esa-sceva/satcom-chunk-collection huggingface dataset:
# A Quantitative Study of Axial Performance of Rockbolts with an Elastic-Debonding Model
## 4 Parameter Study and Discussion
### Rockbolt Length  
Rockbolt length directly affected the axial performance of rockbolts. Within a certain range, the bearing capacity of rockbolts can be improved by increasing rockbolt length. However, the rockbolt length should be restricted to a critical range to meet economic requirements.  
To analyse the impact of rockbolt length on the axial performance of rockbolts, this article used five lengths, ranging from 1.6 m to 3 m. This article focused on full-length anchorage rockbolts. Therefore, the rockbolt length equalled the grout length.  
When the rockbolt length increased from 1.6 m to 3 m, the axial performance of rockbolts increased dramatically (Figure 8). When the rockbolt length was 1.6 m, the peak force of rockbolts was only 173 kN. When the rockbolt length increased to 3 m, the peak force of rockbolts increased to 314 kN, increasing by 81.5%. Therefore, increasing the rockbolt length can significantly benefit the axial performance of rockbolts.  
Moreover, there was a strong linear relationship between the rockbolt length and the peak force, which can be calculated with Equation (8). The linearly dependent coefficient was 1.  
\[F_{bp}=100.5L+12 \tag{8}\]  
where \(L\): rockbolt length.  
[PERSON] et al. [28] conducted experimental tests on rockbolts and found that when the rockbolt length varied from 150 mm to 320 mm, the peak force of rockbolts increased linearly with the rockbolt length. The linearly dependent coefficient was 0.9936. Therefore,  
Figure 8: Axial performance of rockbolts when the rockbolt length varied: (**a**) force-deformation curves; (**b**) peak force variation trend.  
Figure 7: Axial performance of rockbolts when the borehole diameter varied: (**a**) force-deformation curves; (**b**) peak force variation trend.  
there was a satisfactory correlation between the results obtained by [PERSON] et al. [28] and this article. This confirmed the accuracy of this article. However, in the experiment study, the rockbolt length was limited to 320 mm, which was commonly used in the lab test scenario. In this article, the rockbolt length was extended to 3 m, which was more consistent with the rockbolt length used in the field application scenario. Therefore, this article extended previous experiment research.  
When the rockbolt length increased, deformation at the peak force also increased significantly (Figure 9). When the rockbolt length was 1.6 m, the deformation at the peak force was just 4.1 mm. When the rockbolt length increased to 3 m, the deformation at the peak force increased to 11.8 mm, increasing by 187.8%.  
Moreover, there was an apparent non-linear relationship between the rockbolt length and the deformation at the peak force. The corresponding equation is shown below.  
\[u_{bp}=1.83L^{1.69} \tag{9}\]
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 3 Materials and Methods
### Method  
Empirical data are shown in the fundamental diagram (Figure 3). They are represented by the scattered cloud of empirical data for the entire range of traffic conditions. The phenomena are reported by many studies and explained by e.g., heterogeneity of drivers, non-stationary dynamical features of traffic flow, randomness in individual driving behaviors, and errors in measurement methods or data processing [32; 33; 34; 35]. The existence of the scatter raises some questions: What should be the functional form of the deterministic model to ensure the highest empirical accuracy? Should it be represented by a single or multi-regime model? How to estimate boundary parameters and what should be their values?  
To answer the questions, work was divided into the following stages: (stage I) data preparation and determination of expected values of boundary parameters, (stage II) selection of criteria for model assessment and adoption of detailed principles for criteria assessment and criteria acceptance levels, (stage III) model calibration, (stage IV) model assessment and comparison.
## Materials and Methods  
### Animal Models  
Sixty animals with colorectal cancer (CRC) from the strain PIRC (polyposis in the rat colon) rat F344/NTac-Apcam1137 model (sex ratio: 50/50) from the Rat Resource and Research Centre (RRRC) were used for the extraction of neoplastic colonic samples. This animal model was used in the study for the following main reasons: (a) it is an excellent model for studying human familial colon cancer; (b) ENU (N-ethyl-N-nitrosourea)-induced point mutation results in a truncating mutation in the APC (adenomatous polyposis coli) gene at a site corresponding to the human mutation hotspot region of the gene; (c) heterozygotes develop multiple tumors in the small intestine and colon by 2-4 months of age; (d) PIRC tumors closely resemble those in humans in terms of histopathology and morphology as well as distribution between intestine and colon; (e) provides longer lifespan compared to related mouse models (10-15 months); and (f) tumors may be visualized by CT (computerized tomography), endoscopy, or dissection. Moreover, the absolute incidence and multiplicity of colonic tumors are higher in F344-PIRC rats than in carcinogen-treated wild-type F344 rats, or in mice [48; 49].  
