esa-sceva/satcom-chunk-collection huggingface dataset:
# Relationship between Fault Level and System Strength in Future Renewable-Rich Power Grids
## 3 Fault Level Estimation Using Measurement-Based Thevenin Impedance
### Thevenin Impedance Measurement Using Step Load Change  
Load side variations can be utilized to estimate the Thevenin impedance at the load nodes [29; 30; 31]. Note Figure 2 which shows the equivalent system at the load bus where the system side equivalent is kept unchanged. Any step change in the load side would be followed by a change in the voltage. By recording consecutive measurements of both the currents and the voltage before and after the load variations, the equivalent impedance at the load bus can be calculated as expressed in Equation (3):  
\[\overline{E_{th}} = \overline{V_{L,i}}+\overline{Z_{th}}\times\overline{I_{L,i}} \tag{3}\]  
Note that the variables in the above equation (voltages and currents) represent the actual RMS values measured in the positive sequence. While both \(\overline{E_{th}}\) and \(\overline{Z_{th}}\) are kept constant, any change in the current \(\overline{I_{L,i}}\) would be accompanied by a change in the voltage \(\overline{V_{L,i}}\). Hence, two consecutive measurements of two loading conditions (i.e., \(i=1\), \(2\)) would lead to two different equations, as follows:  
\[\begin{array}{rl}\overline{E_{th}}&=&\overline{V_{L,1}}+\overline{Z_{th}} \times\overline{I_{L,1}}\\ \overline{E_{th}}&=&\overline{V_{L,2}}+\overline{Z_{th}}\times\overline{I_{L,2}}\end{array} \tag{4}\]  
By solving these two equations, the value of the Thevenin impedance can be calculated as follows:  
\[Z_{th} = \left|\frac{\overline{V_{L,1}}-\overline{V_{L,2}}}{\overline{I_{L,1}}-\overline{I_{L,2}}}\right| \tag{5}\]  
It is worth noting that the outcome of this method might be limited to cases where the system side is assumed unchanged during the measurements. In this regard, some literature can be found to account for source side variations [8], which is beyond the scope of this paper. However, it would be satisfied for the purpose of a steady-state Thevenin impedance estimation where variations are considered insignificant. In this work, a large step change in the voltage at the load bus is guaranteed for the purpose of improving the accuracy of this method. This voltage change is obtained by a large step change in the load that might be enough to neglect the continuous steady-state small variations in the source side. More specifically, the changes (5%, 10%, and 15%) are used and averaged to improve the accuracy of this method. This will be further discussed in the results section.
## 2 Materials and Methods
### Finite Element Method  
The finite element method was employed to further investigate the sound absorption coefficients using the obtained 5 intrinsic parameters for each RPUF density considered, in parallel with the transfer matrix method. A 3D finite element model of the impedance tube and the considered RPUF specimen, with their dimensions, was created and frequency domain analysis was performed using the pressure acoustics module of Comsol Multiphysics software, as shown in Figure 5 [36].  
Free tetrahedral mesh was generated for the air medium, as well as for the porous layer. The maximal size of the mesh elements was less than 1/6 of the lowest considered acoustic wavelength. The incident sound field was applied as a boundary pressure wave with amplitude 1 Pa on the inlet boundary and all the other boundaries were considered as sound hard boundaries. The porous layer was represented with the Johnson-Champoux-Allard (JCA) rigid model. The surface impedance was obtained, followed by the reflection coefficients and absorption coefficients.  
Figure 5: 3D FEM model—discretization: (**a**) Impedance tube with porous layer backed with rigid wall; (**b**) Impedance tube with air gap between the porous layer and the rigid wall termination.
# An Efficient Task Autonomous Planning Method for Small Satellites
## 4 The Experiment and Analysis
### Stability Analysis of the Method  
The comparison and summary of the running time and task total revenue of the four groups of experiments, after repeating 20 times, are shown in Tables 3 and 4.  
In the four groups of experiments, the distribution of the running time of IGA is more concentrated, and the average running time is significantly lower than that of GA. This phenomenon shows that IGA not only has a shorter average running time, but also has better stability than GA, in terms of running time.  
In Experiment 1 and Experiment 2, the distribution of the total revenue of GA and IGA is basically the same, which shows that the two genetic algorithms are basically the same in terms of task revenue, when the time constraints are relatively loose.  
In Experiment 3 and Experiment 4, IGA gains the highest revenue and has a more concentrated revenue distribution, which indicates that IGA has better stability in terms of task total revenue in the case of strict time constraints.  
