esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Multi-User Broadcast Protocol for the \"Platform as a Service\" Model
## 3 Multi-User Broadcast Protocol Based on _Qcm_
### Group Routing Tables Building Process  
The quantum multicast router needs to collect members' information and share it with other multicast routers, and then construct group routing table containing group members' information. The graph of quantum nodes and links is called tree, so the quantum multicast router can be regarded as the root node. All other nodes \(F_{i}(i=1,2,\cdots,M)\) can only be reached from the root node through a single path. Multicast communication means that a sender sends messages to a group of recipients who are members of the same group. Since a copy of the message is sent by the sender and then copied and forwarded by the router, each multicast router needs to know the list of groups.  
The group routing tables carry four core data: the quantum nodes identity, the list of links, a sequence number, and the age. The quantum nodes identity and the list of links are needed to make the quantum topology. The sequence number distinguishes new routing tables from old ones. The age prevents old routing tables from remaining in the domain for a long time. When the topology of a domain changes, any quantum nodes in the domain are quickly notified by the router to update their topology.  
Figure 2: Pruning process of an undependable neighbor quantum node.
## 2 Materials and Methods  
### Materials and Components  
Nonylphenol (NP), bisphenol A, Triton X-100, Tween-20, Tween-80, luminol, p-iodophenol, gelatin, formaldehyde solution, soybean trypsin inhibitor (STI), dimethylsulfoxide, and Tween-80 were from Sigma-Aldrich (St. Louis, MO, USA). Rabbit antiserum was obtained as described in [20] by rabbit immunization with NP-BSA conjugate, stored at \(-20\)\({}^{\circ}\)C, and used in this work. Goat polyclonal antibodies against rabbit IgG labeled with horseradish peroxidase (GARI-HRP) were obtained from IMTEK (Moscow, Russia). Liquid colorimetric substrate 3,3',5,5'-tetramethylbenzidine (TMB) with H\({}_{2}\)O\({}_{2}\) (substrate solution) was obtainedfrom Immunotech (Moscow, Russia). Methanol was obtained from Fluka (Switzerland). Buffers and water solutions were prepared with the use of purified water with a resistance of not more than 18.6 M\(\Omega\)-cm at 25 \({}^{\circ}\)C (Simplicity Water Purification System, Millipore, Bedford, MA, USA).  
Black polystyrene plates (96-well black Maxisorp Nunc microplates (NUNC 437111)) were obtained from Thermo Fisher Scientific, Roskilde, Denmark). Transparent 96-well microplates were obtained from Costar (Corning Costar, MO, USA). Syringe filters of 0.22 \(\upmu\)m for purifying samples and filtration of water solutions were obtained from Sartorius (Gettingen, Germany).  
We used 1 mM, 10 mM, and 50 mM phosphate-buffered saline, pH 7.4 (1 mM PBS, 10 mM PBS, 50 mM PBS, correspondingly), 50 mM PBS with 0.1% gelatin (pH 7.4), 50 mM PBS with 0.05% Tween-80 (PBST, pH 7.4), and 0.1 M carbonate buffer (pH 10.0).
# Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference
## 3 Simulations
### Simulation of Lateral Control
#### 3.1.2 Adaptability Analysis  
In this section, PPC-FAR and PPC-FAR are compared in curve-following. In PP, the stability is related to the look ahead distance, so the minimum look-ahead distance is 4 m. The radius of the curve is 10 m. In addition, the initial lateral error of driverless bus to desired path is 0 m. The velocity is 10 km/h. The parameters are selected according to Equation (1).  
Figure 16 lists the maximum lateral error with different parameters. The kinematic model does not describe the driverless bus well in large curvature. Thus, if the \(k\) is set at 1, the driverless bus tracks the desired path with a large lateral error. When the look-ahead distance is 4 m, and the \(k\) is 0.5, the PPC-FAR obtains the minimum lateral error. PPC-FAR performs best when the look-ahead distance is 4 m, and \(k\) is 0.8. The minimum lateral error of PPC-FAR is 0.58 m, but the minimum lateral error of PPC-FAR is 0.72 m. The results ascertained that PPC-FAR improves the tracking accuracy of PP under large curvature conditions.  
