esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Data from and Results of Experiment 2
The cognitive task data for the subjects showed that the overall cognitive correctness of the three scenarios was ranked as \"S1 (81.104%) > S2 (64.566%) > S3 (49.566%).\" In S1, the significance of the five digits was high and similar, and the cognitive correctness was ranked from highest to lowest as \"5 > 2 > 3 > 1 = 4.\" The number 5 was slightly significant (84.87%); In S2, the digits began to show more significant differences, ranked as \"4 > 2 > 3 > 1 > 5,\" and the number 4 was the most significant (76.09%). In S3, the numerical significance was the most prominent, with a ranking of \"1 > 3 > 2 > 5 > 4.\" The number 1 was the most significant (77.17%) (see Figure 6). With the same observation times, subjects were able to complete their searches quickly and obtain more information about the scenes in S1. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Eye-Tracking Dependent Variable** **(within 10 s)**} & \multicolumn{3}{c}{**Average Value**} \\ \cline{2-4}  & **S1** & **S2** & **S3** \\ \cline{2-4}  & **N = 92** & **N = 92** & **N = 92** \\ \hline FC (N) & 22 & 11 & 13 \\ SC (N) & 69 & 78 & 77 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of Eye-Tracking Index Data by Scene. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Name**} & \multicolumn{1}{c}{**Panoramas**} & \multicolumn{1}{c}{**Heat Maps**} & \multicolumn{1}{c}{**Track Maps**} \\ \hline S1 & & & \\ S2 & & & \\ S3 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Visualization of Eye-Tracking Data by Scene. In summary, the higher the cognitive correctness, the greater was the significance. The visual saliency was greater in spaces with simple environmental structures and reduced in spaces in which the environmental structures were more complex.
## 4 Results and Discussion
### Influence of Inclined Angle of the Nozzle on the Coating Removal for the Metal Wall  
For the designed wall-climbing robot, the rotary movement of the coating removal device is driven by the shear force induced by the water pressure impacting on the target surface. So the water jet direction cannot be completely perpendicular to the target surface. Here, the angle \(\alpha\) between the water jet direction and the normal direction of the target surface is called inclined angle. If \(\alpha\) is smaller, most of the water pressure is reacting on the normal direction of the target surface and thus the impacting force is larger while the shear force is lower. If the shear force is too small, the coating removal device may not rotate at all and thus the gap between each experimental nozzle cannot be cleaned as expected. However, if \(\alpha\) is too big, the normal force reacting on the surface may not large enough and much energy could be lost. So, the real reaction effect of the inclined angle on the coating removal results should be investigated. Considering the limitation of the existing experimental conditions, the numerical simulations under various inclined angles (0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\) and 30\({}^{\circ}\)) were analyzed.  
For this study, the inlet water pressure and the stand-off distance are 200 MPa and 30 mm, respectively. Figure 20 shows the predicted velocity cloud chart under various inclined angles. It can be seen that the interaction between water and air are more complicated with larger inclined angles. The predicted maximum static pressures reacting on the target surface under various inclined angles are also obtained as shown in Figure 21. Results show that when the inclined angle is larger, the maximum static pressure is decreased. When the inclined angle is 30\({}^{\circ}\), the working efficiency of the high pressure water is only 19.5%, so if the maximum static pressure is too small, the water jet energy can be greatly lost. Considering both the working efficiency and the rotation movement, the proper inclined angle should be chosen.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Travelling Speed (m/min)** & **Cleaning Efficiency (m2/H)** \\ \hline
1 & 1.5 & 27 \\
2 & 2.25 & 40.5 \\
3 & 3 & 54 \\
4 & 3.75 & 67.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relationship between travelling speed and cleaning efficiency.  
Figure 20: Cont.
## 3 Analysis of Factors Affecting Water Inflow
### Analysis of Seepage Field  
An analysis of influential factors reveals a reciprocal influence between the seepage field of the main tunnel and the service tunnel under the three-hole tunnel working condition. As a result, the water inflow into the main tunnel is reduced compared to a single-hole tunnel under identical circumstances.  
