esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Modeling  
This study focuses on surface raft foundations in clay and in sand with the applications of the Mohr-Coulomb soil model to investigate nonlinear foundation behaviors. Considering the local basin soils, the shear wave velocities (\(V_{s}\)) of the soil were assigned to be 120 m/s, 150 m/s, and 180 m/s. The unit weight of the soil (\(\gamma_{\mathrm{s}}\)) was assumed at 20 kN/m\({}^{3}\). The Poisson's ratio was assumed to be 0.4 and 0.3 for clay and sand, respectively. The Young's moduli (\(E\)) of the clay can be thus calculated as 82 MPa, 128 MPa, and 185 MPa, respectively. These clays are termed as Clay 1, Clay 2, and Clay 3. Corresponding values of the Young's moduli for sandy soils were computed as 76 MPa, 119 MPa, and 171 MPa, respectively. The soils are termed as Sand 1, Sand 2, and Sand 3. For simplicity, the ground water influence was ignored in this study. Table 1 presents the material properties used in the numerical studies. To capture more realistic phenomena, nonlinear soil behavior was considered. For the clays, the empirical equation between \(V_{s}\) and undrained shear strength, \(s_{u}\) of the clays (where \(V_{s}=23\,s_{u}^{0.475}\)) suggested by [PERSON] [28] and [PERSON] et al. [29] was used to compute the undrained shear strengths of the clays. As a result, the undrained shear strengths of the clays (Clay 1-Clay 3) were found to be 32 kPa, 52 kPa, and 76 kPa. For the sands, the drained internal friction angles (\(\phi^{\prime}\)) were calculated using the correlations (\(\phi^{\prime}\) vs. SPT-N value) suggested by [PERSON] and [PERSON] [30], and the SPT-N value vs. \(V_{s}\) relation suggested by the local Seismic Design Code [31]. The corresponding values of internal friction angles for these sands (Sand 1-Sand 3) were obtained as 28\({}^{\circ}\), 31\({}^{\circ}\), and 34\({}^{\circ}\). Cohesion was taken as zero for sands in drained conditions. For square rafts with different widths (i.e., 16 m, 26 m, and 36 m) and a fixed thickness of 1 m, the \(K_{r}\) values of the foundation were computed using Equations (1)-(3). Table 2 presents the \(K_{r}\) values of the foundations evaluated using Equation (3). It is clear that nearly all the cases are semi-rigid foundations because \(K_{r}\) was in the range of 0.005-0.1. \(K_{r}\) will decrease with the increase in soil stiffness and foundation dimension. When the width of the foundation reaches 36 m and the soil becomes stiffer, \(K_{r}\) was found to be less than 0.005. If Equation (1) was applied, nearly all the foundations could be defined as
# Effect of Target Size, Location, and Input Method on Interaction in Immersive Virtual Reality
###### Abstract  
Although new virtual reality (VR) devices and their contents are actively being released, there are still not enough studies to prepare its interface/interaction standard. In this study, it was investigated whether specific interaction factors influenced task performance and the degree of virtual reality sickness when performing pointing tasks in immersive virtual reality. A smartphone-based VR device was used, and twenty-five targets were placed in a \(5\times 5\) layout on the VR experimental area that extended to a range similar to the human viewing angle. Task completion time (TCT) was significantly affected by target selection method (\(p<0.001\)) and target size (\(p<0.001\)), whereas the error rate (ER) significantly differed for the target selection method (\(p<0.001\)) and not for the target size (\(p=0.057\)). Target location was observed to be a factor affecting TCT (\(p<0.001\)), but it did not affect the ER (\(p=0.876\)). VR sickness was more severe when the target size was smaller. Gaze selection was found to be more efficient when accuracy is demanded, and manual selection is more efficient for quick selection. Moreover, applying these experimental data to Fitts' Law showed that the movement time was found to be less affected by the device when using the gaze-selection method. Virtual reality provides a three-dimensional visual environment, but a one-dimensional formula can sufficiently predict the movement time. The result of this study is expected to be a reference for preparing interface/interaction design standards for virtual reality.  