Figure 17 shows the two best experiment results in the curve. The trajectories of the driverless bus are similar for these two controllers. The steering angles are illustrated in Figure 17b. Although the curvature of the path is changed at the connection of a straight arc, the steering angle changes smoothly. Figure 17c,d illustrate the lateral and heading errors. The heading errors are close, but the maximum lateral error of PPC-FAR is larger than PPC-FAR. Thus, the PPC-FAR performs better than PPC-FAR both in lane-keeping and curve-following. Thus, the PPC-FAR is more suitable to driverless buses. Meanwhile, suboptimal \(l^{\prime}{}_{d}\) and \(k\) in PPC-FAR are easy to obtain in experiments, which indicate the good adaptability of PPC-FAR.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \(\mathbf{v}\) & \(\mathbf{l_{d}}\) & **Lateral** & **Final** & \multirow{2}{*}{**Algorithm**} & \(\mathbf{v}\) & \(\mathbf{l_{d}}\) & **Lateral** & **Final** \\  & **(km/h)** & **(m)** & **Error** & **State** & & **(km/h)** & **(m)** & **Error** & **State** \\ \hline \multirow{4}{*}{PPC-FAR} & 10 & 15 & 0.02 & oscillate & & & & & \\  & 20 & 0.03 & oscillate & & & & & & \\ \cline{1-1}  & 30 & 15 & 0.05 & oscillate & PPC-FAR & & 15 & 0.01 & stable \\ \cline{1-1}  & 60 & 20 & 0.05 & oscillate & & & 20 & 0.01 & stable \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances of PPC-FAR and PPC-FAR with different \(l_{d}\) and \(k\).  
Figure 16: Maximum lateral error. (**a**) Look-ahead distance is 4 m. (**b**) Look-ahead distance is 6 m.
## 2 Anisotropic Strength Criteria  
### Jager's Plane of Weakness Model  
The single plane of weakness was first proposed by [PERSON] [7] based on the Mohr conception, which is most widely used to predict anisotropic strength of sedimentary rocks, especially shales. The failure model for homogeneous material with cohesion \(S_{\text{o}}\) and internal friction angle \(\phi_{\text{o}}\) is given in Equation (1):  
\[\sigma_{1}-\sigma_{3}=2(S_{\text{o}}+\sigma_{3}\tan\phi_{\text{o}})\bigg{(} \sqrt{1+\tan^{2}\phi_{\text{o}}}+\tan\phi_{\text{o}}\bigg{)} \tag{1}\]  
The sliding failure occurring along the weak bedding plane, inclined at an angle \(\beta\) between the bedding plane normal and the maximum principal loading, with cohesion \(S_{\text{bp}}\) and internal friction angle \(\phi_{\text{bp}}\), is given in Equation (2),  
\[\tau_{\text{m}}=\frac{\sigma_{\text{m}}\sin\phi_{\text{bp}}+S_{\text{bp}}\cos \phi_{\text{bp}}}{\sin\Big{(}2\beta-\phi_{\text{bp}}\Big{)}} \tag{2}\]  
where \(\sigma_{\text{m}}\) is the mean normal stress, \(\sigma_{\text{m}}=(\sigma_{1}+\sigma_{3})/2\), \(\tau_{\text{m}}\) is the maximum shear stress on the bedding plane, \(\tau_{\text{m}}=(\sigma_{1}-\sigma_{3})/2\), \(\sigma_{1}\) is the maximum principal stress, MPa; and \(\sigma_{3}\) is the minimum principal stress, MPa.  
According to the single weak plane criterion, the trend of rock strength concerning the angle \(\beta\) is illustrated in Figure 1. Based on the physical significance and geometric features of the Mohr circle, it can be deduced that the upper and lower boundaries of the angles between the crack surface normal and the maximum principal stress correspond to the internal friction angle of the crack surface along which sliding failure occurs. When the angle \(\beta_{1}\) approaches infinity close to the internal friction angle of the bedding plane \(\phi_{w}\) or \(\beta_{2}\) approaches \(90^{\circ}\), the crack surface strength tends toward infinity.
# Beyond Mitigation: New Metrics for Space Sustainability Assessment +
## 3 Proposed Approach Towards the Definition of a Space Sustainability Index
### Performance-Related Efficiency  
The efficiency related to system performance has no unique standard definition, being related to the particular service provided. In the frame of this work, we will focus on satellite services for broadband user internet access; under this assumption, we can state that the performance efficiency consists of two main components:  
* Coverage efficiency, \(e_{cov}\), which has a value of \(1\) when the coverage requirement is met with the minimum number of satellites; this value decreases when the constellation is oversized (i.e., the coverage requirement is met with a number of satellites higher than the minimum one). The coverage efficiency has a value equal to 0 when the coverage requirement is not met.
