esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Prediction Models and Accuracy Assessment
#### 2.6.2 Random Forest  
The RF is an ensemble of randomized classification and regression trees that generates multiple decision trees from a randomly selected subset of training samples and uses averaging to increase prediction performance and control overfitting [100]. Many individual trees are trained from bootstrapped subsets of the data, which results in individual learning algorithms being run (Figure 4a). The final prediction equals the mean of these suboptimal trees. The number of trees (n_estimators), the maximum depth of the trees (max_depth), and the number of samples used in each node are user-defined parameters that influence the prediction performance and model efficiency. For example, if the n_estimators value is too large, it will affect the model computation; if it is too small, the model error will not stabilize [101, 102]. A higher number of samples used in each node helps to prevent overfitting by avoiding more specific trees [102]. Like other machine-learning algorithms, RF is increasingly used to predict soil properties. It has been shown to increase prediction performance due to its excellent non-linear learning ability and flexibility. In this study, optimal hyperparameters (max_features and max_depth corresponding to the number of features to consider when looking for the best split and the maximum depth of the tree, respectively) were adjusted based on the RMSECV using the \"RandomizedSearchCV\" and \"RandomForestRegressor\" functions implemented in the scikit-learn 1.0.2 package (Python 3.7.4) (Tables A2 and A5 in Appendix B). Randomized search is an alternative method to the grid search method and it performs a random search for hyperparameters within a predefined grid. A fixed number of iterations is performed, and at each iteration the algorithm selects a combination of hyperparameters, tunes the model, and evaluates its performance using RMSECV [97]. The number of trees was fixed at 500 and the number of iterations at 100 for all RF models.  
Figure 3: Schematic diagram of k-fold cross validation principle (adapted from [PERSON] and [PERSON] (2019) [99]).
## 4 Remote Base Unit
### Operating Principle  
Information is requested from the central server through a \"get\" command on an HTTP webpage, then, information will pass through a gateway to be interconnected to a public network. After that, a Very Small Aperture Terminal (VSAT) will send this information to a satellite KU band. This band has the advantage of using lower power transmitters but with the inconvenience of low rate data transmission. However, in order to test the inbound and outbound data rates, preliminary tests were performed having the system transmitting each ten minutes the collected data of climatic variables. This test is shown in Figure 8.  
In Figure 8 it can be appreciated that the maximum inbound data rate (from the RTU to the satellite) is around 3.5 Kbps, while the maximum outbound data rate (from the satellite) to the Central Server is around 20 Kbps. With this test, it was proved that bandwidth used for the system was really low, in addition if this information is requested only every ten minutes, the amount of data transmitted is minimum, so that, a low-speed satellite link was rented from a private ISP.
# Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study
## 3 Overview of the Encryption Algorithms under Study
### AES Algorithm  
Here, images are transformed from gray-levels to respective strings; upon the converted strings, the AES algorithm performs image encryption. It is a symmetric key block cipher to safeguard the confidential information. AES is used to encrypt the images and was implemented on board a circuit or in a programmatic form in a computer. AES comes with three forms with key sizes of 128, 192, and 256, respectively. In the case of an AES-128 bit key, for both encryption and decryption of strings, it uses a key length of 128 bits. It is symmetric cryptography, also known as private key cryptography, and encryption and decryption uses the same key; therefore, the transmitter and recipient both should know and utilize a certain secret key. The sensitive and security levels can be protected with any key length. Key lengths of 192 or 256 bits are required for a high level of security. In this article, we used an AES-512 scheme for encryption of images; in addition, a bit wise shuffling was applied [21]. AES performance depends on key size and number of iterations chosen for shuffling and is proportional to size of the key and iterations. In the encryption stage, in general, 10, 12, or 14 iterations are used. Except for the last round, each round comprises four steps, shown in Algorithm 1:  
Figure 8: EffNet architecture.  
```
1. KeyExpansion: Using the AES key schedule, round keys are produced from cipher keys. Each round of AES requires a distinct 128-bit round key block, plus one extra.
2. SubBytes: By means of an \"s-box\" nonlinear lookup table, one byte is substituted with another byte [21].
3. ShiftRows: Byte transposition occurs, one, two, or three bytes are used to cyclically change the state matrix's of the second, third, and fourth rows to the left.
