esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Approach in the Development of RF MEMS Switches
## 2 Analytical Approach to the Development of RF MEMS Switches of Electrostatic Type
### Coefficient of Stiffness of Elastic Suspension Elements
Table 3 shows the material properties of the sandwich structure. \begin{table}
\begin{tabular}{c c} \hline \hline
**Structural Element** & **Material** \\ \hline Coplanar waveguide & Al \\ membrane & TiN/Al/TiN \\ Fixed down electrode & Al \\ Fixed upper electrode & TiN/Al/TiN \\ Second electrostatic drive & TiN/Al/TiN \\ Anchor area & Al \\ Dielectric layer & Si\({}_{3}\)N\({}_{4}\) \\ Elastic suspension elements & TiN/Al/TiN \\ Substrate & SiO\({}_{2}\)-well in \(Si\)-substrate \\  & SOI technology \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Materials of integrated RF MEMS switch. \begin{table}
\begin{tabular}{c c} \hline \hline
**Structural Element** & **Dimension, \(\upmu\upmu\)** \\ \hline Signal line & 600 \(\times\) 150 \(\times\) 1 \\ Ground line & 600 \(\times\) 100 \(\times\) 1 \\ membrane & 300 \(\times\) 300 \(\times\) 2 \\ Hole in the membrane & 5 \(\times\) 5 \(\times\) 2 \\ Fixed down electrode & 400 \(\times\) 150 \(\times\) 1 \\ Fixed upper electrode & 400 \(\times\) 300 \(\times\) 2 \\ Elastic suspension elements & 50 \(\times\) 2 \(\times\) 2 \\ Dielectric film & 300 \(\times\) 150 \(\times\) 0.15 \\ \hline Air gap & 0.8 \\ Anchor area & 50 \(\times\) 10 \(\times\) 0.8 \\ Substrate & 600 \(\times\) 600 \(\times\) 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The topological dimensions of the structure elements of integrated RF MEMS switch. In the proposed design of the integrated RF MEMS switch serpentine flexures are used (see Figure 7). Equation (16) presented in [72] was used for the calculation the effective stiffness coefficient \(k^{\prime}\) of the elastic suspension:  
\[ \begin{split} k^{\prime}&=\left[\left(\frac{8N^{3}a^{ 3}+2 Nb^{3}}{3 EI_{x}}\right)+\left(\frac{abN(3b+(2N+1)(4N+1))a}{3 GJ}\right)- \right.\\ &\left.-\left(\frac{Na^{2}\left[\frac{2 Na}{EI_{x}}+\frac{(2N+1)b} {GJ}\right]^{2}}{2(\frac{a}{EI_{x}}+\frac{b}{GJ})}\right)-\frac{Nb^{2}}{2} \left(\frac{a}{GJ}+\frac{b}{EI_{x}}\right)\right]^{-1}\end{split}  \tag{16}\]  
where \(t\)--the thickness of meander; \(a\), \(b\)--the geometrical parameters of the meander; \(E\)--the Young's modulus; \(v\)--the Poisson's ratio; \(l_{x}\)--the moment of inertia about the axis \(x\); \(l_{z}\)--the moment of inertia about the axis \(z\); \(l\)--the torsion constant defined by the following expression: \(J=0.413l_{p}\); \(l_{p}\)--the polar moment of inertia: \(l_{p}=l_{x}+l_{z}\); \(N\)--the number of meanders in the elastic mount.
# VENuS-Derived NDVI and REIP at Different View Azimuth Angles
## 3 Results and Discussion
It was found that those areas of nearly zero \(\Delta\)REIP (i.e., the half-circle crop fields with vegetation) also had magnitude values smaller than the 1-STD threshold. Therefore, the change in the view azimuth angle did not affect the REIP in these areas as obtained by VEN\(\mu\)S.  
