esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Direct-Vibration Actuator for
## 3 Design Optimization
### Thickness Optimization  
The thicknesses of the top plate, yoke, and permanent magnets directly affect the force factor. The total thickness, including the top plate, yoke, and permanent magnet, should be 2.6 mm. The thickness of each component is briefly shown in Figure 6.  
Increasing the permanent magnet size will strengthen the magnetic field but will decrease the top plate and yoke thickness. A decreased top plate and yoke thickness will cause magnetic flux saturation and thus less magnetic flux to pass through the coil. Consequently, the force factor decreases. Increasing the top plate and yoke thickness will decrease the size of the permanent magnets, which also leads to a decrease in the flux density and force factor.  
The thickness of each component is changed to find out the maximum force factor. Table 3 shows seven cases for sensitivity analysis and the results when the coil is at a neutral position (0 mm). Considering the limited total height which is 2.6 mm, the changes more than 0.2 mm result in poor force factor. Thus, the optimization is conducted by changing thickness by 0.2 mm.  
In optimizing the thickness, the changing thickness from \(-0.2\) mm to 0.2 mm shows that the thickness of the yoke greatly affects the force factor. Increasing the size of the magnets lowers the force factor, which means that magnetic flux saturation occurs at the yoke. The magnetic flux flow on Case 5 and Case 3 at the actuator cross section is depicted in Figure 7. From Figure 7, the decreased yoke thickness, Case 3, is causing magnetic flux saturation. The force factor in Case 4 varies more than in Case 5 depending on the coil position. The force factor should be consistent in every position. Thus, Case 5 is chosen owing to the higher force factor at every coil position than in the original prototype. The force factor result in every coil position is shown in Figure 8.  
Figure 6: Thickness of components.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Top Plate** & \multirow{2}{*}{**Magnet Thickness**} & **Yoke** & **Total** & **Force Factor** \\  & **Thickness** & & **Thickness** & **Thickness** & **(0-mm Position)** \\ \hline Original & 0.75 & 1.15 & 0.70 & 2.60 & 1.259 \\ Case 1 & 0.95 (+0.2) & 0.95 (\(-\)0.2) & 0.70 & 2.60 & 1.222 \\ Case 2 & 0.55 (\(-\)0.2) & 1.35 (+0.2) & 0.70 & 2.60 & 1.235 \\ Case 3 & 0.95 (+0.2) & 1.15 & 0.50 (\(-\)0.20) & 2.60 & 1.171 \\ Case 4 & 0.55 (\(-\)0.2) & 1.15 & 0.90 (+0.20) & 2.60 & 1.261 \\ Case 5 & 0.75 & 0.95 (\(-\)0.2) & 0.90 (+0.20) & 2.60 & 1.263 \\ Case 6 & 0.75 & 1.35 (+0.2) & 0.50 (\(-\)0.20) & 2.60 & 1.210 \\ \hline \hline \end{tabular}  
Unit: mm.  
\end{table}
Table 3: Seven cases for thickness optimization.  
Figure 8: Thickness optimization results for seven cases.  
Figure 7: Magnetic flux density at actuator cross section.
## 3 Results and Discussion
### Groundwater Analyses  
The milligram equivalent percentage of each ion was calculated according to the content of each component of the groundwater (Table 4). It is easy to see that sodium ions account for the largest milligram equivalent percentage of cations, at 55.31%. Calcium ions were the second, accounting for 35.91%. Among the anions, chlorine ions accounted for 55.22%, which was the largest proportion of anions. The bicarbonate ions accounted for 37.92%. Water sample was analyzed by Piper diagrams [57; 58]. The sample plots fall under zone 2, 4, and 7 (Figure 7). According to the Shukalev classification method, the groundwater chemistry in the study area is of type HCO\({}_{3}\) + Cl-Na + Ca. The calculated Na\({}^{+}\)/(Na\({}^{+}\) + Ca\({}^{2+}\)) value is greater than 0.6 and Cl\({}^{-}\)/(Cl\({}^{-}\) + HCO\({}^{-}\)\({}_{3}\)) value is close to 0.5. The groundwater test data was plotted in the Gibbs model. The water sample plots the slightly upper right side of the model center (Figure 8). This reflects that the water characteristics in this region are controlled by evaporation and crystallization.  
