esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 3D High-Speed Wheel/Rail Rolling Contact Finite Element Model
### Rail Weld in 3D  
With a smooth wheel/rail surface upon mesh generation, the form of 3D geometry irregularity at rail welds in the calculated area was applied by modifying the coordinates of related nodes on the rail surface by a self-compiled program. For easy understanding, a 3D right-handed Cartesian coordinate system is defined, of which the origin \(O\) is located at the initial position of the contact patch (i.e., position A as shown in Figure 7a). The \(x\)-axis is defined along the rolling direction and the \(y\)- and \(z\)-axes are in the lateral and vertical directions. The rail weld model used in this paper for the calculations is shown in Figure 8. Depth (or thickness) \(D\) at any element of the simulated weld is cosine in the direction of \(x\) and expressed in Equation (3):  
\[D=[1-cos2\pi\cdot(x-x_{0})/\lambda]\cdot\delta/2 \tag{3}\]  
For any \(x\) within the welding zone, \(D\) is parabolic in the y direction,  
\[D=[1-(y/w)^{2}]\cdot\delta \tag{4}\]  
where \(\lambda\) and \(\delta\) represent wavelength and maximum depth of a rail weld, respectively; \(x_{0}\) is the starting position of a rail weld applied; \(w\) is one-half width of the weld and assumed as 15 mm in this paper according to field observations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \hline \multicolumn{2}{c}{Lumped sprung mass} & 7500 kg \\ \hline Stiffness of primary suspension & 880 kN/m \\ \hline \hline \multicolumn{2}{c}{Damping of primary suspension} & 4 kNs/m \\ \hline \multicolumn{2}{c}{Stiffness of fastenings} & 22 MN/m \\ \hline \hline \multicolumn{2}{c}{Damping of fastenings} & 200 kNs/m \\ \hline \multirow{3}{*}{Wheel and rail material} & Young’s modulus & 205.9 GPa \\  & Poisson’s ratio & 0.3 \\  & Density & 7790 kg/m\({}^{3}\) \\ \hline \multirow{3}{*}{Material of pre-fabricated slabs} & Young’s modulus & 34.5 GPa \\  & Poisson’s ratio & 0.25 \\  & Density & 2400 kg/m\({}^{3}\) \\ \hline \multirow{3}{*}{Mortar material} & Young’s modulus & 8 GPa \\  & Poisson’s ratio & 0.2 \\ \cline{1-1}  & Density & 1600 kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of the parameters involved in this work.
# An Intelligent Measurement Method and System for Vehicle Passing Angles
## 2 Design of the Measurement System  
The primary challenge in efficiently automating the measurement of vehicle passing parameters is how to obtain data from underneath the vehicle without modifying the testing site or the testing platform. To address this issue, we propose a solution involving a wheeled robot equipped with sensing devices that passes underneath the vehicle. Subsequently, a three-dimensional point cloud is built and the parameters are calculated based on the data extracted from the point cloud. The schematic diagram in Figure 1 illustrates the operation of the passing angle measurement system. In this system, the wheeled robot equipped with a LIDAR and a camera longitudinally passes underneath the vehicle to acquire the data on the vehicle chassis, tires and the ground. The overall point cloud is built by the processor and subsequently analyzed to derive the passing angles. The final results are transmitted to the handheld device to be recorded and displayed.
## 3 Results
### Validation on Machine-Made Parts
When reviewing Figure 14, it must be taken into account that the percentage values are based on a few parts only and are therefore not trustworthy from a statistical point of view. Additionally, the defect position is not evenly distributed, because roughly half of the defective parts feature a through-hole in the bar area. However, the results for the good parts are poor when performing the classification based on the noiseless training data, except for the model F, based on the eigenfrequencies alone. For example, model F-G2 recognized even the smallest defect size perfectly, but misclassified nearly all of the good parts. This is because the inevitable noise effects within the measurement data are interpreted as defect impacts, which arises when the geometric data were used to compensate for the frequency variations caused by the geometric scatter. In contrast, the noisiest training data set led to a perfect detection of the good parts, but significantly poorer results for the detective parts. It is obvious that the amount of training data noise controls the classification border between the good parts and the defective ones. The most satisfying results were achieved with the training data noise in the realistic range from \(-0.1\%\) to \(+0.1\%\). All of the good parts were classified correctly by all of the models and many of the defects were recognized accurately. For example, model F detected the parts with defect sizes from 2.5 mm, using the eigenfrequencies alone. In contrast, the models F-g2 or F-G2 reached an improved defect detection from 2.0 mm or 1.5 mm in size on the basis of additional half or full geometric data, including interactions. The results are very satisfying, even in comparison with the results achieved on the synthetic validation data. Figure 13: Deviations of model-like predicted eigenfrequencies of machine-made connecting rods versus their ART measured eigenfrequencies depending on part no. and quality state.
