esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Testing and Experimental Results
### Experimental Results  
The practical sales campaign was carried out in the marketing campaign to experiment ADVO system. The profiles of social influence and sales outcome were recorded to compare the effectiveness of two phases in the campaign. The total of interactions on the brand is shown in Figure 8. In those results, the posts only count for influencers. Table 2 gives the details of interactions for each phase.  
Table 3 analyzes the sentiment of comments on posts of influencers. Those comments are concerned with the product in two phases.  
The rate of \"not concerned\" comments and total comments in phase 1 is (49%). It is near double the rate of that in Phase 2 (27%). Influencers attracted the audience of influencers in phase 1; they were not attracted to the brand. However, the audience of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of** & **Posts** & **Reactions** & **Comments** & **Shares** & **Total of** \\
**Interactions** & & & & **Interactions** \\ \hline Phase 1 & 13 & 5139 & 869 & 390 & **6398** \\ Phase 2 & 10 & 4849 & 509 & 51 & **5409** \\ \hline
**Total** & **23** & **9988** & **1378** & **441** & **11,807** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total of interactions for each phase.  
Figure 8: The interactions rate between two phases in the influencer marketing campaign from 24 February–12 March 2020.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Phase** & **Positive** & **Negative** & **Neutral** & **Not concern** & **Total** \\ \hline Phase 1 & 337 & 7 & 93 & 432 & 869 \\ Phase 2 & 281 & 4 & 83 & 141 & 509 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sentiments of posts and comments in two phases.  
influencers in phase 2 was attracted by the brand mentioned by their influencers. Besides, the rate of positive comments in phase 2 was also higher than in phase 1.  
The rate of click per interaction reflects the attraction of a post to the audience. The conversion rate for each click in a marketing campaign is required to measure the conversion from interesting to consumers' purchase decisions. Hence, those rates are essential to evaluate the effectiveness of a marketing campaign. Table 4 shows their values in our customers' campaign from 24 February-12 March 2020.  
Although the total clicks in phase 2 are lower than in phase 1, the conversion rate per click of phase 2 is better than in phase 1. In phase 1, the audience may be clicked to the link to the product by the authority of influencers, and they are not ready to buy the product. In phase 2, the audience is interested in the product through the attraction of the influencers. Hence, when they click on the link to a product, they want to reserve it.  
Besides, the conversion rate of phase 2 displays that the revenue of this phase is better than another. The average influencer efficiency of phase 1 is also lower than phase 2. This is shown as Figure 9.
## 2 Energy Security
#### Poland's Energy Policy
Against the background of European guidelines and environmental goals, the development of Poland's energy policy will be focused in the near future at the stabilization of the energy system with the existing hard coal and lignite resources at a constant level, which determine the security of supply, with a focus on increasing the use of renewable sources to meet energy demand. The regulations contained in the new EU energy policy will have an impact on energy companies in Poland. The legal basis for the development and implementation of Poland's energy policy is the Act of April 10, 1997--the Energy Law, which defines the principles of shaping the state's energy policies as well as rules and conditions for the supply and use of fuels and energy, including heat, and the operation of energy enterprises. This act also determines the competent authorities in matters of  
Figure 2: Reduction goals of EU countries in the non-ETS (Emissions Trading System) area in the perspective of 2030, according to ESR Regulation [14], source: own study based on data [15]. fuel and energy management. The purpose of the act is to create conditions for the sustainable development of the country, ensuring energy security, economical and rational use of fuels and energy, development of competition, counteracting the negative effects of natural monopolies, taking into account environmental protection requirements. The last document adopted by the Council of Ministers on November 10, 2009, is Poland's Energy Policy until 2030. The document adopts the following directions for the development of Polish Energy Policy (PEP 2040) [1]:  
* improving energy efficiency,
* increased security of fuel and energy supplies,
* diversification of the electricity generation structure by introducing nuclear energy,
* development of the use of renewable energy sources, including biofuels,
* development of competitive fuel and energy markets,
* limiting the impact of the energy sector on the environment. Improving energy efficiency is one of the priorities of the EU energy policy, with the goal of reducing energy consumption by 20% [16]. Poland has made great progress in this area. Development resulting from the use of new technologies indicates a significant increase in electricity consumption with a relative decline in other forms of energy. Therefore, all possible measures will be taken, which include:  
* increasing the efficiency of electricity generation through the construction of highly efficient generation units,
* a twofold increase in the production of electricity generated in the high-efficiency cogeneration technology by 2020, compared to production in 2006,
* reduction of the network loss ratio in transmission and distribution, through inter alia, modernization of the existing networks and construction of new ones, replacement of low-efficiency transformers, and development of distributed generation,
* increase in end-use energy efficiency,
* increasing the ratio of the annual demand for electricity to the maximum demand for power at peak load, which allows reducing the total cost of meeting the demand for electricity [16].