The results obtained in the present work, based on the VEN\(\mu\)S mission, suggest that the REIP index was less affected by the view azimuth angle than the NDVI, thus making it a better tool for analyzing and comparing the spectral response in vegetative areas measured at different azimuth angles. In addition to the VEN\(\mu\)S mission, described in Section 1, the MultiSpectral Imager (MSI) onboard the Sentinel-2 mission captures the reflected light signal at the four red-edge bands [8; 9] with no charge for users. In recent years, several space agencies and companies have advanced the concept of satellite constellations that consist of mini/micro/nano space systems for earth observation [16]. Such large fleets of satellites strive to enable global monitoring of the earth with many images while minimizing the revisit time. This formation includes satellites  
Figure 3: ANDVI, AREIP, and AAlbedo maps and the value distributions for the three land-use polygons (i.e., Urban, CircleField-N, and CircleField-S) colored in brown, green, and yellow, respectively, and the three dates (i.e., 18 April, 27 June, and 11 September 2018). (**a**) VEN\(\mu\)S RGB images acquired on the three dates, including the three land-use polygons. (**b,d**), and (**f**) AVI maps (Equation (4)) for the same area of interest and three selected dates, showing the spatial variability of positive (red color) and negative (blue color) difference values. (**c,e,g**) Histogram plots showing the distribution of the AVI values for each date and each land-use polygon. The color of each bin matches the color of its polygon. that simultaneously point to the same earth target but with different view azimuth angles. Therefore, the findings of the current study are crucial for processing vegetation properties in the forthcoming era of satellite constellations. Figure 4: Change vector analysis (CVA) of NDVI-albedo (**a**,**b**), and (**c**) and REIP-albedo (**d**,**e**), and (**f**) for the three dates (i.e., 18 April, 27 June, and 11 September 2018). (**a**,**d**) Magnitude maps calculated by Equation (6). (**b**,**e**) Direction maps calculated by Equation (6); the colors are based on Figure 2. Pixels colored in black have magnitude values smaller than the one standard deviation (1-STD) threshold, determined as no-change pixels. (**c**,**f**) Polar plots illustrating the changed magnitude (i.e., distance from the center) and changed direction (i.e., degree angle between 0\({}^{\circ}\) and 360\({}^{\circ}\)) value distribution for the three dates and the three polygons (i.e., Urban, CircleField-N, CircleField-S), colored in brown, green, and yellow, respectively. The color of each point matches the color of its polygon; black circles mark the areas below 1-STD-presented as black (**a**,**b**,**d**,**e**).
## 2 Materials and Methods
### Statistical Analysis
#### 2.4.2 Logistic Function  
After distinguishing the tidal flats from ocean and land in the SAR data with a certain threshold of GVF, the logistic model was applied to characterize the temporal evolution of the SAR backscattering coefficients and estimate the intertidal topography in this study. The temporal variation of the radar backscattering coefficients can be formulated by the logistic function [29] as below.  
\[\gamma^{0}(x,\,y)=\frac{k1}{1+e^{-a(h_{i}-h_{i})}}+k2,\,\,\,\,\,\,\,\,i=1,\, \ldots,\,\,M \tag{2}\]  
where \(\gamma^{0}(x,y)\) is the corresponding gamma naught at the tidal flat pixel (\(x,\,y\)), \(h_{t}\) is the tidal flat topography, \(h_{i}\) is the tide height and \(M\) is the number of SAR images used in the time series. The parameter a is related to the steepness of the logistic function, which ranged from \(-7\) to \(-10\) in the unit of m\({}^{-1}\) for our study area. The two parameters \(k1\) and \(k2\) are related to the average radar backscattering coefficients for the tidal flat exposure period at low tide and inundation period at high tide, respectively. In this study, our JNB optimization algorithm estimated both parameters \(k1\) and \(k2\) on a pixel-by-pixel basis. The JNB algorithm divides the data into two groups: high \(\gamma^{0}\) values for the tidal flat exposure period and low \(\gamma^{0}\) values for the inundation period with respect to the tide heights. The parameter \(k1\) was calculated as the difference between the average \(\gamma^{0}\) values of the tidal flat exposure and inundation periods. The parameter \(k2\) was the average \(\gamma^{0}\) for the inundation period. After the tidal flat topography \(h_{t}\) was initialized from our JNB algorithm, \(h_{t}\) was optimized in an iterative manner for the best fitting logistic model to derive our final Sentinel-1 DEM.
# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 4 Results
### Interaction Space
It should be noted that Evizeon [39] and Orko [15] also automatically zoom in/out to the part of the visualisation that is related to users' query, though users cannot ask it to zoom in directly using NL. The abstract/elaborate method is used in four V-NLIs [40; 63; 84; 88] to drill down to show more details. For example, Ref. [84] transforms data to show average hours of sleep over various months, and users can choose the visual mapping to see each month separately in more detail using NL. Similarly, Ref. [40] uses NL to do drill downs, while, on the other hand, TransVis [63] uses direct manipulation. InChorus [88] uses both modalities. Finally, the connect method is only used by two V-NLIs [15; 80]. Both of these V-NLIs have network visualisation and use the connect method to highlight the relationships between links using Advanced interactions (i.e., using Focus+Context visualisations). While [66] performs this at the data transformation stage, Ref. [15] does this at the visual mapping stage. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**V-NLI** & **Select** & **Explore** & **Reconfigure** & **Encode** & **Abstract/** **Elaborate** & **Filter** & **Connect** \\ \hline ACUI [80] & N & & & & N & N \\ \hline Ava [81] & N & & & & & \\ \hline Boomerang [82] & N & & & N & & \\ \hline Chat2 Vis [83] & & & N & & N & \\ \hline ConVisQA [69] & & & & & W \& N \\ \hline Data@Hand [84] & N & & & N & N \\ \hline DataBreeze [85] & W \& N & W \& N & W \& N & W \& N \\ \hline Evizeon [39] & N & W & N & & & W \& N \\ \hline FlowNL [70] & W \& N & & & & \\ \hline GameBot [86] & & & & & W & \\ \hline GeCoAgent [87] & & & & & N & \\ \hline InChorus [88] & W & W \& N & W \& N & W \& N & W \& N \\ \hline Iris [89] & & & & N & & \\ \hline MIVA [90] & & & & & W \& N & \\ \hline ONYX [91] & & & & W \& N & W \& N & \\ \hline Orko [15] & W \& N & W & W \& N & & W \& N & N \\ \hline Snowy [40] & & & & W \& N & N & W \& N & \\ \hline Talk2 Data [64] & N & & & N & & & \\ \hline TransVis [63] & N & W & N & W & N & \\ \hline Valetto [92] & & & W & W \& N & N & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Use of seven interaction methods in V-NLIs. N: Natural Language and W: WIMP—Windows Icons Menus Pointer.
## 2 Literature Review
[29] built a hybrid prediction model, modeled separately according to different features, and introduced an attention module to adaptively assign feature weights. The traffic system is extremely time varying and shows a certain periodicity in the time sequence; there is a dependency between the current traffic state change and the historical traffic state [30], and the network topology of traffic makes the traffic flow of adjacent sections affect the target section [31]. [PERSON] et al. [32] proposed a deep spatio-temporal neural network based on the spatio-temporal characteristics of traffic flow, which consists of predicting the spontaneous variation part of traffic flow, the periodic part, and the external features part, and the model predicts better with other models established wavelet. The external environment affects the traffic system all the time [33], making it highly uncertain, so the integration of external features is necessary to achieve accurate traffic flow prediction. [PERSON] et al. [34] explored the influence of three external features of time, weather, and day of the week on traffic flow, and input the features into the gradient-boosted decision tree (GBDT) model for prediction, where the experimental results verified the feasibility of the method. [PERSON] et al. [35] established an LSTM-SVR model based on the influence of holidays on highway traffic flow, and they used LSTM to extract features from holiday data and input them into the SVR model for prediction. [PERSON] et al. [36] used CNN to extract spatial features, LSTM, and the gated recurrent unit to extract temporal features; built models for weekday and holiday prediction; and verified the prediction accuracy of the models, but such models containing temporal memory units are difficult to tap potential features in discontinuous data. For the decomposition-integration model, [PERSON] et al. [37] compared the effects of different decomposition methods, such as empirical mode decomposition (EMD), VMD, and wavelet decomposition (WD), on the prediction effect; obtained the components containing different features by clustering; and finally predicted each component by a two-way long and short-term memory neural network. However, the influence of external factors on the traffic flow was not considered, and there were a large number of unsteady features in the high-frequency components obtained from the decomposition, as well as the limited ability of a single model to capture potential features in the traffic flow [38], [PERSON] et al. [39] decomposed the traffic flow data using STL, extracted spatial and temporal features using CNN and LSTM, respectively, introduced the attention mechanism to assign feature weights, and finally weighted and summed the predicted values of each component.
