esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### TPCB's Effect on the Pavement Performance of an Asphalt Mixture
#### 3.4.2 High-Temperature Performance  
The dynamic stability (DS) and rutting depth of GF70 and TPCB/GF70 asphalt mixtures were tested by a wheel tracking test to evaluate TPCB's and AHO's effects on the high-temperature performance of asphalt mixtures. Figure 8 illustrates the DS and rutting depths of GF70 and TPCB/GF70 asphalt mixtures.  
It can be seen from Figure 8 that the DS values of TPCB/GF70 asphalt mixtures are all higher than those of GF70 asphalt mixtures, while the rutting depths of TPCB/GF70 asphalt mixtures are all smaller than those of GF70 asphalt mixtures. In detail, with the addition of 15% dosage of TPCB, the DS of the AC-13 mixture increases by 31.8% and the rutting depth decreases by 29.8%; the DS of AC-20 mixture increases by 27.2% and the rutting depth decreases by 14.8%. The higher the DS, and the smaller the rutting depth, the better the anti-rutting performance of the asphalt mixture. Therefore, TPCB can improve the anti-rutting performance of asphalt mixtures, and the improvement effect of TPCB on AC-13 mixture is more obvious than that of the AC-20 mixture. The enhanced effect of TPCB on the high-temperature performance of GF70 asphalt mixture can be explained in two ways: one reason is that TPCB is a kind of inorganic nanoparticle that has a hardening effect on GF70 and decreases the temperature sensitivity of GF-70, so GF-70 tends to be harder after being modified by TPCB; the other reason is that the TPCB has a large specific surface area, so needs more asphalt binder to cover its surface. The free asphalt content in mixtures decreases while the structural asphalt contents increase; as a result, the high-temperature performance of asphalt mixtures is enhanced.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{8}{c}{**Passing Ratio of Every Mesh (\%)**} \\ \cline{2-11}  & **26.5** & **19** & **16** & **13.2** & **9.5** & **4.75** & **2.36** & **1.18** & **0.6** & **0.3** & **0.15** & **0.075** \\ \hline AC-13 & 100 & 100 & 100 & 95.2 & 80.4 & 53.2 & 36.9 & 25.8 & 17.2 & 11.5 & 8.1 & 6.4 \\ AC-20 & 100 & 93.6 & 84.3 & 71.4 & 61.2 & 41.8 & 29.4 & 21.8 & 15.2 & 10.0 & 7.2 & 5.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Aggregate gradation of AC-13 and AC-20 mixtures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Mixtures**} & **Asphalt** & **Optical Oil Aggregate Ratio (\%)** \\ \hline \multirow{2}{*}{AC-20} & GF70 & 4.5 \\  & TPCB/GF70 & 4.6 \\ \hline \multirow{2}{*}{AC-13} & GF70 & 5.1 \\  & TPCB/GF70 & 5.2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optical oil aggregate ratio of GF70 and TPCB/GF70 asphalt mixtures.
## 3 Design Method for the Vibration Isolation System
### Static Analysis of the QZS System  
The aim of the following analysis is to set the equilibrium position and to determine when the equivalent stiffness of the system is equal to zero. The equilibrium position is determined when the angle of inclination for the oblique springs is equal to zero. At  
Figure 3: Dynamic model of the proposed system.  
this equilibrium position and under a compression force acting on the slider mass (\(m\)), the vertical spring and the two oblique springs are compressed, as shown in Figure 4.  
It is found that the summation of the forces acting on block \(m\) is  
\[F=k_{\triangledown}(x+h_{0})+2k_{n}x(1-\frac{L_{0}}{\sqrt{(a_{0}-x_{h})^{2}+x^{2 }}}) \tag{12}\]  
where \(k_{\triangledown}=k_{p}+k_{ss}\), and \(k_{as}\) is the air-spring stiffness. It is worth noting that, in the situation where the applied force can be handled by the vertical spring alone, no air is delivered to the air spring and thus the vertical stiffness approximately equals \(k_{p}\) (see Sections 3.2 and 4.1 for more details). Conversely, when the force exceeds what the vertical spring can withstand to maintain equilibrium, this over-weight can be supported by the air spring, but at the expense of an increase in the stiffness. However, this poses no problem thanks to the presence of a horizontal-adjustment mechanism that can negate this situation by keeping the geometrical ratio and stiffness ratio equal.  