Additionally, thirty rats from the strain Fischer344--F344 wildtype model (sex ratio: 50/50) were used for the development and extraction of hyperplastic colonic samples. A rat surgical model of hyperplasia in the colon was developed in novo for endoscopic applications. It recreates important features of human hyperplasia, such as the generation of new cells in the colonic mucosa and tissue growth, as well as the corresponding angiogenesis. It consists of an extracolonic suture on which lesions are inflicted with a biopsy extraction forceps during a period established in different weekly follow-ups for the correct induction of the model [50; 51].  
Finally, as a control group, ten healthy tissue samples from three specimens were extracted from the colon of rats from the strain Fischer344--F344 wildtype model (sex ratio: 50/50). Uninvolved areas of the hyperplasia animals (ascending colon, transverse colon, and regions of the descending colon without lesion) were used as healthy tissue samples. This ensured meeting one of the three r's of animal research that aims to maximize the information obtained per animal, making it possible to limit or avoid further use of other animals, without compromising animal welfare.
## 3 Results
### Effects of Fault Angle on Displacement  
Figure 10 shows the importance of fault angles on tunnel displacements. Fault angles of 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) were named as FA30R, FA60R, and FA90R for reverse faults, and FA30N, FA60N, and FA90N for normal faults. The results showed major tunnel displacements between fault angles of 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\). FA30R recorded 5 times bigger displacement than FA30N when the vertical component was changed to 20 mm.  
The major recorded numbers can be seen in FA90R and FA90N, with approximately 13.8 mm and 12.8 mm, respectively. The numbers had changed dramatically in 40 mm of movement by the vertical component, and the results showed that the displacements caused by FA90R and FA90N were more than twice those caused by FA60R and FA90N. However, the results were not dramatic when comparing fault angles of 30\({}^{\circ}\) to 60\({}^{\circ}\) in reverse and normal faults. The domination of changes between a fault angle of 90\({}^{\circ}\) with those of 30\({}^{\circ}\) and 60\({}^{\circ}\) also remained steady in 60 mm of movement by the vertical component in reverse and normal faults. The results showed that when the fault angle was changed to vertical, the forces became sharper and the displacements by the vertical component affected the tunnel more. The difference between normal and reverse faults revealed that under a normal fault condition, the soil experienced sinking and the fractures on the ground surface were not as sharp as under a reverse fault condition. The reason for this is that normal faults caused sinkholes and deformations [16].
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
## 3 Algorithm
### Detection of Type 1 Vibrations
#### 3.3.1 Identification of Type 1 Vibrations  
Those discrete frequency bins \(k\) were identified for which peaks \(\hat{p}_{i,n}\) were most frequently detected. The prominence of peaks \(p_{i,n}\) was analysed with respect to discrete frequencies as  
\[C(k)=\sum_{i}^{L_{Q}-1}\sum_{n}^{N_{\hat{p}_{i}\text{single}}}\alpha_{i,n}p_{i,n }\text{ \ with \ }\alpha=\begin{cases}1\text{ \ if \ }n=k\\ 0\text{ \ if \ }n\
eq k,\end{cases} \tag{7}\]  
in other words, all peaks were projected on the frequency axis and corresponding prominences were summarised. To enable automatic detection of those locations, the projected prominence \(C(k)\) was smoothed by accumulating \(C(k)\) within frequency bands \(\delta k=f_{width}\cdot k\). The width factor \(f_{width}\) was selected empirically for each test turbine.  
The resulting cumulative prominence \(C_{cum}(k)\) was then used to detect most prominent peaks \(P_{C}\) with a minimum peak prominence of 0.1. Here, the minimum peak prominence was empirically selected and resulted in the detection of \(J_{1}\) peaks in total. Those peaks corresponded to frequency bins \(k\) in the linear-scale spectrogram, which: (i) occurred across a significant range of the operational range (here, rotation frequency; general, reference variable \(\hat{r}\)); and (ii) only slightly varied with \(\hat{r}\). Hence, they fulfilled the properties of Type 1 vibrations as defined in this section. Those peak locations and peak widths determined the set of Type 1 vibrations \(V_{1,j_{1}}\) at frequency bins \(k_{v1,j_{1}}\) and with width \(w_{v1,j_{1}}\) for \(j_{1}=1,...,J_{1}\), respectively, in the following analysis. Figure 7 displays the locations of all peaks, cumulative prominence \(C_{cum}(k)\) and most prominent peaks \(P_{C}\) corresponding to identified Type 1 vibrations \(V_{1,j_{1}}\).
