esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Classification in Software-Defined Networking Using Genetic Programming Tools
## 2 Material and Methods
### The GenClass Method  
The main steps of the used method are provided below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Number of Flows** & **Proportion** \\ \hline WWW & 2441 & 57.65\% \\ P2P & 710 & 16.77\% \\ ICMP & 409 & 9.66\% \\ VOIP & 256 & 6.05\% \\ FTP & 217 & 5.53\% \\ DNS & 184 & 4.35\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The distribution of the various application types.  
\begin{tabular}{l l} \(1\). & **Initialization Step:** \\  & (a) & **Set** the parameters of the method: \(N_{c}\) for number of chromosomes, \(N_{g}\) for maximum number of allowed generations, \(p_{s}\) for the selection rate, and \(p_{m}\leq 1\) for the mutation rate. \\  & (b) & **Obtain** the train set \(\text{TR}=\{x_{i},y_{i}\},\ i=1\dots\ M\) of the used dataset. \\  & (c) & **Set**\(k=0\), the iteration number. \\ \(2\). & **Fitness Calculation Step:** \\  & (a) & **For**\(i=1\dots N_{c}\) **do** \\  & (a) & **Create** a classification program \(C_{i}\) using the Grammatical Evolution procedure and the grammar defined in [12]. \\  & (b) & **Calculate** the fitness value \(f_{i}\) of chromosome \(i\) as \\  & (c) & \\  & (b) & **EndFor**. \\ \(3\). & **Genetic Operations Step:** \\  & (a) & **Selection procedure.** All chromosomes are sorted according to their fitness values calculated before and the first \((1-p_{s})\times N_{C}\) of chromosomes with the lowest fitness values are transferred intact to the next generation. The remaining chromosomes are substituted by offsprings produced during the crossover procedure. \\  & (b) & **Crossover procedure**. In order to produce new offsprings, the tournament selection is used to select a pair of \((z,w)\) chromosomes from the current population. This set of selected chromosomes will produce two new offsprings \(\bar{z}\) and \(\bar{w}\) using one-point crossover. \\  & (c) & **Perform** the mutation procedure. During this procedure, a random number \(r,r\in[0,1]\) is drawn for each element of every chromosome. If \(r\leq p_{m}\), then the corresponding element is altered randomly. \\ \(4\). & **Termination Check Step:** \\  & (a) & **Set**\(k=k+1\); \\  & (b) & **If**\(k\geq N_{g}\), terminate; or else, go to the Fitness Calculation Step. \\ \end{tabular}
## 4 Evaluation and Test Cases
### Test-3  
In the third test case, the design of the previous test cases is applied for physical core isolation and vCPU pinning. To perform the process of isolation, basic Linux commands are used, and the kernel of the host operating system isolates all physical hosts CPU core (excluding one). To get baseline performance, the HEPSPEC06 benchmark is used for executing isolation cores on the cloud's physical hosts. One-to-one strict pinning policy is adopted for pinning the vCPUs of virtual machines or containers. OpenStack supported method for pinning the vCPUs of virtual machine or containers is utilized. As the NUMA node topology can also affect the performance, it is taken under consideration. We executed configuration #2 (which consists of two virtual machines or two containers each having eight vCPU cores). In this configuration, eight vCPU cores of the first virtual machine or container were pinned with NUMA node 0, and second virtual machines or containers  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Configurations**} & **Number of** & **Number of** & **Number of vCPUs** & **Per VM/Container** \\  & **Containers** & **Number of VMs** & **Per VM/Container** & **RAM Allocation** \\ \hline Configuration \#1 & 1 Container & 1 VM & 16 vCPUs & 96 GB \\ Configuration \#2 & 2 Containers & 2 VMs & 8 vCPUs & 48 GB \\ Configuration \#3 & 4 Containers & 4 VMs & 4 vCPUs & 24 GB \\ Configuration \#4 & 8 Containers & 8 VMs & 2 vCPUs & 12 GB \\ Configuration \#5 & 16 Containers & 16 VMs & 1 vCPU & 6 GB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Five different configurations of VMs/containers for test-1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Configurations**} & **Number of** & **Number of** & **Number of vCPUs** & **Per VM/Container** \\  & **Containers** & **Number of VMs** & **Per VM/Container** & **RAM Allocation** \\ \hline Configuration \#1 & 1 Container & 1 VM & 16 vCPUs & 96 GB \\ Configuration \#2 & 2 Containers & 2 VMs & 8 vCPUs & 48 GB \\ Configuration \#3 & 4 Containers & 4 VMs & 4 vCPUs & 24 GB \\ Configuration \#4 & 8 Containers & 8 VMs & 4 vCPUs & 24 GB \\ Configuration \#4 & 8 Containers & 8 VMs & 4 vCPUs & 12 GB \\ Configuration \#5 & 16 Containers & 16 VMs & 2 vCPUs & 6 GB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Five different configurations of VMs/containers for test-2.  
