esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application Example with SmartSync
### Applying SmartSync to the Study Case
#### 4.2.1 The Use of SmartSync  
The SmartSync approach was operated through the following steps:  
* We translated the models to S2 ML.
* The SmartSync tool associated the main elements of both models and asked the user to align the children elements of the mains.
* When given the associated children of the main, the SmartSync tool iterated the previous steps for these elements, asking the user to align their children.
* Such iterations were done until the tool had entirely explored the models.  
If an element has no equivalent in the other model, the user can give it the attribute forget if he can justify why it is not an inconsistency.  
The SmartSync tool takes two S2 ML compiled models (in the XML format) as an input and outputs a CSV comparison file. The user aligns the elements from both models in the CSV file, then re-iterates the SmartSync comparison with the two S2 ML compiled models and the CSV file as an additional input. The SmartSync tool is then able to identify the children elements and provides a new CSV comparison file to the user for the next iteration.  
Translation to S2 ML  
Before conducting each comparison, we first had to abstract the SysML model. The abstraction was made towards the S2 ML language, which is used in the SmartSync methodology.  
We translated the information from both the BDD and the IBD to conduce the abstraction. The S2 ML model followed the hierarchy from the BDD and the structure of the IBD. We represented SysML \"partProperties\", i.e., components of the system in the S2 ML model, with blocks. We also used S2 ML ports to represent SysML ports and S2 ML connections to represent SysML connections.  
An example of the transformation over part of the SysML model (namely the battery and drivetrain) is shown in Figure 13.  
Figure 12: Illustration of the Zipline Flyer drone.  
Once we translated the SysML model, we operated the comparison with the AltaRica model and the Modelica model. This was done by translating those models, then operating the SmartSync comparison.
## 3 Implementation of Vehicular VLC System
### Main Board  
The proposed vehicular VLC system utilizes an Arduino Mega board as the main control unit, which provides numerous input/output ports and supports asynchronous serial communication. The proposed vehicular VLC system incorporates the MPU-9250 nine-axis IMU sensor from SMG, which includes a three-axis gyroscope, three-axis accelerometer, and three-axis magnetometer. The MPU-9250 delivers real-time data, with the digital low-pass filter frequency set at 10 Hz, an accelerometer range of \(\pm\)2 G, and a gyroscope range of \(\pm\)250 degrees per second. A complementary filter is applied to combine data from the magnetometer and gyroscope, enabling precise calculation of the yaw rotation angle. The yaw rate is then used to adjust the orientation of the LED tail-light in proportion to the vehicle's turns for seamless VLC between consecutive vehicles; this is achieved through a servo-motor that operates in conjunction with the LED tail-light.  
To ensure the reliable and real-time operation of multiple time-sensitive processes, the proposed vehicular VLC system employs two separate Arduino Mega boards. One board is dedicated to acquiring orientation data from the IMU, while the other is responsible for processing the data from LED and PD, controlling the servo-motor for beam alignment, and managing vehicle movement. This separation of tasks is necessary to prevent timing conflicts and ensure deterministic performance, as initial testing with a single microcontroller showed instability in both communication and actuation. The interconnection between the two boards is managed via serial communication, as illustrated in Figure 2. The proposed vehicular VLC system requires up to 140 ms to ensure entire functionality in the worst-case timing scenario. The first Arduino Mega running the IMU operates with a 4.7 V battery pack, while the second board is powered by a 5.1 V battery pack.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 3 Impulsive Noise Measurements
### Results  
Figure 4 represents an example of measurements made with the generator bar at 16 kV. It corresponds to the plot of the noise level (amplitude) versus the time in milliseconds. In this figure, we notice that a short duration and high amplitude characterize the impulsive event, which is highlighted by the red-dashed contour. As can be seen, the alternative method for measuring partial discharges is also accurate. It represents the impulse event well and the background noise compared to the results from measurements obtained in a power substation [2].
## 2 The Theory of SST  
Commonly used time-frequency analysis methods, such as short-time Fourier transform [34], wavelet transform [35; 36], Wigner-Ville distribution [37; 38], and s-transform [39], are limited by the Heisenberg uncertainty principle [40].To improve the precision of the time-frequency plane, researchers have combined the rearrangement algorithm [41] with these methods [18; 42; 43; 44]. One such transformation that has shown good time-frequency resolution is the SST [18], which recalculates a position near the real coordinates of the time-frequency energy spectrum from continuous wavelet transform (CWT) and rearranges the energy accordingly.  
