esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variation**} & **Caffeine** & **Dose** & **Time** & **Mean \(\pm\) SD** & **F** & \(p\) & **Factor 1** & **Factor 2** &
\begin{tabular}{c} **Mean Difference \(\pm\) SE** \\ **(Factor 1-Factor 2)** \\ \end{tabular} & **t** & \(p\)**Tukey** \\ \hline \multirow{4}{*}{PWV} & 0 min & 1 & & & & 30 min & \(-0.00245\pm 0.0254\) & \(-0.0966\) & 0.995 \\  & 100 mg & 30 min & \(1.005\pm 0.13648\) & 2.860 & 0.066 & 0 min & \(60\) min & \(-0.05081\pm 0.0208\) & \(-2.4374\) & 0.055 \\  & 60 min & \(1.053\pm 0.11166\) & & & 30 min & \(60\) min & \(-0.04836\pm 0.0253\) & \(-1.9090\) & 0.156 \\ \cline{2-10}  & 0 min & 1 & & & & 30 min & \(-0.02844\pm 0.0313\) & \(-0.9090\) & 0.639 \\ \cline{2-10}  & 200 mg & 30 min & \(1.014\pm 0.16218\) & 0.695 & 0.503 & 0 min & \(60\) min & \(-0.00319\pm 0.0229\) & \(-0.1390\) & 0.989 \\ \cline{2-10}  & 60 min & \(1.001\pm 0.12751\) & & 30 min & \(60\) min & \(0.02525\pm 0.0243\) & \(1.0390\) & 0.559 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of PWV using RM ANOVA. Figure 4: Comparison of changes in the PWV of the CCA at each time point (the error bars indicate standard error). Figure 3: Comparison of changes in the MaxD and MinD of the CCA at each time point (the error bars indicate standard error). * \(p\) < 0.05.  
after 100 mg of caffeine intake. For the 200 mg intake, significant differences were observed from 0 to 30 min and 0 to 60 min (\(p\) = 0.002 and \(p\) = 0.001, respectively) but not between 30 and 60 min (\(p\) = 0.666) (Table 5 and Figure 7). The accuracy rate significantly differed between 30 and 60 min (\(p\) = 0.009); however, no significant differences were observed between 0 and 30 min and 0 and 60 min (\(p\) = 0.356 and \(p\) = 0.613, respectively) following 100 mg of caffeine intake. For the 200 mg intake, significant differences were observed between 0 and 60 min (\(p\) = 0.020) but not observed from 0 to 30 and 30 to 60 min (\(p\) = 0.634 and \(p\) = 0.055, respectively) (Table 6 and Figure 8). The response time showed significant differences over time following both the 100 mg and 200 mg caffeine intake, with reductions observed between 30 min (0.963 \(\pm\) 0.042, 0.976 \(\pm\) 0.034) and 60 min (0.934 \(\pm\) 0.046, 0.938 \(\pm\) 0.047) (\(p\) < 0.05) (Table 6 and Figure 9). Caffeine dosage significantly affected SBP at 60 min (\(p\) = 0.016) but not at 30 min (\(p\) = 0.145) (Table 7). Similarly, DBP significantly differed at 60 min (\(p\) = 0.031) but not at 30 min (\(p\) = 0.059) (Table 7). Furthermore, MaxD and MinD exhibited significant differences at 30 min (\(p\) = 0.019 and \(p\) = 0.029, respectively) but not at 60 min (\(p\) = 0.052) (Table 7). PWV, PVV, and HR showed no significant dose-dependent effects over time (\(p\) > 0.05), whereas the SpO\({}_{2}\) differences were significant at 60 min (\(p\) < 0.05) but not at 30 min (\(p\) = 0.338) (Table 7). The accuracy rate and response time were not significantly affected (\(p\) > 0.05) (Table 7).
