esa-sceva/satcom-chunk-collection huggingface dataset:
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 3 Methods
### Gaussian Process Regression  
A collection of random variables that have a joint Gaussian distribution can be completely described by their mean and co-variance (i.e., kernel) functions [50]. Gaussian Process Regression estimates the distribution of functions, \(x\), that are most likely given the training data. The mean of those likely functions \(m(x)\) estimates the most-likely prediction. The spread of the functions is described by the covariance \(k(x_{i},x_{j})\) of the \(i\)-th and \(j\)-th random variable. Therefore, the covariance function provides the uncertainty envelope: a measure of the confidence interval for \(m(x)\). Therefore, GPR is able to provide not only the prediction of InSAR deformation for a particular location, but also the uncertainty associated with its predictions.  
The GPR noise is handled by introducing an extra variable for representing the noise variance in the modelling [51]. The exact mathematical treatment of the Gaussian noise within GPR carries a high computational load and has been shown to compare well with computational shortcuts. In short, the noise variance is inferred from the data and is represented as extra hyper-parameters, which are trained alongside the other hyper-parameters.  
Critically, prior knowledge can be encoded through the choice of the kernels. The commonly used Radial Basis Function (RBF) kernel was adopted here and is described by Equation (2). It has a length scale parameter \(l^{2}\), which describes the smoothness of the function. This parameter controls whether the model captures the data similarity of measurements very close or far away from each other. Therefore, a large \(l^{2}\) results in a smoother function. Figure 5 shows the ability of the RBF kernel in capturing the InSAR deformation. It is clear from Figure 5a that the RBF kernel on its own tends to return to zero and, therefore, does not adequately capture the expected behaviour of the InSAR deformation. This tendency to return to zero was overcome through the combination of the RBF kernel with a linear kernel, as shown in Figure 5b. GPR models are described by hyper-parameters, which influence the predictive performance. Therefore, these hyper-parameters were optimised using gradient descent methods and the likelihood function.  
\[K_{RBF}(x,x^{\prime})=exp(-\frac{1}{2l^{2}}(x-x^{\prime})) \tag{2}\]
## 4 Discussion  
In this study, male subjects exhibited the greatest amount of energy expenditure especially during vigorous activities and showed a markedly larger PHV than females. Female participants were more powerful in science and arabic and spent the largest amount of time sedentary. In all parameters mentioned, gender differences reached the relevance level (\(p\) < 0.05 and \(\mathrm{n_{p}}^{2}\) > 0.10). The parameters of different dimensions (anthropometric vs. PA vs. AP) did not show any relevant relationship with each other.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Male** & **Female** & \multicolumn{2}{c}{**Variance Analysis**} \\ \cline{3-5}  & **(n = 23)** & **(n = 29)** & \(p\) & \(\mathrm{n_{p}}^{2}\) \\ \hline Mathematics & **82.2 \(\pm\) 8.64** & 75.9 \(\pm\) 16.8 & 0.108 & 0.05 \\ Science & 77.0 \(\pm\) 8.76 & **82.9 \(\pm\) 8.61** & **0.017** & **0.11** \\ Arabic & 73.0 \(\pm\) 8.22 & **80.9 \(\pm\) 8.25** & **0.001** & **0.19** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of academic performance parameters depending on gender and time. Values are given as mean \(\pm\) SD. Relevant differences (\(p\) < 0.05 and \(\mathrm{n_{p}}^{2}\) > 0.10) and academic performance maxima marked in bold.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Parameters** & **Male** & **Female** & \multicolumn{2}{c}{**Variance Analysis**} \\ \cline{3-5}  & **(n = 23)** & **(n = 29)** & \(p\) & \(\mathrm{n_{p}}^{2}\) \\ \hline Vigorous physical activities & & & & \\ Days/week (d) & **3.09 \(\pm\) 1.76** & 2.10 \(\pm\) 1.18 & 0.020 & 0.10 \\ Minutes/week (min) & **47.8 \(\pm\) 20.0** & 36.9 \(\pm\) 22.4 & 0.073 & 0.06 \\ MET-minutes/week & **1318 \(\pm\) 932** & 646 \(\pm\) 525 & **0.002** & **0.18** \\ Moderate physical activities & & & & \\ Days/week (d) & 2.22 \(\pm\) 1.51 & **2.38 \(\pm\) 1.32** & 0.682 & 0.01 \\ Minutes/week (min) & **40.0 \(\pm\) 19.1** & 36.9 \(\pm\) 20.6 & 0.579 & 0.01 \\ MET-minutes/week & **395 \(\pm\) 352** & 363 \(\pm\) 301 & 0.725 & 0.01 \\ Walking & & & & \\ Days/walk for at least 10 min (d) & **3.96 \(\pm\) 1.85** & 3.10 \(\pm\) 1.93 & 0.113 & 0.05 \\ Minutes per walking days (min) & **39.1 \(\pm\) 19.8** & 32.4 \(\pm\) 19.2 & 0.222 & 0.03 \\ MET-minutes/week & **547 \(\pm\) 413** & 368 \(\pm\) 343 & 0.093 & 0.06 \\ Sitting & & & & \\ Hours per weekday (h) & 3.17 \(\pm\) 2.23 & **4.45 \(\pm\) 2.71** & 0.075 & 0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of physical activity parameters depending on gender. Values are given as mean \(\pm\) SD. Relevant differences (\(p\) < 0.05 and \(\mathrm{n_{p}}^{2}\) > 0.10) and maxima marked in bold.  
