esa-sceva/satcom-chunk-collection huggingface dataset:
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 2 Materials and Methods
### Investigative Methodology  
To develop a comprehensive approach to DNB destriping required four major steps:  
* We conducted a thorough survey of DNB imagery to discover all conditions where striping occurs. We have collected data from many days taken over the span of the S-NPP mission, and viewed all images taken over a 24-h period, including daytime and nighttime. We specifically chose dates near the phases of the moon--new moon, first quarter, full moon, last quarter and some between these.
* In order to observe features in twilight imagery striping we used the Near Constant Contrast algorithm to produce imagery from DNB radiance that minimizes the contrast due to changes in twilight illumination. This algorithm divides the DNB radiance by a factor that is dependent on the computed direct or indirect solar and lunar irradiance. The solar and lunar illumination levels are estimated in the twilight region using a fit to the twilight radiances during new moon [29]. The NCC images here are not from the official version, but use a similar technique to minimize twilight contrast. These NCC images were plotted on a gray scale for evaluation.
* Along with the radiance plots, we evaluated the associated geolocation to understand the solar or lunar illumination conditions associated with a particular striping artifact. This provides clues as to the root cause of the artifact.
* We collected and analyzed several days of on-board calibrator data, and in some cases calibrated the counts into radiances using nominal linear calibration coefficients. We produced plots of this local sector data as a function of time, ASN and/or detector number. In some cases we simulated the calibration process by computing gain ratios and offsets. The on-board calibrator data reveals uncertainties in the calibration process that can lead to striping.
* We applied the HMM algorithms to SDR data where striping was found and compared the uncorrected images with the corrected images plotted over the same radiance range. We thus evaluated the performance of the destriping algorithm. We also plotted the correction tables used because the detector-to-detector variation seen in the plots are a characterization of the level of striping produced by the DNB.
## 3 Experiments and Results
At the same time, the compactness and spatial distribution of GG clustering, FCM clustering, and GK clustering are similar. However, from the contour shape, it can be seen that the contour of the FCM clustering algorithm is approximately spherical, which indicates that FCM can only reflect the standard distance specification of a hyperspherical data structure; meanwhile, the contour map of the GK clustering algorithm is improved compared with FCM, but it is still approximately spherical; in contrast, the contour map of the GG clustering algorithm has no fixed shape, which indicates that GG can reflect the degree of dispersion of mapping data in any direction or subspace. So, based on the analysis above, we can conclude that the GG clustering algorithm is superior to the FCM and GK clustering algorithm. According to the fault diagnosis method proposed in this paper, if we want to analyze a piece of an original fault diagnosis signal, first, we need to perform empirical mode decomposition, getting the signal entropy value. Then, we perform LDA dimensionality reduction, which will facilitate the subsequent cluster analysis to realize the classification of signal data. In this process, there are three methods of empirical mode decomposition, such as EMD, EEMD, and MEEMD. Similarly, there are three types of entropy values, such as fuzzy entropy, sample entropy, and permutation entropy. Clustering methods usually include FCM clustering, GK clustering, and GG clustering. In the analysis and discussion above, we control the variable method to keep two processes unchanged and compare the third process to determine the optimal approach. However, this analysis is not comprehensive enough, because there are a total of 27 ways to obtain fault diagnosis through the above process. Through the above analysis, it is not fully proved that the optimal method we selected is the best among the 27 types. To confirm the superiority of our proposed fault diagnosis method, we analyze each combination, and the clustering index of each combination is obtained as shown in Figure 10.  
From Figure 10, it can be seen that the clustering indicators PC and CE are both below 1., and the index XB is relatively large except for in Method 12 (PE + EMD + GG) and Method 15 (PE + EEMD + GG). That is to say, as far as the XB indicator was concerned, Method 12 and Method 15 are optimal. For  
Figure 10: Clustering indicator for each type of fault diagnosis method. clustering, we hope that the clustering index PC indicator is close to 1, and the CE and XB indicators are close to 0, because this shows a good clustering effect. Looking at the three indicator curves on the graph, we can clearly see that the best approach to satisfy this condition is Method 15 (PE + EEMD + GG), whose corresponding clustering index values are PC = 1, CE = NaN, XB = NaN. This further certifies our conclusion in the foregoing: through EEMD decomposition, and then the GG clustering of permutation entropy, we can separate the fault signal very well.
