esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Security in Wireless Sensor Networks
#### 2.3.1 Elliptic Curve Cryptography  
The concept of public-key cryptography was introduced in 1976 by [PERSON] and [PERSON] (DH) [24] to solve the issue of key management. In their concept, each party gets a pair of keys: one called a \"public key\" and the other called a \"private key\". These two keys are mathematically related to each other. DH is intended for two-party communication. However, it does not work well in situations where group members change frequently. The extension of Diffie-Hellman is Group Diffie-Hellman key exchange (GDH) [25], which supports group communications in which more than two members can participate.  
Many methods have been proposed to improve the Diffie-Hellman key exchange protocol. Cliques [25] proposed two distinct extensions of the DH. The results demonstrate efficient performance in the rekeying process, and they do so by just using a single message. The drawback of [26] has been mentioned in paper [27]. The authors approved the control of communication of the last two particular parties for only the duration of the key exchange using active attack. [PERSON] [28] made some modifications to the previous protocol and used double DH key exchange. In all the above-mentioned protocols, the issue is sending many messages and processing many rounds during the communication while establishing an initial key agreement. The use of public key cryptography based on elliptic curves could solve problems related to key management in WSNs.  
Using elliptic curve cryptography (ECC), which is most representative of lightweight asymmetric-key algorithms, can provide 128-bit cryptographic security using a 256-bit key, which is significantly smaller than the 3072-bit key of the most widely used public-key encryption algorithm RSA [29]. ECC has been applied to various cryptographic algorithms, including elliptic curve Diffie-Hellman (ECDH) and the elliptic curve digital signature algorithm (ECDSA).
# A Novel Optimization Framework to Replicate the Vibro-Acoustics Response of an Aircraft Fuselage
## 6 Discussion  
Figures 11 and 13 show the comparisons between the SCs AIs at the microphones and the AIs for the RC. The reference pressure field in the volume surrounding the barrel is shown in Figure 15. The best metric values (Equation (1)) and the RSM quality R2 press index are reported in Table 1.  
The final metrics values are equal to nearly 4%-5%, stating that the average error between the reference and the simulated data was equal to nearly 4%-5% for each microphone. Such discrepancies were judged as acceptable, especially if considering the complexity of the study case here proposed for the presented optimization algorithm.  
The evolutions of the metric values for the two considered SCs vs. iterations are reported in Figure 16. Scattering of data was given by the space filling algorithm, since a \"far\" candidate point was evaluated for each iteration, thus generally giving a higher metric value. However, the general trend happens to be decreasing for both SCs. This can be confirmed also in terms of R2 press index (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Best Metric Value [-]** & \(\mathbf{R^{2}_{press}}\) [–] \\ \hline SC1 & 0.039 & 0.951 \\ SC2 & 0.047 & 0.983 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Best metric values and \(\mathbf{R^{2}_{press}}\) for both SCs.  
Figure 14: Overall pressure field [dB] on the external skin for SC2.  
Figure 15: Overall pressure field [dB] on the external skin for RC.  
As a final outcome, the SC2 was judged as more adequate than SC1 to replicate the reference pressure field. This was attributed to the fact that as an overall the pressure field was better captured by the optimization procedure, see Figures 12, 14 and 15 for a qualitative comparison and Table 1 (correlation in terms of R2 press index). Thus, the solution with an increased number of microphones rings (3 for SC1 to 5 for SC2) should be selected to better drive the optimization toward a more accurate sound field replication.
## 4 Parameters Design and Experimental Results
### Parameters Design  
The considerations and calculations of component parameters are as follows.  
A. Choose duty ratio and dc-link voltage.  
As shown in Figure 2, the gate-source voltages of \(S_{1}\) and \(S_{2}\) should briefly overlap when they are at high levels, that is, the duty cycle is slightly higher than 0.5. However, a high-duty cycle requires a higher dc-link voltage for the PFC converter to operate in DCM. Here, the duty ratio is chosen to be 0.54.  
\[D=0.54\]  
The design constraint for dc-link voltage can be obtained by combining (27) and (37), as follows.  
\[V_{o}>V_{B}>\frac{V_{m}}{1-D}-V_{o}, \tag{45}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline Input voltage, \(V_{in}\) & 110 \(\pm\) 10\% V\({}_{\text{rms}}\), 60 Hz \\ Output voltage, \(V_{LED}\) & 216 V (60 \(\times\) 3.6 V) \\ Output current, \(V_{LED}\) & 0.84 A (3 \(\times\) 0.28 A) \\ Output power, \(P_{o}\) & 180 W \\ Switching frequency,\(f_{s}\) & 50 kHz \\ (at rated power) & \\ \hline \end{tabular}
\end{table}
Table 1: Specification of the proposed LED driver.  
