esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network
## 5 Results  
A simulation script was written using the developed algorithms for movement and distribution. There are two base stations in the scenario, and five subscriber nodes are placed around each of them. The distance range between base stations and nodes is 100 to 150 m. The maximum removal distance is 1000 m. When studying interference, a rather large contribution to its change is made by the power of the signal source and the path loss model. For both experiments, the power was 46 dB. To compare the results obtained, it was decided to consider a scenario with the use of various models of signal propagation. Okumura and 3 GPP were chosen as path loss models.  
After completion of the distribution, the subscribers will have randomly located in an area of specified radius with the center at the base station location. The distribution result is shown in Figure 4.
## 5 Discussion  
This paper presents the idea of obtaining a trajectory to achieve the best performance or a motion in the joint-space that fulfills some preliminary conditions that a user can state. This idea is reached by using the method presented and named after this work as OISTV \((\zeta)_{v}\)-HF. Some of these many essential conditions have been tested that make a safe trajectory while controlling a robotic arm. First, the method OISTV \((\zeta)_{v}\)-HF avoids and reduces any undesired oscillation through the trajectory. The trajectory retains a finite velocity, acceleration, and jerk that obeys the rule of thumb for a mechanical design/motion; the algorithm OISTV \((\zeta)_{v}\)-HF works by iteration. This method creates a trajectory that is as accurate as wanted, generating many trajectories. In contrast, the number of iterations increases until a trajectory passes through the initial position \(q_{0}\) at \(t=0\), then \(\theta_{v}\) at the via point time \(t_{v}\), and finally reaches the final position \(S_{f}\) at \(t_{s}\) at the end time.  
Nevertheless, we have found some disadvantages while using this method. First, a desirable trajectory can be obtained, but some drawbacks must be made; for example, a trajectory can obtain a no-continuous velocity and more significant spikes in the acceleration or jerk. At this time, there is not an existing method to obtain the best parameters that can allow us to generate a trajectory that fulfills all the desirable conditions. Therefore, we had to run this algorithm many times to obtain these parameters and obtain the desired trajectory, which means much time is spent on tests. Moreover, the algorithm can run for an extended period, and the time the convergence occurs can vary depending on the parameters. For example, in these results, the longest-running time was about 17.5 s, and the shortest-running time was about 0.052 s (the running time was obtained using a function of MATLAB called the tic toc function). So, a method to generate trajectories with  
Figure 9: Trajectory solution with continuous acceleration starting at \(q_{0}=30\), via point \(\theta_{v}=180\) and \(S_{f}=120\).  
a faster running time and find the best parameters to generate a trajectory that fulfills all the desired conditions must be developed in future works.
### Importance of Path Planning and Energy Management  
Path planning is crucial for managing the energy consumption of UAVs. By optimizing their routes, UAVs can avoid areas where energy expenditure is high and take advantage of conditions favorable for solar energy capture. The integration of heuristic algorithms and machine learning techniques, particularly neural networks, offers promising avenues for improving flight path optimization. These technologies enable adaptive management of UAV flight paths in real time, enhancing their responsiveness to dynamic environmental conditions that impact solar energy availability. For instance, if a UAVencounters unexpected cloud cover, it can adjust its flight path to move to an area with better sunlight [17; 18; 19; 29; 31; 32; 33; 34; 35; 36; 37; 38].  
The literature presents a range of strategies for path planning, from real-time adaptive algorithms to predetermined paths with minor in-flight adjustments. Real-time adaptive algorithms allow UAVs to continuously update their flight paths based on current conditions, while predetermined paths with minor adjustments provide a balance between flexibility and predictability. This diversity highlights the need for a balanced approach that harmonizes adaptability, energy efficiency, and operational reliability. An effective path planning strategy must be able to adapt to changing conditions without consuming too much energy in the process [9; 14; 15; 17; 19; 39; 40].
# A Survey of CNN-Based Network Intrusion Detection
## 4 Performance Metrics  
It is impossible to visually interpret machine learning algorithms' performance, meaning that quantitative metrics (e.g., PPV, TPR, F1, and confusion matrix) must be employed to perform assessments. As with other classification problems, the IDS task's confusion matrix includes terminologies such as true positive (TP), false positive (FP), true negative (TN), and false negative (FN). In IDS tasks, TP, FP, TN, and FN typically refer to attack data that are correctly classified as an attack, normal data that are incorrectly classified as an attack, normal data that are correctly classified as normal, and attack data that are incorrectly classified as normal, respectively. Table 4 presents the terminologies and performance assessment measures employed in IDS studies. For additional clarification, all terminologies used for the same measurement are shown in the table.  