4. The final step is to find final state matrix calculation by multiplying fixed polynomial and current state matrixes.
```  
**Algorithm 1** AES encryption flow.
## 3 Results
### Characteristics of the Aeroelastic Model Tree
#### 3.3.1 Crown Morphology
It is worth noting that the drag coefficients of the model decrease with increased wind speed, but this declining rate of the model is slower than that of the real trees (Figure 10a). The declining trend of the drag coefficients is mainly caused by streamlining effects [28]. Although the current aeroelastic model enables the streamlining behaviour observed in the crown, as shown in Figure 10b, the leaf clusters are suspected to have excess rigidity, as pointed out by [PERSON] et al. [5], which leads to slower declination of the measured \(C_{f}\). Figure 10: (**a**) Drag coefficients of the aeroelastic model for cases C1 to C8 and other broad-leaved trees, including rose of Sharon, poplar trees, black cottonwood, trembling aspen, red alder, paper birch and bigleaf map, and (**b**) crown deflection for case C8 at full-scale wind speeds of 0 m/s to 22.8 m/s. In this analysis, the contributions of branch and leaf to the total observed streamlining effects are further quantified, using the Vogel exponent [28], i.e.,  
\[F_{D}/U^{2}\propto U^{v_{total}} \tag{6}\]  
\[\
u_{total}=\
u_{branch}+\
u_{leaf} \tag{7}\]  
where \(\
u_{total}\) is the total Vogel exponent, \(\
u_{branch}\) is the Vogel exponent associated with the branch streamlining effects, and \(\
u_{leaf}\) is the Vogel exponent associated with the leaf streamlining effects. Figure 11a depicts relationships between speed specific drag (\(F_{D}/U^{2}\)) versus wind speed (\(U\)) of the model for cases C2 to C8. \(F_{D}/U^{2}\) decreases with increased wind speed, which indicates that Vogel exponents for cases C2 to C8 are negative. Subplot (b) in Figure 11 further depicts relationship between total Vogel exponent (\(\
u_{total}\)) versus ratio of frontal area between leaf and branch (\(A_{leaf}/A_{branch}\)). When \(A_{leaf}/A_{branch}=0\), \(\
u_{branch}\) is equal to \(\
u_{total}\). When \(A_{leaf}/A_{branch}>0\), \(\
u_{leaf}\) is obtained by subtracting \(\
u_{branch}\) from \(\
u_{total}\). The \(\
u_{branch}\) of the model is \(-0.11\), which is close to that of real broad-leaved trees, including poplar (\(-0.11\)), maple (\(-0.13\)) and oak (\(-0.08\)), as shown in Table 4. This indicates that the model replicates the branch streamlining effects of the broad-leaved trees. While the \(\
u_{leaf}\) for cases C3 to C8 are \(0.08\), \(0.05\), \(0.05\), \(0.10\), \(-0.01\) and \(0.01\), respectively, which are of the opposite sign and \(1/10\) smaller in magnitude than the crown exponents. As compared to the real broad-leaved trees shown in Table 4, which includes poplar (\(-0.60\)), maple (\(-0.64\)) and oak (\(-0.72\)), the obvious excess rigidity of the model leaf clusters can again be observed. Furthermore, the contribution of the branch accounts for more than \(10\%\) of the crown streamlining effects for the broad-leaved trees shown in Table 4. Although the branch streamlining effects accounts for less than \(20\%\) of the crown streamlining effects, it determines crown deflection in along-wind direction.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 3 Link Budget Analysis
### Link Budget Equation  
The general link budget equation is shown below (Equation (2)):  
\[P_{RX}=P_{TX}+G_{TX}-L_{TX}-L_{FS}-L_{M}+G_{RX}-L_{RX} \tag{2}\]  
where the following applies:  
\(P_{RX}\): received power (dBm).  
\(P_{TX}\): transmitter output power(dBm).  
\(G_{TX}\): transmitter antenna gain (dBi).  
\(L_{TX}\): transmitter losses in dB.  
\(L_{FS}\): free space path loss in dB.  
\(L_{M}\): other losses in dB.  
\(G_{RX}\): receiver antenna gain (dBi).  
\(L_{RX}\): receiver losses in dB.  
For the given Equation (2), the transmitter power (\(P_{TX}\)), transmitter loss (\(L_{TX}\)), and receiver antenna gain (\(G_{RX}\)) are given in Table 1. To calculate the transmitter gain, the following Equation (3) is used:  
\[G_{TX}=10\log\left(\eta\times\left(\frac{\pi D}{\lambda}\right)^{2}\right) \tag{3}\]  
where the following applies:  
\(\eta\): antenna efficiency.  