Considering the 10% variation in the input voltage, the applicable range of the dc-link voltage is calculated as  
\[216>V_{B}>\frac{110\sqrt{2}\times 1.1}{1-0.54}-216=156\text{ V}\]  
Here, the dc-link voltage is chosen to be  
\[V_{B}=160\text{ V}\]  
* Calculate PFC inductance. First, calculate the value of \(k\) and the value of \(y\) using (28) and (30) in sequence. \[k=\frac{160+216}{110\times\sqrt{2}}=2.42\]  
\[y=\frac{\left(2.42\right)^{3}}{\sqrt{\left(2.42\right)^{2}-1}}\bigg{(}1+\frac{ 2}{\pi}sin^{-1}\frac{1}{2.42}\bigg{)}-\left(2.42\right)^{2}-\frac{2}{\pi} \times 2.42=0.78\]  
Assuming a circuit efficiency of 95%, the PFC inductance can be calculated by using (29).  
\[L_{p}=\frac{0.95\times\left(0.54\right)^{2}\times\left(110\times\sqrt{2} \right)^{2}}{180\times 50\times 10^{3}}\times 0.78=0.58\text{ mH}\]  
* Determine the leakage inductance and the mutual inductance of \(T_{1}\).  
Selecting a coupling coefficient of 0.9, the leakage inductance can be calculated by using (44).  
\[L_{l}=\frac{0.95\times 160\times 216\times\left(0.9\times 160+216\right)}{ \left(1+0.9\right)\times\left(160+216\right)\times 180\times 50\times 10^{3}}=1.84 \text{ mH}\]  
Then, the mutual inductance is calculated to be  
\[L_{m}=16.54\text{ mH}\]
# Developing Security Assurance Metrics to Support Quantitative Security Assurance Evaluation
## 3 Our Methodological Approach
### Assurance Metrics Calculation: The Core Concept  
SA evaluation is a systematic process of assigning meaningful scores to the assurance target that indicate its security posture [27]. The higher the value, the better the trustworthiness of the system product against its security mechanisms. For deriving the final score, in our approach, metrics of assurance components are computed using a bottom-up approach, which involves the estimation of at the lowest possible level of detail. It is essential to be able to define what is meant and how to measure it when SA is evaluated. We suggest an aggregation method for doing so by using the model as the structure for estimating values related to SA into a single measure. Figure 2 depicts the hierarchical structure of the SA evaluation based on the proposed SA evaluation model, while Table 4 describes each notion. Our quantitative approach divides the SA evaluation into three sequential phases: the first phase of evaluation is responsible for the assessment of the assurance elements; the second phase for the evaluation of the assurance criteria; and the third for the assurance perspectives, in turn, of the overall assurance level of the assurance target. With the term \"evaluation\", we refer to the assignment of a metric to each component in the model. Metrics represent measurement or evaluation indexes that are given attributes to satisfy the security assurance evaluation. The three-phase quantitative process is discussed in the following subsections.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Security Requirement Criteria** & **Security Requirement Element** \\ \hline \multirow{6}{*}{Authentication} & Password Security \\  & Credential Storage \\ \cline{1-1}  & Credential Recovery \\ \cline{1-1}  & One Time Verifier \\ \cline{1-1}  & Input Validation \\ \cline{1-1}  & Sanitization and Sandboxing \\ \cline{1-1}  & Output Encoding \\ \cline{1-1}  & Deserialization Prevention \\ \hline \hline \end{tabular}
\end{table}
Table 2: Exemplary assurance elements (security requirement elements).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Security Requirement Element** & **Security Requirement Condition** \\ \hline \multirow{6}{*}{Password Security} & The passwords should be at least 64 characters, and passwords of more than 128 characters are denied. \\ \cline{1-1}  & Password truncation is not performed. However, consecutive multiple spaces may be replaced by a single space. \\ \cline{1-1}  & Any printable Unicode character, including language-neutral characters such as spaces and emojis, are permitted in passwords. \\ \cline{1-1}  & Password change functionality requires the user’s current and new password. \\ \cline{1-1}  & A password strength meter is provided to help users set a stronger password. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Exemplary assurance conditions (security requirement conditions).