At the equilibrium position, \(x=0  
In addition, the geometric and stiffness ratios are expressed in the dimensionless form, as follows:  
\[\lambda=\frac{\hat{a}_{0}-\hat{x}_{h}}{2(1-\hat{a}_{0}+\hat{x}_{h})}\;\;and\;\; \alpha=\frac{k_{n}}{k_{p}} \tag{18}\]  
Based on this, Figure 5 can be now obtained by changing the values of the factors that affect the stiffness of the system at different ratios between the geometry and the stiffness. As expected, the best range is achieved when the geometric ratio was equal to the stiffness ratio.  
Results shown in Figure 5 lead to defining the best angle of inclination for the oblique springs that achieves the best range of stability (see Figure 6).  
The next step is to find a good approximated representation of the force and stiffness equation. Therefore, a third-order Taylor series is used, as follows:  
\[\hat{F}=[1+2\alpha(1-\frac{1}{\hat{a}_{0}-\hat{x}_{h}})]\hat{x}+\frac{\alpha}{( \hat{a}_{0}-\hat{x}_{h})^{3}}\hat{x}^{3} \tag{19}\]  
When the geometric and stiffness ratios are equal, Equation (19) can be reduced as follows:  
\[\hat{F}\approx\frac{\alpha}{(\hat{a}_{0}-\hat{x}_{h})^{3}}\hat{x}^{3} \tag{20}\]  
By taking the derivative of the above equation with respect to \(\hat{x}\), the dimensionless stiffness parameter may be expressed as  
\[\hat{K}\approx\frac{3\alpha}{(\hat{a}_{0}-\hat{x}_{h})^{3}}\hat{x}^{2} \tag{21}\]  
Figures 7 and 8 show that the exact and the approximated solutions are approximately the same, and thus can be used for the analysis discussed in Section 4.  
Figure 6: Force and stiffness versus displacement for different oblique angles.
### Network Architecture and Requirement of Uwsn
#### 1.3.1 1D-Uwsn Architecture  
One-Dimensional (1D) [8,9] UWSN refers to sensors that sense, process, and relay the data to the base station all by themselves [10]. An example of this is a buoy used to sense the water properties, it can submerge for a certain period of time to collect information and then float back to transmit the signal to the base station.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 4 Results and Analysis
### Comparative Analysis of Model Prediction Performance
#### 4.3.2 Analysis of Formation Adaptability of Different Models  
For an in-depth analysis of the adaptability of different models in stratigraphy, the prediction results for each of the three soil types, namely medium weathered sandstone, silty clay, and composite strata, are detailed in Figure 14.  
When advancing in the medium weathered sandstone, the penetration will have a sudden drop phenomenon, such as in area (a) in Figure 14. According to the absolute deviation results of the model, we find that the deviation of the prediction results of the LSPP in this section is relatively small, especially after the sudden change, the LSPP can track the actual situation in time, and the deviation is only 4 mm/rev, as shown in Figure 15. In the composite strata, due to the constant change in the strata occupation ratio, the change in the shield penetration does not have any obvious pattern, such as in the (b) area in Figure 14, in which the LSTM and the GRU network only fluctuate around a mean value, which cannot effectively predict the future state of the shield structure to provide valuable guidance. Furthermore, the LSPP model can more clearly portray the fluctuation trend, indicating that the shield driver should pay attention to changes in the stratum and alter  
Figure 13: Next 5 rings prediction sequence.  
crucial construction factors, such as advance speed. Under the pulverized clay stratum, such as area (c) in Figure 14, the shield propels smoothly, so even if the LSTM and GRU fluctuate around the mean value, better results can be achieved.  
From the absolute deviation between the predicted and true values of each model, as shown in Figure 15, it can be seen that the overall prediction deviation of the proposed LSPP is small, especially when there is a large change in the shield penetration, the area marked by the red box in the figure, the deviation of the LSTM and the GRU appears to be a significant increase, but the prediction error of the LSPP does not have a significant change. It can be seen that LSPP is also better than LSTM and GRUs in terms of stratigraphic adaptability.  
Figure 14: Predicted results of each model at the predicted length of 5 rings. (**a**) Predicted results under medium weathered sandstone. (**b**) Predicted results under composite strata. (**c**) Predicted results under silty clay.  