## 3 Results and Discussion
### Voltage Stability Margin  
As mentioned earlier, the VSM should be obtained to compare the operation point and nose point. Using the results above, the PV curves are represented in Figure 17.  
As shown in Figure 17, the nose curves are located at 0, 10, 20, 30, 40, 50, and 60 s from the right side. The circle indicates the operating point. The scale of the x axis is per unit; therefore, the present operating point is approximately 6165 MW. As shown in the figure, the VSM is large at first time. However, the margin decreases with time, and almost vanishes at 60 s. Table 2 shows the specific margin at each curve.  
Figure 16: Progress of the index in case 2. (**a**) \(Z_{Th}\) and \(Z_{L}\). (**b**) \(Z\) index.  
Figure 15: Voltage magnitude of virtual bus in the power sending part depends on the contingencies.  
As shown in Table 2, the margin decreased steadily until the simulation ended and had even exhibited a minus value. Theoretically, the margin cannot be smaller than zero, as this implies that the system does not have an operation point and voltage instability occurs. However, this simulation was operated dynamically, and the load is not a constant power. The load parameters should be applied in the PV curves. Figure 18 shows both the PV and load characteristic curves.  
As shown in Figure 18, the first red curve is the operating curve and the other red lines are the maximum load demand curves. Even though the PV curve is reduced to the last one (left end blue curve), an operating point exists. Table 3 shows the margins with each load character curve.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Time (s)** & **Margins (MW)** \\ \hline
0 & 2177 \\
10 & 2080 \\
20 & 2045 \\
30 & 1191 \\
40 & 429 \\
50 & \(-\)44 \\
60 & \(-\)96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific margins with each PV curve.  
Figure 17: PV curves using Thévenin equivalent impedance.  
Figure 18: PV curves and load characteristic curves.  
From Table 2, the maximum load demand factors with each load character curve are arranged. The proportions of the ZIP model parameter are 35.1%, 14.1%, and 50.8% for the constant impedance, current, and power, respectively. The VSM is expressed in the load demand factor because it is not merely an active power such as a constant power; thus, it cannot be matched with the MW units.
## 4 Discussion  
The aim of the present work was to evaluate the effects of running with an additional load on peak physiological values and ventilatory threshold during a maximal incremental test in trained trail runners. In addition, the running power and cost of transport associated  
Figure 2: Comparison of the acute physiological and mechanical responses of experienced trail runners during an incremental running protocol under three different overload conditions (+0%, +5%, and +10% BM). Peak values and values at ventilatory threshold intensities 1 and 2 are presented for the main variables of interest. * \(p\) < 0.05; ** \(p\) < 0.001.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Peak Values** & **Extra load** & **+0 vs. +5\%BM** & **+5 vs. +10\%BM** & **+0 vs. +10\%BM** \\  & _p_**-Value (ES)** & _p_**-Value (ES)** & _p_**-Value (ES)** & _p_**-Value (ES)** \\ \hline
**VT2** & & & & \\ \hline VO\({}_{2}\) (mL\(\cdot\)min\({}^{-1}\cdot\)kg\({}^{-1}\)) & 0.047 * (0.20) & 1.000 (0.06) & 0.445 (0.14) & 0.050 (0.34) \\ rVO\({}_{2}\) (\%) & 0.618 (0.03) & 1.000 (0.00) & 1.000 (0.05) & 1.000 (0.04) \\ Power (W) & 0.021 * (0.27) & 1.000 (0.00) & 0.108 (0.28) & 0.019 * (0.42) \\ nPower (W\(\cdot\)kg\({}^{-1}\)) & 0.012 * (0.32) & 1.000 (0.03) & 0.058 (0.33) & 0.006 * (0.51) \\ Speed (km\(\cdot\)h\({}^{-1}\)) & \textless{}0.001 ** (0.62) & 0.084 (0.30) & 0.001 * (0.59) & \textless{}0.001 ** (0.70) \\ RER & 0.499 (0.05) & 1.000 (0.04) & 0.880 (0.08) & 1.000 (0.02) \\ CoT (J\(\cdot\)kg\({}^{-1}\cdot\)m\({}^{-1}\)) & 0.013 * (0.27) & 1.000 (0.02) & 0.070 (0.32) & 0.042 * (0.36) \\ \hline  & & & & \\ \end{tabular}
* * \(p\) < 0.05; ** \(p\) < 0.001; ES: effect size; +0% BM: body mass running; +5% BM: running with 5% of body mass weighted west; +10% BM: running with a 10% of body mass weighted west; \(\dot{\text{VO}}_{2}\): rate of oxygen consumption; rVO\({}_{2}\): rate of oxygen consumption relative to peak value; nPower: power normalized to kg: RER: respiratory exchange ratio; CoT: cost of transport.  