Through a comprehensive analysis of the above four groups of experiments, we can draw the following conclusions: In the case of the more relaxed time constraints, the two genetic algorithms are basically the same in revenue, and IGA has shorter running time. In the case of strict time constraints, IGA is not only superior to GA in running time, but also has better stability in terms of task revenue. Therefore, IGA is more suitable for onboard computation, because of the limited resources and computing power on board.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Shortest/Average/Longest**} & \multicolumn{2}{c}{**SD of Running**} & \multicolumn{2}{c}{**Minimum/Average/**} & \multicolumn{2}{c}{**SD of**} \\  & & \multicolumn{2}{c}{**Running Time (s)**} & \multicolumn{2}{c}{**Time (s)**} & \multicolumn{2}{c}{**Maximum Revenue**} & \multicolumn{2}{c}{**Revenue**} \\ \hline \multirow{2}{*}{Experiment 1} & GA & 1.72 & 2.37 & 4.63 & 1.57 & 514 & 514.2 & 516 & 0.61 \\  & IGA & 0.83 & 1.03 & 2.1 & 0.36 & 514 & 514.1 & 516 & 0.45 \\ \hline \multirow{2}{*}{Experiment 2} & GA & 14.01 & 20.88 & 31.56 & 8.47 & 462 & 470 & 479 & 5.03 \\  & IGA & 2.19 & 5.31 & 9.73 & 2.56 & 463 & 470 & 478 & 4.83 \\ \hline \multirow{2}{*}{Experiment 3} & GA & 107.3 & 122.5 & 153.3 & 39.56 & 397 & 420 & 453 & 23.52 \\  & IGA & 69.1 & 85.7 & 98.4 & 20.17 & 417 & 431 & 463 & 18.57 \\ \hline \multirow{2}{*}{Experiment 4} & GA & 173 & 189 & 217 & 34.17 & 352 & 383 & 405 & 21.92 \\  & IGA & 74.5 & 99.7 & 121 & 17.79 & 380 & 409 & 437 & 18.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The running time and task total revenue of the experiments. SD—standard deviation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Overlapping of Time Windows** & **Adequacy of Resources** \\ \hline Experiment 1 & Low & Yes \\ Experiment 2 & Low & No \\ Experiment 3 & High & Yes \\ Experiment 4 & High & No \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental arrangements.
## 1 Introduction
However, in a recent development, a method which is supported by both theory and experimental data has been proposed by [PERSON] et al. [25]. This new method, which has been designed to measure soil moisture, is based on a linear model in shortwave infrared (SWIR) wavelengths, termed as 'SWIR-transformed reflectance' (STR). We chose this measure to use in this study to explore its feasibility as a substitute of the TIR-derived surface temperature for the mapping of irrigated land cover in the Central Goulburn Irrigation District (CGID) of Victoria, Australia, during the peak irrigation season (i.e., the summer) of 2020-2021. \begin{table} \begin{tabular}{l l l} \hline \hline
**Index** & **Equation \({}^{\star}\)** & **Reference** \\ \hline Vegetation Condition Index, VCI & \(VCI=100*\frac{(NDVI-NDVI_{min})}{(NDVI_{min}-NDVI_{min})}\) & [PERSON], 1995 [26] \\ Normalized Difference Water Index, NDWI & \(NDWI=\frac{R_{min}-R_{min}}{R_{max}+R_{min}}\) & [PERSON], 1996 [27] \\ Normalized Multiband Drought Index, NMDI & \(NMDI=\frac{R_{min}-(R_{min}-R_{230~{}m})}{R_{800~{}m}+(R_{160~{}nm}-R_{230~{} m})}\) & Wang and Qu, 2007 [28] \\ Perpendicular Drought Index, PDI & \(PDI=\frac{1}{\sqrt{M_{d}^{2}+(1}}\frac{R_{d}}{R_{d}}+M_{NIN})\) & [PERSON] et al., 2007a [29] \\ Modified Perpendicular Drought Index, MPDI & \(MPDI=\frac{R_{d}+M_{NIN}-f(R_{d},R_{d}+M_{R_{min}})}{(1-f)\sqrt{M_{d}^{2}+(1 })}\) & [PERSON] et al., 2007b, [30] \\ Modified Shortwave Infrared Perpendicular Water & \(MSPSI=\frac{1}{\sqrt{1+M}}(R_{s}+MR_{d})\) & [PERSON] et al., 2013 [31] \\ Stress index, MSPSI & \(DDI=\frac{\sqrt{R_{d}^{2}+R_{min}^{2}}}{1+NDVI}\) & [PERSON] et al., 2008 [32] \\ Distance Drought Index, DDI & \(VDDI=1-(R_{SWIR}+R_{Rad}-2R_{Blue})\) & [PERSON] et al., 2013 [33] \\ Visible and Shortwave Infrared Drought Index, VSDI & \(SINSI=\frac{R_{min}-R_{NIR}}{R_{NIR}+NIR}\) & [PERSON] \& [PERSON], 2003 [34] \\ \hline \hline \end{tabular}  \({}^{\star}\)  
The objectives of this study were (1) to assess the feasibility of using STR for mapping irrigated landcover and (2) to compare the TIR-based landcover classification (thermal approach) with the STR-based landcover (optical approach).
