esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussions
### Description of the Wedge  
The cross section of the 3D wedge is shown in Figure 2. The wedge is symmetry and contains two same stiffened plates. As shown in Figure 3, 7 longitudinal bulb stiffeners and 2 T-shape transverse frames are arranged at equal intervals in the stiffened plate. The bulb stiffener is converted into an equivalent L-shape stiffener in the finite element analysis. For the L-shape stiffener and the T-shape transverse frame, \"tw\" is the thickness of the web plate, \"hw\" is the height of the web plate, \"tf\" is the thickness of the face plate, \"bf\" is the breadth of the face plate. Structural parameters of the stiffened plate are listed in Table 1. The locations for the analysis of structural stress responses are presented in Figure 3. S1-S3 and S7-S9 are located at the face plate of the longitudinal stiffeners. S4-S6 are located at the face plate of the transverse frames.  
Figure 2: The cross section of the 3D wedge.  
The geometric model is meshed by referring to the finite element model in the work of [PERSON] et al. (2018). The spacing of longitudinal stiffeners is divided into 10 parts. The spacing of transverse frames is divided into 20 parts. The web plate of the longitudinal stiffener contains 4 elements in its height. The face plate of the longitudinal stiffener contains 1 element in its width. The web plate of the transverse frame contains 8 elements in its height. The face plate of the transverse frame contains 4 elements in its width. The local coordinate system \(o^{\prime}-\xi\eta\zeta\) which is fixed to the stiffened plate is shown in Figure 3. \(o^{\prime}\zeta\) can be determined using the right-hand rule. As the stiffened plate is symmetrical about the plane \(o^{\prime}-\xi\zeta\), only half of the stiffened plate needs to be analyzed. The symmetry boundary \(\theta_{\zeta}=U_{\eta}=\theta_{\zeta}=0\) is applied to nodes in the symmetry plane \(o^{\prime}-\xi\zeta\) and the supported boundary \(U_{\xi}=U_{\eta}=U_{\xi}=0\) is applied to nodes in the other three sides, as shown in Figure 4. As there are 30 elements in the direction of the \(o^{\prime}-\eta\) axis, the number of 2D sections in the Equation (21) is set as \(n=31\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Particulars** & **Value** & **Unit** \\ \hline Density & 7850 & kg/m\({}^{3}\) \\ Modulus of elasticity & \(2.07\times 10^{11}\) & pa \\ Poisson ratio & 0.3 & - \\ Thickness of the plate & 12 & mm \\ Distance between longitudinal stiffeners & 675 & mm \\ \hline \multirow{3}{*}{Dimensions of longitudinal stiffeners} & tw & 9 & mm \\  & hw & 200 & mm \\  & tf & 19.7 & mm \\  & bf & 39.8 & mm \\ \hline Distance between transverse frames & & 2400 & mm \\ \hline \multirow{3}{*}{Dimensions of transverse frames} & tw & 10 & mm \\  & hw & 400 & mm \\ \cline{1-1}  & tf & 16 & mm \\ \cline{1-1}  & bf & 200 & mm \\ \hline \end{tabular}
\end{table}
Table 1: Structural parameters of the stiffened plate.  
Figure 3: One side of the 3D wedge.
## 4 Proposed Work
### Pre-Identification Threats on Zigbee Network Using RBF  
In this subsection, we present a threat detection on Zigbee network keys which are stored on both PUF and blockchain ledger. We implemented an RBF-based threat detection mechanism to detect attacks on network keys. With Zigbee Light Link profile (ZLL), the devices that communicate over Zigbee possess an AES 128-bit network key that is shared with all devices in the network, even though confidentiality and integrity are ensured through an AES-CCM* block-based protection mechanism using a network key, nonce, and Message Integrity Code (MIC). However, the network keys are prone to spoofing attacks, replying to attacks and flooding attacks [30]. Whenever new Zigbee devices join a network, the IEEE 802.15.4 MAC association procedure is invoked, and Beacon Request frames are issued. The Zigbee router that responds with a Beacon frame issues 16-bit logical ID and a network key ciphered with a pre-installed link key to the Zigbee device. However, whenever an attacker gets access to this network key, the whole Zigbee network becomes compromised. To defend the attacks on Zigbee network keys, we propose an RBF-based threat detection framework. The design and implementation details of the proposed scheme are presented as follows:  
1. We leveraged the J48 decision tree algorithm to perform threat detection on Zigbee network keys, based on the existing works on RBF-based Intrusion Detection Systems (IDS).