# Analysis of Flow Characteristics and Effects of Turbulence Models for the Butterfly Valve
## 5 Sensitivity Analysis  
According to the assumption of the two-equation method of \(k-\varepsilon\) model and \(k-\omega\) model, the time-averaged values based on the simplified assumptions: Reynolds averaging, Boussinesq approximation, Prandtl hypotheses, etc. [53] These time-averaged assumptions require turbulence model constants to account for turbulence, therefore, in this study, the effects of turbulence model constants should be assessed by the sensitivity study. For the present sensitivity study, the methods developed by the [PERSON] [37; 54] and the group of [PERSON] [55] were adopted. The main flow properties based on the time-averaged values were described as follows: the stream-wise flow-velocity (\(u\)), turbulence kinetic energy (\(k\)), turbulence dissipation rate (\(\varepsilon\)), turbulent eddy viscosity (\(\mu_{t}\)) for the two-equation of \(k-\varepsilon\) turbulence model, and stream-wise flow-velocity (\(u\)), turbulence kinetic energy (\(k\)), turbulence specific dissipation rate (\(\omega\)), turbulent eddy viscosity (\(\mu_{t}\)) for the two-equation of \(k-\omega\) turbulence model. The sensitivity of the flow properties to the constants for each turbulence model was examined with sensitivity coefficients using finite difference approximations taking the following form [37; 54]:  
\[\widetilde{\mathrm{S}}_{\mathrm{m}}=\frac{w_{2}-w_{1}}{\Delta C_{m}} \tag{16}\]  
where \(\widetilde{\mathrm{S}}_{\mathrm{m}}\), \(\Delta C_{m}\), \(w_{1}\) and \(w_{2}\) are the sensitivity coefficient, increment of examined constants, and calculation results at \(C_{m}-\Delta C_{m}/2\) and \(C_{m}+\Delta C_{m}/2\), respectively.  
For the case of the DN 80 valve, sensitivity analyses were conducted using Equation (16) under different opening percentage conditions. For the flow properties, the examined constants for the each turbulence model are as follows: C\({}_{\varepsilon 1}\), C\({}_{\varepsilon 2}\), C\({}_{\mu}\), \(\sigma_{k}\), \(\sigma_{\varepsilon}\) for the \(k-\varepsilon\) model, and \(\alpha\), \(\beta\), \(\beta^{\prime}\), \(\sigma_{k}\), \(\sigma_{\omega}\) for the \(k-\omega\) model. Increment of examined constants for the sensitivity analysis with turbulence model is as follows:\(\Delta C_{\varepsilon 1}\), \(\Delta C_{\varepsilon 2}\), \(\Delta\sigma_{k}\), \(\Delta\sigma_{\varepsilon}\) are 0.1 and \(\Delta C_{\mu}\) is 0.01 [37; 54] for the \(k-\varepsilon\) model, and \(\Delta\alpha\), \(\Delta\beta\), \(\Delta\beta^{\prime}\), \(\Delta\sigma_{k}\), \(\Delta\sigma_{\omega}\) are 0.01 for the \(k-\omega\) model.
## 2. Mature Technologies for Water and Wastewater Remediation
### Ozonation  
#### 2.2.1 Applications of Ozonation  
Ozonation is a trusted technology in the beverage industry and is utilised by Coca- Cola, SABMiller, Dasani and other companies for water purification processes [17]. Ozonation is also used in Olympic pools [18] and in hospitals [19] due to its reliably high oxidising potential.
## 3 Simulation Results  
The proposed approach is applied to some IEEE test systems and a real power system. The scenarios, that are evaluated, are summarized in Figure 6. The experiments are implemented in MATLAB(r) (R2009b, Torrance, CA, USA, 2009), and run on an Intel(r) Core(tm2) Duo 2.66 GHz computer (ASUS, Taipei, Taiwan) with 4 GB of RAM (random access memory). In addition, CPU (central processing unit) time is an important metric for verifying the performance of the proposed approach. The CPU time was measured by using the MATLAB _cputime_ function [(30)] in our program. The simulation results obtained were all compared with those found by the conventional CPFLOW approach. More details are illustrated and discussed in the cases below.
## 4 Experiments and Discussion
### Ablation Studies
#### 4.3.2 Ablation Study on Training Step #1  
To investigate the contribution of training step #1, we conduct a test after the training and compare it with the network (b) in Figure 3. Different from network (b), the network used in training step #1 employs a Siamese network to evaluate the similarity between the new sample and randomly selected \(N\) samples from the dataset. The value of \(N\) is empirically set to 10.  
