esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 GAs for Classification Task  
### Computational Evolutionary Environment (CEE)  
[PERSON] and colleagues [9] introduced a genetic algorithm (GA) capable of generating IF-THEN classification rules, a development that laid the foundation for subsequent advancements in GA-based methods. One notable example is the computational evolutionary environment (CEE) [10], designed specifically to extract IF-THEN rules from the gene ontology (GO) database. Its chromosome structure was inspired by the earlier work of [PERSON] and collaborators, with each gene representing a distinct attribute of the dataset.  
In CEE, the chromosome structure is composed of three components for each gene: weight (\(W_{i}\)), operator (\(O_{i}\)), and value (\(V_{i}\)), as shown in Figure 1. In the CEE approach, the dataset used contained 34 attributes, which is why the notation 'Gene\({}_{34}\)' is employed in Figure 1 to accurately represent the number of features in the dataset. These components together define conditions for the antecedent (IF part) of a rule, while the entire chromosome represents the complete antecedent. The weight field (\(W_{i}\)) determines the inclusion of a condition based on its threshold, enabling flexibility in rule generation. If the weight surpasses the threshold, the condition is incorporated; otherwise, it is omitted.  
In CEE, the weight field can be a number between 1 and 10 (\(W_{i}\) = 1,..., 10), and its threshold is usually 8 or 9. The operator (\(O_{i}\)) field determines which relational operator will appear in the condition, and the two possible operators are \(\geq\) or \(<\). Finally, the value (\(V_{i}\)) field represents a numeric value in the attribute domain.  
The GA iteratively searches for optimal classification rules by executing \(n\) runs, one for each target class. The evaluation of individuals (rules) relies on a fitness function (FF) that quantifies their classification performance. When evaluating a rule for a class C, four outcomes are possible depending on the prediction [21]:  
Figure 1: CEE chromosome structure.  
* True Positive (_tp_): The predicted class matches \(C\), and the case belongs to \(C\);
* True Negative (_tn_): The predicted class does not match \(C\), and the case does not belong to \(C\);
* False Positive (_fp_): The predicted class matches \(C\), but the case does not belong to \(C\);
* False Negative (_fn_): The predicted class does not match \(C\), but the case belongs to \(C\).  
To enhance classification performance, the FF combines two widely used medical domain indicators: sensitivity (_Se_) and specificity (_Sp_), which are mathematically defined as  
\[Se=\frac{tp}{tp+fn} \tag{1}\]  
\[Sp=\frac{tn}{tn+fp} \tag{2}\]  
The FF aims to maximize these metrics simultaneously, as demonstrated in Equation (3). Consequently, CEE operates as a binary classifier, treating all non-target classes as a single group (one-vs-all strategy) during each execution.  
\[fitness=\frac{Se+Sp}{2} \tag{3}\]
## 3 Proposed MISO Converter
### Basic Operating Principles
#### 3.2.8. State 8 [\(t_{7}\leq t\leq t_{8}\)]  
As shown in Figure 13, the switches \(S_{1a}\), \(S_{1b}\) and \(S_{2b}\) are off, and the diodes \(D_{2}\) and \(D_{4}\) are off, and the diodes \(D_{1}\) and \(D_{3}\) are on. During this state, the input voltage \(V_{d2}\) continues to excite the magnetizing inductance \(L_{m2}\) and the leakage inductance \(L_{llp2}\), whose voltages on them are \(k_{2}V_{d2}\) and \((1-k_{2})V_{d2}\), respectively. At the same time, the magnetizing inductance \(L_{m1}\) and the leakage inductance \(L_{llp1}\) are demagnetized and the voltages on them are \(k_{1}(V_{d1}-V_{C4})\) and \((1-k_{1})(V_{d1}-V_{C4})\), respectively, forcing diodes \(D_{1}\) and \(D_{3}\) to conduct. There are three key paths: path 1 is that the current \(i_{llp1}\) flows through the body diode of \(S_{1b}\) and hence the leakage inductance \(L_{llp1}\) releases energy to the clamping capacitor \(C_{4}\); path 2 is that the energy-transferring capacitor \(C_{1}\) charges the energy-transferring capacitor \(C_{3}\); path 3 is that the output capacitor \(C_{o}\) releases energy to the load. It is worth mentioning that the two coupling inductors have transformer behavior. In addition, the diode \(D_{2}\) is connected in parallel with the energy-transferring capacitor \(C_{1}\) and has a reverse-biased voltage of \(V_{C1}\), and the diode \(D_{4}\) has a reverse-biased voltage of \(V_{o}-V_{C2}-V_{C3}+nV_{d2}\), so that both the diodes \(D_{2}\) and \(D_{4}\) are cut off in this state. As the switch \(S_{1b}\) is turned on, state 8 ends.  