\end{table}
Table 3: _Cont._with VO\({}_{2}\) peak and ventilatory thresholds 1 and 2 were also compared between three overloaded conditions (i.e., +0, +5, and +10% BM).  
The main findings of this work were as follows: (i) the maximum speed and speed at ventilatory thresholds were significantly affected by the excess in the west's load (\(p\leq 0.003\)), with large effect sizes (0.34-0.62); (ii) the increase in wearable resistance had a moderate effect on VO\({}_{2}\) peak, although the rVO\({}_{2}\) values (in %) at VT1 and VT2 were not affected; (iii) power was not significantly influenced by the overload of the test at peak and VT1 values, but it showed a detrimental effect at VT2 (ES \(\approx\) 0.30).
# Gauge-Invariant Formulation of Time-Dependent Configuration Interaction Singles Method
###### Abstract  
We propose a gauge-invariant formulation of the channel orbital-based time-dependent configuration interaction singles (TDCIS) method [Phys. Rev. A, 74, 043420 (2006)], one of the powerful ab initio methods to investigate electron dynamics in atoms and molecules subject to an external laser field. In the present formulation, we derive the equations of motion (EOMs) in the velocity gauge using gauge-transformed time-dependent, _not_ fixed, orbitals that are equivalent to the conventional EOMs in the length gauge using fixed orbitals. The new velocity-gauge EOMs avoid the use of the length-gauge dipole operator, which diverges at large distance, and allows us to exploit computational advantages of the velocity-gauge treatment over the length-gauge one, e.g., a faster convergence in simulations with intense and long-wavelength lasers, and the feasibility of exterior complex scaling as an absorbing boundary. The reformulated TDCIS method is applied to an exactly solvable model of one-dimensional helium atom in an intense laser field to numerically demonstrate the gauge invariance. We also discuss the consistent method for evaluating the time derivative of an observable, which is relevant, e.g., in simulating high-harmonic generation.  
Keywords:high-harmonic generation; Attosecond science; TDCIS +
Footnote †: journal: Applied Sciences
# Contact Force Surrogate Model and Its Application in Pantograph-Catenary Parameter Optimization
## 3 Sensitivity Analysis of Pantograph-Catenary Parameters  
### Definition of Sensitivity  
Sensitivity analysis is used to study the contribution of input parameter uncertainty to the uncertainty in the outputs of a model [24]. Sensitivity analysis can be divided into local sensitivity analysis and global sensitivity analysis, which focus on how input parameters affect output results both near their baseline values and within their entire range of values, respectively [25]. In this section, a variance-based method [26; 27] is employed for sensitivity analysis of the SDCF with respect to pantograph-catenary parameters, and sensitivity indices are calculated. The higher the sensitivity indices of the input parameter, the greater the influence of the parameter on the SDCF.  
