esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Implementation of GPU-MOC Method
### Parallel Characteristics of MOC  
The basic equations for unsteady flow are a pairs of hyperbolic partial differential equations as follows.  
Continuity equation:  
\[A\frac{\partial H}{\partial t}+\frac{a^{2}}{g}\frac{\partial Q}{\partial x}=0 \tag{1}\]  
Momentum equation:  
\[\frac{\partial Q}{\partial t}+gA\frac{\partial H}{\partial x}+\frac{fQ|Q|}{2 DA}=0 \tag{2}\]  
where \(H(x,t)\) is piezometric head; \(Q(x,t)\) is discharge; \(D,A,a,g,\) and \(f\) are the diameter, cross-section area of the pipe, wave speed, acceleration of gravity, and the Darcy-Weisbach friction factor, respectively.  
The continuity and momentum equations are transformed into two pairs of equations which are grouped and identified as C\({}^{+}\) and C\({}^{-}\), Equations (3) and (4), by the method of characteristics [25], and the final formulas can be expressed as Equations (5) and (6):  
\[C^{+}:\left\{\begin{array}{l}\frac{dH}{dt}+\frac{a}{gA}\frac{dQ}{dt}+\frac{a }{g}\frac{fQ|Q|}{2 DA^{2}}\\ \frac{dx}{dt}=+a\end{array}\right. \tag{3}\]  
\[C^{-}:\left\{\begin{array}{l}-\frac{dH}{dt}+\frac{a}{gA}\frac{dQ}{dt}+\frac {a}{g}\frac{fQ|Q|}{2 DA^{2}}\\ \frac{dx}{dt}=-a\end{array}\right. \tag{4}\]  
\[Q_{P}=\frac{C_{n}-C_{m}}{B_{n}+B_{m}} \tag{5}\]  
\[H_{P}=\frac{C_{n}B_{m}+C_{m}B_{n}}{B_{n}+B_{m}} \tag{6}\]  
where \(Q_{L}\), \(Q_{R}\), \(Q_{P}\)\(H_{L}\), \(H_{R}\), and \(H_{P}\) are the discharge and head of sections L, R, and P, in which discharge and head of sections L and R are known; the coefficients \(C_{n}=H_{L}+BQ_{L}\), \(B_{n}=B+R|Q_{L}|\), \(C_{m}=H_{R}-B\)\(Q_{R}\), \(B_{m}=B+R|Q_{R}|\), \(R=f\Delta x/(2 gDA^{2})\), and \(B=a/(gA)\) are known constants; \(\Delta x\) is the space interval. As shown in the Cartesian mesh in the x-t plane (Figure 1), through Equations (5) and (6), the values of section P can be calculated by those of sections L and R. The values of other sections at the same time level as P can be solved with the same process.  
The solution process described above reflects the explicit and local features of MOC, which is the most critical point for parallelizing MOC on GPU. The explicit feature refers to that the values at section P at the unknown time level is only related to the values at section L and R at the previous time level, having nothing to do with the values at other sections P\({}_{\text{i}}\) at the same time level as P. The local feature refers to that the values of each section are just connected with the values of its adjacent sections. These features match the multi-thread parallel characteristics of GPU quite well. Therefore, MOC can be built conveniently on a GPU-based CUDA architecture platform, which may fully take advantage of GPU (strong ability of floating-point computing) to compensate the drawbacks of MOC.
## 6 Strengths, Limitations, and Future Research Directions
### Strengths
#### 6.1.1 Dataset Considerations  
CNNs have been widely used and extensively studied for image-related tasks, resulting in a rich literature, established architectures, and pre-trained models, making them accessible and convenient for many datasets. On the other hand, ViTs can process patches in parallel, which can lead to efficient computation, especially for large-scale datasets, and allow faster training and inference. ViTs can also handle images of different sizes and aspect ratios without losing resolution, making them more scalable and adaptable to different datasets and applications.
# Single-Layer Pressure Textile Sensors with Woven Conductive Yarm Circuit
## 3 Results and Discussion  
### Conductive Wool Yarns  
A scanning electron microscope (SEM) is employed to characterize morphological changes of the wool yarns at different steps of the synthesis of the conductive fibers through the process proposed. Figure 2a,b describes the fabricated sensors. Furthermore, Figure 2c-f shows SEM images of the standard wool yarns with the magnified view showing no coating on the yarns and the coated yarns under high and low magnification with CNTs. The diameter of a single yarn is about 10 \(\upmu\)m and appears loosely twisted with ample free space between the microfibers. Conductive particles (CNTs) can be observed in the form of the thin coatings and stuck randomly onto wool yarns with an 80% coating area.
