esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fully-Automatic Gap Filling Approach for Motion Capture Trajectories
## 4 Experiments
### Gap Generator  
In order to test our gap filling approach, we needed to artificially generate gaps in the selected samples. To automatize this process, we created a script in Python. We have defined three parameters to influence the properties of the generated gaps: gap length, temporal location and number of missing markers. We have set the following possible values for each parameter:  
* _Gap length_: varying from 0.5, 1 and 2 to 5 s, which corresponds to 60, 120, 240 and 600 frames;
* _Temporal location_: The temporal location of two non-overlapping gaps were randomly defined for each sample. These non-overlapping gaps could be located at any frame but they were explicitly not located on the initial and final frames of the samples to allow comparison with other methods that, by design, do not allow reconstructions on the initial and final frames of the sample (see Section 4.4). These generated samples are hereinafter referred to as samples with gaps in the middle. As an additional experiment, we also generated samples with gaps in the initial or in the final frames of the trajectories.
* _Number of missing markers_: The possible values of 1, 2, 4, 6, 8, 10, 12 and 14 missing markers were considered, where the markers that will be dropped were randomly selected. For each possible number of missing markers, we have randomly generated four combinations of markers to drop.  
By combining all the possible values of each parameter, we generated 128 versions with gaps (4 gap lengths \(\times\) 8 missing markers \(\times\) 4 combinations), each including two non-overlapping gaps located in the middle of the sample. Additionally, we generated 128 samples with gaps in the initial or final frames of the trajectories. As such, a total of 1024 CSV files containing trajectories with gaps (4 MoCap sequences \(\times\) 128 versions with gaps \(\times\) 2 temporal locations) were used in the experiments.
## 2 Engineering Conditions
### Model Design  
To minimize boundary effects, at least 100 m of solid coal is retained between the mined-out area and the model boundary. The model dimensions are set to 675 m \(\times\) 97 m, including nine layers of strata and one cluster layer. The initial porosity is set at 0.05, and the particle radius ranges from 0.36 m to 0.54 m. A parallel bond model is employed between particles, while a linear bond model is applied between the particles and the walls. The particles reach equilibrium under an initial gravitational setting of 9.81 m/sec2. Afterremoving excess particles, different groups are assigned and parameters specified. The model is considered stable when the unbalanced force is reduced to below \(10^{-5}\).  
The mining process involves two coal seams (3 upper and 3 lower) and four working faces, with their spatial distribution shown in Figure 2a. Four measurement circles, each with a diameter of 2 m, are placed at the roof of the 3 upper coal seam and within the siltstone interlayer between the two coal seams, totaling 110 points, as illustrated in Figure 2b.  
Prior to the simulation, a trial-and-error iterative method was employed to calibrate the mesoscopic parameters of each rock layer. Through this iterative process, the microscopic mechanical parameters were continuously adjusted to achieve the best possible match between the macroscopic parameters--such as the elastic modulus, peak stress, and Poisson's ratio--derived from the numerical simulations and those obtained from laboratory experiments. The calibration results are presented in Table 3.  