We tested our model using 10 kinds of malware. As can be seen in Table 8, the accuracy of our model reaches to 0.87, which is 0.0332 higher than the network with the DAT encoder (see Table 7), demonstrating the contribution of our training step #1. At  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Category** & **F1-Score** & **Support** & **Overall Accuracy** \\ \hline Worm & 0.78 & 81 & \\ P2P-Worm & 0.12 & 8 & \\ Trojan-Spy & 0.72 & 81 & \\ Net-Worm & 0.92 & 20 & \\ Packed & 0.84 & 145 & \\ Trojan-Downloader & 0.45 & 57 & 0.8368 \\ Trojan-PSW & 0.82 & 99 & \\ Trojan-Ransom & 0.58 & 67 & \\ Misc & 0.80 & 20 & \\ Trojan-FakeAV & 0.96 & 487 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantitative results of DAT (D = 2).  
Figure 4: The confusion matrix (with data on accuracy) of the results of DAT (D = 2). From top to bottom, from left to right, there are Worm, P2P-Worm, Trojan-Spy, Net-Worm, Packed, Trojan-Downloader, Trojan-PSW, Trojan-Ransom, Misc, and Trojan-FakeAV.  
the same time, Trojan-Ransom's F1-score has improved from 0.58 to 0.72. Although the accuracy has improved a lot, we can find that the F1-score of some kinds of malware classification is unsatisfactory, such as P2P-Worm (F1-score: 0.20), Misc (F1-score: 0.47) and Trojan-Downloader (F1-score: 0.55), which is unacceptable in practice. The f1-score of P2P-Worm, Net-Worm, and Misc is lower than that of the DAT encoder. The same result can be derived from the confusion matrix in Figure 5 as well, in which the second category, i.e., P2P-Worm, is so light and is prone to be predicted as Trojan-Spy, Packed, and so on. We guess the reason for this gap is caused by the rare data of these sorts of malware.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 4 Performance Evaluation
### Field Experiments
#### 4.1.1 Verify Settings for Location Estimation  
An example of RSSIs collected on Path 1 and Path 2 is shown in Figure 15. Although RSSIs increased as Krypto moved closer to the mobile phone, the RSSIs fluctuated as a result of noise and other factors. From the experiments, \(n=15\) obtained good results in terms of noise reduction with the _median filter_. After applying the noise filter and RSSI-based localization technique that mapped signal strength into distances using the _Path Loss Model_ (1), the distance between the signal source (i.e., a mobile phone) and Krypto was 10 m in order to get the result of location error that was under 2 m of the radius with cumulative destruction function (CDF) of 95%. Therefore, we set the \"effective\" sensing range \(r\) for drones to 10 m.
# Biochar Amended Soils and Water Systems: Investigation of Physical and Structural Properties
## 2 Materials and Methods
### Soil-Biochar Samples Preparation  
Air-dried soil, \(P_{s}\) (g), crushed to pass a 2 mm sieve, with a selected amount of biochar, \(P_{BC}\) (g) were mixed to prepare soil-biochar samples, and packed at the bulk density values, \(\rho_{b}\), reported in Table 1. A biochar fraction, \(f_{BC}\), equal to 0.091, equivalent to 91 g/Kg, was selected to carry out the measurements, with \(f_{BC}\) defined as:  
\[f_{BC}=\frac{P_{BC}}{P_{s}+P_{BC}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Properties** & **2.1** & **3A** & **6S** & **Biochar** \\ \hline Org C (\%) & 1.23 \(\pm\) 0.30 & 2.2 \(\pm\) 0.1 & 1.9 \(\pm\) 0.3 & 43.5 \(\pm\) 2.3 \\ pH value (0.01M CaCl\({}_{2}\)) & 6.2 \(\pm\) 0.7 & 7.1 \(\pm\) 0.0 & 6.7 \(\pm\) 0.6 & 9.5 \(\pm\) 0.7 \\ Cation exchange capacity (meq/100 g) & 8 \(\pm\) 0.1 & 17 \(\pm\) 4 & 17 \(\pm\) 3 & - \\ Particle sizes according to USDA (\%) & & & & \\
0.002 mm & 3.6 \(\pm\) 1.2 & 16.9 \(\pm\) 0.1 & 41.5 \(\pm\) 1.4 & - \\
0.002–0.05 mm & 9.5 \(\pm\) 2.2 & 34.6 \(\pm\) 2.7 & 36.4 \(\pm\) 3.1 & - \\
0.05–2.0 mm & 86.9 \(\pm\) 1.9 & 48.5 \(\pm\) 2.6 & 22.1 \(\pm\) 2.2 & - \\ Soil texture & Loamy Sand & Loam & Clay & - \\ Bulk density (g/cm\({}^{3}\)) & 1.38 \(\pm\) 0.040 & 1.11 \(\pm\) 0.100 & 1.20 \(\pm\) 0.060 & 0.173 \(\pm\) 0.060 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physico-chemical characteristics of standard soils and biochar.  
