esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Load-Displacement Curves  
Figure 12 shows the load-displacement curves of the 25 components where the displacement \(\Delta\) is the average of the vertical LVDTS. Most of the curves show a descending stage characterized by a negative gradient. The larger the diameter is, the steeper the gradient. The failure process can be divided into three stages:  
(1) Elastic stage. At the beginning of loading, both the steel and RPC were in the elastic stage, and the curve of \(N_{e}\) vs. \(\Delta\) increased linearly. No significant deformation appeared on the tube's surface, and as a consequence of different lateral deformation coefficients,no obvious interaction acted on the two materials. Compared with steel pipes, the elastic stages of the CFST specimens were shortened. This occurred at 70-90% of the peak load but contracted to 60-80% when increasing the prestress.  
(2) Elastic-plastic stage. With the increase in load, the materials entered the plastic stage and expanded gradually, followed by a binding of steel and RPC. Thus, the slope of the curve decreased by degrees. Compared with steel pipes, infilled RPC prolonged the elastic-plastic stage of the component, and the effect of initial stress made the steel pipe enter plasticity in advance. With an increase in initial stress, the slope of the curve decreased earlier.  
(3) Descending stage. The curve dropped because of the sharp decrease in bearing capacity caused by the lateral bending of bars after peak load. A small confinement coefficient (\(\xi\leq 0.69\)) of specimens led to less confinement effect from the steel tube, which meant it continued to be loaded onto the core RPC when the steel pipe entered the plastic rheological state. The good ductility of the RPC produced a smooth area in the descending section of the curve, and the inner RPC gradually formed a shear-slip zone along the 45\({}^{\circ}\) direction. The curve of the specimen with a large confinement coefficient (\(\xi>0.69\)) indicated a typical buckling failure mode, and the plastic zone of the two materials expanded together. With the increase in the lateral bending of the specimen, the load drop was accelerated.
## Appendix A C\({}_{4}\) Photosynthesis Equations for DCHM-V
### Carboxylation Equations  
Carboxylation rate [94]:  
\[A_{CARB}=\min\bigl{\{}(V_{p}+g_{bs}C_{m}-R_{m}),\ V_{cmax}-R_{d}\bigr{\}} \tag{2}\]  
Rate of PEP carboxylation [94]:  
\[V_{p}=\min\biggl{\{}\frac{C_{m}V_{pmax}}{C_{m}+K_{p}},\ V_{pr}\biggr{\}} \tag{3}\]  
Rate of Rubisco carboxylation at high irradiance [94]:  
\[V_{c}=\frac{C_{s}V_{cmax}}{C_{s}+K_{c}(1+O_{s}/K_{o})} \tag{4}\]  
Photorespiratory Compensation Point [94]:  
\[\Gamma_{s}=\frac{\gamma_{*}O_{m}+K_{c}(1+O_{m}/K_{o})(R_{d}/V_{cmax})}{1+R_{d} /V_{cmax}} \tag{5}\]  
\[\Gamma=\frac{g_{s}K_{p}\Gamma_{s}}{V_{pmax}}+\frac{R_{m}K_{p}}{V_{pmax}} \tag{6}\]  
Appendix 2.2. RuBP Regeneration/Election Transport Rate Equations  
Rate of RuBP regeneration under drying conditions [94]. Dry conditions correspond to less than 6.5% soil moisture in the median layer where most roots are located.  