# Arabic Hate Speech Detection Using Deep Recurrent Neural Networks
## 3 Materials and Methods
### Proposed Deep Recurrent Neural Network Model for Hate Speech Detection  
With the advancement of more sophisticated processing power in computing machines, deep learning has been widely adopted in almost all data processing problems. A convolutional neural network (CNN) is characterized by very deep neural networks that can utilize the advantage of the inherent properties of data, particularly from image data but text as well. Recurrent neural networks (RNNs) are similar to CNNs; however, RNNs also manage the time domain. In a unidirectional (feedforward) RNN, the model learns word by word from start to end.  
In the Arabic language, most meanings of words and sentences change based on where the word has occurred as well as the next words in the sentence. In bidirectional recurrent neural networks, two sequences are considered: the forward sequence (left to right) and the backward sequence (right to left). For example, an Arabic sequence can be used in the same order for the forward layer; however, in the backward layer, the order of the text will  
Figure 2: Word2 Vec architecture based on CBOW and SG (“_l  
be reversed. In Arabic text classification, the time domain, which represents the order of the words in the input text, plays a vital role by solving the problem more effectively and enhancing the classification results. Long short-term memory (LSMT) can be used to solve the vanishing gradient problem, which occurs if a long bidirectional RNN is used alone.  
The proposed deep bidirectional RNN model for hate speech detection from the Arabic social media comments consists of 10 layers, including the input layer, bidirectional RNN layers, flatten layer, dense layers, dropout layers, and output layers. Figure 3 shows the details of the RNN forward and backward layers used to extract the features. The embedding layer produced by Word2 Vec and GloVe detailed earlier is then input into the backward RNN layer followed by the forward RNN layer. The output of the RNN is then concatenated, flattened, and input into the dense and dropout layers, as detailed in Figure 4. The data are then classified into one of the seven classes.  
In the proposed deep bidirectional recurrent neural network, the tanh activation function was used for the dense layers while the sigmoid activation function was used for the output layer. The experiments were performed using a learning rate of 0.001 with 32 batch sizes for 50 iterations. The best validation accuracy-based model was saved out of 50 iterations, which were further used for the test dataset to generate and compare results.
## Materials and Methods
### Simulation Method  
#### 2.3.1 Geometric Model  
According to the actual spray operation, the spray flow field model (Figure 4a) was constructed in ANSYS ICEM CFD 18.0 [37; 38]. As shown in Figure 4b, the model is mainly composed of the front turbulent airflow duct, the main spray airflow duct, the rear anti-drift airflow duct, and the external spray basin. The external spray basin from the nozzle to the edge of the canopy is simplified as a cuboid in the model, in which the length, width, and height of the cuboid are 900 mm, 500 mm, and 2600 mm, respectively. The nozzle models were set on the air outlet of the main air duct. These parts, including the ducts and the external spray basin adjacent to them, were divided into tetrahedral meshes. The rest parts were divided into hexahedral meshes. Grid-independent test showed that the grid quality was better. The total number of cells in the model was 2,864,939. RNG \(\mathrm{k}-\varepsilon\) was taken as the turbulence model.  
There are three DPM [39; 40] boundary conditions, which are namely \"reflect\", \"escape\", and \"trap\". The canopy boundary was set to trap according to the work object; thus, the droplets would have been captured when they reached the surface. One side of the model was set to escape, which meant that the droplets would escape when they reached the surface. The parameters of the fan nozzle are shown in Table 1. The material of the discrete phase model replaced pesticide with water.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name (Unit)** & **Value** \\ \hline \(X\)-Fan Normal Vector & 1 \\ \(Y\)-Fan Normal Vector & 0 \\ \(Z\)-Fan Normal Vector & 0 \\ Flow Rate (kg/s) & 0.01316 \\ Spray Half Angle (deg) & 40 \\ Orifice Width (m) & 0.00091 \\ Flat Fan Sheet Constant & 3 \\ Atomizer Dispersion Angle (deg) & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Work parameters of flat sector nozzle.  
Figure 4: Calculation model structure diagram. Schematic diagram of the spray flow model (**a**). Top view of the spray flow field model (**b**): 1. Anti-drift airflow duct; 2. Spray airflow duct; 3. Turbulent airflow duct; 4. Crosswind; 5. Droplet concentration calibration line (paralleling the X-axis direction and coinciding with the origin of the model coordinates).
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 5 Experimental Setup
### Datasets  
We selected 5 datasets of various sizes from a recent benchmark [8]. **Wikipedia** is a dataset of edits of Wikipedia pages recorded over one month. **Mooc** is a dataset of online behavior of students interacting with content (items) on a MOOC platform. **Lastfm** is a dataset of interaction between users and songs listened by users. **Uci** is a network of university students communicating over a social network. **Enron** is a dataset of emails between employees of a company, over a period of three years.