Figure 15: Absolute deviation in predictions of different models.
## 3 Results and Discussion
### Characterization of the N-TiO\({}_{2}\) Powder
#### 3.1.4 UV-Vis Spectrophotometry Analysis  
The E\({}_{\text{g}}\) of the nano-powder was estimated via a UV-Vis spectrophotometry analysis. Firstly, the reflectance of the N-TiO\({}_{2}\) powder is measured in Kubelka-Munk (K-M) units, based on the equation:  
\[\text{F(R)}= \frac{\left(1-\text{ R}\right)^{2}}{2\text{R}} \tag{3}\]  
Figure 2: Raman spectra of N-doped TiO\({}_{2}\) powder and pure anatase.  
where R is the reflectance. This method is usually applied when the samples measured exhibit high absorbance or light scattering [48; 49; 50; 51]. Figure 4 illustrates the change in the reflectance of the powder within the visible light spectrum.  
Figure 4: Reflection F(R) to wavelength (nm) graph for N-doped TiO\({}_{2}\) powder.  
Figure 3: XPS spectra of N-TiO\({}_{2}\) particles: (**a**) survey scan, (**b**) high resolution of Ti2p peak, (**c**) high resolution of deconvoluted O1s peaks, and (**d**) high resolution of N1s peak.  
In order to calculate a semiconductor's E\({}_{\text{g}}\), [PERSON]'s equation is used:  
\[\text{ahv}=\text{A}(\text{hv}-\text{ E}_{\text{g}})^{\text{n}} \tag{4}\]  
where E\({}_{\text{g}}\) is the energy band gap, a is the absorption coefficient, A is a constant, and n = 1/2 for direct and n = 2 for indirect transition band gaps [52]. Consequently, the band gap energy is obtained by applying the K-M method and extrapolating the linear region of the spectra \((\text{F}(\text{R})\text{hv})^{\frac{1}{2}}\) vs. hv, as presented in Figure 5.  
The measured band gap of the produced N-TiO\({}_{2}\) nano-powder revealed that there is a decrease of E\({}_{\text{g}}\) compared to pure anatase, due to doping with N. This may be related to a better photocatalytic activity under visible light irradiation, since the catalyst needs lower energy in order to be activated. These findings are in close agreement with similar studies, with the observed red shift of E\({}_{\text{g}}\) attributed to a combination of both substitutional and interstitial doping of N [7].
## 4 Results  
An example of the workflow to determine the anatomical relevant space is shown in Figure 3. Figure 3 illustrates the segmented myocardial boundaries as overlays over the three orthogonal slices through the middle of the ultrasonic volume. Given these binary coordinates, the myocardial coverage per line is quantified as displayed in Figure 3. As expected, the lines crossing the ventricular walls have the highest percentage of coverage while the ones crossing the apex have the lowest. Finally, the percentage of coverage can be chosen using the ring-shaped template matching. Logically, the smaller the inner radius and the bigger the outer radius, the higher the total myocardial coverage, Figure 3.  
Figure 2: View of the tested multi-line transmit (MLT) systems looked from a C-plane at 60 mm with respect to the rectangular aperture (central blue square). The MLT beams are spread out in an equiangular manner in the circumferential direction of the cone while steered in the radial direction. In this way, the beams are as far apart as possible and therefore the inter-beam interference is reduced.  
For all datasets tested, the mean opening angle (\(\Phi\)) for the conical scan was \(19.7^{\circ}\pm 8.5^{\circ}\) while the mean \"thickness\" of the cone (\(d\Phi\)) was \(19^{\circ}\pm 3.4^{\circ}\). Therefore, a reduction of 48.9% in the number of scan lines required to cover the FOV at full-line density was achieved, approximately doubling the frame rate. Given this estimated \"mean\" conical FOV, the cross-talk energy level of all the MLT setups was calculated. Figure 4 shows an example of the two-way beam profiles in a C-plane used to calculate the inter-beam interference of a 10 MLT system, where a beam was transmitted in one direction and received in each of the MLT directions. The cross-talk energy levels of the tested systems are presented in Figure 5. As can be noted, the 8 MLT and 9 MLT systems reached the pre-defined threshold of \(-30\) dB, while the 10 MLT was only half a dB above. The 12 MLT system on the other hand was about 4 dB above the cut-off.  
