esa-sceva/satcom-chunk-collection huggingface dataset:
# Magneto-Optical Kerr Effect Driven by Spin Accumulation on Cu, Au, and Pt
## 3 Results
From the real part of the Kerr rotation of Figure 3a,b, I determine the \(\Delta\theta/\Delta M\) values to be \(-4\times 10^{-9}\) and \(-2.5\times 10^{-8}\) rad m A\({}^{-1}\) for Cu and Au, respectively. The \(\Delta\theta/\Delta M\) value of \(-4\times 10^{-9}\) rad m A\({}^{-1}\) for Cu is consistent with a previous report from Ref. [18] that used the nearly the same film structure. The six times larger \(\Delta\theta/\Delta M\) value for Au can be explained by a much larger spin-orbit coupling of Au [10]. From the imaginary part of the Kerr rotation of Figure 3c, I determine the \(\Delta\varepsilon/\Delta M\) of Pt. In this case, I assume that the imaginary part of the Kerr rotation mostly originates from Pt and thus ignore the contribution from Cu, because the imaginary part of the Kerr rotation on Cu is  
Figure 4: Calculation of the spin accumulation on NM. Spin chemical potentials along the thickness of NM of the Pt(10)/FM(3.8)/NM structure, where NM is (**a**) Cu(100)/MgO(3), (**b**) Cu(100)/Au(10), or (**c**) Cu(100)/Pt(2). Solid lines represent the spin chemical potentials of Cu, and every line is an average of 10 nm thickness starting from the bottom (the FM/Cu interface) to the top surface. Red dashed lines represent the spin chemical potentials of Au (average of 10 nm thickness) in (**b**) and of Pt (average of 2 nm thickness) in (**c**). Figure 5: Thickness resolved magneto-optic Kerr effect (MOKE). Relative contribution to the Kerr rotation through the thickness of NM of the Pt(10)/FM(3.8)/NM structure, where NM is either (**a**) Cu(100), (**b**) Cu(100)/Au(10), or (**c**) Cu(100)/Pt(2). Black solid lines are the contribution from Cu, and red solid lines are the contribution from Au in (**b**) and from Pt in (**c**). about thirty times smaller than the real part. The determined \(\Delta\varepsilon/\Delta M\) value is \(-3\times 10^{-9}\) rad m A\({}^{-1}\) for Pt. Recently, a theory calculated the \(\Delta\varepsilon\) for Pt to be \(-30\)\(\upmu\)rad at a spin accumulation of 0.02 \(\upmu\)g per atom [17]. With an atom density of \(6.6\times 10^{28}\) m\({}^{-3}\) of Pt, the \(\Delta\varepsilon/\Delta M\) value becomes \(-2.4\times 10^{-9}\) rad m A\({}^{-1}\), which is similar to our result. The \(\Delta\theta/\Delta M\) of Pt is more difficult to estimate because the \(\Delta\theta\) data of the Cu/Pt sample have contributions from the \(\Delta M\) of both Cu and Pt. In addition, the \(\Delta\theta\) from the demagnetization of FM can have a significant contribution to the measured \(\Delta\theta\) data when the \(\Delta\theta\) from the spin accumulation of NM is too small. I expect that a small negative offset of the real part of the MOKE data of Figure 3c at 8 ps originates from the demagnetization signal of [Co/Ni]. The imaginary part of the MOKE data of Figure 3c shows a negligible offset because the imaginary part of the Kerr rotation of [Co/Ni] is smaller than the real part by an order of magnitude [31; 32].
## 4 A Case Study of Hidake Tunnel in Japan
### Digital Inspection Test  
To prevent the possible collapse of tunnel lining, digital inspection test was conducted to assess the extent of damage in different segments of the tunnel. The tunnel lining images were obtained by a photographing vehicle equipped with line sensor cameras. It is a non-contact detection technology that can track the information of the tunnel surface in a very short time [26; 27]. The process of extracting  
Figure 8: The Hidake Tunnel in Japan.  