Figure 12: Circuit operation for state 6.  
Figure 13: Circuit operation for state 8.  
3.2.9. State 9 [\(t_{8}\leq t\leq t_{9}\)]  
As shown in Figure 14, before the start of state 9, the body diode of \(S_{1b}\) is on, so \(S_{1b}\) has zero voltage switching (ZVS) turn-on.
## 5 Simulation Results Analysis
### Adaptability Test
#### 5.2.2 Test Scenario II  
Based on test scenario I, a more complex driving scenario was constructed, with the six environmental vehicles within the 200 m range before and after the autonomous driving vehicle being sensed increased to 10. At the same time, the policy update frequency was set to 0.5 Hz so that the model could adapt to environmental changes more slowly. Other parameters remained the same as scenario I.  
Figure 12 shows the collision of the three decision algorithms during the test, where collision is defined as two values: collision = 0 means that the autonomous vehicle has not collided with other vehicles; collision = 1 means that the autonomous vehicle has collided with other vehicles. The results show that DPD3 QN and DDQN successfully avoided collisions after 733 and 827 rounds, respectively. This shows that the DPD3 QN method performs particularly well when dealing with complex scenarios. This advantage is attributed to the introduction of dual-priority experience replay, which helps autonomous vehicles identify and utilize key experiences more quickly, thereby accelerating the learning process and making better choices. In addition, the existence of the dueling network structure enables the algorithm to take into account the state value when updating the action value, thereby more accurately obtaining the Q-value corresponding to each action. The specific test results are shown in Table 4:  
Figure 11: (**a**,**b**) Autonomous driving vehicle lane changing scenario.  
Figure 12: Collision situation in test scenario II.  
This result is due to the increase in the complexity of the test scenarios, which leads to a decrease in the success rate and average speed of autonomous vehicles in the decision-making process. At the same time, the number of collisions increases, which, in turn, leads to a decrease in the reward obtained per round and, ultimately, a decrease in the average reward.  
As demonstrated in Table 4, the DPD3 QN algorithm achieves a success rate surpassing DDQN and DQN by 8.8% and 22.2%, respectively, in scenario II testing. It also exhibits superior performance in both average reward and speed metrics. Upon test completion, DPD3 QN yielded an average reward of 12.65 and maintained a speed of 26.95 m\(\cdot\)s\({}^{-1}\), outperforming DDQN (11.01 reward, 26.11 m\(\cdot\)s\({}^{-1}\)) and DQN (9.75 reward, 25.89 m\(\cdot\)s\({}^{-1}\)). These results validate the enhanced learning efficiency, decision-making precision, and operational stability of the proposed method compared to conventional reinforcement learning frameworks.
## 4 Damage Detection Results by TD-BOCDA Sensor System
Using this integrated frequency shift and the proportional coefficient between  
Figure 8: One-dimensional deformation graph along (**a**) the vertical axis, and (**c**) the horizontal axis, respectively, of (**b**) the damage map of 20 J impact with 16 Gbps modulation measurement. the frequency shift and strain, the total amount of residual strain was obtained at each impact event. Figure 9 shows the result according to the impact energy. Because the impact events of 5, 10, 15, and 20 J were repeated three times, each integrated residual strain is shown with the measurement deviation. The graph shows that the total amount of residual strain at each impact point is linearly proportional to the applied impact energy, although as the impact energy is reduced, there is some difference from the fitted proportional line.