## 3 Results and Discussion  
### Grid Test Results  
Prior to computation, we explored the grid sensitivity of the results. The minimum grid size was 2.25 cm in the 1 \(\times\) 1 \(\times\) 1-m\({}^{3}\) region (Region I) near the leakage source, considering the small pipe diameter. For the 20 \(\times\) 20 \(\times\) 20-m\({}^{3}\) space of the intermediate Region II (outside of Region I), around the leakage source, the grid size was gradually enlarged from 2.25 cm to 0.2, 0.3, 0.5, and 1.0 m. In the outermost Region III, the grid size gradually increased to a maximum of 10 m. Table 3 lists the grid sizes and numbers for Region II, as well as the computation times necessary for each grid system. These times were 132 h for the smallest grid (0.2 m) and 48 h for the largest grid (1.0 m).  
Figure 5 shows the computed and experimental variations in hydrogen concentration (the mole fractions) at radii of 30 and 50 m for a leaked mass flow rate (\(\dot{\rm m}\)) of 0.73 kg/s. Although the experimental concentrations considerably changed over time, the computed concentrations were nearly constant except initially. The computational trend toward a constant hydrogen concentration over time reflects the RANS approach toward turbulent flow simulation. For grid sizes of 0.2 and 0.3 m for Region II, the computational results were similar, and the hydrogen concentrations were within the experimental ranges. Considering these data and the computation times shown in Table 3, a grid of 0.3 m for Region II was appropriate; we used this grid in subsequent analyses. Although not shown here, it was confirmed that the computational results were not significantly affected by the grid size in the outermost Region III.
## 4 Discussion
### BK510 Lys and AgNP-BK510 Lys Optimal Parameters  
To enhance the lytic activity of BK510 Lys and AgNP-BK510 Lys, an experimental design for response surface methodology Box-Behnken was carried out. Data showed that to obtain the maximum experimental lytic activity, the optimal conditions of endolysin and conjugate were pH 5.00, temperature of 20.0 \({}^{\circ}\)C and endolysin concentration of 0.14 mg/mL and, pH 5.00, temperature of 40.0 \({}^{\circ}\)C and conjugate concentration of 0.01 mg/mL, respectively.  
[PERSON] et al. [31; 32] reported that, under similar conditions to those studied in the present work, the endolysin LysK from bacteriophage K of _S. aureus_ presented a similar lytic activity (Table 8).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **BK510 Lys** & **LYSK [30]** & **LYSH5 [31]** & **Lys-phiSA012 [28]** \\ \hline pH & 5.00 & 5 & 7.00 & 7.00 \\ Temperature (\({}^{\circ}\)C) & 20.0 & 30 & 37.0 & 37.0 \\ Lytic Activity (\(\Delta\)DO/(s)(mg)) & \(2.74\times 10^{-3}\) & \(1.5\times 10^{-3}\) & 0.19 & 0.11 \\ Endolysin concentration (mg/mL) & 0.14 & 0.5 & 0.3 & 0.109 \\ \hline \end{tabular}
\end{table}
Table 8: Results comparison of similar studies with _S. aureus_ bacteriophages endolysins.  
There is not much information about the kinetic parameters of conjugate nanoparticles-endolysins (Table 9). Nevertheless, all the results hold significant implications for the development of biocontrol agents aimed at discerning and selectively eliminating super-resistant bacteria. More studies are necessary to enhance the applications in veterinary, specifically for Gram-negative bacterial diseases.  
To determine if there is any significant effect of pH, temperature, endolysin or conjugate concentration, or some combination of these, an analysis of variance (ANOVA) for the lytic activity was performed. The ANOVA table (Tables 6 and 7, for BK510 Lys and AgNP-BK510 Lys, respectively) partitions the variability in LA into separate pieces for each of the effects. Then, it tests the statistical significance of each effect by comparing the mean square against an estimate of the experimental error. In both cases, when one of the effects has P-values less than 0.05, indicating that they are significantly different from zero at the 95.0% confidence level: BK510 Lys and AgNP-BK510 Lys concentration, respectively.  
To determine the magnitude and importance of the effects, a Standardized Pareto Chart was made for BK510 Lys (Figure 6a) and AgNP-BK510 Lys (Figure 6b). This shows that the main effects were:  
* BK510 Lys: Concentration (C).
* AgNP-BK510 Lys: Concentration (C).  