Figure 7: Fractal dimension of different distributed cracks.  
cracks was shown in Figure 9. The round surface was automatically spread out to a flat surface as shown in Figure 9a,b. The cracks were extracted from the image as shown in Figure 9c,d by a crack identification program. The cameras were fixed on a car, and it can detect cracks with 0.2-mm resolution at a speed of 70 km/h. The cracks were extracted for all the 65 spans in Hidake Tunnel, and the results of 12 typical spans were shown in Figure 10. The basic condition of the 12 typical spans is shown in Table 1.  
Figure 10: The distribution of cracks in tunnel lining for 12 typical spans.  
Figure 9: Extraction of cracks on tunnel lining surface: (**a**) field test; (**b**) unfolded crack image; (**c**) identification of cracks; (**d**) extraction of cracks.  
A large number of cracks were found in spans 46, 47, 50, 53, and 57, including some wide cracks, which would increase the risk of tunnel lining collapse greatly. There were a large number of small cracks in span 65, but no wide cracks were found. Few cracks were found in spans 16 and 18, which may mean a smaller risk of lining collapse. Moderate degree of cracks were found in spans 34, 36, 42, and 63.  
The causes of cracking in Hidake Tunnel include: tectonic stress, earthquakes and concrete deterioration. The tectonic stress could result in a large number of axial cracks as shown in spans 47, 50, 53, 57, and 63. The earthquake, namely the Kumamoto earthquakes in 2016, could result in ring cracks as shown in spans 46, 47, 50, 53, and 57. The small cracks in spans 18, 34, 42, and 65 were mainly caused by the deterioration of concrete. Moreover, the cracking was influenced by the surrounding rock types. All the heavily cracked spans, including spans 46, 47, 50, 53, and 57, were located in turf breccia or lapilli tuff, which belong to soft rock. In general, the actual cracking was affected by multiple factors.
## 6 Conclusions  
As the deployment of mega-constellations accelerates, ensuring equal access and non-interference in outer space becomes increasingly important. The growing number of satellites and mega-constellations occupying crucial orbits calls into question the fundamental ideals stated in the Outer Space Treaty, such as the \"province of all mankind\" and the prohibition on appropriating space. To resolve these concerns, space actors must accelerate the legislative process and establish strong, nonpolitical solutions through UNGA resolutions, supplemented by multilateral agreements, as well alternative governance models such as adaptive forums, communication networks, and collaborative governance frameworks that reflect the global nature of these issues. To prevent mega-constellations from posing a substantial threat to space safety and sustainability, strict legislation and severe penalties for infractions are required.  
Given the current and dynamic nature of this topic, extensive research and analysis are required. Future research should focus on global space governance, the long-term sustainability of space operations, and the imposition of sanctions or limits on operators to ensure compliance. By doing so, we can reap the benefits of mega-constellations while mitigating the potential negative consequences, promoting the more balanced and equitable usage of outer space. This approach will not only protect the interests of all space-faring nations, but will also ensure that outer space remains viable for future generations.  
Methodology, K.Y.; supervision, K.Y.; writing--original draft, K.Y. and S.A.; writing--reviewing and editing, K.Y. and S.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Social Science Foundation of China, for the research projects \"International Regulation of Large Constellations and China's Response\" (grant number 20 CFX078) and \"Research on Strengthening the Construction of Space Safety System in the New Era\" (grant number 23&ZD137).  
The data used to support the findings of this study are available from the corresponding author upon reasonable request.  
The authors greatly appreciate the insightful and constructive comments and suggestions from the anonymous reviewers.  
The authors declare no conflicts of interest.
