esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Surface Acoustic Waves for Crack Detection on Railway Track Components--Laboratory Tests
## 3 Results
### EFIT Visualization of Surface Wave Propagation in a Rail
In general, the EFIT simulation results for R-wave transmission showed a stronger weakening of the signal for deeper cracks and for a higher number of cracks, but different trends can be noted:  
Crack number and crack depth  
The attenuation trends for the one-crack and two-crack models show similar behavior for all three sensing locations. For 0.5 mm crack depth, corresponding to a ratio \(d/\lambda_{R}=0.17\), almost identical attenuation is observed for the one- and two-crack models. However, for a crack depth of 1 mm, i.e., a ratio \(d/\lambda_{R}=0.3\), the two-crack model exhibits significantly higher attenuation than the one-crack model. The presence of three cracks changes the picture fundamentally. Here, the attenuation values differ markedly between sensing locations for the shallower cracks of 0.5 mm depth;  
Figure 13: Change of transmitted R-wave received at position WR with increasing number of cracks and crack depths up to 1 mm; (**a**) 1 crack (**b**) 2 cracks (**c**) 3 cracks. on the other hand, for each individual location the attenuation barely changes between 0.5 mm and 1 mm crack depth. Receiver position  
Generally speaking, when examining Figures 11-13, the R-wave packet sensed at the right rail head (location HR, Figure 11) shows the most distinct shape compared with the sensing locations below the rail head (location HBR, Figure 12) and web (location WR, Figure 13). This is due to increasing signal overlaps caused by rail-geometry-born reflections. This can also be observed for the increase in noise level with increasing sensing distance. Looking at Figure 14 it can be noted that the R-wave attenuation was strongest at the HR sensing location, especially with an increasing number of cracks; a logical consequence of the higher signal-to-noise ratio and less overlap of mode converted bulk waves at this location. The strong attenuation in the case of the three-crack model and bad signal-to-noise ratio (i.e., signal interference) at the farther sensing locations led to lower attenuation values there.
## 5 Results and Discussion
### Impact of Template Length  
To answer RQ3, we vary the template length in \(\{5,10,20,30,50\}\) and keep other settings to our default configurations. We re-examine the performance of the original and the hand-crafted version of our proposal on all tasks. The model performance under different template lengths is shown in Figure 3 and the model performance of each epoch  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variants** & **AFQMC** & **OCNLI** & **TNEWS** & **INEWS** & **BQ** \\ \hline w/o demo & 70.46 \({}^{\blacktriangledown}\) & 68.32 \({}^{\blacktriangledown}\) & 66.71 \({}^{\blacktriangledown}\) & 64.18 \({}^{\blacktriangledown}\) & 73.19 \({}^{\blacktriangledown}\) \\ w/o demo (full) & 76.53 & 72.86 & 70.51 & 69.34 & 74.26 \\ w/o demo (random) & 75.44 & 71.82 & 69.13 & 68.52 & 73.92 \\ w/o generation (man) & 74.53 & 72.95 & 68.49 & 69.02 & 74.37 \\ \hline Ours (original) & **77.21** & **74.39** & **71.17** & **71.35** & **76.88** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study results of our proposal on 5 datasets. The results of the best performer in each column are bolded. The biggest drop in each column is appended with \({}^{\blacktriangledown}\).  
with a template length of 20 is shown in Figure 4. Clearly, with the increase in the template length, both versions of our proposal show a consistent pattern in the model performance, i.e., increases first to reach the top and then goes down. While the dropping tendency reflects the fact that the overlong templates inevitably add noise, making it difficult for demonstration and classification.  
Interestingly, comparing the performance drop caused by the increase in the template length, the drop on TNEWS is more obvious than that on other tasks. It can be stemmed from the dataset itself, in which the category number of TNEWS is larger than other datasets.  
Figure 4: _Cont._  
Figure 3: Model performance of TaxonPrompt and its variants with various language models on Few-Event. (**a**) Accuracy on AFQMC. (**b**) Accuracy on OCNLI. (**c**) Accuracy on TNEWS. (**d**) Accuracy on INEWS. (**e**) Accuracy on BQ dataset.