It should be highlighted in this context that individuals of the same gender might report relatively substantial changeability in the timing of biological maturation, resulting in late and early matures, which may reflect a difference in PA factors.
## 2 Experimental Program
### Measurement  
The temperature on the side not exposed to fire was measured with 9 type-K thermocouples (TC1-9) with precision of \(\pm\)2.5 \({}^{\circ}\)C. The sketch showing the location of thermocouples following the requirements of DIN EN 1363-1 [21] can be found in Figure 5. The thermocouples TC1-5 were used to determine the mean temperature increase, while the thermocouples TC6-9 served to determine the maximal temperature increase on the side  
Figure 4: (**a**) Experimental set-up for fire tests under vertical eccentric loading. (**b**) Test set-up for the impact load in starting position—as in Test 2.  
not exposed to fire. While thermocouples TC6-7 followed the recommendations of the testing code, the thermocouples TC8 and TC9 were placed at the positions of connectors in the uppermost (TC8) and the middle (TC9) connector rows to measure the temperature increase considering the potential formation of thermal bridges due to presence of the connectors. Additionally, the temperature at the depth of interface between insulation and wythe not exposed to fire was measured with a thermocouple set in concrete, TC 10 (see Figure 3).  
The deformations of the walls were measured with linear-variable inductive transducers (LVDTs). The horizontal deformations were measured at the three points at the mid-height of the wall (LVDT 2) in the middle and on both left (LVDT 1) and right (LVDT 3) sides, close to the edges. The locations of the measuring points are shown in Figure 5. The vertical deformations were measured on both left (LVDT 4) and right (LVDT 5) sides of the head of the wall (see Figure 5). Additionally, in Test 1 and Test 3, the relative displacement of the facing wythe in relation to the load-bearing wythe was determined by measuring the vertical displacement of the facing wythe on the bottom edge (LVDT 6). In Test 2, this measurement was not possible because the facing wythe was enclosed within the furnace. The 400-kilonewton load cell was used to control applied vertical load. All data records were taken at the frequency of 0.2 Hz.  
Figure 5: Locations of the temperature and deformation measuring points, as well positioning of the test specimen in the testing frame of the furnace, in mm.
# Multi-Beam Circular Polarized Reflectarray on Parabolic Reflector by Variable Rotation Technique
## 1 Introduction
This independent phase control provides more flexibility in the design process in order to correct phase errors. For example, a small phase correction can be added on both polarizations to improve the beam shaping. Second, the reflectarray cells are full-wave analyzed under local periodicity assumption [20] using a method of moments (MoM) formulation in the \"space domain\" for planar periodic multilayer structures [21], which provides a great flexibility for the fast and accurate design of unit cells with arbitrarily shaped elements. Third, in spite of using a fast and accurate tool for the analysis of periodic multilayer structure [21], we propose an easy strategy for the optimization and interpolation of the geometric parameters of the reflectarray element to provide few calls to full-wave analysis routine in the design process--this will provide reduction in the consumption of the CPU time in the design process. Fourth, although in this work a specific design is carried out on a parabolic surface, the proposed scheme for designing multi-beam antennas using the VRT is shown on an arbitrarily shaped surface.
## 5 Experiments
### Staff Line Detection
#### 5.2.3 Results of Cross-Part Training  
The results of cross-part training with and without data augmentation are shown in Table 4.  