2. The training and testing of RBF-based threat detection model is performed using the ZBDS2023 dataset.  
The ZBDS2023 dataset consists of Zigbee traffic and the network key regulated data as multiple PCAP files. These PCAP files are merged into a single PCAP file using Wireshark.
4. Using a Python package \"pcap handler 0.1.3\", the PCAP file is converted into a CSV file for training and testing the threat detection model.
5. The extracted features in the CSV file of the ZBDS2023 dataset using Principle Component Analysis (PCA) are: \"version\", \"ihl\", \"tos\", \"len\", \"id\", \"flags\", \"frag\".
6. We performed binary classification of attacks on Zigbee security keys; we considered the two labels as \"Benign\" and \"Attacks\".
7. Th attacks are detected on Zigbee network keys and the further defense mechanisms can be employed based on the detection results for securing the Zigbee devices in IoT networks and the results presented by the confusion matrix for evaluating threat detection Zigbee Figures 5 and 6.
## 2 Materials and Methods
### Modified Raman Spectroscopy Setup
To implement this, a measurement setup incorporating a transparent diffraction grating and a cylindrical lens was constructed, as illustrated in Figure 1.  
To verify the effectiveness of this measurement system, the Raman spectra of Si and GaN on Si substrates were measured. Figure 2 presents the Raman spectrum of GaN on Si. In this spectrum, the Raman peaks of GaN and Si are clearly separated, enabling the simultaneous measurement of temperature distributions in both materials. Additionally,  
Figure 1: Schematic diagram of spectrometer for high-speed Raman thermography using tilted bandpass filter and cylindrical lenses. the time required to distinguishably measure the anti-Stokes signal of GaN was only 50 ms at 150 mW laser power. Consequently, the mapping speed for Raman thermography in GaN on Si-based HEMTs is remarkably fast, reaching up to 50 ms per point. In order to verify the spatial resolution of the Raman thermography measuring system configured by the above method, a reference pattern sample for verifying the spatial resolution was produced. The sample was manufactured using p-doped Silicon on Insulator (SOI). As shown in Figure 3, it has a structure in which one ground electrode and electrodes are attached to the 1, 2, 3, and 4 \(\upmu\)m wire patterns, respectively. The Si layer is 4 \(\upmu\)m thick, and a bridge structure connecting equal-width lines was introduced to prevent pattern collapse. In addition, to suppress collapse caused by the undercutting of the Si patterns, a few hundred nanometers of Si were intentionally left unetched. When a voltage of 150 V is applied to a 1 \(\upmu\)m line width pattern using this sample, the temperature increases due to Joule heating, and the temperature distribution is confirmed using Raman thermography. Figure 4a shows the image of the SOI reference pattern viewed through an optical microscope and the result of the Raman thermography overlay. Interestingly, a temperature of about 200 \({}^{\circ}\)C is measured even for the 2-4 \(\upmu\)m pattern without applied voltage. This is likely due to the effect of leakage current from the less  
Figure 3: Schematic diagram of SOI reference pattern for spatial resolution verification. (**a**) is the electrode structure, (**b**) is a cross-sectional structure. Figure 2: A Raman spectrum of GaN on Si wafer measured by the modified Raman spectroscopy system for thermography described in this study (Model # AccuOptote SD-r24). etched p-doped Si in the bridge structure designed to prevent pattern collapse, as shown in Figure 4b. The two images coincide in a local area, while the temperature difference is clear with and without the pattern, and even the 1 \(\upmu\)m pattern can be identified. This demonstrates that the measurement system is capable of taking thermal images at a sub-micro spatial resolution.
