esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of the Effective Width for Simply Supported Generally Loaded Box-Girders
## 6 Numerical Results
### Case Study
#### 6.1.3 Use of the Average Effective Width  
The goodness of the results obtained by using the effective width \(b_{en}\) has already been tested by comparison with FEM results, so it is now intended to compare the normal stresses obtained by considering the effective width \(b_{en}\) by Equation (2), taken as a reference, with those by considering the average effective width \(\bar{b}_{e}\) derived both from the displacement equivalence and the normal stress equivalence (see Section 5). For this comparison, the three simple loading conditions already mentioned in Section 5 are considered, in particular, the uniformly distributed load \(p_{0d}=-100\,\mathrm{kN}/\mathrm{m}\) (DL), the segment of load \(p_{0s}=-100\,\mathrm{kN}/\mathrm{m}\) of extension \(\Delta=\frac{\ell}{10}\) centered at midspan (SL-centered) and concentrated load \(P=-100\,\mathrm{kN}\) applied at midspan (CL-centered).  
Figure 20 shows that for the DL case, the three curves are almost superimposed, while, on the contrary, for the CL-centered case, the two curves related to the average effective widths are notably different; in particular, deriving from the normal stresses equivalence captures the maximum value quite well, while deriving from the displacement equivalence follows the curve better but is not able to approximate the maximum value. The case of SL-centered is an intermediate case. The amount of the difference between the two approximate curves depends on the extension \(\Delta\) of the segment loaded.
## 5 Discussion  
A single-difference multipath hemispherical map is presented in this paper to improve the accuracy of BDS-2/BDS-3 short baseline positioning by mitigating the multipath in real-time. Considering the ORTs of the BDS MEO satellites are approximately seven sidereal days, the consecutive seven-day residuals are established as a multipath model. Moreover, since the spatial geometric structure of GEO is basically unchanged, partial ambiguity resolution is used to improve the fixed ambiguity efficiency. The authors of [25] proposed that multipath is spatially repetitive on the sky map. We use KNN to search for the multipath value on the space position closest to the current satellite to avoid the null value in the current satellite position caused by the slight orbit offset, which improves the computational efficiency and the spatial resolution of multipath.  
The sidereal filtering methods are applied in conventional receivers, which can only use double-difference residuals or reconstructed single-difference residuals to mitigate the multipath. These methods need to dynamically update the multipath model, which will occupy a large amount of data memory and reduce the efficiency. Furthermore, when there are hardware problems and data interruptions in the observation data, that will seriously affect the solution results. However, the proposed method avoids all the above-mentioned problems and provides the conventional receivers with a new solution for multipath mitigation.  
Figure 11: The nine-day positioning accuracy in the east, north, and vertical directions and the baseline accuracy.
## 3 Results and Discussion
### Seasonal Energy Performance and TEWI
From the graphs of Figure 8, it is evident that the value of TEWI has been monotonically decreasing for years, for both refrigerants, since a drop in the electricity emission factor causes a significant reduction in the indirect emissions of the system. In particular, compared to the situation in 2020, by the year 2050, the overall CO2 emissions drop by 63% and 89% in the R-410A and R-454B systems, respectively. A significant outcome for both refrigerants is that, in the analyzed scenario, the share of direct emissions becomes more and more important with respect to indirect emissions, in agreement with the electricity emission-factor trend. More in detail, from Figure 8a, it is evident that for traditional refrigerants, such as R-410A, the weight of direct emissions increased from 21% in 1990 to 37% in 2020, highlighting how the adoption of more environmental-friendly fluids, characterized by lower GWP values imposed by the current regulation, is essential to improve the sustainability of air-conditioning systems. Indeed, the comparison of Figure 8a,b points out that it is possible to strongly reduce the overall environmental impact with R-454B since the contribution of indirect emissions will become negligible with respect to that of direct emissions in the next years. As an example, direct emissions are expected to represent the totality of the system's GHG emissions in 2050. Figure 8: Trend of TEWI and contributions of direct/indirect emissions expected for the next years for the GCHP-based system operating with R-410A (**a**) and R-454B (**b**). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **2025** & **2030** & **2035** & **2040** & **2045** & **2050** \\ \hline \(\beta\) [g\({}_{\text{CO2}}\) kWh\({}^{-1}\)] & 216.8 & 170.9 & 127.5 & 86.7 & 38.3 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Values of the emission coefficient \(\beta\) expected for the next years. In order to complete the overview of the environmental performance of the studied configurations, the TEWI trend related to the ASHP-based system is illustrated in Figure 9. Comparing Figures 8 and 9, it is clear that the contribution of direct/indirect emissions out of the total remains almost the same even in the case of aerothermal units. Indeed, compared to the GCHP, the rise in the annual electric consumption of the ASHP systems weakly increases the weight of indirect emissions by about 2.5% for both refrigerants. Another interesting aspect is that when R-454B is used, the indirect emissions share remains stable for a prolongated time (up to 2040) before plunging, whereas when R-410A is adopted, the indirect emissions share constantly decreases. This trend for R-410A mostly depends on the large weight of the direct emissions and thus on the high R-410A GWP value. Finally, the results confirm that in the next years the share of direct emissions will be predominant, and thus the switch to eco-friendly low-GWP fluids will be necessary to accomplish the European targets.
## 5 Evaluation
### Networks Performance  
For our experiments in OPNET, let \(M=\{\)1,2,3,4\(\}\), \(N=28\), and four source nodes are randomly deployed in a 100 m \(\times\) 100 m rectangular region. Radio radius constrained at 20 m. In these experiments, the simulation period \(\tau\) is set from 0 to 10 minutes. The data rate created by each source node is 1,000 bits/s.  
Figure 18 shows network load for different sink numbers. The more sinks deployed, the lower a network load is created. When more sink nodes are deployed, the source node has a chance to choose a nearer sink to transport its data packet to. This results in a significant decrease in the total communication hops, and results in a greatly lower load on the system.  
In Figure 19, end-to-end delay of MSCWSNs-PC decreases as sink number increases. The performance of end-to-end delay when using four sink nodes always outperforms in terms of less sinks. In MSCWSNs-PC, more sink nodes result in shorter data transport paths and lower loads which have a significant impact on decreasing networks congestion. Thus, congestion is more likely to happen when less sinks are deployed.  
Figure 18: Network load at different sink numbers.
## 3 Modal Simulation Analysis
### Verification of ACS-SSI Applied on MED  
To further verify the correctness and accuracy of the proposed ACS-SSI algorithm, the failures of the leaf spring and the damper were simulated for error analysis. A noise signal with signal-to-noise ratio (SNR) equaling two is added to a select normal vibration response to simulate the fault vibration signal. The sampling frequency of the simulation system is 200 Hz, the sampling time is 20 s, and the average correlation calculation is carried out 50 times. Then, the proposed ACS-SSI algorithm was used to perform modal identification. The input data is set for a period of 100 s and averaged 50 times. Monte Carlo tests were performed 10 times for each simulation, and its corresponding identification error is shown in Tables 4 and 5.  
It can be seen from Tables 4 and 5 that the identification error is always within a reasonable range, and the recognition error does not increase due to the change of the suspension parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stiffness Changes** & 0\% & 5\% & 10\% & 15\% & 20\% \\
**Natural Frequency Error** & 3.16\% & 2.28\% & 2.14\% & 2.07\% & 3.25\% \\
**Modal Shape Error** & 4.78\% & 3.67\% & 3.16\% & 2.96\% & 4.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Identification error of simulated suspension spring faults.  