Figure 6: Geomorphologic features of the infant stage of mud forest evolution. (**a**) The lack of vegetation on a small area of the earth’s surface leaves the loess-like soil exposed; (**b**) Many holes are shaped by subsurface erosion; (**c**) Vertical sinkhole are formed by subsurface erosion.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Item** & **Value (mg/L)** & **Test Item** & **Value (meq/L)** &
\begin{tabular}{c} **Milligram Equivalent** \\ **Percentage (\%)** \\ \end{tabular} \\ \hline Visible substances & White and turbid & \(\text{SO}_{4}^{2-}\) & 2.065 & 6.86 \\ Water temperature & 15 (\({}^{\circ}\)C) & Cl\({}^{-}\) & 16.617 & 55.22 \\ pH & 7.01 & HCO\({}^{-}\) & 11.411 & 37.92 \\ ORP & 127.1 & Na\({}^{+}\) & 20.955 & 55.31 \\ Water hardness & 842.8 & K\({}^{+}\) & 0.018 & 0.047 \\ Total dissolved solids & 2184 & Ca\({}^{2+}\) & 13.605 & 35.91 \\ Water alkalinity & 571.1 & Mg\({}^{2+}\) & 3.307 & 8.73 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical and chemical properties and main ion content of groundwater.  
Figure 8: Plot of the major ions within the Gibbs model for groundwater in study area.  
Figure 7: Piper diagrams of major ions in groundwater sample in the mud forest area.
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 5 Simulation Results
### Simulation Settings  
We assumed that the binary source data were generated independently and uniformly at the receiver. The classifier at the eavesdropper was a convolutional neural network (CNN). The input to the CNN was of two dimensions \((2,16)\) corresponding to 16 in-phase/quadrature (I/Q) data samples. The CNN consisted of a convolutional layer with kernel size \((1,3)\), a hidden layer with dropout rate 0.1, a rectified linear unit (ReLU) activation function at the convolutional and hidden layers and a softmax activation function at the output layer that provides the label _signal_ or _noise_. We applied a backpropagation algorithm with the Adam optimizer to train the CNN using cross-entropy as the loss function. The CNN was implemented in Keras with the TensorFlow backend. We assumed that the eavesdropper already knew the signal type that was used at the transmitter. Thus, the classifier at the eavesdropper was only trained with two labels, _signal_ and _noise_. For each signal type, we trained a separate classifier using different datasets, where 20,000 symbols were generated and split into blocks of 16 I/Q symbols. The channel between the nodes had path-loss effects and Rayleigh fading such that the channel gain from node \(i\) to node \(j\) was \(h_{ij}=\left(\frac{d_{0}}{d_{ij}}\right)^{\gamma}h_{i,j}\), where \(d_{ij}\) is the distance from node \(i\) to \(j\), \(d_{0}\) is the reference distance, \(h_{i,j}\) is Rayleigh fading between node \(i\) to \(j\), and \(\gamma\) is the path loss exponent. We set \(d_{0}=1\) and \(\gamma=2.8\) throughout the simulations. Note that there was only a path loss component in the channels for the simulations with CJ for the case of one antenna.  
We used the perturbation-to-noise ratio (PNR) metric from [15] that captures the relative perturbation power at the CJ with respect to the noise and measured how the increase in the PNR affected the accuracy of the classifier at the eavesdropper. As the PNR increases, the perturbation generated by the CJ is more likely to be detected by the eavesdropper and increases the BER at the receiver.
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### Intelligent Algorithm-Based Methods
#### 5.2.8 Summary  
In this subsection, we introduced seven different kinds of intelligent algorithm-based routing protocols. Due to the intelligence of the intelligent algorithm, these routing protocols are more effective and robust than traditional routing protocols; moreover, they have capability to deal with the multiple constraints of a network. The learning-based intelligent algorithms such as the FL, the NN, the RL, and the ant colony have good performance in routing discovery and routing building; the SA, the GA, and the PSO are similar intelligent algorithms which are mainly used to form clusters or choose the cluster head for the network.  