\(D\): antenna diameter.  
\(\lambda\): wavelength.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 5 Performance Evaluation
### Time for the Detection System  
We first calculate the memory footprint. We need to record three benchmark windows; the number of each benchmark window is 100 and the window size is 80. For a CAN frame, the data we need are only the ID and data fields. The size of the data field is 64 bit, and the ID field is only 11 bit but we expanded it to 16 bit. So the size of a data frame is 10 B. After calculation, the memory footprint we need is about 240 KB, which does not burden us at all. If we need to record more windows in the future, we only need to add 80 KB of memory for each window.  
Regarding the time complexity, we do not calculate the time complexity of the training, because that can be calculated elsewhere; we only discuss the detection time. For a window, we need to calculate the Hamming distance for \(N\) windows of size \(K\) and calculate the threshold for comparison. The time complexity of the Hamming distance for a pair of strings is O(\(logn\)), so the overall time complexity is O(\(NKlogn\)), where \(n\) equal to 80 represents the string length of a data frame.  
We conduct a comparison between our method and the DCNN method in terms of detection time and memory consumption. The DCNN method's average detection time is presented for the minimum batch size, considering both GPU and CPU. The results of these two methods are presented in Table 6. As shown in Table 6, DCNN [9] has nearly 9.8 million parameters in space complexity, and our model optimizes at least 1000 times the space size. In terms of time, our model runs about 20 times faster.
# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 2 Gravitational Search Algorithm
### Algorithm Flowchart  
The detailed flowchart of the algorithm is shown in Figure 2, and the optimization procedure is described as follows.  
Step 1: Initialize the positions and accelerations of all particles, the number of iterations and the parameters of the GSA;  
Step 2: According to the Equation (12), calculate the fitness value of each particle and update the gravity constant;  
Step 3: According to the Equations (5)-(7), calculate the quality of the particles based on the obtained fitness values and the acceleration of each particle according to the Equations (8) and (15);  
Step 4: Calculate the velocity of each particle and update the position of the particle according to the Equation (17);  
Step 5: If the termination condition is not satisfied, turn to Step 2, otherwise output the optimal solution.
## 3 Modeling of Distributed Denial of Service (DDoS) Attack  
Distributed denial of service (DDoS) against the communication channels between the control center and the metering units can restrict the data flow from descending to their intended locations on time by way of distribution or forfeiting the connectivity, affecting the MG's performance [10]. The behavior of this attack involves flooding a target computer or network with traffic coming from multiple sources, seeking to prevent legitimate users from accessing the targeted system or network by consuming its resources. In the DDoS attack, using User Datagram Protocol (UDP) flooding represents one of the used techniques. The attacker conveys many packets to the victim's destination port. If the sent packets are large enough, the communication channels may become overburdened, and packets may not be able to reach their destination. Modeling UDP flooding attacks involves switching UDP traffic to the intended target point [38]. The attackers might utilize botnets to engage in illicit  
Figure 5: Construction of Linux containers to create virtual networks.  
behaviors for hacking devices by sending UDP flooding attacks, causing the interruption of the targeted point, as Figure 6 depicts.  
In this work, The DDoS attack can interrupt the communication channels between the MG's centralized secondary control and the local controllers in the primary control layer within the developed ns3-based communication model, affecting the MG's frequency stability. To mimic the DDoS attacks as a real-time simulation in ns3 that represents the cyber layer in this work, we emulated the UDP flooding behavior in ns3 to represent the DDoS attack that targeted the central control of the MG. Consequently, this overwhelms the centralized controller with many UDP packets sent from the attacker bots, increasing the contingency of the communication channels between the central and local controllers of the MG. This makes the controllers unable to send or receive any information and affects the system's performance. For emulating the DDoS in the developed communication model in ns3, it is necessary to identify the attack parameters and the application of the attack using UDP flooding within the communication network topology designed for communication between the secondary and primary control layers, as described in Algorithm 2.  
```
1: Identify the DDoS attack experimental parameters (attack rate, max Bytes, number of bots).
2: Create nodes in the ns3-based communication model to identify the number of attackers' bots.
3: Create a link using point-to-point communication between the bot nodes and the target node (central controller node in LAN-2).