# Blind Spot Detection Radar System Design for Safe Driving of Smart Vehicles
## 3 Tracking System Algorithm
### Target Filter  
In BSD radar, the Kalman filter [24] and the \(\alpha\)-\(\beta\)-(\(\gamma\)) filter are commonly used as prediction filters for target tracking. Based on range measurements, the \(\alpha\)-\(\beta\)-(\(\gamma\)) filter can predict range, velocity, and constant acceleration. As a fixed gain formulation of  
Figure 6: Condition of Gating Algorithm.  
Figure 7: Structure of BSD Radar Tracking Processor.  
the Kalman filter, it can be easily implemented at low cost, making it primarily used in tracking prediction systems [18]. The \(\alpha\)-\(\beta\) filter for range tracking is given by Equations (8)-(10). \(r_{0}(n)\) represents the measured range, \(\hat{r}_{p}(n)\) represents the predicted range at the measurement time, \(\hat{r}_{s}(n)\) represents the smoothed range after the nth observation, \(\hat{o}_{s}(n)\) represents the smoothed velocity after the nth observation, and \(T_{s}\) represents the time between measurements.  
Smoothed range is shown as Equation (8):  
\[\hat{r}_{s}(n)=\hat{r}_{p}(n)+\alpha\cdot[r_{o}(n)-\hat{r}_{p}(n)] \tag{8}\]  
Smoothed velocity is shown as Equation (9):  
\[\hat{o}_{s}(n)=\hat{o}_{s}(n-1)+\frac{\beta}{T_{s}}\cdot[r_{o}(n)-\hat{r}_{p}(n)] \tag{9}\]  
Predicted range is shown as Equation (10):  
\[\hat{r}_{p}(n+1)=\hat{r}_{s}(n)+T_{s}\cdot\hat{o}_{s}(n) \tag{10}\]  
In the equations, the output noise variance of the \(\alpha\)-\(\beta\) filter at steady state is minimized when the gain coefficients are related as shown in Equation (11):  
\[\beta=\frac{\alpha^{2}}{(2-\alpha)}\,\ 0<\alpha<1 \tag{11}\]  
For range and velocity tracking in a system, the \(\alpha\)-\(\beta\)-\(\gamma\) filter can be defined by the following equations.  
Smoothed range is shown as Equation (12):  
\[\hat{r}_{s}(n)=\hat{r}_{p}(n)+\alpha\cdot[r_{o}(n)-\hat{r}_{p}(n)] \tag{12}\]  
Predicted velocity is shown as Equation (13):  
\[\hat{o}_{s}(n)=\hat{o}_{s}(n-1)+T_{s}\cdot a_{s}(n-1)+\frac{\beta}{T_{s}}\cdot [r_{o}(n)-\hat{r}_{p}(n)] \tag{13}\]  
Smoothed acceleration is shown as Equation (14):  
\[\hat{a}_{s}(n)=\hat{a}_{s}(n-1)+\frac{2\cdot\gamma}{T_{s}^{2}}\cdot[r_{o}(n)- \hat{r}_{p}(n)] \tag{14}\]  
Predicted range is shown as Equation (15):  
\[\hat{r}_{p}(n+1)=\hat{r}_{s}(n)+T_{s}\cdot\hat{o}_{s}(n)+\frac{T_{s}^{2}}{2} \cdot\hat{a}_{s}(n) \tag{15}\]  
The gain coefficients are defined as below:  
\[\left\{\begin{array}{l}\alpha=1-\mu^{3}\\ \beta=1.5\cdot(1-\mu^{2})\cdot(1+\mu)\\ \gamma=(1-\mu)^{3}\\ 0<\mu<1\end{array}\right. \tag{16}\]  
Significant smoothing occurs when the value of \(\mu\) approaches 1, while no smoothing is applied when the value of \(\mu\) is 0. Figure 8 illustrates the structure of the \(\alpha\)-\(\beta\) tracking filter and the \(\alpha\)-\(\beta\)-\(\gamma\) filter using the equations described earlier.