The CWT of the signal s(t) is defined by  
\[W_{s}(a,b)=\int s(t)\frac{1}{\sqrt{a}}\psi^{*}\left(\frac{t-b}{a}\right)dt, \tag{1}\]  
where \(\psi^{*}(t)\) is the complex conjugate of the mother wavelet \(\psi(t)\), and \(b\) is a time shift factor, which is scaled by \(a\). However, the energy of wavelet coefficients often diffuses along the scale in \(a\) direction, which generates the smearing effect in the time-frequency representation. Previous research [45] revealed that smearing has an insignificant effect along the time \(b\)-axis. Therefore, it is possible to estimate the instantaneous frequency \(w_{s}(a,b)\) by calculating partial derivatives for all \(W_{s}(a,b)\
eq 0\), as indicated below.  
\[w_{s}(a,b)=-i(W_{s}(a,b))^{-1}\frac{\partial}{\partial b}W_{s}(a,b). \tag{2}\]  
Notably, each point \((a,b)\) can be mapped to \((b,w_{s}(a,b))\) using this equation. To improve the smearing problem, we can convert the sum of every wavelet coefficient at the point \((b,a)\) to \((b,w_{s}(a,b))\). As \(a\) and \(b\) are discrete values, we can define a scale step \(\Delta a_{j}=a_{j}-a_{j-1}\) and frequency step \(\Delta w_{i}=w_{i}-w_{i-1}\). As a result, the time-frequency spectrum after SST can be expressed as follows:  
\[T_{s}(w_{i},b)=\Delta w^{-1}\sum_{a_{j};|w(a_{j},b)-w_{i}|\leq\Delta w/2}W_{s} \big{(}a_{j},b\big{)}a_{j}^{-3/2}\Delta a_{j}. \tag{3}\]  
In essence, SST redistributes the energy of the time-scale plane to the time-frequency plane, where it is rearranged to concentrate the energy. For ultrasonic signals, SST allows for better visualization of instantaneous energy changes when defects appear, which can be very helpful in resolving the defect signal overlapping with blind zone signals.
## 6 Data Analysis
### Analysis of BYOD Security Policy Techniques Based on Privacy and Security Requirements
\begin{table}
\begin{tabular}{c c c c c} \hline
**Ref** & **Technology Employed** & **Performance Analysis** & **Advantage** & **Limitation** \\ \hline
[44] & MDM-based model. & Prototype implementation. & It can identify whether the BYOD device is disabled or enabled. & Only a limited number of devices and operating systems. \\  & & & Protect against & \\
[45] & Pattern lock method and four-digit PIN(CirclePIN). & Experiment analysis and real dataset. & side-channel, shoulder-surring and single-recording threats. & Less secure technologies. \\
[46] & Based on WPA2-Enterprise. & Experimental analyses, case study. & It eliminates shared password risks. & Increase processing power. \\
[47] & Set policies based on IEEE & Case study of a Greek school. & Introduces security issues and their resolution. & Secure but vulnerable if authentication policies are simple. \\
[48] & Co-proximity authentication protocol(fingerprint sensors and biometric). & Prototype implementation and behavioural, biometric dataset. & Context-aware authentication. & Complex and time-intensive. \\
[49] & EZ-Net system. & Experimental analysis, Case study on campus. & Low-cost, high-performance. & Each user’s monthly authentication time is limited. \\  & Lightweight network access control (NAC) & Prototype implementation, OpenWrt, NAC module. & Improves administrator management and OpenWrt is a free wireless router. & Unsuitable for all systems. \\
[50] & Pseudo-code-based two-factor authentication. & Mathematical analysis. & Simple to implement and inexpensive. & It is difficult to do while utilising a mobile phone rather than a laptop because the keyboard is different. \\
[51] & NFC technology. & Simulation (computer simulation experimental). & More secure, fast and convenient authentication. & Limited number of nodes. \\
[52] & AppShield scheme, certificate-based authentication. & Prototype implementation, synthetic dataset (1000 data access operations). & Most secure BYOD infrastructure control. & Complicated and time-consuming. \\ Fine-grained security policies (set policy as an individual, group for user and device). & Prototype implementation. & Very low cost. & Network address interpretation is weak. \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of authentication techniques.