## 3 Photonic Technologies
### Fiber Optic Sensors (FOS)
#### 3.5.2 Dental Applications  
Prosthetics  
[PERSON] _et al._[119] was the first one using fiber optic sensors to monitor force and temperature in dental splints worn by patients suffering from sleep apnea. Dental splints are used as a clinical method to mitigate the effects of apnea, during sleep and must be used by patients during night. A way to attempt if patients are wearing it correctly and in a proper manner, during sleep, is to monitor the temperature and force exerted on the dental splint. FGB were inscribed in simple strands of optical fiber, embedded within the splint. The pressure sensor consists of an FBG embedded within three layers of glass fiber composite material (Figure 6). The glass fiber insulates the FBG from the temperature effect. Both the force and temperature sensors used in the trials were independently calibrated and were found to have accuracies of better than 0.5 N and 0.1 \({}^{\circ}\)C, respectively. The obtained results have suggested that monitoring of both the pressure and temperature of the dental splint as a function of time, can give a clear indication of the compliance of the patient with regard to the dental splint.  
[PERSON] _et al._[120] also have proposed a new experimental scheme, utilizing FBG sensors, to investigate and to measure the impact absorption capability of custom-made mouth guards. They used a customized pendulum device to simulate the impact loads and its response was observed using pairs of FBGs bonded to the surface of custom-made mouth guard and a typhodontic jaw model, representing normal occlusion, on similar positions.  
FBG sensors were used to study the biomechanics of a mandible, in order to develop the design of temporo-mandibular joint (TMJ) implant [121]. An experimental testing device was used to apply the occlusion and muscular forces to the mandible and four FBGs were glued at the surface of a synthetic mandible. The obtained results were correlated with similar ones obtained with a numerical finite element model.  
Figure 6: Schematic representation of a dental splint with embedded fiber optic sensors, to monitor force and temperature (adapted from [119]).
# Aerospace
## 1 Introduction
[7] for small spacecraft, a preliminary estimate of the cost savings offered by aerocapture for small satellites is presented. The results of the mission analysis can be extended to other related small satellite concepts at Mars and Venus, which are briefly discussed. These include standalone SmallSat missions using a dedicated low-cost launch vehicle [22], relay orbiter for lander/balloon missions [23], a global small lander network [24], and global imaging and radar constellations for future planetary missions [25].
## 5 Results and Discussion  
### Comparison between Monitoring and Finite Element Simulation Results  
The comparison between the FE simulation and monitoring results of the horizontal displacement and settlement of the ground is illustrated in Figure 6. As shown in Figure 6a, both the monitored and simulated horizontal ground displacements exhibit a nonlinearly accelerating reduction trend as the distance from the monitoring point to the caisson increases. The maximum monitored horizontal displacement is 5.3 mm, while the simulated value is 6.0 mm, resulting in a 13.2% error. Figure 6b demonstrates that the settlement curves of both monitoring and simulation conform to typical characteristics of ground  
Figure 5: Schematic diagram of disease reinforcement: (**a**) loose soil, (**b**) erosion void, and (**c**) tilting.  
settlement during excavation, reaching their maximum at monitoring point S3, with a monitored maximum of 3.9 mm and a simulated maximum of 4.3 mm, resulting in an error of 10.2%.  
The comparison between the FE simulation and monitoring results of the horizontal displacement and longitudinal strain of the caisson is depicted in Figure 7. From Figure 7a, it can be observed that both monitored and simulated caisson horizontal displacements reach their maximum at monitoring point M6, with a monitored maximum of 7.4 mm and a simulated maximum of 7.8 mm, resulting in an error of 5.4%. Figure 7b shows that the longitudinal strain of the caisson increases nonlinearly with depth, and the error between the monitoring and simulation results increases with depth, with a maximum error of 12.5%.  
