esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Wind Field around Three Wind-Break Walls  
The horizontal wind speeds in different terrains around three wind-break walls measured with the Pitot tube are shown with black, blue, and red lines in Figure 7. The wind-break wall is located at the zero point of the x-axis, and the measuring height in Figure 7a is 15 cm, and 30 cm in Figure 7b. A wind speed of \(u_{*}=0.9701\) m/s was utilized as an example to analyze the wind speed variation. As shown in Figure 7, the horizontal wind speeds in three terrains were decreased then increased with x distance. The horizontal wind speed reached its peak on the leeward side and was gradually increased with x distance, and the peak of the concave subgrade was much lower than that of the convex and flat subgrade. Meanwhile, as shown in Figure 7a, the minimum speed of convex, flat, and concave subgrade on the leeward side was 2.73, 2.23, and 0 m/s, respectively. Although, according to numerical simulations, the speed on the leeward side of the concave subgrade should be negative at the height of 15 cm, the speeds of convex and flat were both positive because the Pitot tube measurements were all positive and could not be negative in wind-tunnel experiments. Therefore, the numerical simulation was applied to supplement the complete flow field information around wind-break walls.  
Figure 5: The size distribution of sand in the sand bed.  
Figure 6: Wind profiles with experimental data and the profile curves fitted to the logarithmical law.  
In order to verify the reliability of numerical simulations in this study, numerical wind speeds of the convex wind-break wall at different heights were compared with those from the wind-tunnel experiments, as shown with the pink line in Figure 7. The results show that all simulation results are basically consistent with those collected in the wind tunnel.  
The major stream-wise wind fields around these three wind-break walls under different terrains are shown in Figure 8a-c (symmetry plane in x-z direction). The results show that the acceleration and vortex zone appear successively, and a large vortex zone was formed on the leeward side of the wind-break walls, which is consistent with reported results [28]. As shown in Figure 8, when the wind rolled back to the track under the action of the vortex zone, the wind speed was reduced due to the obstruction of convex slope P2. However, concave slope P4 could increase the wind speed towards the railway track, and slope P3 could slow down the wind speed that carried away a lot of sand particles, indicating that a large number of sand particles would be deposited in the concave subgrade. The wind speed on the leeward side of the flat subgrade was between those of concave and convex terrains.  
Figure 8: Wind fields around wind-break walls (\(u_{*}=0.9701\) m/s): (**a**) convex subgrade, (**b**) flat subgrade, and (**c**) concave subgrade.  
Figure 7: Wind speeds around three wind-break walls (\(u_{*}=0.9701\) m/s), and the measuring heights are: (**a**) 15 cm and (**b**) 30 cm.
## 5 Results and Discussion
### Sentiment Analysis  
As we mentioned earlier, we applied the sentiment analysis using the sentiment analyzer based on the VADER lexicon. This technique helps to detect the text polarity and classify it into three different classifications: positive, neutral, or negative. The generated PDF synthesis reports show the results of the sentiment analysis (see Figure 7). As in [28,29] we used the following thresholds to classify the scores of sentiment analysis:  
* Positive (score between 0.05 and 1);
* Negative (score between \(-\)0.05 and \(-\)1);
* Neutral (score between \(-\)0.05 and 0.05).  
Figure 7 shows the percentage of the students' feedback that is positive, negative, or neutral for the feedback files reviewed in our experiments. By looking at the results, which show the sentiment scores to the text collected from each lesson, we can conclude the overall sentiment of the students are positive, which means that their feedback includes a majority of positive words that conveys their positive emotions about the lessons.
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 6 Application
### Information of Specimen of Impact Damage on CFRP Laminate
#### 6.1.2 Internal Delamination Defect Detected by Ultrasonic Testing (UT)  
To verify the correctness of infrared detection, the specimen was firstly detected by the ultrasonic testing of water immersion. A V312 water-immersion focused ultrasound probe was used (OLYMPUS Corporation, Japan). The piezoelectricity chip size of the ultrasonic transducer is 6.35 mm, the probe emission frequency is 10 MHz, and the focal length of the probe in water is 11.684 mm. During the  
Figure 28: The specimen of impact damage of carbon fiber-reinforced plastics (CFRP).  
