esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 5 Simulation and Analysis
### Simulation Results
#### 5.2.3 Network Lifetime  
In this section, another set of experiments conducted to measure the lifetime of the network under the operation of the five algorithms is presented. Once more, two sets of experiments are conducted, one for small-scale networks, 500 nodes, and another for large-scale networks, 15,000 nodes. Here, the experiment is conducted only with different sinkholes, 1, 2, 3, and 5. It is believed that experimenting with different sink node will not add that much information to the lifetime analysis.  
Figure 15 shows the lifetime of small-scale networks. The lifetime is computed in terms of simulation running time and measured in milliseconds (ms). It is clear from the figure that TBDS has the largest lifetime with different sinkholes, between 15,000 ms and 14,700 ms, for 1-5 sinkholes, followed by MBIDS. ABC still gives a reasonable lifetime with different sinkholes, and its performance is much better than S-LEACH and MS-LEACH. For instance, the lifetime of MS-LEACH and S-LEACH  
Figure 14: Energy consumption for a large-scale network, 8000 nodes, with \(10,20,30\) sink nodes, and 5 sinkholes.  
Figure 13: Energy consumption for a large-scale network with 1, 2, 3, and 5 sinkholes.  
is the same when three and five sink nodes are used, 6000 ms. However, they reach almost 7000 ms with one and two sink nodes.  
Figure 16 shows the network lifetime for large-scale networks, 15,000 nodes. Again, TBIDS gives the best lifetime, followed by MBIDS and ABC, with different numbers of sinkholes. The lifetime of S-LEACH and MS-LEACH is the worst with all of the sinkholes. However, it is worth noting that the lifetime difference between MBIDS and ABC is not that large; it is, on average, 2000 ms. Besides, the lifetime of the MBIDS is affected by the overhead control messages by a small value, on average, 3.5%, where the control messages drop messages with almost 0.01%.  
Figure 17 examines the network lifetime with 8000 sensor nodes and 10, 20, and 30 sink nodes. For these experiments to be conducted, huge resources are required to get the average results. However, it is important to look at the network from a different point of view. As can be seen in the figure, the results confirm the enhancement of the network operations mentioned in the previous sections where the lifetime of TBIDS is still much better than others. Besides, the algorithms could be ranked as TBIDS, MBIDS, ABC, S-LEACH, and MS-LEACH in terms of lifetime. At the same time, the average  
Figure 16: Lifetime for large-scale networks.  
Figure 15: Lifetime for small-scale networks.  
lifetime of the algorithms seems to be much better when the number of sink nodes increases. It is also noticeable that ABC, S-LEACH, and MS-LEACH are enhanced with a better rate than TBIDS and MBIDS. Nonetheless, it has been noticed that the best lifetime is accomplished at three special cases where sink nodes are distributed on the borders of the monitored field, around the center of the monitored area, and when even clusters are generated.
# An Effective Algorithm for Finding Shortest Paths in Tubular Spaces
## 2 ESP in Tubular Space  
### Problem Description  
Euclidean geometry is the geometry in daily life [1] where the distance between two points \(\mathbf{p}=(x_{p},\ y_{p},\ z_{p})^{T}\) and \(\mathbf{q}=(x_{q},\ y_{q},\ z_{q})^{T}\) in 3D space is defined as follows:  
\[d_{e}(\mathbf{p},\ \mathbf{q})=\sqrt{(x_{p}-x_{q})^{2}+(y_{p}-y_{q})^{2}+(z_{p}-z_{q})^{2}} \tag{1}\]  
From the discrete point of view, a path \((\alpha)\) from the source \(\mathbf{\mathfrak{P}}\) to the destination \(\mathbf{\Omega}\) is a finite sequence of nodes \(\mathbf{x}_{i}\), starting at \(\mathbf{\mathfrak{P}}\) and ending at \(\mathbf{\Omega}\). We obtain the length of the path as in Equation (2):  
\[L(\alpha)=\sum_{i=0}^{n-1}d_{e}(\mathbf{x}_{i},\ \mathbf{x}_{i+1}),\quad\mathbf{x}_{0}= \mathbf{\mathfrak{P}},\ \mathbf{x}_{n}=\mathbf{\Omega} \tag{2}\]  
Then, the ESP is the path connecting \(\mathbf{\mathfrak{P}}\) and \(\mathbf{\mathfrak{Q}}\), which has the minimum length and has to be through a given tubular space. The mathematical definition of tubular space is given as follows [35]:  
Definition 1: Let \(\mathbf{\epsilon}(s):I\to\mathbb{R}^{3}\) be a smooth, regular space curve. A tubular surface \(\partial\Omega\) associated to \(\mathbf{\epsilon}(s)\), of radius \(\rho\), is, by definition, the envelope of the family of spheres of radius \(\rho\), with the center on the curve.  
