esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Test Results and Analysis
### Test Stress and Analysis of U-Rib Side  
The schematic representation of the location of the U-rib side measurement points is shown in Figure 11a. From the figure, it is observed that the G#, H#, and I# strain rosettes are pasted on the junction area of the U-rib web and the diaphragm, 10 cm away from the A diaphragm. Figures 21 and 22 show the stress-load position relationship curves on the U-rib web, which are summarized as follows:  
Figure 20: The curve of the relationship between the stress of the arc-shaped incision and the position of the longitudinal load: (**a**) A# measuring point, (**b**) B# measuring point, (**c**) # measurement point, (**d**) b# measuring point.  
1 All measuring points show similar laws, and the peak stress is located about 10-20 cm away from diaphragm A. This indicates that the U-rib is subjected to the maximum shear force when the rear axle of the vehicle is just passing through the transverse spacer A. Furthermore, the stress values at all measurement points are relatively close to each other, indicating that the stress distribution in the U-rib web is relatively uniform.
2 After optimizing the arc-shaped cut, the stress at the U-rib measuring point near the 105# diaphragm did not increase or decrease significantly. It shows that the reinforcement at the fatigue crack of the curved cut of the transverse spacer did not affect the force in the side of the nearby U-rib.
3 After optimizing the arc-shaped cut and reinforcing the steel plate, when compared with the optimization of only the arc-shaped cut, it is observed that the stress of the U-rib measuring point near the 100# diaphragm does not decrease but slightly increases. There are negative effects, however, the effect is not significant.
4 The stress-load location curves before and after the optimization of the arc notch reinforcement showed the same pattern of change, both showing the trend of first increase and then decrease. In addition, no significant increase or decrease in the peak stress was observed after reinforcement, indicating that the optimization of the arc notch or steel plate reinforcement had little effect on the U-side stress.  
Figure 21: Longitudinal loading 100# diaphragm (steel reinforcement) U rib side stress-load position relationship curve: (**a**) Before reinforcement, (**b**) after reinforcement.  
Figure 22: Longitudinal loading 105# diaphragm (nott optimization) U rib side stress-load position relationship curve: (**a**) Before optimization, (**b**) after optimization.
## 3 Model and Parameter Identification  
### Equivalent Circuit Models  
In this study, we chose the popular and widely reported nRC, nRCH, and PNGV models as battery models. The schematics of the three models is shown in Figure 2. In these models, \(U_{OCV}\) denotes battery voltage source, \(U_{L}\) is the terminal voltage, \(R_{0}\) denotes equivalent ohmic resistance, and \(R_{i}\) and \(C_{i}\) denote diffusion resistance and diffusion capacitance, respectively. \(u_{h,k}\) is the hysteresis voltage, \(k_{p}\) is a decaying factor, and M is the maximum amount of hysteresis voltage. \(C_{b}\) in the PNGV model is an equivalent capacitance, which describes the changes in the OCV caused by time accumulation. The equations of ECMs are presented in Table 2.  
Figure 1: Battery test bench and experimental results under the New European Driving Cycle (NEDC) working cycles. (**a**) Schematic of the test bench; (**b**) current profile; and (**c**) voltage profile.  
Figure 2: Schematics of equivalent circuit models (ECMs): (**a**) Resistance-capacitance (nRC) model and (**b**) the Partnership for a New Generation of Vehicle (PNGV) model.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 2 Electrical Subsystems in Satellites
### Features of DC-DC Converters for Satellite Applications  
DC-DC converters are crucial components in satellites, and their design affects the overall performance of the satellite. In the era of the miniaturization of satellites, a converter capable of providing high power without increasing the mass or volume of the EPS is highly relevant. In power electronics, improved power density is achieved at high switching frequencies, leading to reduced dimensions of magnetics. Nevertheless, high switching frequency increases switching losses, resulting in larger heat-sink requirements. Thus, a trade-off exists between power density and heat dissipation requirements. Considering the harsh and stringent environmental conditions in space, the converter components must be carefully chosen to perform well under such conditions. Thus, in summary, the following design requirements are desirable for DC-DC converters in satellite applications [67; 68]:  
* High power density.
* High efficiency.
* Minimum increase in volume/weight/heat management.
* Reliability.
* Modularity.
* Good performance in harsh environments like large temperature and pressure variations, mechanical vibrations, etc.
* High switching frequency to minimize the size of magnetic components.
* Lower Electromagnetic Interference (EMI).  
These features are schematically represented in Figure 7. Several DC-DC converter topologies are mentioned in the literature for various applications. However, they cannot be directly adopted for satellite applications due to the strict design requirements in space. The following section provides a detailed review of the topologies that can be adopted for satellite applications.  