\[A_{RuBP}=\min\biggl{\{}\biggl{(}\frac{xJ_{t}}{2}-R_{m}+g_{bs}C_{m}\biggr{)}, \ \biggl{(}\frac{(1-x)J_{t}}{3}-R_{d}\biggr{)}\biggr{\}} \tag{7}\]  
\[J_{t}=J_{m} \tag{8}\]  
Rate of RuBP regeneration under wet conditions [94]:  
\[A_{RuBP}=\frac{(1-x)J_{t}}{3}-R_{d} \tag{9}\]  
\[J_{t}=J_{m}+J_{s} \tag{10}\]  
\[J_{m}=p_{I}=\frac{J_{max}*p1 APAR}{p1 APAR+2.1J_{max}} \tag{11}\]\[J_{s}=4.5\bigg{(}1+\frac{7\Gamma}{3C_{s}}\bigg{)}V_{c} \tag{12}\]  
Appendix A.2.3. Temperature Dependencies for Maximum Enzymatic Rates  
Maximum enzymatic rates (i.e., \(V_{cmax}\), \(V_{pmax}\), and \(J_{max}\)) [95]:  
\[f(T_{k})=k_{25}\exp\bigg{[}\frac{E_{a}(T_{k}-298.15)}{298.15\ R\ T_{k}}\bigg{]} \frac{1+\exp\bigg{(}\frac{298.15\ AS-H_{d}}{298.15\ R}\bigg{)}}{1+\exp\bigg{(} \frac{T_{k}\ AS-H_{d}}{T_{k}\ R}\bigg{)}} \tag{13}\]  
Michaelis-Menten constants (\(K_{c}\), \(K_{p}\), \(K_{o}\)) [95]:  
\[f(T_{k})=k_{25}\exp\bigg{(}\frac{E_{a}(T_{k}-298.15)}{298.15\ R\ T_{k}}\bigg{)} \tag{14}\]  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Model Parameter** & **Description** & **Value** & **Units** & **Reference** \\ \hline \(g_{bs}\) & Bundle sheath conductance & 3 & mmol m\({}^{-2}\) s\({}^{-1}\) & [96] \\ \(C_{m}\) & CO\({}_{2}\) concentration in mesophyll cell & 0.7\(C_{air}\) & mol m\({}^{-3}\) & [67] \\ \(C_{air}\) & CO\({}_{2}\) concentration outside the leaf & 0.0145 & mol m\({}^{-3}\) & [67] \\ \(R_{m}\) & Mesophyll mitochondrial respiration & 0.5\(R_{d}\) & umol C m\({}^{-2}\) s\({}^{-1}\) & [94] \\ \(R_{d}\) & Leaf mitochondrial respiration & 0.01\(V_{cmax}\) & umol C m\({}^{-2}\) s\({}^{-1}\) & [94] \\ \(V_{pr}\) & PEP regeneration rate & 80 & umol C m\({}^{-2}\) s\({}^{-1}\) & [94] \\ \(O_{i}=O_{m}=O_{s}\) & Intercellular O\({}_{2}\) concentration & 210 & mmol mol\({}^{-1}\) & [97] \\ \(x\) & Partitioning factor of electron transport rate & 0.4 & unity & [94] \\ \(\gamma^{*}\) & Half of reciprocal of Rubisco specificity & 0.000193 & unity & [94] \\ \hline \hline \end{tabular}
\end{table}
Table 10: C\({}_{4}\) Photosynthesis Model Variables in DCHM-V.
## 2 Positioning in Cellular Networks
### Cell-Identity-Based Localization Techniques  
The Cell identity (CID) or proximity-based technique is the simplest of the four techniques, as it mainly relies on checking whether or not the object to be positioned is present in a particular radio coverage area. It is necessary to know the location of the serving base station and the area of the serving cell that is incorporated to estimate the UE's location. It requires many base stations to achieve comparable accuracy to other methods and is not suited for large areas or areas with low-density populations [27].
# \(HM^{3}a1D\): Polymorphic Malware Detection Using Program Behavior-Aware Hidden Markov Model
## 5 Experimental Evaluation
### Comparing with Other Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Dataset Size (Benign/Malware)** & **DR(\%)** & **FAR(\%)** & **Accuracy (\%)** \\ \hline [PERSON] (2015) [13] & 370/760 & 88.95 & **0.2** & 97.58 \\ \(HM^{3}alD\) & 370/760 & **99.12** & 0.23 & **99.38** \\ \hline [PERSON] (2012) [16] & 8/200 & 100 & 12.5 & 99.52 \\ \(HM^{3}alD\) & 8/200 & **100** & **0** & **100** \\ \hline [PERSON] (2015) [5] & 2330/1020 & 98.9 & 4.5 & — \\ \(HM^{3}alD\) & 2330/1020 & **98.99** & **1.27** & **98.85** \\ \hline [PERSON] (2012) [12] & 2595/3639 & 98.4 & 2.7 & 97.85 \\ \(HM^{3}alD\) & 2595/3639 & **98.81** & **1.36** & **98.70** \\ \hline [PERSON] (2013) [17] & 3760/4410 & 97.3 & — & 91.2 \\ \(HM^{3}alD\) & 2676/4410 & **98.79** & **1.85** & **98.39** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of \(HM^{3}alD\) with the current static malware detection methods. and Figures 10 and 11, the performance of \(HM^{3}alD\) is always higher than the other methods in terms of DR and Accuracy and is significantly better than the other methods in term of FAR.
# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
## 5 Experimental Evaluation
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Average CPU Use (\%)**} \\ \cline{2-5}  & **JanusGraph** & **Nebula Graph** & **Neo4j** & **TigerGraph** \\ \hline SF = 0.1 & 17\% & 22\% & 11\% & 15\% \\ SF = 0.3 & 30\% & 35\% & 14\% & 26\% \\ SF = 1 & 40\% & 47\% & 22\% & 35\% \\ SF = 3 & 49\% & 56\% & 28\% & 46\% \\ SF = 10 & 69\% & 78\% & 57\% & 63\% \\ _Average_ & 41\% & 48\% & 26\% & 37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average CPU use (%) for each graph database. Figure 12: Query execution times for all queries in minutes for each graph database. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Average RAM Use (\%)**} \\ \cline{2-5}  & **JanusGraph** & **Nebula Graph** & **Neo4j** & **TigerGraph** \\ \hline SF = 0.1 & 33\% & 39\% & 29\% & 31\% \\ SF = 0.3 & 43\% & 48\% & 32\% & 40\% \\ SF = 1 & 54\% & 62\% & 37\% & 50\% \\ SF = 3 & 61\% & 70\% & 43\% & 57\% \\ SF = 10 & 83\% & 89\% & 67\% & 76\% \\ _Average_ & 49\% & 62\% & 42\% & 51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average RAM use (%) for each graph database. Neo4j excels at using less RAM. When we ran queries, Nebula Graph had to use more RAM in its internal data reading processes.
# Adaptive Network Routing Technology for Near-Moon Space Cross-Domain Transmission
## 3 Routing Scheduling Algorithm
### Sub-Network Clustering
#### 3.1.2 Fixed Topology Sub-Network Set and Sub-Network Clustering  
For fixed topology sub-networks, the link state routing method is used to achieve rapid deployment of data transmission. Considering that the fixed topology described here is actually relatively fixed at a finite time scale, the method of sub-network clustering and partitioning as nodes approach the link transmission state can obtain a uniform sub-network partitioning structure under fixed topology conditions, forming flexible communication under different application requirements.  
In the sub-network search process, similar to Equation (2), the criterion is that the communication rate variance of each node in sub-network \(G_{w^{\prime}}\) (\(w\in Z^{+}\) ) is less than a certain threshold \(\Omega\), and the calculation starts from the initial fixed node neighboring nodes to the distant nodes gradually until the threshold is met.  
The clustering process of fixed topology sub-networks is as follows:  
Step 1: Sub-network tables \(G_{w^{\prime}}\), \(w\in Z^{+}\) are constructed with \(G_{w^{\prime}}\in\{U_{a},U_{e},U_{t}\}\), and node \(S^{\prime}(0)\) is taken as an example to define \(S^{\prime}(i)\in G_{w^{\prime}}\), \(i\in Z^{+}\), \(i\
eq 0\), which is a neighboring node. Then, for node 0 of the \(G_{w^{\prime}}\) sub-network at time \(t\), the communication rate variance \(\delta_{f,G_{w^{\prime}}}(0,t)=\text{var}\{f(q_{0},q_{i},q_{0}^{\prime},q_{i} ^{\prime},t)\}\) is determined;  
Step 2: It is determined whether \(\delta_{f,G_{w^{\prime}}}(0,t)\) exceeds the threshold \(\Omega\). If it does, the variable \(i\) in step 1 is expanded. After the expansion, the selection range of \(i\) is expanded to the neighboring nodes of node 0, and then the process skips to step 1 for calculation. If it does not exceed the threshold, it indicates that the neighboring node of node 0 is the minimum envelope of the sub-network combination. Other nodes are selected to search for the next sub-network, and the process proceeds to step 3;  
Step 3: The sub-network table \(G_{w^{\prime}}\) is updated, one node from the remaining nodes is randomly selected, and the process skips to step 1 for the next sub-network search until all nodes belong to a certain sub-network.
## 3 Numerical Example
### Granular Dam Break Simulation
We completed a regression analysis based on this dependence, and the fitting parameters of \(h_{\max}\) and \(\tau\) are evaluated and listed in Table 1.  