Figure 10: Changes of MED in different positions of damper faults. (**a**): MED changes caused by faults in left-front damper; (**b**): MED changes caused by faults in right-front damper.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Left**} & \multicolumn{3}{c}{**Right**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Bounce Mode**} & \multicolumn{2}{c}{**Pitch Mode**} & \multicolumn{2}{c}{**Bounce Mode**} & \multicolumn{2}{c}{**Pitch Mode**} \\ \cline{2-7}  & **Front MED** & **Rear MED** & **Front MED** & **Rear MED** & **Front MED** & **Rear MED** & **Front MED** & **Rear MED** \\ \hline Front spring & + & + & - & - & - & - & + & + \\ Rear spring & + & + & + & + & - & - & - & - \\ Damper & + & - & + & - & - & + & + & + \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tendencies of stiffness and damping ratio.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stiffness Changes** & 0\% & 5\% & 10\% & 15\% & 20\% \\
**Natural Frequency Error** & 3.16\% & 2.28\% & 2.14\% & 2.07\% & 3.25\% \\
**Modal Shape Error** & 4.78\% & 3.67\% & 3.16\% & 2.96\% & 4.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Identification error of simulated absorber faults.  
In order to investigate whether the different fault states can be clearly distinguished according to the modal energy difference in the presence of noise interference, the Monte Carlo test was repeated 50 times for three different spring stiffness conditions. The modal energy difference corresponding to the three spring stiffness states is shown in Figure 11.
# Research on Grooved Concrete pavement Based on the Durability of Its Anti-Skid Performance
###### Abstract  
The objectives of the present study are to investigate the anti-skid performance of concrete pavement and to attempt to enhance its durability by two different methods: using a longitudinally-transversely grooved (LT) form, and using a self-developed composite curing agent containing paraffin and Na\({}_{2}\)SiO\({}_{3}\) as the main ingredients. The friction coefficient (\(\mu\)) was measured by self-developed equipment to evaluate the anti-skid performance of samples with three different groove forms (LT, longitudinally grooved (L), and transversely grooved (T)). Abrasion tests were then carried out to evaluate the durability of the anti-skid performance. The results indicated that anti-skid performance of LT samples was approximately 46.2% greater than that of T samples, but its durability was not as significant as that of T samples. However, the resistance to abrasion could be improved by using the aforementioned curing agent. Comparisons were carried out between samples sprayed the curing agent and control samples without any curing agent under standard conditions. It was found that the application of the curing agent increased the anti-skid durability of concrete by 35.4%-47.8%, proving it to be a useful and promising technique.  
concrete pavement; anti-skid performance durability; self-developed equipment; self-developed composite curing agent; longitudinally-transversely grooved form +
Footnote †: journal: Applied Sciences
## 3 The Proposed Multimodal Model
### Stage 2: Feature Extraction  
Feature extraction is the process of extracting feature vectors from pre-processed data. The model first embeds the pre-processed data for feature extraction and then extracts important features through several layers.  
First, we use an embedding layer to vectorize the pre-processed text and code data tokens. The embedding layer is a technique for generating random vectors that can represent tokens [10]. The text and code embedding is to represent the input data as a set of embedded tokens with zero padding. As the image data are vectorized during the pre-processing stage, the image data are not embedded in this stage.  
Second, the embedded or vectorized text, image, and code data undergo computations through their respective CNN\(-\)based channels. We used the same CNN\(-\)based channel for text and code data. Figure 6 represents the structure of the CNN\(-\)based text and code channels.  
The model in Figure 6 consists of four kernels of size (2, vector size), (3, vector size), (4, vector size), and (5, vector size), respectively. These kernels perform convolution operations with 64 output channels. The model also maintains three other layers: max-pooling, concatenation, and fully connected layers. The results of the convolutional operations go through a max-pooling layer and concatenation layer, resulting in a single vector. Then, a fully connected layer applies ReLU to the single vector, returning only positive values and performing linear operations. Finally, the features of the text or code data are extracted.  
Figure 5: Example of image pre-processing.  
Figure 6: CNN\(-\)based text and code channels.  
