 & 1.12 & 2250 & 5.5 \\
82 & 3.7/878 & 2.00 & 3750 & 3.7 \\
110 & 2.7/656 & 2.66 & 5000 & 2.7 \\
210 & 1.4/342 & 3.72 & 7000 & 1.4 \\
1257 & 0.2/57 & 5.32 & 10,000 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 10: Comparison of clutch engagement conditions with the equivalent pin-on-disc tests.
## 2 Data and Methods
### [PERSON]'s Line Algorithm  
[PERSON]'s line algorithm is a simple and efficient method for drawing lines on a grid, such as pixels on a computer screen, originally published by [PERSON] [18]. The algorithm is based on the observation that the slope of a line between two points can be expressed as a ratio of the change in the y-coordinate to the change in the x-coordinate. This ratio is known as the slope of the line. The algorithm determines which points in an \(N\times M\) grid should be selected to form a close approximation to a straight line between two points. The algorithm works by incrementally plotting the pixels that are closest to the line, one at a time, as illustrated in Figure 1. It computes the coordinates of the successive points by incrementing the x- and y-coordinates. An error term is computed at each step to determine if x or y should be incremented. Hence, it uses integer arithmetic to draw the line can be drawn rather than floating-point arithmetic (division or multiplication operations) making it both faster and more precise. All of this is encapsulated in the pseudocode presented in Algorithm 1.  
```
1:Input values for \(x_{1},y_{1},x_{2},y_{2}\) {\(x_{1},y_{1}\) are coordinates of the starting point} {\(x_{2},y_{2}\) are coordinates of the ending point}
2:\(dx\gets x_{2}-x_{1}\)
3:\(dy\gets y_{2}-y_{1}\)
4:\(i_{1}\gets 2\times dy\)
5:\(i_{2}\gets 2\times(dy-dx)\)
6:\(d\gets i_{1}-dx\)
7:if\(dx<0\)then
8:\(x\gets x_{2}\)
9:\(y\gets y_{2}\)
10:\(x_{end}\gets x_{1}\)
11:else
12:\(x\gets x_{1}\)
13:\(y\gets y_{1}\)
14:\(x_{end}\gets x_{2}\)
15:endif
16:Generate point at coordinates \((x,y)\)
17:while\(x<x_{end}\)do
18:if\(d<0\)then
19:\(d\gets d+i_{1}\)
20:else
21:\(d\gets d+i_{2}\)
22:\(y\gets y+1\)
23:endif
24:\(x\gets x+1\)
25: Draw point at \((x,y)\)
26:endwhile
```  
**Algorithm 1** Bresenham Line Drawing Algorithm.  
Figure 1: An example demonstrating the outcome of [PERSON]’s line algorithm [18]. The grid’s origin \((0,0)\) is positioned in the upper-left corner, with point \((1,1)\) marking the line’s starting point at the top left, and point \((11,5)\) indicating the line’s endpoint at the lower right.
## 3 Methodology
### Data Sources  
As part of this research, detailed analysis was performed to characterise payload entropy values for a range of well-known and registered services averaged across a range of environments, as shown in Figure 4. Raw packet data were sourced from several widely used public sources (described in [5]) as well as recent live capture traces. Raw PCAP files were converted into flow records, with payload entropy reconstructed for common TCP and UDP protocols.  
In total, over 54 million packets were sampled. Datasets were selected to avoid sources with known large distortions to ensure that values were statistically consistent across datasets (and where possible, labelled anomalies are excluded). Results were also weighted per service type with respect to sample size, so that the contribution of each packet trace is proportionate (i.e., small dataset samples containing outliers do not distort overall results). Where anomalies were labelled, we excluded these labelled events from the calculations--therefore, the estimates were for known 'normal' traffic.