## 4 Construction and Analysis of Sliding Mode Control Based on RBF Neural Network
### Analysis of Synovial Control Stability  
Define the Lyapunov function as:  
\[L=\frac{1}{2}s^{T}Ds+\frac{1}{2\gamma}\tilde{\alpha}^{2} \tag{31}\]  
where \(\gamma>0\). Then  
\[\begin{array}{ll}\dot{L}&=s^{T}D\dot{s}+\frac{1}{2}s^{T}\dot{D}s+\frac{1}{ \gamma}\tilde{\alpha}\dot{\tilde{a}}\\ &=s^{T}\left[-(K_{d}+C)s-\frac{1}{2}\hat{a}s\cdot(H\cdot H)+(g+\tau_{\tau})+v \right]+\frac{1}{2}s^{T}D\dot{s}+\frac{1}{\gamma}\tilde{\alpha}\dot{\tilde{a}} \\ &=s^{T}\left[-K_{d}s-\frac{1}{2}\hat{a}s\cdot(H\cdot H)+(W\cdot H)+(\varepsilon +\tau_{\tau}+v)\right]+\frac{1}{2}s^{T}\left(\dot{D}-2C\right)+\frac{1}{\gamma} \tilde{\alpha}\dot{\tilde{a}}\\ &=s^{T}\left[-\frac{1}{2}\hat{a}s\cdot(H\cdot H)+(W\cdot H)\right]-s^{T}K_{d} s+s^{T}(\varepsilon+\tau_{\tau}+v)+\frac{1}{\gamma}\tilde{\alpha}\dot{\tilde{a}} \end{array}\]  
due to  
\[\left\{\begin{array}{l}s^{T}[\varepsilon+\tau_{r}-(\varepsilon_{0}+b_{d}) \text{sgn}(s)]\leq 0\\ s^{T}\left(\dot{D}-2C\right)s=0\\ s^{T}[W\cdot H]=\sum\limits_{i=1}^{m}s_{i}w_{i}^{T}h_{i}\\ 2s_{i}w_{i}^{T}h_{i}\leq s_{i}^{2}\|w_{i}\|^{2}\|h_{i}\|^{2}+1\leq s_{i}^{2} \
ot{\varphi}h_{i}+1\end{array}\right.\]  
Organized based on extrapolation:  
\[\begin{array}{ll}&s^{T}\left[-\frac{1}{2}\hat{a}s\cdot(H\cdot H)\right]\\ &=&-\frac{1}{2}\hat{a}\left[s_{1},s_{2},\ldots,s_{m}\right]\cdot\left(\left[ \begin{array}{c}s_{1}\\ \vdots\\ s_{m}\end{array}\right]\cdot\left[\begin{array}{c}h_{1}^{T}h_{1}\\ \vdots\\ h_{m}^{T}h_{m}\end{array}\right]\right)\\ &=&-\frac{1}{2}\hat{a}\left(\frac{s_{1}^{2}\|h_{1}\|^{2}+\ldots+s_{m}^{2}\|h_ {m}\|^{2}}{s}\right)\\ &=&-\frac{1}{2}\hat{a}\sum\limits_{i=1}^{m}s_{i}^{2}\|h_{i}\|^{2}\end{array} \tag{32}\]  
According to the lower limb exoskeleton rehabilitation robot walking rehabilitation training in each joint influence factors, set the number of joints affecting gait rehabilitation training to three, that is \(n=3\), then, the, collation formula can be obtained:  
\[\begin{array}{ll}\dot{L}\leq&-\frac{1}{2}\hat{a}\sum\limits_{i=1}^{m}s_{i}^{ 2}\|h_{i}\|^{2}+\frac{1}{2}\alpha\sum\limits_{i=1}^{m}s_{i}^{2}h_{m}^{T}h_{m}+ \frac{n}{2}-s^{T}K_{d}s+\frac{1}{\gamma}\tilde{\alpha}\dot{\tilde{a}}\\ &=&-\frac{1}{2}\hat{a}\sumFrom Equation (33), the design adaptive law is:  
\[\dot{\dot{a}}=\frac{1}{2\gamma}\sum_{i=1}^{m}s_{i}^{2}\|h_{i}\|^{2} \tag{34}\]  
Also, since \(\dot{L}\leq\frac{n}{2}-s^{\mathrm{T}}K_{d}s\), in order to ensure that \(\dot{L}\leq 0\), it is only necessary to ensure that the adaptive law converges gradually when \(\frac{n}{2}\leq s^{\mathrm{T}}K_{d}s\). The convergence result is:  
\[\|s\|\leq\sqrt{\frac{n}{2K_{d}}} \tag{35}\]
## 3 Critical Technical Approach
### Design Method for Optical Filters Addressing Power-Spectrum Nonlinearity Characteristics
Additionally, to address potential fabrication errors in high-precision multilayer designs, a transmittance tolerance band is incorporated into the filter design. This tolerance band ensures that the fabricated filter maintains compliance with spectral uniformity constraints, even in the presence of deviations in layer thickness or material refractive index, thereby enhancing the overall stability and engineering robustness of the system. To define the input boundaries for filter design, the raw spectral distribution of the fiber laser source in the 1540-1560 nm band was measured and analyzed. As shown in Figure 4, the laser exhibits significant spectral drift and non-uniformity under different power levels, which fails to meet the spectral consistency requirements for accurate solar radiation simulation. According to the optimization principle described above, the transmittance tolerance curves of the spectral filter are shown in Figure 5.