## 3 Proposed Method
### State Estimation Based on the UKF
\begin{array}{l}\chi_{i,k/k-1}=h_{k}(\mathbf{x}_{i,k/k-1})+\mathbf{v}_{k}\\ \overset{\wedge}{\hat{\mathbf{z}}}_{k/k-1}=\overset{2n}{\underset{i=0}{\sum}}W_{i}^{m }\mathbf{x}_{i,k/k-1}\\ P_{\hat{z}_{k}}=\overset{2n}{\underset{i=0}{\sum}}W_{i}^{c}\Big{(}\mathbf{x}_{i,k/ k-1}-\overset{\wedge}{\hat{\mathbf{z}}}_{k/k-1}\Big{)}\Big{(}\mathbf{x}_{i,k/k-1}- \overset{\wedge}{\hat{\mathbf{z}}}_{k/k-1}\Big{)}^{\mathsf{T}}+R_{k}\\ P_{\hat{x}_{k}\overset{\wedge}{\hat{x}}_{k}}=\overset{2n}{\underset{i=0}{ \sum}}W_{i}^{c}\Big{(}\mathbf{x}_{i,k/k-1}-\overset{\wedge}{\hat{\mathbf{x}}}_{k/k-1} \Big{)}\Big{(}\mathbf{x}_{i,k/k-1}-\overset{\wedge}{\hat{\mathbf{z}}}_{k/k-1}\Big{)}^{ \mathsf{T}}\end{array} \right. \tag{16}\]  
where \(\mathbf{v}\) is the measurement noise, \(R\) is the covariance matrix of the measurement noise, \(R=\mathrm{E}[\mathbf{v}\mathbf{v}^{\mathsf{T}}]\). (4) Update the Kalman gain \(K_{k}\), state, and covariance:  
\[\left\{ \begin{array}{l}K_{k}=P_{\hat{x}_{k}\overset{\wedge}{\hat{x}}_{2}}P_ {\hat{z}_{k}}-1\\ \overset{\wedge}{\hat{x}}_{k}=\overset{\wedge}{\hat{x}}_{k/k-1}+K_{k}\Big{(} \mathbf{z}_{k}-\overset{\wedge}{\hat{x}}_{k/k-1}\Big{)}\\ P_{k}=P_{k/k-1}-K_{k}P_{\hat{z}_{k}}K_{k}^{\mathsf{T}}\end{array} \right. \tag{17}\]  
\(\mathbf{x}_{k}\) and \(P_{k}\) are used as prior information at time \(\mathrm{k}+1\) for a new round of the filtering process. When faults occur, Equation (12) can be rewritten as:  
\[\mathbf{z}=h\mathbf{x}+\mathbf{v}+\mathbf{f} \tag{18}\]  
where \(\mathbf{f}=\left[f_{V},f_{\alpha},f_{\beta},0,0,0\right]^{\mathsf{T}}\) represents the faults of the corresponding measurement signals for airspeed \(V\), angle of attack \(\alpha\), and sideslip angle \(\beta\). All components of the missile are part of a complete closed-loop system, so the impact of faults will continuously propagate and increase over time. The UKF model estimates the state of the air data system under normal conditions and lays the foundation for subsequent fault diagnosis.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 4 Deep Learning Models
### Multilayer Perceptron Network (MLP)  
Among the many deep neural networks, the MLP was among the first to be applied in short-term traffic flow forecasting. The MLP is the simplest type of deep neural network, and a typical MLP consists of an input layer, hidden layers, an output layer, and nonlinear activation functions, forming a multilayer feedforward artificial neural network, as shown in Figure 1. This method only involves matrix operations in the fully connected layer, resulting in high computational efficiency and a low computational cost. In the MLP, the input layer receives the input data and performs feature normalization, while the hidden layers process the input signals. The output layer makes decisions or predictions based on the processed information. Figure 1 illustrates a single-neuron perceptron model, where the activation function \(\varphi\) (Equation (1)) is a nonlinear function that maps the summation function (\(\mathrm{xw}+\mathrm{b}\)) to the output value \(\mathrm{y}\). In Equation (1), the terms \(\mathrm{x}\), \(\mathrm{w}\), \(\mathrm{b}\), and \(\mathrm{y}\) represent the input vector, the weighted vector, the bias, and the output value, respectively [16]. Figure 2 shows the structure of the MLP model.  
\[\mathrm{y}=\varphi(\mathrm{xw}+\mathrm{b}) \tag{1}\]  
The multilayer perceptron (MLP) is capable of mapping multidimensional data to a one-dimensional output, solving the nonlinear issues that a single-layer network cannot address. The MLP is trained using the backpropagation (BP) algorithm, and, by applying a simple MLP, high prediction accuracy can be achieved for short-term traffic flow forecasting.  