## 2 Experimental Setup and Procedures
### Nozzles and Flow Conditions  
The experiment was aimed at analyzing the influence of a double-walled confined width on the waterjet flow field through measuring the mean velocity and turbulent intensity.  
As the velocity and turbulent intensity of waterjet had a close relation with the structure of nozzle, the nozzle was designed based on previous literature [38]. The nozzle that had been used in the experiment is illustrated in the Figure 2. The contraction angle is \(\alpha=14^{\circ}\) which could reduce the pipeline pressure loss as much as possible. The exit diameter is 4 mm. The jet velocity is 26 m/s, corresponding the operating pressure of 0.34 MPa.  
Figure 3 shows the generation method of the double-walled confined waterjet in a plexiglass tank as well as the measuring region. The pipe and nozzle were connected by a metallic flange through the baffle at the water tank inlet, and it was sealed by a rubber blanket to prevent water leakage. In order to minimize the influence of the right wall and left wall, the water tank was designed as a 1 \(\times\) 1 m horizontal square. The measuring regions of the flow field are marked in the Figure 3a. Four plexiglass water tanks were used in the experiment with the distance between the upside wall and the downside wall (called the confined width) being: 20, 40, 52 and 60 mm. The dimensionless width was defined as the ratio of the actual width to the diameter of the nozzle exit. Therefore, the dimensionless widths  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & **Power** & **Impulse Frequency** & **Scanning Speed** & **Wavelength** & **Width** \\ \hline Value & 120 mJ/Pulse & 30 HZ & 3.75/s & 532 nm & 1 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detailed information of the pulsed laser.  
Figure 2: Profile and photo of nozzle.  
were 5, 10, 13 and 15, respectively, as shown in Table 3. Figure 3a,b show the profile section of the horizontal and vertical planes in the three-dimensional coordinate.  
At the beginning of the experiment, the plexiglass tank was filled with water to eliminate air bubbles that attached to the upside wall since the air bubbles dramatically affect the accuracy of the PIV. Then, the mean velocity and the turbulent intensity of the horizontal plane \(X\)-\(Y\) and the vertical plane \(X\)-\(Z\) was measured in the direction of axis \(X\) and radius \(Y\) or \(Z\). Each test of each section of 160 sets of instantaneous data was acquired and averaged in order to minimize the adverse effect of single measurements and poor data points.
## 5 Discussion  
At present, there are few reports about full-spectrum hyperspectral remote sensing payloads on geostationary orbit, and we present an optical system, the GeoFWHIS, in this paper. The system comprises an afocal telescope, three imaging objectives, and spectrometers for five bands. The hyperspectral imaging of the ground coverage is completed by internal scanning. The scanning mirror is located in the parallel beam behind the telescope and before the imaging objectives, and this method has the advantage of high stability. Due to the relatively stationary state, the system's need for large relative apertures is not as high as the LEO payloads, which also avoids a larger volume. Design results show that GeoFWHIS has a wide width, high spatial resolution, and excellent spectral imaging performance, which meet the application requirements of continuous monitoring, and fine identification in disaster prevention and mitigation, environment, agriculture, forestry, ocean, meteorology, and resources. We developed prototypes of spectrometers for each sub-band and tested their performance. The results show that the long-slit convex grating spectrometers have high spectral uniformity, minor spectral distortions, high imaging quality, and low stray light. Such high-fidelity performance is also necessary for high-precision quantitative hyperspectral remote sensing.  
The GeoFWHIS reported in this paper also has limitations. First, spectrometers are spliced in GeoFWHIS, which greatly reduces the size, difficulty, and cost of spectrometers, but a total of 20 spectrometers will pose other problems. For example, it brings challenges to image stitching and may have the risk of reducing reliability. Secondly, although spectrometers are high-fidelity, it is still not easy to ensure that the performance of different spectrometers in one band is completely consistent. The inconsistencies of slits, gratings, mirrors, and alignment processes all contribute to this difference, leading to the complexity of calibration and the increasing difficulty of image stitching. Therefore, based on the scheme proposed in this paper, reducing the number of spectrometers and adding spectral multiplexing is an optimization direction in the future, which requires the promotion of detector technology simultaneously.
