esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 5 Performance Evaluation
### Evaluation Results
#### 5.2.2 Delay  
We also examine the average delay of three kinds of algorithms in the uniform and hotspot distributions. Figures 7 and 8 show the delay increases along with the increase of clients, owing to the higher channel conflict with the client density increasing. When the number of clients is 200, the delay of CAA-BD algorithm gets a 16% improvement in the uniform distribution and 24.2% improvement in the hotspot distribution respectively, compared with that of the Categorized algorithm.  
Figure 5: AP utilization in the uniform distribution.  
Figure 6: AP utilization in the hotspot distribution.  
Under the uniform distribution, the SSF obtains the shortest delay, which is because many clients can achieve a high transmission rate according to the algorithm associating to the AP with the strongest signal and all APs can almost keep load balance to a certain extent under the uniform distribution scenario. However, the delay of CAA-BD algorithm is the shortest under the hotspot distribution. The reason is that the CAA-BD algorithm can avoid clients gathering, and make full use of APs, which alleviates the conflict partly among clients. While in SSF and Categorized algorithms, some APs are overloaded, and the others are idle or light-loaded, which result in the wasting of some APs. Conversely, the CAA-BD can redistribute some transmission traffic of the overloaded APs to some light-loaded APs.
# Large Scale Implementations for Twitter Sentiment Classification
## 6 Results and Evaluation  
### Our Datasets for Evaluating MapReduce versus Spark Framework  
#### 6.1.1 Classification Performance  
In this subsection, we measure the classification performance of our solution using the classification accuracy. We define classification accuracy as \(acc=|CT|/|TT|\), where \(|CT|\) is the number of test set tweets that were classified correctly and \(|TT|\) is the cardinality of \(TT\). We present the results of two experimental configurations, the multi-class classification and the binary classification. Under the multi-class classification setting, we attempt to assign a single sentiment label to each of the vectors in the test set. In the binary classification experiment, we check if a sentence is suitable for a specific label or does not carry any sentiment inclination. As stated and in [7], the binary classification is a useful application and can be used as a filter that extracts sentiment sentences from a corpus for further processing. Moreover, we measure the influence of Bloom filters in the classification performance. The value \(k\) for the \(k\)NN classifier is set to 50. The results of the experiments are displayed in Table 2. In the case of binary classification, the results depict the average score for all classes.  
Looking at the outcome in Table 2, we observe that the performance of multi-class classification in not very good, despite being way above the random baseline. We also observe that the results with and without the Bloom filters are almost the same. Thus, we deduce that, for multi-class classification, the Bloom filters marginally affect the classification performance. Furthermore, the outcome for emoticons is significantly better than hashtags which is expected due to the lower number of sentiment types. This behavior can also be explained by the ambiguity of hashtags and some overlap of sentiments. In the case of binary classification, there is a notable difference between the results with and without Bloom filters. These results may be somewhat unexpected but can be explicated when we take a look in Table 3. Table 3 presents the fraction of test set tweets that are classified as neutral because of the Bloom filters and/or the weight threshold \(w\) (no matching vectors are found). Notice that the integration of Bloom filters leads to a bigger number of tweets with no matching vectors. Obviously, the excluded tweets have an immediate effect on the performance of the \(k\)NN classifier in the case of binary classification. This happens since the number of tweets in the cross fold validation process is noticeably smaller compared to the multi-class classification. Overall, the results for binary classification with Bloom filters confirm the usefulness of our approach.
## 3 Results and Discussion
### Interpretation of CB Model
#### 3.6.4 PDPs Analysis  
Even though the SHAP plot in Section 3.6.2 successfully visualized the global impact of input features, the detailed relationships between input features and their SHAP values were not clarified. In this part, the PDP was adopted to clarify these relationships in detail  
Figure 14: Local SHAP interpretation based on CB model. (**a**) Specim No. 111 in the database. (**b**) Specim No. 297 in the database.  
based on CB model prediction. Figure 15 provides a rich visualization of the PDPs for the six critical input features over the entire database. PDPs can also explore the interaction between pairs of features. For each feature, its corresponding correlated feature is displayed on the right side of the plot by a colored bar. It is observed that SHAP values were increased almost linearly with increasing of \(d\), \(h\) and \(\rho_{l}\), indicating a positive correlation between these features and the SS. In addition, features \(b\) and \(f^{\prime}_{c}\), exhibited nonlinear relationships with SHAP values. Increasing their values up to approximately 300 mm and 80 MPa, respectively, increased the SHAP values. Beyond these threshold values, the corresponding SHAP values were not increased. On the other hand, SHAP values were decreased linearly by increasing the features \(a/d\), indicating a negative correlation between this feature and the SS. This observation is compatible with scientific knowledge and mechanics-driven models, which indicate that the SS of RCDBs mainly depends on geometric dimensions and concrete strength, followed by reinforcement properties.