Figure 12: The left image shows typical errors during the staff line detection. Green pixels indicate TPs, red FPs, blue FNs. Either all lines in a staff are too long (third staff) or bled background is recognised (last staff). The right image shows a page with weak bleeding. In this image all staff lines are detected correctly. The zoomed sub images focus on the start and end of a staff.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Staff Detection**} & \multicolumn{3}{c}{**Hit Lines**} \\
**Model** & **Prec.** & **Rec.** & \(\mathbf{F_{1}^{8}}\) & **Prec.** & **Rec.** & \(\mathbf{F_{1}^{\text{HL}}}\) & **Total \(\mathbf{F_{1}}\)** \\ \hline Default & 0.996 & 0.998 & 0.997 & 1.000 & 1.000 & 1.000 & 0.997 \\ Data aug. & 0.988 & 0.992 & 0.990 & 0.999 & 0.999 & 0.999 & 0.989 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Staff detection and the number of recognised lines in a detected staff with and without data augmentation. The last column combines the two individual \(F_{1}\) measures by multiplication.  
In general, it can be observed that on any split our algorithm obtains an \(F_{1}^{\text{D}}\)-score of 98.8% or higher in the staff line detection. On part 1 even 100% are reached, thus all staff lines are found with an overlap of at least 50%. This shows that the staff line detection algorithm is very robust to new layouts. It is significant that part 2 suffers from data augmentation more clearly than part 3, while on part 1 still a score of 100% is reached. The prediction of the line length is untouched. This can be explained by the different kind of quality of the parts. While part 3 suffers very weakly from bleeding staves and its lines are drawn very clearly, part 2 consists of pages where bleeding lines are present and some staff lines are barely visible and thus are more similar to background noise. Data augmentation creates, among other things, pages where the contrast of data is adjusted, which can explain why it is harder to distinguish actual staff lines, background and bleeding in part 2. In general, part 2 also yields the lowest \(F_{1}^{\text{D}}\)-score of 96.8%, which also shows that this part is the most complicated one.  
The staff line lengths are detected very similar across the parts with a mean \(F_{1}^{\text{LF}}\)-value of 98.6% and 98.7% (with and without data augmentation), which is almost identical to \(F_{1}^{\text{LF}}\) when training and evaluation on all data of 98.5% (compare Table 2). Hence, we conclude that the crucial component of the algorithm is to detect where staff lines are located and not to find the correct length of staff lines.  
Note that the accuracies of these experiments are averaged over the parts while the results in the previous Table 3 are the mean of all pages, which is why the values should not be directly compared since part 2 consists of more instances than the other parts.
## 4 Performance Evaluation
### Noise Faults
From these results, it can be seen that AT is well adapted to the large changes from the small changes by using the proposed thresholds for all velocities, and any FP has not occurred at all. In the noise faults, except for 1.3 m/s, as KT does not have FPs, its precision achieves 100%. However, at 1.3 m/s constant FPs occur regardless of the intensity and the duration, but TPs increase  
Figure 11: Recall according to each duration as a function of noise fault intensity: (**a**) Cumulative sum of the recall at 0.7 m/s of Jackal UGV; (**b**) Cumulative sum of the recall at 1.0 m/s; (**c**) Cumulative sum of the recall at 1.3 m/s; (**d**) Cumulative sum of the recall at 1.6 m/s. Figure 12: Precision according to each duration as a function of noise fault intensity: (**a**) Cumulative sum of the precision at 0.7 m/s of Jackal UGV; (**b**) Cumulative sum of the precision at 1.0 m/s; (**c**) Cumulative sum of the precision at 1.3 m/s; (**d**) Cumulative sum of the precision at 1.6 m/s. as the duration becomes higher. In addition, KT at 1.3 m/s shows better precision than KT in random faults. This is because the magnitude of the noise faults is large enough to be detected even though the intensity for them is the lowest (\(\eta_{0}=0.5\)). Nevertheless, the FPR (false positive rate) of the KT is in the range of about 58% to about 18% at 1.3 m/s. In RT, the FP is increased significantly as the duration increases at the certain levels of intensity (\(\eta_{0}\geq 1.5\)). When the intensity is below 1.5, RT is not capable of detecting noise faults.