## 2 System Configuration  
As shown in Figure 1, two laser terminals were deployed on opposite shores of Qinghai Lake, separated by over 100 km. The link was established using custom-designed optical communication terminals with a stare-scan acquisition strategy. The scanning PAT terminal (Figure 1b) employs an L-shaped folded-axis design with a 100 mm aperture telescope. To achieve high atmospheric transmittance and low solar background noise, the communication wavelength was set to 1550 nm. A Charge Coupled Device (CCD) camera (\(-\)70 dBm sensitivity) served as the detector. Two beacon laser (1540 nm/400 \(\upmu\)rad and 650 nm/160 \(\upmu\)rad) were integrated to enhance acquisition probability and facilitate manual calibration. A dichroic beam splitter separated transmit (1544 nm) and receive (1556 nm) channels.  
Table 1 outlines the coarse beacon acquisition link budget for the 100 km ground-based experiment.  
Table 2 presents the communication link budget for the 100 km/100 Gbps experiment. The schematic diagram of the key parameters of the beacon light and communication light of the laser communication link is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Remarks** \\ \hline Transmit Power & 30 dBm/1550 nm & 1 W \\ Transmit Antenna Gain & 89 dB & 200 μrad (full angle) \\ Receive Antenna Gain & 104.2 dB & 80 cm aperture \\ Space Loss & \(-\)238.2 dB & 100 km line-of-sight \\ Receive Path Loss & \(-\)1.5 dB & \\ Transmit Path Loss & \(-\)1.5 dB & \\ Spot Averaging Loss & \(-\)9.54 dB & 3 \(\times\) 3 grid \\ Atmospheric Loss & \(-\)15 dB & \\ Pointing Loss & \(-\)0.98 dB & \\ Detector Received Power & \(-\)43.52 dBm & \\ Detector Sensitivity & \(-\)70 dBm & \\ Link Margin & 26.5 dB & \\ \hline \end{tabular}
\end{table}
Table 1: Coarse beacon acquisition link budget.  
Figure 1: Schematic of the ground-based ultra-long-distance atmospheric optical communication link over 104.8 km. The experiment was conducted near Qinghai Lake, China, and the satellite image shows the optical communication terminals placed in a temporary outdoor experimental site. (**a**) The scanning terminal’s temporary experimental site, located at an elevation exceeding 3400 m. (**b**) The scanning terminal optical communication terminal, placed in an insulated box equipped with a heating plate. (**c**) Optical schematic of the optical communication PAT terminal. (**d**) The staring reception terminal experimental site, located on the top floor of the Qinghai Lake View Hotel, at an elevation exceeding 3300 m. (**e**) The staring reception terminal using star calibration for optical axis pointing in the experimental scenario.
## 3 Underwater Communication Experiment and Discussion
### Verification of Demodulation Optimization  
First, the fully digital AGC performance was tested; the output can be stabilized by adjusting the AGC gain coefficient by a certain amount, as shown in Figure 9. The blue curve depicts the original sampling signal, with fluctuations increasing in line with bubble strength. The red curve, on the other hand, represents the output signal after AGC module control. To avoid amplifying noise, the digital AGC gain is maintained within 0.2-2 V. Specifically, when the sampling signal Din is 0.2 V, the AGC gain adjusts to 1.26 V; when Din is 1.98 V, the AGC gain output stabilizes at 1.37 V. These results indicate that the AGC module can consistently maintain an output level of approximately 1.37 V, with a gain adjustment range spanning from \(-\)3.2 dB to 16 dB, thereby meeting the system's gain control requirements.  
Subsequently, the MF performance in the demodulation structure was analyzed, and the results are presented in Figure 10. The X-axis represents the sampling time, while the Y-axis indicates the relative magnitude of the digital signal in arbitrary units (a.u.). The figure demonstrates the effect of median filtering on the envelope signal. The blue dashed line corresponds to the original envelope signal, which exhibits significant spike noise. In contrast, the red solid line represents the median-filtered signal, where the spike noise has been effectively suppressed, resulting in a smoother waveform. This filtering process enhances the reliability of subsequent threshold calculations, thereby improving the overall demodulation performance.  
Figure 8: Upper computer display. **(a)** Picture of sending side, and **(b)** picture of receiving side.  
Figure 9: Diagram of AGC effect.  