eight vCPU cores were pinned with NUMA node 1. It should be noted that NUMA nodes were assigned to each VM in all of our configurations. At the end, all configurations are run independently on the physical machine of the cloud, which is designed for pinning and isolation purposes. The HEPSPEC06 benchmark runs simultaneously on virtual machines or containers in single configuration.
## 3 Proposed Improved ADRC Strategy
### Characteristic Analysis of Improved ADRC
#### 3.3.2 Disturbance Suppression  
This section simplifies the ADRC outer loop control block diagram in Figure 9 to a two-degree-freedom closed-loop system combining Equations (18)-(21), as shown in this figure.  
Figure 8: Comparison of disturbance estimation capabilities between traditional ESO and cascaded. (**a**) error plot with an increasing \(\omega_{o}\). (**b**) error plot with an increasing \(k\).  
\(H(s)\) and \(C(s)\) in the figure can represent the setpoint filter and feedback controller, respectively, and their expressions are:  
\[C(s)=\frac{C_{n1}s^{4}+C_{n2}s^{3}+C_{n3}s^{2}+C_{n4}s+C_{n5}}{b_{0}s^{2}(s+2 \omega_{o})^{2}} \tag{24}\]  
\[H(s)=\frac{\omega_{c}(s+\omega_{o})^{4}}{C_{n1}s^{4}+C_{n2}s^{3}+C_{n3}s^{2}+C_ {n4}s+C_{n5}} \tag{25}\]  
In Equations (24) and (25) above, \(C_{n1}=k_{p}\), \(C_{n2}=4k_{p}\omega_{o}+2{\omega_{o}}^{2}\), \(C_{n3}={\omega_{o}}^{2}\big{(}6k_{p}+4\omega_{o}\big{)}\), \(C_{n4}={\omega_{o}}^{3}\big{(}4k_{p}+\omega_{o}\big{)}\), and \(C_{n5}=k_{p}{\omega_{o}}^{4}\).  
The authors can obtain the transfer function of the system output regarding reference inputs and generalized disturbances by simplifying the voltage control circuit shown in Figure 7 to a standard structure with feedback channels and controlled objects.  
\[u_{dc1}=\frac{b_{0}H(s)C(s)}{s+b_{0}C(s)}u_{dc1}^{*}+\frac{1}{s+b_{0}C(s)}f_{1 }=\frac{k_{p}}{s+k_{p}}u_{dc1}^{*}+\frac{s^{2}(s+2\omega_{o})^{2}}{\big{(}s+k_ {p}\big{)}(s+\omega_{o})^{4}}f_{1} \tag{26}\]  
The overshoot generated by the control system based on ADRC during the tracking process depends on the disturbance term. Figure 10 plots a frequency curve for the disturbance term with control parameters \(\omega_{\rm o}\) = 300 rad/s, \(k_{p}\) = 300.  
As shown in the figure, the improved ADRC possesses better disturbance suppression when the frequency is below 300 rad/s. Limitations of the observer bandwidth \(\omega_{\rm o}\) are the main factors to affect its ability to resist disturbance, especially in the frequency band of more than 300 rad/s.  
Figure 11 shows the frequency curves of the disturbance term for the improved ADRC under different control parameters. It means that the greater the selected \(\omega_{\rm o}\) or \(\rm kp\) is, the stronger the robustness of the Grid-tie inverter to disturbances.  
Figure 10: Contrast between the Cascaded ESO and the Conventional one Regarding the Disturbance-rejecting ability.  
Figure 9: Equivalent Closed-loop System with Two-degree-of-freedom (“\(\times\)” in the figure means conjugate complex number).  