## 6 Conclusions
### Three-Layered MMs for Surface Plasmon Polariton Guiding  
SPP supported by graphene/dielectric multilayered MMs was studied theoretically. It is concluded that SPP can be supported by a three-layered MM. Also, one can easily manipulate the characteristics of the multilayered SPPs by manipulating the structural parameters. Thus, the active materials can be included into the MM design, which might open wide avenues for future devices.  
The dielectric permittivity should be smaller than the absolute value of \(Re(\varepsilon_{m})\), aiming to fulfill the EM boundary condition and statements of continuity [41] in the case of a traditional metal SPP. If not, the SPP mode would be cut off. The property of anisotropic material is represented by multilayered MMs if the case is considered from the effective medium perspective. Further, it is shown in Figure 9 that one may deal with multilayered MMs as with anisotropic metal near the upper bound of its SPP frequency region (in band 2). The \(\text{Re}(\varepsilon_{\perp})\) is far from zero, while \(\text{Re}\Big{(}\varepsilon_{||}\Big{)}\) is much closer to zero within band 2 of Figure 9, compared with \(Re(\varepsilon_{m})\) of pure metal.  
The effective permittivity is caused by the interaction of the EM field with the free electrons in graphene sheets and the SPP mode coupling and resonance in multilayered MM. Consequently, the SPP frequency region is extended due to the SPP mode coupling and resonance in the multilayered MMs, characterized by anisotropic permittivity.  
It is worthwhile noting that the multilayered SPPs could also be excited by means of the evanescent EM field generated by a prism or waveguide. Extended frequency range, lower propagation loss, and controllable features are possible due to the changes of the graphene/dielectric ratio, and these properties might open a gateway for possible applications in biosensor and active SPP devices.  
Herein, we have theoretically considered the SPP guided by dielectric/graphene/dielectric multilayered MMs. One may conclude that an SPP can propagate when the MM is at the frequency where a conventional metal SPP cannot occur, as in the case of high permittivity of dielectric on metal. In addition to the variance of a high frequency cut-off point in comparison with metal SPPs, it was observed that a low cut-off frequency, below which SPP could not be supported by the MM, exists.  
These outstanding properties open wide avenues for the usage of an SPP mode for some potential applications, which are impossible for metal SPPs. In addition to the characteristics of the multilayered SPPs being easily controlled by tuning the composite parameters, the active materials can also be incorporated into the MM. This described feature might also create new possibilities for future devices.
# Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System
## 4 Empirical Study
### Physical Simulation  
#### 4.2.1 Description of the Physical Testing Ground  
Prospects for further development of theoretical models involve the consideration of their practical application on real physical objects. To understand the possibilities of implementing the presented model based on reputation and trust in real world, we have developed the physical testing ground, which includes a model of the intersection and three MAVs, assembled from available components.  
Figure 4: Average total number of vehicles.  
Figure 3: Histograms of percentage of FN and FP error rate: (**a**) FN from total number of AVs, which transmit incorrect data; and (**b**) FP of total number of vehicles, which transmit correct data.  
The following is a description of physical testing ground, MAVs, setup and methodology of the physical simulations, and results of the experiments.  
We have developed a group of MAVs to implement the theoretical model on the physical ground. Figure 5 represents how each model looks. All MAVs have the same characteristics: appearance, software, hardware, speed, etc. Each MAV consists of:  
* single on-board computer;
* servos' control module;
* two servos;
* video-camera;
* ultrasonic range finder (URF);
* wireless module; and
* power supply unit.  
To conduct physical simulations, the model of the intersection should provide an imitation of a part of the urban road network. To establish the current location of any MAV, the roads were split into elementary square sections. Figure 6 presents the developed model of the roads and intersections. The roads consist of elementary sections that were colored in black and white and have borders colored in red.  
The main task of the MAVs is to drive according to the generated path. This path is compiled by an algorithm that generates a random endpoint and finds the shortest path. The MAVs should drive without any crashes. Therefore, they need to interact with each other to exchange the data on their location. MAVs transmit their current location each time moment and can predict conflicts. To solve possible conflicts on the intersections, models rely on the priority rules.  
Figure 5: Model of unmanned autonomous vehicle.  
MAVs use video cameras to detect their position. The cameras detect the color of the section on which they are located. Then, this color is compared with the color of the previous section and the decision on the current location is made. It is possible because sections color is only black or white. Red-colored borders of the road help MAVs to detect borders using video cameras. Each MAV has a URF to detect other MAVs in front of it.  
The implementation of the reputation and trust model allows solving these problems and helps detect intruders within the information interaction of the MAVs' group.