Figure 27: The curve of average temperature elevation over time and the acquired frame image.  
experiment, the mechanical transmission controls the ultrasonic water-immersion probe to automatically scan the test block in the X and Y direction. According to the material characteristics of the specimen and the thickness of the specimen, the ultrasonic bottom echo imaging method was adopted. Set the equipment parameters as follows: the pulse voltage was 200 V, the gain of the system was set to 21 dB, the scanning speed was 40 mm/s, and the step interval was 0.2 mm. The experiment took 42 min in total. The scanning result is shown in Figure 29a. The region of interest is selected as shown in Figure 29b and the delamination defect is extracted after image processing, as shown in Figure 29c. The calculated damage area is 1836 mm\({}^{2}\).
## 4 Results
### Tests with Three Classification Classes
#### 4.1.1 Tests with Three Classification Classes and Decimated Data  
The same tests carried out with the full sampling frequency were repeated with a sampling frequency decimated. These tests were carried out because the sampling frequency usually assumes lower values and the fault characteristic patterns searched are at lower frequencies, i.e., ranging from 0 to 2 kHz.  
Figure 6: Training and validation accuracy progress for the radial stray flux signal: training in light blue and in blue (smoothed), validation in black dashed.  
Figure 7: Confusion matrices (test set) for (**a**) radial flux signal and (**b**) for current signal.  
For the decimation of the sampling frequency, the \"decimate\" MATLAB function was used. This function includes a lowpass Chebyshev Type I infinite impulse response (IIR) filter of order 8 for the purpose of antialiasing.  
The reduction factor chosen was 10, so the new sampled frequency was then 12 kHz and the signal length was 10,000 samples.  
For the sake of a comparison, a decimated sampling image obtained from the current signal and a non-decimated one are shown in Figure 8.  
The validation accuracies obtained with these decimated data were 97.5% and 40.0% for the radial flux and current signals, respectively. This means that the images could also be recognized with a decimated sampling frequency, although with a decrease in the validation accuracy for both flux and current signals (from 100% to 97.5% for the radial flux and from 50% to 40% for the current).
## 4 Results and Discussion
### Discussion of This Paper  
#### 4.3.1 Qualitative Analysis on the Effectiveness of the Model  
The FE modeling approach employed in this study originated from [PERSON] and [PERSON] [26]. This approach has been validated by [PERSON] and [PERSON] for high-frequency vehicle-track interaction at squats [19; 20], and by [PERSON] and [PERSON] for local rolling contact fatigue occurring on high-speed wheel tread [22]. This section tries to further validate it through measured ABA in time and frequency domains. It should be noted that no corresponding weld geometry for measuring ABA in Section 2 was available so only a qualitative analysis will be performed to discuss the effectiveness of the model.  
[PERSON] and [PERSON] [5] indicated that on Chinese high-speed railway lines the average weld geometry irregularity in 0.11 m (0.05-0.39 m) accounted for 78%. Meanwhile, weld straightness should be no more than 0.2 mm for tracks allowing speeds of 300 km/h, according to requirements of maintenance regulations. Therefore, we assumed that geometry wavelength of rail welds corresponding to the measured data in Figure 4 is from 0.05 to 0.4 m with a depth less than 0.2 mm. Figure 5b shows that the wavelength of the rail weld excites two high-frequency vibrations that are well reflected in Figure 10b; similar to the frequency domain response of the 0.2 m wavelength welds. Figure 10a indicates that the wavelength raises the ABA amplitude to about 100 m/s\({}^{2}\) in the time domain, less than the measured data (148 m/s\({}^{2}\)) in Figure 5a. The difference might be caused by the low frequency response generated without the consideration of an actual irregularity in the model built in this paper. For example, a high-energy low-frequency response is seen in Figure 5b, while there is no such response in Figure 10b. It should be emphasized that the results of two measured ABAs in the literature [18] did not overlap completely due to vehicle hunting. In Figure 11a, variations in ABA ranged from 35.6 to 204.6 m/s\({}^{2}\) under wavelengths from 0.05 to 0.4 m. It agrees well with the measured data in Figure 6 when we consider that the measured ABA induced by other irregularities is about 50 m/s\({}^{2}\). Therefore, the 3D transient finite element model established in Section 3 can effectively simulate the ABA response that is excited by the rail welds.  