Figure 6: Summary of various aspects of EP (power sources for EP, electric thrusters along with their power levels, and the evolution of solar EP).
# A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks
## 4 Heterogeneous WBAN Simulator
### The Results of Measurements Used in the Simulator  
Most currently designed WBANs are used in indoor environments in which radio wave propagation is significantly disturbed compared to outdoor ones. This is mainly due to the stronger multipath propagation phenomena. For this reason, it was decided to carry out the measurements in a harsh environment, i.e., the passenger ferryboat.  
Since the AMASD method is based on the radio channel characteristics, the determination of its parameters is the key element. However, developing a reliable WBAN simulator also requires consideration of other aspects that are related, in particular, to the used RIs. These are:  
* The accuracy of RDMs,
* the probability of the correct detection of the LOS conditions,
* the dependence of the LQI on the RSSI,
* the noise characteristics of both RIs.
## 1 Introduction
(**b**) Modeled scattering cross-section vs. frequency for a unidirectional fiber-reinforced composite in the normal fiber direction by [PERSON] [53]. Data reads from his plot shown in red circles fitted with a cubic frequency dependence. 27 MHz in this Nb study. While the combined damping-scattering attenuation apparently rationalizes the observed behavior, no theoretical basis is currently available to explain the level of damping, including the KGL theory noted above [33; 34; 35; 36; 37]. Since the damping term was not often utilized after 1960 outside the composite field, one of the objectives of the present study is to examine how broadly the Mason-McSkimin relation exists in various materials. It is anticipated that more detailed studies on dislocation effects, including atomistic or molecular dynamic simulations, will be required to establish improved quantitative mechanisms for damping. The extent of applicability of Datta-Kinra and Biwa relations is also of interest as these have not been tested with real attenuation data. These three relations (Mason-McSkimin, Datta-Kinra, and Biwa) are named in this report. Other aims of this work are to experimentally obtain the attenuation coefficients (\(\alpha\)) of a wide range of materials in the low MHz range (below 15 MHz), so as to present them in tabular form, to give information that is useful in material selection, and to provide rational interpretation of attenuation behavior. This information will also aid in improving model calculations for distance-amplitude curves for UT and predicting the detectability of AE signals on large structures. Over 300 samples were used, mainly based on the availability in the author's laboratory, as well as some new acquisitions. Experimental procedures describe transmission difference methods used for attenuation measurements without requiring the reflectivity of interfaces. The Results and Discussion section provides separate tables for different types of materials, metals (steels, non-ferrous alloys, and cast iron), polymers and wood, fiber-reinforced composites, and ceramics and rocks. This part covers general observations and includes comments on peculiar material behavior observed for some groups, such as cold-worked metals and cast iron. Also noteworthy is a transition discovered in  
\[\alpha_{\mathrm{d}}=\mathrm{C_{d}}\;\mathrm{f}=\pi\,\eta/\lambda=\pi\,\eta\; \mathrm{f}/\
u_{\mathrm{L}}, \tag{2}\]0 Attenuation coefficient vs. frequency. Experimental data represented by circles, Mason–McSkimin fit represented by + marks, and the linear component represented by dots. (**a**) The 0.2% C steel normalized at 900 (blue) and 1100 \({}^{\circ}\)C (red). Data read from [PERSON] and [PERSON] [55]. (**b**) Nb with two different grain sizes: 32 (blue) and 60 \(\upmu\)m (red). Data read from [PERSON] et al. [56]. mortar spectral results, which changed from the Mason-McSkimin to Datta-Kinra spectrum when void content exceeded 20%. This represented a change from independent to multiple scattering.
## 3 Methods
### Model for Storing Trajectory Data  
In the face of the challenges posed by the vast volume of traffic trajectory data, devising an efficient storage model for storing and retrieving extensive trajectory data within big data frameworks becomes exceedingly crucial. Currently, research on space-time data primarily focuses on spatial data, while time information is often treated in a simplistic manner, such as using timestamps [39], strings [40], and time-counting methods for auxiliary processing. However, the reality is that big data contains a variety of time information that is not fully utilized. Furthermore, in many cases, achieving uniform organization of multi-scale time information and enhancing the efficiency of multi-scale time calculations pose significant challenges [41]. Therefore, this research, grounded in the architecture of MapReduce and HBase, has devised a data storage model predicated on multi-level time encoding. However, the trajectory data points of taxis within the same period are not stored continuously on the storage device. Accessing them can generate a significant amount of IO [42], thereby reducing data access efficiency [43]. Other storage methods, such as vehicle-based trajectory [44] or spatial-information-based [45] storage methods, do not guarantee adequate support for the query conditions required for this type of analysis. Therefore, this paper has optimized the time encoding storage model based on real-world usage scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline
**Data Type** & **Data Format** & **Data Volume** \\ \hline Taxi trajectory point data & csv & 32 G \\ Road network data & shp & 20.5 M \\ Building contour data & shp & 17.9 M \\ Boundary data of each district & shp & 7.51 M \\ \hline \end{tabular}
\end{table}
Table 1: Data basics.  