## 2 Background Theory
### Random Forest Algorithm  
The decision tree is one of the ML techniques that can provide classification and regression by dividing the pattern of data step by step in the form of tree branches. If the data is plotted in two-dimensional of x-axis and y-axis, the analysis can be visually performed through graphs and however, it is difficult to directly understand the graph when there are many variables. Thus, the decision tree based on binary classifier is used. Even though this method has an advantage of easily checking the data structure, it has limitation to increase reliability because the error is continuously accumulated to a next level. The final value, which is affected by small noise, may be biased due to characterization of architecture in decision tree. To improve the disadvantage, the algorithm of random forest (RF) was suggested. Even though the principle and architecture of RF is similar with decision tree, RF constructs the forest with various numbers of trees as an ensemble technique [29]. In RF, the bootstrap aggregating method is applied to create multiple training data sets from the same variable as shown in Figure 2. This method allows overlap when selecting data as sampling with replacement to have an equal size with the number of original data. The random subspace is also applied to determine the number of trees, and the various leaf nodes are divided from root node considering minimization of cost function. The various number of trees suppress overfitting occurred in the general decision tree using only one tree by strong law of large numbers. The input variable, that is not selected in bootstrap aggregating, is used as validation data to verify the reliability of constructed RF, and it is called to out of bag (OOB). The data sets can be randomly mixed to analyze the importance of each variable. If the errors based on OOB of original data and randomly mixed data are expressed by \(\mathrm{e_{i}}\) and \(\mathrm{r_{i}}\), the important score among input variables can be calculated through Equation (8).  
\[\mathrm{v_{i}}=\frac{\frac{1}{t}\sum_{i=1}^{t}\left|\mathrm{e_{i}-r_{i}}\right|} {\sqrt{\frac{1}{t-1}\sum_{i=1}^{t}\left[\left(\left|\mathrm{e_{i}-r_{i}} \right|\right)-\left(\frac{1}{t}\sum_{i=1}^{t}\left|\mathrm{e_{i}-r_{i}} \right|\right)\right]^{2}}} \tag{8}\]  
The grey relational grade (GRG) has been applied to find the numerical correlations between two variables and it can be calculated through Equation (9) based on referencing (\(\mathrm{X_{R}}\)) and comparing (\(\mathrm{X_{C}}\)) variables.  
\[\mathrm{GRG}=\frac{1}{n}\sum_{j=1}^{n}\frac{\underset{i}{min}\underset{j}{min} \left|\mathrm{X_{R,j}-X_{C,j}}\right|+\delta\underset{i}{max}\underset{j}{max} \left|\mathrm{X_{R,j}-X_{C,j}}\right|}{\left|\mathrm{X_{R,j}-X_{C,j}}\right|+ \delta\underset{i}{max}\underset{j}{max}\left|\mathrm{X_{R,j}-X_{C,j}}\right|} \tag{9}\]  
Figure 2: Architecture of random forest. The R and L in decision tress denote the root node and leaf node, respectively.  
where j is sequence of each variable. The \(\delta\) denotes the resolving coefficient and the value is generally assumed to 0.5 [30].
# Smart Learning Technological in the Economy 5.0--The Polish Perspective
## 5 Results
### Instrumental Support in the Model of Learning and Teaching Development in the Future  
The instrumental support of Smart learning (Figure 3) will be according to respondents integrated communication systems as the basis of the information system (81.1%). This is the opinion of the dominant group of younger people up to 39 years of age (79.13%), graduate students (87.5%), and part-time students (84.6%), which confirms the statistically significant relationship \(\chi^{2}\) (Figure 3).  