To investigate the reciprocal impact of tunnels in-depth, seepage velocity vector maps (Figures 9 and 10) and pore water pressure cloud maps (Figure 11) for both the main tunnel and the single-hole tunnel are drawn when the stable seepage state is completed after the tunnel excavation. The flow velocity vector field around the single-hole tunnel exhibits a symmetrical distribution, with the maximum flow velocity occurring at the left and right arch foot of the tunnel, reaching a peak value of 4.06 \(\times\) 10\({}^{-7}\) m/s. Under the parallel working condition of three holes, the pressure relief effect of the service tunnel causes a decrease in seepage velocity at the right arch foot of the main tunnel. The maximum seepage velocity is observed at the left arch foot, reaching a peak value of 3.5 \(\times\) 10\({}^{-7}\) m/s. As a result, the seepage velocity field in the main tunnel exhibits significant asymmetry.  
Figure 11: Distribution of pore water pressure during steady seepage in tunnels (unit: kPa). (**left**) Three-hole tunnel. (**right**) Single-hole tunnel.  
Figure 9: Seepage velocity of the main tunnel in a three-hole parallel tunnel.  
Examining the contour of pore water pressure reveals that, under the condition of three parallel holes, mutual pressure relief between tunnels results in a decrease in pore water pressure in the surrounding rock between tunnels, leading to a sparse contour. The reduction in seepage rate and smaller water inflow of the main tunnel can be attributed to the force mechanism.
# Thermal Fatigue Life Prediction of Thermal Barrier Coat on Nozzle Guide Vane via Master-Slave Model
## 2 Modeling and Simulation Methods
### Simulation Procedure  
The high-integrity life prediction approach for the TBC on a NGV with the master-slave model is shown in Figure 2.  
To find the coefficients (\(a\) and \(b\)) in Equation (2), we need the TFL test data of the NGV. Due to the insufficient TFL measurement data of the NGV in an open database [23], the TFL of a typical ceramic metal tube, a similar plasma sprayed TBC material, and the working environment of the model described in this paper, was selected to calculate the value of parameters \(a\) and \(b\) in Equation (2). The ceramic tube test has been proven to be an effective alternative test method [13]. The prediction procedure of the TFL for the TBC is described as follows:  
**Step 1**: Based on the geometry parameters and test conditions of a ceramic metal tube, a 2D axisymmetric FE model was established and solved by the thermo-elastic method. Both shear strain ranges and axial strain ranges (i.e., strain ranges in the \(Z\) direction) at the risk point in one cycle were simulated with different TGO thicknesses, and the relationship of the strain and thickness was obtained by fitting polynomials. The relationship was inputted into Equation (2). According to the cyclic life test of the ceramic metal tube, the parameters \(a\) and \(b\) were obtained by nonlinear regression analysis.  
**Step 2**: The typical thermal shock test of the NGV was divided into two states (i.e., heating stage and cooling stage). The convection heat transfer coefficient of each stage was solved by the CFD method, and then imported into the master model (i.e., macro NGV with TBC) as the boundary conditions. Then, the temperature and node deformations were solved by the thermo-elastic coupling method. The results at each time were inputted into the slave model (i.e., micro TBC) by the volume element intersection mapping algorithm [24]. The master-slave model was based on two assumptions: (a) The TGO hardly affects the temperature field in the TBC, and (b) the grid node deformation mainly depends on the temperature field at the macro scale [25].  
**Step 3**: The strain ranges at the risk points of the slave model were imported into the TFL model to predict the cyclic life of TBC.
# Coupled Vibration Analysis of Multi-Span Continuous Cable Structure Considering Frictional Slip
## 4 Engineering Examples
### Analysis of Results
#### 4.3.2 Analysis of Impact Effect after Hoisting Weight Detachment  
In order to investigate the impact effect generated by the cable hoist loading system when hoisting heavy loads out of the lifting cable, this section takes the hoisting of heavy loads from the left bank to the mid-span stage of detachment for analysis and obtains the mid-span displacement of the main cable, as shown in Figure 15.  
Figure 14: Comparison of results under the action of crane–cable coupled vibration at the design speed: (**a**) time course diagram of the vertical displacement in the span, (**b**) time course diagram of the cable force in the span, (**c**) time course diagram of the deflection of the left–bank cable tower, (**d**) time course diagram of the deflection of the right–bank cable tower.  