virtual reality; head-mounted display; user experience; virtual reality sickness 2021 11 9846 10.3390/app11219846
## 2 Background
### Sparse Representation
Note that compared with Equation (2), Equation (3) only replaces the \(l_{0}\) norm in the regularization term with the \(l_{1}\) norm. Convert it to the following form:  
\[\min_{x}\tfrac{1}{2}\|\mathbf{y}-\mathbf{D}\mathbf{x}\|_{2},s.t.\|\mathbf{x}\|_{1}\leq\lambda \tag{4}\]  
It is assumed that \(\mathbf{x}\) is two-dimensional to facilitate visual analysis, as shown in Figure 1a. Contour lines of Equation (4) can be drawn on the (\(x_{1}\), \(x_{2}\)) plane, and the constraint is a region with a size of \(\lambda\) on the plane (within the square contour). The place where contour lines intersect with this region for the first time is the optimal solution. It can be seen that the region constrained by the \(l_{1}\) norm has \"angles\" at the intersection of each coordinate axis, while the geodesic of the objective function will intersect at angles and generate sparsity in most cases unless the position is very well placed. In contrast, the \(l_{2}\) norm does not have such properties, as shown in Figure 1b. Because it is a circle, the probability of the first intersection occurring at the position with sparsity is very small. Therefore, converting the \(l_{0}\) norm to the \(l_{1}\) norm can also produce sparsity, while the \(l_{2}\) norm can produce smoothness but not sparsity.
## 4 Testing of the Solution
### Software Components Tests  
Local tests were made as a way to perform the operations required to carry on with the implementations works, due to the fact that they had to be regarded as successful before moving forward to more complex scenarios. These local tests were carried out in a machine with an Ubuntu operating system that was capable of handling simultaneously the Publish/Subscribe manager, CDT and VDT software components. Among other features, the connection between CDT and VDT, data transfers among them and the Publish/Subscribe manager sending commands and receiving information were checked. Overall, the performance was as it had been expected, so it was made possible to move the testing to another scenario. The appearance of all the software components being tested locally has been placed in Figure 5.
## 3 Characteristics of the Waveforms
### Contrasts in the Parameters
#### 3.4.2 Analysis of the 20 Hz Data
Due to finger rafting of the thin sheets, nilas ice usually have roughness surface [29]. In Figure 13, the average waveforms of the data from the old ice with PP values less than 3 and the average waveforms of the seawater are shown. By comparing Figure 13a with Figure 13b, it could be seen that the trailing edge slopes of the old ice were greater than those of the seawater. Meanwhile, the trailing edges of the seawater from the C Band were almost parallel to the axis. In contrast, the trailing edges of the old ice were not. This study fitted the trailing edges of the aforementioned waveforms using a least square method and the statistical data of the slopes were obtained, as shown in Figure 14.  
Figure 12: Distributions of the classifications of the HY-2A/B: **A**. Distributions of the classifications of the Ku Band’s PP values of HY-2B ALT data; **B**. distributions of the classifications of the C Band’s PP values of the HY-2B ALT data; **C**. AARI ice chart; **D** and **E**. enlarged drawings of zone 1 (old ice); **F** and **G**. enlarged drawings of zone 2 (nilas). Note: in Figure 12A,B, the same colors used in Figure 12C were used to indicate the edges of different ice types. The average waveforms of the old ice with PP values less than 3, along with those of the seawater, had presented significant differences in regard to their trailing edges. Meanwhile, the single waveforms were not found to be significantly different. As shown in Figure 14, there were many overlapping regions of seawater and old ice (PP \(>\) 3). Furthermore, there were many effects observed in the trailing edges which had resulted from the directional angles, physical properties in the scattering cells, and the noise of the echoes. It was found in this study that those effects could not be ignored in the 20 Hz data.
