esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Improvement of C3 Module  
The traditional Convolutional Neural Network (CNN) [39] has been very successful in extracting features as well as structure for image classification with good fitting ability, and the structure has been optimized with the addition of residual structure and the deep neural network has led to a better combination of context and corresponding generalization ability. From the perspective of human vision, the Transformer [40] based on the self-attention mechanism is more in line with human visual logic, but in the application process, it is found that it cannot adapt to a large range of target size changes and the attention mechanism brings a correspondingly large amount of computation, which makes the accuracy and speed less than that of CNN. Swin Transformer [41] proposes a hierarchical approach to construct multi-layer feature maps to solve the above problems, as well as to introduce a sliding window operation to restrict attention to a finite window to make the computational complexity linearly related to the input image size.  
Swin Transformer is similar to ViT in that the input image with size \(H\times H\times 3\) is first chunked, then the feature dimension of the image block is changed by linear embedding in stage1, and then it is fed into Swin Transformer Block and the input features are calculated; stage2-stage4 is similar in that the \(2\times 2\) adjacent image blocks are merged and stitched together, and then the convolutional network is used to downscale them to achieve the desired feature dimension.  
Swin Transformer Blocks are shown in Figure 4, and the normalized data first enter the Windows Multi-head Self Attention (W-MSA) module that further divides the image blocks into different regions and performs self-attentive calculations on them; immediately into the Multilayer Perception (MLP) machine using the GeLU function as the activation function to implement the nonlinear transformation of the data. Finally, the introduction of the Shifted Windows Multi-head Self Attention (SW-MSA) mechanism, which enables the interaction of information within partial windows between different levels; and connected using residual modules after each MSA module and MLP, as shown in Equations (8) to (11).  
\[\hat{Z}^{l}=W-MSA\left(LN\left(Z^{l-1}\right)\right)+Z^{l-1} \tag{8}\]  
\[Z^{l}=MLP\left(LN\left(\hat{Z}^{l}\right)\right)+\hat{Z}^{l} \tag{9}\]  
\[\hat{Z}^{l+1}=SW-MSA\left(LN\left(Z^{l}\right)\right)+Z^{l} \tag{10}\]  
\[Z^{l+1}=MLP\left(LN\left(\hat{Z}^{l+1}\right)\right)+\hat{Z}^{l+1} \tag{11}\]  
Figure 4: Swin Transformer Blocks.  
The C3 module, which incorporates Swin Transformer Blocks, is named the C3 STR module, and its specific structure is shown in Figure 3. It is experimentally demonstrated that the C3 STR module, which introduces the Swin Transformer module, can achieve the same effect of introducing an attention mechanism to improve model accuracy with minimal additional overhead.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 6 Discussion, Limitations, and Future Work
### Technical Limitations
#### 6.3.1 Bias in the Results  
In this study, we analyzed real-world data in a SOC using the proposed scheme and evaluated it based on an escalation set obtained through actual daily operations. The reported improvements in the escalation results indicate a significant enhancement in the system's performance. However, incident response practices are influenced by various factors. In particular, the escalation set was initially obtained from installed security appliances, integrated by the SIEM system currently in use, and then manually selected by the security operators. The results are heavily reliant on the security policy and implementation of the enterprise network as well as the experience and condition of the technicians and analysts involved in the process, e.g., the occurrence of alert fatigue.  
Thus, rather than pursuing an all-encompassing security solution, we presented a SIEM scheme that shows promise for a specific SOC implementation as well as easy adaptability and customization. Achieving a fully objective and impartial comparison on a larger scale dataset that covers more attack types and different case studies considering other network environments is beyond the scope of this paper. These issues will be pursued in future work.
## 4 Prediction Results and Analysis
### Feature Importance Analysis Based on SHAP Methodology  
SHAP is a method employed to elucidate the rationale behind machine learning predictive models [56], which is particularly applicable to black-box models. It determines the extent to which each input variable contributes to the final prediction of the model by using the concept of Shapley values. For the rockburst intensity level prediction problem, the SHAP method can be employed to analyse the relative importance of sample features. The specific results are presented in Figure 11.  
