esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Work  
This research introduced a feature selection approach founded upon GA principles, augmented by localized optimization techniques, with the specific objective of classifying phishing websites instead of exhaustively using the entire set of 73 URL-based features. The application of our proposed methodology led to significant improvements in classical performance indicators for ML models, encompassing accuracy, recall, and precision. Furthermore, it is noteworthy that our approach consistently yielded feature subsets comprising fewer than 45 elements across all models, resulting in a substantial reduction in inference-related computational costs.  
Although the current study utilized URL-based features, our future work aims to enhance the feature set's versatility by incorporating content-based features such as HTML and CSS attributes. This expansion is intended to improve the effectiveness of phishing attack detection. Additionally, we plan to transform our research into a multi-objective optimization problem, taking into account the phishing detection scores of the model (e.g., accuracy and recall) alongside feature extraction time, which directly impacts costs, especially considering that content-based features are more computationally intensive than URL-based features. As part of our forthcoming efforts, we intend to develop a multi-objective feature selection methodology that adequately addresses these time-related complexities. Additionally, an extension of this research could involve applying the same algorithm to other related optimization problems, such as hyperparameter tuning for ML/DL models using GA. The process of finding the best hyperparameter combinations presents computational challenges similar to the feature selection task, making the proposed algorithm potentially valuable in this context as well.  
**Author Contributions:** Conceptualization, E.K. and O.K.S.; Methodology, M.K. and B.D.; Software, E.K. and M.K.; Resources, E.K.; Data curation, M.K.; Writing--review & editing, B.D.; Supervision, O.K.S.; Project administration, B.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding  
**Data Availability Statement:** The data presented in this study are openly available in [[https://www.kaggle.com/datasets/mehmetorkomaz/high-risk-url-and-content-dataset](https://www.kaggle.com/datasets/mehmetorkomaz/high-risk-url-and-content-dataset)]([https://www.kaggle.com/datasets/mehmetorkomaz/high-risk-url-and-content-dataset](https://www.kaggle.com/datasets/mehmetorkomaz/high-risk-url-and-content-dataset)) (accessed on 20 May 2024).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Experimental Tests and Aeroacoustic Simulations of the Control of Cavity Tone by Plasma Actuators
## 4 Results and Discussions
### Control Effects on Flow Structures and Pressure Fields
#### 4.2.2 Power Spectra of Velocity Fluctuations  
Figure 17 shows the predicted power spectra of velocity fluctuations at \(x/L=0.5\) and \(z=z_{\text{pa}}\) at the peak of maximum power for \(y/\theta=6.4\) and \(3.6\), respectively, with and without control, along with the measured data. The figure shows that the power at the fundamental frequency is weakened by the control in both computations and experiments. This corresponds to the weakening of the above-mentioned large-scale vortices with spanwise axes that produce the cavity tone.
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 5 Experiment and Analysis
### Multi-Domain Dialogue Modeling
#### 5.1.3 Human-in-the-Loop Evaluation  
Although previous offline evaluation results show the superiority of our MSTOD model, we also perform an online interactive human evaluation [32, 33] to estimate its practical ability. We compare our STOD with the strong baseline Galaxy by interacting with real human users. In each dialogue session, we randomly sample a user goal to guide the dialogue. (We use the tools of ConvLab-2 ([[https://github.com/thu-coai/ConvLab-2](https://github.com/thu-coai/ConvLab-2)]([https://github.com/thu-coai/ConvLab-2](https://github.com/thu-coai/ConvLab-2)) (accessed on 1 December 2023)) to construct new user goals. Note that these goals are different from existing dialogues in the original MultiWOZ corpus.) Then, a user is instructed to converse with STOD or Galaxy to complete the task by following the sampled user goal. At the end of each session, the user is asked to give explicit feedback from three perspectives: (1) **Success** measures if the model successfully completes the user goal (i.e., whether the movie tickets were booked with all the user constraints satisfied). (2) **Coherency** measures whether the model's response is logically coherent with the dialogue context. (3) **Fluency** measures the fluency of the model's response. Each user goal is independently evaluated by three real users on a 3-point Likert scale (0 for the worst, 1, or 2 for the best). We randomly select 50 user goals and report the average score of three users via online interaction for each user goal.  