Nevertheless, practical applications cannot unlimitedly increase \(\omega_{\rm o}\) and \(k_{\rm p}\) due to observer noise and closed-loop stability. To ensure the system performance, usually select \(\omega_{\rm o}\) as 5-10 times \(k_{p}\).
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 2 Background
### Hybrid-Copy  
The hybrid-copy algorithm works by running the pre-copy algorithm just once as the first step of the migration. In this phase, the source VM continues providing service to users while all the memory data are copied to the destination. The VM is then suspended and its processor state is copied to the destination without the remaining dirty memory pages. Further, the VM resumes at the destination immediately, and the post-copy algorithm kicks. The rest of the memory pages will be synchronized in the post-copy phase.  
Page faults in post-copy phase always incur a significant performance loss; sometimes page faults can even cause the services in migrated VM to be totally unavailable. Hybrid-copy algorithm can avoid many page faults because of the pre-copy algorithm. Further, as with the post-copy mechanism, it also helps solve the trouble of the pre-copy algorithm in write-intensive workloads. In the original hybrid-copy algorithm, the pre-copy algorithm only works for a single round. The page faults occurring in the post-copy phase can be further reduced if more copy iterations are executed. However, a predetermined number of rounds cannot be satisfied with various workloads. If the network is in good condition and the VM runs a read-intensive workload, more iterative copy round should be executed, reducing the page faults further. On the contrary, if the network condition is poor or the VM runs a write-intensive workload, less iterative copy behavior should be performed to save the network resources and the migration time. In this paper, we proposed a new parameter to determine the right moment to stop the iterative copy phase based on the real situation.
## 2 Numerical Model
### Mathematical Equations
#### 2.1.1 Discretization of the Shallow-Water Equation  
The FVM-Godunov approach solves the two-dimensional shallow-water equations by integrating the source term.  
\[\text{U}_{\text{t}}+\text{F}(\text{U})_{\text{x}}+\text{G}(\text{U})_{\text{y}} =\text{S}(\text{U}) \tag{5}\]  
By integrating Equation (6) into each cell and time step, the formulation becomes:  
\[\int_{\text{x}_{\text{i}-1/2}}^{\text{x}_{\text{i}+1/2}}\int_{\text{y}_{ \text{j}-1/2}}^{\text{y}_{\text{j}+1/2}}\int_{\text{p}^{\text{n}+1}}^{\text{ p}^{\text{n}+1}}\biggl{(}\frac{\partial\text{U}}{\partial\text{t}}+\frac{ \partial(\text{F}(\text{U}))}{\partial\text{x}}+\frac{\partial(\text{G}(\text {U}))}{\partial\text{y}}\biggr{)}\text{d}\text{x}\text{d}\text{y}\text{d} \text{t}=\int_{\text{x}_{\text{i}-1/2}}^{\text{x}_{\text{i}+1/2}}\int_{\text{y}_ {\text{j}-1/2}}^{\text{y}_{\text{j}+1/2}}\int_{\text{p}^{\text{n}+1}}^{\text{ p}^{\text{n}+1}}(\text{S}(\text{U}))\text{d}\text{x}\text{d}\text{y}\text{d} \text{t}\,. \tag{6}\]For a better resolution of Equation (7) and to ensure its stability, system decomposition is recommended. This will have to be performed in the \(x\)-direction on the one hand and the \(y\)-direction on the other hand.  
The resolution is split into a sequence of one-dimensional equations.  
In the \(x\)-direction:  
\[\int_{x_{i-1/2}}^{x_{i+1/2}}\int_{t^{n}}^{t^{n+1}}\biggl{(}\frac{\partial U}{ \partial t}+\frac{\partial(F(U))}{\partial x}\biggr{)}dxdt=0\;. \tag{7}\]  
The numerical solution at time \(t_{n+1/4}\) is obtained from the resolution of Equation (8), as follows:  
\[\frac{U_{i,j}^{n+1/4}-U_{i,j}^{n}}{\Delta t}+\frac{F_{i+1/2,j}^{n}-F_{i-1/2,j} ^{n}}{\Delta x}=0 \tag{8}\]  
where \(U_{i,j}^{n}\) is the first solution along each row of cells, with \(j\) fixed; \(F_{i+1/2,j}^{n}\) is the numerical flux between two consecutive cells (\(i-1,j\)) and (\(i,j\)).  