4: For each attacker bot node in the model: \(\bullet\) Assign an IP address. \(\bullet\) Define the behavior of the DDoS attack using UDP flooding and install the developed DDoS application in each created bot node. \(\bullet\) Set the DDoS attack's start and stop times.
5: Run the simulation.
```  
**Algorithm 2** DDoS attack using UDP flooding
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 4 Selected Studies on CPS Water Supply Network Management  
In this section, research papers reporting the application of CPSs in the water domain to monitor water system facilities are reviewed. As illustrated in Figure 6, we categorized the water supply network management (WSNM) application domain as water quality monitoring (WQM), leak detection and monitoring (LDM), pressure control and monitoring (PCM), parameter estimation and monitoring (PEM), state estimation and monitoring (SEM), demand prediction and monitoring (DPM), pipe health monitoring (PHM), pump energy consumption monitoring (PECM), and water resource management (WRM).  
Of these categories, the application of a CPS to WQM and LDM are famous and have been reported in the literature. Thus, the discussion in this section is based on these two applications. It is worthy to note that, in water supply systems, the quality of water can be successfully monitored at the treatment plant and pumping stations, the major problem occurs in the distribution networks due to  
Figure 6: Water supply network management WSNM application domain.  
pipe burst and leaks occurrence along the network. Thus, the application of a CPS to water quality and leak monitoring in the distribution networks was our focus. As such, we categorized these discussions under water quality monitoring applications, and leakage detection applications.
## 2 Materials and Methods
### Saliency Value  
X and Y coordinates from the eye-tracking database were plotted on the GBVS Map images of each video for saliency/pixel value calculation. The mean and standard error of each video pixel value was calculated for each video separately. Figure 3 shows some of the video frames categorically.  
The GBVS model starts by working on a computing feature map for saliency detection. It usually creates three features depending on the intensity channel, colour channel, and local orientation map according to the input image. Initially, it creates its first feature map by taking the average over red, green and blue (RGB) channels of the image.  
\[\text{Image intensity total = Red + Blue + Green/3} \tag{1}\]  
The process was repeated over several spatial scales using Gaussian pyramid computation. The image was repeatedly low pass filtered to create activation maps that represent different scales. Feature maps for other features (colour and orientation) were also computed in a similar manner. At the end all maps were combined to form one activation map, using the Markovian approach in Equation (2).  
For calculation of final saliency maps through GBVS model, Markov chain image interpretation [19; 20] is used for mapping various images and the resulting equilibrium distribution on the map is treated as a saliency value. For this, the first step includes the calculation of a features map graph consisting of vertexes and nodes connected by edges. The dissimilarity and proximity between two nodes give us the weight of the two nodes. A saliency map involving a GBVS algorithm is named M, while the dissimilarity between nodes \(M\)(_i,j_) and \(M\)(_p,q_) with the given two locations (_i,j_) and (_p,q_) is described by Equation (2)  
\[d((i,j)k(p,q))\triangleq\left|log\frac{M(i,j)}{M(p,q)}\right| \tag{2}\]  
The equilibrium distributions of these Markov chains are activation maps that are further normalized, forming the saliency map [21; 22]. The saliency mean for different emotional videos/contents was calculated for all of the frames of the GBVS Map image, and their bar graphs were plotted using STATISTICA.  
Figure 3: (**A**) Happy video frames, (**B**) Neutral video frames and (**C**) Sad video frames.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### Codebook Design
#### 4.4.5 Cutting Diversity  
Target matrices that are not wide or tall are cut into wide or tall submatrices via (9) and (10), respectively. However, there are various ways to cut them. There is no need to form each submatrix from adjacent rows or columns of the original matrix. In fact, the choice of rows or columns is arbitrary. Some of the cuts may lead to submatrices that are good codebooks, other cuts to worse ones. These various possible cuts provide many options, which allow us to avoid submatrices that are bad codebooks. They provide diversity to ensure a certain quality in case of codebook evolution.
# Spectrum Demand Forecasting for IoT Services
## 5 Study Case Implementation: Colombia
### Spectrum Forecasting: Required Capacity  
The required capacity in non-cellular or non-congested scenarios is exactly the offered traffic calculated in the previous step. For services carried over cellular networks, the Cobham model is carefully applied. Several services in the same scenario must be organized by non-peremptory priority (which means, the server or gateway gives priority, after serving the actual packet). As this model is based on the mean packet size (and its second moment, i.e., \(E[{S_{n}}^{2}]\)) and the mean required delay, if a group of services sets very stringent values in both parameters, the required capacity would be extremely high, since it needs a system able to process both noticeably big packets and with truly short delays. This could be the case, for example in smart car services: on-board entertainment systems need large multimedia packets and on-board safety systems need short delays. We recommend separating these services in different scenarios since they would certainly be offered in different spectrum bands.  