The effect of differences between actual parameters and model parameters of a vehicle-track system on ABA should be further investigated in the future. These include the change in stiffness of rail pads and fatigue damage of the fastening system [1], the location of the rail weld between the adjacent sleepers [27], existence of track lateral irregularity [28], noise from braking pads, and damage appearance due to an increase in total train mass, etc.
# Spatial Monitoring of Wafer Map Defect Data Based on 2D Wavelet Spectrum Analysis
## 3 Wavelet Spectrum Based 2D Statistical Process Control
Hereafter, the calculation of the control limit is conducted based on the density of the distance matrices, which enables us to detect the abnormal status containing a high amount of energy distance from the original background. The suggested method gives robust results in a nonparametric framework with computational efficiency, regardless of the hyperparameters or pre-specified distribution. Using this procedure, a spatial monitoring scheme for 2D image data, as well as the calculation of fault strengths in the divided region can be provided.
# The Quest for Low Loss High Refractive Index Dielectric Materials for UV Photonic Applications
## 3 Materials  
In the search for materials with an appropriate HRI dielectric properties in the UV range, we look for real materials whose optical properties fulfill the two following constrains on their dielectric constant \(\varepsilon=\varepsilon_{1}+\mathrm{i}\varepsilon_{2}\),  
* A real part of the dielectric permittivity \(\varepsilon_{1}>4\) in the UV allow the generation of electric and magnetic dipolar responses in the nanoparticle. This is necessary to satisfy the zero backward and minimum forward conditions.
* An imaginary part of the dielectric permittivity that meets the condition \(\varepsilon_{2}<1\) at energies higher than 3 eV. This opens a low-absorption window above 3 eV, since it can be easily derived that, provided that \(\varepsilon_{1}>\varepsilon_{2}\), the corresponding imaginary part of the refractive index (\(n_{i}\approx\varepsilon_{2}/2\sqrt{\varepsilon_{1}}\)) would be of the order of 0.1.  
The materials we have analyzed that fulfill these requirements are the following: aluminum phosphide (AlP), aluminum arsenide (AlAs), aluminum nitride (AlN), diamond, cerium dioxide (CeO\({}_{2}\)), and titanium dioxide (TiO\({}_{2}\)). Table 1 shows the photon energy at which \(\varepsilon_{2}=1\) for each of the selected materials. In the case of AlN and diamond, the photon energy at which this condition is reached is out of the spectral range considered in this study (1-6 eV).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & \(\mathbf{E(\varepsilon_{2}=1)/eV}\) \\ \hline AlP & 3.90 \\ AlAs & 3.25 \\ AlN & - \\ Diamond & - \\ CeO\({}_{2}\) & 3.53 \\ TiO\({}_{2}\) & 3.39 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Photon energy at which \(\varepsilon_{2}=1\) for each of the selected materials. For AlN and diamond, the dash indicates that the \(\varepsilon_{2}=1\) is produced outside of the spectral range under study (1–6 eV).  
Some of the wide-bandgap materials have been rejected because they do not fulfill one of the two constraints. This includes GaN, ZnS, ZnO, and AlSb. For all the selected materials, with the exception of AIP and AlAs, NP fabrication methods have been reported and are available in the literature [35; 42; 43; 44].  
Figure 1a-f shows the dielectric permittivity (\(\varepsilon=\varepsilon_{1}+\mathrm{i}\varepsilon_{2}\)) of the selected materials. The region where \(\varepsilon_{1}>4\) and \(\varepsilon_{2}<1\) within the UV is shadowed in gray. In Figure 1g,h, the dielectric function of Rh and Al is shown as a reference because these metals are widely used in UV plasmonic applications [20], Figure 1i plots the dielectric permittivity of the HRI dielectric materials taken as reference, like Si, GaAs and Ge. In the case of the reference materials (Al, Rh, Si, GaAs, and Ge) the shadowed region indicates the UV spectral range.