The degree of feature importance can be used to demonstrate the extent to which each feature affects the prediction results. As illustrated in Figure 11, each rectangle represents the corresponding feature importance. The more important the feature, the taller the rectangle; the less important the feature, the shorter the rectangle. It becomes apparent that the characteristic importance of the elastic strain energy index \(W_{el}\) is the largest, 0.21, indicating that the feature has the greatest impact on the intensity level of the rockburst; the stress ratio \(\sigma_{\theta}/\sigma_{c}\) feature importance is as high as 0.201, indicating that the feature has a greater impact on the intensity level of the rockburst, and the feature and the elastic strain energy index \(W_{et}\) are close to the characteristic importance of the feature; it can be speculated that there may be a certain degree of correlation between these two features. In addition, the maximum tangential stress \(\sigma_{\theta}\) (MPa), rock uniaxial compressive strength \(\sigma_{c}\) (MPa), rock tensile strength \(\sigma_{t}\) (MPa) and rock brittleness ratio \(\sigma_{c}/\sigma_{t}\) characteristic importance are lower than the first two, but their impact on the intensity level of rockbursts cannot be ignored. Therefore, these features should be taken into account in engineering applications and research to make the prediction of rockburst intensity levels more reliable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline NGO-CNN-BiGRU-Attention & 98.28\% & 97.92\% & 97.73\% & 97.82\% \\ CNN-BiGRU-Attention & 96.55\% & 94.95\% & 94.95\% & 94.95\% \\ RF & 94.83\% & 94.68\% & 95.88\% & 95.28\% \\ BiGRU & 93.10\% & 93.73\% & 90.91\% & 92.30\% \\ ELM & 89.66\% & 90.65\% & 89.06\% & 89.85\% \\ CNN & 86.21\% & 84.85\% & 89.90\% & 87.30\% \\ KNN & 82.76\% & 82.51\% & 78.45\% & 80.43\% \\ SVM & 79.31\% & 77.78\% & 83.00\% & 80.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of the performance of the models.
# Research on Key Parameters and Engineering Experiments of Coal Gangue Slurry Filling Technology
## 4 Industrial-Grade Circular Pipe Conveying Test of Gangue Slurry
### Experimental Analysis of Long-Distance Pipeline Transportation
#### 4.3.2 Evaluation of the Effect of Long-Distance Pipeline Transportation  
This long-distance pipeline transportation test transported about 180 m\({}^{3}\) of clean water, 60 m\({}^{3}\) of mortar, and 500 m\({}^{3}\) of gangue slurry. There were no pipeline blockage incidents throughout the entire process. In addition, when the pumping flow rate was changed three times, except for the feedback data from the pressure gauge, there were no other abnormal phenomena. After the experiment, no large gangue particles were deposited at the bottom of the pipeline. That is to say, with the cooperation of the six transportation processes, 71% of the gangue slurry formed by \"gangue + water\" could be stably transported over a long distance.  
Figure 7: Relationship between pumping flow rate and conveying resistance.
# Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression
## 3 Preliminaries
### Classification Algorithms  
#### 3.2.1 Support Vector Machine (SVM)  
SVM is a supervised machine learning algorithm for classification and regression tasks. SVM works by representing each data point as a point in an n-dimensional space, where n is the number of features. The algorithm then finds the optimal hyperplane (n - 1 dimensions). This hyperplane is positioned so that all points of one category reside on one side of it, and all points of the other category are on the opposite side, maximizing the margin between the classes [38]. Although there may be multiple hyperplanes, SVM seeks the one that best separates the two categories. This optimal hyperplane maximizes the distance to points in either category, termed the margin. The points falling precisely on the margin are labeled as the supporting vectors.