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 4 Evolution of Gas-Sensing Technologies
With the IoT paradigm, gas sensors are becoming key devices to measure ambient gases, generate warnings related to the presence of unwanted gases and allow other systems, such as smart windows, smart curtains, automated exhaust systems, and automated heating ventilation and air conditioning (HVAC) systems, to automatically act in order to avoid damages from leakages [11; 12].
## 3 Numerical Simulations
### Model Accuracy Verifications and Validation
Regarding surface temperatures, the values are quite similar at both sensor locations (around 40 \({}^{\circ}\)C and 5 \({}^{\circ}\)C). As expected, the major difference is related to the heat flux values, these being much bigger (around 19.9 W/m\({}^{2}\)) near the steel stud (Figure 5a) when compared to the cavity location (near 8.9 \({}^{\circ}\)C), due to the thermal bridge effect caused by the high thermal conductivity of steel. Making use of the measured values (surface thermal resistances and environmental air temperatures on both sides of the wall), it was possible to model a similar representative partition and facade LSF wall, making use of the four tested FEM computational tools. Afterwards, we computed the predicted conductive (or surface-to-surface) thermal resistance (\(R_{cond}\)) for each wall and each software model, as displayed in Table 6.  
When comparing the results predicted by the FEM models with the measurements, we can see very good agreement, whereby the differences vary between \(-\)0.1% and \(+\)2.3%. Moreover, these deviations are much smaller (almost negligible) for the facade LSF wall, this being explained by the steel studs' lower thermal bridge relevance, given the effect of the continuous external thermal insulation layer (ETICS). These results allow us to validate \begin{table}
\begin{tabular}{l l c c} \hline \hline \multirow{2}{*}{**Test N.**} & \multirow{2}{*}{**Sensors Location**} & \multicolumn{2}{c}{\(R_{cond}\) **(m\({}^{2}\cdot\)K/W)**} \\ \cline{3-4}  & & **Partition** & **Facade** \\ \hline
**1** & Top & 1.607 & 3.247 \\
**2** & Middle & 1.576 & 3.121 \\
**3** & Bottom & 1.491 & 3.232 \\ \hline \hline  & **Measurement Average** & **1.558** & **3.200** \\ \hline  & **Computed in THERM** & **1.594** & **3.200** \\  & Percentage Deviation & \(+\)2.3\% & 0.0\% \\ \hline  & **Computed in FLIXO** & **1.590** & **3.196** \\  & Percentage Deviation & \(+\)2.1\% & \(-\)0.1\% \\ \hline  & **Computed in PSIPLAN** & **1.591** & **3.198** \\  & Percentage Deviation & \(+\)2.1\% & \(-\)0.1\% \\ \hline  & **Computed in ANSYS** & **1.586** & **3.206** \\  & Percentage Deviation & \(+\)1.8\% & \(+\)0.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: LSF walls’ conductive \(R\)-values for both experimental and numerical methods. the accuracy of the implemented models, making use of the four computational tools compared in this research.
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 2 Construction of Failure Model
### Failure Model of the Soil Region at the Front Surface of the Anchorage Segment
The discrete points representing the contour of the failure model in any radial plane could be generated through those located in the previous radial plane according to the normality condition between the newly generated points and the points in the previous plane. Following this method, the contours of the failure model were successively generated; hence, the 3D velocity discontinuity surface was also  
cretization technique was applied in this paper, as shown in Figure 3:1 In this case, according to whether the plane passed through the front-end surface of the anchorage segment, the failure range could be divided into Range I or Range II, as shown in Figure 6a. Range I contained the radial plans that intersected with the front-end surface of the anchorage segment, and Range II contained the radial plans that did not intersect the front-end surface of the anchorage segment. In Range I, the generation of new points began with the use of points \(A_{1}\) and \(A_{1}^{\prime}\) of the front-end surface of the anchorage segment (Figure 6b). These two points belonged to plane \(\prod_{1}\) and could be renamed \(P_{i,1}\) and \(P_{i+1,1}\). Then, the new point \(P_{i,2}\), which belonged to plane \(\prod_{2}\), could be generated by these two points. Similarly, the points \(A_{2}\) and \(A_{2}^{\prime}\), belonging to \(\prod_{2}\), could be renamed \(P_{i-1,2}\) and \(P_{i+1,2}\). Therefore, two new points (\(P_{i-1,3}\) and \(P_{i,3}\)) could be created by the three points \(P_{i-1,2}\), \(P_{i,2}\), and \(P_{i+1,2}\). This operation was repeated until the end of Range I. In each plane \(\prod_{j+1},j\) points were generated, with two other points located on the contour of the front-end surface of the anchorage segment already present. Thus, the total number of points in plane \(\prod_{j+1}\) was \(j+2\). In Range II, the contours were successively generated until the end of Range II, following the same method. Finally, the entire velocity discontinuity surface in the logarithmic spiral region was generated. For more details concerning the spatial discretization technique, the reader can refer to [20,21].
# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 2 Related Work  
### Sentimental Analysis in Social Media  
This section explores the Use of Sentiment Analysis in understanding social media Opinions and emotions. The use of sentiment analysis in understanding the opinions and emotions expressed on social media platforms, particularly Twitter, has been widely studied in recent years. Research in this area has focused on various topics, such as COVID-19 tweets [7], shared mobility services [8], and emotional intelligence in social media posts [9].  
In [7], sentiment analysis was applied to COVID-19 tweets in English using TextBlob and TwitterScrapper in Python, achieving an accuracy of 79.34%. The study also applied sentiment analysis to medical documents in English using SVM and KNN, achieving an accuracy of 80%. However, the scope of this study was limited to textual data from Twitter and only a limited number of languages were considered.  
Similarly, in [8], sentiment analysis was applied to tweets related to shared mobility services using a sentiment-emotion detection model, survey, and manual annotation and labeling. The study found that 67% of respondents preferred passenger reservation services over other available routes such as public transportation. The majority of tweets were neutral in the pre-COVID-19 era, but there was a 2% change in trend from positive to negative in the COVID-19 era. The study also found that words and frequency distribution of phrases gathered from significant Twitter data were common phrases such as \"Uber\", \"Rideshare\", \"Drivers\", and \"Lyft\".  
In [9], sentiment analysis was applied to social media posts using preprocessing, feature extraction, matrix formulation, and classification. The study found that LRA-DNN achieved high performance values, with a classification accuracy of 94.77%, sensitivity of 92.23%, and specificity of 95.91%.  
Recently, in 2022, authors in [10] introduced a novel concept of'sentiment scope' within social network analysis, presenting a multi-dimensional framework to quantify users' influence on public sentiment over time and space, tested on a Reddit dataset. One year later, in 2023, the authors extended their work and proposed a scalable framework for analyzing sentiment dynamics among users and communities on social media, focusing on community influence, sentiment evolution, and inter-community interactions. Their approach has been empirically validated through extensive experiments [11].  
Despite the progress made in the field, there are still limitations in these studies. The scope of these studies is limited to Twitter data, and the focus is on a limited number of languages and topics. Additionally, the methods used in these studies may not be able to accurately capture the full range of emotions and sentiments expressed in social media posts. Therefore, further research is needed to expand the scope and improve the accuracy of sentiment analysis on social media platforms.
# Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV
## 4 Simulation Analysis
### Simulation Results  
#### 4.2.1 Velocity Contour  
The velocity contour of the coaxial Tri-rotor considering the horizontal wind is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Nodes**} & \multirow{2}{*}{**Elements**} & **Average** & **Turbulence** & **Pressure** & **Spatial** \\  & & **Skewness** & **Model** & **Interpolation** & **Discretization** \\ \hline
1164307 & 6472340 & 0.21734 & Spalart-Allmaras & Standard & Second-order \\  & & & & & upwind \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mesh parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & \(\mathbf{C_{T}}\) & \(\mathbf{C_{T}}\) & **Relative** & \(\mathbf{C_{P}}\) & \(\mathbf{C_{P}}\) & **Relative** \\  & **Experiment** & **Simulation** & **Error (\%)** & **Experiment** & **Simulation** & **Error (\%)** \\ \hline
1 & 0.404 & 0.385 & 4.94 & 0.198 & 0.181 & 9.39 \\
2 & 0.578 & 0.562 & 2.85 & 0.215 & 0.196 & 9.69 \\
3 & 0.783 & 0.742 & 5.53 & 0.218 & 0.197 & 10.66 \\
4 & 0.854 & 0.878 & \(-\)2.73 & 0.274 & 0.248 & 10.48 \\
5 & 0.924 & 0.952 & \(-\)2.94 & 0.408 & 0.375 & 8.80 \\
6 & 0.968 & 0.988 & \(-\)2.02 & 0.524 & 0.572 & 8.39 \\
7 & 0.925 & 0.945 & \(-\)2.12 & 0.798 & 0.764 & 4.45 \\
8 & 0.965 & 0.925 & 4.32 & 0.834 & 0.848 & \(-\)1.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Computational Fluid Dynamics (CFD) and experiments.  