## 4 Case Studies: Impact on Pavement Asset Value Modeling
### Unevenness Deterioration  
From the perspective of pavement serviceability value, a decisive parameter is the decrease in pavement serviceability represented by the IRI, which leads to increased user costs. However, pavement serviceability decline is not linear and is, therefore, expressed through a polynomial degradation function. The resulting increase in user costs due to worsening IRI can be monetized using tools such as the Highway Development and Management program. Pavement rehabilitation improves IRI, generating benefits through reduced user costs. The relationship between road user benefits, degradation functions, and increased traffic loading is determined through Equation (11). The impact of diminishing benefits relative to worsening IRI parameters for semi-rigid arterial pavements, represented by degradation functions (shown in Figure 8) across different traffic intensities, is calculated in Figure 11. The unevenness parameter deteriorates according to applied design axle loads (DALs), with user benefits decreasing proportionally to unevenness. While the function shapes remain consistent for higher traffic loads, the benefit values vary as they depend on traffic intensity. By incorporating these values into Equation (7), a new road asset's pavement serviceability value can be determined and evaluated using the pavement user value index (shown in Table 7).  
Applying the user benefits evaluation principle to different unevenness deterioration functions and comparing functions from accelerated testing facilities with long-term monitoring sections (Figure 9) allows for the analysis of their varying impacts. The pavement user value index appears in Table 8, while Figure 12 displays the user benefit evaluation for different unevenness deterioration functions over time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Year** & **2023** & **2024** & **2025** & **2026** & **2027** & **2028** & **2029** & **2030** & **2031** & **2032** \\ \hline User benefits (EUR) & 6583 & 4959 & 3972 & 3434 & 3174 & 3040 & 2895 & 2619 & 2103 & 1254 \\ Pavement User Value Index & 100\% & 75\% & 60\% & 52\% & 48\% & 46\% & 44\% & 40\% & 32\% & 19\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Pavement User Value Index.  
Figure 11: Different users benefits in relation to the same function, but with different traffic intensity values over time.  
As shown in Tables 7 and 8, the total value of user benefits and the pavement user value vary depending not only on traffic intensity but also on the degradation functions used in their calculation. The average total cumulative user benefits amount to EUR 100,433.15, calculated as the mean of user benefits derived from APT and three LTPP sections. Among the evaluated degradation functions, the minimum value reaches 91% (APT) of the average user benefits, while the maximum value reaches 109% (LTPP 1). This represents an 18% dispersion, which is statistically significant.
# Analysis of the Working Performance of a Back-to-Back Geosynthetic-Reinforced Soil Wall
## 3 Results and Discussion
### Post-Construction Performance of the BBGRSW
#### 3.1.2 Strain on the Geogrid of the BBGRSW On-Site  
The distribution of the strain on the geogrid inside the wall over the period of 70 months after construction is shown in Figure 9. During the period from the completion of the wall to 72 months, the distribution of the strain was nonlinear along the reinforcement with an increase over time.  
With the progression of time, the strain appeared to increase. The strain 72 months after construction was completed was approximately 295.2% of the end of the construction value. The geogrid at the panel restricted the deformation of the wall and balanced the lateral pressure on the panel to maintain the stability of the wall.  
Avoiding tensile failure has always been one of the most critical aspects of the internal stability design procedure. According to the studies by [PERSON] et al. [8], the strain on geogrids inside walls is usually less than 1%, even after 12 years post-completion; it is considered that the tensile strength for geosynthetics is too conservative. With respect to the TGDG EG130R uniaxial geogrid, the measured maximum strain was approximately 2.58%, only 28.9% of the peak strain. This indicated that the safety redundancy for the reinforcement strength chosen by the designers was sufficient.  
The strain in the reinforcement from FEM was approximately 116.5% of that from the field test, as shown in Figure 10, which was in good agreement and shared the same trend along the wall height as the results obtained by [PERSON] [14] and [PERSON] et al. [29]. The comparison indicated that the input parameters were reasonable.