## 4 The 5G Use Cases and Spectrum Requirements
### ITU-Defined Generic 5G Use Cases
#### 4.1.2 URLLC  
This set of services refers to delay-intolerant applications that require secure End-to-End (E2E) data transmission and low-latency delivery. The term low-latency implies that data must arrive at its destination at a defined time; otherwise, the data will be of no use, resulting in a loss of reliability [58]. Emerging applications under this scenario should have strict requirement specifications for throughput, latency, and availability, as these capabilities cannot be compromised. URLLC is majorly designed to guarantee a low-latency transmission [59]. The use cases of ultra-reliable communication include applications that perform safety functions and monitoring, such as an autonomous vehicle. Other areas include remote medical procedures, robotics, mobile-enabled industrial manufacturing, power grid management, drone-based delivery, and cloud-based entertainment [60].
## 5 Pharmacologic Impact of Exosomes on the Treatment of Metabolic Diseases
### Role in Metabolic Regulation  
Diabetes Mellitus. Exosomes derived from mesenchymal stem cells (MSCs) have demonstrated a potential to enhance insulin sensitivity and support pancreatic \(\beta\)-cell survival [88; 105]. Exosomes deliver miRNAs, such as miR-29a, which alter insulin signaling pathways and glucose uptake and are a key mediator in decreasing insulin-induced glucose uptake of skeletal muscle cells [90]. Likewise, miR-29a, a crucial member of the miR-29 family, is a key regulator of insulin resistance in obesity and diabetes, influencing glucose and lipid metabolism in fat, muscle, and liver cells [88; 105]. It has been associated with glucose-induced \(\beta\)-cell dysfunction in humans and rats because glucose-induced upregulation of miR-29a in human islets of Langerhans reduces glucose-stimulated insulin secretion [89; 106]. The proposed mechanism suggests that miR-29a interferes with the cellular machinery involved in granular exocytosis and disruption of the functionality of peroxisome proliferatorator-activated receptor-\(\delta\)[89; 106].  
Obesity. Exosomes from adipose tissue affect metabolism by modifying liver function and insulin sensitivity. In obese individuals, the white adipocytes secrete exosomes that promote the differentiation of monocytes into M1 macrophages, including the polarization of adipose tissue macrophage, which release pro-inflammatory cytokines such as IL-13/IL-4 [107]. These white adipocyte-derived exosomes can also directly impact inflammation, as they often carry pro-inflammatory signals such as IL-6, TNF\(\alpha\), miR-155, miR-29a, and HMGB1, which lead to insulin resistance. However, those from individuals with a normal weight may have protective effects [82; 97]. miR-122 and miR-192 are other examples of exosome-transported miRNAs that impact metabolism. In this regard, it has been shown that plasma levels of miR-122 and miR-192 correlate with waist circumference, visceral fat, and an increased TG/HDL ratio in humans. Inhibiting miR-122 reduces cholesterol and fatty acid synthesis in mice and primates [103]. Additionally, miR-27a-3p and miR-27b-3p regulate adipose function, and miR-27 mimicking increases mouse TG levels [87; 103]. Elevated levels of miR-122 have been detected in exosomes derived from adipose tissue and in cells treated with Exo-AT. The overexpression of miR-122 enhanced adipogenesis, while its inhibition suppressed the process by modulating vitamin D3 receptor (VDR), sterol regulatory element-binding transcription factor 1 (SREBF1), peroxisome proliferatorator-activated receptor-Y, lipoprotein lipase, and adiponectin. Furthermore, reducing SREBP1 expression or increasing VDR expression effectively blocked adipogenesis [91; 104]. Real-time RT-PCR analysis revealed that miR-122, miR-192, and miR-27a-3p levels decreased in the epididymal white adipose tissue (eWAT) of mice fed a high-fat diet (HFD), while miR-122 and miR-192 increased in the liver. This phenomenon indicates that miRNA expression is tissue-regulated in obesity, leading to changes in circulating exosomal miRNA [87; 103].