Figure 8: Mesh distribution.  
Figure 9: The velocity contour of coaxial Tri-rotor (2200 \(\rm\tau/min\)): (**a**) 0 m/s; (**b**) 2.5 m/s; (**c**) 4 m/s.  
In Figure 9, it can be clearly noted that the downwash flow is moved along with the wind direction. Compared with the case with no wind effect, the velocity variation becomes even more complex, which will affect the aerodynamic performance of the coaxial Tri-rotor. Moreover, with the increase of the horizontal wind velocity, the downwash velocity of the coaxial Tri-rotor gradually decreases and the velocity gradient arrangement of downwash becomes closer, leading to the strong rotor interference. It can be seen that the aerodynamic coupling will be affected by the external airflow and aggravate the aerodynamic interference between the rotors. Moreover, the enhancement of aerodynamic interference will bring the increase of required power. This also verifies that in Figures 5 and 7, the overall power consumption of the coaxial Tri-rotor system with the influence of external airflow is significantly greater than that without airflow.
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 3 Proposed ECDH Architecture
### Routing Networks (\(RoutingNetwork_{1}\) and \(RoutingNetwork_{2}\))  
The \(RoutingNetwork_{1}\) consists of four multiplexers, i.e., \(M_{1}\), \(M_{2}\), \(M_{3}\) and \(M_{4}\). The length of \(M_{1}\), \(M_{2}\) and \(M_{4}\) are \(2\times 1\) while the length of \(M_{3}\) multiplexer is \(3\times 1\). The objective of \(M_{1}\) and \(M_{2}\) is to select coordinates of either the initial point on ECC (\(xp\) and \(yp\)) or coordinates of the public key (\(Q_{xp}\) and \(Q_{yp}\)) for the generation of a shared key. The multiplexer \(M_{3}\) drives the output of \(M_{1}\) and \(M_{2}\) along with the curve constant parameter \(b\). It is important to note that this work has utilized NIST-standardized ECC parameters (\(xp\), \(yp\) and \(b\)) [39]. The \(M_{4}\) multiplexer is responsible to select an operand either from multiplexers \(M_{1}\), \(M_{2}\) and \(M_{3}\) or from an operand from the memory block. The output of  
Figure 3: Proposed ECDH accelerator architecture for key-agreement.  
\(M_{4}\) goes to ALU as an input for further computations. In Figure 3, the multiplexer \(M_{5}\) determines the \(RoutingNetwork_{2}\). The purpose of \(M_{5}\) is to select an appropriate output, either after the ADDER or MULT unit, for writing back in a memory block.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 2 Theoretical Background
### Long-Term Evolution (LTE)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Scope and Focus** & **Contributions** & **Limitations** \\ \hline \multirow{4}{*}{[1]} & A comprehensive analysis of the existing MBB performance in an urban area: Cyberjaya City, Malaysia. & Provides suggestions, such as the deployment of more indoor hotspots for MBB providers to improve their performance networks. & The suggestions and methods used are solely for the environment studied. \\ \cline{2-4}  & & Extensive documentation of possible & No measurement of actual \\ \cline{2-4}  & Examination of important challenges in the implementation of 5G. & 5G technological challenges common in mmWave communication, D2D communication, and security. & data to ascertain the degree of impact of the challenges discussed. \\ \hline \multirow{4}{*}{[7]} & Development of a 5G navigation system that utilizes the downlink channel in an opportunistic approach. & Presentation of a 5G structure capable of exploiting the downlink channels using an experimental approach in evaluating the performance of the countries such as Nigeria. \\ \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the related works. In conclusion, the studies presented here have shown how 2G, 3G, and 4G LTE networks can be improved in terms of their performance. These include the use of KPIs, reducing interference, deploying MIMO-OFDMA multiple access technology, optimizing handover mechanisms, and modulating network parameters. In addition, these studies highlight the importance of continued research and development in mobile network performance optimization as technology advances and new challenges arise.