As can be seen from Figure 15, the moment of hoisting the weight detached from the impact effect is very obvious. In order to describe the dynamic effect produced at the moment when the weight is detached from the pulling cable, this paper expresses it by calculating the impact coefficient \(\mu\). The ratio of the deflection or force added to the suspension structure at the moment the weight breaks away from the pulling cable to the deflection or force generated when the load is at rest is called the impact coefficient. The vertical maximum dynamic deflection generated in the span is up to 13.6 m, while the vertical static deflection generated only no-load action is only 4.78 m, with an impact coefficient of up to 2.0. The mid-span cable force generated by the maximum dynamic cable force is up to 4123 kN, and the no-load action generated by the static cable force is 1912 kN. The impact coefficient is 2.16. The left-bank cable tower maximum dynamic deflection is 0.031 m, and the no-load action generates a static deflection of 0.011 m. The impact coefficient is 2.82, and that on the left-bank cable tower is 2.16. The right-bank cable tower maximum dynamic deflection is 0.031 m, and no-load action produces a static deflection of 0.011 m; the impact coefficient of the left-bank cable tower is 2.82. The right-bank cable tower maximum dynamic deflection is 0.048 m, and no-load action produces a static deflection of 0.012 m; the impact coefficient of the left-bank cable tower is 4.00.
# 3D Documentation with TLS of Caliphal Gate (Centa, Spain)
## 3 Results
### Results of the Previous Works: Micro-Geodetic Network  
After defining the main areas of work, six vertices were located on the bailey (101 to 106), two support points were set on the National Parador (343, 341), two other vertices on the surveillance path of the counterscarp (601, 603), and a last vertex on the Weapon Square (602). Figure 5 shows their location, and Table 1 lists their coordinates.  
The accuracy of the network had to be enough to guarantee the coherence with the subsequent representation of the 3D model, considering a scale of 1:500. The vertices of the micro-geodetic network and the support points were referred to with respect to the permanent station in Ceuta Heliport, which belongs to the National Network of Reference Stations (ERGSNSS) and the European Network of Permanent Stations (EUREF, EPN). Two GPS devices were used to observe in RTK mode, recording the coordinates of each basis four times. Final coordinates were obtained by calculating the average of the observed values, as long as the difference between them was lesser than 3.5 cm. This value can be assumed as the accuracy provided for the absolute coordinates of the point-clouds that were produced, and it is sufficient for most works in the fields of topography and cartography. However, 4 mm can be offered as the accuracy provided by TLS for simple measurements when working in relative coordinates.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Name** & **X** & **Y** & **z** & **Comments** \\ \hline
101 & 290,757.78 & 3,974,068.26 & 21.46 & Vertex \\
102 & 290,760.11 & 3,974,058.87 & 21.74 & Vertex \\
103 & 290,762.17 & 3,974,048.03 & 22.07 & Vertex \\
104 & 290,764.04 & 3,974,034.92 & 22.53 & Vertex \\
105 & 290,763.48 & 3,974,020.65 & 23.09 & Vertex \\
106 & 290,756.43 & 3,974,017.03 & 23.35 & Vertex \\
341 & 290,789.26 & 3,974,024.05 & 12.28 & Support point \\
343 & 290,776.47 & 3,974,031.61 & 12.50 & Support point \\
601 & 290,704.39 & 3,974,111.90 & 9.51 & Support point \\
602 & 290,662.62 & 3,974,094.87 & 7.94 & Support point \\
603 & 290,712.23 & 3,974,059.13 & 11.39 & Support point \\ \hline \end{tabular}
\end{table}
Table 1: Coordinates of the micro-geodetic network vertices and the support points.  
Figure 5: Micro-geodetic network and support points.