## 3 Results
### Macrotexture and Noise
#### 3.2.2 Longitudinal Groove Concrete Pavement  
The statistical results of correlation analysis between macrotexture and noise of longitudinal groove concrete pavement are shown in Figure 5.  
Figure 5 shows that there is a quadratic relationship between the macrotexture and tire/pavement noise of longitudinal groove pavement, and the correlation coefficient (\(R^{2}\)) is \(\sim\)0.60. This indicates that there is a certain correlation between macrotexture and tire/road noise of longitudinal groove concrete pavement. Likewise, SPSS 19.0 statistical analysis software was used to test samples for the two variables correlation analysis. Table 6 presents the calculation results of SPSS correlation analysis of descriptive statistics.  
As can be seen from Table 6, the sample mean of macrotexture is 0.71, which meets the relevant requirement of the current industry standard. The standard deviations of the two variables are relatively small, which implies that the individual sample is closer to the sample mean with a less variation degree. Macrotexture of samples are consistent with the new concrete pavement construction, furthermore, validate the accuracy of the test data.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Sample Mean** & **Standard Deviation** & **N** \\ \hline Macrotexture (mm) & 0.71 & 0.729 & 36 \\ OBSI (dB(A)) & 101 & 1.48 & 36 \\ \hline \end{tabular}
\end{table}
Table 6: Statistical description of macrotexture and OBSI of longitudinal groove pavement.  
Figure 5: Regression analysis between macrotexture of longitudinal groove pavement and tire/road noise.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Statistical Test Parameters** & **Macrotexture/mm** & **OBSI/dB(A)** \\ \hline \multirow{4}{*}{Macrotexture/mm} & Pearson correlation coefficient & 1 & 0.79 ** \\  & Significant (bilateral) & & 0.001 \\  & Covariance & 0.427 & 5.19 \\  & Covariance & 0.011 & 0.133 \\  & N & 40 & 40 \\ \hline \multirow{4}{*}{OBSI/dB(A)} & Pearson correlation coefficient & 0.790 ** & 1 \\  & Significant (bilateral) & 0.001 & \\ \cline{1-1}  & sum of squared & 5.19 & 98.8 \\ \cline{1-1}  & Covariance & 0.133 & 2.53 \\ \cline{1-1}  & N & 40 & 40 \\ \hline \end{tabular}  
** means the correlation was significant at the level of 0.01 (both sides).  
\end{table}
Table 5: Correlation analysis between macrotexture and noise of transverse groove concrete pavement.  
The Pearson correlation coefficient and \(p\)-value from the T statistics test are shown in Table 7. The Pearson correlation coefficients of the two variables are \(\sim\)0.78, indicating that there is a high, positive correlation between the macrotexture and the tire/road noise of longitudinal groove concrete pavement. In addition, the corresponding pavement noise level will also increase when increasing the macrotexture under the same test conditions. Meanwhile, it is worthwhile noting that there is no saturation of noise level when varying the macrotexture.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 5 Experiments and Results
### Results
#### 5.2.4 Ablation Experiment  
The ablation study results in Table 5 clearly demonstrate the systematic impact of the cross-gated feature fusion mechanism, cross-domain knowledge learning, and dual-channel GNN architecture on model performance. The experimental data show that the dual-channel GNN served as a critical foundational component. When this module was enabled independently, the F1 score rose significantly from the baseline of 70.66 to 77.76, representing a 7.1 percent improvement. This result confirms the effectiveness  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Epochs**} \\ \cline{2-5}  & **1** & **2** & **3** & **4** \\ \hline FedAvg & 85.02 & 86.94 & 88.58 & 89.70 \\ FedSage & 85.37 & 87.41 & 89.17 & 88.94 \\ AD-FG & 90.20 & 91.12 & 93.41 & 92.29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of the number of local training rounds on model accuracy.  
Figure 4: Performance metrics during multi-round communication. (**a**) Accuracy curve during multi-round communication. (**b**) F1 score curve during multi-round communication.  
of the feature-structure decoupling design. By learning the topological and behavioral characteristics of network traffic in parallel, the model was able to capture anomalous patterns more comprehensively.  
Further incorporating the cross-gated feature fusion mechanism led to a slight increase in the F1 score to 77.81. Although the improvement was relatively small, this mechanism introduces nonlinear feature fusion, which proves beneficial in processing complex and heterogeneous network traffic. It enables the model to emphasize important feature channels while suppressing irrelevant noise.  