* Data rate efficiency, \(e_{rate}\), which has its maximum value when the designed constellation manages to serve the planned number of users (according to the business plans) with the minimum required data rate; it is 0 when it does not manage to serve the planned users with the minimum data rate.  
Therefore, the performance-related efficiency will be a combination of these two efficiencies, properly weighted, namely:  
\[e_{p}=0.5(e_{cov})+0.5(e_{rate}) \tag{9}\]In the rest of this section, the above efficiencies are defined.
## 5 Multiobjective Optimization of Main Beam Structure Based on RSM
### Multiobjective Optimization of Main Beam Structure
#### 5.2.2 Selection of Sample Points and Calculation of Real Response Value  
Four design variables with a certain value range together constituted the design space, which contained a large number of design variables with different permutations and combinations. A group of sample points were selected in the design space through the experimental design method, and the structure was optimized based on the sample points. Whether the sample points were selected reasonably affected the accuracy of the analysis results.  
The central composite design (CCD) was adopted to select the sample points. The CCD sample points were composed of cubic points, axial points, and central points [45]. The number of sample points \(n_{c}\) was as follows  
\[n_{c}=2^{n_{s}-\zeta}+2n_{s}+1 \tag{22}\]  
where \(n_{c}\) is the number of sample points, \(\zeta\) is the factorial coefficient, and \(n_{s}\) is the number of design variables. Among them, the factorial coefficient corresponding to the number of design variables and the number of sample points are shown in Table 8.  
There were four design variables in the optimal design of the main beam. According to Table 8, the factorial coefficient was 0 and the number of sample points was 25. Some sample points generated through the central composite design in ANSYS Workbench are shown in Table 9.  
The true response values of the objective functions, such as the maximum deformation and maximum equivalent stress of the main beam corresponding to each sample point in Table 9, are shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Variables** & **Initial Value** **(mm)** & **Lower Limit Value** **(mm)** & **Upper Limit Value** **(mm)** & **Physical Meaning** \\ \hline P1 & 25.0 & 22.5 & 27.5 & Thickness of left and right walls of longitudinal beam \\ P2 & 25.0 & 22.5 & 27.5 & Thickness of upper and lower walls of longitudinal beam \\ P3 & 30.0 & 27.0 & 33.0 & Thickness of left and right walls of cross beam \\ P4 & 30.0 & 27.0 & 33.0 & Thickness of upper and lower walls of cross beam \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameter values of main beam design variables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(n_{s}\) & \(\zeta\) & \(n_{c}\) \\ \hline
1 & 0 & 5 \\
2 & 0 & 9 \\
3 & 0 & 15 \\
4 & 0 & 25 \\
5 & 1 & 27 \\
6 & 1 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Number of central composite test design sample points.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 5 Results and Discussion
### Simulation 2: Performance Evaluation under Average Density Nodes Scenario (49 Nodes)
#### 5.2.3 Discussion for Jitter  
The results of the scenarios in Figure 16 show a rise in jitter levels for the two scenarios as compared to when there were 49 nodes in the network. The malicious scenario had extremely higher levels of jitter than that of the proposed framework. This result is strongly attributed to the framework's key components, such as admission control, resource reservation, and the IDS. In the normal scenario, the first two nodes show a steady hike in jitter from 0 until it reaches 101,938.83 microseconds. The 11 nodes maintain a constant level of 101,938.83 microseconds but node 14 dropped to 78,282.15 microseconds. The rest of the nodes maintain this level until the end of the application. The highest recorded jitter level remains at 101,938.83 microseconds which is fairly normal based on the number of nodes in the network. This scenario was not part of the comparison between the sinkhole and framework scenarios but rather acted as a litmus to the two scenarios. In the malicious scenario, the first two nodes show a steady hike in jitter from 0 until it reaches 119,335.24 microseconds. The rest of the nodes maintain this level until the end of the application except node 14, which has a jitter level of 78,282.16 microseconds. The framework scenario outperformed the sinkhole scenario by reducing the jitter levels to almost four times lower than when the sinkhole nodes were in action. The framework's route selection mechanism is well equipped to deal with various changes in the route or queuing network. The prioritization of packets by the framework further helped in reducing network congestion.
# Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
## 5 Conclusions  
This study applies hierarchical Bayesian modeling, using the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) approximation models to predict bus counts. The GPP model does not apply for data with spatial points close to each other. Additionally, the data distribution has a significant impact on the model accuracy; however, the normality of the data distribution can be improved by using the transformation log and the square-root function when running the MCMC algorithm. We use the Gibbs sampler to obtain the samples from the bus count data and use these samples to build spatial prediction and temporal forecasting. Different covariance matrices are used with the Gibbs sampler, including exponential, Gaussian, spherical, and Matern. We apply the these models on two different datasets with different distributions. The results show that the GPP model outperforms the AR, and GP models. In the GP model, the exponential, spherical, and Matern provide a higher accuracy, compared to the Gaussian covariance matrix. The AR model provides better prediction accuracy in terms of temporal forecasting. The results also confirm that HBM can be used effectively in spatiotemporal analysis and yields high prediction accuracy.  
Conceptualization, T.A., K.E. and T.S.; methodology, T.A.; software, T.A.; validation, T.A., K.E. and T.S.; formal analysis, T.A.; investigation, T.A., K.E. and T.S.; resources, T.A. and K.E.; data curation, T.A.; Writing - original draft, T.A; Writing - review & editing, T.A., K.E. and T.S.; visualization, T.A.; supervision, K.E. and T.S.; project administration, K.E.; funding acquisition, K.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Not Applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
Several elements that might affect the precision of facial expression analysis include: (1) the degree of detail, (2) shifts between expressions, (3) conditions that trigger expressions, (4) the consistency and accuracy of both training and testing datasets, (5) variations in facial features among individuals, (6) head position and complexity of the scene, (7) characteristics of the images, and (8) the connection to non-verbal communication (2018). Relying solely on facial expressions for emotion recognition is insufficient. A more comprehensive approach that incorporates audio, written expressions from text, and physiological data sourced from wearable devices can significantly enhance classification accuracy and improve the overall efficiency of the model (2018). However, the previous limitations were identified concerning model accuracy, the integration of human expertise (19), data privacy, security challenges, and issues related to generalizability and algorithmic bias [PERSON] et al. (2018). As with any machine learning algorithm, a large amount of training data is required. Videos with varied frame rates, from various viewpoints, with varying backgrounds, with individuals of various genders, nations, races, and so on, must be included in these data. Most publicly available datasets, however, are insufficient. They lack racial and gender diversity, as well as have only a narrow range of emotional expressions. Compared to prior studies, the current one is unique in integrating various technologies to create an integrated system for remote monitoring of COVID-19 patients. The use of an AI camera, wearable IoT devices, and a web application, can provide healthcare professionals with a more comprehensive view of the patient's health status and allow for more accurate risk assessment. Furthermore, the use of MySQL as the database system can provide an efficient way of storing and analyzing patient data. The demonstrated system can facilitate the transfer of patients between hospitals and healthcare organizations to enhance efficiency.
## 4 Discussion  
### Definition and Distribution of Key Factors to Describe Variation in the Velocity Profile  
For the UFMs under test, Daniel 3400, there are three important factors proposed by [PERSON] [18] and Daniel Inc. for examining the variation in the tested UFM performance with respect to the distortion in velocity profile. These three helpful ratios are defined as  
\[K_{\mathrm{S}}=(U_{\mathrm{B}}+U_{\mathrm{C}})/(U_{\mathrm{A}}+U_{\mathrm{D}}), \tag{6}\]  
\[K_{\mathrm{A}}=(U_{\mathrm{A}}+U_{\mathrm{B}})/(U_{\mathrm{C}}+U_{\mathrm{D}}), \tag{7}\]  
\[K_{\mathrm{C}}=(U_{\mathrm{A}}+U_{\mathrm{C}})/(U_{\mathrm{B}}+U_{\mathrm{D}}), \tag{8}\]  
where \(K_{\mathrm{S}}\), \(K_{\mathrm{A}}\), and \(K_{\mathrm{C}}\), are the swirl ratio, asymmetry ratio, and crossflow ratio, respectively. The swirl ratio compares the inner chords to the outer chords and is an indicator of swirl due to both the different radial locations and planes. In good conditions, \(K_{\mathrm{S}}\) should be close to 1.042/0.89 = 1.17 [19]. The asymmetry ratio \(K_{\mathrm{A}}\) compares the flow in the top half of the pipe (path A and B) with that in the bottom half (path C and D) and, in good condition, should be close to 1. The crossflow ratio is the ratio of the flow velocity along the paths in one plane divided by those in the other plane at right angles; in good conditions, it should be close to 1. Here, the \(K_{\mathrm{S}}\) is shown in Figure 7 for both DN250 and DN150, which was reported to reveal the power-law velocity profile rule for fully developed turbulent flow by [PERSON] et al. [20]. Furthermore, the swirl ratio is also presented as the \"profile factor\" by Daniel Inc., which is defined as the reciprocal of the meter factor, and which is sometimes called the velocity distribution correction factor in other documents [21]. The symbol shapes indicate the streamwise location of the tested UFMs (triangles: UFM1, diamonds: UFM2, circles: UFM3, squares: UFM4, gradient triangles: UFM5), and the symbol colors denote \(n\) (red: only one tested UFM, blue: two UFMs, green: three UFMs, purple: four UFMs, orange: five UFMs), as shown in Figure 8. We can see overall  
Figure 7: Relative measurement error \(E\) as a function of flowrate \(Q\) for two to three tested flowmeters.  
