esa-sceva/satcom-chunk-collection huggingface dataset:
The HEA interaction network was created based on six descriptors, including \(\delta\) as atomic size difference, \(\Delta H_{mix}(\mathrm{kJ}/\mathrm{mol})\) as the mixing enthalpy, \(S_{c}\) as the configurational entropy of mixing for the ideal solid solution, \(\varphi\) as a single dimensionless thermodynamic parameter for designing HEAs, \(\varepsilon_{RMS}\) as the square root mean residual strain, and VEC as the valence electron concentration of the alloys. The descriptors' similarity to a pair of compounds was calculated using content and structure similarity parameters. Then, an interaction network between each pair of HEA compositions was constructed, which was mapped to a social network graph. The strength of the relationship between the two compounds was determined by the similarity value called weight, where a stronger connection had greater weight. As ML methods are effective in social network analysis (SNA), the proposed method can also be effective for phase prediction in HEAs. To the best of our knowledge, other studies have not used any HEA phase prediction based on an HEA interaction network. In this study, a new phase prediction method is presented for use when the phase of a compound is unknown. The proposed approach first finds the position and connections of the new compound in the HEA network. Then, it predicts the phase based on the nearest neighbor in the network. The proposed approach is a graph-based \(k\)-nearest neighbor (KNN) algorithm for phase prediction by supervised ML. The proposed method can predict compound phases, which are used in new alloys and alloy structure design. Therefore, accurate phase prediction plays an important role in the selection of a combination of elements to form HEAs with desirable properties. The rest of this study is organized as follows: The definition of notations, the HEA interaction creation, and the process of the proposed approach using the graph-based KNN algorithm for phase prediction are introduced in Section 2. In Section 3, the experimental results are explained with an example of an unknown phase compound. Additionally, the proposed approach is compared with other ML methods. Finally, Section 4 concludes and outlines the future work to improve the proposed approach.
## 2 Modeling of Wind Curtailment and EV Charging  
### Mitigation of the Wind Power Forecast Uncertainty  
The available wind power generation (\(p_{AW}\)) of a wind farm is assumed to be subjected to a normal distribution, having a mean of the forecasted wind generation (\(p_{AW}^{f}\)) and a standard deviation derived from the forecast error (\(\varepsilon_{W}\)) as follows:  
\[p_{AW}=p_{AW}^{f}+\varepsilon_{W} \tag{1}\]When wind power curtailment is executed with certain absolute power limit (\(l_{W}\)) [15], then the wind power (\(p_{W}\)) can be mathematically expressed as the following min function:  
\[p_{W}=\min\left(p_{AW}^{f}+\varepsilon_{W},\ l_{W}\right) \tag{2}\]  
As wind generation is restricted to be below the absolute power limit and as the wind generation range is reduced, the uncertainties associated with the forecasting error will be reduced as shown in Figure 1. This advantage of uncertainty reduction leads to operational benefits that can reduce the required spinning reserve against the forecasting error.  
As the wind energy in excess of the absolute power limit is spilled, the expected (forecasted) value of wind power production is decreased to less than that of the available wind power. The expected value of the delivered wind generation is derived from the definition of the expected value [16] for a non-negative random variable as follows:  
\[p_{W}^{f}=\int_{0}^{P_{W}^{\max}}\left(1-F_{W}(p_{W})\right)dp_{W}=\int_{0}^{ l_{W}}\left(1-F_{AW}(p_{AW})\right)dp_{AW} \tag{3}\]  
where \(F_{W}\) and \(F_{AW}\) represent the cumulative distribution functions (CDF) of the delivered wind generation and the available wind generation, respectively. Note that the CDF of the delivered wind generation between \(l_{W}\) and \(P_{W}^{\max}\) is equal to one, and the CDF of the delivered wind generation is equal to that of the available wind generation between \(l_{W}\) and zero.