Then, we solve the equation again, this time along the \(y\)-direction.  
\[\int_{y_{1-1/2}}^{y_{i+1/2}}\int_{t^{n}}^{t^{n+1}}\biggl{(}\frac{\partial U}{ \partial t}+\frac{\partial(G(U))}{\partial y}\biggr{)}dydt=0 \tag{9}\]  
The numerical solution at time \(t_{n+2/4}\) is obtained from the resolution of Equation (10), as follows:  
\[\frac{U_{i,j}^{n+2/4}-U_{i,j}^{n+1/4}}{\Delta t}+\frac{G_{i+1/2,j}^{n+1/4}-G_{ i-1/2,j}^{n+1/4}}{\Delta y}=0 \tag{10}\]  
where \(U_{i,j}^{n+1/4}\) are present as data along each row of cells, with \(i\) fixed; \(G_{i-1/2,j}^{n+1/4}\) is the numerical flux between two consecutive cells (\(i\)\(j\)\(-\)\(1\)) and (\(i\)\(j\)).  
The integration of the friction and source terms (rainfall, infiltration, and bottom slope) are established as follows:  
\[\frac{U_{i,j}^{n+\frac{3}{4}}-U_{i,j}^{n+\frac{2}{2}}}{\Delta t}=S_{f}\biggl{(} U_{i,j}^{n+\frac{2}{4}}\biggr{)}+S_{S}\biggl{(}U_{i,j}^{n+\frac{2}{4}} \biggr{)}; \tag{11}\]  
\[\frac{U_{i,j}^{n+1}-U_{i,j}^{n+\frac{3}{4}}}{\Delta t}=S_{p}\biggl{(}U_{i,j}^{ n+\frac{3}{4}}\biggr{)}\;. \tag{12}\]
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 2 Data and Method
### The Training of the Network
#### 2.3.2 The Parameter Settings Related to Training the Network  
During model training, hyperparameter tuning is performed using the training and validation datasets. Due to the depth of the network, to ensure that the output of the ReLU activation function does not completely vanish, the Kaiming method (He initialization) [27] is first employed to initialize the network parameters. The network utilizes stochastic gradient descent with a mini-batch size of 8 [28] and a momentum of 0.7. To help the gradient descent method escape local minima or saddle points, thereby finding better local optima and improving the model's robustness and generalization capability, a cyclical learning rate is adopted during training. The maximum value of the cyclical learning rate [29] is set to \(10^{-2}\), the minimum to \(10^{-5}\), with a step size of 5 and a shrinkage factor of 2. Each cycle involves the learning rate decreasing from the maximum to the minimum value and then returning to the maximum, after which the learning rate is divided by the shrinkage factor. The maximum number of training iterations is set to 100, but early stopping is triggered if there is no improvement in validation loss for 30 consecutive iterations.
## 3 Results
### Clinical Measurements for CAL and GRD
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Mean Value in mm \(\pm\) Standard Deviation**} \\ \hline
**Group/Time** & **CAL** & **PD** & **GRD** & **GRW** & **AG** & **BGT\({}_{1}\)** & **BGT\({}_{2}\)** & **BGT\({}_{3}\)** \\ \hline  & Baseline & 4.19 \(\pm\) 1.07 ** & 1.47 \(\pm\) 0.48 & 2.72 \(\pm\) 1.00 ** & 3.47 \(\pm\) 0.92 & 1.33 \(\pm\) 1.41 & 1.04 \(\pm\) 0.21 & 1.17 \(\pm\) 0.20 & 1.02 \(\pm\) 0.28 \\  & 6 months & 1.17 \(\pm\) 0.67 ** & 1.22 \(\pm\) 0.33 & 0.21 \(\pm\) 0.37 & 0.69 \(\pm\) 1.14 & 3.73 \(\pm\) 1.00 & 2.09 \(\pm\) 0.25 ** & 1.93 \(\pm\) 0.28 ** & 1.85 \(\pm\) 0.27 ** \\  & 12 months & 1.12 \(\pm\) 0.72 ** & 1.18 \(\pm\) 0.31 & 0.22 \(\pm\) 0.36 & 0.73 \(\pm\) 1.13 ** & 3.70 \(\pm\) 0.94 ** & 1.96 \(\pm\) 0.27 ** & 1.96 \(\pm\) 0.23 ** & 1.67 \(\pm\) 0.18 ** \\  & 24 months & 1.14 \(\pm\) 0.77 ** & 1.19 \(\pm\) 0.34 & 0.27 \(\pm\) 0.44 ** & 0.80 \(\pm\) 1.21 ** & 3.69 \(\pm\) 0.98 ** & 1.79 \(\pm\) 0.28 ** & 1.60 \(\pm\) 0.28 ** & 1.55 \(\pm\) 0.19 ** \\ \hline  & Baseline & 4.62 \(\pm\) 1.26 ** & 1.54 \(\pm\) 0.48 & 3.08 \(\pm\) 1.14 & 3.64 \(\pm\) 1.25 & 1.51 \(\pm\) 1.78 & 1.11 \(\pm\) 0.27 ** & 1.17 \(\pm\) 0.29 ** & 1.08 \(\pm\) 0.39 ** \\ Control group & 6 months & 1.25 \(\pm\) 0.75 ** & 1.13 \(\pm\) 0.26 & 0.30 \(\pm\) 0.49 ** & 0.85 \(\pm\) 1.24 ** & 3.93 \(\pm\) 1.33 ** & 1.63 \(\pm\) 0.31 ** & 1.70 \(\pm\) 0.33 ** & 1.60 \(\pm\) 0.46 ** \\ (CAF + NA - CTG) & 12 months & 1.25 \(\pm\) 0.77 ** & 1.08 \(\pm\) 0.29 & 0.34 \(\pm\) 0.9 & 0.95 \(\pm\) 1.16 ** & 3.91 \(\pm\) 1.29 ** & 1.63 \(\pm\) 0.27 ** & 1.64 \(\pm\) 0.34 ** & 1.55 \(\pm\) 0.43 ** \\  & 24 months & 1.31 \(\pm\) 0.81 ** & 1.11 \(\pm\) 0.26 & 0.37 \(\pm\) 0.55 ** & 0.98 \(\pm\) 1.24 ** & 3.87 \(\pm\) 1.33 & 1.51 \(\pm\) 0.27 ** & 1.57 \(\pm\) 0.33 ** & 1.42 \(\pm\) 0.45 ** \\ \hline \multicolumn{10}{l}{*   Sattionally significant difference in comparison to baseline (Standard text for dependent variable) *  } \\ \multicolumn
# A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems
## 5 Conclusions  
In this study, we introduced a CNN-based equalizer designed for HDS channels. Using a pretrained CNN, we processed the received signal from the HDS channel output to mitigate the 2D ISI and achieve an equalizer output signal close to the desired target signal. In addition, we conducted experiments to determine the optimal CNN equalizer structure. The proposed equalizer demonstrated superior accuracy compared to traditional equalizers. Simulations showed that the proposed model outperformed those of prior studies. Specifically, at a BER of \(10^{-3}\), the proposed model yielded a gain of approximately 3.5 and 2.5 dB in contrast to conventional equalizers with 0% and 10% misalignment, respectively. Furthermore, even in the presence of misalignments and blurred conditions, the proposed model consistently exhibited exceptional performance. The proposed model can resist the blur effect up to 2.8. However, our proposed model still trades off between performance and complexity/latency.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Latency (ms)** \\ \hline Structure 1 & 4.1 \\ Structure 2 & 6.448 \\ Structure 3 & 6.151 \\ Structure 4 & 7.25 \\ Structure 5 & 8.23 \\ Structure 6 & 8.532 \\ Structure 7 & 8.623 \\ Structure 8 & 8.854 \\ Conventional equalizer in [18] & 0.0902 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The processing time for eight CNN structures.  
Figure 10: BER performance of the CNN equalizer according to blur.  
**Author Contributions:** Conceptualization, T.A.N. and J.L.; methodology, T.A.N. and J.L.; software, T.A.N.; validation, T.A.N. and J.L.; formal analysis, T.A.N.; investigation, T.A.N. and J.L.; writing--original draft preparation, T.A.N.; writing--review and editing, T.A.N. and J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2023-2020-0-01602), supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP).  
Not applicable.  
**Data Availability Statement:** The data presented in this study are available in article.  
The authors declare no conflict of interest.