Table 3 shows the results with the first row denoting strong inter-annotator agreements as measured by the Fleiss kappa coefficient [34]. Compared with Galaxy, our STOD achieves better scores on all metrics. Moreover, we find that STOD significantly outperforms Galaxy on the Success and Coherency metrics, suggesting that STOD generates more semantically coherent responses and achieves a higher task completion rate. On the Fluency metric, both systems achieve decent performance due to the pre-trained language models. Future research should pay more attention to the contextual coherency and task success rate in practice.
# Investigating Supercomputer Performance with Sustainability in the Era of Artificial Intelligence
## 5 Results and Discussion
### Performance Analysis of Top-Performing and Most Energy-Efficient Supercomputers  
An analysis of the performance (Rmax) of top-performing supercomputers and the performance (Rmax) of top-EE supercomputers has been conducted to determine the relationship between the two groups. Generally, Table 7 indicates a negative correlation between the performance of top-performing supercomputers and the performance of top-energy-efficiency supercomputers, with the exception of three weak positive correlations. This inverse relationship suggests that as the performance of top-performing supercomputers increases, the performance of energy-efficient supercomputers tends to decrease. This trend has been consistently observed over the five-year period from 2020 to 2024.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Nov24** & **June24** & **Nov23** & **June23** & **Nov22** & **June22** & **Nov21** & **June21** & **Nov20** \\ \hline June24 & 0.984 & & & & & & & & \\ Nov23 & 0.943 & 0.938 & & & & & & & \\ June23 & 0.917 & 0.909 & 0.967 & & & & & & \\ Nov22 & 0.930 & 0.919 & 0.965 & 0.998 & & & & & \\ June22 & 0.899 & 0.902 & 0.931 & 0.989 & 0.989 & & & & \\ Nov21 & 0.880 & 0.874 & 0.945 & 0.993 & 0.989 & 0.990 & & & \\ June21 & 0.883 & 0.876 & 0.945 & 0.993 & 0.989 & 0.990 & 1.000 & & \\ Nov20 & 0.890 & 0.885 & 0.955 & 0.995 & 0.991 & 0.990 & 0.999 & 0.999 & \\ June20 & 0.903 & 0.903 & 0.967 & 0.998 & 0.994 & 0.989 & 0.995 & 0.994 & 0.997 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance analysis with _correlation_ for top-performing supercomputers.  
In other words, achieving higher computational power among top-performing supercomputers often comes at the expense of EE. Consequently, supercomputers with the highest computational performance may not necessarily operate at peak EE, highlighting a trade-off between maximizing computational power and optimizing EE. The likely reasons for the differences in performance and EE between the two groups of supercomputers are different system sizes, different types of processors and accelerators (CPU vs. GPU or other accelerators), different energy systems, and different hardware generations (i.e., \"age\" of systems leading to different silicon sizes).
# Analysis, Generation, and Validation of New Boards for the Game Micro Robots
## 2 Background
### Search Algorithms  
Search algorithms are crucial computational tools for exploring the structure of a graph. They operate systematically, following a set of predefined rules to analyze and extract information about the relationships and connections within it [32]. Some examples are:  
* **Depth-first search (DFS):** The depth-first search algorithm is used to traverse graphs. This algorithm explores in depth from an initial vertex, searching as far as possible along each path before backtracking and continuing with the next vertex [32; 33]. One advantage of this algorithm is that nodes are visited only once. Additionally, it is easily modifiable and can be used to solve simple problems such as connectivity, cycle detection, and simple paths [34].
* **Breadth-first search (BFS):** The breadth-first search algorithm is also used to explore graphs. It starts at an initial vertex and explores all adjacent nodes before proceeding to more distant vertices [32; 33]. The main difference between BFS and DFS lies in their search strategy. BFS tends to be more effective for finding the shortest path between two nodes because it explores the nearest nodes first. Beyond that, it has the same characteristics as DFS [34].
* **Dijkstra:** Dijkstra is a search algorithm for finding minimum-cost paths in directed and undirected graphs, as long as their weights are not negative. This algorithm finds the shortest path from a source node to all other reachable nodes in the graph [32]. It is widely used in graph representations to find solutions to maze problems [35].
