esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Modeling and Analysis in Ansys
### Material Assignment  
After specifying the dimensions of the IGBT components, it is important to consider the material characteristics. The materials used in the IGBT include aluminum (Al), ceramic (Al\({}_{2}\)O\({}_{3}\)), silicon (Si), copper (Cu), and solder (SAC305). The key material properties that are considered are density (\(\rho\)), Young's modulus (elastic modulus), and Poisson's ratio (\(\
u\)).  
[PERSON]'s modulus measures the stiffness of a material and represents the relationship between stress and strain in the linear elastic region of a material's deformation. It is crucial for determining how much a material will deform under a given load. Poisson's ratio, on the other hand, describes the ratio of lateral strain to axial strain when a material is stretched. It provides insights into the material's behavior when subjected to stress in different directions [27]. These properties are essential for ensuring the mechanical integrity and performance of IGBT components. These material properties are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Component** & **Material** & **1 (mm)** & **w (mm)** & **t (\(\mu\)m)** \\ \hline \hline \multirow{2}{*}{Baseline} & Cu & 107.5 & 38 & 3000 \\ \hline \multirow{2}{*}{Solder 1} & SAC305 & 23 & 27.8 & 250 \\ \hline \multirow{2}{*}{Copper 1} & Cu & 23 & 27.8 & 300 \\ \hline \multirow{2}{*}{C ceramic} & Al\({}_{2}\)O\({}_{3}\) & 24 & 29 & 380 \\ \hline \multirow{2}{*}{Copper 2} & Cu & 23 & 27.8 & 300 \\ \cline{2-5}  & Si & 4.8 & 4.8 & 302 \\ \cline{2-5}  & SAC305 & 4.8 & 4.8 & 85 \\ \cline{2-5}  & Si & 3.3 & 2.98 & 106.8 \\ \cline{2-5}  & SAC305 & 3.3 & 2.98 & 85 \\ \hline \multirow{2}{*}{Brake chopper diode} & Si & 5.45 & 4.99 & 111.8 \\ \cline{2-5}  & SAC305 & 5.45 & 4.99 & 85 \\ \hline \multirow{2}{*}{Inverter IGETs} & Si & 7.25 & 6.84 & 111.8 \\ \cline{2-5}  & SAC305 & 7.25 & 6.84 & 85 \\ \hline \multirow{2}{*}{Inverter diodes} & Si & 6.3 & 4.5 & 106.8 \\ \cline{2-5}  & SAC305 & 6.3 & 4.5 & 85 \\ \hline \end{tabular}
\end{table}
Table 2: Physical specifications of FP50R12 KT4 IGBT module.  
Figure 4: A 3D model of the FP50R12 KT4 IGBT module in ANSYS SpaceClaim.  
Another important property to consider in the context of vibration analysis is the S-N curve. The S-N curve or Wohler curve illustrates the relationship between the stress amplitude (S) and the number of cycles to failure (N) for a material subjected to cyclic loading. It helps predict the fatigue life of materials by showing how much stress a material can withstand before failure under repeated loading. The curve typically has a downward slope, with high stress leading to failure in fewer cycles, and low stress allowing for more cycles. Data for S-N curves for various materials used in IGBT modules can be found in references [33; 34; 35; 36; 37].