## 8 Conclusions and Future Remarks  
In order to pave ways for smart services, FANET needs to promise uninterrupted wireless connectivity in a ubiquitous fashion. An apparent outcome is the emergence of new possibilities in the form of rapid, efficient, and low-cost deployment of future flying ad-hoc networks. In this context, we explored different architectures and wireless technologies that can be used in the communication links between UAV and the ground station. We also proposed a hybrid wireless communication scheme in order to employ the low-power consumption feature of Bluetooth (802.15.1) and the high data transmission rate capability of the Wi-Fi technology (802.11). The proposed scheme was designed, implemented, and simulated with the OPNET. The simulation results, in terms of throughput and delay, demonstrated the viability of our scheme for FANET. Our research contribution is hoped to enable researchers and engineers to explore more avenues for coming forth with improved UAV wireless communication systems. For instance, the proposed hybrid scheme does not cater to the backhaul link for the FANET system. Existing FANET system(s) mainly rely on simple point-to-point communication over the unlicensed band (e.g., ISM 2.4 GHz). Such an unlicensed band is of a low data rate and operates over a limited range. The number of UAVs and their associated applications are poised to grow in the near future. This further stresses the need to develop new hybrid schemes such as WiMAX, LTE, and 5G at the backhaul to enable enhanced UAV-ground communications.  
Conceptualization, M.A.K.; Methodology and Implementation, M.A.K. and I.M.Q.; Simulation, M.A.K.; Validation, M.A.K.; Data Curation, I.M.Q.; Writing--Original Draft Preparation, F.K. and M.A.K.; Writing--Review & Editing, F.K. and M.A.K.; Supervision, I.M.Q. This research received no external funding.  
A part of this paper was published in Proceedings of the 2 nd International Conference on Future Networks and Distributed Systems (ICFNDS '18), Amman, Jordan, 26-27 June 2018.  
The authors declare no conflict of interest.
# Efficacy of Fire Protection Techniques on Impact Resistance of Self-Compacting Concrete
## 2 Experimental Investigation
### Test Procedure  
The details of the fresh properties of SCC are elaborated below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & \multicolumn{8}{c}{**Mix Proportions (kg/m\({}^{3}\))**} & \multicolumn{8}{c}{**Aggregates**} \\ \cline{2-11}  & **OPC** & **w/p** & **SP** & **VMA** & **Water** & **FA** & **GGBFS** & **EPA** & **CA** & **MS** \\ \hline FA & 320 & 0.42 & 0.8 & 0.10 & 218 & 144 & - & - & 804 & 963 \\ FA/EPA & 320 & 0.42 & 0.9 & 0.15 & 218 & 144 & - & 24 & 804 & 939 \\ GGBFS & 320 & 0.42 & 0.9 & 0.15 & 218 & - & 144 & - & 804 & 963 \\ GGBFS/EPA & 320 & 0.42 & 1.0 & 0.20 & 218 & - & 144 & 24 & 804 & 939 \\ \hline \hline \end{tabular} Note: w/p = water–powder ratio.  
\end{table}
Table 4: SCC mix proportions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Aggregate Type** & **Density (kg/m\({}^{3}\))** & **Specific Gravity** & **Water Absorption (\%)** \\ \hline MS & 1624 & 2.73 & 0.59 \\ CA & 1792 & 2.82 & 0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical properties of MS and CA.  
SCC Rheological Properties  
The facility of SCC to effectively fill densely packed formwork and prevent segregation was assessed using a range of test methods in accordance with the EFNARC guidelines. The filling capacity was evaluated through tests such as slump flow and T\({}_{500}\). The flowability and consistency of the concrete were assessed through the J-ring and V-funnel tests to evaluate its passing ability and viscosity. Figure 2 provides a detailed presentation of the workability tests conducted on the SCC. Results obtained from the workability tests conducted is detailed in Table 5.