Definition 2: The storage space of the tube \(\Omega\) is the 3D space enclosed by the lateral wall (\(\partial\Omega\)) and the two ending cross-sections of the tube.  
In Definition 1, \(s\) is the arc length parameter of the centerline curve. In this work, we consider the tubular surface \(\partial\Omega\) to be regular (Figure 1). The condition underlying the regularity of a tube is given in detail in [28]. By \(\kappa(s)\), we denote the curvature of the centerline curve \(\mathbf{\epsilon}(s)\). In order to avoid singularities as well as self-overlapping, the following condition is required:  
\[\kappa(s)<\rho^{-1},\forall s\in[0,L] \tag{3}\]  
where \(L\) is the length of \(\mathbf{\epsilon}(s)\).  
Figure 1: The regular and self-overlapping tube. The regular tube ensures the correctness of the directed graph in the following section.
# Tunable Effect of the Calcination of the Silanol Groups of KIT-6 and SBA-15 Mesoporous Materials
## 3 Results and Discussion
### Effect of Calcination Temperature on the Mesostructure of the MNS  
As already stated, the structure of mesoporous materials was monitored by XRD analysis to determine the effects of calcination temperature. The results are presented in Table 3.  
All XRD diffractograms show the formation of materials with angles of 0.8\({}^{\circ}\) < 20 < 2\({}^{\circ}\), characteristic of mesoporous materials, and are displayed in Figure 3. An analysis of Figure 3 shows that, among the calcined samples, all SBA samples have good structural organization except that prepared by Method 1. All KIT-6 samples showed good structural organization, indicating that the different calcination methods had little influence on the mesostructure, as expected [13; 14; 15]. Moreover, in all calcinated materials, the pores shrink more than in the noncalculated materials, but among all calcination methods, there is no linear relationship between pore shrinkage and the calcination temperature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **d\({}_{110}\) (nm)** & **a\({}_{0}\) (nm) 1** & **Samples** & **d\({}_{211}\) (nm)** & **a\({}_{0}\) (nm) 2** \\ \hline SM0 & 11.2 & 13.0 & KM0 & 10.7 & 26.2 \\ SM1 & 10.4 & 12.0 & KM1 & 9.4 & 23.0 \\ SM2 & 10.3 & 11.9 & KM2 & 9.8 & 24.0 \\ SM3 & 10.8 & 12.5 & KM3 & 9.6 & 23.5 \\ SM4 & 10.8 & 12.5 & KM4 & 9.7 & 23.8 \\ SM5 & 10.6 & 12.2 & KM5 & 9.8 & 24.0 \\ SM6 & 10.4 & 12.0 & KM6 & 9.4 & 23.3 \\ \hline \hline \end{tabular} \({}^{1}\) calculated by the formula [62] a\({}_{0}\)\(\,=\,\) 2.d\({}_{100}\).100. \(\sqrt{3}\), 2 calculated by the formula [66] a\({}_{0}\)\(\,=\,\) d\({}_{211}\). \(\sqrt{6}\).  
\end{table}
Table 3: Results of the X-ray diffraction (XRD) analyses of the mesoporous nanosilica (MNS) samples.  
Figure 2: The residual carbon content for KIT-6 and SBA-15 after each calcination method.