## 5 Results and Discussion
### Orientation  
Effective RF index for varying frequency and optical mode index is calculated for the two orientations (0\({}^{\circ}\) and 30\({}^{\circ}\)). Using Equations (3)-(6), values for effective Pockel's coefficients are calculated, and using the results in Figure 4, \(V_{\pi}L\) is calculated by substituting the values in Equation (1). \(V_{\pi}L\) and \(\Delta V_{\pi}L\) are plotted against the D and RF frequency for the two orientations in Figure 7. The results obtained in Figure 7a show that \(\Delta V_{\pi}L\) increases initially with D toward the center of \(\mathrm{w_{g}}\) and then reduces. These results are consistent with the results obtained in Figure 4b. It is evident from the results in Figure 7b that to maintain the same order of performance in the device rotated by an angle of 30\({}^{\circ}\), the \(V_{\pi}\) increases. \(\Delta V_{\pi}L\) also increases due to increase in the difference between effective RF and optical mode index with the frequency.
## 2 Steering and Vehicle Systems Modeling  
### Dynamic Model of Steering System  
To incorporate the steering system model into the vehicle dynamics control framework, only the torque of the steering motor is used as the control input for the vehicle's steering mechanism [28]. \(N_{s}\) represents the motion ratio between the motor reducer and the steering system, whereas \(N_{m}\) stands for the motion ratio between the steering knuckle and the steering wheel. Figure 1 provides a summary of the essential system configuration. In this model, all forces are evenly applied to the central pin. The correlation between the two motion ratios is defined as stated in [29].  
\[J_{sq}\ddot{\delta}+B_{eq}\dot{\delta}+\tau_{dis}+\tau_{f}\operatorname{sgn}( \dot{\delta})=N_{s}N_{m}\tau_{m} \tag{1}\]  
where the torque of the steering motor is denoted as \(\tau_{m}\), while \(\tau_{f}\) represents the equivalent friction constant of the steering system. In addition, \(\tau_{dis}\) refers to the steering resistance torque resulting from the force of the front tires. The equivalent inertia and damping of the steering system with respect to the kingpin are represented by \(J_{eq}\) and \(B_{eq}\), respectively, with specific values provided as follows:  
\[\begin{split}& J_{eq}=J_{fw}+(N_{m}N_{s})^{2}J_{m}+N_{s}^{2} \Big{(}J_{c}+m_{r}r_{p}^{2}\Big{)}\\ & B_{eq}=B_{fw}+(N_{m}N_{s})^{2}B_{m}+N_{s}^{2}\Big{(}B_{c}+B_{r }r_{p}^{2}\Big{)}\end{split} \tag{2}\]  
where \(J_{fw}\) and \(B_{fw}\) are the moment of inertia and viscous friction of the front wheel, respectively, \(J_{m}\) and \(B_{m}\) are the moment of inertia and damping coefficient of the motor, \(J_{c}\) and \(B_{c}\) are the moment of inertia and damping coefficient of the lower steering column, \(r_{p}\) is the radius of the pinion, \(B_{r}\) is the rack damping coefficient, and \(m_{r}\) is the mass of the rack.  
Based on Equation (2), taking the steering wheel angle \(\delta\) and steering wheel angle rate \(\dot{\delta}\) as the state variables of the steering system, the state equation can be represented as follows:  
\[\dot{x}_{1}=A_{1}x_{1}+B_{1}\tau_{m}+N_{1}\omega_{\tau} \tag{3}\]  
\[y_{1}=C_{1}x\]  
where  
\[A_{1}=\left[\begin{array}{cc}0&1\\ 0&-\frac{B_{eq}}{I_{eq}}\end{array}\right]\quad B_{1}=\left[\begin{array}{ cc}0\\ \frac{N_{1}N_{1}}{I_{eq}}\end{array}\right]N_{1}=\left[\begin{array}{cc}0\\ -\frac{1}{I_{eq}}\end{array}\right] \tag{4}\]  
\[C_{1}=\left[\begin{array}{cc}1&0\end{array}\right]\quad\omega_{\tau}=\tau_{ dis}+\tau_{f}\,\text{sign}(\delta)\]  
According to Equation (3), the dynamic relationship between the motor torque \(\tau_{m}\) and the steering wheel angle \(\delta\) can be obtained. The steering resistance torque \(\tau_{dis}\) and the friction torque \(\tau_{f}\) are external disturbances of the steering system.