## 4 Imaging of Bragg-Dip Profiles and Evaluation of Detailed Inner-Grain Information
### Imaging of Integrated Intensity of Bragg-Dip  
#### 4.4.1 Crystal Thickness Evaluation from the Integrated Intensity of Bragg-Dip  
In this section, the information of the crystal thickness, obtained by analyzing the integrated intensity of Bragg-dip, is discussed. Figure 8a,b show the results of imaging (\(\sigma_{\text{Bragg}}(\lambda_{\text{J\&I}})\times 2.35\sigma\)) of Grain 3. Figure 8c,d show the neutron transmission spectra at entire Grain 3, Region (8) around Region (\(\alpha\)) and Mini Region A. Here, the integrated intensity of Bragg-dip is \(\sigma_{\text{Bragg}}(\lambda_{\text{J\&I}})\times 2.35\sigma\), the product of the height and the FWHM of Bragg-dip.  
Figure 8: (**a**) Imaging of the integrated intensity of (101) Bragg-dip in Grain 3. (**b**) Imaging of the integrated intensity of (110) Bragg-dip of Grain 3. (**c**) Comparison of the neutron transmission spectra at entire Grain 3 and Region (8). (**d**) Comparison of the neutron transmission spectra at entire Grain 3 and Mini Region A.  
At Region (8), the integrated intensities of both (101) and (110) Bragg-dip were small. Region (8) is the region where Grains 2 and 3 were found to be overlapping in the IPF map presented in the previous study [19]. In actuality, on the neutron transmission spectrum at Region (8) shown by Figure 8c, Bragg-dips of both Grains 2 and 3 appeared. Here, we note that the sample thickness was constant, 5 mm. Hence, overlapping of Grains 2 and 3 at Region (8) means that the thickness along the neutron transmission path of Grain 3 was smaller than 5 mm here. Therefore, the reduction in the integrated intensity of two Bragg-dips at Region (8) indicates that the thickness of Grain 3 became smaller. In other words, the reduction in the integrated intensities of Bragg-dips which are observed at Region (8) in Figure 8a,b indicate that Grain 3 became thinner. This means that the information of crystal thickness can be evaluated from the integrated intensity of Bragg-dip.
# Reverse-Net: Few-Shot Learning with Reverse Teaching for Deformable Medical Image Registration
## 3 Materials and Methods  
The multimodal medical image registration framework based on few-shot learning is shown in Figure 1. It consists of boundary a enhancement network, a pixel alignment network, a coarse registration network, and a reverse teaching network. The boundary enhancement network is used to enhance the ROI boundaries of T1 images to provide high-quality data for the subsequent pixel alignment network and reduce registration distortion and misalignment caused by blurred ROI boundaries. The coarse registration network roughly aligns the T1 image with the T2 image, which can reduce the image discrepancy, effectively reduce the iteration time for subsequent neural network model training, and improve the efficiency of model training. The pixel alignment network generates more smooth deformation fields. Reverse teaching aligns the warped, segmented, labeled image with the warped image and maps the supervisory information in the warped, segmented, labeled image to the unlabeled image. Through the above method, rich structural knowledge is transmitted for the border enhancement network in reverse, which improves the performance of the border enhancement network. With the four networks mentioned above, we achieve deformable medical image registration with only a few segmentation labels. The detailed process of training is described in the subsequent sections.  
Figure 1: Overall architecture of our reverse-net.
## 3 Methods
### Classification Algorithms  
The emotion classification approach is based on the minimum distance to Riemannian mean (MDRM), as illustrated in Figure 1. The MDRM principle is applied to a binary classification problem in a curved space, where the mean of each class covariance matrix represents the class center or the center of mass [7; 16; 41]. The MDRM classification using the Frechet mean and AIRM is employed to classify a test trial, as described in Algorithms 1 and 2. The test trial is assigned to the class mean with the shortest distance to it in the Riemannian space. This approach improves classification accuracy by using a distance metric that accounts for the specific geometry of the space of covariance matrices, as implemented in Algorithm 2.  