The most substantial performance gain was observed when all three components were enabled together: the dual-channel GNN, the cross-gated feature fusion mechanism, and cross-domain knowledge learning. In this setting, the model achieved its best performance with an F1 score of 79.19, accuracy of 90.20%, and recall of 70.94%. This indicates a strong synergistic effect among the components. The dual-channel GNN provides a rich multi-view representation, the cross-gated feature fusion mechanism facilitates fine-grained feature interaction, and the cross-domain knowledge learning mechanism injects global structural prior knowledge by sharing server-side information. Among these, the cross-domain knowledge learning mechanism contributed most notably to the improvement in recall, raising it from 69.25% to 70.94%. This enhancement demonstrates its effectiveness in helping the model identify rare types of attacks, which is especially critical when dealing with highly imbalanced network attack data.
# Electromagnetic Field Variation of ELF Near-Region Excited by HED in a Homogeneous Half-Space Model
## 5 Conclusions  
In this research, we employed the \"air-seawater\" homogeneous half-space model to obtain EM field integral expression in ELF near-field region under HED antenna excitation based on the general expression of the Sommerfeld integral using the quasistatic approximation method. The EM field distribution of an underwater dipole was simulated to calculate the field distribution in shallow seawater. After the calculation, the EM field distribution law in the vicinity of the HED antenna in seawater was analyzed. The simulated results show different attenuations with distance for each component. The vertical electric component \(E_{2}\) and radical magnetic components \(H_{\varphi}\) are found to be of the greatest potential for long-distance propagation among the electric and magnetic components, respectively. Finally, we verified the correctness of the proposed method by comparing it with [PERSON]'s approximation method and [PERSON]'s exact expression approximation method, exhibiting the great potential of the proposed method for various kinds of applications in shallow sea environments. The proposed method could be also applied to the excitation of vertical and horizontal magnetic dipoles (VMDs/HMDs) and vertical electric dipoles (VEDs).  
Conceptualization, T.D.; methodology, H.X.; software, S.H.; validation H.X. and S.H.; formal analysis, H.X. and S.H.; investigation, H.X., S.H., and T.D.; resources, S.H. and T.D.; data curation, T.D.; writing--original draft preparation, S.H.; writing--review and editing, H.X. and S.H.; visualization, S.H.; supervision, H.X.; project administration, H.X.; funding acquisition, H.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author on reasonable request.  
We would like to thank [PERSON] for his support of the article and for providing some help during the preparation of this manuscript.  
The authors declare no conflict of interest.  
Figure 5: Magnitudes of \(E_{12}\) versus the directional distance in near field (\(\varphi=\pi/2\)). (**a**) Magnitudes of \(E_{12}\) for 3 Hz; (**b**) magnitudes of \(E_{12}\) for 30 Hz.
# Design and Research of a Virtual Laboratory for Coding Theory
## 3 Results  
### Implementation of a Coding Theory Virtual Laboratory  
A web-based virtual lab has been created that offers an innovative learning environment for coding theory. The lab includes four interactive models designed to solve coding theory problems, allowing students to practice and refine their skills in real time. In addition, the platform has four statistical modules that provide valuable information about the usability of the models, student activity, analysis of errors made in solving the tasks, and ranking of the best students. To facilitate the administration of the laboratory, three administrative modules have been integrated that allow efficient management of user profiles, task conditions, and control work groups. These functionalities create a comprehensive and flexible learning platform that supports both educators and learners in the process of mastering complex coding theory concepts.  
Figure 3(a) shows the page with the list of developed interactive models that the user can run and solve tasks. Figure 3(b) shows the page about the statistics of solved tasks, and Figure 3(c) shows the summary statistics of errors made when solving the tasks. Figure 3(d) shows the best students ranking page, with a total of 5 rankings available, one general and one for each interactive model.  
Figure 3: General architecture of the virtual laboratory.  
Figures 5 and 6 illustrate the use-case diagrams of the virtual laboratory for students and teachers, respectively. Figure 5 presents the main actions that students can perform on the platform, including logging into the system, accessing the interactive models for solving coding theory problems, viewing their own results and errors, and participating in leaderboards (ranking of the best students). Figure 6 shows the use-case diagram for educators, which includes managing user profiles, setting assignment conditions, monitoring student activity, and generating statistical reports. These diagrams provide a visual overview of the functionalities available to the two main groups of users of the lab, highlighting the interactive and administrative capabilities of the platform.  
The system was tested in a real learning process as the students sent their opinions, comments, remarks, and recommendations to improve the system. Based on the students' feedback:  
* The interface design of individual interactive models was improved;  
Figure 4: Screenshots of some components of the implemented virtual laboratory: (**a**) model list page; (**b**) statistics page for task solutions; (**c**) error statistics page; (**d**) best students ranking page.  