The respondents admit that virtual e-books and e-materials, which will replace paper materials, will also be a significant support (80.1%). This opinion is shared by the dominant group of younger people up to 39 years of age (75.3%), graduate students (86.4%), and part-time students (84.6%), which is confirmed by a statistically significant relationship \(\chi^{2}\) (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Gender**} & \multicolumn{4}{c}{**Age**} & \multicolumn{4}{c}{**Level**} & \multicolumn{4}{c}{**Mode of Studies**} \\ \cline{3-13}
**Variants** & & & & & & & & & **Graduate** & & **(f-t and p-t)** & \\ \cline{2-13}  & \(\chi^{2}\) & \(\alpha\)* & **k***b & \(\chi^{2}\)** & \(\alpha\) & **k** & \(\chi^{2}\) & \(\alpha\) & **k** & \(\chi^{2}\) & \(\alpha\) & **k** \\ \hline each part of the home will become an integral part of education & 17.986 & 0.001 & 4 & 33.321 & 0.001 & 12 & 45.41 & 0.000 & 4 & 23.278 & 0.000 & 4 \\ \hline classrooms will be equipped with intelligent infrastructure & 13.559 & 0.009 & 4 & 25.21 & 0.014 & 12 & 40.739 & 0.000 & 4 & 15.379 & 0.004 & 4 \\ \hline LMS platforms will be the backbone of the teaching system & 13.469 & 0.009 & 4 & 23.878 & 0.021 & 12 & 38.646 & 0.000 & 4 & 16.659 & 0.002 & 4 \\ \hline e-books and e-materials will replace paper versions & 3.535 & 0.474 & 4 & 38.747 & 0.000 & 12 & 26.065 & 0.000 & 4 & 51.661 & 0.000 & 4 \\ \hline integrated communication systems will be the basis of the information system & 5.374 & 0.251 & 4 & 36.003 & 0.000 & 12 & 25.624 & 0.000 & 4 & 40.147 & 0.000 & 4 \\ \hline \hline \end{tabular}  
* \(\alpha\)—confidence level b k—number of the degrees of freedom c f-t—full-time studies, p-t—part-time studies.  
\end{table}
Table 3: Instrumental support for Smart learning in terms of gender, age, level, and mode of study–test of independence \(\chi^{2}\).  
Figure 3: Instrumental support of Smart learning in practice.  
According to respondents another instrument will be LMS platforms as the basis of the didactic system (64.0%). This opinion is shared by slightly more younger people up to 39 years of age (61.56%), men (61.5%), graduate students (76.2%), and full-time students (64.9%), which confirms the correlation statistically significant \(\chi^{2}\) (Table 3).  
Few of the respondents are of the opinion that each part of the home will become an integral component of education in the future (14.7%), which is understandable due to the specificity of some fields and scientific disciplines.
## 3 Transactive DR Based on Flexibility Potential Evaluation  
### Functional Process of Transactive DR Framework  
The functional overview of the proposed transactive DR framework is represented in Figure 2, and characterized as a five-step process:  
(1) A pre-dispatch would be performed for each prosumer aggregator with the forecasted multi-energy supply-demand. All flexible and inflexible multi-energy resources are economically optimized.  
(2) A flexibility potential quantification method is developed in order to compute the maximum possible deviation of every flexible resource from the cost-optimal pre-dispatch  
Figure 2: Functional process of the transactive DR framework.  
at any given time. Once the positive and negative flexibility are quantified from the aspects of time and power, these can be aggregated to form the overall flexibility potential of prosumer aggregators.  
(3) A pricing mechanism is developed to price the flexibility potential of prosumer aggregators and account for their profitability in an ancillary service.  
(4) Once receiving the real-time DR task from the distribution system operator, cost-optimal scheduling is performed to obtain the scheduling plan for each prosumer aggregator.  
(5) After receiving the scheduling plan, each prosumer aggregator performs real-time DR to re-schedule the flexible and inflexible multi-energy resources, while meeting its own supply and demand balance.
## 4 Conclusions  
Previous works on dust control in unpaved roads have mainly focused on dust emission by vehicle (wheels) traveling. The results of the control soils (no-treatment) in this study clearly show that dust emission by wind from unpaved road can substantially contribute to the mass transfer, and thus should be considered. The experiment on dust control products in laboratory conditions with no vehicle transport on the soil revealed high efficiency in dust emission suppression for all the products (Brine, Lignin, Resin, Bitumen, PVA), reducing at least 77% of the dust emission by wind. However, the efficiency of most of the products was reduced significantly when applied and tested under field  
Figure 7: The dependence of PM10 emission in the Brine concentration applied on the soil under two wind 6.5 and 9.5 m s\({}^{-1}\). The efficiency is represented by the emission coefficient (\(Y\) axis) that is the ratio between the dust fluxes of the Brine sample (different concentration) and the control (no-treatment) sample, in which, the lower value, the higher dust emission suppression.  
conditions in quarry-haul truck road. Two weeks later and after 446 fides of the quarry trucks, the products were not efficient to prevent dust emission by wind, except of the Brine.  