The ANOVA analysis, the Standardized Pareto Chart, and the response surface show the great importance and effect of endolysin concentration in the LA of BK510 Lys and the concentration of conjugate in the LA of this. Therefore, these factors must be considered to continue deeper studies of the mechanism of action and applications of endolysin and AgNP-BK510 Lys conjugates.
## 1 Introduction  
There is a fierce competition among the online applications and services in the market which has accordingly put larger pressure on developers to constantly attract customers' attention [1]. Moreover, the expectation levels of software users are constantly rising, which requires extensive user involvement during RE [2]. Therefore, leveraging the principle of crowdsourcing could present a practical means for wide-scale user involvement [3]. Crowdsourcing is the act of outsourcing a task to a crowd to benefit from the distributed talent and experience [4].  
Due to the unparalleled growth in mobile and social apps, a great rise in crowd-based settings has been realized [1], and in order for a software application to attain worldwide acceptance and recognition, it should fulfill the needs of a wide range of users [5].  
A pivotal stage prior to crowdsourcing is selecting a suitable subset of the crowd to ensure quality contributions [6]. The need for this practice could increase when crowdsourcing RE tasks which require a crowd with certain domain knowledge [7]. Hence, finding a crowd which possess the required domain knowledge is a crucial step when conducting crowdsourcing for any RE task. The best crowd that would most likely contribute valuable mobile apps requirements and features are users and developers of a mobile app for a specific domain similar to the domain of the mobile app having its requirements crowdsourced. Moreover, social network sites such as Twitter are considered as a suitable platform to conduct the crowd selection since a large crowd with diverse backgrounds, roles and knowledge can be found.  
This article is an extension of our in-progress work [8] where the crowd selection approach was first proposed. In addition, it extends our previous article [9] which described building classification models for identifying mobile app users and developers on the social network Twitter which constitutes the role identifier component in the approach. Therefore, this article presents the remaining building blocks of the proposed crowd selection approach which is the expertise retrieval component. Additionally, it empirically evaluates the effectiveness of the expertise retrieval component and the complete proposed approach on a real crowd.  
The article consists of seven main sections, where it presents a background about crowd selection for crowdsourcing and crowdsourcing requirements engineering (Section 2), related work (Section 3), the design of the proposed crowd selection approach (Section 4), expertise retrieval (Section 5), an evaluation of the proposed approach (Section 6) and finally concludes (Section 7).
## 3 Dataset and Methods
### AD Definition as a Data Preprocessing Method (Substudy 1)
To better integrate the effect of data splitting on the performance of the models, five different training/test splits were considered. These five splits were defined so that each molecule belongs to the test set exactly once among the different splits. Note that outlier elimination was performed on preprocessed data and before data training/test splitting in order to keep the training/test ratio intact. Finally, the Lasso models were trained and validated for each of these splits, without performing dimensionality reduction. In the results section, the presented model performances are averaged over these splits, and the error bars displayed on the figures are the corresponding standard deviations. The considered performance metric is the mean absolute error (_MAE_). In particular, four configurations are implemented and compared, as summarized in Table 1. In the first configuration, the effect of X-outliers is investigated. For each X-outlier detection method, different amounts of X-outliers are eliminated from the preprocessed data, namely 0%, 10%, 30% and 50% (based on anomaly score). The subsequent preprocessed data without X-outliers, for the different elimination thresholds and X-outliers detection methods, are then submitted to data splitting, scaling and model training/test, as described in the previous paragraph. Concerning the second configuration, the effect of Model-outliers is studied via a procedure similar to that adopted for the X-outliers. The only difference lies within the tested standardized residual thresholds, namely 1, 2, 3, 5 and 10, in absolute values. This means that molecules with absolute standardized residual value above 1, 2, 3, 5 and 10 are removed from the preprocessed data. In the third configuration, the combined effect of X-outliers and Model-outliers is analyzed. In particular, the three following layouts are compared, for a given X-outlier detection method (_RF confidence_) and for given thresholds in the detection of X-outliers and Model-outliers:  
* Layout 1: simultaneous elimination of X-outliers and Model-outliers. * Layout 2: elimination of Model-outliers followed by elimination of X-outliers. * Layout 3: elimination of X-outliers followed by elimination of Model-outliers. Finally, for y-outliers, the thresholds are those as defined earlier using the IQR method, and the effect of their elimination on the model performance is analyzed.