Overall, the FE simulation results are in good agreement with the prototype test, and the FE simulation tends to be conservative (greater than monitoring values), indicating that the FE model constructed in this study possesses the capability to accurately predict  
Figure 6: Horizontal displacements and vertical settlement of the ground: (**a**) horizontal displacements and (**b**) vertical settlement.  
Figure 7: Horizontal displacements and longitudinal strains of the caisson wall: (**a**) horizontal displacements and (**b**) longitudinal strains.  
the mechanical performance of caissons and can provide theoretical guidance for the construction of such caissons.
# Vibration Energy Harvesting from Plates by Means of Piezoelectric Dynamic Vibration Absorbers
## 5 Experimental Results
### Tests with Air Excitation  
The fan depicted in Figure 10 was used to excite the plate by means of random pressure oscillations. Several tests were carried out with air velocities ranging from 4.1 to 9.0 m/s (the maximum value corresponds to the maximum flow rate of the fan). In the first series of tests, the simple piezoelectric patch was bonded under the center of the plate, whereas in the second series of tests, the CPDVA was fixed under the center of the plate. The OC voltage generated by the piezoelectric layer was analyzed in the frequency domain by means of NI Signal Express to obtain the power spectral density (PSD). In every test condition, the test was repeated seven times and good repeatability was found. Figure 18 refers to the voltage generated by the patch. The voltage PSD shows a peak at the natural frequency of the first mode of the plate. The height of this peak strongly increases with air velocity and reaches the maximum value of about 0.02 V\({}^{2}\)/Hz when the air velocity is 9.0 m/s. It is worth noting that at the maximum air velocity, a minor peak at the natural frequency of  
Figure 17: Magnitude of the FRF between the OC voltage and the impulse in the presence of tuning error \(\Delta f\approx-1\) Hz.  
the second mode of the plate appears as well. Since the air flow is able to strongly excite only the first mode of vibration of the plate, pressure fluctuations generated by the air flow can be considered to be spatially uniform on the plate surface and are dominated by low frequency components, as happens in the excitation of buildings by wind.  
Figure 19 shows the PSD of OC voltage generated by the CPDVA. Also, in this case, the generated voltage strongly increases with air velocity, but amplitudes are much larger than the ones in the previous case, with peaks ranging from 0.5 to 1 V\({}^{2}\)/Hz when air velocity reaches the maximum value. The effect of the CPDVA is clearly shown by the presence of the two peaks around the frequency of the first mode of the plate: like in the impulsive tests, the first peak is higher.  
Figure 18: PSD of the OC voltage generated by the patch directly bonded on the plate excited by the air jet. Experimental results are reported for three values of air velocity.
## 5 Computational Simulation Results and Discussion  
The results are organized into four sections. The first section deals with the investigation of the effect of damping forces on the steady-state response under the assumption of circular centred orbit. Both stable and unstable synchronous responses are identified. The second section describes the nonsynchronous response and the conditions of its emergence. The third section provides basins of attraction to demonstrate the effect of the choice of the initial conditions on the response character. Finally, the fourth section deals with the presence of isolated solutions in the frequency response and provides information on the regions where they occur.
# Finite Element Analysis of Thermal Stress and Thermal Deformation in Typical Part during SLM
## 6 Conclusions  
In this paper, the simulation of laser additive manufacturing was studied through thermodynamic coupling and elastoplastic theory. The simulation results demonstrated that the thermal stress and its strain corresponded to the geometrical deformation, when the part was quickly heated and the material rapidly solidified. Through simulation case analysis, the main conclusions were as follows:  
1. According to the reasonable design simulation algorithm and process parameters, the simulation process of the SLM parts was completed. Consequently, the thermodynamic coupling algorithm was simple and effective, while the thermal stress and thermal strain data were calculated to draw the curve.
2. For the simulated sample, the thermal stress increased from the substrate to the upper surface layer by layer. While the stress was reduced when the substrate was removed, the stress decreased from the substrate to the upper surface.
3. For the forming plane, the uniaxial stress reached the tension and pressure equilibrium state. Through the \(\sigma_{\mathrm{x}}\)/\(\sigma_{\mathrm{y}}\)/\(\sigma_{\mathrm{z}}\) comparison, the same three areas--Areas I, II, and III (in Figure 7d-f)--showed tensile and compressive stress, but the amplitude and breadth were highly different. Furthermore, the maximum residual stresses of the 316L stainless steel under these process parameters were \(\sigma_{\mathrm{xMax}}\) = 295 MPa, \(\sigma_{\mathrm{yMax}}\) = 283 MPa, and \(\sigma_{\mathrm{zMax}}\) = 305 MPa.