## Nomenclature  
\begin{tabular}{l l l l} \multicolumn{4}{c}{**Nomenclature**} \\ \(h_{f}\) & Wall heat transfer coefficient & \(Sc=\frac{v}{D_{g}}\) & Schmidt number \\ \(k_{m}\) & Wall mass transfer coefficient & \(\rho_{f}\) & Density of fluid \\ \(\tau\) & Ratio of \((\rho C)_{p}\) to \((\rho C)_{f}\) & \(C_{p}\) & Specific heat \\ \(C_{\infty}\) & Ambient concentration & \((\rho c)_{p}\) & Operative heat capability \\ \(T_{w}\) & Surface temperature & \(u,v\) & Velocity components \\ \(\rho\) & Density of fluid & \(\sigma=\frac{K^{2}}{A}\) & Reaction rate parameter \\ \(D_{T}\) & Thermophoresis diffusion coefficient & \(N_{t}\) & Thermophoresis parameter \\ \(U\) & Stretching velocity & \(\it{Rex}\) & Local Reynolds number \\ \(\alpha\) & Thermal diffusivity & \(I\) & Identity tensor \\ \(v\) & Kinematic viscosity & \(n\) & Power law index \\ \(A=\frac{a_{0}^{2}\beta}{4v}\) & Unsteadiness parameter & \(A_{1}\) & First Rivlin-Ericksen tensor \\ \(D_{B}\) & Brownian diffusion coefficient & \(B\) & Magnetic parameter \\ \(Sc\) & Schmidt number & \(M=\sqrt{\frac{\sigma^{2}B^{2}}{\rho^{4}}}\) & Magnetic parameter \\ \(We=\frac{\Gamma N_{w}x}{4^{2}}\) & Local Weissenberg number & \(\delta=\frac{T_{w}-T_{w}}{T_{w}}\) & Temperature constant \\ \(\sigma=\frac{K^{2}}{\alpha}\) & Reaction rate parameter & \(Pr=\frac{\pi c_{p}}{K_{w}}\) & Prandtl number \\ \(N_{b}\) & Brownian motion parameter & \(E=\frac{\pi c_{p}}{K_{w}}\) & Activation energy \\ \(x\) & Distance along the axial direction & \(\beta\) & Expansion or contraction strength \\ \(r\) & Distance along the radial direction & \(f^{\prime}\) & Dimensionless velocity \\ \(\theta^{\prime}\) & Dimensionless temperature & \(a(t)\) & Radius of cylinder \\ \(\phi^{\prime}\) & Dimensionless concentration & \(\gamma_{1}=\frac{h_{r}a(t)}{2K_{w}}\) & Thermal Biot number \\ \(\gamma_{2}=\frac{k_{w}a(t)}{2D_{m}}\) & Concentration Biot number & \(t\) & Time \\ \(We\) & Local Weissenberg number & \(V\) & Velocity field \\ \(\varepsilon\) & Thermal conductivity parameter & \(C\) & Concentration field \\ \(Cf\) & Skin friction coefficient & \(\tau\) & Cauchy stress tensor \\ \(hf\) & Wall heat transfer coefficient & \(p\) & Pressure \\ \(q_{m}\) & Wall mass flux & \(\mu\) & Apparent viscosity \\ \(q_{w}\) & Wall heat flux & \(\mu_{0}\) & Zero shear rate viscosity \\ \(\tau_{rx}\) & Wall shear stress & \(\mu_{\infty}\) & Infinite shear rate viscosity \\ \(T_{w}\) & Surface temperature & \(\tau\) & Shear rate \\ \(Nb=\frac{\tau D_{g}(C_{f}-C_{m})}{\
u T_{w}}\) & Brownian motion parameter & \(\Gamma\) & Time material constant \\ \(Nt=\frac{\tau D_{T}(T_{f}-C_{m})}{\
u T_{w}}\) & Thermophoresis parameter & \(\eta\) & Local similarity variable \\ \(K(T)\) & Variable thermal conductivity & & \\ \end{tabular}
# A Novel Strutless Double-Row Structure for Deep Excavation: Working Mechanism Study and Analysis
## 3 Analysis of Experimental Results
### Earth Pressure Analysis
#### 3.2.3 E\({}_{\rm fr}\) and E\({}_{\rm rf}\)  
The double-row structure effectively capitalizes on the spacing effect between the front and back structures, as well as the stiffness of the soil between the piles, to achieve a more balanced stress distribution within the structure. Obtaining accurate measurements of earth pressure between the piles is challenging due to the complexity of soil behavior and the limitations of testing conditions, which can introduce significant disturbances. Figures 21a and 22a reveal that the measured earth pressure values Efr and Erf in this test lack a discernible regular distribution.  