The accuracy metric, which refers to the ratio of the total number of correct classifications, is widely utilized to measure performance. However, when we have an imbalanced dataset, accuracy is not a proper measurement technique. For example, assume that 99.9% of the dataset is labeled as \"normal\", and accuracy is used to measure the model's performance. If the model is biased to \"0\" and all testing samples (model output) are classified as normal, then the model accuracy will be 99.9%. In this example, the accuracy of 99.9% shows an excellent result, but obviously, this model is not delivering any valuable information. In this case, using other suitable evaluation metrics, such as Precision (specificity), Recall (sensitivity), or F1 Score (harmonic mean of Precision and Recall), can be helpful. Precision measures the number of correct classifications penalized by the number of incorrect classifications, Recall or sensitivity measures the number of correct classifications penalized by the number of missed entries, and the false alarm rate measures the proportion of benign events incorrectly classified as malicious. The F1 Score calculates the harmonic mean of Precision and Recall, which serves as a derived effectiveness measurement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Terminology** & **Other Terminologies or Explanation** & **Equation** \\ \hline ACC & Accuracy & \(\frac{TP+TN}{TP+TP+FN}\) \\ PPV & Precision and positive predictive value & \(\frac{TP}{TP+FP}\) \\ TPR & Recall, sensitivity, true positive rate, and & \(\frac{TP}{TP+FN}\) \\  & detection rate & \(\frac{FN}{TP+FN}\) \\ FNR & Miss rate and miss false negative rate & \(\frac{FN}{TP+FN}\) \\ TNR & Specificity, selectivity, and true negative rate & \(\frac{FN}{TP+FN}\) \\ FPR & False alarm and false positive rate & \(\frac{TP}{TP+FN}\) \\ F1 Score & F1 Score and F Measure & 2* \(\frac{TP}{TP+FN}\) \\ ROC & ROC curve: a graph with the x-axis shows FPR, and y-axis shows TPR \\ AUC & The area under the curve or area under the ROC curve \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Equations and Explanations of Each Performance Measure.
# The Locating-Chromatic Number of Origami Graphs
## 2 Results and Discussions
### Locating-Chromatic Number of Origami Graphs  
**Theorem 1**. : _Let \(O_{n}\) be an origami graph for \(n\geq 3\). Then, the locating-chromatic number of \(O_{n}\),_  
\[\chi_{L}(O_{n})= \begin{cases}4,&\text{for $n=3$}\\ 5,&\text{otherwise}\end{cases} .\]  
Proof. : Let \(n\in\mathbb{N}\) with \(n\geq 3\). An origami graph \(O_{n}\) is a graph with \(V(O_{n})=\{u_{i},v_{i},w_{i}:i\in\{1,...,n\}\}\) and \(E(O_{n})=\{u_{i}w_{i},u_{i}v_{i},v_{i}w_{i}:i\in\{1,...,n\}\}\cup\{u_{i}u_{i+1 },w_{i}u_{i+1}:i\in\{1,...,n-1\}\}\cup\{u_{n}u_{1},w_{n}u_{1}\}\). Next, to prove the theorem, we consider the following two cases:  
**Case 1. **\(\chi_{L}(O_{3})=4\)  
First, we determine the lower bound of \(\chi_{L}(O_{3})\). In the origami graphs \(O_{n}\) for \(n\geq 3\), there are three adjacent vertices (complete graph with three vertices, denoted by \(K_{3}\)); we then need at least 3-locating coloring. Without loss of generality, we assign three colors for any \(K_{3}\) in \(O_{n}\) for \(n\geq 3\), and then the three vertices are dominant vertices. As a result, if we use three colors it is not enough because there are more than one \(K_{3}\) in \(O_{n}\) for \(n\geq 3\). Therefore, \(\chi_{L}(O_{3})\geq 4\). Next, we determine the upper bound of \(\chi_{L}(O_{3})\leq 4\). To show that 4 is an upper bound for the locating-chromatic number for the origami graph \(O_{3}\) we describe a locating coloring \(c\) using four colors as follows:  