Unfortunately, excepting the fuzzy logic algorithm, the other six intelligent algorithms have not been used yet in the underwater environment. Although the availability and the performance of these intelligent algorithms have been talked about in terrestrial environments, considering the differences between the terrestrial environment and the underwater environment, the performance of these intelligent algorithms in the underwater environment also deserves to be investigated. The applications and research on intelligent algorithms in underwater acoustic sensor networks are just beginning; many excellent intelligent algorithms have not been implemented yet in underwater environments. Therefore, this is a highly potential research area for underwater acoustic sensor network routing protocol design, and we anticipate many open issues and challenges.
## 5 Experiments and Results
### Reconstruction Evaluation
#### 5.1.2 Three-Dimensional Reconstruction Model--Raytrix  
The methods presented by [PERSON] et al. [37; 38] to estimate the depth map of an image of a multi-focus plenoptic camera can be used with the Raytrix camera. This method takes advantage of the different focal lengths to perform ray tracing in order to obtain depth (back-projecting the pixels to the array of microlenses). The method begins with finding salient points and their matches in neighbor microlenses using a scaled value from the sum of absolute differences. To perform the ray tracing and to obtain more robust results, the method filters the noisy results using a RANSAC approach, eliminating unwanted results.  
Raytrix cameras have additional calibration problems as they are multifocal. As such, there are several parameters that need to be adjusted. The main objective during our vision system configuration was to determine the best camera setup for estimating a depth map, including both the photographic environment and calibration parameters.  
The results obtained the method of [PERSON] et al. [38] are presented in Figure 12, and a 3D reconstruction example using the software of the manufacturer Raytrix is presented in Figure 13.
Multi-layered structures can be easily modelled using one element per layer having either isotropic or anisotropic material properties. This method is also highly efficient for modelling of the wave propagation in structures which have a constant cross-section. Because only the cross-section is discretised, the number of finite elements used in the SBFEM reduces drastically when compared to common finite element methods, as can be seen as a comparison between the SBFEM discretisation in Figure 1b and the FEM discretisation in Figure 1c, respectively. Another feature of the SBFEM is that an infinite domain, a so-called unbounded domain, as shown in Figure 1a, can be coupled with the domain of interest, thus avoiding unwanted reflections and by that simplifying the analysis of the wave propagation. To be able to distinguish a damage in the aluminium from a damage in the CFRP in the hybrid composite addressed above, fundamental and higher-order GW modes have been determined and analysed. Additionally, to some research performed and reported in [13], a delamination case has been studied and is reported here proposing a new damage detection methodology, which allows damage localisation within the different layers. The results show that most of the wave energy (displacement) concentrates in a certain layer of the hybrid composite depending on the mode being considered and with this a damage present in the respective layer may be detected. The results obtained help to understand the wave propagation in such a multi-layered structure which may further help GW based techniques to be enhanced in the sense of NDT and SHM systems to be developed for hybrid composite structures.
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 3 GNSS Performance Characterization
### Error Modelling
#### 3.1.2 Atmospheric Errors
For single-frequency receivers, the Klobuchar model accounts for at least 50% of the root-mean-square raw code ranging error due to ionospheric delay. The residual code ranging error for GPS L1 C/A is given by:  
\[\sigma_{iono,L1}=\text{Fr}_{v} \tag{11}\]where F is the obliquity factor presented in (7), and \(\tau_{v}\) is approximated from TEC\({}_{\text{v}}\) to be:  
\[\tau_{v}=\left\{ \begin{array}{c}9\text{m for }0\leq\left|\phi_{m}\right|\leq 2 0\\ 4.5\text{m for }20\leq\left|\phi_{m}\right|\leq 55\\ 9\text{m for }\left|\phi_{m}\right|>55\end{array} \right. \tag{12}\]  