## 5 Parameter Optimization
### Steepest Climb Test  
To establish a regression model using response surface methodology for determining optimal values, it is crucial that these values fall within the predefined high and low levels of the parameters. The steepest climb test can efficiently identify the interval where the optimal values of the parameters reside. According to the results of the PB test, the parameter effects of the first four significance parameters, i.e., the minimum drive trajectory of the second driving pair of limb \(l_{\text{2 min}}\), the width of moving platform \(m\), the angle between \(P_{3}\) and \(P_{2}\) axis \(\theta\), and the length of fixed platform \(N\), are negative; that of the fifth significance parameter, i.e., the width of the fixed platform \(M\), is positive. Therefore, in this paper, the first four significance parameters are gradually reduced according to the selected step, the fifth significance parameter is gradually increased according to the selected step, and the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Symbol** & **Effect** & **Sum of Mean Squares** & **Impact Rate** & **F-Value** & **\(p\)-Value** & **Significance Ranking** \\ \hline Model & — & 0.0085 & — & 112.92 & 0.0088 & significant \\ \(X_{0}\) & \(-\)0.0150067 & 0.0011 & 13.255 & 254.71 & 0.0039 & 2 \\ \(X_{1}\) & 0.00233326 & 0.0000 & 0.320429 & 6.16 & 0.1312 & 10 \\ \(X_{2}\) & 0.00794674 & 0.0003 & 3.71693 & 71.43 & 0.0137 & 5 \\ \(X_{3}\) & \(-\)0.00894674 & 0.0004 & 4.71124 & 90.53 & 0.0109 & 4 \\ \(X_{4}\) & \(7.326\times 10^{-5}\) & 2.684 \(\times 10^{-8}\) & 0.000315893 & 0.0061 & 0.9450 & 11 \\ \(X_{5}\) & \(-\)0.00937326 & 0.0004 & 5.17115 & 99.37 & 0.0099 & 3 \\ \(X_{6}\) & 0.00772674 & 0.0003 & 3.51397 & 67.53 & 0.0145 & 6 \\ \(X_{7}\) & 0.00251326 & 0.0000 & 0.371776 & 7.14 & 0.1161 & 9 \\ \(X_{8}\) & 0.00391326 & 0.0001 & 0.901329 & 17.32 & 0.0532 & 8 \\ \(X_{9}\) & \(-\)0.0152267 & 0.0012 & 13.6465 & 262.23 & 0.0038 & 1 \\ \(X_{10}\) & 0.00603326 & 0.0002 & 2.14245 & 41.17 & 0.0234 & 7 \\  & & \(R^{2}\) = 0.999, \(R^{2}_{\text{adj}}\) = 0.9901, \(CV\) = 11.57\%, Adeq Precision = 34.0778 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of significance parameters in PB test.  
Figure 14: Pareto chart.  
other parameters are selected to the middle level to carry out the steepest climb test. The test design and results are shown in Table 3.  
As can be seen from Table 3, the volume ratio increases and then decreases, and the volume ratio of the third group of tests is the largest. Thus, the optimal interval of the significance parameter is near the level selected in the third group, so the levels selected in the second, third, and fourth groups of tests are chosen for the response surface test.
# Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index
## 3 Modelling Process and Results
### Conqueror Models  
#### 3.2.1 Random Forest Model  
The RF model was developed using four input variables, including \(V_{p}\), \(D\), \(R_{n}\), and \(Is_{50}\) to predict the rock \(BI\). The present study employed several parameters to develop the RF model. After trial and error procedure, the number of models to build was set as 100, the sample size was set as 0.95, the maximum number of nodes was set as 10,000, and maximum tree depth and minimum child node size were set as 10 and 2, respectively. Predicted \(BI\) values by RF, along with their actual values for training and testing datasets, are displayed in Figure 6. The obtained \(R^{2}\) values of 0.89 and 0.75 for train and test stages of RF model, respectively revealed a high and suitable accuracy level of train and test stages. In addition, the RF model identified the importance of the input variables (Figure 7). As can be seen, the \(R_{n}\) with importance of 0.37 was identified as the most important variable and followed by \(V_{p}\) with importance of 0.35 and \(\text{Is}_{50}\) with importance of 0.29. It is noteworthy to mention that the RF model did not consider \(D\) as an important factor.  