# Preventing Black Hole Attacks in AODV Using RREQ Packets
## 3 Proposed Method  
### Summary  
MANET devices typically operate with limited CPU processing power, constrained battery life, insufficient bandwidth support, and limited storage. Moreover, the most significant source of energy consumption in MANETs is due to wireless communications, not to computing tasks using the mobile device microprocessor. In fact, the energy consumed by computation is at least 50 percent lower than that consumed by communication [18]. In addition, MANETs lose network connectivity if a node runs out of power; thus, nodes must be able to operate with a limited amount of power for critical operations. Therefore, the communication protocol applied to determine whether a communicating node is a black hole node should be reduced as much as possible to ensure that the amount of required computation is minimal. One problem with existing sequence number-based methods is that the sequence number of RREQ packets flowing through the network tends to increase as the number of nodes in the network increases, which makes misidentification of nodes more likely. To solve this problem, in this paper we consider a mechanism to prevent misidentification. One of the characteristics of a black hole node is that it forges an RREP packet and replies with a forged RREP packet even if it receives an RREQ packet with a destination address that does not exist in the network. Therefore, a normal node can identify a black hole node if it sends back an RREP packet using a forged RREQ packet. In the proposed method, sequence number-based decision mechanisms are not used; instead, a decision mechanism used in related research based on forged RREQ packets for blacklist redesign is incorporated into the transmission of ordinary RREQ packets. This prevents more false positives compared to the standard AODV protocol. Each node corresponding to the proposed method has a dummy address list and a blacklist; these lists consist of entries in which a single unit is a combination of a node's address and time-to-live information.
## 3 Performance of IWP with Steel Anchor Box
### Parametric Study  
To study the pylon wall stresses, steel anchor box utilization, and the force characteristics at the steel-concrete interface for the IWP detail, parameters such as the thickness of the perforated side plates, end plate, and surface plate; the diameter of perforations on the perforated plates; and the arrangement of connectors are varied to observe the performance of the IWP with a steel anchor box as a parametric study.
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 2 Group Communication
### Hybrid Group Communication  
In the previous subsection, we have briefly described the two main approaches for CoAP group communication. The first approach uses CoAP NON over IPv6 multicast, and the second one uses CoAP CON over IPv6 unicasts. We have shown that each approach has its strong and weak points. The multicast approach is generally fast and has little overhead, but is not flexible and not reliable and, thus, cannot be used when reliability is required. One the other hand, the unicast approach is reliable and flexible, but is often slower and has more overhead.  
In this paper, we extend our previous unicast-based solution with multicast support. We do so by maintaining the overall architecture of our approach. Clients still create entities on the Entity Manager (EM) to represent the groups and have to query the CoAP group resource on the EM to access the group. However, at creation time, clients can specify the communication type between the EM and the entity members (i.e., multicast or unicast). We have extended our existing solution so that all of its features become valid for entities communicating by using multicast as they are valid for entities using unicasts. In this subsection, we discuss the most important features and how they are extended to allow entities to use multicast communication.
## 3 Privacy and Security Threats in IoT Domain
### Privacy Attacks
#### 3.2.2 Rationale for Choosing Tree-Based Models  
In this study, we selected tree-based models--specifically XGBoost, Random Forest, and Decision Classifier--due to their robustness and interpretability, which are crucial for the following reasons:  
* Handling Mixed Data Types: Tree-based models are well-suited for datasets with a mix of numerical and categorical features. Given the nature of our smart home dataset,which includes diverse types of sensor data, tree-based models handle these mixed types effectively without the need for extensive preprocessing.
* Model Interpretability: Tree-based models, particularly Decision Trees and Random Forests, provide more interpretability compared to complex deep learning approaches. This interpretability is valuable for understanding how different features contribute to the model's predictions, which is important for analyzing and validating results in practical applications.
* Performance with Encrypted Data: Tree-based models are known to perform well with encryption techniques, including Fully Homomorphic Encryption (FHE). Their structure allows for efficient handling of encrypted data, which is crucial for maintaining both security and performance in our study.
* Computational Efficiency: Compared to deep learning models, tree-based models generally require less computational power and training time, making them more practical for the scope of our study, especially when evaluating performance with FHE.  
We considered alternatives such as MLP, SVM, and deep learning approaches but determined that the advantages of tree-based models in terms of handling mixed data types, interpretability, and computational efficiency aligned better with the objectives of our research.