## 5 Results and Discussion
### Water Absorption  
The results of water absorption are represented in Figures 18 and 19. According to Figure 18, using SF resulted in raising the water absorption. Moreover, adding 10% SP significantly improved the water absorption of SF reinforced HSC. On the other hand, 5% SP reduces this property while using 1% NS considerably increased it when less than 5% SP was used. Using 1% NA dropped the water absorption when using 10% SP, and using a combination of 2% NS and 10% SP with 0.5% SF had a significant impact on increasing the water absorption. This could be attributed to the high surface area of NS. As shown by [PERSON] et al. [67], the water absorption increased by adding NS, due to incremented specimens destruction, so that increasing the concrete specimens capillary porosity and permeability resulted by raising the acidity of the solution, while SP could act as a filler and reduce the porosity and permeability of HSC mixes. Additionally, [PERSON] et al. [68] found that using a high SP content (20%) declined the workability by up to 20 mm. So, the incorporation of 0.5% SF and 10% SP with 0% and 2% NS raised the water absorption by about 56% and 68%, respectively. A similar trend was observed when PPF was used; however, the influence of PPF on the water absorption was more significant than that of SF. Therefore, using 10% SP and 0.5% PPF together with 0% and 2% NS increased this property by 70% and 82%, respectively (Figure 18b).  
Figure 17: Effect of the fibres on the secant modulus of elasticity of HSC (**a**) SF and (**b**) PPF.  
Figure 18: Impact of the fibres on the water absorption of HSC containing NS and SP (**a**) SF and (**b**) PPF.  
Figure 19 shows the impact of a combination of NS, SP, SF and PPF on the porosity of HSC. SF increased the water absorption of SF reinforced HSC, and further using 10% SP considerably it. Conversely, using 5% SP decreases the value of this property, whereas using 1% NS substantially enhanced it when less than 5% SP was employed. Besides, using 1% NA led to reducing the water absorption when using 10% SP and using 2% NS and 10% SP with 0.5% SF has a far-reaching influence on raising the water absorption. Therefore, a combination of 0.5% SF and 10% SP with 0% and 2% NS increased this property by nearly 10% and 115%, respectively. The same trend was experienced when PPF was used; however, the impact of PPF on water absorption was greater than that of SF. Consequently, using 0.5% PPF and 10% SP together with 0% and 2% NS raised this property by 147% and 173%, respectively (Figure 19b).
# A Programmable SRv6 Processor for SFC
## 3 Protocol-Independent Pipeline Design
### Overall Architecture of Programmable SRv6 Processor  
As shown in Figure 1, this processor consists of ingress RMT, deparser plus, and egress RMT. The original deparser module of ingress RMT has been replaced by deparser plus, so the final phase of ingress RMT does not merge the packet payload with the header, but sends parsing results (metadata and PHY) and cached payload directly to the deparser plus module.  
The ingress RMT module adopts a reduced instruction set and is mainly responsible for receiving and determining the type of packets to complete the simple preprocessing of  
Figure 1: Overall architecture of programmable SRv6 processor.  
packets. It writes the parsing results needed to process the packet to metadata, including the packet type and flow ID, which is then processed by the deparser plus module.  
The deparser plus module is responsible for receiving PHVs and metadata sent by the ingress RMT and for stateful processing of packets based on the flow ID and packet type in the metadata. After modifying any PHV fields based on the complex instructions in the action table, the modified PHV is sent to the Egress RMT for modification and forwarding.  
The Egress RMT uses a reduced instruction set and receives PHVs sent by the Deparser plus module to change the PHVs' destination IP addresses, search the forwarding table and adjacency table to generate layer 2 headers, and finally forward the packets.  
After the packet enters the pipeline, the packet header is parsed by the parser to extract the information waiting to be matched in the next stages. The ingress RMT performs simple processing on packets, such as by modifying specific fields and searching flow tables. Deparser plus handles complex operations on packets after receiving parsing results, such as inserting headers and encapsulating headers. The modified packet is then passed to the egress RMT for final processing, before it is then sent out.  
Ingress RMT, deparser plus, and egress RMT are programmable modules that form the programmable SRv6 processor. Programmers can change a processor's functionality as needed. Therefore, it meets the requirement of a programmable data plane and realizes pipelined processing architecture independent of protocol.
