esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 2 Materials and Methods
### Incidence-Based Reproduction-Number-Calculation Method  
In contrast to estimating the effective reproduction number \(\mathcal{R}_{t}\) as a time-dependent parameter by using the SIRD-AKS method, it can also be calculated directly from incidence data based on the incidence-based method formulated in [65]. This method requires both incidence data and information about the empirical serial interval distribution, which represents the time period between the onset of illness in an infected case and the onset of illness in a subsequent case.  
Due to the unavailability of reliable data on the serial interval distribution during the early stages of the SARS-CoV-2 pandemic, the German federal center for disease control and prevention (Robert Koch Institute or RKI) chose to use the incidence-based method from [65] and assumed a Dirac \(\delta\)-distribution centered at \(s=4\) days for the serial interval [66]. This assumption led to a straightforward formula for calculating the effective reproduction number \(\mathcal{R}_{t}^{s}\) as the ratio between the number of newly infected individuals at time point \(t\) and the number of newly infected individuals at time point \(t-s\):  
\[\mathcal{R}_{t}^{s}=\frac{n_{l,l}}{n_{l,l-s}} \tag{23}\]  
To account for fluctuations in the reported incidence data, such as weekday dependencies, the method employed a moving average over \(\tau\) days, resulting in a more stable estimate denoted as \(\mathcal{R}_{t}^{s,\tau}\):  
\[\mathcal{R}_{t}^{s,\tau}=\frac{\frac{1}{\tau}\sum_{i=t-\tau+1}^{t}n_{l,i}}{ \frac{1}{\tau}\sum_{i=t-\tau+1}^{t}n_{l,i-s}}=\frac{\bar{n}_{l,i}^{\tau}}{ \bar{n}_{l,i-s}^{\tau}}. \tag{24}\]  
The RKI reported two different \(\mathcal{R}_{t}^{s,\tau}\) values on a daily basis: (i) a sensitive 4-day \(\mathcal{R}_{t}^{4A}\) value and (ii) a more-stable 7-day \(\mathcal{R}_{t}^{4,\tau}\) value, using averaging windows of 4 or 7 days. The advantage of this incidence-based method lies in its simplicity, as it does not require the explicit formulation of a compartmental model or parameter estimation. Additionally, the resulting \(\mathcal{R}_{t}\) values are easy to interpret, even for non-experts, as they correspond to the doubling time of newly infected individuals within \(s=4\) days. Consequentially, the daily updated value of \(\mathcal{R}_{t}\) calculated from this incidence-based method was used by the RKI to inform the public as well as political decision makers about the status of the pandemic situation and therefore had a considerable influence on the installed non-pharmaceutical interventions [66].  
However, a limitation of this method is its strong dependence on the assumption of a \(\delta\)-distributed serial interval at \(s=4\) days, which can impact the interpretation of the calculated \(\mathcal{R}_{t}\) values. In contrast, the SIRD-AKS method presented earlier does not rely on such assumptions, providing a more flexible and robust approach for estimating the effective reproduction number and other parameters in the SIRD model.
# Efficient Gene Transfection by Electroporation--In Vitro and In Silico Study of Pulse Parameters
## 4 Discussion
### Cell Membrane Permeabilization and Cell Survival  
For GET, it is critical to use pulse parameters which achieve cell membrane permeabilization and at the same time maintain high cell survival. It was postulated that membrane permeabilization is prerequisite for successful GET. GET, however, is a complex process comprised of several consecutive steps, with cell membrane permeabilization alone not being a guarantee for successful GET [1,55]. Nevertheless, cell membrane permeabilization was first determined by using PI. Because the electric field at the intersection of the permeabilization and survival curves is not necessarily optimal also for GET, we also determined GET efficiency at slightly larger and slightly lower electric fields. With all tested pulse protocols, we were able to determine the optimal electric field for GET, which usually resulted in around 80% cell membrane permeabilization and higher than 60% cell survival. As expected, using longer pulses and/or higher numbers of applied pulses, cell membrane permeabilization was reached at lower electric fields [48,68]. The optimal electric field for GET was similar for both cell lines (Supplementary Tables S3 and S4). We electroporatedcells in suspension, which means they are of similar round shape, their size does not differ significantly (Table 1), and they are evenly distributed between electrodes, which all leads to a similar electric field needed for having the majority of cells permeabilized [69].