## 2 Materials and Methods
### Definition and Design of the System's Components
#### 2.2.5 Merged Internal Interfaces in a PCB  
To minimize system noise, we decided to integrate the FPGA/LED driver interface and PD driver/FPGA interface into the internal interface. As a result, the LED driver was also included within the internal interface. This action effectively reduced the number of interacting PCBs in the system. Following this revision, the internal interface now performs three functions: (i) on the transmitter side, to ferry the output bits of the FPGA to the LED for emission, (ii) on the receiver side, to transport the analog signal received by the photodetector to the FPGA, and (iii) to link the FPGA to the external interface, which houses the input and output ports and power supply. As depicted in Figure 20, various blocks interact in the aforementioned functions, and their connectors, labelled as JB\({}_{1}\), JB\({}_{2}\), JB\({}_{3}\), JM\({}_{1}\), JM\({}_{2}\), and JM\({}_{3}\), are joined with their corresponding connectors on the TE0720 [42] and external interface boards.  
Figure 19: TE0720 SoC includes the FPGA Xilinx XA7z020-1 CLG484Q.  
Figure 20: Diagram of the interaction between external interface, TE0720 SoC, and internal interface.  
In reference to function (i), the interface is utilized on the LiFi router side to output the video signal on the VLC-based link and on the USB dongle to output the user requests on the IR link. For function (ii), the interface is needed on the USB dongle side to receive the video signal on the VLC-based link and on the LiFi router to receive the user requests on the IR link. With regard to function (iii), the interface is necessary to connect the FPGA to the input and output ports of the carrier board and to the appropriate power ports, which supply the required voltage and current to both the LiFi router and the USB dongle. The FPGA is embedded in the TE0720 board depicted in Figure 20.  
The blocks associated with the transmitter in Figure 20 are responsible for processing the signals generated in the FPGA into an analog signal that is channeled to the LED driver. The signal introduced to the LED driver is the modulated signal emitted by the LED.  
The receiver-related blocks in Figure 20 convert the analog signal from the receiver into a digital signal, which is then transferred to the FPGA for demodulation and decoding. By using a jumper, the received signal can be selected, i.e., either an OOK modulated signal or an OFDM signal [43]. This latter modulation scheme allows for the simultaneous transmission of multiple data streams at orthogonal carrier frequencies [43; 44].  
As visible in Figure 20, the LED driver module is incorporated into the internal interface within the blocks related to the Tx side.  
Figure 21 depicts the actual appearance of the internal interface PCB following the manufacturing and soldering process.
# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
## 4 Experiment Analysis and Results
### Influence of Flood on L4 for GPS Satellites  
#### 4.1.1 Ground Track Repeat Period of GPS Satellite  
To analyze the influence of flood on L4 combination observation for GPS satellites, the ground track repeat period of GPS satellite is investigated first. As is well known, the repeat period of the satellite relative to the ground stationary receiver is twice as long as the operation period of the satellite. The operation period of a GPS satellite is approximately 11 h and 58 min. Thus, the ground track repeat period of a GPS satellite is almost equal to one solar day. The ground track of the G10 satellite on DOY 199, DOY 201, and DOY 202, 2021 in the view of the sky plot is presented in Figure 5.  
From Figure 5, it can be observed that the satellite elevation angle and azimuth of the G10 satellite on these three days are totally consistent with each other. This phenomenon denotes that the ground track of the G10 satellite on DOY 199, DOY 201, and DOY 202, 2021 is identical. Based on the relationship between multipath error and L4 observation, if the surrounding environment of the observation station keeps constant, the L4 should also remain stable. This can indicate that the changes in L4 are caused by the environment around the observation station, not by the satellites. Thus, to analyze the influence of flood on L4, data sets collected from two adjacent days can be used to compare and analyze. In addition, considering that DOY 200 and DOY 201 are affected by rainfall and local small-scale floods, DOY 199 is used to compare with DOY 202 in this experiment.  
Figure 5: Ground track of G10 satellite on DOY 199 (red), DOY 201 (green), and DOY 202 (black), 2021 in the view of sky plot.