\(c(u_{i})=i,i=1,2,3\). \(c(v_{i})= \begin{cases}2,&\text{for $i=1,3$}\\ 3,&\text{for $i=2$}.\end{cases} \)  
The coloring \(c\) will create the partition \(\Pi\) on \(V(O_{3})\). We shall show that the color codes of all vertices in \(O_{3}\) are different. We have: \(c_{\Pi}(u_{1})=(0,1,1,1)\); \(c_{\Pi}(u_{2})=(1,0,1,1)\); \(c_{\Pi}(u_{3})=(1,1,0,1)\); \(c_{\Pi}(v_{1})=(1,0,2,1)\); \(c_{\Pi}(v_{2})=(2,1,0,1)\); \(c_{\Pi}(v_{3})=(2,0,1,1)\); \(c_{\Pi}(w_{1})=(1,1,2,0)\); \(c_{\Pi}(w_{2})=(2,1,1,0)\); \(c_{\Pi}(w_{3})=(1,1,1,0)\). Since the color codes of all vertices \(O_{3}\) are different, \(c\) is a locating-chromatic coloring. Thus, \(\chi_{L}(O_{3})\leq 4\). **Case 2. **\(\chi_{L}(O_{n})=5\), for \(n\geq 4\)  
To determine the lower bound, we will show that four colors are not enough. For a contradiction, assume that there exists a 4-locating coloring \(c\) on \(O_{n}\) for \(n\geq 4\). We assign \(\{c(u_{i}),c(v_{i}),c(w_{i}),c(u_{i+1})\}=\{1,2,3,4\}\), where \(c(v_{i})\
eq c(u_{i+1})\) because \(d(v_{i},x)=d(u_{i+1},x)\), \(x\in\{u_{i},v_{i}\}\). Observe that, on \(O_{n}\) for \(n\geq 4\), there are \(n\) vertices \(u_{i}\) whose degree is 5. As a result, at least two vertices \(w_{k},w_{l}\), \(k\
eq l\) have the same color codes, which is a contradiction. Therefore, \(\chi_{L}(O_{n})\geq 5\), for \(n\geq 4\). To show the upper bound for the locating-chromatic number of origami graphs \(O_{n}\) for \(n\geq 4\), let us differentiate some subcases. **Subcase 1.
# Numerical Study of Mode Conversion between Fundamental Lamb and Rayleigh Waves
## 3 Results
### Rayleigh to Lamb Wave Conversion  
The Rayleigh wave interaction with the discontinuity is shown in Figure 6a. Although the Rayleigh wave was travelling towards the right-hand side, the bulk wave diffracted energy from the discontinuity can be seen travelling in the opposite direction. Both P and SV waves are generated from the mode conversion and propagate though the half space at angles nominally similar to what was observed for the incident \(A_{0}\) mode case. The mode conversion efficiencies are plotted in Figure 6b and show that \(>\)50% of the incident energy is scattered away. Interestingly, the \(R\to S_{0}\) conversion is higher than the \(R\to A_{0}\).  
Figure 4: Lamb to Rayleigh mode conversion for an incident \(A_{0}\) mode: (**a**) snapshot showing the vector plot of total displacement in the quarter-space configuration. P and SV refer to the longitudinal and shear waves, respectively, and R is the Rayleigh wave. (**b**) Mode conversion efficiency of the different modes.  
Figure 5: Lamb to Rayleigh mode conversion for an incident \(S_{0}\) mode: (**a**) snapshot showing the vector plot of total displacement in the quarter-space configuration. P and SV refer to the longitudinal and shear waves, respectively, R is the Rayleigh wave and SSL is the sub-surface longitudinal wave. (**b**) Bar plot for percentages of converted modes.
# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 2 Review on Time-Series Forecasting Studies
### Statistical Model
#### 2.1.2 Stochastic Model  
Hidden Markov Model (HMM)  
HMM was first introduced in 1996 by [PERSON] and [PERSON] [101]. HMM is a double stochastic process that introduces the hidden states with the unobservable underlying stochastic process. HMM has been applied to time-series forecasting in different domains such as forecasting prices of stock and option [36; 37; 38], commodity market [39], electricity [40; 41], crude oil [42], and currency exchange rate [43] from 2007 to 2018. As the real-life market is complex and highly volatile with nonlinear relationships, HMM helps model the time-series data with the hidden states. The model can find the hidden states of the time-series data to improve its forecasting capability and accuracy [102]. HMM is suitable for data with high volatility, as hidden states of the model can simulate different volatility regimes. In addition, the model considered the times-series data correlation in interpreting the data characteristic. Hence, the model is suitable for dynamic pricing when the time-series data are non-stationary and high in volatility. It is noticed that HMM has a better performance compared to regression models such as GARCH when the data are high in volatility and has a larger sample size [38; 103]. HMM can also outperform some Deep Learning (DL) models due to its utterly probabilistic state architecture [36; 104]. In addition, the model does not require a huge amount of training data as DL models do. In short, HMM is suitable for nonlinear and highly volatile data, as it can capture the changes of every time step well. Table 4 summarizes the studies using HMM.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Domain** & **Author** & **Dataset** & **Model** & **Result** \\ \hline \multirow{4}{*}{**Stock**} & [PERSON] and [PERSON], 2005 [36] & Four airline stock indexes & HMM & MAPE of less than 6.850 for included stocks \\ \cline{2-4}  & [PERSON], 2009 [37] & Six different stock prices & Fuzzy logic model and HMM & MAPE of less than 4.535 \\ \cline{2-4}  & [PERSON], 2016 [38] & Nordic BM & HMM, ARIMA accuracy of 73\% \\ \hline \multirow{2}{*}{**Commodity**} & [PERSON] et al., 2013 [39] & Financial market commodity prices & HMM & RMSE of 0.08502 \\ \hline \multirow{4}{*}{**Electricity**} & [PERSON] and [PERSON], 2007 [40] & Spanish spot market of 2005 & HMM & \\ \cline{2-4}  & [PERSON] et al., 2010 [41] & Electricity market data of August 2009 & HMM & MAPE of 4.1598 \\ \hline \multirow{2}{*}{**Oil**} & Bon and Isah, 2016 [42] & WTI dataset for oil prices of 2015 & HMM & \\ \hline \multirow{2}{*}{**Financial**} & [PERSON], 2015 [43] & Foreign currency exchange rate of Euro against USD (April 2007 to February 2011) & HMM, ANN MSE of less than 0.04 \\ \cline{2-4}  & & & \\ \hline \end{tabular}
\end{table}
Table 4: HMM time-series forecasting from 2007 to 2018.
## 3 Application Results
### Diagnosis-Oriented Data Filtering and Threshold-Based Classification (Approach B)  
In Figure 10 measurements obtained from two set-ups (Z15 and Z20) are shown, where the same operating conditions were applied. The filtering steps from the pressure equivalent value (Figure 10 (first row)) to the time behavior of smoothed \(AS\) gradient (Figure 10 (third row)) are shown. The same threshold values were used to classify the wear state, because the values of the centered moving average of the \(AS\) gradient are in the same range for both measurements after filtering. In principle, the classified machine states (Figure 10 (last row)) have similar structures, which comprise the run-in phase (at the beginning of the measurements), interim phase of permanent wear, and the final destruction phase. Phases with constant wear progression classify time periods with steady state behavior. Some phases were present that includes significant changes in the surface condition (State 4), but the system returned to stable and error-free operation (State 1). Based on the experimental results, it can be concluded that in spite of the different time behavior of wear processes, they can be compared from a structural point of view by distinguishing different phases, which are obviously identical to the development of the wear processes.  
This approach based on the use of easy to measure operating data is simple to implement. The measurements can be readily taken from operation.  
The modules can be implemented in the decision logic modules to allow condition assessment. The complexity of the application for users is also low. This approach has a high real-time capability, but the method might not yield directly identifiable information. Based on the classification of the machine state, operating conditions such as the lubrication interval can be changed according to the state, thereby reducing the changes in the surface conditions.  
Figure 9: Establishing cumulative AE energy based on Acoustic Emission measurement during the measurement Z15 **(left)** showing damage accumulation (right) (approach A).
## 3 Modelling and Simulation  
The geometrical profiles of typical inclusions in the steel observed with SEM were captured as coordinates with MATLAB (version 2016a, MathWorks, Natick, MA, USA, 1984). Due to the differences in contrast between inclusion and steel matrix in the SEM picture, the MATLAB codes can assist to recognize the details of inclusion shape in a desirable way. Thereafter, the coordinates of inclusion shape were processed by a Python script using the Abaqus (Dassault Systemes Simulia Crop., Providence, RI, USA, 1994) extension of the Python programming language to create the inclusion component of the Abaqus model. The version of Abaqus is Abaqus 2017. The steel matrix was set as a square area (30 \(\times\) 30 \(\upmu\)m\({}^{2}\)) and assumed to be a homogeneous material with a single phase and elastic only. The inclusions were positioned in the middle of the matrix area so that the residual stress around the inclusion was not affected by the matrix interface.  