# Joint Resource Allocation in Secure OFDMA-Based Networks Taking a Base Station as a Two-Way Relay
## 3 Problem Solution
For the given subcarrier pairing strategy \(\pi_{i,j}=1\), \(\eta_{i,j}=1\), the subcarrier assignment can be expressed as:  
\[\max_{\pi,\rho}\Biggl{\{}\sum_{j=1}^{N}L_{j,k}(\pi_{i,j,}\rho^{i,j}_{k}, \widehat{P}^{j}_{R,T_{k}},\lambda)+\sum_{j=1}^{N}L_{j,m}(\eta_{i,j},\rho^{i,j}_ {m},\widehat{P}^{j}_{R,(A_{m},B_{m})},\lambda)+\lambda P_{totalB}\Biggr{\}} \tag{29}\]  
The solution is:  
\[\widehat{\rho}^{i,j}_{k}= \begin{cases}1,&k=k(i,j)=\underset{k}{\arg\max}\Bigl{(} L_{j,k}(\pi_{i,j},\rho^{i,j}_{k},\widehat{P}^{j}_{R,T_{k}},\lambda)+L_{j,m}( \eta_{i,j},\rho^{i,j}_{m},\widehat{P}^{j}_{R,(A_{m},B_{m})},\lambda)\Bigr{)}\\ 0,&\text{otherwise}\end{cases} . \tag{30}\]  
\[\widehat{\rho}^{i,j}_{m}= \begin{cases}1,&m=m(i,j)=\underset{m}{\arg\max}\Bigl{(} L_{j,k}(\pi_{i,j},\rho^{i,j}_{k},\widehat{P}^{j}_{R,T_{k}},\lambda)+L_{j,m}( \eta_{i,j},\rho^{i,j}_{m},\widehat{P}^{j}_{R,(A_{m},B_{m})},\lambda)\Bigr{)}\\ 0,&\text{otherwise}\end{cases}  \tag{31}\]  
Step 3. For the given \(\widehat{P}^{j}_{R,T_{k}}\), \(\widehat{P}^{j}_{R,(A_{m},B_{m})}\), \(\widehat{\rho}^{i,j}_{k}\) and \(\widehat{\rho}^{i,j}_{m}\), the subcarrier pairing is expressed as:  
\[\max_{\pi}\Biggl{\{}\sum_{j=1}^{N}\left(L_{j,k}(\pi_{i,j},\widehat{\rho}^{i,j }_{k},\widehat{P}^{j}_{R,T_{k}},\lambda)+L_{j,m}(\eta_{i,j},\text{wide}\rho^{i,j}_{m},\widehat{P}^{j}_{R,(A_{m},B_{m})},\lambda)\right)\Biggr{\}}. \tag{32}\]  
Inserting \(\widehat{P}^{j}_{R,T_{k}}\) and \(\widehat{P}^{j}_{R,(A_{m},B_{m})}\) into Equations (15) and (16), we get \(\widehat{R}^{i,j}_{\sec,m}\) and \(\widehat{R}^{i,j}_{\sec,T_{k}}\). Formula (32) can be expressed as:  
\[\text{maxmize}\sum_{i=1}^{N}\sum_{j=1}^{N}\pi_{i,j}\widehat{R}^{i,j}_{\sec,T_{ k}}+\sum_{i=1}^{N}\sum_{j=1}^{N}\eta_{i,j}\widehat{R}^{i,j}_{\sec,m}-\lambda \left(\sum_{j=1}^{N}\left(\rho^{j}_{R,T_{k}}+\widehat{P}^{j}_{R,(A_{m},B_{m})} \right)\right). \tag{33}\]  
So, we only need to maximize:  
\[\sum_{i=1}^{N}\sum_{j=1}^{N}\pi_{i,j}\widehat{R}^{i,j}_{\sec,T_{k}}+\sum_{i=1 }^{N}\sum_{j=1}^{N}\eta_{i,j}\widehat{R}^{i,j}_{\sec,m} \tag{34}\]  
We define two profit matrices \(\left[\widehat{R}^{i,j}_{\sec,m}\right]_{N\times N}\) and \(\left[\widehat{R}^{i,j}_{\sec,T_{k}}\right]_{N\times N}\). In order to maximize (34), we need to pick elements in the two profit matrices to make the sum of profits maximization. This is obviously a standard linear assignment problem [27], which can be efficiently solved by the Hungarian method.