* Errors (bugs) related to the operation of the interactive models were fixed;
* Additional modules and functionalities were developed both for the entire virtual laboratory and for individual software models for studying the various codes.
# Internet of Things for Modern Energy Systems: State-of-the-Art, Challenges, and Open Issues
## 3 IoT Challenges in Energy Systems
### Standards
In addition to their long-range data transmission, which makes it possible to be utilized for many applications such as agriculture processing, energy management systems, smart homes, smart parking, etc. Regarding LoRa technology, LoRaWAN is a protocol designed for low-powered devices specifically for the LoRa technology. This is suitable for some applications in which a small amount of data need to be transferred, such as in electric vehicles [111]. These features lead to the integration of the LoRaWAN protocol with cellular communication technologies such as 4G in the IoT-driven systems [112]. The LoRa technology has been widely accepted by the related organizations. In Sigfox technology, however, ultra-narrow band technology is employed in order to transmit information in long-distance communication [109]. Considering the bandwidth dedicated to the Sigfox technology, only a small amount of data may be transferred. While LoRa technology may be used in bidirectional communication links, for instance, grid monitoring, network density needs to be higher in Sigfox technology. Apart from this difference, these technologies are very similar [113]. Considering characteristics of the Sigfox technology, it is widely used in remote devices that need to transfer small amounts of data in a long distance. In a similar way, several attempts have been done by many organizations such as oneM2M and IEEE to define a global standard that fits every IoT system [100; 114]. For instance, in the OneM2M organization, several partners participate in defining a standard for Machine-to-Machine (M2M) with more focus on the service layer. On the other hand, IEEE organization is developing a standard for IoT focusing on communication layer. In [115], a great attempt is done to collect standards for IoT introduced by organizations to make a coordination among them. A complete list of communication standards regarding to the IoT is mentioned in Table 1.
## 4 Evaluation and Results
### User Acceptance Testing  
User acceptance testing is a phase of testing in which targeted end users test the software to determine whether it can be accepted or not. We conducted our test with 5 children ranging in age from 7 to 10 years old. We planned for the test session to last under an hour. We gave the participants a VR headset to explore VR videos, and each user was tested one-on-one by a researcher. Table 5 below illustrates the user acceptance testing results for one of the participants and the given feedback.  
### User Acceptance Testing  
User acceptance testing is a phase of testing in which targeted end users test the software to determine whether it can be accepted or not. We conducted our test with 5 children ranging in age from 7 to 10 years old. We planned for the test session to last under an hour. We gave the participants a VR headset to explore VR videos, and each user was tested one-on-one by a researcher. Table 5 below illustrates the user acceptance testing results for one of the participants and the given feedback.  
Figure 8: Memory performance testing.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Task** & **Time Needed** & **User Feedback** & **Completion** \\  & & & **Status** \\ \hline Enter name & 00:04:89 & - & Pass \\ Choose section & 00:01:05 & - & Pass \\  & & \"\(\cup\)\(\  
In summary, the user acceptance testing showed successful results among different children, indicating that we have achieved our goal of developing the Dr. Barea system. Furthermore, the system encountered no crashes.
## 3 Results and Discussion
### Analytical Approximation
Yet, this agreement shows how detuning-dependent line-shape asymmetry changes from impulsive dressing at high intensity can be captured and approximated within an analytical framework of the well-known Autler-Townes energy-level splitting of a strongly coupled two-level system using the generalized Rabi formalism. Figure 3: (**a**) dipole phase shifts \(\Delta\varphi_{\mathrm{D}}\) calculated analytically with Equation (11). A jump from positive to negative dipole phase shifts at zero detuning is clearly present as well as the monotonic increase (decrease) of \(\Delta\varphi_{\mathrm{D}}\) for blue (red) detuned pulses. (**b–d**) lineouts of the dipole phase shift (of Figure 3a), blue) are convoluted along the detuning axis (violet) with an intensity-dependent Gaussian profile (depicted at the resonance position, red) and compared to the numerical results (Figure 2d), black). (**b**) for the lowest intensity, \(\mathrm{I}_{0}=0.1\times 10^{15}\) W/cm\({}^{2}\), the Gaussian convolution function with a FWHM \(=0.61\) eV corresponds to the spectrum of the 3 fs Gaussian driving pulse. (**c,d**) for higher intensities, \(\mathrm{I}_{0}=2.4\times 10^{15}\) W/cm\({}^{2}\) and \(\mathrm{I}_{0}=4.7\times 10^{15}\) W/cm\({}^{2}\), respectively, a decreasing FWHM of the Gaussian convolution function provides the best fit of the convoluted analytically calculated dipole phase shifts to the corresponding numerical results.