\begin{table}
\begin{tabular}{c c c} \hline
**Field Name** & **Data Sample** & **Description** \\ \hline TIME & 31 May 2019 & Date \\ POSITION\_TIME & 23:31:00 & Point of time \\ LNG & 118.023224 & Longitude \\ LAT & 24.49147 & Latitude \\ CAR\_NO & 300 bF5568114 df822 bed19e86e821e8 & License plate number \\ \hline \end{tabular}
\end{table}
Table 2: Structure and exemplar of taxi trajectory data.
# Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction
## 4 Balanced Realization/Truncation Technique for 2-D IIR Digital Filters  
Balanced realizations are known to be useful realizations for model reduction. The internally balanced realization gives an indication of the dominance of the system states in the input/output behavior [49]. The idea of a balanced realization model reduction, in general, is to remove from the system matrices the blocks corresponding to the smaller Hankel singular values [19,20,50,51]. In the following section, we present the design procedures for nearly linear-phase 2-D IIR digital filters with separable denominators.
## 3 Results and Discussion
### Discrimination Analysis
#### 3.2.4 Discriminant Models Based on Feature Wavelength Variables  
The results of the new discriminant models calibrated with the selected wavelength variables are summarized in Table 3. The optimal pre-processing methods for full-range wavelength models, smoothing and MSC, were also applied to the selected wavelength variables to observe the variations in accuracies. Overall, the modeling algorithms SIMCA and PLS-DA generated the same prediction accuracies as the best model results built on the full-range wavelengths in Table 2, but the KNN and SVM-DA algorithms could no longer obtain prediction accuracies as high as those based on the full-range wavelengths. The prediction accuracy of the KNN model dropped significantly from 97.56% to 89.84%. Even with the optimal modelling parameters, 13 samples in cultivar H8 and 12 samples in cultivar H168 were misclassified for the testing set. This showed that it was more difficult for the KNN model to identify all samples well when the number of wavelength variables was reduced. SIMCA and PLS-DA retained equivalent accuracies at 98.78% and 99.19% for the testing set, respectively. However, they increased the classification rates slightly during the calibration and cross-validation process. This may be explained by the fact that the MLR was chosen as the fitness function for the GA to select feature wavelengths, and a linear relationship between the spectral variables and cultivar categories was maintained. The accuracies of the SVM-DA for the training set and testing set all reduced slightly in this study, which agreed with previous research while training the SVM-DA model with feature wavelengths selected by the successive projection algorithm (SPA) method [45]. This indicated that the new hyperplane in the characteristic spectral space could no longer classify the samples as the hyperplace managed to do in the full-range spectral space. The results in previous literature [12; 23] showed that the performances of those models established on the selected variables had decreased slightly. However, [PERSON] et al., (2015) showed the opposite result, where the classification rates of the SVM model based on feature wavelengths increased while using SPA methods to select the feature wavelength variables [6]. These differences were most likely due to the experimental data,  
Figure 5: The feature wavelengths identified by 3315 optimal models and their root mean square error calculated from cross-validation (RMSECV) results after performing iterative operations with the genetic algorithm.  
pre-processing methods, wavelength selection algorithms, and modeling methods. Overall, the high accuracy of the simplified models in the current study indicated that using feature wavelengths for modeling could significantly simplify the model without sacrificing too much accuracy.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 4 Bleach of Photodarkened Fiber
### Photobleaching  
At present, the PB wavelengths of ytterbium-doped fiber are 355 nm [1], 405 nm [32; 88], 514 nm [46], 543 nm [1; 89], 550 nm [88; 90], 633 nm [1], 793 nm [33], and 810 nm [33]. However, only [PERSON] et al. used visible light to PB in Tm-doped fiber PB experiment [12].  
In 1994, [PERSON] et al. [91] showed that when using 514 nm bleached Tb-doped fiber, the reason is that the two-photon excitation can release electrons from the color center and make the color center disappear. In 2014, [PERSON] et al. [32] used Yb-doped fiber photondarkened by 405 nm light radiation, and they observed the partially recovered PB phenomenon. They showed that the reason for the incomplete PB phenomenon is the strong absorption of 405 nm radiation by excited ions (excited-state absorption), and PD loss caused by ground-state absorption is also observed.  