Meanwhile, we used another CNN\(-\)based channel for image data. Figure 7 illustrates the structure of the CNN\(-\)based image channel. The model in Figure 7 receives inputs of size (258 \(\times\) 258). The model also maintains three convolution layers with a kernel size of (5, 5) followed by the ReLU operation. The model also applies a max-pooling operation twice with a kernel size of (5, 5). After the final max-pooling operation, the results go through a fully connected layer. Finally, it extracts the image features in a feature size of (\(1\times 1\times 256\)). Now, we obtain all of the representation vectors from the input data of each single modality.
## 3 Results and Discussion
This occurs because, under a constant data rate, more subcarriers result in longer OFDM symbol durations, extending the decision feedback duration and causing more significant channel variations. However, more appropriate weight coefficients can improve positioning performance. For instance, in Route 4, when the number of subcarriers is 1024, the mean positioning error is optimized with \(\alpha\) = 0.7, resulting in 4.8 cm, compared to 6.0 cm with equal weight coefficients. Increasing the weight for estimated CSI in VLCP data blocks adjacent to the current channel can enhance the accuracy of channel estimation due to their high correlation. However, higher weights also amplify the impact of noise, underscoring the importance of selecting appropriate weights. Similarly, in the MIMO system, optimal weight coefficients occur when \(\alpha\) = 0.7 in both Route 3 and Route 4. Finally, considering the variation in terminal size in our MIMO-based VLCP system, we assess the positioning performance by adjusting the distance between the two PDs. Figure 13 compares the mean positioning errors with and without DFCE at different PD distances. Along Route 1, where the SNR is sufficient, increasing the distance between PDs does not notably enhance the positioning performance. However, in Route 2, without DFCE, the mean positioning error can be reduced from 20.0 cm to 17.5 cm as the distance between PDs increases from 20 cm to 50 cm. After employing DFCE, the mean positioning error can be further reduced from 15.5 cm to 12.5 cm. This improvement is attributed to the decreased correlation between channels as the distance between PDs increases, resulting in reduced interference between signals and less additional noise being introduced. Figure 11: Comparison of mean positioning errors with and without DFCE at different receiver velocities: (**a**) MISO scenario; (**b**) MIMO scenario.
## 3 Data Acquisition
### Landslide Conditioning Factors
#### 3.2.3 Elevation  
An elevation map was extracted from DEM, and values placed in five categories using the natural break classification method: 1557-1751, 1751-1917, 1917-2096, 2096-2300, and 2300-2515 m asl. Nearly half of the landslides are located in the lowest elevation class (Figure 3c).
# Topological Navigation and Localization in
## 3 Topological Navigation and Localization for LHD
### Topological Map and Physical Representation
* A delta threshold and a radius are defined:
* The last LIDAR-landmark for which the next highest similarity value does not belong to one of the current LIDAR-landmark's local neighbors is selected (searching from lowest to highest similarity value). * This similarity value between the current LIDAR-landmark and the selected LIDAR-landmark is stored as the threshold of this LIDAR-landmark. This value represents the minimum ICP's fitness value that must be met for a valid geometric fit. * The difference between the threshold and the next similarity value is stored as the delta threshold of this LIDAR-landmark. This value represents how different the current LIDAR-landmark is from the other LIDAR-landmarks that are not its neighbors. The distance to the selected LIDAR-landmark is stored as the radius index of the current LIDAR-landmark. This value represents the distance (in LIDAR-landmark measurement/sample distance units) from which a valid geometric fit can be achieved. An empty list is created for the LIDAR-key-landmarks. The LIDAR-landmark with the largest radius among the first 10 LIDAR-landmarks is added to the list. The LIDAR-landmark with the largest radius among the last 10 LIDAR-landmarks is added to the list. The remaining LIDAR-landmarks are ordered by highest to lowest radius, and highest to lowest delta threshold (if they have the same radius). For each of these sorted LIDAR-landmarks with radius greater than 2 LIDAR-landmark measurement distances (4 [m] in this case):  
If it is not near an existing LIDAR-key-landmark (farther than 5 LIDAR-landmark measurement distance) then it is added to the LIDAR-key-landmark list. In the case of an intersection node, the LIDAR point clouds obtained in the intersection are integrated and stored as a single cloud point. This cloud point is the LIDAR-landmark of the intersection.