## 3 Non-Isolated Single Tree Radio Channel Characterization  
Once the radio channel characterization of an isolated single tree has been performed by means of measurements and simulations, in this section a step towards a more realistic scenario has been taken. Specifically, simulations and measurements of a single tree nearby a dense forest zone with thick in-leaf trees have been performed. This scenario is located in the Orgi Forest. This forest is a 77-hectares millennial forest with a high ecological value which extends into the Ultzama Valley (Navarra). The oak groves populated the Navarra valleys 4000 years ago. Orgi Forest was declared a Natural Recreational Area in 1996, after the intense forest exploitation of wood, pasture for livestock and hunting. This was to encourage the natural regeneration of forest's flora and fauna, while regulating the use of people.  
Orgi has been included in the European Natura 2000 network. Natura 2000 is the largest network of protected areas in the world and it offers protection to most valuable and threatened species in Europe. In the case of Orgi, the oak (Quercus robur) is the main species protected. There are oak trees of two types where many of them are centenarians: the common oak and the American oak.  
The forest is organized into three zones (shown in Figure 10). The first called Arigartzeta, is the welcome zone that gives access for the oak grove. It has parking and picnic areas. The second zone is called Tomaszelaieta, and has a large walk area, where the visitor can discover other tree species such as holly and elms as well as some animal species such as birds or amphibians. The third zone is called Munagorri, which is a conservation area. The public cannot access this last zone due to restrictions in order to facilitate the process of natural regeneration. The measurements have been performed within the accessible Tomaszelaieta zone (see zoomed zone in Figure 10), which is prepared for visitors (see Figure 11).
# Predicting the Loose Zone of Roadway Surrounding Rock Using Wavelet Relevance Vector Machine
## 2 Wavelet Relevance Vector Machine
### Wavelet Kernel Function  
RVM transforms the input space into a high-dimensional space through the pre-selected kernel function while realizing data linearization in this space. In square integrable space, besides the inner product kernel form \(K(x,x^{\prime})=K(\langle x,x^{\prime}\rangle)\), there are translation invariant kernels \(K(x,x^{\prime})=K(x-x^{\prime})\) for kernels \(K(x,x^{\prime})\).  
Assuming that \(\varphi(x)\) is a wavelet generating function, the translation invariant form of the wavelet kernel function can be constructed as [51; 54]:  
\[K(x,x^{\prime})=\prod_{i=1}^{d}\varphi\bigg{(}\frac{x_{i}-x^{\prime}{}_{i}}{a }\bigg{)} \tag{9}\]  
In the formula, \(d\) is the input vector dimension, and \(a\) refers to the scaling factor of the wavelet kernel function which is a constant that needs to be optimized. In the following section, several commonly used wavelet generating functions and their kernels are given.  
1. The Mexican hat wavelet generating function is \(\varphi(x)=\left(1-x^{2}\right)\exp\left(-\frac{x^{2}}{2}\right)\), and the corresponding Mexican hat wavelet kernel function is given: \[K(x,x^{\prime})=\prod_{i=1}^{d}\left[1-\frac{(x_{i}-x^{\prime}_{i})^{2}}{a^{2}} \right]\exp\left[-\frac{(x_{i}-x^{\prime}_{i})^{2}}{2a^{2}}\right]\] (10)
2. The Morlet wavelet generating function is \(\varphi(x)=\cos(1.75x)\times\exp\left(-\frac{x^{2}}{2}\right)\), and the corresponding Morlet wavelet kernel function is: \[K(x,x^{\prime})=\prod_{i=1}^{d}\cos\left[1.75\frac{x_{i}-x^{\prime}_{i}}{a} \right]\exp\left[-\frac{(x_{i}-x^{\prime}_{i})^{2}}{2a^{2}}\right]\] (11)
3. The DOG wavelet generating function is \(\varphi(x)=\exp\left(-\frac{x^{2}}{2}\right)-\frac{1}{2}\exp\left(-\frac{x^{2 }}{8}\right)\), and the matching DOG wavelet kernel function is: \[K(x,x^{\prime})=\prod_{i=1}^{d}\left\{\exp\left[-\frac{(x_{i}-x^{\prime}_{i}) ^{2}}{2a^{2}}\right]-\frac{1}{2}\exp\left[-\frac{(x_{i}-x^{\prime}_{i})^{2}}{ 8a^{2}}\right]\right\}\] (12)