* **A*:** A* is a pathfinding algorithm that uses a heuristic function to find the shortest path between a start node and a target node in a graph. The heuristic guides the search towards the target vertex, prioritizing the exploration of nodes with the lowest estimated total cost [32; 36; 37]. This function allows A* to adapt efficiently to different problems [38].  
This work uses DFS because we do not need to know the path length, only whether the graph is fully connected.
## 2 SBRT in Ovarian Primary Oligometastases
Similarly, a single-institution retrospective study by [PERSON] et al. evaluated the efficacy of SBRT in patients with oligometastatic ovarian cancer ineligible for surgery or systemic therapy [15]. A total of 82 patients with 156 lesions were included and received a median dose of 24 Gy in three fractions. Complete response was reported in 60% of patients after a median follow-up of 17.4 months. The median systemic treatment-free interval after SBRT was 7.4 months while the 2-year local progression-free survival was 68%. Most failures (90%) were outside of the radiation field [15]. Overall, SBRT is an effective and well-tolerated treatment option for ovarian primary oligometastases, with high rates of complete response and local control, as well as favorable overall survival and low rates of late toxicity. Prospective studies are ongoing, including the MITO RT3/RAD study (NCT04593381) [16], which is a multicenter prospective phase II study evaluating SBRT for the treatment of oligometastatic, persistent or recurrent ovarian cancer not amenable to surgery, local therapy and systemic therapy. SBRT is delivered in one, three or five daily fractions to a total dose of 30-50 Gy to all sites of active metastatic disease reported on imaging. The primary endpoint is a clinical complete response on a per lesion basis which aims to complete enrollment and analysis in 2023. While awaiting the results of prospective data, select patients with oligometastatic, recurrent or persistent ovarian cancer should be considered for treatment with SBRT especially in patients who are not candidates for surgery and systemic therapy.
## 6 Application of HTLS Conductors to Increase the Ampacity of Transmission Overhead Lines
This line utilizes the conductor 352-AL1/59-ST1A, and weather data were obtained on 25 November 2021. Current load data were obtained from PMU units in the electric power system of the Slovak Republic, and weather data were obtained from the distribution system. Figure 14 illustrates the resulting steady-state dynamic ampacity with the original ACSR conductor for a 24 h interval, with the graph also depicting surrounding meteorological conditions. Figure 15 displays the replacement of the ACSR conductor with its compatible ACCC conductor (LUBBOCK). The ampacity increased by at least 694.26 A and at most 973.29 A with the replacement of the ACCC conductor, resulting in an average increase of 778.22 A. The ACCC conductor LUBBOCK operates at a temperature of 200 degC, which is its maximum allowable operating temperature. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ACSR Conductor** & **243-AL1/39-ST1A** & **352-AL1/59-ST1A** & **382-AL1/49-ST1A** & **429-AL1/56-ST1A** & **445-AL1/74-ST1A** \\ \hline Conductor diameter (mm) & 21.75 & 26.50 & 27.00 & 28.70 & 29.70 \\ \hline Approx. weight (kg/km) & 980.1 & 1433.45 & 1442.5 & 1620.8 & 1831.81 \\ \hline ACCC conductor & LISBON & LUBBOCK & STOCKHOLM 2L & HAMBURG & ROME \\ \hline Conductor diameter (mm) & 21.79 & 26.42 & 26.39 & 28.63 & 29.90 \\ \hline Approx. weight (kg/km) & 946 & 1375 & 1395 & 1627 & 1775 \\ \hline Diameter of the aluminium wire in the aluminium conductor (mm) & 7.11 & 8.76 & 8.76 & 8.76 & 9.53 \\ \hline Electrical resistance of the conductor (\(\Omega\)/km) & 0.0887 & 0.0608 & 0.0605 & 0.0514 & 0.0474 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Alternative replacement for ACSR conductors. Figure 14: Analysis of the dynamic steady-state ampacity \(I_{\text{max}}\) for transmission line V427 utilizing ACSR conductor 352-AL1/59-ST1A.