Figure 1: Single-neuron perceptron model.  
In the context of short-term traffic prediction, the MLP is used to model the mapping relationship between the historical time series data (input) and the predicted results (output), enabling accurate traffic flow predictions. [PERSON] et al. [17] utilized the MLP, SARIMA, and support vector regression (SVR) algorithms to predict the traffic flow over a 42-day period on a road segment in Morocco, incorporating factors such as whether a given day was a holiday. Their experiment demonstrated that the MLP outperformed the other two models in terms of prediction accuracy. [PERSON] et al. [18] proposed a multivariate machine learning-based highway traffic flow prediction model for non-recurrent events, using a dataset that included five features, namely the traffic flow, speed, density, road accidents, and rainfall, as well as two evaluation metrics, the root mean square error (RMSE) and mean absolute error (MAE). The MLP model used as a benchmark produced favorable forecasting results.
# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 3 GARSU: Automatic RSU Deployment
### Mutation  
The mutation operator introduces diversity into the population, thereby avoiding local minima solutions [23]. GARSUD makes use of probabilistic mutation for each gene, meaning that a gene may vary its value using a constant probability. The mutation probability is adapted to the size of the genotype to make one change in each individual on average.
# Using Science and Technology to Unveil The Hidden Delicacy Terfezia arenaria, a Desert Truffle
## 3 Results and Discussion
### Showcasing T. arenaria's Nutritional Value
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Minerals** & _Tefezia_ & _Agaricus_ & _Lentinula_ & _Pleurotus_ & _Tuber_ & & & & \\ _arenaria_ & _Bisporons_ & _edodes_ & _ostreatus_ & _melanosporum_ & **Pork** & **Beef** & **Chicken** \\ \hline Major essential elements & & & & & & & & \\ Ca & 26 & 580 & 438 & 730 & 817 & 60 & 70 & 60 \\ K & 3695 & 38,400 & 21,700 & 14,244 & 7356 & 3180 & 2730 & 3020 \\ Mg & 128 & 1300 & 1330 & 2800 & 241 & 190 & 164 & 205 \\ Na & 23 & 491 & 144 & 35 & 67 & 540 & 550 & 630 \\ P & 1407 & 8210 & 4080 & 6204 & 2678 & 1730 & 1440 & 1660 \\ S & 299 & n.a & n.a & n.a & n.a & n.a & n.a \\ Essential trace elements & & & & & & & \\ Cr & 0.9 & 7.0 & 0.3 & n.a & n.a & n.a & n.a \\ Cu & 6.6 & 34.2 & 7.1 & 39 & 18 & 0.7 & 0.6 & 0.4 \\ Fe & 19.6 & 49.4 & 35.7 & 130 & 12 & 7.9 & 19.6 & 5.9 \\ Mn & 1.4 & 6.6 & 19.3 & 14 & 1 & -0.125 & +0.125 & 0.1 \\ Mo & 0.6 & 0.3 & 0.2 & \textless{}0.01 & n.a & n.a & n.a & n.a \\ Ni & 0.1 & 0.7 & 0.1 & 0.7 & n.a & n.a & n.a & n.a \\ Se & 0.5 & 1.7 & 1.1 & 0.3 & n.a & n.a & n.a & n.a \\ Zn & 11.0 & 51.5 & 76.3 & 110.4 & 37 & 22.3 & 38.5 & 11.8 \\ Non-essential elements & & & & & & & \\ Al & 10.7 & 17.9 & 5.8 & n.a & n.a & n.a & n.a & n.a \\ Li & 0.4 & \textless{}0.1 & 0.1 & 0.3 & n.a & n.a & n.a & n.a \\ Elements with detrimental health effects & & & & & & & \\ As & 0.1 & 0.3 & 0.5 & \textless{}0.1 & n.a & n.a & n.a & n.a \\ Ba & 0.3 & 2.8 & 1.7 & n.a & n.a & n.a & n.a & n.a \\ References & This study & [75] & [76] & [91–93] & [94] & [90] & [90] & [90] \\ \hline \hline \end{tabular} n.a–data not available.  