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 2 Preliminaries and Problem Formulation
### Model Descriptions of AC Microgrids  
Assuming that all distributed renewable energy generation units (DREGUs) are connected to the grid through voltage source inverters equipped with output LC filters, as depicted in Figure 1. Each inverter incorporates four local control units responsible for voltage, current, power, and frequency control. Specifically, when local droop controls for \(P-f\) and \(Q-V\) are utilized in primary regulation, this complete high-order nonlinear model for each distributed generator is detailed as follows [17]:  
\[\dot{\bar{\phi}}_{i}=f_{i}(\bar{\varphi}_{i})+k_{i}(\bar{\varphi}_{i})D_{i}+g_ {i}(\bar{\varphi}_{i})\bar{v}_{i}, \tag{1}\]  
\[\dot{\bar{z}}_{i}=v_{odi}=h_{i}(\bar{v}_{i}), \tag{2}\]  
The state vector \(\bar{\varphi}_{i}\), which represents the set of variables defining the status of the \(i\)-th DREGU, is formally defined as \(\bar{\varphi}_{i}=[\delta_{i},P_{i},Q_{i},\phi_{di},\phi_{qi},\gamma_{di}, \gamma_{qi},i_{idi},i_{iqi},v_{odi},v_{odi},i_{odi},i_{ogi}]^{\top}\). Where \(\delta_{i}\) represents the angular position of the \(i\)-th DREGU's coordinate system relative to a common reference frame, with an angular velocity of \(\delta_{i}=\omega_{i}-\omega_{\text{com}}\). This indicates that the coordinate system of the \(i\)-th DREGU rotates at a frequency \(\omega_{i}\), while a different DREGU is designated as the common reference system, rotating at \(\omega_{\text{com}}\). For the \(i\)-th DREGU, \(P_{i}\) and \(Q_{i}\) represent the active and reactive power outputs, respectively. Here, the voltage regulation PI controller utilizes \(\phi_{di}\) and \(\phi_{qi}\) as auxiliary state variables, while \(\gamma_{di}\) and \(\gamma_{qi}\) serve similar purposes in the current control loop. For the \(i\)-th voltage source  
Figure 1: Control block diagram of DREGUs. In the figure, the asterisk (*) is used to represent the reference value.  
inverter, \(i_{idi}\) and \(i_{iqi}\) denote the direct and quadrature current components, respectively, with corresponding voltage components \(v_{adi}\) and \(v_{odi}\). Similarly, \(i_{odi}\) and \(i_{odi}\) represent the direct and quadrature current components, respectively. It is assumed that the disturbance vector \(D_{i}=[\omega_{\text{com}},v_{bidi},v_{bqi}]\) is known. Here, \(v_{bdi}\) and \(v_{bqi}\), denoting the voltage components at \(i\)-th DREGU's location, are labeled \(v_{bi}\). Details on the functions \(f_{i}(\tilde{v}_{i})\), \(k_{i}(\tilde{v}_{i})\), and \(g_{i}(\tilde{v}_{i})\) can be found in [17].
# Bacterial Biofilm Growth on Various Dental Stabilization Systems for Avulsed and Luxated Teeth  
[PERSON]  
1 Department of Endodontics, College of Dentistry, University of Florida, Gainesville, FL 32610, USA; 1 [EMAIL_ADDRESS]
2 Oral Biology, College of Dentistry, University of Florida, Gainesville, FL 32610, USA; 2  
[PERSON]  
2 Oral Biology, College of Dentistry, University of Florida, Gainesville, FL 32610, USA; 2  
[PERSON]  
3 Department of Periodontics, College of Dentistry, University of Kentucky, Lexington, KY 40536, USA; 3  
[PERSON]  
1 Department of Endodontics, College of Dentistry, University of Florida, Gainesville, FL 32610, USA; 1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/article/pdf/10](https://www.mdpi.com/journals/article/pdf/10)]([https://www.mdpi.com/journals/article/pdf/10](https://www.mdpi.com/journals/article/pdf/10))  
a scanning electron microscope and found a direct association between bacterial infection and resorptive defects [16]. Another study has demonstrated a tight association between inflammatory root resorption and periradicular bacterial infection in a mouse model observed by MicroCT [17].  