Figure 2: Basic information of the model: (**a**) Distribution and Direction Diagram of the Mining Area, and (**b**) Measurement Circular Distribution Information.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Layer**} & \multicolumn{4}{c}{**Micromechanical Parameters**} \\ \hline
**Serial Number** & **Rock Type** & \(\mathbf{\tilde{E}}\) **(GPa)** & \(\mathbf{k_{n}/k_{s}}\) & \(\mathbf{\widetilde{\sigma_{c}}}\) **(MPa)** & \(\mathbf{\tilde{c}}\) **(MPa)** & \(\mathbf{\tilde{\mu}}\) \\ \hline
1 & Fine sandstone & 3.05 & 1.1 & 31 & 15.5 & 0.5 \\
2 & 2\# coal seam & 3.50 & 1.40 & 23.4 & 11.7 & 0.5 \\
3 & Alumina mudstone & 3.88 & 1.40 & 22 & 11 & 0.5 \\
4 & Medium to fine sandstone & 8.62 & 1.33 & 43 & 21.4 & 0.5 \\
5 & 3 upper coal seam & 3.50 & 1.40 & 23.4 & 11.7 & 0.5 \\
6 & Silstone & 5.3 & 1.2 & 57.8 & 28.9 & 0.5 \\
7 & 3 lower coal seam & 3.50 & 1.40 & 23.4 & 11.7 & 0.5 \\
8 & Silstone & 5.1 & 1.40 & 56.8 & 27.8 & 0.5 \\
9 & Medium to fine sandstone & 5.15 & 1.40 & 48 & 24 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calibration results of mesoscopic parameters.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 3 Experiments
### SD Rat Experiments  
To verify the reliability of the algorithm, twelve nine-week-old SD rats injected with 18F-FDG were scanned using a Siemens Inveon micro-PET scanner. Sinograms of 128 pixels \(\times\) 160 pixels \(\times\) 130 slices and activity images of 128 pixels \(\times\) 128 pixels \(\times\) 130 slices were obtained. The reconstruction method used was the ordered subset expectation maxi  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phantom** & **Sampling Time** & **Sampling Interval** & **Counts** & **Dataset** \\ \hline \multirow{8}{*}{Zubal thorax} & \multirow{2}{*}{20 min} & \multirow{2}{*}{\(14\times 50\), \(2\times 100\), \(2\times 150\) s} & \(5\times 10^{6}\) & \multirow{2}{*}{training} \\  & & & \(5\times 10^{7}\) & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \hline \multirow{8}{*}{Zubal head} & \multirow{2}{*}{30 min} & \multirow{2}{*}{\(12\times 50\) s} & \(5\times 10^{6}\) & \multirow{2}{*}{training} \\  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \cline{2-5}  & & & & \\ \hline \multirow{8}{*}{Zubal head} & \multirow{2}{*}{30 min} & \multirow{2}{*}{\(5\times 30\), \(7\times 100\) s} & \(5\times 10^{6}\) & \multirow{2}{*}{training} \\  & & & & \\ \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset for simulation experiments.  
Figure 4: Two PET phantoms: (**a**) Zubal thorax phantom; (**b**) Zubal head phantom.  
mization 3D algorithm (OSEM 3D) packaged in Siemens scanning software. Three rats were randomly selected for testing, and the other nine rats were used for training. Thus, there were 1170 image pairs in the training dataset and 390 image pairs in the testing dataset. Here, the images reconstructed using OSEM 3D were treated as the ground truth.  
For the SD rat dataset, the relative root mean squared error (rRMSE) was used to evaluate the image quality.  
\[\text{rRMSE}=\sqrt{\text{MSE}}/\overline{x} \tag{9}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(\hat{x}_{i}-x_{i})^{2} \tag{10}\]  
where \(x\) is the ground truth, \(\hat{x}\) is the reconstructed image, \(\overline{x}\) is the ground truth average pixel value, and n is the number of image pixels.
# Numerical Assessment of the Seismic Vulnerability of Bridges within the Italian Road Network
## 5 Discussion
### Comparison between the Two Approaches  
The present comparison aims to qualitatively evaluate the differences between two approaches with different safety levels applied to the same case studies. Since the assessment of the Seismic Class of Attention is based on decisional trees and visual inspections, it is expected that it will be more conservative than an analytic approach based on NLTHAs.  
Comparing the results obtained in terms of Seismic Class of Attention with those obtained in terms of fragility curves, it can be noted that for bridges in high seismicity regions (Bridge 1, Bridge 2, Bridge 3, and Bridge 5), there is an agreement between the results of the two approaches while for Bridge 4, located in a low seismicity region, there is a discrepancy. The differences in the results obtained for Bridge 4 are quite important. The Seismic Class of Attention of the bridge is Medium-High, while the NLTHAs did not highlight particular issues in terms of the response of bearings and structural elements. Considering that only the curve corresponding to \(\gamma\) = 0.5 was constructed, since even for \(T_{r}\) = 2475 years the seismic action was not particularly intense, it seems that for Bridge 4, the approach of Level 2 gives a result that does not capture in a proper way the seismic behavior of a bridge in a low seismicity region. One possible reason for this difference could be the different levels of conservatism of the methods. An extended sample of bridges has to be considered to improve the reliability of this qualitative comparison and to evaluate in a more accurate way the framework proposed by the Guidelines. Considering that the topic of the seismic vulnerability assessment of bridge structures has been significantly developed in recent years, comparing the results with other approaches based on seismic vulnerability would increase the accuracy in the evaluation of the approach proposed by the Guidelines. For instance, [PERSON] et al. [59] explored the use of simulated records in the assessment of R.C. bridges; [PERSON] et al. [60] proposed and implemented a procedure based on a two-level structure-network risk assessment for bridge prioritization, while the work of [PERSON] et al. [61] introduces a technique that employs artificial intelligence for the large scale vulnerability assessment of bridge structures.
# Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles
## 3 Methodology  
In this paper, we developed a model to classify breast density from mammograms based on pre-trained CNNs and weighted average ensembles. The key steps of our methodology are illustrate in Figure 4. The first stage entailed collecting the dataset from KAMC. Next, we prepared the data. Then, several pre-trained models were applied to classify breast density. Finally, a weighted average ensemble was employed to enhance the CNN performance. Below, we describe each of the steps in sequence.
# Research on Lifespan Prediction Methods Using Ultrasonic Microimaging for Electronic Packaging
## 3 Ultrasonic Image Data Processing and Feature Extraction
### Flip-Chip Image Calibration and Solder Joint Image Segmentation  
When using an ultrasonic microscopy imaging system to collect life-cycle data of flip chips, achieving ideal consistency is often challenging due to operational and environmental factors. Consequently, precise horizontal calibration of the images is required. Figure 5a depicts a perfectly aligned C-scan image of a flip chip. In this image, a Cartesian coordinate system is established with the origin at the lower-left corner, the bottom edge of the chip serving as the \(x\)-axis and the left edge as the \(y\)-axis. The horizontal calibration of the flip-chip image is performed accordingly.  
Figure 4: Schematic diagram of intensity compensation method for flip-chip images.  
The procedure begins with aligning the flip-chip image from Figure 5a, positioning its bottom-left endpoint at the origin of a Cartesian coordinate system. This alignment ensures the longer side of the chip image parallels the \(x\)-axis and the shorter side aligns with the \(y\)-axis. In Figure 5b, the angle \(\alpha\) is defined at \(\angle COD\), where point \(C\) has coordinates \(C_{(x_{c},y_{c})}\). A perpendicular line from point \(C\) to the \(x\)-axis intersects at point \(D\), with coordinates \(D_{(x_{c},\theta)}\). The angle \(\alpha\) is calculated by:  
\[\alpha=arccos(\frac{x_{c}}{\sqrt{x_{c}^{2}+y_{c}^{2}}}) \tag{2}\]  
To finalize the calibration, the image is rotated by \(\alpha\) degrees using Python's rotation function (version 3.11.9), ensuring the chip image is horizontally aligned.  
Given that the diameter of the solder joint is 140 \(\upmu\)m and the system's lateral resolution is 3 \(\upmu\)m per pixel, the expected diameter of the solder joint image is approximately 47 pixels. To ensure complete coverage, images are captured using an 80 \(\times\) 80 pixel window centered on the geometric center of the solder joint. Figure 6 presents the results from the collection of solder joints at different positions.