### Casualty Assessment Method Based on Seismic Parameters  
Earthquake casualties are closely related to seismic intensity, occurrence time, population density, and other factors. Magnitude and intensity are parameters commonly used to characterize seismic strength. [PERSON] et al. [11] analyzed data from major earthquakes in China over a 20-year period, examining the correlation between different intensity levels and factors such as house collapses, people becoming trapped, and rescue operations, and proposed empirical criteria for quantitatively estimating casualty rates. [PERSON] [12] developed an empirical model linking earthquake casualties to magnitude, the area of intensity zones, and population density by analyzing global strong earthquake data from the 20 th century. [PERSON] and [PERSON] [13] employed an optimization approach to establish empirical functional relationships between seismic intensity and death rates for different  
Figure 1: Statistics of the annual damage caused by earthquakes from 1991 to 2020.  
countries or regions, as shown in Equation (1). This method is suitable for countries or regions with extensive historical records of earthquake casualties.  
\[v(S)=\Phi\left[\frac{1}{\beta}\ln\left(\frac{S}{\theta}\right)\right] \tag{1}\]  
In the equation, the death rate \(v\) and seismic intensity \(S\) follow a two-parameter lognormal cumulative distribution relationship; the parameters \(\beta\) and \(\theta\) of the lognormal cumulative distribution correspond to the country or region where the earthquake occurs.  
Furthermore, by multiplying the exposed population \(S_{j}\) within different intensity levels \(P(S_{j})\) by the corresponding death rate \(v(S_{j})\) and summing the results, the total estimated number of deaths \(E[L]\) can be calculated:  
\[E[L]=\sum_{j}v(S_{j})\cdot P(S_{j}) \tag{2}\]  
Empirical assessment methods based on seismic parameters require fewer parameters and are simple and rapid. However, the fitting formulas rely on reasonable assumptions and abundant historical earthquake data, and these methods overlook the significant influence of building seismic capacity. Such approaches are typically applied at a global or national scale, resulting in lower accuracy when used for smaller regional areas.
## 2 Finite Element Model of FSW
### Geometric Model  
The FSW process is a dynamic nonlinear analysis based on the Lagrange method. On the basis of previous process experiments, the welding parameter interval was roughly determined by considering  
Figure 1: Schematic of the friction stir welding (FSW) process: (**a**) Plunging, (**b**) dwelling, (**c**) welding.  
the strength and plasticity of the welded joint. The whole process of friction stir welding of a 2A14 aluminum alloy was numerically simulated by finite element software Deform-3D. The material of the tool was W6, and the workpiece was 2A14-T6 lightweight aluminum alloy of 150 mm \(\times\) 100 mm \(\times\) 6 mm size. The workpiece was divided into 15,256 nodes and connected to form 65,450 tetrahedral elements. Tool steel W6 was connected to 9584 nodes to form 43,268 tetrahedral elements. Tool and workpiece meshing are shown in Figure 2a. The yield strength of the workpiece was much less than the strength of the material used in the tool, so the tool was defined as a rigid body that could conduct heat conduction in the numerical simulation process. The diameter of the shoulder of the tool was 16.3 mm, the maximum use diameter of the tool pin was 8.15 mm, and the length of the tool pin was 5.6 mm. The specific dimensions are shown in Table 1.  
In the process of numerical simulation, the powerful adaptive mesh re-division technique of the software is used to solve the problem of mesh distortion caused by material flow in the welding process. The mesh of the contact area is finely divided through the mesh window to obtain more accurate results, and the refined mesh window moves along with the mixing head during welding feed. The welding simulation process and mesh window move with the welding feed as shown in Figure 2b. The boundary condition set by the FSW model is that the workpiece is constrained by setting zero speed in three directions, thus ensuring that all degrees of freedom of the workpiece are limited [25; 26; 27]. The movement of the FSW tool is defined by the rotation in the \(Z\) direction and the feeding movement in the X direction. The convection heat coefficient between the workpiece and the environment is set to 20 W/m\({}^{2}\)/\({}^{\circ}\)C [28], and the convection heat coefficient of the workpiece bottom surface is set to 200 W/m\({}^{2}\)/\({}^{\circ}\)C to consider the thermal conduction effect of the backing plate [28].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Pin height & 5.6 mm \\ Shoulder diameter & 16.3 mm \\ Mix diameter of pin & 8.15 mm \\ Tool tilt angle & 25\({}^{\circ}\) \\ Plunge depth & 0.25 mm \\ Concave angle of shoulder & 10\({}^{\circ}\) \\ Taper angle of pin & 15\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimension parameters of the tool.  