## 3 Preliminaries
### Matrix Key Establishment Scheme  
KDC generates two matrices. One matrix is secret symmetric matrix (\(S\)) of order \((\lambda+1)\times(\lambda+1)\) and other is public matrix (\(P\)) of order \((\lambda+1)\times N\), where \(N\) is the total number of devices in the network and \(\lambda\) is the security parameter.  
Symmetric matrix construction: Matrix \(S\) is said to be symmetric if \(S=S^{T}\).  
Public matrix construction: KDC chooses independent key seeds \(S_{1}\), \(S_{2}\),..., \(S_{N}\in F_{p}\). The public matrix \(P\) is called as Vandermonde matrix and is generated as shown in Equation (2).  
\[P=\begin{pmatrix}S_{1}&S_{2}&\cdots&S_{N}\\ (S_{1})^{2}&(S_{2})^{2}&\cdots&(S_{N})^{2}\\ \vdots&\vdots&\ddots&\vdots\\ (S_{1})^{\lambda+1}&(S_{2})^{\lambda+1}&\cdots&(S_{N})^{\lambda+1}\end{pmatrix} \tag{2}\]  
KDC performs matrix multiplication operation (Equation (3) for \(S\) and \(P\) matrices to obtain the matrix \(A\) of size \(N\times(\lambda+1)\). Transpose operation is performed on the resultant matrix, i.e., \(A=(S\cdot P)^{T}\).  
\[A\cdot P=(S\cdot P)^{T}\cdot P=P^{T}\cdot S\cdot P=(A\cdot P)^{T} \tag{3}\]  
Each device is loaded with a secret row of matrix \(A\) and a key seed of matrix \(P\). Two devices can establish the pairwise key \(P_{ij}\) by exchanging their key seeds, followed by matrix multiplication operation (Figure 3). The scheme is \(t\)-collision resistant, if the columns of public matrix \(P\) are linearly independent.
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 4 Network Indicator System Construction
### Task Arrival Rate  
A series of network issues during data transmission can result in some tasks losing packets or accumulating at nodes, thereby decreasing the success rate of tasks reaching their intended destinations. Therefore, the successful task arrival rate is a crucial parameter in assessing network performance, as it reflects the reliability of data transmission. This indicator is calculated as the ratio of packets successfully reaching the destination to the total number of packets sent at the initiation of the task. The formula is as follows:  
\[arrive\_rate=\frac{\sum\frac{\gamma_{i}}{\rho_{i}}}{M} \tag{7}\]  
where \(\gamma_{i}\) denotes the total number of packets at which the task \(i\) reaches the destination, \(\rho_{i}\) denotes the total number of packets at which the task \(i\) is initiated, and \(M\) represents the total number of generated tasks. A lower task arrival rate indicates that most of tasks are congested in the network or packet loss occurs, which will significantly affect the transmission performance of the IoV network and even cause complete network failure.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 5 SST--An Efficient Heuristic Algorithm for the GPP Problem  
The ES algorithm is memory-less in the sense that it finds a trajectory each time it is called without taking advantage of the fact that the planned trajectories for different altitudes are not entirely uncorrelated. As a result, time-consuming operations are performed repeatedly, resulting in long computation times. This section presents a heuristic algorithm that exploits the mission properties to reduce the computation times while maintaining good performance.