## 3 Results
### Effect of Soil Characteristics on Aroma Profiles of Wines  
To investigate the impact of soil characteristics on the aroma profiles of wines, Pearson's correlation analysis was performed on 21 aroma compounds after AF and 10 aroma compounds after MLF in 2020, which were selected by two-way ANOVA (Figure 5). The result showed that 10 and 8 aroma compounds had a strong correlation with soil physico-chemical properties after AF and MLF, respectively. In AF wines, 1-heptanol had a strong-positive correlation with soil Ca and Mg; 2-heptanol and 1-octanol had a strong-negative correlation with N, P, K, Fe, EC, and a strong-positive correlation with soil pH value. 1-octen-3-ol had a strong to extremely strong positive correlation with soil Ca and Mg, respectively. Linalool had a strong positive correlation with Mg, and m-cresol had a strong negative correlation with soil K.  
Figure 4: Heat maps for statistical difference in the concentration of aroma compounds between plots in wines. AF: Alcohol fermentation; MLF: Malolactic fermentation. The components listed in this figure were shown via two-way ANOVA to be significantly influenced by plot across two vintages (Tables S7 and S8). The concentrations were converted to log2 fold for standardization. The capital letters represented the differences between four plots in 2020 by one-way ANOVA (\(p\leq 0.05\)) and the lowercase letters represented the differences in 2021.  
Figure 5: Pearson’s correlation coefficients among the concentration of aroma compounds of wines and the physico-chemical properties of soil. Data from soils at a depth of 30–60 cm was used for analysis.  
It is worth noting that ethyl 3-methylbutanoate, ethyl hexanoate, isoamyl acetate, and isopentanoic acid in AF wines had a strong to extremely strong negative correlation with N, P, K, Fe, and EC (except for ethyl 3-methylbutanoate and K) and an extremely strong and strong negative correlation with soil pH (Figure 5), and the four compounds had a significant aroma contributionin the wine aroma due to their concentrations far exceeding the thresholds. In addition, the four aroma compounds showed very significant differences in concentration between the two years; the concentration was higher in 2020 than in the 2021 (Table S7).  
In the MLF wines, the correlation of several compounds such as higher alcohol was consistent with the compounds in the AF wines. Ethyl acetate had a strong to extremely strong negative correlation with N, P, K, Fe, and EC in soil and a strong-positive correlation with soil pH. Phenethyl acetate had a strong to extremely strong positive correlation with N, P, K, Fe, and EC in soil and an extremely strong and strong-negative correlation with soil pH.
## 4 Gain Correction  
Consider again the sensor network defined above, but assume that, in the general sensor model (1), the nonlinear term \(v_{t}^{i}\) exists, the coefficients \(\alpha^{i}\) are possibly different from each other and unknown, and the measurement noise term \(\xi_{t}^{i}\) is absent, \(i=1,\ldots,n\). In this case, it is possible to follow either the methodology from [34, 36, 35] based on the joint estimation of offset and gain parameters or the methodology from [44, 45], where the gain correction represents an autonomous process, based on the acquisition and processing of _measurement increments_. We adopt the latter approach, bearing in mind that it allows more flexibility for practice, i.e., the offset and gain parameters can be independently corrected.