Plaque level one of the paradoxes of dental splinting after trauma is that stabilizing the displaced tooth with a splint may lead to the accumulation of plaque, which hinders proper oral hygiene, causing bacterial contamination intra- and extraradicularly and causing root resorption [18]. Bacteria from the gingival sulcus have been detected at the root surface at the site of external resorption after traumatic injury [14,19]. In addition, periradicular bacteria may indirectly cause internal root resorption or pulpal necrosis [20]. A study by [PERSON] et al. concluded that an introduced nonoral bacteria to the oral cavity of monkeys with traumatized teeth found its way to the dental pulp [21]. Moreover, most traumatic injuries occur in children and adolescents who have larger dental tubules, allowing for the faster passage of oral bacteria [22,23]. With the advances in microbiological techniques, recent studies found a close resemblance of periodontal bacteria in the necrotic dental pulp after trauma [24,25]. Our new understanding of the importance of the bacterial load to the healing of the splinted avulsed or subluxated tooth emphasizes the importance of oral hygiene and reducing oral biofilm, especially during the first 2-3 weeks of treatment [18,26,27].  
Furthermore, the International Association for Dental Traumatology recommends a splint that offers physiological retention and proper hygiene for a favorable clinical prognosis [28]. Previous studies have also reported that bacterial load and biofilm formation play a critical role in healing [8,29,30]. Yet, there has not been a study that investigated biofilm growth levels on the splint systems available on the market. In our study, we aim to compare biofilm growth on four different commonly used splinting systems currently being used by clinicians.
## 6 Simulation Setup and Results
### Simulation Scenario  
Figure 4 shows the simulation scenario on Earth's surface. It is a small-scale simulation scenario that includes three ESIMs (one GSO-ESIM and two NGSO-ESIMs) communicating to three satellites. ESIMs are moving in a straight line with variable speeds of 25-55 m/s (i.e., between 90 and 200 km/h, representing a range of typical train speed) between the 'START' and the 'END' points. We have assumed a straight-line motion of ESIMs in favor of implementation simplicity. However, ESIMs could follow any path without implication in the operation of the proposed techniques. With any arbitrary trajectory, achieved results would remain the same, as long as some interference events are triggered by ESIMs passing near the FWA links or being within the coverage area of each other when their beams spatially align. The reason for having three ESIMs in the collision domain (overlapping of satellite footprints) of the GSO-SAT and the two NGSO-SATs, and later on in the collision domain of the FWA links when they come closer to the FWA nodes, is to demonstrate at minimum the performance of the DSS techniques and how they manage to solve the interference when spatial separation is not possible. Further, there are two NGSO orbits at an altitude of 2000 km, an inclination of 50\({}^{\circ}\) from the Equator, and ascending nodes at 27.38 W and 142.61 W. In each orbit, only one NGSO-SAT is considered, as the scenario lasts 250 s and there is no need for link roaming between satellites. Both NGSO-SATs point towards their respective focal points around 50 N, 5 E, covering an area of 70-75 km around their focal points on the Earth's surface.There is one GSO-SAT at an altitude of 36,000 km and it associates with one ESIM on the Earth's surface. There are two point-to-point MW FWA links: (1) FWA 7-8; and (2) FWA 9-10. The first FWA link is parallel to the surface of the Earth, whereas for the second link, the elevation angle from FWA 9 to FWA 10 is 32\({}^{\circ}\) aiming to provide an alignment with the GSO-SAT.  
The presented configuration of the simulation scenario is designed so as to trigger interference events between some of the participating entities per event. These are the generic interference events we aimed to create in this scenario:  
* Alignment between ESIMs, NGSO-SATs, and GSO-SAT occurs around the 100 th second of the simulation.
* ESIMs getting very close to FWA 10 and also aligning with the FWA 9-10 link.
* ESIMs passing through the FWA 7-8 link while also being very close to both transmitter FWA 7 and receiver FWA 8.  
All satellite links start the scenario by using channel 1. FWA 7-8 and 9-10 links use channels 0 and 1, respectively. By assigning the same channel to multiple links, the advantage of spatial separation of all nodes at the start is exploited, while enabling the detection of interference during the scenario in order to study the impact of the proposed DSS techniques. The initial spectrum allocation status is presented in Figure 5.