## 4 Results and Discussion
### Characteristic Comparison of Scratch Detected by CD and DVD Pick-Up Heads  
An important detail of the scratch needed to study is the shape characteristics and those detected by the CD pick-up head, DVD pick-up head, and SEM are essentially compared. It is seen in Table 3 that shape characteristics of scratches detected by CD and DVD pick-up heads are similar to those measured by SEM. These 3 scratches are examined to confirm their shapes by SEM as shown in Figure 9. The shape characteristics of the scratches detected by this proposed technique can specify the cause of scratches on magnetic disks in the hard disk manufacturing processes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of** & \multicolumn{3}{c}{**1 st Scratch**} & \multicolumn{2}{c}{**2 nd Scratch**} & \multicolumn{2}{c}{**3 rd Scratch**} \\
**Measurement** & **Width (\(\upmu\)m)** & **Error (\%)** & **Width (\(\upmu\)m)** & **Error (\%)** & **Width (\(\upmu\)m)** & **Error (\%)** \\ \hline CD Pick-up head & 68.97 & 25.40 & 110.35 & 17.39 & 337.18 & 1.56 \\ DVD Pick-up head & 68.94 & 25.35 & 110.30 & 17.34 & 337.82 & 1.75 \\ SM & 30 & 45.45 & 70 & 25.53 & 230 & 30.72 \\ SEM (Reference) & 55 & - & 94 & - & 332 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of width measurements and percentage errors of 3 scratches measured by CD, DVD, SM, and SEM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Equipment** & \multicolumn{3}{c}{**Shape Characteristics of the Scratches**} \\ \cline{2-4}  & **1 st Scratch** & **2 nd Scratch** & **3 rd Scratch** \\ \hline
**Scale** & \(\cdot\cdot\) 30 \(\upmu\)m & \(\cdot\cdot\) 80 \(\upmu\)m & 200 \(\upmu\)m \\ \hline CD & & & \\ DVD & & & \\ SEM & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Shape characteristics of 3 scratches measured from the CD pick-up head, DVD pick-up head, and SEM.
## 2 Mathematical Models Used for Route Development
### Environmental Forces Originating from Ocean Currents  
The equations describing the force of ocean currents acting on the ship's hull are presented in Equations (4)-(6).  
\[Current_{X}=q_{c}\cdot K_{x}(\varphi_{C})\cdot A_{FC} \tag{9}\]  
\[Current_{Y}=q_{c}\cdot K_{Y}(\varphi_{C})\cdot A_{LC} \tag{10}\]  
\[Current_{M}=q_{c}\cdot K_{M}(\varphi_{C})\cdot A_{FC}\cdot H_{FC} \tag{11}\]  
where \(Current_{X}\) --sea current force acting on the \(X\)-axis of the ship [N]; \(q_{C}\) --the factor determining the sea current pressure on the ship's hull [kg/m\(\cdot\)s\({}^{2}\)]; \(\varphi_{C}\)--angle of sea current impact relative to the \(L_{pp}/2\) point [\({}^{\circ}\)]; \(K_{X}\)--shape coefficient for the \(X\)-axis [-]; \(A_{FC}\)--cross-sectional area of the hull under waterline [m\({}^{2}\)]; \(Currently\) --sea current force acting on the \(Y\)-axis of the ship [N]; \(K_{Y}\)--shape coefficient for the \(Y\)-axis [-]; \(A_{LC}\)--lateral surface area of the hull under waterline [m\({}^{2}\)]; \(Current_{M}\)-torque generated by the sea current force [N\(\cdot\)m]; \(H_{FC}\)--offset of the centroid of the underwater lateral surface relative to the \(L_{pp}/2\) point [m].  
The factor determining the sea current pressure on the ship's hull is as follows  
\[q_{c}=\frac{1}{2}\cdot\rho_{c}\cdot V_{c}^{2} \tag{12}\]  
where \(\rho_{c}\)--water density [kg/m\({}^{3}\)]; \(Vc\)--sea current speed [m/s].  
The hull shape coefficients (\(K_{X}\), \(K_{Y}\), \(K_{M}\)) are determined similarly using Equations (5)-(8); however, in this case, the longitudinal and transverse underwater surface areas are taken into account.