Results are shown in Table 3.
## 4 Experiment and Analysis
### Mango Dataset
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Total Acidity**} & \multicolumn{4}{c}{**Vitamin C**} \\ \cline{2-9}  & **RMSEP** & **Variance** & **Lower** & **Upper** & **RMSEP** & **Variance** & **Lower** & **Upper** \\  & & & **RMSEP** & **RMSEP** & **RMSEP** & **RMSEP** \\ \hline FPLS & 0.2731 (10) & 0.0191 & 0.2310 & 1.7415 & 0.6988 (10) & 0.0196 & 0.5006 & 2.1907 \\ PLS & 0.4328 (5) & 0.0239 & 0.3696 & 1.8053 & 1.1015 (5) & 0.0248 & 0.9996 & 2.4569 \\ OSCPLS & 0.4837 (2) & 0.0004 & 0.3832 & 1.8122 & 1.2581 (3) & 0.0753 & 0.9129 & 2.3978 \\ VCPLS & 0.4912 (8) & 0.0043 & 0.3248 & 1.7831 & 1.3499 (1) & 0.1033 & 1.1249 & 2.5569 \\ PoPLS & 0.4396 (10) & 0.0081 & 0.4225 & 1.8334 & 1.1491 (4) & 0.0141 & 1.2111 & 2.6385 \\ LoPLS & 0.4332 (5) & 0.0239 & 0.4222 & 1.8333 & 1.1023 (5) & 0.0248 & 1.0504 & 2.4951 \\ DOSC & 0.4818 (2) & 0.0025 & 0.3832 & 1.8122 & 1.2568 (3) & 0.0743 & 0.9129 & 2.3978 \\ OPLS & 0.4433 (4) & 0.0013 & 0.3615 & 1.8011 & 1.0847 (4) & 0.0167 & 1.0376 & 2.4852 \\ MSC & 0.3825 (6) & 0.0014 & 0.3841 & 1.6631 & 1.0797 (6) & 0.0192 & 0.5573 & 2.2137 \\ SNV & 0.3829 (6) & 0.0014 & 0.3844 & 1.6632 & 1.0820 (6) & 0.0194 & 0.5573 & 2.2137 \\ SGFilter & 0.4201 (5) & 0.0014 & 0.3696 & 1.8053 & 1.0969 (7) & 0.0186 & 1.0002 & 2.4573 \\ Lasso & 0.5035 (1) & 0.0074 & 0.3696 & 1.8053 & 1.2863 (1) & 0.0488 & 1.0002 & 2.4573 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative analysis results in terms of RMSEP and RMSEP bounds. The proposed FPLS compared to PLS, OSCPLS, VCPLS, PoPLS, LoPLS DOSC, OPLS, MSC, SNV, SGFilter, and Lasso using the Mango dataset. Figure 9 depicts the CS plot of the predicted values for TA and VC. The data in the figure indicate that CS and its lower bound satisfy Equation (33). Considering the correlation between prediction and target, it is evident that the FPLS model proposed in this paper yields the best predictive results. According to Table 4 and Figures 8 and 9, the proposed FPLS outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of RMSE for TA and VC prediction. The first filters learned for Mango TA and VC analysis are \(h_{TA}=[-0.0630,0.5889,0.8057]\) and \(h_{VC}=[-0.4626,-0.8547,-0.2355]\); thus, \(\frac{w^{T}(\lambda h_{TA})^{T}Y}{w^{T}X^{T}Y}=1.7297<3\) and \(\frac{w^{T}(\lambda h_{VC})^{T}Y}{w^{T}X^{T}Y}=2.4060<3\), which are consistent with our previous analysis. Figure 8: RMSEP values of the TA and VC prediction using FPLS, PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, and SGFilter models with 1–10 components. Lasso is different from the previous methods. It is a feature selection method. The horizontal axis in the figure represents the number of random experiments for Lasso. Figure 9: CS plot of TA and VC prediction using FPLS, PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso. The horizontal axis represents methods, while the vertical axis represents CS and its lower bound.