## 2 HSM for Base Station PA
### Linear Amplifier Design
#### 2.3.3 Frequency Response of LA
It is determined by the output impedance of the LA (\(\mathrm{R_{out,LA}}\)), the PA equivalent resistance (\(\mathrm{R_{PA}}\)), and a decoupling capacitance (\(\mathrm{C_{PA}}\)), as shown in \[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]0 The \(\mathrm{R_{out,LA}}\) is determined by the parallel combination of the output resistances of transistors \(\mathrm{M_{P17}}\) and \(\mathrm{M_{N21}}\). It can be expressed as \(\mathrm{R_{out,LA}}\approx\mathrm{r_{o,P17}}\parallel\mathrm{r_{o,N21}}\). When \(\mathrm{R_{out,LA}}\gg\mathrm{R_{PA}}\), the output pole can be approximated as shown in (13). In this design, \(\mathrm{R_{PA}}=8\)\(\Omega\) and \(\mathrm{C_{PA}}=200\) pF, placing the second pole around 100 MHz. \[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]1  
The LA system contains two internal poles, along with an additional pole near the unity-gain bandwidth (UGBW), which may affect overall stability. To address this, a zero is introduced in the feedback path, as shown in \[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]2 The frequency-dependent feedback factor \(\mathrm{\beta(s)}\) is expressed in (14) and is simplified in (14a) and (14b), where the resulting zero and pole are given in (15) and (16), respectively. With \(\mathrm{R_{FI}}=14\) k\(\Omega\), \(\mathrm{R_{F2}}=1\) k\(\Omega\), \(\mathrm{C_{F}}=133.25\) fF, the zero and pole are located at \(\mathrm{f_{z}}=80\) MHz and \(\mathrm{f_{p}}=1.1\) GHz, as illustrated in \[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]3  
\[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]4  
\[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]5 \[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]6 (14b) \[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]7 (15) \[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]8  
\[\mathrm{i}_{\mathrm{d,p}}=\mathrm{g}_{\mathrm{mP}}\cdot\mathrm{v}_{\mathrm{test}} \tag{7}\]9 Simplified modeling of the SM output pole. Figure 9 shows the frequency response of the LA for different common-mode levels of \(V_{env}\) (Min: 400 mV, Typical: 1.13 V, and Max: 1.86 V). The plots include both the loop gain and phase response of the LA. Owing to the feedback path, both the loop UGBW and phase margin (PM) are improved. The UGBW increases from 76 MHz to a maximum of 128 MHz, while the PM improves from 22.6\({}^{\circ}\) to 56\({}^{\circ}\). At V\({}_{\rm env}\) = 1.13 V, which corresponds to the Class-AB crossover point, the total transconductance of the output power transistors decreases, resulting in a reduced LA loop gain. The UGBW remains above 100 MHz across the remaining V\({}_{\rm env}\) range from 400 mV to 1.86 V.
## 4 Case Study
### IEEE-118 Bus System  
In this case study, the multi-area non-linear state estimation model is applied to a partitioned \(IEEE\)-118 bus system. Since the number of states in this system is large compared to the \(IEEE\)-14 bus system, it is difficult to visualize the final estimates. However, the same error metric used in the \(IEEE\)-14 bus system is applied to this system. Hence, a 100 Monte Carlo simulation is generated and the average error curves for each area is shown in Figure 8. From the shown results, one can see that the presented model has achieved error below the order of \(10^{-2}\) within less iterations compared to the system in Figure 1. The main reason for such is that the number of shared states within an area is less, which depicts a realistic scenario in real-life. Tables 3 and 4 present the error statistics per area per state for the considered system. In Table 3, local and shared states are included in the calculation while in Table 4 only local states are considered. Table 4 presents statistics regarding area states final estimates.  
Figure 7: Performance index for different noise level on the \(IEEE\)-14 bus system 4 regions.  
The behavior of the performance index \(J(x)\) is recorded over the iteration process and is presented in Figures 9 and 10. Upon convergence, the performance index is less than the threshold value. Hence, no gross errors are detected based on \(\chi^{2}\) test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **State** & **Area 1** & **Area 2** & **Area 3** \\ \hline \multirow{2}{*}{Mean} & Angle (degree) & \(1.01e-2\) & \(0.79e-2\) & \(1.27e-2\) \\  & Magnitude (pu) & \(0.35e-3\) & \(0.29e-3\) & \(0.30e-3\) \\ \multirow{2}{*}{Average std.} & Angle (degree) & \(0.74e-2\) & \(0.58e-2\) & \(1.0e-2\) \\  & Magnitude (pu) & \(0.29e-3\) & \(0.25e-3\) & \(0.21e-3\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistic metric per state per area on \(IEEE\)-118 (local states only).  
Figure 8: Per area error curves on the \(IEEE\)-118 bus system 3 regions.  
The robustness the presented model against the noise level in the measurements is also evaluated considering this test system. Similar to the previous case study, the noise level is varied from 0 to 1.4% of the measurements' standard deviation. The result is presented in Figure 11. The results show that with noise level up to 1.1%, the performance index \(J(x)\) per area is below the threshold value, which reduces the false positive alarms.  
Figure 10: Per area mean of \(J(x)\) on the IEEE-118 bus system 3 regions with standard deviation.  
Figure 9: Per area mean of \(J(x)\) on the IEEE-118 bus system 3 regions.
## 3 Results and Discussion  
### Indirect Tensile Strength (ITS) and Water Sensitivity Test (ITSR)  
Table 6 shows the results of Indirect Tensile Strength and Water Sensitivity Test for all the series. The same as [PERSON] et al. [14] indicated, the values of ITS are similar or even better for mixtures exposed to salt than those submerged in distilled water.  