## 4 Evaluation Study
### Data Collection and Analysis  
#### 4.3.1 Quantitative Data Collection and Analysis  
This study included quantitative and qualitative data. The quantitative data were collected through a five-point Likert questionnaire after the IVR game experience. The questionnaire comprised three parts. Part 1 of the questionnaire featured basic information about the participants, including their personality, collaborative competence, and VR readiness (see Appendix A). Collaborative competence measured participants' communication skills, teamwork ability, leadership, and collaboration experiences; these items were modified from the scales designed by [PERSON] et al. [57] and [PERSON] [58].  
Parts 2 and 3 of the questionnaire assessed the IVR collaboration experience (collective efficacy and social experience) and the IVR presence (spatial presence and social presence) (see Appendix A). The items related to IVR collaboration experience were based on the scale for digital collaborative games proposed by [PERSON] [38], and the items related to IVR presence were based on the scale for collaborative augmented-reality games proposed by [PERSON] et al. [59]. As shown in Table 3, the results of [PERSON]'s alpha coefficients are considered acceptable when the number of items is less than 10, according to [PERSON] and [PERSON] [60], and the results of composite reliability (CR) and average variance extracted (AVE) meet the criteria in general, according to [PERSON] and [PERSON] [61].  
A descriptive statistical analysis and regression analysis were applied to the quantitative data. The former was used to understand the participants' experience based on IVR experience and basic information. The latter was used to explore the factors that influenced students' collaboration experience in IVR. Consequently, collective efficacy and social experiences were considered outcome variables, and all other variables (e.g., personality, collective competency, social presence, and spatial presence) were entered into regressions as predictors.  
Figure 5: Real-time mapping between virtual and physical world: (**a**) a collector observing their teammates during the game; (**b**) five students facing the enemy ship.
# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 5 Practical Simulations, Analysis, and Discussion
### Cloud Computing Resource Scheduling Simulations  
In this section we show the empirical results that our proposed WOA-AEFS algorithm obtained when tackling the resource scheduling problem in cloud computing environments. In order to more accurately measure the performance of our proposed approach, we employed two types of simulations: first with a real data set, and second with an artificial data set.  
In simulations with a real data set, we used the single-objective model that was described in Section 3.1, while for the purpose of simulations with an artificial data set, we utilized the multi-objective model that was in detail presented in Section 3.2.  
Both simulations were conducted in a CloudSim 3.0.3 environment, which is a self-contained framework that provides an extensible toolkit for cloud environments simulations [98]. For more information about CloudSim, please refer to [98].  
Moreover, we have executed both simulations on the computing platform with Intel CoreTM i7-4770K processor at 4 GHz with 32 GB of RAM memory, Windows 10 Professional x64 operating system and Java Development Kit 11 (JDK 11) and IntelliJ integrated development environment (IDE). Since the basic CloudSim 3.0.3 is not CUDA(tm) technology enabled, we have executed algorithms on the central processing unit (CPU).  
A potential solution is encoded in both simulations as a set of tasks, where for each task, an available VM (resource) is mapped. Thus, the length of a solution is the total number of submitted tasks to the cloud system.  
In the following two subsections we show our empirical results, along with comparative analysis and discussion, for both conducted simulations.
## 6 Earth Rotation Parameters Evaluation  
Detailed investigation into the ERP, as a fundamental aspect of the reference frame definition, is essential. We evaluated ERP from BDS-3 with B1C/B2a combination observations by using the formal error index and comparing with the external IERS 20C04 products. For purpose of comparison, we also calculated and evaluated the ERP generated by the B1I/B3I combination observations of BDS-3 satellites and by GPS satellites.
# Dry Test Methods for Micropumps
## Materials and Methods
### Methods
#### 2.2.2 Valve Leakage  
The micropump status during a compression test is schematically represented in Figure 5. The inlet and outlet valves are in the closed position, and the residual fluid pathways can be modeled by fluidic resistance \(R_{in}\) and \(R_{out}\).  