The effect of BEDF is illustrated in Figure 11. The smooth envelope signal after median filtering is shown in black, while the red dashed line represents the threshold value calculated by the joint judgment threshold module. The judgment signal, output under a 50 MHz system clock, is represented by the blue curve. This signal exhibits obvious burrs, which can lead to incorrect demodulation outputs under the bit synchronization signal. After the BEDF process, these burrs are effectively removed, as indicated by the red dashed line.
## 2 Materials and Methods
### Instruments and Outcomes  
In the present study, different outcomes were assessed:  
1. Before and after the session, the wellness questionnaire recommended by [PERSON] and [PERSON] [28] was used to assess general fatigue, muscle soreness, stress level, and mood state. Each dimension (general fatigue, muscle soreness, stress level, and mood state) was measured by a Likert scale (1 to 5), where one represents the worst (always retired, very sore, highly stressed, or highly annoyed depending on the dimension) and five the best state (very fresh, feeling great, very relaxed, or very positive mood depending on the dimension). Therefore, lower scores represented higher soreness, stress level, general fatigue, or mood state. This questionnaire has been previously used in the field of sports [29; 30; 31];
2. Before and after the session, the subjective perception of mental fatigue was measured using a 0 to 100 VAS scale. This scale allowed us to assess the perceived mental fatigue, where 0 indicated \"not mental fatigue\" and 100 \"extreme mental fatigue\". The VAS for mental fatigue has been employed in previous studies to assess mental fatigue in sports [32; 33];
3. During the session, the Polar Team software version 1.9 (Polar Electro, Kempele, Finland), using H10 polar sensors, assessed the mean heart rate, maximum heart rate, mean speed, and maximum speed. Heart rate variables refer to internal physical load, while speed variables refer to external physical load. Additionally, this software reported the root mean square of successive differences between normal heartbeats (RMSSD). A higher RMSSD has been related to higher parasympathetic modulation [34]. Therefore, the RMSSD was registered at rest (5 min before and 5 min after each session). Participants were required to sit quietly during this period without talking to anyone;
4. After each session, the Questionnaire to Quantify the Mental Load in Sports (QQMLS) [35] was administered. This instrument has four items: physical effort (how demanding would you quantify the physical effort of this session?), cognitive effort (how demanding would you quantify the cognitive effort of this session?), emotional effort (how demanding would you quantify the effort made to manage your emotions during this session?), and affective (how demanding would you quantify the effort made in this session to manage emotional relationships with the rest of the participants?). However, due to the individual character of the session, the affective question was not included in our procedure. Therefore, we selected the physical, cognitive, and emotional demand dimensions. All these questions were evaluated using a Likert scale with responses ranging from 0 to 10, where 0 means nothing and 10 is the maximum required value for each item. This questionnaire showed an internal consistency of Cronbach's \(\alpha\) (0.73).
## 5 Results and Discussion
### Modality Comparison  
To further explore the influence of three modalities on our proposed model, we used all possible inputs to evaluate our model: uni-modal (T, A, V/\(V^{*}\)), bi-modal (T + A, T + V/\(V^{*}\), A + V/\(V^{*}\)), and tri-modal (T + A + V/\(V^{*}\)), the performance is shown in Table 5.  
As can be seen from Table 5, whether it is the SVM classifier-based method proposed by [PERSON] et al. [9] or our method, the model using only face image features (V) or text features (T) performs well, but the combination of them (T + V) improves the F1-score by 4.4% (from 65.2% to 69.6%) and 2.9% (from 74.6% to 77.5%), respectively. Therefore, for sarcasm detection, the blessing of the two is beneficial to improving the model's performance. It is worth noting that the blessing of speech modality (A) did not improve our model greatly. On the contrary, the blessing of speech modality improves the performance of the baseline method. Specifically on the method proposed by [PERSON] et al. [9], the blessing of speech modality (T + A, A + V) improves the performance of the model by 1.6% (from 64.6% to 66.2%) and 2.9% (from 65.2% to 68.1%), respectively. On the sequential context encoder proposed by [PERSON] et al. [30], the blessing of speech modality (T + A, A + \(V^{*}\)) improves the performance of the model by 7.0% (from 53.4% to 60.4%) and 0.7% (from 71.2% to 71.9%), respectively. This shows that using the baseline method can better integrate the features of speech modalities. In addition, in the model with three modalities (T + A + V/\(V^{*}\)) as input, our method outperforms the baseline method by 6.5% (from 72.5% to 79.0%) and 6.7% (from 72.3% to 79.0%), respectively. This shows that our proposed model can better fuse the features of the three modalities.