Figure 3: Example of the workflow to determine the anatomical relevant space: (**a**) Myocardial segmentation shown as a mask overlaid in three orthogonal slices through the middle of the ultrasound volume (from left to right: azimuth, elevation and depth); (**b**) Computed coverage function expressing the percentage of pixels along each image line that belong to the myocardium; (**c**) Percentage of myocardial coverage as a function of the inner radius (_Ri_) and the outer radius (_Ro_) of the ring-shaped template.
# Studies regarding the Use of Pneumatic Muscles in Precise Positioning Systems
## 2 Materials and Methods  
### Components of Experimental Setup  
The experimental setup used for determining the relation between the axial contraction (\(\Delta\)I) of the pneumatic muscle and the internal pressure (\(p\)) applied gradually consisted of several components, as presented in the Figure 1.  
The investigated pneumatic actuated muscle (Figure 1(a)) is manufactured by FESTO, Germany (MAS-10-N-100-AA-MCFK), and its characteristics are presented in Table 1.  
Figure 1: Experimental setup. (**a**) Picture; (**b**) control diagram.  
The control diagram is presented in Figure 1b. The pressure of the compressed air delivered by the compressor was set by the pressure control valve with a manometer. The value of the compressed air pressure was read at the display of the analog pressure sensor. The resulted displacement of the muscle was measured after each experiment.  
The components of the experimental stand were from Festo Ag & Co., and their main characteristics are presented in the table below (Table 2):
## 4 Satellite Precision Orbit Determination and Accuracy Analysis
### Influence of PCV on HY-2B/C/D Satellite Orbit Determination
As a post-processing method, the residual method has high computational efficiency and is similar to the PCV model estimated by the direct method. The improvement of orbit determination accuracy is basically the same. Considering both computational efficiency and accuracy, this paper opts for the residual method to estimate the PCV model for reduced-dynamic precision orbit determination. Figure 6: PCV model (**(a)** HY-2B Residual method PCV model, **(b)** HY-2B Direct method PCV model, **(c)** HY-2C Residual method PCV model, **(d)** HY-2C direct method PCV model, **(e)** HY-2D Residual method PCV model, **(f)** HY-2D Direct method PCV model).
# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 2 Related Work
### Research Gap  
Despite breakthroughs in IoD security, some important gaps still exist. Current authentication schemes typically do not handle the peculiar combination of mobility, resource limitations, and timeliness typical of drone networks. Moreover, although machine learning has exhibited great promise in strengthening security in a variety of contexts, its use in the context of IoD authentication is under-investigated. In addition, holistic security analysis that combines formal verification with performance evaluation in realistic scenarios is largely missing from the existing literature. Though recent contributions have considered separate features such as energy efficiency [49] or behavior-based authentication [58], there areno prior works that combine these factors within a unified framework verified using both rigorous formal security verification and genuine performance analysis. Our contribution fills this gap with anintegrated approach.
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 7 End-To-End Delay Analysis
### Algorithmic Procedure  
Here we summarize the procedure. First, we obtain the output and the delay of the contention process, \(L(z)\) and as \(AC(z)\), with mean values \(L^{{}^{\prime}}(1)=\overline{L}\) and \(AC^{{}^{\prime}}(1)=\overline{AC}\), respectively. Observe that this is executed for each CH. Second, the ergodicity conditions \(\rho_{1}<1\) must be satisfied at all CHs located at ring 1. Otherwise, the offered traffic at local level must be reduced until the ergodicity condition is met. Third, for each CH of every ring we obtain the sojourn time, Equations (20) and (21); first for all CHs located in the outermost ring, second for all CHs located in the inner ring next to the previous one, and so on, until reaching the first neighbor ring of the sink. Fourth, from Equation (23) we obtain \(\overline{W}_{(x,y)}^{\prime}\) for all CHs of every ring. Finally we evaluate the end-to-end delay using Equations (30) and (31).