## 4 Mechanical Model and Numerical Analysis
### Spring-Mass Model
\begin{pmatrix}\text{K}C4+\text{K}C3+\text{K}B4&-\text{K}B4&-\text{K}C3&0&0&0&0&0&0 \\ -\text{K}B4&\text{K}B4+\text{K}A4&0&-\text{K}A4&0&0&0&0&0\\ -\text{K}C3&0&\text{K}C3+\text{K}C2+\text{K}B3&-\text{K}B3&-\text{K}C2&0&0&0&0\\ 0&-\text{K}A4&-\text{K}B3&\text{K}A4+\text{K}A3+\text{K}B3&0&-\text{K}A3&0&0&0\\ 0&0&-\text{K}C2&0&\text{K}C2+\text{K}C1+\text{K}B2&-\text{K}B2&-\text{K}C1&0&0\\ 0&0&0&-\text{K}A3&-\text{K}B2&\text{K}A3+\text{K}A2+\text{K}B2&0&-\text{K}A2&0\\ 0&0&0&0&-\text{K}C1&0&\text{K}C1+\text{K}B1&-\text{K}B1&0\\ 0&0&0&0&-\text{K}A2&\text{K}B1&\text{K}A2+\text{K}B1+\text{K}A1&-\text{K}A1\\ 0&0&0&0&0&0&-\text{K}A1&\text{K}A1\\ \end{pmatrix} . \tag{22}\]  
The displacement vector, \(X=(x_{1},x_{2},x_{3},x_{4},x_{5},x_{6},x_{7},x_{8},x_{9})^{\text{T}}\), represents the displacement of \(C4\), \(A4\), \(C3\), \(A3\), \(C2\), \(A2\), \(C1\), \(A1\), and \(A0\), respectively. The load vector, \(F_{t}\), can be expressed by Equation (23):  
\[ \begin{pmatrix}-2 KB4\text{$\Delta$}_{\text{c}4}\\ 2 KB4\text{$\Delta$}_{\text{c}4}\\ -2 KB3\text{$\Delta$}_{\text{c}3}\\ 2 KB3\text{$\Delta$}_{\text{c}3}\\ -2 KB2\text{$\Delta$}_{\text{c}2}\\ 2 KB2\text{$\Delta$}_{\text{c}2}\\ -2 KB1\text{$\Delta$}_{\text{c}1}\\ 2 KB1\text{$\Delta$}_{\text{c}1}\\ F_{t}\end{pmatrix} . \tag{23}\]  
The simulation curves were obtained using the data in Table 3 and by building finite element models for different river arrangements. Theoretical values were also calculated, and they were compared with the curves calculated by the finite element model, as shown in Figure 23. The theoretical curves agree well with the curves produced by the finite element simulations, which indicates the accuracy of the mechanical model developed during this study. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(G_{b}\) (GPa) & \(E_{r}\) (GPa) & \(E_{b}\) (GPa) & \(W\) (mm) & \(t_{1}\) (mm) & \(t_{2}\) (mm) & \(r\) (mm) & \(\Delta\) (\(\mu\)m) \\ \hline
28 [25] & 69 [25] & 68 [25] & \(2c+t\) & 2 & 2 & 4 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Spring–mass model input data. Figure 22: Force–displacement fitting curve. In this chapter, the riveted lap structure is simplified to a spring-mass point model. The forces of the whole model under tensile loading are studied by analyzing the forces at each mass point. The beneficial effects of the developed model are:  
(1) The spring-mass model is established according to [PERSON]'s law. The variation of force with displacement during the tensile process of the riveted lap specimen is derived completely. At the same time, the equivalent spring stiffness coefficient-displacement load equation system is established. (2) The system of equivalent spring stiffness coefficient-displacement-load equations can be used to calculate the force-displacement curves for different rivet spacing, edge distance, and row spacing under tensile loading. It can be used to study the prediction of the strength of riveted lap joints.