Soil samples of 5 cm diameter and 4.5 cm height were used for the pressure plate measurements. For the sample bulk volume (\(V_{b}\)) of 88.4 cm\({}^{3}\), the \(P_{s}\) value corresponding to the fixed \(f_{BC}\) was determined as:  
\[P_{s}=\frac{\rho_{b,s}\,\rho_{b,BC}\,V_{b}(1-f_{BC})}{\rho_{b,s}\,f_{BC}+\rho_{ b,BC}(1-f_{BC})} \tag{2}\]  
where the dry soil bulk density and the dry biochar bulk density are respectively \(\rho_{b,s}\) and \(\rho_{b,BC}\). The soil-BC mixtures were denoted as loamy sand + BC, loam + BC and clay + BC.
# Fast and Efficient Lunar Finite Element Gravity Model
## 1 Introduction
### Orbit Propagation  
The last application in this project includes using the FEM model to approximate a satellite's orbital trajectory over a provided time span. Similar works which inspired this method of orbital propagation can be found in the following literature: [33; 34; 35; 36; 37]. This application will be achieved using the resulting Chebyshev polynomial coefficients from the original FEM routine at specific radii above the lunar surface to approximate the acceleration a satellite with negligible mass would experience during the course of a short- and long-term orbit around the Moon. This propagation considers the two-body problem, which consists of a satellite with negligible mass orbiting a larger astronomical body. This problem allows us to use [PERSON]'s second law force equations to then come up with a finalized two-body equation of motion for the theoretical satellite orbiting the Moon. In this paper, first the orthogonal approximation techniques used alongside Chebyshev polynomials are covered in detail in Section 2. Then, in Section 3, the process of utilizing FEM on the LP-100k model is discussed and compared to the original SH model. In Section 4, a discussion is presented on how the coefficients produced by the FEM model on a two-body orbit propagation problem about the lunar surface are implemented. Lastly, in Section 5, a summary of the findings and concluding remarks is provided.
# Ontology-Based Feature Selection: A Survey
## 3 Feature Selection
### Filter Models  
Filter models determine subsets of features to perform pre-processing, independently of the chosen classifier. In the first step, features are analyzed and ranked on the basis of how they correlate to the target classes. This analysis can either consider features separately and perform ranking independently of the feature space (univariate), or evaluate groups of features (multivariate). Multivariate analysis has the advantage that interactions between features are considered during the selection process. In the second step, the highest ranked (i.e., scored) features constitute the final input variables of the classifier.  
Some of the most common evaluation metrics that have been used for ranking and filtering are Chi-square, ANOVA, Fisher score, Pearson correlation coefficient, and mutual information [39; 40; 41].  
Chi-Square: The \(\chi^{2}\) correlation uses the contingency table of a feature target-pair to evaluate the likelihood that a selected feature and a target class are correlated. The contingency table shows the distribution of one variable (the feature) in rows and another (the target) in columns. Based on the entries, the observed values are calculated under the assumption that the variables are independent (null hypothesis); the expected values are then derived. Small values of \(\chi^{2}\) show that the expected values are close to the observed values, thus the null hypothesis stands. On the contrary, high values show strong correlation between the feature and the target value.  