# High-Fidelity Fin-Actuator System Modeling and Aeroelastic Analysis Considering Friction Effect
## 3 Modeling of the System
### Modeling of the Electromechanical Actuator
#### 3.4.4 Model of the Controller and Sensor  
The fin deflection angle is measured with an angular displacement sensor that gives position feedback to the controller. The transfer function of the sensor can be considered as a second-order element, which is given by:  
\[G(s)=\frac{\omega^{2}}{s^{2}+2\zeta\omega s+\omega^{2}} \tag{15}\]  
The controller is used to adjust the input signal so that the output signal of the actuator can keep the track of the command signal. The most commonly used control law is to control the error between the command signal and the measured signal using proportional (P), integral (I), and derivative (D) terms [50]. The conventional actuator is a closed-loop system, and its controller usually includes amplification, filtering, and a PID control. The principle diagram of a PID control is shown in Figure 8, where \(\delta_{\text{cmd}}\) is the command signal.  
Figure 8: The controller and sensor model of the actuator.
## 3 Results and Discussion
### Performance of Multi-GNSS Static PPP Survey (Test B)  
In this section, the results obtained processing the measurements of Test B, with the standard PPP algorithm, are shown. All possible combinations of GPS, GLONASS, and Galileo are considered, the Estimate ZTD tropospheric model is adopted, and the outlier rejection strategy is applied (Table 6).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**GNSS**} & \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Technique**} & \multicolumn{3}{c}{**Standard Deviation (mm)**} & \multicolumn{3}{c}{**Error (mm)**} \\ \cline{3-10}  & & & \(\sigma_{\mathbf{x}}\) & \(\sigma_{\mathbf{y}}\) & \(\sigma_{\mathbf{z}}\) & \(\mathbf{x}\) & \(\mathbf{y}\) & \(\mathbf{z}\) \\ \hline GPS & Clock 15 min & PPP & 23 & 27 & 38 & 17 & 3 & 18 \\ GPS & Clock 30 s & PPP & 6 & 5 & 4 & \(-\)13 & 2 & \(-\)3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Standard deviations and errors of GPS-PPP (Precise Point Positioning) with 15 min clock data and GPS-PPP with 30 s clock data. Estimate ZTD tropospheric model and outlier rejection strategy are used.  
For Test B, all the considered configurations demonstrate worse performance with respect to homologous configurations in Test A; this is probably related to the different equipment adopted in the two tests and, above all, to different amounts of processed data, significantly larger in Test A.  
About the single GNSS configurations, the GLONASS-only case provides the best results in terms of both standard deviation and error, while degraded performances are evident for the GPS-only and, above all, for the Galileo-only case. The double constellations cases demonstrate better performance with respect to single constellation ones; the best performance between the double constellation configurations is GPS+GLONASS with a tridimensional error of about 6 cm.  
Triple constellation GPS+GLONASS+Galileo has the best performance according to all the figures of merit; in particular, in this case the tridimensional standard deviation and error are, respectively, about 6 cm and 5 cm.
# Method to Characterize Potential UAS Encounters Using Open Source Data
## 2 Materials and Methods
### Use Cases and Data Sources
Federal and state freeways and primary roads were sourced from Natural Earth Data ([[https://www.naturaleardhata.com/downloads/10m-cultural-vectors/roads/](https://www.naturaleardhata.com/downloads/10m-cultural-vectors/roads/)]([https://www.naturaleardhata.com/downloads/10m-cultural-vectors/roads/](https://www.naturaleardhata.com/downloads/10m-cultural-vectors/roads/))). While electric power lines, railways, and roads are mostly above ground, the majority of oil pipelines are buried underground. This results in relatively straighter infrastructure layouts. Polygons of golf course perimeters were also sourced from the Geofabrik OSM extracts as a representative recreational feature. Other recreational features such as beaches and lakes were considered but ultimately not included to manage computation resources. Agricultural land use features such as farms and vineyards were not included because we heuristically assessed that OSMdid not have sufficient coverage and we could not find an appropriate alternative dataset with nationwide coverage. Line vector and polygon features were interpolated to enforce a consistent spacing between points. Interpolation was calculated based on the arc length between points along the vector using a linear chordal approximation. This was more efficient than using a piecewise cubic Hermite interpolating polynomial (pchip) used by previous modelling efforts [1]. The spacing of 500 feet was selected because it preserves details along curves and was equivalent to the 500 feet horizontal dimension of the near mid-air collision (NMAC) safety metric, which is commonly used to evaluate airborne collision risk. This spacing also heuristically determined to be sufficiently efficient, as a linear reduction in spacing results in a non-linear increase in computation time. This trade-off is further discussed in Section 4.1.