## 6 Automatic Learning Approach
[PERSON]'s test \(p\)-value indicates the \(p\)-value from [PERSON]'s test, which evaluates the significance of any changes in error rates between the two models. In this instance, the \(p\)-value is 0.134, showing no statistically significant difference between the error rates of the evaluated models [63]. Sensitivity represents the actual positive rate or the proportion of positive samples accurately identified as positive [64]. In this instance, the sensitivity is 0.84, indicating that the model accurately recognizes 84% of positive samples. Specificity denotes the true negative rate or the proportion of actual negative samples accurately categorized as negative. A specificity of one suggests that the model correctly detects 100% negative samples [64]. The Positive Predictive Value is the proportion of samples anticipated to be positive that are, in fact, positive. In this instance, the positive predictive value is one, which indicates that around 100% of the anticipated positive samples are positive. A negative predictive value of 0.87 suggests that around 87.1% projected negative samples are negative [65]. Prevalence denotes the frequency or proportion of positive samples within a dataset [66]. In this instance, the prevalence is 0.481, which indicates that around 48.1% of samples are positive. The detection rate is the fraction of positive samples that the model correctly classifies as positive. A detection rate of 0.403 suggests that the model detects or identifies 40.3% of positive samples. The detection prevalence is the proportion of projected positive samples that are positive. In this instance, the detection prevalence is 0.404, which indicates that approximately 40.4% of the anticipated positive samples are positive [64]. Balanced accuracy is the average of sensitivity and specificity and measures classification performance as a whole [67]. A balanced accuracy of 0.92 means that positive and negative samples are correctly classified. The positive class column identifies the label or class regarded as positive. In this instance, \"No\" is the positive class. Root Mean Square Error (RMSE) is a metric utilized to assess the precision of predicted values by computing the average discrepancy between predicted probabilities and actual outcomes [61]. It is especially advantageous for assessing models that generate probabilities instead of solely class labels. The classification model obtained an RMSE of 0.228, suggesting that the model's predicted probabilities are highly accurate and closely match the actual class labels. This demonstrates the model's effectiveness in making dependable predictions. Overall, the results indicate that the KNN model performs effectively, exhibiting a high and balanced accuracy. As evidenced by the sensitivity and specificity scores, it performs well in correctly categorizing positive and negative combinations. Positive and negative predictive values reflect the model's ability to reliably predict positive and negative cases.
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 2 Principles  
To sweep the beam in free space for wireless communications, PAAs tune the phase or induce a time delay at each antenna element. For PAAs using OBFNs, the working principles relate to the beam pattern of one-dimensional (1D) and two-dimensional (2D) PAAs, as well as the RF signal processing of OBFNs. The beam pattern is formed by the integral of the electromagnetic field of each antenna in free space. Categorized by the delay, OBFNs have two types: TTD architectures and phase shifter architectures [20]. The former produce different time delays for RF signals transmitted or received by different antenna elements, while the latter produce various phase shifts.
# Simulation of Two-Phase Flow of Shotcrete in a Bent Pipe Based on a CFD-DEM Coupling Model
## 4 Calculation Results and Analysis
### Flow Field of Particle Phase
When the particle leaves the bent pipe section, it is picked up again by the airflow and accelerated under the actions of turbulence and secondary flows. When the particle passes through the 90\({}^{\circ}\) bent pipe, its movement is not heavily affected by the turbulence and secondary flows; thus, the particle velocity does not increase further and stabilizes. In addition, the turbulence and secondary flows in the bent pipe act as a resistance to the particles. Because the 90\({}^{\circ}\) bent pipe provides the lowest resistance, the particle velocity experiences the lowest loss. The CFD-DEM model provides detailed data about particle movement and collisions. Figure 12 presents the main wear areas and wear effect of the concrete particles on the bent pipe. The particle velocity is increased by 100%. The particle velocity is increased by 100%. pipe with different bend angles. The results show that within the same time interval, the 45\({}^{\circ}\) bent pipe experiences significant wear, and its maximal depth of wear is 0.042 mm. In addition, the main wear position diffuses radially toward the outside wall of the bent pipe with an increasing bend angle. This is because the wall roughness promotes particle-wall collisions. The reflection angle at a rough wall is much greater than that at a smooth wall. The rougher wall causes more particles to diffuse radially along the outside wall of the lifted bent pipe, while the higher axial velocity causes the particles to suspend uniformly. Thus, when the particles hit the bent pipe, they are uniformly distributed across the curved section of the bent pipe, thereby causing elliptical wear; these results are consistent with those presented by [PERSON] [44]. Figure 13 shows the numbers of particle-pipe wall contact times and normal contact forces of the shotcrete particles in the pipes with different bend angles. With increasing bend angle, the number of particles-pipe wall contact times and normal contact force first increase and then decrease. At a bend angle of 45\({}^{\circ}\), both values exhibit a maximum, which is consistent with the results of [PERSON] [45]. In addition, in pneumatic conveying, the normal contact forces of most particles affect the bent pipe; it is assumed that the 45\({}^{\circ}\) bent pipe is subjected to stronger impact and wear. Figure 12: (**a–f**)Amount of wear and main wear position of particles in bent pipes with different angles. Figure 14 presents the variation in the particle energy loss concerning time. When the particles enter the bent pipe, the particle-pipe wall collisions significantly increase the energy loss. As the particle rope gradually forms, the number of particles-pipe wall contact times decreases, and the energy loss gradually decreases and stabilizes. The particle energy loss is not correlated with the bent pipe structure, and the particle energy loss in the bent pipe is approximately 30 times that in the straight pipe.