## 2 Materials and Methods
### Methods
#### 2.3.4 Selection of Optimal Spectral Indices and Texture Features  
RF is an ensemble of learning algorithms proposed by [PERSON] [35]. During the forest construction process, the randomness is not only in the selection of the data set, but also in the selection of the feature set. Using Bootstrap sampling technology to extract N samples from the original sample as a training set, and rather than choosing the best split among all attributes, m attributes are randomly chosen from M at each node and then these m attributes are used to split the node according to the principle of the decision tree algorithm, where m << M, to find the best segmentation feature. This study uses the Gini index to measure the effect of feature segmentation when performing node segmentation. Suppose there are \(k\) classes in the sample \(D\), then its Gini index is  
\[Gini(D)=1-\sum_{j=1}^{k}{P_{j}}^{2} \tag{1}\]  
where, \(P_{j}\) is the probability of occurrence of the first \(j\) sample. If after a split, the sample set \(D\) is divided into parts \(D_{1},D_{2}\cdots,D_{i}\), then the Gini index for this split is  
\[Gini_{split}(D)=\frac{D_{1}}{D}Gini(D_{1})+\cdots+\frac{D_{m}}{D}Gini(D_{m}) \tag{2}\]  
The value of the Gini index is related to the segmentation effect. The larger the value, the more important the feature is. This study uses the RF algorithm to select the sensitive spectral indices and texture features for identifying cotton root rot.  
The values of 10 spectral indices and 16 texture features of the 384 sample points were extracted using ArcGIS 10.2 as the sample set. In order to avoid the instability of the experimental results, the cross-validation method was used in feature selection, and the sample set was divided into a training set and a test set according to a 3:1 ratio, and the proportion of the class before segmentation was maintained.
## 4 Challenges for Ground Times and Respective Measures to Reduce Risks
### Operational and Technical Measures for Individual Stakeholders
#### 4.2.1 Efficient Planning and Operations
This is a complex task that requires good taxi time estimation to decrease traffic overload. Many studies are concentrating on this issue. Since taxi times depend on individual taxiway layouts, [PERSON] et al. found the development of suitable airport-specific forecasting models and predicting tools for surface movement time is an option to minimize taxi times [38]. Furthermore, [PERSON] et al. point out that gate reassignment and taxi scheduling are closely connected and should be considered simultaneously to assure smooth surface movement, passenger satisfaction, and simplified ramp operations [18]. This is particularly important when bottlenecks tend to shift to the apron and taxiway areas. [PERSON] et al. state that the pushback is the current measurement to determine the departure time. This encourages to pushback as soon as possible, resulting in extended taxi times. They propose to measure departure punctuality with respect to take-off times, consequently reducing delay formation on the taxiways [30]. For airlines in particular, the use of an apron stand instead of a gate position speeds up the boarding time and additionally does not require a pushback, which reduces overall GT [23]. Further, airlines should assure appropriate flight scheduling and allocation of aircraft, crew, and ground resources. This affords the execution of operations research and consideration of KPIs. Especially in the field of GH, bad scheduling has direct or indirect effects on other airlines and stakeholders since they all share GH resources [4]. Still, if flight schedule disruptions occur, schedule recovery is an option that aims at keeping the system in balance. Therefore, the airline operation control centres develop flexible solutions for irregular operations management [19]. However, higher load factors and denser flight schedules challenge the ability of airlines to recover from disruptions [4]. Lastly, the growth in air traffic comes along with new challenges in the area of passenger handling at airports. More people with hearing impairment and reduced mobility chose to travel by air. However, barrier-free service systems for such passengers are not yet mature. This intensifies the need to invest in new suitable strategies and processes, making use of technologies not only to provide a good travel experience but also to prevent delays caused by unexpected difficulties and waiting times in this area [39; 40].
## 3 Results and Discussion  
### Effects of Pulse Correction on Bead Penetration  
Bead penetration is crucial in WAAM as it directly influences the mechanical and structural integrity of the deposited material. Adequate penetration ensures strong inter-layer bonding and proper fusion between the deposited material and the substrate. Lesser penetration of the bead may reduce the final part's intended performance; hence, it needs to be investigated in WAAM of Cu1897.  
In CMT+P, PC impacts the droplet's detachment force. Generally, when the PC is 0 for a specific synergic line, the droplet detachment force is equal to an initially programmed value, and in such cases, the weld behavior is governed by WFS and TS in the absence of any other correction factors.  
Changing PC from \(-5\) to \(+5\) allows diversion from the pre-set detachment force. A lower PC value, i.e., \(-5\), results in a higher pulse frequency and small size drops [19] and, hence, lower penetration. For the samples presented in Figure 2, grinding and polishing were performed, and it was observed under an optical microscope for penetration study. The results showed that by changing PC from \(-5\) to \(+5\) at intervals of 5, penetration depth increased, and the average of three measured values for bead penetration are plotted in Figure 5.  