## 4 Experimental Study
### Stribeck Curve Measurement and Parameter Identification  
This section provides a concise and precise description of the experimental results, interpretation, and conclusions. In order to accurately obtain the Stribeck friction curve of the precision reducer, the friction torque measurement experiment was carried out under different speed conditions, from low speed to high speed. When the drive motor works in the speed mode, the speed fluctuation is very small and it can be approximately ignored. The friction torque of the precision reducer is equal to the drive torque of the motor at the input end, which is acquired by the torque sensor at the input end in real time. Figure 5 shows the variation of the friction torque with the meshing position under a speed of 2.0933 rad/s. In order to obtain the one-to-one corresponding relationship between the friction torque and speed, the mean value filtering method is used to deal with the measured data of friction torque under a steady speed [19]. In other words, the mean value of friction torque is used as the corresponding friction torque under steady speed. This method can also filter out the noise interference at the same time.  
The friction torque-speed data of the RV reducer were obtained by changing the measurement speed (Table 1). The Stribeck curve of the precision reducer was fitted with the least square method (Figure 6). Table 2 shows the parameters of the Stribeck model that were obtained by least square fitting. The RSS (residual sum of squares) is \(5.10453\times 10^{-4}\) and the Adj. R-Square (adjusted coefficient  
Figure 4: RV reducer comprehensive performance tester. (**a**) Structure diagram; and, (**b**) Physical view.  
Figure 5: Friction torque of a RV reducer (2.0933 rad/s).  
of determination) is 0.97998. The two parameters indicated that the forward fitting could achieve the ideal fitting effect.
## 3 Numerical Simulation and Analysis
The value of \(e_{T}\) and \(e_{C}\) are reduced with increasing the numbers of views and laser beams. The relative errors of temperatures are less than the concentration reconstruction results. The reason is that the integral area errors caused a greater impact on concentration results from the weak absorption in the peripheral low temperature regions of flame. For the centrosymmetry phantoms of flame as shown in Figure 2a,b,d, we have better reconstruction results when the laser beam paths are vertical and parallel to the symmetry axis of the flame as shown in Figure 4a and the beam number is 16. However, for the non-centrosymmetric phantoms of flame, like in Figures 2c and 3c, we do not have an acceptable and practical reconstruction result when the beam number is 16 and the beam paths are not vertical and parallel to the symmetry axis of the flame. Increasing the number of views and beams, \(e_{T}\) and \(e_{C}\) are reduced to less than 0.05 and 0.1, respectively, when the number of beams are 32 and 44, as shown in Figure 4c,d. Therefore, the better spatial resolution could be gained only when the laser beam paths are vertical and parallel to the symmetry axis of the combustion field under the limited number of light paths.
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
## 4 Experimental Validation and Discussion
### Results and Discussion  
#### 4.2.1 Time-Domain Signals and Their Spectrums of Current Signals  
Figure 3 shows the three-phase current signals and their spectrums of the healthy motor and faulty motor. It can be seen from Figure 3 that the time domain signals of the faulty motor are slightly larger than those of the healthy motor. The signature frequency components are concealed by the supply frequency and are difficult to distinguish. It requires advanced signal processing methods to remove the supply frequency and make the signature frequency obvious.
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
## 3 System Model  
Figure 1 represents the system model of the CP-OFDM system utilizing the proposed confidence level channel estimation method [17]. At the OFDM transmitter, the incoming binary bits are first grouped and mapped according to a pre-specified modulation scheme: 16 QAM. After inserting the block-type pilots, the 16 QAM symbols are converted into parallel streams by a serial to parallel block. The inverse fast Fourier transform (IFFT) block is used to transform the data sequence \(\hat{\mathbf{X}}_{i}(n)\) with \(N\) rows into time-domain signal \(\hat{\mathbf{x}}_{i}(n)\), where \(N\) is the number of sampling points within one OFDM symbol. Then, the CP is inserted into the time-domain OFDM symbols in order to reduce the ICI and ISI. The transmitted signals are then passed through the multipath fading channels \(\hat{\mathbf{h}}_{i}(n)\) with AWGN \(\hat{w}_{i}(n)\) after the parallel to serial block.  