The equivalent fluidic resistance \(R_{eq}\) of these two resistances in parallel is:  
\[R_{eq}=\frac{R_{in}R_{out}}{R_{in}+R_{out}} \tag{6}\]  
The \(R_{eq}\) value is therefore driven by the lowest resistance between \(R_{in}\) and \(R_{out}\). The airflow is assumed to be laminar, and the expansion is isothermal. The elasticity of the pumping chamber when the pumping membrane is pushed, a full compression, against a mechanical stop is here neglected. In these conditions, the differentiation of the ideal gas equation leads to:  
\[\frac{dP}{P}=\frac{dn}{n}=-\frac{Q}{V_{D}}dt \tag{7}\]  
where \(n\) is the number of air moles in the pumping cavity, and \(Q=Q_{in}+Q_{out}\) is the net flow from the cavity to the outside.  
After the initial compression at \(t=t_{0}\), the pressure in the pumping chamber satisfies the following differential equation:  
\[dt=-R_{eq}V_{D}\frac{dP}{P(P-P_{atm})}=\frac{R_{eq}V_{D}}{P_{atm}}\bigg{(} \frac{1}{P}-\frac{1}{P-P_{atm}}\bigg{)}dP \tag{8}\]  
Figure 5: Schematic representation of the micropump during leak tests. The pressurized air in the pumping chamber flows through the residual fluid pathways of the inlet and outlet valves in closed positions. The direction of the airflow is indicated by blue arrows.  
Solving Equation (8) with the initial condition \(P=P_{atm}\) at \(t=t_{0}\) leads to:  
\[P(t)=\frac{P_{atm}}{1-\left(1-\frac{P_{atm}}{P_{max}}\right)e^{-\frac{P_{atm}(t-t_ {0})}{R_{eq}V_{D}}}} \tag{9}\]  
The value of \(R_{eq}\) can be evaluated at each time \(t\):  
\[R_{eq}(t)=\frac{P_{atm}(t-t_{0})}{V_{D}\;ln\left[\frac{P(P_{max}-P_{atm})}{P_{ max}(P-P_{atm})}\right]} \tag{10}\]  
The time \(\tau_{1/2}\) necessary to lower the maximum pressure by a factor of two is:  
\[\tau_{1/2}=\frac{R_{eq}V_{D}}{P_{atm}}\;\ln\left[1+\frac{P_{atm}}{P_{max}}\right] \tag{11}\]  
\(R_{eq}\) and \(\tau_{1/2}\) provide useful quantitative characteristics of the pumping cavity's tightness. A hypothesis about the relative contribution to each valve will be made to estimate the maximum free flow of the micropump.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 2 Problem Description
### Mathematical Formulation
#### 2.3.2 Optimization Objective  
The specific observation time selection of the satellite is simplified, and the intermediate of the visible time window of the mission is temporarily set as the optimal observation time [19]. This is attributed to the characteristics of agile-satellite observation, where the optimal imaging quality and observation angle can be achieved through observation at the intermediate of the visible time window of the mission.  
If \(t_{i}^{s}\) is the dynamic selected, this introduces a joint problem involving mission planning and optimal control, which will be explored in future research. Then, the agile-satellite mission planning problem will become a constraint satisfaction integer programming problem. To maximize the total observation profit, more tasks will be scheduled. Hence, the objective function \(f\) is designed to maximize the total profit by the sum of scheduled tasks. In this paper, we only consider the equal profit task, which means \(w_{i}=1\).  