# Systematic Literature Review on Test Case Selection and Prioritization: A Tertiary Study
## 4 Results and Analysis
### RQ1-Basic Information of Available Texts in TCS&P  
The three parts of the question were answered as follows:  
**RQ 1.1:**_What is the distribution of secondary texts on TCS&P over various online databases?_  
Figure 2 depicts a pie chart representing the spread of the chosen 22 studies over the different online databases. ACM and Elsevier account for 70% of the selected studies, followed by IEEE Explore and Springer digital library, respectively. Only one survey from Wiley online library was found relevant to our SLR. Thus, there is a stronger presence of TCS&P in ACM and Elsevier.  
**RQ 1.2:**_What is the evolution of the number of secondary studies published in TCS&P over the years?_  
Growing surveys in the area reveal the growth in the research field of TCS&P. The need for new secondary studies arises from the obsolescence of existing ones or from missing aspects in the analysis. The 22 selected studies were published between Jan 2010 to May 2020. Figure 3 shows a line graph with the number of secondary studies published during this period per year. There was rapid growth in the number of surveys conducted in TCS&P after 2015. The number for 2020 is clearly lower as our study only considers the year until May; it is likely that the number for the remainder of 2020 would be consistent with the early trend. Five valuable and rigorous surveys in TCS&P were published in 2019. This demonstrates the growing interest of researchers in the field.  
Figure 2: Spread of 22 texts over online databases.  
_RQ 1.3: Which are the key publishing journals for TCS&P secondary studies?_  
Figure 4 shows the type of publication of studies. Almost 70% of studies were journal papers, and the remainder were conference communications or book chapters. The length of text of a survey, SLR, or mapping study is generally larger than a typical general primary study. Not all journals in the software engineering field publish secondary texts due to domain and size constraints. Table 4 lists the names of the ten journals and the number of surveys on TCS&P. This list is not exhaustive for journals that might publish secondary texts; it simply shows where the selected secondary texts were published, as possible targets of publication for future contributions of researchers.
# Fundamental Matrix Computing Based on 3D Metrical Distance
## 4 Computing \(F\) in Metric Space Based on Geometric Distance
### The Framework of Computing F Visually  
The input data of our algorithm are a pair of images of a scene that need to be reconstructed, which are taken with a hand-held camera with a fixed focal length. The framework of the experiment is as follows:  
1. Extract SIFT features on both images. Compute the matchings of features \([u,u^{\prime}]\).
2. Use a traditional method such as the eight-point method to compute \(F\) as the initial input of next step.
3. Optimize \(F\) using algorithm in Section 4.2.
4. If the metrical projection error is smaller than Step 2, output the result as the final \(F\). If not, go to Step 2 to repeat.  
In step (3), we can deduce \(P_{E},\ P_{E}^{\prime}\) from \(E\). Then, the figure of reconstructed 3D points \(X\) can be shown in a Cartesian coordinate system even in each iteration.
# Comparing New Wireless Sensor Network Protocols through Simulation and Data Analysis +
## 3 Simulation and Empirical Study
### Simulation and Simulation Model
#### 3.1.1 Simulation Tool (OPNET)  
The Optimized Network Engineering Tool (OPNET version 14.5) modeler is a network simulator that offers solutions for managing networks and applications. Some of these solutions include network engineering, research and development, operation, planning, and performance management. It can represent communication devices, protocols, and technologies, and it can mimic the performance of these technologies in a dynamic virtual network environment. These are some of its uses. Academic research in the field of OPNET technology offers support for wireless protocols, Mobile Ad hoc network protocols, and core network technologies [12].