This increase in penetration depth with an increase in PC value is attributed to the fact that higher PC results in large droplets and, hence, deep penetration. A similar trend was observed in WAAM of magnesium alloy [19], where an increase in penetration was noted at the higher values of PC.  
The porosity study is focused on the dilution and reinforcement area of the weld bead. The actual images obtained from the optical microscope are presented in Figure 6a, and porosity numbering and its distribution using ImageJ are depicted in Figure 6b (the large-size pores are marked with a red color, and small pores are overshadowed by numbers), and the corresponding porosity area (in %) is presented in Table 3. In the dilution zone, the porosity is lower, and as the second and third layers of Cu1897 are added, the porosity increases. This behavior is due to the chemical composition of the base material, as there is less chance of porosity in WAAM of mild steel compared to copper-based materials, as it is less prone to oxidation. It can be seen in  
Figure 4: Cu1897 samples prepared by varying ALC (in %) at constant PC = \(+5\).  
Figure 6b that with the addition of second and third layers, the Cu1897 amount increases as more layers are added, and hence, porosity increases.  
Figure 5: Weld bead penetration vs. pulse correction.  
Figure 6: Optical images of Cu1897 samples prepared by varying PC. (**a**) before porosity analysis; (**b**) after porosity analysis.  
The problem of porosity persists, and further investigations were made to reduce the porosity by varying the ALC. At +5 PC, penetration was significantly improved, as tabulated in Table 3. Therefore, +5 PC was selected to carry out further investigation by varying the ALC to mitigate the porosity.
## 3 Offline Optimization and Adaptation Under Nonlinear Task Constraints
### Constraints Definitions in Joint Space and Cartesian Space  
A robot may encounter constraints in Joint space and Cartesian space at one time. Joint space constraints depend directly on \((\mathbf{\mu}_{t},\mathbf{\sigma}_{t})\) of each joint freedom, which usually refer to the motion range of the robot's links. The Cartesian space constraints depend on the nonlinear forward kinematics function of \((\mathbf{\mu}_{t},\mathbf{\sigma}_{t})\), standing for the 3D positions of the end effector or any other interest points on the robot links. In the following, we formulate several key constraints commonly used in obstacle avoidance, like the joint range limit, waypoints, hyperplane, and repellers.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 4 Dimensioning Tools: Interference and Fading Characterization
### Single-User Case with Multiantenna Receiver
#### 4.2.2 Maximum Ratio Combiner (MRC)  
Using a MRC, the equivalent fading coefficient \(A_{f}=F_{1}+\ldots+F_{n_{r}}\) is the sum of \(n_{r}\) random exponential i.i.d. random variables and it is distributed as chi-squared random variable with \(2n_{r}\) degrees of freedom. The CDF of \(A_{f}\) is,  
\[\text{Prob}\{A_{f}\leq u\}=1-\sum_{p=0}^{n_{r}-1}\frac{1}{p!}u^{p}e^{-u}. \tag{37}\]  
**Proposition 7** (MRC decoder).: _The interference Laplace transform with MRC is identical to the SISO case in (32)_  
\[\mathcal{L}_{I}^{\text{MRC}}(s)=\mathcal{L}_{I}(s). \tag{38}\]  
_Given a node position \(r=|x|\), the SIMO fading distribution with MRC averaged on the random noise and interference,_  
\[\mathbb{E}_{I,P_{n}}\ \mathbb{E}_{A_{f}}[\mathds{1}_{\{A_{f,k}\leq A_{f}<A_{ f,k-1}\}}]=\sum_{p=0}^{n_{r}-1}\frac{1}{p!}\Big{(}\Xi_{p}(s_{k})-\Xi_{p}(s_{k -1})\Big{)} \tag{39}\]  
_with_  
\[\Xi_{p}(s)=(-1)^{p}\sum_{j=0}^{p}s^{p}\binom{j}{p}\frac{d^{j}\mathcal{L}_{P_{ n}}(s)}{ds^{j}}\frac{d^{p-j}\mathcal{L}_{I}(s)}{ds^{p-j}}, \tag{40}\]  
_where \(\frac{d^{j}f(s)}{ds^{j}}\) is the derivative of order \(j\) of the function \(f(s)\) for \(j\in\mathbb{N}^{*}\) and \(\frac{d^{0}f(s)}{ds^{0}}=f(s)\)._  
Proof.: The proof of this proposition is detailed in Appendix E.