## 3 Statistical Tolerance Analysis of 3D Printed Non-Assembly Mechanisms in Motion
### Sampling Technique  
Sampling techniques are used in tolerance analysis to determine the influence of part deviations on a certain quality criterion due to their greater capability and more independent applicability compared to simplified statistical formulas. Therefore, techniques like Monte-Carlo and Latin-Hypercube are well-established [38; 39]. The dimensional part deviations of the linkages \(L_{j}\) and the resulting joint clearances \(c_{ij}\) as a result of the FDM process are represented in terms of tolerances and are therefore used for sampling. These values are determined through the empirical predictive models, which are trained on previously gathered research data (see Section 3.2). Consequently, through using a sampling technique for the tolerance analysis based on vector loops, the motion behaviour of the shown 4-bar mechanism can be simulated. The influence of deviations can thus be considered for a realistic representation of the motion behaviour. Using a certain sampling size \(N\), the motion behaviour of \(N\) different virtual systems is realistically described [32]. The requirement or a certain quality criterion of a system on which the influence of tolerances is evaluated on its fulfilment is defined as the functional key characteristic (FKC) [40]. In this contribution, the motion accuracy of the kinematic joints is defined as the FKC. Figure 5 shows the procedure for the statistical tolerance analysis based on vector loops using sampling technique as an adaption of the conceptual framework (see Figure 2). The missing information for numerically and iteratively solving the vector loop equation (see Equation (2)), viz. the dimensional deviation of the linkages \(L_{j}\) and the resulting clearance vector \(\vec{c_{ij}}\) consisting of the joint clearance \(c_{ij}\) and the force angle \(\gamma_{ij}\) are determined and integrated through predictive models, respectively MBS. As a result, the unknown angles \(\theta_{unknown}\) of all kinematic joints \(l_{ij}\) and thus the motion behaviour of the mechanism can be determined for every discrete step \(i\) to \(i_{n}\) depending on the given starting angle \(\theta_{1}\). This angle is known since the position of the first linkage \(L_{1}\) is defined by the drive as a function of time \(\tau\). The influence of geometric part deviations resulting from the FDM process on the motion accuracy are considered through sampling for a certain sampling size \(N\) (illustrated by the dotted line). Therefore, the mobility band of specific kinematic joints consisting of \(N\) coupling curves for different virtual mechanisms and thus the FKC can be calculated.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 4 Case Studies, Results, and Discussion
### Unbalanced Load (Scenario II)
#### 4.2.2 Locating the Fault (Scenario II)  
In Table 6, the values of root mean square error and mean absolute percentage error are relatively small, whereas the values of \(R^{2}\), NSEC, and WIA are almost in unity, indicting the high performance of both MLP and ELM. The MLP method needs 10.0988-11.7969 s to train the network and ELM trains the network in a fraction of a second. Like the balanced load scenario, some performance measures and testing times shows that MLP is slightly better than ELM for some faults, while ELM is slightly better for the others, and both of them perform equally in a few cases. Considering all the parameters, ELM performs slightly better than MLP.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & \\ \cline{3-13}  & **AG** & **BG** & **CG** & **AB** & **BC** & **AC** & **ABG** & **BCG** & **ACG** & **ABC** & **ABCG** & **No** & **Fault** \\ \hline \multirow{6}{*}{**Predicted**} & **AG** & 885 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & **BG** & 0 & 885 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & **CG** & 0 & 0 & 885 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & **AB** & 0 & 0 & 0 & 885 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & **BC** & 0 & 0 & 0 & 0 & 885 & 0 & 0 & 0 & 0 & 0 & 0 \\  & **ABC** & 0 & 0 & 0 & 0 & 0 & 0 & 885 & 0 & 0 & 0 & 0 \\  & **BCG** & 0 & 0 & 0 & 0 & 0 & 0 & 885 & 0 & 0 & 0 & 0 \\  & **ACG** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 885 & 0 & 0 & 0 \\  & **ABC** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 228 & 228 & 0 \\  & **ABCG** & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 657 & 657 & 0 \\  & **no** & & & & & & & & & & & & \\  & **fault** & & & & & & & & & & & & & \\ \cline{3-13}  & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection and classification confusion matrix for scenario II.