# A Proposal for Rapid Assessment of Long-Distance Oil and Gas Pipelines After Earthquakes
## 2 Analysis of Pipeline Safety Assessment Elements Under Seismic Impacts  
### Relationship Between Post-Earthquake Hazards and Pipelines  
Seismic impacts on long-distance pipelines can be categorized into direct and indirect mechanisms (Figure 1). Direct damage primarily involves shear/compression failures caused by fault displacement, with severity dependent on the pipeline-fault intersection angle [19]. Cyclic ground motions induced by seismic waves (P- and S-waves) generate abrupt stress concentrations at pipeline joints (flanges, welded connections), leading to structural failure. Indirect effects arise from seismic-induced geotechnical instability: in mountainous areas, ground shaking reduces soil cohesion and internal friction angles or promotes rock fracture propagation, ultimately triggering landslides and collapses. Thesehazards may bury pipelines, induce suspended spans, or impose excessive lateral pressures, escalating post-event deformation risks. Additionally, seismic damage to ancillary structures (e.g., bridges, tunnels) can propagate to pipelines--for instance, bridge displacements exceeding the tolerance of pipeline expansion joints may cause tensile fractures [5]. Studies indicate that fault activity and seismic landslides dominate pipeline safety risks [19], with other factors posing relatively minor threats.
# Wave Propagation in Periodic Metallic Structures with Equilateral Triangular Holes
## 3 Results  
In this section we study the dispersion characteristics of the mirror- and glide-symmetric periodic structures with equilateral triangular holes. The results are calculated following the formulation presented in the previous section and validated with the commercial software _CST Microwave Studio_ (version 2018, Dassault Systemes, 78140 Velizy-Villacoublay, France). Initially, we characterize the  
Figure 2: Normalized magnitude of the tangential modal functions in the equilateral triangular waveguide.  
influence of the different geometrical parameters that conform the unit cell on the wave propagation through the structure. Then, its unexpected isotropic behavior is discussed in detail, giving a physical insight on the matter. Finally, we study the capabilities of triangular holes as electromagnetic bandgap (EBG ) structures.
# Simultaneous Prevention of Rotational and Translational Slip for a Humanoid Robot
## 3 Rotational Slip Prevention
### Yaw Moment Compensation Mechanism for Human Walking  
The gait pattern of the human walking is recorded by the optical motion capture system, which consists of 12 optical cameras located around the ceiling board and 38 retro-reflective markers fixed on each human subject. Several of these markers are intentionally located on the shoulder and waist parts for the motion capture in this study. The cameras receive the returned signals from the markers, capture their varied positions, and thus record the gait pattern of the subjects, as Figure 3 shows. The frequency for the data update is 100 frames per second. The yaw moment between feet and ground in the human walking can be measured using the yaw moment measurement system [23].  
The gait pattern data are acquired from several subjects for universality, and each subject is required to walk at different speeds (ranging from 4 to 6 km/h) and walk with varied upper body motions (walk naturally with arm-swing and waist rotation, walk without waist rotation or arm-swing, and walk without arm-swing and with waist rotation). The details of the human walking data collection experiments are shown in [23]. Several important rules can be drawn after studying the yaw moment compensation mechanism in human walking [22].  
* The arm swing and waist rotation in the vertical direction contribute to the yaw moment compensation in the walking.
* The arm swings in the back-and-forth pattern periodically, and the amplitude of the swing arm increases as the step length gets larger during the walking.  
Figure 3: Snapshots of the human walking experiment by the motion capture system.  
* The second derivative of the angle trajectory of the swing arm shows that the acceleration of the swing angle undergoes roughly the five stages: ascent, light fluctuation, descent, light fluctuation, and ascent.
* The waist rotation in the vertical direction occurs in the similar form as the periodic sinusoidal signal in human walking, and the rotation angle becomes larger when the walking speed increases.