## 4 RL-Based Decision Making Solution
### RL-Based Solution
#### 4.2.2 Action Space  
The action in the proposed RL-based solution determines the selection of a switch neighbour to be the next forwarder to deliver video streaming packets to a destination. It also determines the optimal video streaming traffic policy. The policy is intended to minimise the reward value in the Q-routing method. In the process of assigning a routing path for video streaming flows, the agent learns to choose routes with low delay, jitter and loss ratio while prioritising paths with high available bandwidth. Moreover, the actions should be modified according to the reward value.  
In Q-learning, the RL agent visits all states and tries different actions to approximate the optimal Q-function. It then updates and saves the Q-value after an episode in a Q-table which  
Figure 3: Sequence diagram of OpenFlow with RYU controller and RL-based solution.  
becomes a reference for the RL agent to select the best route for a node pair. The Q-value is defined as a measure of the overall expected reward if the RL agent is in a state \(S\) and takes action \(A\). The learning phase of the RL agent includes a series of stages named episodes \((0,1,2,\ldots,n,\ldots)\). Throughout the \(n\)th episode at time \(t\), the agent determines an action \(A\) on a video streaming packet at a current state \(S\) and obtains a reward \(R\) as it proceeds to the next state, \(S^{\prime}\). The optimal multimedia traffic routing problem can be modeled as a minimisation problem, and the Q-learning algorithm must be adjusted to adapt to minimisation requirements. The RL agent utilises the following modified Q-learning equation to update the Q-value for optimal path routing.  
\[Q(S,A)=\overbrace{Q(S,A)}+\alpha[\underbrace{R(S,A)}_{\text{Reward}}+ \overbrace{\min_{A}Q^{\prime}(S^{\prime},A^{\prime})}^{\text{Minimum predicted}}-Q(S,A)] \tag{7}\]  
where \(\alpha\) is the learning rate that decides how much of the new learned value will be utilised. As it can see from Equation (7), the output is the new Q-value for the state which is resulted by increment the current Q-value by \(\alpha\) multiplied by the chosen action's Q-value. \(\alpha\in[0,1]\), when the value \(\alpha\) is set to 0, the RL agent will not be able to learn from new actions. Contrariwise, if it is set to 1, the agent totally passes over previous knowledge and only values the recent learned information taking into consideration the instant reward for the state-action pair. Higher \(\alpha\) values allow newly learned Q-values to change quickly.
## 3 Experiments and Results
### Results
#### 3.2.1 Recovery Performance Analysis
Since there is almost no recoverable signal at higher frequencies, the reconstruction results with the ptychographic method still have few cloudy features surrounding the recovered image.
## 3 Application to Resistive Driven-Rod Method: Testing with Some Examples
### Real Examples with Internal Resistance
As final comments, it can be stated that the soil resistivity profile could be recovered with a sufficient degree of reliability by using a few grounding resistance measurements provided that the sampling does not smooth possible structures in the grounding resistance profile that may distort the result of the minimization process of (17). Thin soil layers may not be well tested if the rods used in resistance logging are too long. Results improve using finer sampling as long as a greater number of resistive junctions have to be taken into account. The here-analyzed RDR method has been proven to be valid although it has certain drawbacks. Together with the aforementioned problem with the sampling of the grounding resistance, an increase in the difficulty of the minimization process of (17) must be added to achieve adequate values of the soil parameters. The introduction of the internal resistivity of the rod in the semi-empirical formulas for the calculation of the resistance (16), leads to the expression (17) which may have some local minima that the minimization algorithm can take as the solution of the problem. The proper choice of the values of the \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(a\)**(m)** & 1.0 & 2.0 & 3.0 & 4.0 & 5.0 & 6.0 & 7.0 & 8.0 & 9.0 & 10.0 & 11.0 & 12.0 \\ \hline \(\rho_{app}\)**(\(\Omega\)m)** & 77.8 & 68.9 & 52.3 & 39.8 & 30.8 & 24.9 & 21.1 & 18.8 & 17.3 & 16.3 & 15.7 & 15.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Apparent resistivity profile data vs. electrode separation \(a\), for the site located in Castelldefels. Figure 9: Measured and calculated grounding resistance in Castelldefels, Barcelona. parameters that start the minimization process is important. For this reason, it is necessary to repeat the minimization process with different initial values and analyze the final results. This situation is much less frequent in the method based on apparent resistivity.