Figure 2: Mesh model of tool and workpieces used in the simulation process: (**a**) Meshing of tool and workpiece; (**b**) the welding simulation process and mesh window move with the welding feed.
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### Numerical Integration  
Another challenge, associated with the use of discontinuous and singular enrichment functions, lies in the numerical integration of the weak form of Equation (23). Since the functions to be integrated are not smooth, standard Gauss quadrature cannot be used and more sophisticated tools need to be employed.  
For the discontinuous jump enrichment functions, the most common approach would be element partitioning where elements are divided into integration sub-cells based on the crack geometry [44; 132]. Extensions of this technique have also been proposed for higher order elements [146; 100; 147]. Alternatively, other works completely avoid the use of element partitioning by employing either equivalent polynomials [148; 149], or the Schwarz-Christoffel conformal mapping [150].  
As far as asymptotic enrichment functions are concerned, the most widely used solution would involve element partitioning combined with some transformation aiming at removing the singularity. Several such transformations have been proposed, e.g., the almost polar mapping of Laborde et al. [105], the parabolic transformation of Bechet et al. [106], and the Duffy transformation by Mousavi and Sukumar [151]. Element partitioning is used to divide the element containing the crack tip in triangles with one node lying on the singularity, thus also accounting for the discontinuity present in this element. Subsequently, the transformation is used to map quadrilateral elements to the constructed triangles leading to an accumulation of Gauss points around the crack tip and additionally removing the singularity. A promising solution, also including the above steps, is the algorithm introduced in Chevaugeon et al. [114], where a mapping is used for all asymptotically enriched elements, rather than just the ones containing the crack tip, and an adaptive strategy is devised to determine the number of Gauss points required for each element. Similar element partitioning algorithms [152] and mappings [153] have also been introduced for the three dimensional case.
## 4 Discussion
### Limitations and Shortcomings  
This study has certain limitations and uncertainties. Firstly, in terms of data, the spatial resolution of the currently used GOSIF, GLASS NPP, and other environmental variable data is relatively coarse at 0.05\({}^{\circ}\), which may affect the accuracy of the analysis.  
Secondly, regarding the research methodology, although the SIF and NPP data in our experiments exhibit good spatiotemporal consistency, and the relationship between SIF and NPP can be inferred based on the correlation theories between GPP and NPP, as well as between SIF and GPP, it should be noted that the amount of carbon consumed by plant respiration is not a constant value in practice. It varies across different vegetation types and is influenced by environmental factors. Our research findings only discuss the potential of using SIF to monitor the dynamic changes in NPP induced by drought at a theoretical level. We validated our conclusions using satellite measurement data and model inversion data, without incorporating flux tower data or field experimental data. Therefore, the results and conclusions of this study have certain limitations.  
In addition, regarding the analysis, we assessed the ability of SIF to monitor dynamic changes in NPP by using the average SIF/NPP ratio for the entire HHH Plain, which to some extent overlooks local variations in the ratio. Furthermore, we only conducted simple correlation analyses between SIF and NPP, as well as between SIF and five environmental factors, without considering the impact of factors such as topography, elevation, and canopy conditions on land vegetation NPP. Moreover, the experimental results mainly apply to the HHH Plain, and the generalizability to other regions remains unexplored.  
In future research, we plan to integrate ground-based measurements to further investigate the impact of environmental factors and other variables on the relationship between SIF and NPP. This will enable us to uncover the complex nature of the linear or non-linear relationship between these two variables.