\end{table} 
Table 2: Mineral content [mg kg\({}^{-1}\) dw] for _T. arenaria_ and other desert truffles, commercial mushrooms and meat (pork, beef and chicken). Values for _T. arenaria_ were determined in the present study while values for the other mushrooms, truffles and meat were determined in other studies.
# Dual-Band MIMO Antenna for n79 and sub-7 GHz Smartphone Applications
## 3 Results and Discussion
### MIMO Performance Parameters
#### 3.3.1 Envelope Correlation Coefficient (ECC)  
In [25; 26], the theory underlying the ECC formula computation employed in Equation (1) is discussed. Using the following equation, which computes the envelope correlation coefficient (ECC) \(\rho\), Figure 9 displays the ECC of the antennas 1 to 5 that are being presented. It is evident from Figure 9 that the simulated ECC value for all antennas in the frequency range of 4.4 GHz to 7.1 GHz is less than 0.002. The measured ECC is  
Figure 8: Three-dimensional radiation pattern of proposed ten-port MIMO antennas.  
depicted in Figure 9. Equation (1) was used for calculation of the measured correlation from the measured reflection coefficients.  
\[\rho_{eij}=\frac{|S_{ii}^{*}S_{ij}+S_{ji}^{*}S_{jj}|^{2}}{(1-|S_{ii}|^{2}-|S_{ji}|^ {2})(1-|S_{jj}|^{2}-|S_{ij}|^{2})} \tag{1}\]
# Generating \(m\)-[PERSON] Gray Codes and Related Algorithms
Obviously, each of the rank and unrank functions runs in \(\Theta(n)\) time. ```
voidGray_refl_unrank(intg[],intm,intn,intvalue){ inta[Mxm]; for(inti=0;i<n;i++){  a[n-i]=value%m; value=value/m; } g[1]=a[1]; intsum=g[1]; for(inti=2;i<=n;i++){  if(sum&1)g[i]=m-1-a[i]; elseg[i]=a[i]; sum=sum+g[i]; } } intGray_refl_rank(intg[],intm,intn){  inta[Mxm]; a[1]=g[1]; intsum=g[1]; for(inti=2;i<=n;i++){  if(sum&1)a[i]=m-1-g[i]; elsea[i]=g[i]; sum=sum+g[i]; } intvalue=0,mm=1; for(inti=n;i>=1;i--){  value=value+mm*a[i]; mm=mm*n; } } returnvalue; }
```  
**Algorithm 7** Ranking and unranking for the reflected Gray code Algorithm 8 is very convenient if one wants to generate only a part of the codewords, namely the codewords from \(A\) to \(B\), where \(0\leq A<B\leq m^{n}-1\). The main thing in it is to find a codeword by a given number in the sequence (unranking). We follow the algorithm for \(G_{2}(n)\) from [13], but present an extended version for the \(m\)-ary Gray code. Its time complexity analysis is similar to that of Algorithm 5 or can be used as a generalization of that in [13]. Finally, we get that Algorithm 8 is again a CAT algorithm. ```
void Gray_refl_from_A_to_B(intm,intn,intA,intB) { Gray_refl_unrank(g,m,n,A); intS[Maxwell]; inti=A,j,k,br=A,sum=0,t=0; S[1]=i; for(inti=2;j<=n;j++){ sum=sum+g[j-1]&1; if(0==sum)S[j]=1; elseS[j]=-1; } do{ print(g,n,t); if(B==br)return; k=n; i=i+1; j=i; while(0==(j%m)){ j=j/m;k=k-1; } if(k>0){ inttemp=g[k]+S[k]; if((temp<0)|(temp==m)){S[k]=-S[k];} g[k]=g[k]+S[k];br++;t=k*S[k]; } while(k>0); }
```  
**Algorithm 8** Part of the reflected Gray code \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Number** & **Codeword** & **Number** & **Number** & **Number** & **Codeword** \\
**(Rank)** & **(Radix 3)** & & **(Rank)** & **(Radix 3)** & \\ \hline
0 & 000 & 000 & 14 & 112 & 112 \\
1 & 001 & 001 & 15 & 120 & 102 \\
2 & 002 & 002 & 16 & 121 & 101 \\