# Assessing the Impact of Patient Characteristics on Genetic Clinical Pathways: A Regression Approach
## 3 Results
### Statistical Analyses
#### 3.5.3. Binary Models: Assignment to a Clinical Pathway--Not-EMA Sample
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** & **(8)** \\  & **B1** & **B2** & **B3** & **B4** & **B5** & **B6** & **C** & **A** \\ \hline \multirow{3}{*}{Age} & \(-0.00574\) * & \(-0.00111\) & \(-0.00212\) * & \(-2.05\) \(\times\) & \multirow{3}{*}{\(0.0120\) ***} & \(0.00237\) * & \(0.000175\) & \(-0.00371\) *** \\  & (\(-1.718\)) & (\(-1.288\)) & (\(-1.702\)) & \((-0.589\)) & (4.423) & (1.673) & (0.175) & (\(-4.076\)) \\ \multirow{3}{*}{Weeks of pregnancy} & \(-0.00505\) & \(-0.00428\) *** & \(-0.00868\) *** & \(-0.000155\) & \(0.0282\) *** & \(-0.0112\) *** & \(-0.000709\) & \(-0.000872\) \\  & (\(-1.487\)) & (\(-3.594\)) & (\(-5.902\)) & (\(-3.143\)) & (8.317) & (\(-4.007\)) & (\(-0.410\)) & (\(-0.743\)) \\ \multirow{3}{*}{Foreign} & 0.249 *** & \(-0.0133\) & \(0.00325\) & \(-0.000294\) & \(-0.159\) *** & \(-0.00713\) & \(-\) & \(-0.0148\) \\  & (5.643) & (\(-1.040\)) & (0.170) & (\(-0.464\)) & (\(-5.931\)) & (\(-0.370\)) & & (\(-1.138\)) \\ \multirow{3}{*}{Live births} & 0.0567 ** & \(-0.00201\) & \(0.00593\) & \(0.000179\) & \(-0.00395\) & \(-0.111\) *** & \(-0.0191\) & \(0.0121\) \\  & (2.245) & (\(-0.328\)) & (0.624) & (0.464) & (\(-0.231\)) & (\(-4.823\)) & (\(-0.508\)) & (\(1.563\)) \\ \multirow{3}{*}{Miscarriages} & \(-0.110\) *** & \(0.0173\) *** & \(0.0288\) *** & \(-0.0003\) & \(0.0493\) ** & \(-\) & \(-0.00188\) & \(0.0163\)* \\  & (\(-3.421\)) & (\(2.855\)) & (\(2.746\)) & (\(-0.523\)) & (\(2.045\)) & - & (\(-0.0555\)) & (\(1.702\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 18. Clinical path length estimation with different techniques—full sample. The coefficients of path B1 are quite similar to those of the previous estimates. The estimation is robust to the subsample considered. Of course, the age variable is now also present, which is significant and negatively correlated with the probability of being in B1. A similar argument applies to the B5 path, to which more older mothers seem to be assigned. A similar argument can be made for path B6.
## 7 Conclusions  
A novel image alignment algorithm that uses rotation-discriminating ring-shifted projection for AOI applications is presented. It combines hybrid projection transformation and the ring shift technique. The hybrid projection transformation with the image pyramid searching technique can significantly reduce the computation burden and own the unique and robust features in the alignment process. The ring shift technique provides the rotation estimation between the template image and the searching image. The results show that the rotation estimation of the proposed method is superior to other comparative methods. Furthermore, the novel image alignment algorithm can obtain accurate and robust results in the scene image with rotation, translation, and noise. A series of experiments verified the efficiency, accuracy, and robustness of the proposed algorithm. Furthermore, the proposed method not only provides high accuracy with rotation, but also works well under noise influence and translation. The various experiment results indicate that this approach is suitable for accurate image alignment in AOI industrial inspections.  
**Acknowledgments: The authors would like to acknowledge the financial support of the National Science Council of Taiwan, through its grant MOST 104-2221-E-027-029-MY2.**  
**Author Contributions: Contribute the ideas of the research and research supervision: [PERSON]; Performing of the research and writing of the manuscript: [PERSON].**  
**Conflicts of Interest: The authors declare no conflict of interest.**  
Figure 19: PCB case4 images. (**a–c**) real images and (**d**) template image (206 \(\times\) 201 pixels).