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 5 Experimental Results
### Deep Learning Models Setup  
This section provides the details of the learning model setup and architecture. Three DL models, RNN, LSTM, and GRU, were trained based on selected features \((f^{\prime})\). The RNN, LSTM, and GRU architectures are shown in Tables 6-8, respectively. The time batch size and epochs are 500 and 50, respectively. We use the _tanh_ activation function for RNN, LSTM, and GRU. Meanwhile, we use softmax for the output layer and Adam for the optimizer, with a learning rate equal to 0.01.
## 5 Results  
### Parameters Inference  
First, we compare the accuracy of all models on the _parameters inference_ task by computing the magnitude-normalized _Mean Square Error_ (\(MSE_{n}\)) between predicted and original parameters values. We average these results across folds and report variance. We also evaluate the distance between the audio synthesized from the inferred parameters and the original audio with the _Spectral Convergence_ (SC) distance (magnitude-normalized Frobenius norm) and _MSE_ (it should be noted that these measures only provide a global evaluation of spectrogram similarity, and that perceptual aspects of the results should be evaluated in human listening experiments that are left for future work). We provide evaluation results for 16, 32, and 64 parameters on the test set in Table 1.  
In low parameters settings, baseline models seem to perform an accurate approximation of parameters, with the \(CNN\) providing the best inference. Based on this criterion solely, our formulation would appear to provide only a marginal improvement, with \(VAE\)s even outperformed by baseline models and best results obtained by the \(WAE\). However, analysis of the corresponding audio accuracy tells an entirely different story. Indeed, AEs approaches strongly outperform baseline models in audio accuracy, with the best results obtained by our proposed \(Flow_{reg}\) (1-way ANOVA \(F=2.81\), \(p<0.003\)). These results show that, even though AE models do not provide an exact parameters approximation, they are able to account for the importance of these different parameters on the synthesized audio. This supports our original hypothesis that learning the latent space of synthesizer audio capabilities is a crucial component to understand its behavior. Finally, it appears that adding disentangling flows (\(Flow_{dis}\)) slightly impairs the audio accuracy. However, the model still outperform most approaches, while providing the huge benefit of explicit semantic macro-controls.
## 4 Numerical Examples
### Numerical Example 3
#### 4.3.1. Case A: Constant Initial Hydraulic Head  
In Case A, the initial groundwater level is considered to be constant as follows.  
\[h(x,t=0)=0. \tag{24}\]  
The left tidal boundary data are  
\[h(x=0,t)=A\sin(\frac{2\pi}{t_{0}}t). \tag{25}\]  
The boundary data on the inland side is described as  
\[h(x=\infty,t)=0. \tag{26}\]  
Equation (26) describes that the tide has nearly no effect if \(x\) approaches far inland. The length is 3000 m. The transmissivity is 31.25 m\({}^{2}\)/hr. The storage coefficient is \(4.5\times 10^{-4}\). The leakage coefficient is \(5\times 10^{-3}\)/d. The elapsed time is 65 hr. The amplitude of the tidal change (\(A\)) is 2.5 m. The tidal period (\(t_{0}\)) is 24.7 hr. The schematic illustration for case A is shown in Figure 8a.  
[PERSON] et al. (2013) applied the FDM to solve this problem [35]. To apply the FDM, discretization in the spatial and temporal domains were separately considered. For spatial discretization, the governing equation of one-dimensional tide-induced groundwater response for each grid point was approximated using backward difference formulas. For time discretization, the implicit scheme was adopted [35]. Since the proposed method is one of the meshfree approaches, this problem is solved by using the boundary points placed on the spacetime boundary. Therefore, we assign one source point and 163 boundary points uniformly distributed on the boundary. The boundary data are provided on boundary points. We consider the order of the basis function to be 25. To obtain the field solution, we collocate 2046 inner points. The numerical result of our method is then compared with that of the FDM. Figure 9 shows the numerical results adopting the FDM and our method. It is found that the tide-induced groundwater fluctuations computed using our method agreed well with those of the FDM.  
Figure 8: Schematic of finite coastal confined aquifer: (**a**) case A: constant initial hydraulic head; (**b**) case B: inclined initial hydraulic head.