To check the effect of the boundary condition on the simulation results, we moved the midpoint of the breach to one side, i.e., \(y=85\) m (B = 70 m), and applied two types of boundary conditions in the simulations: (1) the open boundary (zero-gradient) condition for all four outer boundaries, and (2) the open boundary condition for the boundary adjacent to the dam and the closed boundary for the other three boundaries. The boundary conditions were found to affect the collapse process of the granular pile (Figure 11); the deposit from the opening was mostly grains that were initially adjacent to boundary 1 with the first type of boundary condition, whereas the deposit collapsed evenly for the accumulated grains initially near the opening with the second type of boundary condition. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Num.** & \(B\)**/m** & \(h_{\max}\)**/m** & \(\tau\)**/s** \\ \hline
1 & 5 & 6.692979 & 6.90883 \\
2 & 10 & 6.135773 & 6.557069 \\
3 & 15 & 9.688248 & 6.989259 \\
4 & 20 & 9.814965 & 7.037584 \\
5 & 35 & 10.08215 & 6.778386 \\
6 & 50 & 11.97468 & 6.661324 \\
7 & 70 & 13.35109 & 6.441027 \\ \hline \end{tabular}
\end{table} 
Table 1: Fitting results of \(h_{\max}\) and \(\tau\). Figure 10: Tracked depth of the mass collapse from a breach of different widths during the dam breaking process.
# Highly Curved Lane Detection Algorithms Based on Kalman Filter
## 1 Introduction
In our paper, we introduce a curve lane detection algorithm based on Kalman filter [17]. This algorithm includes [PERSON]'s threshold method [18; 19] to convert RGB to Black-White image, image pre-processing using top view image transform [20; 21] to create a top-view image of the road, a Hough transform for detecting the straight lane in the near-field of the sensor [22], and parameter estimation of the curve lane using a Kalman filter. Also, we use two different models for a curve lane in the Kalman filter. One is the parabola model [23], another is the circle model [24]. The Kalman-based linear parabolic lane detection is already tested on consecutive video frames using the parabolic model by [PERSON] et al. [25]. The paper presents the method which is extended to the circular model. Our proposed method shows robustness against noise. Effective parameter estimation of a curve lane detection could be used to control the speed and heading angle of the self-driving car [26]. Multiple methods have been introduced to detect lane for the self-driving car and advanced driver assistance systems. The vision-based lane detection methods usually used some popular techniques, an edge detector to create a binary image, the classical Hough transform [27; 28] to detect straight lines, the color segmentation to extract lane markers, etc. Most of the methods focused on only a straight lane detection in the near distance using a Hough transform or some simple methods. For a curve lane, few number methods used to detect curved roads such as parabola [23] and hyperbola fitting and B-Splines [29; 30], Bezier Splines. To enhance the result of lane detection, the area at the bottom of an image is considered as a region of interest (ROI) [31]. Segmenting ROI will increase the efficiency of the lane detection method and eliminate the effect of the upper portion of a road image [32]. The majority of the methods directly detect lanes from the images that are captured by the front-view camera, as shown in Figure 1. However could be robust using the raw image for detect lane, estimating the parameters of road lane may be difficult [33]. This research is considered on a curve lane detection algorithm, which can estimate parameters of the road turning and define geometric shapes based on the mathematical model and the Kalman filter [17].
# An All-Mach Number HLLC-Based Scheme for Multi-Phase Flow with Surface Tension
## 4 Numerical Test Cases
### 2-D Problems
#### 4.2.1 Advecting Bubble in an Oblique Velocity Field  
In this test-case, two key components of the interface capturing scheme are evaluated. First, the accuracy of the VoF method. Secondly, the compatibility of CICSAM with HLLC, i.e., its ability to maintain pressure and velocity equilibrium for uniform flow.  
Here, a circular bubble of radius \(R_{0}=0.16\) is initialised at the bottom left corner, \(\mathbf{x}=(0.25,0.25)\), of a unit domain. The bubble is advected in an oblique uniform and steady velocity field, \(\mathbf{u}=1.0\mathbf{\dot{x}}_{x}+1.0\mathbf{\dot{\mathbf{v}}}_{y}\). The initial pressure is set to 1. The material properties are given as per [PERSON] et al. (2018):  
\[(\rho,\gamma,p_{\infty})=\begin{cases}(1.0,1.4,0.0),&\text{if }|\mathbf{x}-\mathbf{x}_{c}|\leq R _{0},\\ (0.125,4,1.0),&\text{otherwise},\end{cases}\]  
with \(\mathbf{x}_{c}\) the position of the centre of the bubble. Inflow/outflow conditions are enforced at boundaries.  