ANOVA: A metric related to \(\chi^{2}\) is analysis of variance. It tests whether several groups are similar or different by comparing their means and variances, and returns an F-statistic, which can be used for feature selection. The idea is that a feature where each of its possible values corresponds to a different target class, will be a useful predictor.  
Fisher Score: It is based on the intuition that effective feature combinations should result in similar values regarding instances in the same class, and much different values regarding instances from different classes.  
Pearson Correlation Coefficient: It is used as a measure for quantifying linear dependence between a feature variable \(X_{i}\) and a target variable \(Y_{k}\). It ranges from \(-1\) (perfect anti-correlation) to \(1\) (perfect correlation).  
Mutual Information: The information gain metric provides a method of measuring the dependence between the \(i\)th feature and the target classes \(\vec{c}=[c_{1},c_{2},\ldots,c_{k}]\), as the decrease in total entropy, namely \(IG(f_{i},\vec{c})=H(f_{i})-H(f_{i}|\vec{c})\), where \(H(f_{i})\) is the entropy of \(f_{i}\) and \(H(f_{i}|\vec{c})\) the entropy of \(f_{i}\) after observing \(\vec{c}\). High information gain indicates that the selected feature is relevant. IG has been extended to account for feature correlation and redundancy. Other MI metrics are Gini impurity and minimum-redundancy-maximum-relevance.
## 3 Data and Methodologies  
### Satellite Images  
The European Copernicus Sentinel-2 satellites mission consists of a constellation of Sentinel-2A and Sentinel-2B satellites, launched successively in June 2015 and March 2017. The swath width of a frame is 290 km, and the spatial resolutions of the four bands (blue, green, red and near-infrared (NIR) are 10 m. Compared with the revisit time (16 days) of Landsat 8, Sentinel-2 reduces this by 11 days. Remote sensing satellites passing from neighboring orbits could further shorten initial revisit time (5 days) due Petermann in North-West Greenland located high latitudes region. This is of benefit when monitoring the changes of glacier surfaces at a finer temporal scale, especially for short-lived collapse events. In this study, Sentinel-2A/B images were respectively acquired with \(\leq\)10% cloud cover during the summer months (May to July) to derive the ice velocity and melt ponds in PG from the European Space Agency Copernicus Open Access Hub and Google Earth Engine in Tables S1 and S2. In addition, Landsat 8 OLI (Operational Land Imager) was preferred in using to cover the whole extent of sea ice just from one scene of images without mosaics other frame images seen in Table S3.  
The Sentinel-1 synthetic aperture radar (SAR) Level-1 Ground Range Detected (GRD) data product ([[https://developers.google.com/earth-engine/guides/sentinel1](https://developers.google.com/earth-engine/guides/sentinel1)]([https://developers.google.com/earth-engine/guides/sentinel1](https://developers.google.com/earth-engine/guides/sentinel1))) (accessed on 7 February 2021) was employed to quantify the hardness of the landfast sea ice neighboring ice front of PG in July 2017. These data were processed using the SNAP Sentinel-1 Toolbox ([[https://step.esa.int/main/toolboxes/sentinel-1-toolbox/](https://step.esa.int/main/toolboxes/sentinel-1-toolbox/)]([https://step.esa.int/main/toolboxes/sentinel-1-toolbox/](https://step.esa.int/main/toolboxes/sentinel-1-toolbox/))) (accessed on 7 February 2021) to estimate the backscatter (applying a precise orbit, border noise removal, thermal noise removal, radiometric calibration, and terrain correction) via the Google Earth Engine platform. All the corresponding remote sensing images are presented in the Table S4.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Pollution Avoiding Source Location Privacy (PA-Slp)  
In this technique [30], messages are transmitted in specific time slots to avoid Time Correlation attacks. In addition, _PA-SLP_ implements random network coding on the transmitted packets to prevent _Size/Structure Correlation_ attacks. In _PA-SLP_, there are three different types of packets: real, fake, and polluted (packets created or modified by an adversary). Therefore, a mechanism called Triple-Type Homomorphic Signature (_TTHS_) was developed to filter the unwanted packets such as the fake and polluted ones. Moreover, a signature equation with a secret key is used to determine the type of the forwarded packets without exposing their contents. If the packet type is real, it will be forwarded to the next node towards its destination. However, if the packet is fake, it will be discarded to reduce the overhead of the network. Lastly, in the case the packet is polluted, it will be utilized to improve the Intrusion Detection Systems (_IDS_) by locating the captured nodes. The entire process is employed by intermediate nodes to enhance the overall performance of the network.