where \(\phi_{m}\) is the geomagnetic latitude. For dual-frequency receivers, the iono-free pseudorange combinations remove the first-order ionospheric delay, but higher-order errors remain. However, since their magnitude is insignificant compared to other error sources, the residual ionospheric error for these types of receivers is assumed to be zero. The troposphere is the lower part of the atmosphere extending up to approximately 50 km and its local propagations characteristics depend on several factors, including humidity, temperature and altitude. The tropospheric delay for a given slant range can be described as a product of the delay at the zenith and a mapping function, which models the elevation dependence of the propagation delay. In general, the total Slant Tropospheric Delay (STD) is given by the sum of a Slant Hydrostatic Delay (SHD) and a Slant Wet Delay (SWD), and both can be expressed by a relevant Zenith Tropospheric Delay (ZTD) and a Mapping Function (MF). The SHD (or \"dry component\") accounts for about 90% of the total tropospheric delay and accurate estimations are normally available. On the other hand, the \"wet component\" (SWD) estimations are generally less accurate and there is a significant variability depending on the actual models implemented. The total STD is given by [10]:  
\[\text{STD}=\text{SHD}+\text{SWDSTD}=\text{ZHD}\times\text{MF}_{\text{H}}+\text {ZWD}\times\text{MF}_{\text{W}} \tag{13}\]  
where ZHD and ZWD are the zenith hydrostatic delay and zenith wet delay, respectively; (ZTD = ZHD + ZWD) are their corresponding MFs. A standardized and often employed from the RTCA for the residual error after troposphere correction is [10]:  
\[\sigma_{\text{tropo}}=\left\{ \begin{array}{c}0.12\times\frac{1.001}{\sqrt{0 02001+sin^{2}EL}}\text{ }\text{ for }El\geq 4^{\circ}\\ 0.12\times\frac{1.001}{\sqrt{0.002001+sin^{2}EL}}\left[1+0.015\left(4^{\circ}- El\right)^{2}\right]\text{ for }2^{\circ}\leq El\leq 4^{\circ}\end{array} \right. \tag{14}\]
## 4 Simulation Results and Analysis
### Simulation Experiments for a Continuously Changing Road Level  
In order to verify the applicability of the proposed control strategy to the case of the road level changing randomly, simulation experiments were conducted for a light vehicle at a speed of 20 m/s with continuous changes in the road grade, and the road surface changes and body acceleration are shown in Figures 9 and 10. As shown in Figure 9, the road changed from Class B to Class C, then to Class D, and finally, back to Class C. The duration of each section was 10 s, and the total simulation time was 40 s. According to the simulation experiment results in Figure 10, it was proven that the semi-active suspension system based on PPO still had good performance under the continuously changing roads, and the body acceleration value was reduced by 46.93%.  
Figure 8: Body acceleration of fuzzy, PPO, and passive suspension systems for a heavy vehicle on class-B and -C roads.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Body Acceleration** & **B** & **C** & **D** \\ \hline Passive & 0.218 & 0.3083 & 0.436 \\ Fuzzy & 0.1973 & 0.3023 & 0.425 \\ PPO & 0.1625 & 0.255 & 0.4147 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The RMSE values of the two semi-active suspensions for a heavy vehicle under the same conditions.
## 4 Discussion
### Evolution Process and Disaster Chain Effect  
Based on optical remote sensing imagery data from the past 20 years, these landslides have been continuously occurring in the same locations due to irrigation. Early movements are typically manifested primarily as flowslides (Figure 2). After an initial flowslide, the slope body is pulled and stretched. Stress concentration and non-uniformity increase on the slope, leading to the development of tension cracks. These cracks influenced the stress distribution and seepage field of the slope, resulting in continuous landslides (Figure 5). And these cliffs, formed continuously due to the occurrence of flowslides and slides, often experience falls due to unloading effects.  
These three types of landslides continuously occur at different locations of the slope edge, and deposits are accumulated at the slope toe. Based on field geological investigation, water seepage is frequently observed at the middle and the bottom parts of the slope (Figure 6b). The groundwater level of the terraces in the study area is higher than that in other regions, as evidenced by the commonly observed water outflows. Thus, the accumulated deposits had high water content, resulting in a low strength of the accumulated deposits. Subsequent landslides are associated with loess mudflows, such as the mudflow that occurred on 9 March 2016 near Jiangliu Village. The resulting disaster chain has a greater range and distance of impact (Figure 10).