\[f=max(\sum_{i=1}^{n}\frac{x_{i}}{n}w_{i}) \tag{6}\]  
**Remark 2**.: _Agile-satellite mission planning can be described as a multi-objective optimization problem which needs to determine the observation time, observation order, and optimizing satellite control at the same time [36]. In this study, we focus on addressing the decision-making problem of the observation order for large-scale satellite missions._
# An Improved Robust Fractal Image Compression Based on M-Estimator
## 3 Robust M-Estimator
### Huber's M-Estimator  
[PERSON] first introduced a robust M-estimator [58], named Huber's M-estimator. He chose the \(\rho\)-function to be formed by Equation (14)  
\[\rho_{H}(e)=\left\{\begin{array}{ll}e^{2}/2,\text{ for }|e|\leq k\\ k|e|-e^{2}/2,\text{ for }|e|>k\end{array}\right. \tag{14}\]  
Here \(k\) is the tuning constant. In particular, \(k=1.345\sigma\) for the [PERSON] case [34], where \(\sigma\) is the standard deviation of the errors. A common approach [58] is to take  
\[\hat{\sigma}=\text{median}\left\{abs(e-median(e))\right\}/0.6745 \tag{15}\]  
[PERSON]'s M-estimator is a mixed \(l_{2}\) and \(l_{1}\) minimization problem, but LS is only a \(l_{2}\) minimization problem (since in LS case, \(\rho(e)\) is always equal to \(\varepsilon^{2}/2\)). When there are outliers in the measurements, generally the corresponding residuals are relatively large. Since \(\rho_{H}(e_{i})\) of [PERSON]'s M-estimator with \(n\) beyond 7 is smaller than that of LS, [PERSON]'s M-estimator is less sensitive to relatively large residuals. Hence it can suppress the influences of outliers.  
From Equation (14), we obtain  
\[\psi(e)=\rho^{\prime}_{H}(e)=\left\{\begin{array}{ll}e,\text{ for }|e|\leq k\\ ke/|e|,\text{ for }|e|>k\end{array}\right. \tag{16}\]  
Hence, in the [PERSON] case, the weight function, which we denote as \(w_{H}(e)\), is defined by Equation (17)  
\[w_{H}(e)=\psi(e)/e=\left\{\begin{array}{ll}1,\text{ for }|e|\leq k\\ e/k,\text{ for }|e|>k\end{array}\right. \tag{17}\]Considering the general regression model (1), Let \(\varepsilon=e/\sigma\) and \(w_{i}=w(\varepsilon_{i})=w(e/\sigma)\), where \(\sigma\) is defined by Equation (15). The main steps of IRLS for the estimation of the parameter using the Huber M-estimator are described as Algorithm 1.  
```
Input Initial value of estimates \(\hat{\beta}^{(0)}\), random variable \(\hat{\varepsilon}_{i}{}^{(0)}\), and weight \(w_{i}{}^{(0)}\) Output The final value of \(\hat{\varepsilon}_{i}^{(t-1)}\), \(w_{i}^{(t-1)}\) and \(\hat{\beta}^{(t)}\)
1: Select initial estimates : \(\hat{\beta}^{(0)}\), \(\hat{\varepsilon}_{i}{}^{(0)}\) and \(w_{i}{}^{(0)}\)
2:while\(\hat{\beta}\) didn't converge to a convergence value do
3: Compute residuals by \(\hat{\varepsilon}_{i}^{(t-1)}=y_{i}-x_{i}^{T}\hat{\beta}^{(t-1)}\) and \(w_{i}^{(t-1)}\) weights by Equation (17)
4: Compute the new weighted \(-\) least \(-\) squares estimates \(\hat{\beta}^{(t)}\) by Equation (13)
5: Compute the objective value \(E\) based on the objective function by Equation (10)
6:endwhile
```  
**Algorithm 1** IRLS using Huber M-estimator
## 2 State-of-the-Art
### Reducing Computation Time for Generating CPRs  
[PERSON] et al. (2020) describe the challenge of computation time for CPRs in their work. We draw the following conclusions for our presented work: while sequencing with CPRs is comparatively fast, generating CPRs with hyper-heuristics (e.g., with evolutionary algorithm) is often very computationally intensive. If stochastic values are to be used as recommended during the generation of the CPRs (so-called training phase), the computational effort increases further. A first possibility to reduce computational effort is the representation of CPRs (Figure 2). The grammar-based tree representation is a composition of individual components to a configurable function based on mathematical operators. This representation has in theory no fixed length and is suitable for larger computational budgets ([PERSON] et al., 2019). The advantage of this representation is a large search space which fits the large computational budget ([PERSON] et al., 2019). Another variant is parameter-based representation, which is a configurable function with a defined format. The defined format means a fixed length and thus a calculable computational effort but, at the same time, a smaller search space.  