The simulated process was the cooling procedure during oil quenching. The temperature variation was from 835 \({}^{\circ}\)C to the room temperature, which was set at 20 \({}^{\circ}\)C. The temperature was defined with pre-defined field in Abaqus/Standard. The thermal expansion coefficients and the mechanical property parameters of different inclusions and steel matrix were obtained from references and experiments in the present study.  
During the cooling process, the thermal stress of every node during the elastic deformation in the inclusion-matrix system was calculated with Equation (1).  
\[\varepsilon=\alpha_{i}(T-T_{0}) \tag{1}\]  
where \(i\) is the material definition number, which represents steel matrix and different inclusions; \(\alpha_{i}\) is the thermal expansion coefficient; \(T_{0}\) is the initial temperature, and \(T\) is the current temperature.  
To avoid over constraining the thermal expansion of the system and guarantee the accuracy of the simulation, it is important to define proper interface conditions. In the present study, the left side of the square steel area was fixed in the \(x\)-direction and still can move in the \(y\)-direction. The element type is set as \"continuum-plane stress-3 nodes (CPS3)\". The simulation type is static.  
Figure 1: Specimen geometries and their taken positions.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 1 Introduction  
Passive optical networks (PONs) are the most promising solution for access networks. Because the European Union is seeking broadband access and continually rising bandwidth requirements for end users, some technologies are not sufficient, such as asymmetric digital subscriber line (ADSL) or wireless fidelity (WiFi). The current research goal is to not only use optical fibers for data transmission, but also for other special services, such as an accurate time, stable frequency and optical sensing for infrastructure defense [1; 2].  
The first specification of PON was approved in 1998 as the asynchronous transfer mode PON (APON) [3]. The basic topology, a cascade connection of splitters in the optical distribution network (ODN), corresponds to the first specification [4]. The Czech Republic is obliged to develop a broadband access Internet technology with a transmission speed of at least 30 Mbit/s (downstream direction) for current households and 100 Mbit/s for new customers by 2020. A proper technology for the main purpose is gigabit PON (GPON) technology and/or Ethernet PON (EPON). At present, EPON technology is not very popular around the world due to the decreasing price and better efficiency of GPON technology. Furthermore, the International Telecommunication Union (ITU) has more standards of PONs that are compatible with the previous standards. The ITU's last standard next generation PON Stage 2 (NG-PON2) can transfer 40 Gbit/s (by 4 \(\lambda\), each with 10 Gbit/s), but theInstitute of Electrical and Electronics Engineers (IEEE) currently works on the first PON specification at 100 Gbit/s (by 4 \(\lambda\), each with 25 Gbit/s). Note that ITU and IEEE are not compatible due to different encapsulation methods.  
The rest of this paper is structured as follows. Section 2 introduces related works. Section 3 provides an overview of the GPON and EPON physical layer. Section 4 presents the activation processes for the G, XG, NG-PON(2) and EPONs. Section 5 presents the simulation model and a discussion of the results. Finally, Section 6 concludes the paper.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## Methodology  
### Problems Statement  
WSNs are actually facing a range of problems, such as coverage problem, position estimation problem, security information and vulnerability problem, robustness and scalability problem, and sensors energy preservation. Concerning the energy efficiency and energy management, several routing layer protocols have been designed in [16; 39]. Nevertheless, these designed protocols are not as energy efficient as required due to the following reasons:  
* These protocols pick up the head nodes at random. Therefore, the selection measures of these designed protocols are poor and are able to be improved in many ways. These randomly selected head nodes are not optimal in number. Consequently, creates imbalance and uneven clusters in the network. Therefore, the CHs which are in charge of large clusters expend more amount of energy in comparison with the CHs of small clusters that creates an unbalanced energy situation leads to end the network lifetime very earlier.
* Whenever a member node link itself to a CH which is selected on the basis of received signal strength and this CH positioned backward compared to its direction of transmission towards the BS, back transmission occurs. This type of data transmission contributes to extending the general path length traveled by the locally collected data.
* These clustering protocols do not define a clear strategy for dealing with non-associated nodes which do not receive any CH joint request during the selection process. These non-associated nodes directly convey their information to BS even if they are away from CHs. To do this, a heavy amount of energy is consumed, because of which the lifetime of the network is reduced.