## 4 The Proposed Scheme: Architecture and Components
### The COVID-19 Layer
#### 4.3.3 COVID-19 Thermal Inspection
Moreover, persons residing in any region \(R\) have to register themselves to \(E_{MC}\), such that \(E_{MC}\) can track the persons in case the temperature is higher than normal temperature. If the temperature is higher, we identify the home address of person \(P_{c}\), and \(E_{MC}\) is notified of a potential COVID-19 case. A flag value \(f_{3}\) of 1 indicates a high probability of a positive case, otherwise, the person is marked as normal. Lines 15-24 present these conditions. ```
1:UAV swarms assisted with normal and thermal cameras. Output: Critical Boolean flag \(f_{3}\) for thermal screening of \(P_{c}\) in a designated region \(R\). 2:procedureUAV_Thermal_Inspection(\(UAV_{k},R\))
3:for each region \(R\)do
4:for\(i\gets 1\) to \(k\)do
5:\(UAV_{i}\leftarrow\)Video_Frames_Normal \((P_{c},R)\)
6:\(UAV_{i}\leftarrow\)Video_Frames_Thermal \((P_{c},R)\)
7: Store \(F_{norm}\), \(F_{therm}\) into database \(D\)
8:for Each \(F_{norm}\) image do
9:\(RO_{norm}\leftarrow\) HOG(\(F_{norm}\))
10:endfor
11:for Each \(F_{therm}\) image do
12:\(ROI_{therm}\leftarrow\) HOG(\(F_{therm}\))
13:endfor
14:\(M_{n}\leftarrow\) YOLOv3(\(F_{i},ROI_{norm}\))
15:\(M_{t}\leftarrow\) YOLOv3(\(F_{i},ROI_{therm}\))
16: Register \(P_{c}\) to E\({}_{MC}\)
17:for Each registered person \(P_{c}\) do
18: Monitor \(Temp(P_{c})\) from \(M_{t}\)
19:if \(Temp(P_{c})>98.6^{\circ}F\)then
20: Identify \(P_{c}\) from \(M_{n}\)
21: Notify \(E_{NC}\) of potential COVID-19 case
22:\(R_{3}\gets 1\)
23:else
24:\(R_{3}\gets 0\)
25:endif
26:endfor
27:endfor
28:endfor
29:endfor
30:endfor
```  
**Algorithm 3** Thermal inspection and monitoring
## 2 Maximum Likelihood Estimation
\begin{split} l^{*}(\lambda)\propto& n(k+1)\ln \lambda-nk\ln\left[\sum_{i=1}^{n}\sum_{j=1}^{k}\,(e^{x_{ij}{}^{\lambda}}-1)(1+S _{j})\right]-n\ln\left[\sum_{i=1}^{n}\,(e^{y_{i}{}^{\lambda}}-1)(1+T_{i})\right] \\ &+\sum_{i=1}^{n}\sum_{j=1}^{k}\,[(\lambda-1)\ln x_{ij}+x_{ij}{}^{ \lambda}]+\sum_{i=1}^{n}\,[\ln y_{i}+y_{i}{}^{\lambda}\ln y_{i}].\end{split}  \tag{13}\]By taking the derivative of \(l^{*}(\lambda)\) and setting it to 0, we obtain the following equation and its solution is the MLE \(\hat{\lambda}\). \[ \begin{split}\frac{\text{d}l^{*}}{\text{d}\lambda}=& \frac{n(k+1)}{\lambda}-\frac{nk\sum_{i=1}^{n}\sum_{j=1}^{k}\left(1+S_{j} \right)e^{x_{ij}{}^{\lambda}}x_{ij}{}^{\lambda}\ln x_{ij}}{\sum_{i=1}^{n} \sum_{j=1}^{k}\left(e^{x_{ij}{}^{\lambda}}-1\right)(1+S_{j})}-\frac{n\sum_{i=1 }^{n}\left(1+T_{i}\right)e^{y_{ij}{}^{\lambda}}y_{i}{}^{\lambda}\ln y_{i}}{ \sum_{i=1}^{n}\left(e^{y_{ij}{}^{\lambda}}-1\right)(1+T_{i})}\\ &+\sum_{i=1}^{n}\sum_{j=1}^{k}\left(\ln x_{ij}+x_{ij}{}^{\lambda} \ln x_{ij}\right)+\sum_{i=1}^{n}\left(\ln y_{i}+y_{i}{}^{\lambda}\ln y_{i} \right)=0.\end{split}  \tag{14}\]  