In some cases, PB can also cause the same high temperature as TB to bleach the color center. In the study of Tm-doped fibers, [PERSON] et al. [8] speculated that the bleaching phenomenon in some PB experiments was essentially caused by high temperature rather than photon irradiation, and they found that bleaching began when the fiber was heated to 350 \({}^{\circ}\)C. In the theoretical simulation, the core could reach a similar temperature by irradiating the laser with the same wavelength and power.
# Research on the Calculation and Analysis of Ski-Track Areas Based on Laser Point Clouds
## 3 Calculation of Ski Track Area  
### Construction of Triangulation Network Based on Growth Algorithm  
A number of algorithms are available for the calculation of plane point sets, including the incremental method [27], the [PERSON] scan method [28], the volume-wrapping method [29], and the divide-and-conquer method [30]. While [PERSON]'s algorithm is one of the most widely used and efficient algorithms, it is not capable of removing ski-track edge gaps with complex shapes and sizes. The purpose of this paper is to provide an improved method for determining point cloud boundaries, namely an improved growth algorithm, which is used to construct a triangular network using the plane point cloud obtained in Section 2, as shown in Figure 2. In addition, this paper shows that the RANSAC algorithm's time complexity for plane fitting is \(O(n^{2})\), while the improved growth algorithm's time complexity for triangular grid generation is \(O(nlogn)\). A triangular network is constructed using the following growth algorithm:  
1. The initial edge AB in the edge table is formed by taking the closest point B to the starting point A in all point cloud data, as shown in Figure 2.  
2. Select a point C in the remaining point cloud data to maximize \(\angle\)ACB, then place the newly generated two edges AC and BC in the edge table, and place \(\triangle\)ABC as the first  
Figure 1: The flow of the random sampling consensus algorithm.  
triangle in the triangle. Take any edge from the edge table and assume that it is a BC edge, then the BC edge will divide the plane into two half-planes. Find the discrete point D in the remaining point cloud data. The selection principle is to make the point D and point A different from the same half-plane of the BC side and to maximize \(\angle\)BDC. Add the new edges BD and CD to the edge table and \(\triangle\)BCD to the triangle table.  
4. Repeat step 3 until all point cloud data are processed.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 4 Discussion
### K-Fold Cross Validation  
To evaluate the robustness of the architecture, a \(K\)-fold cross-validation was conducted on both original datasets without augmentation, with \(K=5\). For each fold, 10% of the dataset was set aside for testing to ensure that the test size remained constant across all of the experiments. The original random seed was fixed to guarantee consistent testing throughout. In total, 600 and 905 examinations were utilized for cross-validation inference for the two- and four-chamber datasets, respectively. A second randomization seed was fixed to ensure consistent shuffling in the training set. The results for the Dice score and Hausdorff distance are reported in Table 3 and Figure 6.  
The Hausdorff distance exhibited higher variability due to its sensitivity to localized outliers in predictions. Dice coefficient differences remained low across folds, indicat  
Figure 6: Cross-validation scores for each of the trained folds. Cross-validation results using a UNet architecture are also shown here to provide further evidence of consistency of the models and datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**K1**} & \multicolumn{2}{c}{**K2**} & \multicolumn{2}{c}{**K3**} & \multicolumn{2}{c}{**K4**} & \multicolumn{2}{c}{**K5**} \\ \cline{2-11}  & **Mean DSC** & **Mean HD** & **Mean DSC** & **Mean HD** & **Mean DSC** & **Mean HD** & **Mean DSC** & **Mean HD** & **Mean DSC** & **Mean HD** \\ \hline ENet 2 cav & 0.897 & 6.66 & 0.905 & 6.18 & 0.905 & 6.39 & 0.903 & 8.27 & 0.897 & 7.00 \\ UNet 2 cav & 0.896 & 13.31 & 0.896 & 14.00 & 0.892 & 17.05 & 0.894 & 8.83 & 0.891 & 9.54 \\ \hline ENet 4 cav & 0.886 & 13.73 & 0.880 & 12.55 & 0.885 & 13.20 & 0.889 & 10.34 & 0.883 & 13.87 \\ UNet 4 cav & 0.904 & 11.88 & 0.906 & 12.18 & 0.905 & 9.46 & 0.903 & 9.85 & 0.910 & 10.96 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average Dice (DSC) and Hausdorff (HD) scores across the K folds of cross-validation. Experiments were applied to nonaugmented datasets.  
ing consistent model learning. Any discrepancies compared to the reported results in Tables 1 and 4 can be attributed to the training variability.