At the receiver, the CP is removed after the serial to parallel conversion. After removing the CP, the time-domain received OFDM symbols \(\hat{\mathbf{y}}_{i}(n)\) are alternated in the fast Fourier transform (FFT). After that, the frequency-domain OFDM symbols \(\hat{\mathbf{Y}}_{i}(n)\) are transformed from the parallel to serial block. The estimated CIR is obtained in the channel estimation block. There are two blocks in the red dashed box: the first one is the channel estimation block, which represents the channel estimation method based on the confidence level; the second one is statistical buffer block, which represents the number of statistical frames \(T\). The noise in the multipath channel is further suppressed in the statistical buffer block. At last, the binary information bits are obtained after the 16 QAM symbol demodulation scheme.  
Block-Type Pilot-Based LS, IMMSE, Threshold Value and Chi-square Distribution Channel Estimation Methods
## 7 Conclusions
\begin{array}{c}V_{1}\cos(\phi_{v1})+\hat{\sigma}_{p}\cos( 2\pi f_{p}t+\phi_{vp})+\hat{\sigma}_{n}\cos(2\pi f_{p}t+\phi_{vm})\\ V_{1}\cos(\phi_{v1}-\frac{\pi}{3})+\hat{\sigma}_{p}\cos(2\pi f_{p}t+\phi_{vp}- \frac{\pi}{3})-\hat{\sigma}_{n}\cos(2\pi f_{p}t+\phi_{vm}-\frac{\pi}{3})\end{array} \right]\]  
According to (13), small-signal dq voltages can be obtained based small-signal positive-negative voltages as  
\[\left[ \begin{array}{c}\Delta v_{d}\\ \Delta v_{q}\end{array} \right]=\left[ \begin{array}{c}\hat{\sigma}_{p}e^{j \phi_{vp}}+\hat{\sigma}_{n}e^{j\phi_{vm}}\\ -j\hat{\sigma}_{p}e^{j\phi_{vp}}+j\hat{\sigma}_{n}e^{j\phi_{vm}}\end{array}  \right]=\left[ \begin{array}{cc}1&1\\ -j&j\end{array} \right]\left[ \begin{array}{c}\hat{\sigma}_{p}e^{j\phi_{vp}}\\ \hat{\sigma}_{n}e^{j\phi_{vm}}\end{array} \right]=\left[ \begin{array}{cc}1&1\\ -j&j\end{array} \right]\left[ \begin{array}{c}\Delta v_{p}\\ \Delta v_{n}\end{array} \right] \tag{14}\]  
The same relationship is valid for three-phase currents. \[\left[ \begin{array}{c}\Delta i_{d}\\ \Delta i_{q}\end{array} \right]=\left[ \begin{array}{cc}1&1\\ -j&j\end{array} \right]\left[ \begin{array}{c}\Delta i_{p}\\ \Delta i_{n}\end{array} \right] \tag{15}\]  
As already presented in this paper, the small-signal behaviour of a three-phase converter can be modelled in dq domain by  
\[\left[ \begin{array}{c}\Delta i_{d}\\ \Delta i_{q}\end{array} \right]=\left[ \begin{array}{cc}y_{dd}(s)&y_{dq}(s)\\ y_{qd}(s)&y_{qq}(s)\end{array} \right]\left[ \begin{array}{c}\Delta v_{d}\\ \Delta v_{q}\end{array} \right] \tag{16}\]  
By substituting (14) and (15) in (16) we get  
\[\left[ \begin{array}{cc}1&1\\ -j&j\end{array} \right]\left[ \begin{array}{c}\Delta i_{p}\\ \Delta i_{n}\end{array} \right]=\left[ \begin{array}{cc}y_{dd}(s)&y_{dq}(s)\\ y_{dq}(s)&y_{qq}(s)\end{array} \right]\left[ \begin{array}{cc}1&1\\ -j&j\end{array} \right]\left[ \begin{array}{c}\Delta v_{p}\\ \Delta v_{n}\end{array} \right] \tag{17}\]  
Equation (17) can be rewritten as  
\[\left[ \begin{array}{c}\Delta i_{p}\\ \Delta i_{n}\end{array} \right]=\left[ \begin{array}{cc}1&1\\ -j&j\end{array} \right]^{-1}\left[ \begin{array}{cc}y_{dd}(s)&y_{dq}(s)\\ y_{dq}(s)&y_{qq}(s)\end{array} \right]\left[ \begin{array}{cc}1&1\\ -j&j\end{array} \right]\left[ \begin{array}{c}\Delta v_{p}\\ \Delta v_{n}\end{array} \right]=\left[ \begin{array}{cc}y_{pp}(s)&y_{pn}(s)\\ y_{mp}(s)&y_{mn}(s)\end{array} \right]\left[ \begin{array}{c}\Delta v_{p}\\ \Delta v_{n}\end{array} \right] \tag{18}\]Based on (A8), the relationship between sequence-domain admittance definition and dq-domain admittance definition can be obtained by  