A mesh convergence study is conducted by repeating the calculation of a set of increasingly finer meshes from \(64^{2}\) to \(512^{2}\). The VoF accuracy is assessed using the shape error. The \(L_{1}\) norm of the shape error, \(\mathbf{\dot{\mathbf{v}}}_{y}\), is computed via  
Figure 4: Gas–liquid: assessment of HLLC vs. CICSAM—400, 800 and 1600 nodes.  
\[\|e_{g}\|_{1}=\frac{\sum\limits_{l}^{N}|\mathbf{a}_{nn}(\mathbf{x}_{l},t)-\mathbf{a}_{nn}( \mathbf{x}_{l},t)|\mathcal{V}_{l}}{\sum\limits_{l}^{N}\mathcal{V}_{l}}. \tag{44}\]  
Figure 5 demonstrates a better qualitative description of the volume fraction and the energy field. Further, as seen in Figure 6, the shape error with CICSAM is at least an order of magnitude below that of HLLC. CICSAM is clearly superior for advecting the liquid-gas interface.  
Figure 5: Advecting bubble: HLLC (**left**) and HLLC-CICSAM (**right**) on a \(512^{2}\) mesh.
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 5 Conclusions  
In this paper, we presented a real-time algorithm for VO estimation and a novel method for integrating VO and a vision-based tree stem detection system with GNSS positioning to generate accurate stem maps. Our approach is based on existing and widely applied optimization techniques, i.e., GN and LM non-linear least squares, which provide efficient solution procedures to the optimization problems formulated in this paper.  
Although GNSS positioning is used to maintain a globally consistent position track, the heading of the camera is subject to drift; global positioning cannot be used to infer the heading of the camera. This issue did not appear to be significant in our dataset; however, we expect that camera heading will eventually drift since it is unconstrained and only estimated from visual egomotion. This issue can be resolved by incorporating a global direction sensor, i.e., magnetometer, to maintain a globally consistent heading. The inclusion of directional data in the global alignment optimization step requires minimal modifications to the presented algorithm. Since electronic direction sensors are relatively inexpensive, we recommend using such a sensor for large-scale mapping applications.  
The successful integration of real-time localization and mapping into forestry practices presents new opportunities for enhancing decision-making processes and implementing complex silvicultural prescriptions. This research provides a practical, low-cost solution that addresses the need for accurate and efficient positioning and mapping in forested environments.
## 2 Materials and Methods
### Network Scenarios
#### 2.2.1 Wireless Scenario  
The architecture of the wireless network is presented in Figure 3. The most frequent identified services were e-mail, streaming, data and audio transmission through ports 80, 2869, 993 and 5222. There were 100,023 connections during the test time, which is equivalent to around 300 users. Datasets were collected in 51 buffers, and there were 58,679 closed connections.
## 3 Methodology
### Error Analysis and Anomaly Detection  
#### 3.4.1 Anomaly Detection Based on Chebyshev's Inequality  
Chebyshev's inequality is an important theorem in probability theory for estimating the probability of a particular range of a data-set, applicable to all probability distributions. It provides an upper bound based on the standard deviation, allowing us to identify data points that differ from the mean without assuming a data distribution.  
[PERSON]'s inequality states that for any random variable \(X\), if the mean of \(X\) is \(\mu\) and the standard deviation is \(\sigma\), at least \(1-\frac{1}{k^{2}}\) of the proportion (or percentage) of data points lie within the range \(\mu\pm k\sigma\), where \(k\) is a positive number. The arithmetic procedure for anomaly detection using [PERSON]'s inequality is as follows:  
\[e_{t}=\left|\hat{x}^{t}-x^{t}\right| \tag{20}\]  
where: \(e_{t}\) is the error between the predicted value \(\hat{x}^{t}\) and the observed value \(x^{t}\).  
\[\mu_{e}=\frac{1}{n}\sum_{t=1}^{n}e_{t} \tag{21}\]\[\sigma_{e}=\sqrt{\frac{1}{n}\sum_{t=1}^{n}(e_{t}-\mu_{e})^{2}} \tag{22}\]  
where: \(e_{t}\) represents the error of the t-th sample, \(n\) is the total number of samples, \(\mu_{e}\) is the mean of the error, and \(\sigma_{e}\) is the standard deviation of the error.  