At a specific operating speed, the input parameter \(x_{r}\) (\(r=1,2,\ldots,14\)) represents the aforementioned 14 pantograph-catenary parameters, and the output is the SDCF \(\sigma\). The first-order, second-order, and total sensitivities of \(x_{r}\) are defined as follows:  
\[S_{r}=V_{x_{r}}(E(\sigma|x_{r}))/V(\sigma), \tag{2}\]  
\[S_{rt}=[V_{x_{r}x_{t}}(E(\sigma|x_{r},x_{t}))-V_{x_{r}}(E(\sigma|x_{r}))-V_{x_{ t}}(E(\sigma|x_{t}))]/V(\sigma), \tag{3}\]  
\[S_{r}^{\mathrm{T}}=[V(Y)-V_{\mathbf{x}_{r}}(E(\sigma|\mathbf{x}_{\sim r}))]/V (\sigma)=E_{\mathbf{x}_{\sim r}}(V(\sigma|\mathbf{x}_{\sim r}))/V(\sigma), \tag{4}\]  
where \(S_{r}\) represents the impact of \(x_{r}\) on \(\sigma\), \(S_{rt}\) represents the impact of the interaction effects between \(x_{r}\) and other input parameter \(x_{t}\) on \(\sigma\), and \(S_{r}\)T is the sum of the first-order sensitivity of \(x_{r}\) and all higher-order sensitivities. This is used to comprehensively measure the impact of the parameter itself and its interaction effects with other parameters on the model output. In the equation, \(r\), \(t=1\), \(2\), \(\ldots\), \(14\), and \(t>r\); \(\mathbf{x}_{\sim r}\) denotes all input parameters except \(x_{r}\); \(V(\sigma)\) is the total variance of \(\sigma\) when all input parameters vary arbitrarily within their ranges; \(V_{x_{r}}(E(\sigma|x_{r}))\) is the average decrease in variance of \(\sigma\), i.e., \(V(\sigma)-E_{x_{r}}(V(\sigma|x_{r}))\), when \(x_{r}\) is fixed at all possible values within its range, while \(\mathbf{x}_{\sim r}\) varies arbitrarily; and \(E_{\mathbf{x}_{\sim r}}(V(\sigma|\mathbf{x}_{\sim r}))\) is the average variance of \(\sigma\) when \(\mathbf{x}_{\sim r}\) is fixed at all possible values within their ranges, while \(x_{r}\) varies arbitrarily.
# Some Statistical Aspects of the Truncated Multivariate Skew-\(t\) Distribution
## 5 Conclusions and Future work  
In this paper, we showed the property of closedness under affine transformations of random vectors having a truncated multivariate skew-\(t\) distribution. We also provided conditions for the truncated multivariate skew-\(t\) family to be preserved under marginalization. Additionally, we derived the conditional distribution of subvectors of a random vector having truncated multivariate skew-\(t\) distribution. On the other hand, we described a procedure based on the rejection sampling method to generate random vectors from the truncated multivariate skew-\(t\) distribution. This procedure can be useful for performing simulation studies on statistical models based on this distribution.  
The implementation of maximum likelihood estimation for the truncated multivariate skew-\(t\) distribution may be challenging since an efficient computation of the integral involved in (7) is required. This topic will be addressed in a future paper as well as alternative methods for the generation of random vectors, simulation studies, and applications to real data. Another interesting issue is to test the hypothesis that a dataset has a truncated multivariate skew-\(t\) distribution, which may be achieved by following the ideas of [PERSON] and [PERSON] [30] and [PERSON] and [PERSON] [31]. In addition, it is interesting to study the properties derived in this paper in more general truncated distributions, such as the truncated skew-elliptical distributions, which can be defined through the skew-elliptical distributions ([PERSON] [27] Ch. 6).  
Figure 3: Histograms of simulated random samples from: (**a**) marginal distribution \(Y_{1}\) overlaid with the PDF given in (11); (**b**) conditional distribution \(Y_{2}|Y_{1}=-4.7\) overlaid with the PDF established in Theorem 3.  
**Author Contributions:** Conceptualization, R.A.M.-V., E.Z. and D.K.N.; methodology, R.A.M.-V., E.Z. and D.K.N.; investigation, R.A.M.-V., E.Z. and D.K.N.; writing--original draft preparation, R.A.M.-V., E.Z. and D.K.N.; writing--review and editing, R.A.M.-V., E.Z. and D.K.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Comite para el Desarrollo de la Investigacion--CODI, Universidad de Antioquia (Grant No. 2018-21991).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank the reviewers for their helpful comments and suggestions.  
**Conflicts of Interest:** The authors declare that there is no conflict of interests regarding the publication of this article.
## 1 Introduction
### Related Work  
Scientific research on the levels of exposure to radiofrequency (RF) electromagnetic fields and radiofrequency electromagnetic fields (RF-EMFs) in both outdoor and indoor environments has seen significant advancements in recent years [10; 11; 12]. This emerging field has attracted considerable attention due to the widespread proliferation of wireless communication devices and the growing public concern about potential health risks [13; 14]. Studies have increasingly focused on understanding the spatial distribution and temporal variation of RF-EMF exposure, employing advanced measurement techniques and sophisticated modeling approaches [15; 16; 17]. Researchers not only map exposure levels in diverse environments but also investigate the implications of prolonged exposure on human health, thus contributing to the development of more effective safety standards and guidelines.  