From Equation (14), we can derive a nonlinear equation \(\beta(\lambda)=\lambda\), where  
\[ \begin{split}\beta(\lambda)=& n(k+1)[\frac{nk\sum_{i= 1}^{n}\sum_{j=1}^{k}\left(1+S_{j}\right)e^{x_{ij}{}^{\lambda}}x_{ij}{}^{ \lambda}\ln x_{ij}}{\sum_{i=1}^{n}\sum_{j=1}^{k}\left(e^{x_{ij}{}^{\lambda}}-1 \right)(1+S_{j})}+\frac{n\sum_{i=1}^{n}\left(1+T_{i}\right)e^{y_{ij}{}^{ \lambda}}y_{i}{}^{\lambda}\ln y_{i}}{\sum_{i=1}^{n}\left(e^{y_{i}{}^{\lambda}} -1\right)(1+T_{i})}\\ &-\sum_{i=1}^{n}\sum_{j=1}^{k}\left(\ln x_{ij}+x_{ij}{}^{\lambda} \ln x_{ij}\right)-\sum_{i=1}^{n}\left(\ln y_{i}+y_{i}{}^{\lambda}\ln y_{i} \right)]^{-1}.\end{split}  \tag{15}\]  
The above equation has a fixed-point solution for \(\lambda\). The MLE \(\hat{\lambda}\) can be derived using the fixed-point iterative approach as \(\lambda_{(i+1)}=\beta(\lambda_{(i)})\), where \(\lambda_{(i)}\) is the \(i^{th}\) iterative value of \(\lambda\). When \(\left|\lambda_{(i+1)}-\lambda_{(i)}\right|\) is very close to 0, the iteration process can be stopped. Then, according to Equation (11), the MLEs \(\hat{\theta}_{1}\) and \(\hat{\theta}_{2}\) can be obtained using the MLE \(\hat{\lambda}\). \[\hat{\theta_{1}}=\frac{nk}{\sum_{i=1}^{n}\sum_{j=1}^{k}\left(e^{x_{ij}{}^{ \lambda}}-1\right)(1+S_{j})},\ \hat{\theta_{2}}=\frac{n}{\sum_{i=1}^{n} \left(e^{y_{i}{}^{\lambda}}-1\right)(1+T_{i})}. \tag{16}\]  
Based on the invariance property of the MLEs, once we obtain the MLEs \(\hat{\theta_{1}}\), \(\hat{\theta}_{2}\), and \(\hat{\lambda}\), the MLE \(\hat{R}_{s,k}\) is  
\[\hat{R}_{s,k}=\sum_{m=s}^{k}\sum_{n=0}^{k-m}\left( \begin{array}{c}k\\ m\end{array} \right)\left( \begin{array}{c}k-m\\ n\end{array} \right)\frac{(-1)^{n}\hat{\theta_{2}}}{(m+n)\hat{\theta_{1}}+ \hat{\theta_{2}}}. \tag{17}\]
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 3 Deep-Reinforcement-Learning-Based Cooperative Downloading Scheme  
In this section, we propose a DRL-based cooperative downloading scheme. We first explain the overview of the scheme, and formulate the cooperative downloading problem as MDP. After that, we describe a discretized SAC-based training algorithm to train the optimal policy.