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
###### Abstract  
Currently, vehicle classification in roadway-based techniques depends mainly on photos/videos collected by an over-roadway camera or on the magnetic characteristics of vehicles. However, camera-based techniques are criticized for potentially violating the privacy of vehicle occupants and exposing their identity, and vehicles can evade detection when they are obscured by larger vehicles. Here, we evaluate methods of identifying and classifying vehicles on the basis of seismic data. Vehicle identification from seismic signals is considered a difficult task because of interference by various noise. By analogy with techniques used in speech recognition, we used different artificial intelligence techniques to extract features of three, different-sized vehicles (buses, cars, motorcycles) and seismic noise. We investigated the application of a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) to classify vehicles on the basis of vertical-component seismic data recorded by geophones. The neural networks were trained on 5580 unprocessed seismic records and achieved excellent training accuracy (99%). They were also tested on large datasets representing periods as long as 1 month to check their stability. We found that CNN was the most satisfactory approach, reaching 96% accuracy and detecting multiple vehicle classes at the same time at a low computational cost. Our findings show that seismic methods can be used for traffic monitoring and security purposes without violating the privacy of vehicle occupants, offering greater efficiency and lower costs than current methods. A similar approach may be useful for other types of transportation, such as vessels and airplanes.  
traffic monitoring; deep learning; convolutional neural network; recurrent neural network; deep neural network; signal-to-noise ratio +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Design of the Proposed Capacitive Sensors
#### 2.4.2 MNT Sensor Design  
This sensor consists of two \"E\" shaped plates, a design inspired by that of a resistive humidity sensor. In this case, both the area of the plates (\(A=A_{1}=A_{2}\)) and the number of slots interspersed between them are equal, and are separated by a distance (\(d\)). Figure 3 presents the proposed diagram for the MNT sensor, whose parameters are defined by the dimensions \(a\), \(b\), \(c\), and \(d\). Furthermore, illustrates how the positive and negative electric charges are distributed on the plates of the capacitive sensor, showing the electric field lines and their similarity to the model of a parallel plate capacitor, characterized by an area (\(A\)) and separation (\(d\)).  
Figure 3: Diagram of the MNT sensor, illustrating two flat plates in the shape of an ‘E’, separated by the dielectric distance (\(d\)). The dimensions (\(a\), \(b\), \(c\)), the area (\(A\)) and charge distribution are key parameters for calculating the capacitance.  
Therefore, the capacitance value for the MNT capacitive sensor \((C_{MNT})\) can be calculated from Equation (5):  
\[C_{MNT}=\frac{\varepsilon A}{d}=\frac{\varepsilon_{o}\varepsilon_{r}A}{d}=\frac{ \varepsilon_{o}\varepsilon_{r}[3(a-c)b+ac]}{d} \tag{5}\]
# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
## 3 Results  
### Simulation of System Error Correction  
#### 3.1.1 Monte Carlo Analysis Method  
The Monte Carlo method, also known as the random simulation method, is applied to geo-location problems by many researchers [6; 13; 28]. The simulation data are generated by computer and used to replace the actual test data, which are difficult to obtain.  
The error analysis model is established as:  
\[\Delta y=f(x_{1}+\Delta x_{1},x_{2}+\Delta x_{2},\cdots,x_{n}+\Delta x_{n})-f(x _{1},x_{2},\cdots,x_{n}) \tag{23}\]  
where \(\Delta y\) is the error of \(y\) and \(\Delta x_{n}\) is the error of \(x_{n}\).  
The random variable error \(\Delta x_{n}\) obeys the normal distribution, and the error model can be described as:  
\[\Delta x_{n}=R_{i}\sigma_{x_{n}},\ i=1,2,\cdots,N \tag{24}\]  
where \(R_{i}\) is a pseudorandom number, which obeys the standard normal distribution, \(N\) is the size of sample space, and \(\sigma_{x_{n}}\) the measurement standard deviation of parameter \(x_{n}\).  
The nominal value \(y\) of the error analysis is calculated by the real value of each parameter \((x_{1},x_{2},\cdots,x_{n})\). The random error sequences of each parameter \((\Delta x_{1},\Delta x_{2},\cdots,\Delta x_{n})\) are added to each parameter. According to Equation (23), the function value error \(\Delta y\) is calculated, and the error value is analyzed by the statistical method.