The body of literature on the application of machine learning for electromagnetic field strength estimation is relatively limited. Therefore, we will highlight the most relevant and significant works identified in our review. In [18], the authors investigated the mapping of electromagnetic field (EMF) exposure from cellular base station antennas (BSAs) using artificial neural networks (ANNs). The EMF exposure map (EEM) in urban areas is created using data from EMF sensor networks, drive tests, and public databases that detail the locations and orientations of the BSAs. In addition, in [19], the researchers explored the use of artificial neural networks (ANNs) for the spatial reconstruction of exposure to radiofrequency electromagnetic fields (RF-EMFs) in an outdoor urban environment. They conducted drive test measurement campaigns that covered approximately 65 km in Paris, recording electric field strength (E) across a wide band from 700 to 2700 MHz. Furthermore, ref. [20] aimed to accurately reconstruct electromagnetic field exposure maps in urban areas using limited sensor data. By training a model on environmental factors, the authors effectively predicted field propagation and generated detailed exposure maps in a short area. In addition, in [21], the authors presented an algorithm for estimating electromagnetic field exposure maps using U-net architecture based on convolutional neural networks. The model learns the propagation characteristics of a wireless signal in a realistic indoor environment, taking into account various positions of the Wi-Fi access points. The results demonstrate that the model can accurately produce power maps, effectively measuring the electromagnetic field by learning from indoor propagation phenomena and environment models. In [22], the authors proposed a machine learning-based method to estimate electromagnetic radiation levels in the ground plane near fifth-generation (5G) base stations. Experimental results demonstrate the feasibility and effectiveness of the method, with the machine learning model achieving a mean absolute percentage error of approximately 5.98%.
## 4 Discussion
### Highlighted Impressions and Overall Themes
#### 4.1.2 Real-World Clinical Implementation, including Diagnostics, Education, and Technology
Each of these studies not only reflects AIs growing influence in healthcare but also underscores the multifaceted improvements it brings in terms of technology--from remote care to education and rapid disease diagnosis.
# A Novel Overcurrent Suppression Strategy during Reclosing Process of MMC-HVDC
## 3 Operation Principle of the Reclosing Current Limiting Resistance based Fault Current Limiter
### The Calculation Method of RCLR  
There are two main electric elements in the proposed FCL. The main function of CLI is to limit the rise rate of fault currents and ensure the reliable operation of the DCCB, while that of the RCLR is to reduce the overcurrent in the converter during reclosing. Extensive papers have studied the calculation method of the CLI in MMC-HVDC grid [8; 20; 21; 22], so it will not be discussed in this paper. The main contribution of this paper is to propose a novel calculation method of RCLR, which can limit the arm currents to a specified value. According to the aforementioned analysis, the constraint of the RCLR is denoted by Equation (3). \(I_{\text{max}}\) represents the maximum value of arm current during reclosing, \(I_{th}\) represents the threshold of SMs overcurrent protection, and \(k\) is ratio coefficient which denotes the limiting level of fault currents.  
\[I_{\text{max}}\leq k\times I_{th} \tag{3}\]  
By using the RCLR, only SMs discharging will appear during reclosing, whereas it is difficult to describe the accurate relations between arm currents and RCLR through mathematical equations. In order to solve this problem, a method of estimating the maximum arm current is proposed. The FCL reduces the discharging speed of the SMs significantly during reclosing, so the AC output can be considered still under control within the several milliseconds after fault. If the circulating currents in the arm are neglected, the maximum current in the arm is approximately equal to Equation (4)  
\[I_{arm}=\frac{i_{ac}}{2}+\frac{1}{3}|i_{dc}(\Delta t_{c})| \tag{4}\]  
where \(i_{ac}\) is the amplitude of phase current in the AC side of converter, \(i_{dc}\) represents the current measured at positive pole of the converter shown in Figure 1, and the \(\Delta t_{c}\) is protection time from reclosing time to the trip time of the DCCB. By substituting (4) into (3), the following inequality can be obtained as Equation (5). Define the value satisfying the equation condition of (5) as the critical resistance R\({}_{0}\).  
\[\frac{i_{ac}}{2}+\frac{1}{3}|i_{dc}(\Delta t_{c})|\leq k\times I_{th} \tag{5}\]