3 & 010 & 012 & 17 & 122 & 100 \\
4 & 011 & 011 & 18 & 200 & 200 \\
5 & 012 & 010 & 19 & 201 & 201 \\
6 & 020 & 020 & 20 & 202 & 202 \\
7 & 021 & 021 & 21 & 210 & 212 \\
8 & 022 & 022 & 22 & 211 & 211 \\
9 & 100 & 122 & 23 & 212 & 210 \\
10 & 101 & 121 & 24 & 220 & 220 \\
11 & 102 & 120 & 25 & 221 & 221 \\
12 & 110 & 110 & 26 & 222 & 222 \\
13 & 111 & 111 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ranking for the codewords in \(G_{3}(3)\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Number** & **Codeword** & **Number** & **Number** & **Codeword** \\
**(Rank)** & **(Radix 4)** & & **(Rank)** & **(Radix 4)** & \\ \hline
0 & 00 & 00 & 8 & 20 & 20 \\
1 & 01 & 01 & 9 & 21 & 21 \\
2 & 02 & 02 & 10 & 22 & 22 \\
3 & 03 & 03 & 11 & 23 & 23 \\
4 & 10 & 13 & 12 & 30 & 33 \\
5 & 11 & 12 & 13 & 31 & 32 \\
6 & 12 & 11 & 14 & 32 & 31 \\
7 & 13 & 10 & 15 & 33 & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ranking for the codewords in \(G_{4}(2)\).
# Three-Dimensional Reconstruction-Based Vibration Measurement of Bridge Model Using UAVs
## 3 Experiments
### Experimental Schemes  
Figure 7 shows the four reference points (red points on the rectangular frame) and the target point (the yellow point on the bridge model). In the experiment, the bridge model is excited by an arbitrary force.  
For the homography transformation approach, the four reference points can be used to establish the homography relationship between the original frame and the frame to be corrected.  
For the 3D reconstruction approach, the fixed rectangular frame is the Xw-Yw plane of the world coordinate system (Figure 8). The top left corner point is its origin. The world coordinates of the four reference points are \((0.0,0.0,0.0)\), \((500.0,0.0,0.0)\), \((0.0,500.0,0.0)\), and \((500.0,500.0,0.0)\), respectively, and their corresponding pixel coordinates obtained from the first frame are \((577.1,147.0)\), \((1245.2,165.1)\), \((559.2,806.9)\), and \((1228.2,804.9)\), separately. Using the four sets of coordinate values and the intrinsic matrix \(\mathbf{A}\), \(\mathbf{R}_{1}\) and \(\mathbf{t}_{1}\) can be computed using the extrinsic function of MATLAB.  
Figure 6: (**a**) DJI-Spark UAV; (**b**) fixed reference object.  
Figure 7: Measurement points.  
Similarly, the pixel coordinates of the reference points from the \(2^{nd}\) to the \(n^{th}\) frames can also be utilized to calculate the extrinsic matrices \(\mathbf{R}_{i}\) and \(\mathbf{t}_{i}\) for each frame of images. Ultimately, the projection matrices \(\mathbf{M}_{i}\)\((i=1,2,3\dots,n)\) for each frame can be estimated.  
In order to study the influence of the distance between the fixed object and the bridge model on the measurement accuracy, three working conditions are set up: (1) the reference points (on the fixed object plane) are coplanar with the target points, that is, the distance between the fixed object and bridge model is 0.0 m. In addition, the distance between the UAV and the bridge model is 3 m (Figure 9a); (2) the fixed object is 1 m in front of the bridge model, and the UAV-bridge model distance is 5 m (Figure 9b); (3) the fixed object is 1 m behind the bridge model, and the UAV-bridge model distance is also 3 m (Figure 9c).