## 3 Results and Discussion
### Effect of the Variation in Fly Ash-Stabilized Loess on Compaction  
Compaction characteristics have an important impact on the engineering properties of soil, such as strength, compressibility, hydraulic conductivity, and dispersibility [75]. In this study, SPC test was conducted for loess with different FA ratios to determine the impact of FA on the stabilization of loess. Table 6 shows the effect of FA stabilization of loess on compaction test with varying FA ratios.  
Loess treated with 10%, 20%, and 30% FA significantly increased the OMC than the untreated loess. The OMC increased from 4.99% to 11.27%, and the significantly high value was observed at the addition of 30% FA (Table 6). A similar trend was observed in previous studies, which applied FA to loess [55; 76] and clay [72; 75]; however, neither no consistent variation nor gradual decreases in OMC were observed with the increase in the FA ratio in a few studies [57; 77] and soft soil [46]. On the contrary, as OMC increased, a significant decrease from 2.90 to 7.56% in the MDD was observed in loess treated with 10%, 20%, and 30% FA compared with that of the untreated loess (Table 6). This result was consistent with that of recent studies [46; 55; 72; 75; 76; 78]. The increase in OMC might be attributed to progressive hydration of loess-FA mixtures that required additional amount of water to complete the cation exchange reaction [72]. Moreover, the formation of cemented products with the increase in the number of fine particles in the loess-FA mixtures by the particle size of FA was relatively small; this condition resulted in compaction effort, which increased the number of voids, decreased dry densities, and weakened water absorption [55; 72; 76; 79]. In addition, the specific gravity of FA was lower than that of loess. Thus, the specific gravity of loess mixed with FA was decreased with the increase in the amount of FA; this condition decreased the maximum dry density [55; 71]. The aforementioned results showed that FA was a suitable additive agent for improving the engineering characteristics of loess soils.
# Adaptive Polling Medium Access Control Protocol for Optic Wireless Networks
## 5 Experimental Setup and Numerical Results  
We validated our study by simulating our protocol in the NS3 simulator. We considered four performance metrics in evaluating potential benefits of our study, viz., average waiting time, average packet collision, average system utilization, and average system fairness. The average waiting time in this context refers to the average time interval between the instant at which the VLN sends the ARF and the moment at which it starts sending DTF. In a nutshell, this is the duration the VLN is in association phase. Moreover, the average packet collision is the mean value of occurred collisions. The average system utilization refers to the mean time the system is not idle. System fairness was another performance metric we considered in expressing numerical results. We expressed system fairness by using the fairness index defined in [50] as follows:  
\[\text{fairness index}=\frac{\sum(\frac{t_{i}}{\omega_{i}})^{2}}{N(\sum(\frac {t_{i}}{\omega_{i}}))^{2}} \tag{1}\]  
where \(t_{i}\) represents throughput of node \(i\), \(\omega_{i}\) denotes the weight of the node \(i\), and \(N\) represents the number of nodes in the network. In our simulation, we assumed that all VLNs have equal weight and the number of VLNs in each scenario ranges from 1 to 10 per VLAP. The small number of users per cell is attributed by inter-cell interference and contention during resource arbitration with the access point during association phase. The fairness index expressed in Equation (1) ranges from 0 to 1,where the high value of fairness index implies that the system is fairer. In this study, the downlink data rate was 120 Mbps, and the uplink data rate was 4 Mbps. The maximum propagation distance was 10 m, this is due to the fact that within this range VLC signals can be reliably detected. Network traffic was generated randomly at each node following poison process of constant rate \(\lambda\) packets per second. In order to evaluate potential benefits of our study, for each performance metric we compared the APMAC protocol against the medium transparent medium access control (MT-MAC) demonstrated in [29]. Furthermore, for each scenario, we rerun each simulation 50 times and the results presented are averages of the results obtained for each category with 97% confidence interval.