## 4 Results and Discussion
The experimental results indicated that the Gabor and BoW-SRP approaches have shown lower classification results with least average \(accu_{y}\) of 71.83% and 80.40% respectively. Alongside these results, the BoW-LBP and GLCM-SVM methods have accomplished slightly improved average \(accu_{y}\) values of 84.04% and 79.78% respectively. These results are followed by the GoogleNet and VGGNet approaches, which have resulted in reasonable average \(accu_{y}\) values of 84.40 and 84.75% respectively. At last, the MDTL-ICDDC method has gained higher average \(accu_{y}\) of 96.45%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Recall (\%)**} \\ \hline
**Methods** & **Class-0** & **Class-1** & **Class-2** & **Class-3** & **Average** \\ \hline Gabor & 52.00 & 47.40 & 58.80 & 91.00 & 62.30 \\ \hline BoW-SRP & 63.40 & 49.60 & 67.00 & 91.40 & 67.85 \\ \hline Bow-LBP & 74.00 & 61.80 & 67.00 & 93.80 & 74.15 \\ \hline GLCM-SVM & 73.59 & 69.57 & 79.17 & 71.74 & 73.52 \\ \hline GoogleNet & 79.60 & 83.99 & 87.18 & 90.26 & 85.26 \\ \hline VGGNet & 76.08 & 82.46 & 89.82 & 82.75 & 82.78 \\ \hline MDTL-ICDDC & 89.20 & 89.20 & 94.80 & 98.40 & 92.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recall analysis of MDTL-ICDDC technique with existing algorithms under various classes. Figure 12: Recall analysis of MDTL-ICDDC technique under various classes. Table 6 and Figure 14 illustrate a comparative \(F_{score}\) inspection of the MDTL-ICDDC algorithm with other techniques under distinct classes. The experimental results indicated that the Gabor and BoW-SRP models have exposed lesser classification results with minimal average \(F_{score}\) of 61.98% and 67.88% respectively. In addition, the BoW-LBP and GLCM-SVM models have accomplished somewhat higher average \(F_{score}\) values of 74.35% and 87.99% respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \hline
**Methods** & **Class-0** & **Class-1** & **Class-2** & **Class-3** & **Average** \\ \hline Gabor & 55.13 & 80.83 & 65.67 & 85.67 & 71.83 \\ \hline BoW-SRP & 79.65 & 86.95 & 70.86 & 84.15 & 80.40 \\ \hline Bow-LBP & 91.89 & 80.86 & 74.86 & 88.55 & 84.04 \\ \hline GLCM-SVM & 72.43 & 82.21 & 70.99 & 93.48 & 79.78 \\ \hline GoogleNet & 93.00 & 73.23 & 92.88 & 78.50 & 84.40 \\ \hline VGGNet & 86.73 & 82.73 & 78.95 & 90.57 & 84.75 \\ \hline MDTL-ICDDC & 96.00 & 94.90 & 96.60 & 98.30 & 96.45 \\ \hline \end{tabular}
\end{table} 
Table 5: Accuracy analysis of MDTL-ICDDC technique with existing algorithms under various classes. Figure 13: Accuracy analysis of MDTL-ICDDC technique under various classes. Moreover, the GoogleNet and VGGNet approaches have resulted in reasonable average \(F_{score}\) values of 81% and 84.95% respectively. Finally, the MDTL-ICDDC model has gained superior average \(F_{score}\) of 92.87%. These results and discussion pointed out that the MDTL-ICDDC model has proficiently detected and classified the crowd density in enabled video surveillance systems.
## 4 Performance Analysis
### Encryption Throughput  
The entire amount of plaintext data that are properly and quickly encrypted is referred to as the encryption throughput. The throughput for encryption schemes is determined as the average amount of plain text in q bits divided by the average encryption time. The data were effectively and securely encrypted using the suggested approach. Figure 5 and Table 4 show the encryption throughput provided by the proposed and existing methods, demonstrating that the suggested approach offers a reliable and secure encryption throughput.  
\begin{table}
\begin{tabular}{c} ECC-EERP \\ (Proposed) \\ \end{tabular} \\ \hline
10 & 5 & 53 & 29 & 49 & 52 & 97 \\
20 & 7 & 59 & 32 & 88 & 62 & 96 \\
30 & 9 & 63 & 37 & 69 & 73 & 95 \\
40 & 12 & 73 & 39 & 75 & 79 & 98 \\
50 & 15 & 81 & 43 & 80 & 81 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Encryption throughput of the proposed and existing methods.  
Figure 5: Encryption throughput of the proposed and existing methods [41; 42; 43; 44; 45].