# Tuberculosis Vaccines: An Update of Recent and Ongoing Clinical Trials
## 3 TB Novel Vaccines
### Recombinant Live Vaccines
#### 3.2.4 ChadOx1-85A  
A novel chimpanzee adenoviral-vectored vaccine expressing Ag85A is currently being explored in the ChAdOx1-85A vaccine. Researchers believe that simian adenoviral vectors are advantageous because of the low prevalence of antivector antibodies in humans, a consideration that has limited adenovirus use to date [23]. Murine studies show that ChAdOx1-85A is protective when it is part of a BCG-ChAdOx1 85A-MVA85A immunization plan [23]. The first human phase I trial to evaluate the safety and immunogenicity of ChAdOx1-85A alone, and as a prime-boost regime with MVA85A, in healthy BCG-vaccinated adults has been conducted (ClinicTrial.gov: NTC01829490). A total of 42 healthy BCG-vaccinated adults were divided into four groups: six adults receiving ChAdOx1-85A alone (group 1); 12 receiving ChAdOx1-85A at a higher dose (group 2); 12 receiving a ChAdOx1-85A prime-MVA85A boost (group 3); and 12 receiving a ChAdOx1 85A-ChAdOx1 85A prime-MVA85A boost combination (group 4) [23]. Groups 1 and 2 (\(n=18\)) were followed for 168 days, while Groups 3 and 4 were followed for 224 days and 287 days, respectively [23]. Most AEs reported were mild to moderate, with no SAEs [23]. By assessing immunogenicity, it was found that Ag85A-specific ELISpot and intracellular cytokine CD+ and CD8+ T cell responses were induced, while polyfunctional CD+ T cells, and IFN-\(\gamma\), TNF-\(\alpha\), and CD8+ T cells were induced by ChAdOx1-85A and boosted by MVA85A23. ChAdOx1-85A also induced serum Ag85A IgG responses, which were boosted by MVA85A [23]. A further phase I trial was recently completed comparing aerosol and intramuscular administration (ClinicalTrials.gov: NCT04121494), while another evaluating the safety and immunogenicity of ChAdOx1-85A in the adult and adolescent population is currently recruiting (ClinicalTrials.gov: NCT03681860). A phase IIa randomized clinical trial is set to follow.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 4 Proposed Schemes
### B-Dbr  
In this section, we describe the B-DBR routing protocol, which finds the set of forwarders at each hop using the greedy opportunistic forwarding mechanism. Additionally, it uses a fall back mechanism to find an alternative route to deliver the data in the case of the void hole region. On the right side of Figure 4 of B-DBR, the fall back approach is illustrated. When node \(S\) looks up two-hop neighbor information, there is the possibility of encountering a void node. In this case, B-DBR uses backward transmission from node \(n3\) instead of depth adjustment, which consumes high energy. The node \(n3\) forwards the data packet instead of dropping to node \(n4\), which looks for its neighbors in the direction of the destination and finds nodes \(n5\) and \(n6\). Thus, greedy forwarding again is resumed till the time the packet reaches its destination.  
Figure 3: Illustration of cluster formation in C-DBR.
# Identifying Adversary Impact Using End User Verifiable Key with Permutation Framework
## 3 Proposed Permuted Security Framework
### Service Transactions  
The proposed framework achieves high service transactions for different access levels, which is depicted in Figure 11. The initial \(T_{n}\) is required for improving service distributions without reducing the change in service allocation. In the proposed framework, \(D_{T}=\{1\) to \(T\}\) is augmented to improving \(\exists_{i}\) and hence the \(n\
eq 0\) is achieved. In this case, the change in \(T_{n}\) is achieved for multiple iterations as classified by the learning process. The \(Z_{DT}\) update in different instances is required for \((T+1)\) for \(S_{r}=1\), and hence the \(\exists_{i}\) are improved. The classifier performs \((C_{r}\times\beta)\) and \((z-n)\) differentiation for improving service transactions. In the proposed framework, the validation is performed under different instances for \(|C_{f}|\). The \(D_{T}:\rightarrow\ \beta\ \forall\ I_{0T}\) mapping increases \(T_{n}\) for leveraging the distribution. Therefore, for varying access levels, the transactions are improved without increasing the overhead. The procedure is general for various \(Z_{DT}\) overwhelming service failures. Then, various transactions and access level-related comparative analyses are shown in Tables 4 and 5.  
The proposed framework reduces adversary impact, service failure, and service delay by 10.98%, 11.82%, and 10.19%, respectively. Contrarily, it improves the access rate by 7.73%.  
The proposed framework achieves 11.16% less adversary impact, 12.34% less service failure, 10.19% less service delay, 7.1% high access rate, and 10.12% high service transaction.  
Figure 10: Access Rate Analysis.