4. The changes of strain and thermal stress trends were relative, while the macroscopic geometric deformation occurred. From the simulation results, it could be deduced that the thermal deformation and thermal stress were in positive proportion. The higher deformation occurred at the corner of the same plane and the maximum deformation amount was located at the support toward the solid parts.
5. After the simulation, a set of optimizing technological parameters in the experiment was printed in the case model. The characteristics of the experimentally formed parts were consistent with the simulation results. It was observed that the simulation analysis could effectively predict the quality of the forming parts, which proved instructive to the practical production.  
[PERSON] conceived and designed the experiments; [PERSON] designed and performed the simulation and experiments; [PERSON] compared and analyzed the experimental and simulation data.  
This study was funded by the Key Research Projects of Shaanxi Provincial Department of Education (No. 18J5097) and the Shaanxi Key Laboratory of Surface Engineering and Remanufacturing Tianyuan Fund Project (No. tywl2019-07).  
This work was supported by the Shaanxi Key Laboratory of Surface Engineering and Remanufacturing, CH. The authors are also grateful for the support of Prof. [PERSON], University of Cincinnati, UK.  
The authors declare that they have no conflicts of interest.
# Learning Accurate Pseudo-Labels via Feature Similarity in the Presence of Label Noise
## 4 Experiments and Discussion
### Experimental Complexity Evaluation  
The FPL algorithm's superiority is emphasized by using training time as a criterion for evaluating the experiment's complexity. We applied a symmetric noise setting to the CIFAR10 dataset, with a noise rate of 20%. Each comparison method underwent 200 training sessions under identical experimental conditions. Furthermore, to underscore the duration of the feature clustering process, we conducted supplementary experiments devoid of feature labels, designated as FPL (w/o), and their outcomes are displayed in Table 9. Upon reviewing the table, it becomes apparent that our proposed method demonstrates a reduced runtime compared to alternative benchmark algorithms, notably surpassing the performance of the MLC and MSLC pseudo-labeling techniques. This implies that our approach attains superior classification accuracy while retaining lower complexity. Furthermore, we noted a mere 0.73 h reduction in training time after removing the feature labels. This suggests that the feature clustering process has minimal impact on the overall complexity of the training cycle.  
Figure 1: Ablation experiments on CIFAR10.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**CE**} & \multirow{2}{*}{**P**} & \multirow{2}{*}{**F**} & \multicolumn{2}{c}{**Sym**} & \multicolumn{2}{c}{**Pair**} & \multicolumn{2}{c}{**Trid**} & \multicolumn{2}{c}{**Ins**} & \multirow{2}{*}{**Mean**} \\  & & & **20\%** & **40\%** & **20\%** & **40\%** & **20\%** & **40\%** & **20\%** & **40\%** & **20\%** & **40\%** \\ \hline ✓ & & & 83.81 \(\pm\) 0.15 & 66.22 \(\pm\) 0.43 & 81.83 \(\pm\) 0.32 & 59.41 \(\pm\) 0.60 & 82.97 \(\pm\) 0.23 & 64.18 \(\pm\) 0.41 & 81.65 \(\pm\) 0.42 & 59.02 \(\pm\) 0.42 & 72.39 \\ ✓ & ✓ & & 93.18 \(\pm\) 0.06 & 91.88 \(\pm\) 0.06 & 94.10 \(\pm\) 0.04 & 93.74 \(\pm\) 0.05 & **94.43 \(\pm\) 0.04** & 92.90 \(\pm\) 0.04 & **93.72 \(\pm\) 0.04** & 92.91 \(\pm\) 0.02 & 93.36 \\ ✓ & & ✓ & 84.67 \(\pm\) 0.10 & 84.18 \(\pm\) 0.09 & 84.81 \(\pm\) 0.05 & 84.42 \(\pm\) 0.08 & 84.72 \(\pm\) 0.09 & 84.10 \(\pm\) 0.07 & 84.41 \(\pm\) 0.09 & 84.61 \(\pm\) 0.03 & 84.50 \\ ✓ & ✓ & ✓ & **93.55 \(\pm\) 0.03** & **92.92 \(\pm\) 0.04** & **94.13 \(\pm\) 0.07** & **94.08 \(\pm\) 0.09** & 94.23 \(\pm\) 0.06 & **93.71 \(\pm\) 0.06** & 93.70 \(\pm\) 0.06 & **93.59 \(\pm\) 0.07** & **93.74** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average test accuracy (%) of the ablation experiment on CIFAR10. The best mean results are in bold. “P” represents the prediction label, “F” represents the feature label and “✓” represents which module to apply.