## 2 Materials and Methodology
### Calculation of Fractal Dimensions Based on the Transformation of Pore Radius of NMR
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Water-** & **Sand to** & & & **Percentage** & **Percentage** & **Percentage** & **Uniaxial** \\
**Cement** & **Cement** & **Adaxture** & **Porosity** & **of** & **of** & **of** & **Compressive** \\
**Ratio** & **Ratio** & & & **Micropore** & **Mesopore** & **Macropore** & **Strength/MPa** \\ \hline \multirow{8}{*}{0.30} & & 0.07 & 5.7470 & 0.1988 & 0.1725 & 0.6287 & 28.0725 \\  & 0.70 & 0.10 & 5.3261 & 0.1371 & 0.2320 & 0.6309 & 33.3596 \\  & & 0.13 & 6.4188 & 0.1724 & 0.1580 & 0.6696 & 25.8754 \\  & & 0.07 & 7.9812 & 0.0117 & 0.1320 & 0.8563 & 12.3486 \\  & 1.00 & 0.10 & 4.6157 & 0.1865 & 0.1992 & 0.6143 & 34.6290 \\  & & 0.13 & 3.3437 & 0.1682 & 0.2234 & 0.6085 & 36.8885 \\  & & 0.07 & 5.9445 & 0.2496 & 0.1109 & 0.6395 & 27.8376 \\  & 1.30 & 0.10 & 5.6107 & 0.2305 & 0.1209 & 0.6486 & 28.2588 \\  & & 0.13 & 4.9982 & 0.0555 & 0.1566 & 0.7879 & 33.1568 \\ \hline \multirow{8}{*}{0.33} & & 0.07 & 8.7581 & 0.0409 & 0.1389 & 0.8202 & 12.4586 \\  & 0.70 & 0.10 & 6.4043 & 0.1438 & 0.2460 & 0.6103 & 25.2527 \\  & & 0.13 & 5.4177 & 0.1404 & 0.1794 & 0.6802 & 28.3275 \\  & & 0.07 & 7.1317 & 0.0991 & 0.1122 & 0.7886 & 17.5747 \\  & 1.00 & 0.10 & 9.9657 & 0.0695 & 0.0882 & 0.8423 & 11.5799 \\  & & 0.13 & 6.7898 & 0.0614 & 0.1465 & 0.7921 & 20.0908 \\  & & 0.07 & 7.7785 & 0.1005 & 0.0662 & 0.8332 & 12.9857 \\  & 1.30 & 0.10 & 7.3258 & 0.0919 & 0.1022 & 0.8059 & 16.9800 \\  & & 0.13 & 6.7425 & 0.1170 & 0.1705 & 0.7124 & 22.2345 \\ \hline \multirow{8}{*}{0.35} & & 0.07 & 7.3831 & 0.0447 & 0.1292 & 0.8261 & 15.2602 \\  & 0.70 & 0.10 & 9.9875 & 0.0149 & 0.1339 & 0.8512 & 11.2586 \\  & & 0.13 & 6.8863 & 0.0836 & 0.1881 & 0.7283 & 18.9264 \\ \cline{1-1}  & & 0.07 & 7.5579 & 0.0619 & 0.1014 & 0.8367 & 13.5750 \\ \cline{1-1}  & 1.00 & 0.10 & 6.7476 & 0.1158 & 0.2443 & 0.6399 & 22.4181 \\ \cline{1-1}  & & 0.13 & 10.0031 & 0.0379 & 0.1069 & 0.8552 & 10.1587 \\ \cline{1-1}  & & 0.07 & 9.9854 & 0.0469 & 0.1209 & 0.8323 & 11.2789 \\ \cline{1-1}  & 1.30 & 0.10 & 7.0357 & 0.0674 & 0.2093 & 0.7233 & 17.9190 \\ \cline{1-1}  & & 0.13 & 6.2118 & 0.1723 & 0.2055 & 0.6221 & 26.5328 \\ \hline \end{tabular}
\end{table} 
Table 4: Statistics of pore volume and compressive strength of sample.