A further method to reduce computational effort is the generation of an optimized initial population, which is currently of minor interest ([PERSON] et al., 2019). Most researchers generate the initial population randomly (e.g., [PERSON] (2018); [PERSON] et al. (2019); [PERSON] et al. (2020)). Compared to a randomly generated population, the algorithm converges later than with an optimized diverse population. Thus, a decrease of simulation effort will be evolved with an optimized initial population. An approach for improvement consists for example in the use of existing PRs to form the initial population (e.g., [PERSON] et al. (2020); [PERSON] et al. (2020)). However, research potential for the generation of good initial solutions is an ongoing issue which we address in our paper.
## 3 Results and Discussion
### Effect of Compound on Biofilm Persistence and Biofilm Viability  
Biofilm persistence and viability were assessed with the hit compounds in combination with an antibiotic. It has been previously determined that sub-MICs of antibiotics result in increased biofilm formation [69]. Additionally, it has been previously demonstrated that the starvation response mediates high biofilm specific tolerance to antibiotics [70]. To check interactions between antibiotics and (p)ppGpp production, ampicillin was used in all of our assays due to its bactericidal effect and stressing effect. Sub-MIC concentrations of ampicillin were determined by growth measurements (OD\({}_{600}\)). We found that the biggest change in the culture cell density was observed between 40 and 60 \(\upmu\)g/mL ampicillin (Figure S8A). Analyzing the effect of ampicillin on biofilm formation, we observed that the presence of the antibiotic significantly increased the amount of biofilm with the highest biomass observed at relatively high ampicillin concentrations (80 \(\upmu\)g/mL) (Figure S8B). To analyze the effect of our hit compounds in combination with antibiotics, a range of ampicillin concentrations from 30 to 50 \(\upmu\)g/mL was utilized.  
The amount of biofilm biomass was determined in the combined presence of antibiotics and either compound A or B. These combination therapies led to a highly significant reduction in biofilm mass compared to the ampicillin-only treated controls (Figure 5A). As a reference control, we used IDR 1018, an antimicrobial peptide that is reported to target (p)ppGpp directly and degrade ppGpp in vitro [30]. The addition of the hit compounds to ampicillin concentrations of 40 \(\upmu\)g/mL (Amp40) and 50 \(\upmu\)g/mL (Amp50) resulted in a highly significant decreases in biofilm volume compared with their cognate antibiotic only treated control (Figure 5A). At Amp40 the biofilm biomass was reduced by 97.9% (\(p\) = 0.0009) for S3-G1A (50 \(\upmu\)M), by 92.4% (\(p\) = 0.0014) for S3-G1B (50 \(\upmu\)M), and by 75.4% (\(p\) = 0.006) for IDR1018 (6 \(\upmu\)M). Amp50 showed reductions in biofilm biomass of 67.9% (\(p\) = 0.0044) for S3-G1A, of 72.9% (\(p\) = 0.0042) for S3-G1B, and 65.2% (\(p\) = 0.0054) for IDR1018. The difference between Amp40 and Amp50 can be attributed to the larger increase in biofilm volume induced by the higher concentration of antibiotic.  