The Frechet mean of class \(k\) is computed for each class in Algorithm 1, then MDRM is used for predicting the unknown class for the test trial. The test trial is classified to the class mean with the shortest distance, in this case, \(\Sigma_{\mu}^{(1)}\), as shown in Figure 1.  
```
0: a set of labeled trials \(\mathbf{X}_{i}\) for \(i=1,...,I\).
0:\(I^{(k)}\), a set of indices of trials of class \(k\).
0:\(\Sigma_{\mu}^{(k)}\), \(k=1,\ldots,\)K, centers of classes.
1: Compute covariance matrices \(\hat{\Sigma}_{i}\) of \(X_{i}\).
2:for\(i=1\to K\)do
3:\(\Sigma_{\mu}^{(k)}\)=\(\mu\) (\(\hat{\Sigma}_{i}\), \(i\in I^{(k)}\))
4:endfor
5:return\(\Sigma_{\mu}^{(k)}\)
```  
**Algorithm 1** Estimation of Riemannian centers of classes.  
Figure 1: MDRM principle for a binary problem on a Riemannian manifold.  
```
0: a set of \(\mathbf{X}_{i}\) of \(K\) different known classes.
0: X an EEG trial of unknown class.
0:\(\Sigma_{\mu}^{(k)}\), K centers of classes from Algorithm 1.
0:\(\hat{k}\) the predicted class of test trial \(\mathbf{X}\)
1: Compute covariance matrix \(\hat{\Sigma}\) of \(X\).
2:for\(i=1\to K\)do
3:\(\hat{k}=arg\ min_{k}\delta(\hat{\Sigma},\Sigma_{\mu}^{(k)})\)
4:endfor
5:return\(\hat{k}\)
```  
**Algorithm 2** Minimum distance to Riemannian mean.  
The unique structure of covariance matrices belongs to the Riemannian manifold of the symmetric positive definite, allowing manipulation within the Riemannian geometry framework. Hence, the manifold of PSD matrices allows for the use of explicit formulas for essential operations in the Riemannian manifold to easily implement the algorithms.
## 7 Simulation Experiments and Comparative Study
### Case 2  
In another experiment, both algorithms were allowed to reach the MPP point. In this manner, both converters operated in CCM mode during the starting phase, as shown in Figures 14 and 15. Next, the irradiance conditions were varied at 0.1 ms and the operation of both converters was observed in terms of the MPP tracking of the PV system. The irradiance condition was reset to 1000 \(\frac{W}{\text{m}^{2}}\) at 2 ms.  
Figure 14 indicates that during the experiment with variations in irradiance, for the PV system with the conventional \(P\&O\) algorithm the converter enters discontinuous mode at 0.1 ms and returns to CCM mode at 0.265 ms, which was when the irradiance returned to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Algorithm** & **Mode of Operation** & **Time (ms)** \\ \hline \multirow{4}{*}{Case-1} & Conventional \(P\&O\) & Discontinuous & 0–0.08 \\  & Continuous & 0.08–0.3 \\ \cline{2-3}  & & \multirow{2}{*}{Proposed \(P\&O\)} & Discontinuous & 0–0.04 \\ \cline{1-1}  & & \multirow{2}{*}{Continuous} & 0.04–0.3 \\ \cline{1-1} \cline{3-3}  & & & \\ \end{tabular}
\end{table}
Table 1: Time comparison of proposed and conventional \(P\&O\) algorithms.  
Figure 12: Usual operation of the \(P\&O\) algorithm: (**a**) instantaneous photovoltaic voltage, (**b**) voltage across the inductor, and (**c**) duty cycle.  
Figure 13: Exceptional operation of the proposed modified \(P\&O\) algorithm: (**a**) instantaneous photovoltaic voltage, (**b**) voltage across the inductor, and (**c**) duty cycle.  
1000 \(\frac{W}{\text{m}^{2}}\) at 0.2 ms. This clearly depicts that the conventional \(P\)&\(O\) algorithm requires several iterations and a considerable time to return to MPP after entering DCM, compromising the convergence speed and dynamic efficiency of the system. In contrast, the performance of the PV system with the proposed method under the same test is displayed in Figure 15. Similarly, the proposed algorithm enters DCM due to variations in weather conditions after 0.1 ms; however, it does not diverge from the MPP point by a large margin compared to the conventional \(P\)&\(O\). Moreover, it attains the MPP point after only 0.22 ms when the irradiance returns back to 1000 \(\frac{W}{\text{m}^{2}}\) at 0.2 ms. In summary, the proposed method outscores and outperforms the conventional \(P\)&\(O\) algorithm in terms of both convergence speed and the dynamic efficiency of the PV system.  