## 3 Results and Discussion
### Modeling Analysis Based on Characteristic Wavelengths  
The samples for the calibration and prediction sets utilized in this subsection are the same as those used in the full wavelength modeling-based analysis. To further choose the optimal model combination, this study combines SNV preprocessed spectral data with different characteristic wavelength extraction algorithms and the data corresponding to the extracted characteristic wavelengths were input into PLS-DA, LSSVM, RF, and ELM discriminant models. Table 4 shows the results of modeling analysis of different characteristic wavelength extraction algorithms combined with PLS-DA, LSSVM, RF, and ELM discriminant models. It is evident from the analysis of this table that (1) by comparison with the results of the analysis based on full wavelength modeling, it is observed that the four discriminant models combined with different characteristic wavelength extraction algorithms yield good overall classification results. (2) The CARS combined with each discriminant model performs best, different characteristic wavelength extraction algorithms (RFA, UVE, CARS) combined with the same discriminant model, the CARS algorithm combined with the discriminant model acquires the highest accuracy of the calibration set and prediction set, indicating that CARS can eliminate the full wavelength redundancy to the greatest extent possible and preserve usable information, thus improving the accuracy of the model, while RFA and UVE do not show this effect. (3) The same characteristic wavelength extraction algorithm combines four discriminant models (PLS-DA, LSSVM, RF, ELM). Among them, the ELM model combined with the characteristic wavelength extraction algorithm achieves the best performance (the accuracy of both calibration and prediction sets in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **NO.** & **Wavelengths (nm)** & **Molecular Vibration** \\ \hline \multirow{4}{*}{CARS} & \multirow{4}{*}{7} & 890.5 & Second overtone of C-H \\  & & 1081.7, 1088.5 & Second overtone of C-H + Second overtone of C-C \\  & & 1155.7, 1159.1 & Second overtone of C-H \\ \cline{1-1}  & & 1351.3, 1357.8 & Second overtone of C-H + First overtone of C-H \\ \hline \hline \end{tabular}
\end{table}
Table 3: The characteristic wavelengths and the corresponding molecular vibration extracted by competitive adaptive reweighted sampling (CARS).  
Figure 8: Process of selecting characteristic wavelengths based on competitive adaptive reweighted sampling (CARS).  
ELM model exceeds 95%), which indicates that the most efficient data classification can be achieved by ELM.  
In summary, the SNV-CARS-ELM model has the best classification performance (the calibration set and prediction set have the highest accuracy, 99.05% and 98.10%, respectively). Therefore, it is chosen as the optimal classification model.
## 2 Theoretical Background
### The Forward Model
#### 2.2.4 Seismic Model  
Iterative geostatistical seismic inversion and Bayesian linearized seismic inversion are the two most frequently employed seismic inversion techniques in the oil and gas sector [27]. The model requires seismic data with varying acquisition angles \(\theta\) since it relies on the linearized approximation for weak elastic contrasts. It is possible to estimate the sequence of reflection coefficients with the models described in [25; 32; 33]. In terms of math, it is stated as:  
\[d(t,\theta)=\int_{-\infty}^{\infty}s(\tau,\theta)c(t-\tau,\theta)\mathrm{d} \tau+e_{d}(t,\theta), \tag{8}\]  
where \(d(t,\theta)\) represents the seismic amplitude at time \(t\), \(s(\tau,\theta)\) is the wavelet, \(e_{d}(t,\theta)\) denotes the observation error, and \(c(t,\theta)\) represents the reflectivity at the interface, as modeled by:  
\[c(t,\theta)=a_{\alpha}(t,\theta)\frac{\partial}{\partial t}\ln\alpha(t)+a_{ \beta}(t,\theta)\frac{\partial}{\partial t}\ln\beta(t)+a_{\rho}(t,\theta) \frac{\partial}{\partial t}\ln\rho(t), \tag{9}\]  
where \(\alpha\) and \(\beta\) are the P-wave and S-wave velocities, respectively. The \(\rho\) parameter is the density, and the coefficients \(a_{\alpha}(t,\theta)\), \(a_{\beta}(t,\theta)\), and \(a_{\rho}(t,\theta)\) are given by:  
\[a_{\alpha}(t,\theta) =\frac{1}{2}(1+\tan^{2}\theta), \tag{10}\] \[a_{\beta}(t,\theta) =-4\bigg{(}\frac{\beta(t)^{2}}{\alpha(t)^{2}}\bigg{)}\sin^{2}\theta,\] (11) \[a_{\rho}(t,\theta) =\frac{1}{2}(1+a_{\beta}(t,\theta)). \tag{12}\]