The results obtained in the A-Series for ITS and ITSR are the regular ones for a hot mix asphalt. There are no noticeable differences between the series submerged in distilled water (A1) and the series submerged in salt water (A2). This result was expected due to the fact that the A-Series was not submitted to temperature changes.  
In the series subjected to five freeze-thaw cycles (B-Series), although the result of ITS for dry specimens (B0) shows no significant difference when compared to the dry reference specimens (A0), that difference exists for the wet series. This variation is lower between specimens submerged in salt water (B2 and A2) and greater between specimens submerged in distilled water (B1 and A1). Theseresults show that the greatest damage to the hot mix asphalt is not the temperature changes but the amount of time that the mixture is submerged in frozen water. The B2 series obtained a value of ITRS, which is close to the limit established by PG-3 (Spanish Regulation) for surface layers (85%).  
For the series subjected to a year outdoors (C-Series), the results of ITS for dry and submerged specimens are lower than the ones of the reference series (A-Series). However, in the case of submerged specimens (C1 and C2), especially for the series submerged in distilled water, the values of the ITS test are lower than in dry specimens (C0). This result is due to the fact that remaining submerged for a year is a more damaging process for the mixture than remaining dry for the same period.
# Quantifying Auditory Presence Using Electroencephalography
## 3 Results
### Experiment 2  
In Experiment 2, data from one participant in Group II were ruled out considering that she could not tell the difference between Programs A and C. Thus, 24 sets of valid data were collected as shown in Figure 3, in which Participant 1-12 belonged to Group I, and Participant 13-24 belonged to Group II.  
As shown in Figure 4, there was a trend that the presence score for Program A was higher than that for C. Though not as overwhelming as in Experiment 1, this trend still applied to most participants (19 of 24). The paired \(t\)-test showed that \(p\) = 0.02 \(<\) 0.05, indicating a significant difference in the presence score at a significance level of 0.05.  
By the results of Experiment 1, the presence score only showed a significant correlation between T/B within all the indices. Therefore, in Experiment 2, we put emphasis on checking the reliability of this correlation. As shown in Table 5, these two sets of values did not meet normal distribution according to the Kolmogorov-Smirnov method (for Program A, \(p\) = 2.6 \(\times 10^{-5}\); for Program C, \(p\) = 1.58 \(\times 10^{-4}\)). Thus, the Wilcoxon signed rank test was applied, and the results (\(p\) = 0.04) showed that the difference in T/B between the two programs was significant.  
\begin{table}
\begin{tabular}{c c c c} \hline
**EEG Index** & \(p\) **for McNemar** & **Pearson Correlation Coefficient** & \(p\) **for Pearson** \\ \hline Theta & 0.016 * & 0.258 & 0.334 \\ Alpha & 0.002 * & 0.174 & 0.519 \\ Beta & 0.000 * & 0.124 & 0.647 \\ T/A & 0.070 & \(-\)0.228 & 0.396 \\ T/AB & 0.375 & \(-\)0.149 & 0.582 \\ T/B & 1.000 & 0.683 & 0.004 * \\ A/B & 0.125 & 0.383 & 0.143 \\ \hline \end{tabular}  
* Significance level of 0.05.  
\end{table}
Table 4: Results of the McNemar test in Experiment 1.  
Figure 4: Presence score for Programs A and C in Experiment 2.  
Then, the McNemar test was performed to evaluate the correlation between presence score and T/B. The results (\(p\) = 0.625) showed no significant difference between presence and T/B, indicating the existence of correlation. Besides, the Pearson correlation coefficient was 0.574 (\(p\) = 0.003), confirming the correlation from a statistical point of view.  
To summarize, we first found significant differences in both presence score and T/B when presenting different programs. Then, the correlation between the measure of presence score and the measure of T/B was evaluated, showing no significant difference between these two measurement methods. The correlation coefficient was significantly different from 0, as well.