###### Abstract  
Fused Filament Fabrication (FFF) enables the manufacturing of complex polymer components. However, surface finish and dimensional accuracy remain key limitations for their integration into functional assemblies. This study explores the potential of conventional turning as a post-processing strategy to improve the geometric and surface quality of PLA reinforced with carbon fiber (CF) parts produced by FFF. Machinability was evaluated through the analysis of cutting forces, thermal behavior, energy consumption, and surface integrity under varying cutting speeds, feed rates, and specimen slenderness. The results indicate that feed is the most influential parameter across all performance metrics, with lower values leading to improved dimensional accuracy and surface finish, achieving the most significant reductions of 63% in surface roughness (Sa) and 62% in cylindricity deviation. Nevertheless, the surface roughness is higher than that of metals, and deviations in geometry along the length of the specimen have been observed. A critical shear stress of 0.237 MPa has been identified as the limit for interlayer failure, defining the boundary conditions for viable cutting operation. The incorporation of CNC turning as a post-processing step reduced the total fabrication time by approximately 83% compared with high-resolution FFF, while maintaining dimensional accuracy and enhancing surface quality. These findings support the use of machining operations as a viable and efficient post-processing method for improving the functionality of polymer-based components produced by additive manufacturing.  
additive manufacturing; Fused Filament Fabrication; PLA carbon fiber; machinability; dimensional accuracy; surface roughness; post-processing; interlayer failure
## 1 Introduction
The idea behind this selection is to discard the stations with the worst performance in each criterion. However, most of the classical methods of statistical analysis and EPN performance are based on the approach of the normal law of measurement error distribution, which a priori does not assume the presence of minor residual errors of a systematic nature that are not taken into account by models, for example, the tropospheric correction model [13]. The main purpose of this article is an attempt to identify the presence or absence of residual errors of a systematic nature at a single observation station based on the theoretical foundations of a non-classical approach to the statistical analysis of a long time series. To analyze the presence of residual unaccounted-for systematic errors at a particular GNSS station, the authors propose new software that can confirm or reject the hypothesis that residual errors follow the normal Gaussian distribution law, i.e., perform diagnostic mathematical modeling. This paper presents an overview of the non-classical method of geodetic time series analysis (Section 2), describes the process of using the Python v.3.9 software environment to develop a new software package for analyzing GNSS coordinate time series (Section 3), and evaluates the positioning of some stations in the EPN/IGS network using the developed GNSS software (Section 4).
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline Capacity (bit/pixel) & 0.3651 & 0.0980 & 0.0547 & 0.0493 & 0.0582 & 0.0587 \\ Increase in capacity over & 214\% & 188\% & 284\% & 335\% & 61\% & 67\% \\ existing one (\%) & & & & & & \\ MSE between original and & & & & & & \\ recovered images & & & & & & \\ BER between embedded & & & & & & \\ and extracted information & 0.00\% & 0.00\% & 0.00\% & 0.00\% & 0.00\% & 0.00\% \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{l c c} \hline
**Schemes** & **Results in [10]** & **Results with our scheme** \\ \hline Image size id Lena & 512 \(\times\) 512 & 512 \(\times\) 512 \\ Output image quality (dB) & 48.20 & 51.13 \\ Capacity (bit/pixel) & 0.0204 & 0.0546 \\ Increase in capacity over [10] (\%) & – & 168\% \\ MSE between original and recovered images & 0.00 & 0.00 \\ BER between embedded and extracted info & 0.00\% & 0.00\% \\ \hline \end{tabular}
\end{table} 
Table 3: Comparisons with [10] for test image Lena.
## 1 Introduction
Specifically, accurate approximate expressions of ergodic rate and closed-form solutions of the outage probability for each user are obtained by using the theories of order statistics and probability statistics. 3. We verify the mathematical results obtained through Monte Carlo simulations. For the C-NOMA relay network, we discuss the impact of base station transmission power, relay amplification, forwarding power, relay LI level, quantization error level, and channel estimation error level on system performance. Theoretical and simulation results demonstrate the feasibility of the proposed theoretical approach. The rest of this paper is organized as follows. Section 2 introduces the system model of a multi-user FD C-NOMA relay network. In addition, the signal models include channel estimation errors, loop interference, and ADC quantization error. Sections 3 and 4 provide theoretical analysis for the system ergodic rate and outage probability, respectively. Section 5 shows the theoretical and simulation results of system performance metrics under different parameters. Finally, Section 6 concludes the paper. _Notations_: We use \(\mathcal{CN}\) to denote the complex normal distribution and i.i.d. to denote \"independent and identically distributed\". \(\mathbb{E}(\cdot)\) denotes the expected value of random variables. \(\Pr(\cdot)\) indicates the probability of an event.