For DRPW structures, the absolute sums are 20.2 kPa, 15.4 kPa, and 18.9 kPa, respectively, while for ARPW structures, they are 8.1 kPa, 9.1 kPa, and 8.4 kPa. The qualitative observation of the results suggests that the earth pressure values in front of the wall and behind the piles for ARPW are more closely aligned. This indicates that the loss of earth pressure between ARPW is reduced, and the horizontal stiffness coefficient of the soil between ARPW is greater [39].  
Figures 21b and 22b illustrate the earth pressure values between the piles obtained from the numerical model. It is evident that the overall E\({}_{\rm fr}\) is greater than E\({}_{\rm rf}\) in both DRPW and ARPW structures. This can be attributed to the direct soil-structure interaction at the front wall (E\({}_{\rm fr}\)), which experiences higher pressure, while the rear wall (E\({}_{\rm rf}\)) benefits from the spacing effect and soil stiffness, resulting in lower pressure. Furthermore, the earth pressure between the piles of the DRPW structure is consistently greater than that of the  
Figure 21: E\({}_{\rm fr}\) (H = 500 mm) (**a**) model test results and (**b**) numerical simulation results.  
Figure 20: E\({}_{\rm rr}\) (H = 500 mm).  
ARPW structure. This difference arises due to the ARPW structure's increased rear wall stiffness and larger soil-pile contact area, which enhance stress distribution and reduce localized pressures. Interestingly, the minimum earth pressure occurs at a 4D row spacing for DRPW and a 6D row spacing for ARPW. At 4D spacing, the DRPW structure achieves an optimal balance between soil stiffness and spacing, while at 6D spacing, the ARPW structure benefits from greater soil-pile interaction and stress redistribution.  
Hence, the ARPW structure leverages the advantages of increased rear wall stiffness, and an expanded soil-pile contact area, allowing the soil between piles to more effectively contribute its stiffness within the structure. This enhanced interaction between the soil and structure results in enhanced resistance to overturning moments, ultimately providing superior anti-overturning and supporting capabilities for the ARPW structure.
## 4 Discussion  
The study methodology, with a close-to-real dynamic condition, resulted in some significant outcomes, indicating that both seat base and backrest angles affect comfort perception. For the sleeping use case, it was also concluded that users are drawn to choosing a flatter position than the current one in series production cars. This outcome agrees with findings of some previous studies [11, 26, 27].  
On the other hand, the results of the general comfort rating obtained in the study showed an unexpected behavior. Comfort ratings were expected to worsen with time [28], and this was the case for the upright position. However, the flat position had an opposite effect on comfort. This phenomenon can be explained by many comfort perception models, such as the [PERSON] comfort/discomfort perception model [29]. In the mentioned model, the resulting comfort is the result of several factors, such as environment, and psychosocial and cognitive factors, rather than strictly physical qualities. In previous similar models, one of the inherited components of comfort perception is expectation or previous experience [30]. One hypothesis could be that the participants had experience with normal car seats and their angles. In contrast, it is expected that participants did not have similar experiences travelling in a lying position inside a car in a dynamic scenario. Therefore, the initial more negative comfort rating of the flat condition could have been caused by the difference in seat expectations in a moving vehicle. Once the participants experienced the seat in the moving car for 5 min, the expectation was readjusted to a more positive opinion.  
The analysis of discomfort in different body areas indicates that some of the discomfort originated from seat comfort limitations, whereas other discomfort was inherently specific to seat position. The seat design was based on the design of an automotive seat. Although the intention was to acclimate to the specific scenario and use case, in some cases, the seat prototype was insufficient to truly represent a comfortable futuristic reclined seat. Consequently, this resulted in some localized discomfort in the reclined and flat positions, for example, in the legs/feet and head areas.  
Other study limitations were the low number of participants and short duration of the experiment. The constant low speed and accelerations due to safety standards also limited the evaluation of specific driving-related discomorts. These aspects explain the relatively low number of significant results. Another major limitation was the low number of female participants. Gender-based differences in comfort and higher pressure sensitivity have been reported [31, 32] and thus must be taken into account when performing comfort user studies. Furthermore, an additional special COVID-19 protocol, which included full bodysuits, protective facemasks and reduced control of the in-vehicle environment temperatures due to constant airing, may have affected the comfort ratings [33, 34] and, ultimately, the results.