# Bonded-Particle Model with Nonlinear Elastic Tensile Stiffness for Rock-Like Materials
## 2 Method Description
### Contact Model
The range of \(w\) is from 0 to 1 and that \(w\) equals 0 means all the tiny beams gather in the center line of the two contact particles. Bonds with different bending stiffness but with the same normal stiffness and shear stiffness can be modelled by changing the distribution of the tiny beams. The modified Mohr-Coulomb criterion with a tension cut-off and an upper bound is used for the shear strength of the bonds, as shown in Figure 4, where \(\varepsilon_{max}^{s}\) is the upper bound of shear strain, \(\varepsilon_{0}^{s}\) is the ultimate pure shear strain of the bond, \(\varphi\) is the internal friction angle and \(\varphi_{f}\) is the friction angle between particles when the bond between them fails. In this paper, the stresses are replaced by the strains in order to obtain better simulation results by cooperating with the nonlinear tensile model and the effect of the bend on the tensile strain is taken into consideration as follows. \[\varepsilon^{n}+\frac{wr_{m}}{r_{p}+r_{q}}|\theta_{p}-\theta_{q}|\leq \varepsilon_{0}^{n} \tag{6}\]  
If the bond is broken or no bond exists between particles \(p\) and \(q\) which contact with each other, then only normal force and shear force exist between them. The normal force \(F_{pq}^{nr}\) is calculated by Equations (3) and (4). \(di_{pq}^{0}\) in Equation (4) should be replaced by the sum of the radii of \(p\) and \(q\). The shear force \(F_{pq}^{tn}\) is calculated as follows:  
\[F_{pq}^{tn}=\mu F_{pq}^{nr} \tag{7}\]  
where \(\mu\) is the coulomb friction coefficient. Figure 3: Bonds between contact particles. Figure 2: Normal force versus normal strain. The resultant contact force vector \(\mathbf{F}_{p}\) and the resultant contact moment \(M_{p}\) on particle \(p\) are calculated as follows:  
\[\mathbf{F}_{p}=\sum_{q}^{G_{p}}\Bigl{(}\mathbf{V}_{pq}F_{pq}^{nr}+\mathbf{U}_{ pq}F_{pq}^{tn}\Bigr{)} \tag{8}\]  
\[M_{p}=\sum_{q}^{G_{p}}\Bigl{(}r_{p}F_{pq}^{tn}+M_{pq}\Bigr{)} \tag{9}\]  
where \(\mathbf{V}_{pq}\) is an unit vector pointing from the center of particle \(p\) to the center of particle \(q\). \(G_{p}\) is a set of particles which are in contact with particle \(p\).
# Full-Scale Piano Score Recognition
## 3 Method
### CRNN Structure  
Convolutional-Recurrent Neural Network (CRNN) is a model that uses a Convolutional Neural Network (CNN) as an encoder and a Recurrent Neural Network (RNN) as a decoder. The encoder first outputs a feature map, which is then sliced and concatenated into a sequence of feature vectors. The decoder converts this feature vector into the target text form. In this study, a CNN is used as the encoder and an RNN as the decoder.  
Assuming the input image size is \((c,h,w)\), where \(c\) represents the number of channels, \(h\) represents the height, and \(w\) represents the width, the CRNN architecture (shown in Figure 7) consists of the following:  
1. Convolutional Layers: This layer transforms the input image into a feature map of size (512, \(w/16\), \(h/8\)). Then, through a reshape layer, the feature map is converted into a two-dimensional feature vector of size (512, \((w/16)*(h/8)\)).
2. Recurrent Layers: The feature vector output by the Convolutional Layers is passed through a Bidirectional LSTM to generate an output vector. Finally, a fully connected layer transforms this output vector into the final text output.  
Figure 5: An example of image column bounding box.  
Figure 6: (**Left**) Chord with a second interval. (**Right**) Chord without a second interval.  
Figure 8 illustrates a method for converting multi-voice sheet music into target text. This method involves several steps:  
1. Input the multi-voice sheet music image into the CNN.
2. The CNN extracts relevant features from the input image, transforming it into a feature map.
3. Slice all rows of the feature map and concatenate them into a long sequence of feature vectors.
4. The concatenated image sequence is passed to the RNN.
5. The RNN's output is converted into the final text output (KERN encoding) through vocabulary projection.  
It is important to note that the image concatenation step occurs in the feature space extracted by the encoder, not directly on the input image pixels. The visualization in Figure 8 is provided for better understanding. This method can transcribe multi-voice sheet music without explicit staff line segmentation.