Table 2 shows the time requirements from the simulation results for the conventional and proposed \(P\)&\(O\) algorithms operating in continuous and discontinuous modes.  
Figure 14: Usual Operation of \(P\)&\(O\) algorithm under varying irradiance: (**a**) instantaneous photovoltaic voltage, (**b**) voltage across the inductor, and (**c**) duty cycle.  
Figure 15: Exceptional operation of the proposed modified \(P\)&\(O\) algorithm under varying irradiance: (**a**) instantaneous photovoltaic voltage, (**b**) voltage across the inductor, and (**c**) duty cycle.
## 3 Matching expected performance with application requirements  
The expected result for each solution will be returned from the models giving a value for each desired metric. In order to choose a solution they must be compared to find the most appropriate selection as per the application requirements. The appropriate solution is unclear, however, when a comparison is made over each of the metrics. For example: _solution 1_ returns a reliability of 90% and a latency of 0.23 s while _solution 2_ returns a reliability of 81% and a latency of 0.05 s from the given network. If the applications requirements state that reliability is valued as twice as important as latency it is unclear at first look which solution is the most appropriate for use. Is a 9% gain in reliability worth a 0.18 s loss in latency even with the importance weighting? To solve this each solution must be provided a single score which reflects the expected response and its adherence to the desired requirements. From this score a clear winner can be chosen for use in the network. Section 6 presents a statistical approach to achieve this.
## 6 Conclusions  
Two new RFI detection and mitigation algorithms have been presented. Both are based on processing of the radiometric signal's spectrogram and thus operate in the time and frequency domains simultaneously.  
The Smoothing (and thresholding) Algorithm is studied and its performance is estimated using Monte Carlo simulations. The threshold value in the Smoothing Algorithm is the most critical parameter, as it minimizes the retrieved \(T_{\text{A}}\) error depending on the filter size and the RFI power, which is a priori unknown. For a determined filter size, the best threshold can be calculated varying the RFI signal power and keeping the noise power constant. In case that the interference is sinusoidal, it is found that there is an optimal threshold value which minimizes the retrieved \(T_{\text{A}}\) error for any RFI power. This threshold value diminishes with the filter size used in the Smoothing Algorithm. For a sinusoidal RFI, with a threshold value of 1.37 \(\sigma^{2}_{n}\), the retrieved \(T_{\text{A}}\) error is 2 K for a filter size of 25 \(\times\)25 pixels, and any INR value.  
Figure 17: Error in the estimation of the retrieved temperature using the LLMMSE denoising algorithmin as a function of the error in the a-priori estimation of the thermal noise power. Both errors are represented in Kelvin. Colored lines represent RFI contaminated radiometric signals with an INR determined by its label. Black dotted line represents a radiometric signal in abscense of RFI.  
In case that the RFI is broadband, two cases have been studied, which are a PRN RFI, and an OFDM RFI. The PRN RFI behaves like noise and it is found that there is not an optimal threshold value, as increasing the RFI power always increases the retrieved \(T_{\text{A}}\) error. The OFDM RFI behaves like a sinusoidal RFI, so there also exists an optimal threshold value, although the maximum retrieved error in the \(T_{\text{A}}\) is higher than in the sinusoidal case. In addition, broadband RFI's contaminate extense areas of the spectrogram, resulting in a poorer radiometric resolution as many more pixels of the spectrogram have been eliminated.  
A 2D de noising filter based on the optimum Wiener filter (LLMMSE) is also studied to estimate the RFI in the signal's spectrogram and to substract it from the contaminated one. However, it has been found that the Wiener filter has an acceptable performance applied to the spectrogram to detect RFI signals for subsequent substraction only if the noise power of the received signal is precisely known, which is actually the magnitude to be determined. Therefore although the Wiener filter is optimal for signal denoising in signal processing, the accuracy required in the estimation of the noise power is much higher in microwave radiometry than in typical telecommunications applications, and therefore it does not prove to be suitable for RFI mitigation.  
These two algorithms have been tested only with sinusoidal, chirp, PRN and OFDM like signals, testing other types of RFI signals with these algorithms will be performed in the future as well as processing measured data.