decreasing trend of \(K_{S}\) with increasing \(Q\). However, these results are so rough that they cannot be used to evaluate the effects of chordal velocity profiles on \(E\) (or \(\delta_{\text{UFM}}\)) under different \(n\). In contrast, the \(E\)-\(Q\) curves and simulated velocity profiles are more sensitive with respect to this theme.
# A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO
## 4 Discussion  
In this study, the application of CYGNSS v2.1 Level 2 data was examined in a numerical simulation of TC Irving and a WWB event that occurred during the 2018 January MJO event. WRF model simulations and WRFDA 3 DEnVar and 4 DEnVar data assimilation experiments were conducted to investigate the impact of the CYGNSS data when it was assimilated individually, as well as combined with the IMERG rainfall and ASCAT ocean surface wind vector observations. The goal of this study was to report on the benefits and challenge in the assimilation of CYGNSS data.  
The results indicate that CYGNSS data can capture the high winds and sharp wind gradient under heavily precipitating conditions within TC Irving and the WWB event. When CYGNSS data were assimilated individually, a positive impact was found on the wind field for both TC Irving and the WWB event. The intensity and track forecast of TC Irving were also positively affected. However, the main impact of the data appears to only last about 4 h. In addition, the influence of the CYGNSS winds on the precipitation field was modest at best. When CYGNSS winds were assimilated together with IMERG hourly rainfall and ASCAT ocean surface vector winds, significant impacts were found in the wind, pressure, and precipitation fields. The intensity and track forecast of TC Irving were largely improved. Specifically, the structure in wind and precipitation fields in the vicinity of the TC core region and across the WWB event was better represented with the assimilation of the combined satellite data. Unfortunately, one caveat of the current research is the lack of an experiment parallel to DA_com that assimilates IMERG and ASCAT data using 4 DEnVar. Due to the expensive computational cost of 4 DEnVar, we did not have the time or computational resource to conduct this experiment. Therefore, the sole impact of CYGNSS data is unclear when IMERG and ASCAT data are also assimilated. This is quite important for a thorough understanding on the overall benefit and limitation of CYGNSS observation. In future studies, we will conduct these experiments with more case studies to further explore the impact of CYGNSS data when it is combined with other datasets.
# On Ad Hoc Communication in Industrial Environments
## Appendix B Theorem of Circle Intersection Length  
**Theorem A1**.: _The chord length of a circle intersection depends on the radius \(r\) of the circle and the distance \(s\) of the chord to the center of the circle. With these two values \(\alpha\) can be determined as \(\alpha=\arcsin\frac{s}{r}\). The length of the intersection is subsequently defined as \(I(s,r)=2r\cdot\cos\arcsin\frac{s}{r}\). In the scenario of randomly driving nodes through the circle, it is assumed that \(s\) is equally distributed over \(s\in[0,r[\). With this assumption the intersection length can be averaged over the distribution as follows:_  
\[I(r)=\frac{1}{s_{max}-s_{min}}\int_{s_{min}}^{s_{max}}I(s,r)ds=\frac{1}{r-0} \int_{0}^{r}2r\cos\arcsin\frac{s}{r}ds=\frac{1}{r}\frac{\pi r^{2}}{2}=\frac{ \pi}{2}r\] (A1)