\[\left[ \begin{array}{cc}y_{pp}(s)&y_{pn}(s)\\ y_{np}(s)&y_{mn}(s)\end{array} \right]=\left[ \begin{array}{cc}1&1\\ -j&j\end{array} \right]^{-1}\left[ \begin{array}{cc}y_{dd}(s)&y_{dq}(s)\\ y_{qd}(s)&y_{qq}(s)\end{array} \right]\left[ \begin{array}{cc}1&1\\ -j&j\end{array} \right]\] (A9)
# Modularity-Based Incremental Label Propagation Algorithm for Community Detection
## 2 Methods and Materials
### Experimental Datasets
#### 2.4.1 LFR Networks  
LFR benchmark is a widely used program to build artificial networks to test the performance of community detection algorithms. It can flexibly generate high quality synthetic networks which are close to the real networks. The LFR program provides a series of configuration parameters for users to define, including the size of a network N, i.e., the number of nodes in a network; the maximum and minimum of nodes' degree, \(maxx\) and \(mink\) respectively; the maximum and minimum of nodes in a community, \(maxc\) and \(minc\) respectively; the ratio of the number of links between communities and the total number of edges in the network, \(mu\), which indicates the significance of community structure in the network. The smaller the value of \(mu\) is, the more obvious the community structure of a network is. In this paper, the LFR program is used to generate two types of benchmark datasets, i.e., Group A and Group B. Each network in Group A has 1000 nodes and different \(mu\) values ranging from 0.1 to 0.5. Similarly, Group B has five networks with 5000 nodes. The two sets of data simulate small networks and large networks, respectively.
# Sea Purslane as an Emerging Food Crop: Nutritional and Biological Studies
## 3 Results and Discussion
### Total Phenolic and Flavonoid Content  
Halophytes are rich in highly bioactive phytochemicals [15], particularly in phenolic compounds, which are abundantly present in the human diet, and to which are attributed important antioxidant properties, their intake being associated with a decreased risk of development of oxidative stress-related diseases [14]. Flavonoids are one of the most important families of phenolic compounds, thus contributing to their health benefits, namely as anticancer and chemopre agents [17]. Therefore, the measurement of the phytochemical composition of an extract in terms of total phenolic and flavonoid content can be used to estimate its antioxidant potential. Regarding the TPC of the _H. portulacoides_ extract, the results presented in Table 4 support the advantage of using ethanol as extraction solvent, since a higher content of phenolic compounds was obtained when compared with reported extractions using hexane, chloroform, methanol [16], ethyl acetate, and water [59]. Moreover, the TPC for the _H. portulacoides_ extract is in agreement with previous results obtained for this plant harvested in the same region of Portugal [18]. The TPC is higher than those reported for other halophytes, namely _Ipomoa pes-caprae_[60], which can be attributed to the rich content of sulfated flavonoids, particularly derivatives of isorhamnetin-sulfate [18], which proved to possess higher antioxidant activity than \(\alpha\)-tocopherol [61].