The Brine enables to keep on very high efficiency (>90%) to suppress dust emission by wind over time. Additional experiments revealed that Brine can be applied with reduced amounts and still keeping on low emission. There are only few scientific articles on the application of hydrous magnesium chloride (Brine). Those studies showed the high efficiency of the Brine to reduce dust emission due to vehicle (wheels) traveling. In this study, the Brine was found to be very efficient also in wind erosion and may provide a good solution for product application in disturbed soils especially in the water-limited areas of the arid and semi-arid climates. Nevertheless, further study is needed to investigate the possible environmental impacts of the diverse dust suppression substances, including toxicity of atmospheric particulate matter when dust is emitted from the treated soils and/or soil-groundwater pollution because of vertical fluxes of the applied solutions on the surfaces.  
This research was funded by the Ministry of Energy, Israel, grant number 212-17-021.  
The author declares no conflict of interest.
# Drone-Based Vibration Monitoring and Assessment of Structures
## 2 Background
### Unmanned Aircraft Systems in SHM  
One of the major challenges in SHM is accessing remote areas or difficult-to-access segments of structures. UAS (drones) are increasingly used as a solution to this problem. Thus far, much of the research considering drones in SHM applications has focused on the use of onboard cameras for image acquisition [8,9]. The assessment is then performed by a qualified engineer through visual inspection of the images with or without the use of image processing software. Another approach is to use a drone to hold a reflector prism against the bridge for total station measurements instead of holding the prism manually [10]. In this case, the prism is affixed directly to the UAS in a configuration described as a _helping hand_[11]. Moreover, Unmanned Aerial Manipulators (UAMs) are a new type of drone that achieve higher precision by utilizing a gripper or multifingered end effector to provide additional degrees of freedom over _helping hands_[11].  
[PERSON] et al. [10] presented an application of drones for SHM in which a dynamic model and controller were developed to achieve contact between the manipulator arm and the underside of a structure. They proposed to use an ultrasound sensor, attached to the end effector, to assess the depth of cracks in the structure. During the experiments, the proposed aerial manipulator was able to maintain continuous contact with the structure, which is necessary for the assessment; however, the experiments were performed without the actual ultrasound sensor. In another study by [PERSON] et al. [12], a contact-based UAS measured the electrical potential difference between materials in a sample of concrete as a method of nondestructive testing.  
In addition to these contact-based methods, there is ongoing research into the deployment and retrieval of sensors by drones. For SHM applications, these sensors can be any of the variety of options considered in the previous subsection. For example in [13], a drone was developed with a hooked gripper able to attach a dummy package with a preprepared adhesive to a target by using the arm to apply pressure to the package against the desired surface. The package was then removed using a loop attached to the package, with which the hook can engage, and then, reverse thrust was used to remove it from the surface. Closer to this work, drones were attached directly to civil structures using electropermanent magnets for the collection of vibration data from civil structures [14]. In this case, an electropermanent magnet was utilized to maintain contact between the drone and the underside of the structure until the onboard systems have completed the data collection. However, the sensor package was integrated with the UAS and not treated as a separate system for deployment.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 4 Results Analysis
### Prediction
#### 4.2.2 DBSCAN (Density-Based Spatial Clustering of Applications with Noise)  
DBSCAN is a density-based clustering algorithm that can find arbitrary-shaped clusters and handle noise (data points that do not belong to any cluster). It allowed the detection of clusters in noisy data; it was suitable for cookie data that contained noise, such as non-relevant or inconsistent third-party cookies. Clusters of densely connected cookies were obtained, ignoring noise and improving the handling of atypical or unusual cookies.  
For eps = 10, the results indicate that the DBSCAN model has achieved a perfect data grouping. The clusters (n = 2) (Figure 14) are well defined and separated, which is reflected in a high Silhouette Score, a high Calinski-Harabasz index, and a low Davies-Bouldin index (Table 8).