Figure 5: Evaluation of the models proposed using a gain chart.  
Figure 6: Testing and training results of RF model to predict the \(BI\).
## 5 Coating Materials with Embedded Antibiotics and Other Antibofilm Agents
### Silver Nanoparticles and Silver Ions  
Antimicrobial properties of silver have been well-known for a long time, but the mechanism underlying silver nanoparticles-mediated toxicity is still incompletely understood, and the full details of how silver nanoparticles induce toxicity in bacterial cells remain to be elucidated. However, a series of studies have provided important mechanistic insights into the antibacterial properties of both zero-valent silver nanoparticles and silver ions [82; 83; 84; 85].
## 5 Experimental Results
### Experiment 1: Approximation Problem  
In this experiment, we used FNNs with the structure of 1-\\(S_{1}\\)-1 to approximate the function \\(f=\\sin(2x)\\mathrm{e}^{-x}\\), where \\(S_{1}\\) is the number of hidden nodes with \\(S_{1}\\) = 3, 4,..., 7. The training dataset was constructed when \\(x\\) is in the range of [0, \\(\\pi\\)] with increments of 0.03, while the testing dataset was obtained at an interval of 0.04 in the range of [0, \\(\\pi\\)]. So, the numbers of samples in the training and testing dataset are 105 and 78, respectively. The search space was [-10, 10]. For every fixed hidden node number, the three algorithms were run five times. Table 1 gives the performance comparisons on the testing dataset for the three algorithms.  
The results in Table 1 indicate that PSOCS-FNN has the best performance in almost all criteria. Table 1 also reveals that the best architecture for this function approximation problem is with \\(S_{1}\\) = 6. Figure 4 shows the convergence rates of PSO-FNN, CS-FNN, and PSOCS-FNN based on the average values of SSE in 500 iterations. It is clearly seen that during the initial iterations the convergence of the PSO-FNN was very fast; however, in the last iterations, it had almost no improvement and was trapped in local minima of the parameter space. These figures indicate that PSOCS-FNN and CS-FNN have very close results, but PSOCS-FNN is better.  
\\begin{table}
\\begin{tabular}{c c c c c c} \\hline \\hline \\multicolumn{2}{c}{**Hidden**} & \\multirow{2}{*}{**Algorithm**} & \\multicolumn{5}{c}{**SSE**} \\\\ \\cline{3-6} \\multicolumn{2}{c}{**Node (\\(S_{1}\\))**} & & **Min** & **Average** & **Max** & **Std. Dev.** \\\\ \\hline \\multirow{3}{*}{3} & PSO & 1.504569 & 2.197208 & 2.64631 & 0.462301 \\\\  & CS & 1.064576 & 1.765316 & 2.212237 & 0.463635 \\\\  & PSOCS & **1.047247** & **1.737105** & **2.166139** & **0.457843** \\\\ \\hline \\multirow{3}{*}{4} & PSO & 0.861025 & 1.225908 & 1.736935 & 0.348625 \\\\  & CS & **0.414418** & 0.810047 & 1.323472 & 0.360855 \\\\  & PSOCS & 0.427326 & **0.790218** & **1.27989** & **0.342118** \\\\ \\hline \\multirow{3}{*}{5} & PSO & 0.691495 & 0.875569 & 1.299515 & 0.250296 \\\\  & CS & 0.286534 & 0.453186 & 0.877309 & 0.248645 \\\\  & PSOCS & **0.253777** & **0.430846** & **0.822753** & **0.233818** \\\\ \\hline \\multirow{3}{*}{6} & PSO & 0.513046 & 0.828988 & 0.969032 & 0.181192 \\\\  & CS & 0.127328 & 0.414455 & 0.554916 & **0.167307** \\\\ \\cline{1-1}  & PSOCS & **0.088938** & **0.384225** & **0.512852** & 0.171315 \\\\ \\hline \\multirow{3}{*}{7} & PSO & 0.619243 & 0.91505 & 1.150879 & 0.190815 \\\\  & CS & 0.206532 & 0.48647 & **0.670016** & **0.170129** \\\\ \\cline{1-1}  & PSOCS & **0.155654** & **0.462798** & 0.725362 & 0.202521 \\\\ \\hline \\hline \\end{tabular}
\\end{table}
Table 1: The performance comparisons of the PSO-FNN, CS-FNN, and PSOCS-FNN in the function approximation problem.