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Assay** & _H. portulacoides_ Extract \\ \hline \multicolumn{2}{c}{**Chemical composition**} \\ \hline TPC (mg GAE/g extract) & 16.10 \(\pm\) 0.20 \\ \hline TPC (mg QCE/g extract) & 26.60 \(\pm\) 0.80 \\ \hline \multicolumn{2}{c}{**Antioxidant activity**} \\ \hline DPPH (IC\({}_{50}\) mg/mL) & 3.70 \(\pm\) 0.40 \\ \hline ABTS (IC\({}_{50}\) mg/mL) & \textgreater{}5 \\ \hline \(\beta\)-carotene/linoleic acid (IC\({}_{50}\) mg/mL) & 0.15 \(\pm\) 0.03 \\ \hline Lipid peroxidation (IC\({}_{50}\) mg/mL) & \textgreater{}5 \\ \hline Metal chelating ability (IC\({}_{50}\) mg/mL) & 2.30 \(\pm\) 0.50 \\ \hline FRAP (mg TE/g extract) & 19.90 \(\pm\) 1.90 \\ \hline CUPRAC (mg TE/g extract) & 44.00 \(\pm\) 2.30 \\ \hline \multicolumn{2}{c}{**Enzymatic activity**} \\ \hline AChE inhibition (IC\({}_{50}\) mg/mL) & \textgreater{}5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical composition and antioxidant and enzymatic activities of the _H. portulacoides_ leaves extract.
# Performance Model for Video Service in 5G Networks
## 3 Approaches
### Interference Coordination Approach
#### 3.1.1 Network Clustering  
We used the clustering methodology in [29] to group sectors into clusters. Neighbor sectors with higher interference to each other are more likely to be clustered together. The interference is calculated based on all UEs in the neighbor sectors and their allocated beams. The interference is calculated using Equations (6)-(8) and averaged over all UE within the sector, as shown in Figure 2. The cluster starts by adding a randomly selected sector, and sectors are added if they cause highest interference to all existing sectors in the cluster. The cluster increases to a maximum cluster size \(C_{max}\).
# Analysis of Surface EMG Parameters in the Overhead Deep Squat Performance
## 2 Materials and Methods
### Investigated Parameters Description
#### 2.2.2 Frequency Parameters: Mean and Median Frequency  
Median frequency (\(MDF\)) is a frequency at which the EMG power spectrum is divided into two regions with equal amplitude. \(MDF\) is a measure that indicates the frequency of muscle activation, and its relationship to %MVC can be useful in assessing how effectively muscles are engaged during exercise. Mean frequency (\(MF\)) is a weighted average of all frequencies in the EMG signal and is a typical indicator of muscle fatigue. Within sEMG frequency analysis, \(MF\), \(MDF\), and the zero-crossing rate (ZCR) (which is not used in this work) are the most important parameters in fatigue studies [7; 31; 32]. In our analysis, as far as the frequency parameters are concerned, we focus on the \(MDF\) mainly because it is less susceptible to interference than the mean frequency, and its decreasing value, similar to the \(MF\) value, also indicates muscle fatigue. However, \(MDF\) is a more stable measure than \(MF\). \(MF\) and \(MDF\) are calculated according to formulas (6) and (7) [33]:  
\[MF=\frac{\sum_{j=1}^{M}f_{j}P_{j}}{\sum_{j=1}^{M}P_{j}}, \tag{6}\] \[\sum_{j=1}^{MDF}P_{j}=\sum_{j=MDF}^{M}P_{j}=\frac{1}{2}\sum_{j=1} ^{M}P_{j}, \tag{7}\]  
where \(f_{j}\) is the frequency value of the sEMG power spectrum at the frequency bin \(j\), \(P_{j}\) is the sEMG power spectrum at the frequency bin \(j\), and \(M\) is the length of frequency bin.