According to [PERSON]'s inequality, given the mean and standard deviation of the error, the threshold value \(k\) can be chosen to determine the threshold of outliers, i.e.:  
\[P(|e_{t}-\mu_{e}|\geq k\sigma_{e})\leq\frac{1}{k^{2}}. \tag{23}\]  
This formula indicates that the probability of the error \(e_{t}\) deviating from the mean \(\mu_{e}\) by at least k times the standard deviation will not exceed \(\frac{1}{k^{2}}\).
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**FILTER-BASED SUBSET SELECTION METHODS (FSS)**} \\ \hline \hline \multirow{3}{*}{**Attribute Evaluator**} & \multicolumn{5}{c}{**CfsSubsetEval (CFS) Method**} \\ \cline{2-7}  & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No. of Features**} & \multicolumn{5}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \cline{3-7}  & \multirow{2}{*}{**Search Methods**} & & **No. of Features** & \multicolumn{5}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{3}{*}{**Heuristics Method**} & GA & 2 & 80.41 & **82.99** & 80.93 & 78.87 \\  & BAT & 2 & 81.96 & **82.99** & 81.44 & 78.87 \\  & PSO & 3 & 79.90 & 81.96 & 82.47 & 75.77 \\  & FS & 3 & 80.41 & 80.41 & **82.99** & 74.23 \\  & AS & 2 & 80.41 & 82.47 & 82.47 & 75.26 \\ \hline \multirow{3}{*}{**Exhaustive Method**} & GSS & 6 & 80.93 & 80.41 & 82.47 & 77.32 \\  & BF & 3 & 79.90 & 81.44 & 82.47 & 75.77 \\ \hline \multirow{3}{*}{**Exhaustive Method**} & Average & & 80.56 & 81.81 & **82.18** & 76.58 \\ \cline{3-7}  & \multirow{2}{*}{**Search Methods**} & & **No. of Features** & \multicolumn{5}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{3}{*}{**Heuristics Method**} & GA & 11 & 73.49 & 73.75 & 76.59 & 73.49 \\  & BAT & 17 & 73.49 & 75.30 & 76.08 & 73.75 \\ \cline{3-7}  & PSO & 16 & 73.41 & 74.44 & 76.51 & 73.49 \\ \cline{3-7}  & FS & 16 & 73.41 & 74.44 & 76.51 & 73.58 \\ \cline{3-7}  & AS & 17 & 73.49 & 74.44 & **77.11** & 72.81 \\ \hline \multirow{3}{*}{**Exhaustive Method**} & GSS & 6 & 75.39 & 75.56 & 76.33 & 69.88 \\  & BF & 16 & 73.32 & 74.44 & 76.51 & 73.67 \\ \hline \multirow{3}{*}{**Exhaustive Method**} & Average & 73.72 & 74.63 & **76.52** & 72.95 \\ \cline{3-7}  & \multicolumn{5}{c}{**FILTER-BASED FEATURE RANKING METHODS (FFR)**} \\ \hline \hline \multirow{3}{*}{**Attribute Evaluator**} & \multirow{2}{*}{**Search Methods**} & \multirow{2}{*}{**No. of Features**} & \multicolumn{5}{c}{**Performance Metrics (Accuracy)/Classifier**} \\ \cline{3-7}  & & & **NB** & **DT** & **LR** & **KNN** \\ \hline \multirow{3}{*}{**IGF**} & Ranker & 6 & 74.78 & 74.96 & 75.47 & 70.57 \\  & Ranker & 6 & 72.63 & 76.25 & **76.42** & 71.86 \\ \cline{1-1}  & Ranker & 6 & 73.24 & 75.04 & 75.82 & 71.86 \\ \cline{1-1}  & \multirow{2}{*}{**CV**} & Ranker & 6 & 74.61 & 74.01 & 75.65 & 69.97 \\ \hline \multicolumn{7}{c}{Average} & \multicolumn{5}{c}{73.82} & 75.06 & **75.84** & 71.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance Accuracy Values of FS-based Prediction Models on KC3 dataset.