## 3 Results and Discussion
### Results of the Corrosion Inhibition Efficiency Test of IL1 and IL2 by the Tafel Method  
The corrosion inhibition efficiency test by using the Tafel method showed that IL1 and IL2 protect carbon steel surfaces as well as anodic protection as shown in the Tafel extrapolation curve in Figure 7.  
Figure 6: Graphs of the correlation between ln(Icorr/T) towards 1000/T in the corrosion system after the addition of IL1 (**a**) and IL2 (**b**); (**c**,**d**) represent the free energy values \(\Delta\)G* of the corrosion system after the addition of IL1 and IL2 at various temperatures, respectively.  
As a general demonstration of IL1 and IL2 in protecting the metal surfaces, the 3D visualization models of the interaction of each IL1 and IL2 on carbon steel surfaces are shown in Figure 7. This shows that the presence of free electrons on the O atom and the resonance of electrons from the aromatic ring of ILs contribute to interact with the carbon steel surface. In addition, the cations of imidazole salts can electrostatically interact with electron clouds on the carbon steel surface, resulting in a decrease in the oxidation rate of carbon steel.  
The results of the corrosion inhibition efficiency test showed that IL1 and IL2 have potency as corrosion inhibitors using the direct injection method and direct coating method in 1% NaCl solution. Since IL1 and IL2 have larger ionic polarity, the capability to protect carbon steel surface by direct coating was better than the direct injection method.  
Figure 7: (**a**) Extrapolation Tafel curve of IL1 and IL2 and (**b**) the 3D visualization illustration of the interaction between IL1 and (**c**) IL2 on the carbon steel surface (note: the red ball reflects the oxygen atom, the blue ball reflects the nitrogen atom, the purple ball reflects the iodine atom, the grey ball reflects the carbon atom, and the white ball reflects the hydrogen atom).  
The presence of water and other polar compounds in a corrosion system can promote the carbon steel oxidation rate. Hence, water and other polar compounds need to be removed from the system (on the carbon steel surface). The position of propyl and butyl groups in IL1 and IL2 structures were designed to make a distant orientation between the carbon steel surface and corrosion agents. Under certain conditions, the addition of hydrophobic groups can improve corrosion inhibition performance through the process of preventing interactions between water molecules and electrolyte solutions from carbon steel surfaces [8; 15; 40; 41].
###### Abstract  
Due to the strong intermittency of micro-resources, the poor grid-tied power quality, and the high generation-demand sensitivity in micro-grids, research into the control methods of micro-grid systems has always been a notable issue in the field of micro-grids. The inverter is the core control equipment at the primary control level of the micro-grid, and the key factors affecting its output performance can be divided into three categories: control methods, hardware configuration, and control parameter design. Taking the classical active and reactive power (P-Q) control structure and the three-phase, two-stage inverter topology model as an example, this paper designs a parameter for offline tuning, and an online self-tuning optimization method for an inverter control system based on the fruit fly optimization algorithm (FOA). By simulating and comparing the inverter controllers with non-optimized parameters in the same object and environment, the designed parameter tuning method is verified. Specifically, it improves the dynamic response speed of the inverter controller, reduces the steady-state error and oscillation, and enhances the dynamic response performance of the controller.  
micro-grid; voltage source inverter; control parameters; fruit fly optimization algorithm; online parameter optimization; offline parameter optimization +
Footnote †: journal: Applied Sciences