## 9 Green Hydrogen and Ammonia: A Key Investment in the Green Energy Transition  
Governments, private organizations, and investors are investing in green energy research and development to promote rapid use of these clean energy sources. Despite its potential for decarbonization in a variety of industries, such as transportation, industry, and power production, the demand for green hydrogen and ammonia is anticipated to increase significantly globally in the coming years, as shown in Figure 29.  
According to the Hydrogen Council, the world's yearly demand for hydrogen alone might approach 500-680 million metric tons by 2050 [156]. Take the example of Morocco, which has put forward an ambitious plan to develop green hydrogen and ammonia as part of its strategy efforts to decrease its dependence on petroleum and coal and to advance sustainable economic growth. In 2020, the Moroccan government announced a roadmap to create a green hydrogen sector in the country by 2030, which includes the construction of a sustainable hydrogen manufacturing plant, the creation of a regulatory framework, and the promotion of research and innovation. The government has also announced plans to develop a green ammonia project, which will involve the production of ammonia using renewable energy sources such as wind and solar power. The country is a major producer of phosphate, a key ingredient in fertilizer production that accounts for 85%. The data in Table 16 were taken from statistical databases from reports of the Minister of Energy, Mines, and the Environment. Renewable hydrogen and ammonia can cover nitrogen fertilizer requirements. This provides several benefits to Moroccan producers, such as decreasing the need to import from other countries and encouraging the microeconomy to consume local products in accordance with the economy of the country. Three important axes must be depended on to allow the implementation of the national policy for green hydrogen and ammonia in order to meet local demand and optimize national potential, particularly through exports: (1) technology, which includes technological improvements for cost reductions; (2) investment and supply, which includes investment conditions in the green hydrogen sector and its derivative products; and (3) finding market gaps or unmet needs and creating products or services to fill those gaps, ultimately leading to the creation of new markets [157; 158].  
Figure 29: Future demand for ammonia and hydrogen until 2050 [155].
# FaceMashup: An End-User Development Tool for Social Network Data +
## 1 Introduction  
The availability of application programming interfaces (APIs) for accessing social network data is a gold-mine for developers, since they provide a standard, easy and trustworthy way for requesting user's information, useful for a personalized application experience. Social Network APIs, such as the Facebook Graph API [1], apply strict rules in order to protect their users' privacy. They must explicitly grant the access on specific information to the different applications. For instance, a simple application can show the contents created by friends while being in a restaurant, taking its location as input. Provided that the application has the permission to access the data, it costs only a few lines of code for developers to retrieve contents created by the user's friends in that specific place, combining comments, photos and videos.  
In contrast, even if users are the owners of the data, they have less control of it. The social network application mediates their access: they cannot combine sources, filter results or perform specific actions if not explicitly provided by the interface. Considering the increasing growth of social networks' size, users are already overwhelmed by the contents created every day [2]. This opened the market for third party applications that provide visualizations for specific user interests (e.g., travelling, restaurants, music _etc._). However, it is also difficult for third party developers to supportunusual searches and other specific tasks that have a smaller potential audience. Nevertheless, for these small groups, such functionalities are useful.  
In this paper, we propose to fill this gap applying End User Development (EUD) techniques for enabling end-users to inspect and control their social network data, creating applications able to both retrieve and manipulate their contents. We describe the design iterations and the implementation of FaceMashup, an environment allowing users to mix contents their contents and those created by their friends, and to perform actions on them. As an example of a social network, we focus on Facebook.  
FaceMashup represents the Graph API data (e.g., user profiles, photos, comments, videos, _etc._) with the same elements used in the social network application interface (e.g., the Facebook web client in our case), including them in dedicated widgets. Starting from these building blocks, the user can drag interface elements between different widgets for retrieving, combining and filtering data coming from the Facebook graph. In addition, (s)he can perform actions on the selected entries through a set of action widgets. In the last part of the paper, we report on a preliminary user study on the FaceMashup prototype, which shows a good completion rate, even for difficult tasks (e.g., the combination and filtering of more than two data sources), a good overall acceptance of the tool and the proposed EUD techniques, together with the need for more effective help during the widget connection.