## 3 Virtual Admittance Feedforward Compensation and Phase Correction to Reduce Current Distortion
### Virtual Admittance Feedforward Compensation  
With the analysis in Section 2, the input admittance shows the resistance-capacitance characteristic due to the limited current loop bandwidth. It makes the input current lead the input voltage, which degrades the power factor. To solve this problem and improve the power factor, this paper proposes VAFC to generate virtual admittance, which neutralizes the susceptance component and adjusts the input admittance as pure conductance. Figure 8 shows the current control loop diagram with VAFC.  
With the proposed compensation method, the input admittance of the PFC converter \(Y\)(s) is calculated as  
\[Y(s)=\gamma_{1}(s)+\gamma_{2}(s)+\gamma_{3}(s), \tag{7}\]  
where the virtual admittance \(Y_{3}\)(s) is given by  
\[Y_{3}(s)=\frac{G_{id}(s)H_{c}(s)}{1+T_{i}(s)}. \tag{8}\]  
With the input admittance analysis in Section 2.1, the leading phase of \(Y(s)\) is mainly caused by \(Y_{1}(s)\). Therefore, by setting  
\[-Y_{1}(s)=Y_{3}(s), \tag{9}\]  
the susceptance component is neutralized. By solving (9), the transfer function of the feedforward compensator is given by  
\[H_{c}(s)=-\frac{G_{iv}(s)}{G_{id}(s)}\approx-\frac{1}{v_{o}}. \tag{10}\]  
Therefore, the feedforward term is calculated using  
\[d_{2}=1-\frac{v_{in}}{v_{o}}=1-\frac{\sqrt{2}v_{in,rms}\sin(\theta)}{v_{o}}. \tag{11}\]  
Figure 8: Current control loop diagram with the proposed VAFC.  
Figure 7: Equivalent circuit scheme under the proposed VAFC and phase correction strategy.  
With the proposed VAFC, the phase difference between input current and voltage is eliminated, and the input admittance of the PFC converter becomes pure conductance. Therefore, the PF and THD of the converter are improved.
# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 5 Conclusions  
The presented end-to-end deep learning CT reconstruction algorithm was trained with simulated interventional data to mitigate metal artifacts during reconstruction. We showed that the iCTU-Net reconstruction MAR approach is better suited to mitigate metal artifacts than commonly used sinogram pre-processing and image post-processing deep learning approaches. The iCTU-Net is the only studied method that can eliminate the metal streak artifacts. However, the end-to-end reconstruction approach performs worse than the other approaches when no artifacts are present. Reconstructions without any metal showed that the iCTU-Net is prone to blurring. Because the whole reconstruction is learned, the network is able to fully utilize the raw data, which benefits the removal of metal artifacts. In the future, we will try to improve the network architecture by investigating alternative loss functions and post-processing layers to avoid blurring. We will also train networks with data including different kinds of artifacts to investigate simultaneous mitigation of several types of artifacts.  
Conceptualization, D.F.B.; methodology, D.F.B. and C.U.; software, D.F.B., A.-K.G. and C.U.; validation, D.F.B. and T.R.; formal analysis, D.F.B.; investigation, D.F.B.; resources, L.R.S. and F.G.Z.; data curation, D.F.B.; writing--original draft preparation, D.F.B.; writing--review and editing, D.F.B., C.U., T.R., A.-K.G., L.R.S., and F.G.Z.; visualization, D.F.B.; supervision, L.R.S. and F.G.Z.; project administration, F.G.Z.; funding acquisition, F.G.Z. All authors have read and agreed to the published version of the manuscript.  
This research project is part of the Research Campus \(M^{2}OLIE\) and funded by the German Federal Ministry of Education and Research (BMBF) within the Framework \"Forschungscampus: public-private partnership for Innovations\" under the funding code 13 GW0388A.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available.  
The authors declare no conflict of interest.