An alamarBlue cell viability assay also showed that ampicillin killed more bacterial cells when combined with the tested hit compounds (Figure 5B). In the case of ampicillin at 30 \(\upmu\)g/mL (Amp30), the reductions were 55.4% (\(p\) = 0.0024) and 54.2% (\(p\) = 0.0027) for S3-G1A (50 \(\upmu\)M) and S3-G1B (50 \(\upmu\)M), respectively. When higher concentrations of antibiotic were used, the synergetic effects of compounds S3-G1A and S3-G1B were less noticeable, with the decreases being only 29.2% (\(p\) = 0.0278) and 6.5% (\(p\) = 0.6), respectively. This effect was attributed to the greater volume of the biofilm contained in these samples (Figure 5B).
## 5 Analysis of Fluctuating Parameters
### Relations Between Fluctuating Parameters  
Turbulence intensity and Gust factor are two important parameters in determining the fluctuating wind speed component. On the basis of the wind field of the typhoon investigated by [PERSON] (2018) and [PERSON] (2018), [PERSON] et al. established a standardized empirical expression for describing the relations between the gust factor and longitudinal turbulence intensity ([PERSON] and [PERSON], 2018), which is:  
\[G_{u}\big{(}t_{g}\big{)}=1+k_{1}I_{u}^{k_{2}-1}\ln\left(\frac{T}{t_{g}}\right),G_{v}\big{(}t_{g}\big{)}=k_{3}I_{v}^{k_{4}-1}\ln\left(\frac{T}{t_{g}}\right) \tag{9}\]  
where \(k_{1}\) and \(k_{2}\) are two parameters and average time interval \(T\) is set to 10 min. [PERSON] suggested that \(k_{1}=0.62\) and \(k_{2}=1.27\), whereas [PERSON] stated that \(k_{1}=0.5\) and \(k_{2}=1.0\).  
Many field measurements have revealed linear relations between the gust factor and longitudinal turbulence intensity, which means \(k_{2}=1.0\) is widely accepted ([PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018). [PERSON] et al. (2018) conducted a statistical analysis by comparing fitting parameter \(k_{2}\) with Equation (9) or the constant 1, and the results show that the calculations of the two methods are nearly identical. In this study, a linear fitting method was used to determine the relations amongst the gust factors, longitudinal turbulence intensity, and lateral turbulence intensity, which are defined as follows:  
\[G_{u}\big{(}t_{g}\big{)}=1+k_{u}I_{u}\ln\left(\frac{T}{t_{g}}\right),G_{v} \big{(}t_{g}\big{)}=k_{v}I_{v}\ln\left(\frac{T}{t_{g}}\right) \tag{10}\]  
The fitting results of Equations (9) and (10) are shown in Figure 13, and the fitting parameters are shown in Table 4. As indicated in the graph, the Gust factor increased with turbulence intensity, and the fitting curve patterns of Equations (9) and (10) are not very different. However, the fitting curve patterns of Equation (9) are different from the values given by [PERSON] and [PERSON]. By contrast, Equation (10) only considers a single parameter, and the fitting parameters of the three high-rises are in good agreement with the value of 0.42 proposed by [PERSON] (2018). Thus, the relations between the gust factor and turbulence can be easily fitted using Equation (10). The mean values of \(k_{u}\) and \(k_{v}\) are 0.356 and 0.342, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Building** & \multicolumn{2}{c}{**Equation (1)**} & **Equation (2)** \\ \hline \multirow{4}{*}{longitudinal} & A & \(k_{1}=0.333\) & \(k_{2}=1.851\) & \(k_{u}=0.382\) \\  & B & \(k_{1}=0.349\) & \(k_{2}=1.978\) & \(k_{u}=0.357\) \\  & C & \(k_{1}=0.380\) & \(k_{2}=2.087\) & \(k_{u}=0.330\) \\ \hline \multirow{4}{*}{lateral} & A & \(K_{3}=0.258\) & \(K_{4}=1.711\) & \(k_{v}=0.397\) \\  & B & \(K_{3}=0.300\) & \(K_{4}=1.898\) & \(k_{v}=0.348\) \\ \cline{1-1}  & C & \(K_{3}=0.200\) & \(K_{4}=1.814\) & \(k_{v}=0.282\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fitted parameters of the two equations.