# Deformation Behavior of Saturated Soft Clay under Cyclic Loading with Principal Stress Rotation
## 2 Specimen Preparation and Test Apparatus
### Test Apparatus  
The test apparatus used was the GDS dynamic hollow cylindrical apparatus (DHCA) manufactured by GDS Instruments Ltd., UK. It can independently apply axial force \(W\), torque \(M_{\text{T}}\), outer pressure \(p_{o}\), and inner pressure \(p_{i}\), and then control all four stress components in the soil element (axial stress \(\sigma_{z}\), radial stress \(\sigma_{r}\), tangential stress \(\sigma_{\theta}\), and shear stress \(\tau_{z\theta}\)), i.e., the independent control of the magnitudes and directions of all three principal stresses. The apparatus also enables the customization of the cyclic loading waveforms for axial forces and torques. It enables continuous rotation of the principal stress axis and can be used to simulate traffic loading. [PERSON] et al. [30] used a hollow cylinder apparatus to apply cyclic stress and shear stress to specimens, demonstrating that this method can simulate traffic loading better.  
According to [PERSON] et al. [31], the stress and strain components of the soil element in DHCA can be derived from the equations in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basic Properties** & **Value** \\ \hline Specific gravity \(G_{\text{s}}\) (g/cm\({}^{3}\)) & 2.66–2.68 \\ Natural density \(\rho\) (g/cm\({}^{3}\)) & 1.63–1.65 \\ Natural water content \(w\) (\%) & 56.7–59.6 \\ Initial void ratio \(e_{0}\) (-) & 1.64–1.71 \\ Plasticity index \(I_{p}\) (\%) & 42 \\ Plastic limit \(w_{p}\) (\%) & 24 \\ Liquid limit \(w_{L}\) (\%) & 66 \\ Clay content (\%) & 55 \\ Slit content (\%) & 41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Index properties of tested soft clay.
## Appendix A Compliance Matrix of Right-Circular Flexure Hinge  
The elements of the compliance matrix \(\mathcal{C}\) of the right-circular flexure hinge are defined as follows [24]:  
\[\left\{\begin{array}{l}C_{\theta_{z},f_{y}}=\frac{12}{Ew}\int_{0}^{2r}\frac {dx}{(x)^{3}}\\ C_{\theta_{z},m_{z}}=\frac{12}{Ew}\int_{0}^{2r}\frac{dx}{(x)^{3}}\\ C_{\delta_{y},f_{z}}=\frac{1}{Ew}\int_{0}^{2r}\frac{dx}{(x)}\\ C_{\delta_{y},m_{z}}=\frac{12}{Ew}\int_{0}^{2r}\frac{dx}{(x)}\\ C_{\delta_{z},f_{z}}=\frac{12}{Ew^{3}}\int_{0}^{2r}\frac{dx}{(x)}+\frac{1}{ \delta w}\int_{0}^{2r}\frac{dx}{(x)}\\ C_{\delta_{y},m_{y}}=-\frac{12}{Ew^{3}}\int_{0}^{2r}\frac{dx}{t(x)}\\ C_{\theta_{y},f_{z}}=C_{\delta_{z},m_{y}}\\ C_{\theta_{y},m_{y}}=\frac{1}{Ew^{3}}\int_{0}^{2r}\frac{dx}{t(x)}\end{array}\right. \tag{10}\]  
where \(t(x)=t_{0}+2r-2\sqrt{x(2r-x)}\), \(E\) is [PERSON]'s modulus of the hinge's material, \(G\) is the shear modulus of the hinge's material, \(w\) is the width of the flexure hinge, \(r\) is the radius of the flexure hinge, and \(t_{0}\) is the minimum thickness of the flexure hinge. \(C_{\theta_{x},m_{x}}\) is determined by Equation (3.46) in Reference [26].  
Analytical results and FE-results of the compliance matrix of 3-4R and 5-4R compliant parallel pointing mechanisms are as follows:  
\[\begin{array}{l}C_{\theta_{x},f_{y}}^{\text{data}}=\begin{bmatrix}0.74\times 1 0^{-2}&0&1.89
## 2 Materials and Methods
### Data Processing
#### 2.4.2 Data Dimensionality Reduction  
With the aim of improving the performance of the ML-based classification models, two approaches for reducing the dimensionality of the input space were applied. The first one was represented by principal component analysis (PCA), a widely used unsupervised method for mapping the original data into a lower-dimensionality space that captures as much of the data set's variance. The second technique corresponded to a model-based feature selection step based on the partial least squares (PLS) supervised method. The aim of this approach is identifying the subgroup of features (i.e., isotope or elemental determinations) that conduct to the best predictions for a certain classification of the samples. The feature selection method was conducted under the software SOLO 8.9.1, 2021 (Eigenvector Research Inc., 2022 Manson, WA, USA 98831) [31].