## 6 Application of New Technology  
UHS is pivotal for enhancing efficiency, safety, and feasibility. Advanced technologies, such as nanomaterials and smart coatings, can increase storage capacity and containment safety. Monitoring and sensing technologies, including fiber optic systems and wireless sensor networks, provide real-time data for early detection of anomalies. Computational advancements, like high-performance simulation and machine learning, optimize storage strategies and predictive maintenance. Innovations in drilling and injection, such as horizontal drilling and smart injection systems, improve operational efficiency. Environmental and safety technologies, including seismic and environmental monitoring, ensure compliance and protect natural resources. Integrating renewable energy and smart grid technologies, alongside H\({}_{2}\) generation via electrolysis, supports sustainable energy storage. Conducting pilot projects and field trials validates new methods in real-world conditions, providing valuable insights for UHS deployment. Combining these technologies enhances the feasibility, safety, and efficiency of UHS, promoting its role in sustainable energy systems.
## 2 Materials and Methods  
### Sampling  
Samples of Jameed were collected from the local Jordanian market. In total, 80 samples were collected from the districts of Ajloun, Elkarak, Elshobak, Irbed, Jarash, Mafraq, Tafila, and Zarqa. There is a huge variability among districts in terms of climate, agroecology, and the biophysical context, which may affect the production system, milk quality, and the quality of the processed final product (Table 1). Each sample consisted of 2-3 balls of Jameed. Then, in the laboratory, each ball was weighed, broken, and ground into a powder form using a mill (Thomas Wiley Mill Model 4, Swedesboro, NJ, USA) equipped with a \(1\times 1\) mm sieve and stored refrigerated in zipped bags for later analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Districts**} & \multirow{2}{*}{**Type of Climate**} & \multirow{2}{*}{**Area**} & \multirow{2}{*}{**Temperature**} & \multicolumn{2}{c}{**Average Annual**} & \multirow{2}{*}{**Altitude**} \\  & & & & & **Rainfall** & \\ \cline{3-6}  & **km\({}^{2}\)** & & & **Min \({}^{\circ}\)C** & **Max \({}^{\circ}\)C** & **mm** & **m** \\ \hline Ajloun & Mediterranean climate & 420 & 3 & 36 & 611 & 719 \\ Elkarak & Desert climate & 3495 & 19.18 & 29.23 & & 973 \\ Elshobak & Cold desert climate & 32,832 & 13.72 & 24.14 & 41.4 & 1120.61 \\ Irbed & Hot-summer Mediterranean climate & 1572 & 5 & 31 & 449.2 & 620 \\ Jarash & Mediterranean climate & 410 & 5 & 33 & 339.9 & 520–750 \\ Mafraq & Cold semi-arid climate & 26,551 & 3 & 36 & 149 & 915 \\ Tafila & Mid-latitude steppe climate & 2209 & 2 & 31 & 191.5 & 970 \\ Zarqa & Cold semi-arid climate & 4761 & 3 & 37 & 125.2 & 570–660 \\ \hline \hline \end{tabular} Source: Ministry of Agriculture and National Agricultural Research Center.  
\end{table}
Table 1: Climate, agroecology, and the biophysical information of the sampling districts.
## 6 Discussion  
### Efficiency  
During the localization, the MVF was less affected by community size because all attributes of the MVF had a unified baseline. A specific attribute could be used as the basis for screening and positioning, significantly improving localization efficiency. However, no unified baseline was available the EBC column elements, making comparison difficult. Moreover, the MVF can see more attributes in the diagonal corner, and the head rotation amplitude is smaller when browsing all attributes, thus saving more time.  
During the comparison, the EBC was more affected by community size because when too many column nodes were present (M, L clusters), the attributes that were not on the same baseline increased. Moreover, because the column was too long, head rotation was needed to assist observation, so it took more time. Unlike localization, which requires only a single browse, the comparison may require repeated observations between distant areas, which is why the time assumption increased dramatically in M and L clusters. However, in the MVF, users could find a suitable angle to view more attributes in the immersive environment, and the head rotation amplitude and times were less than the EBC, thus taking less time.  
Some users said in the interview that the redundant information of EBC is too much and dense, which disturbs their judgment. Observing large clusters even requires large body movements, which makes it impossible to derive an overview of the data, increasing the burden of memory and making it more time consuming.  
The above findings show that the MVF has higher efficiency than the EBC when localizing and comparing, suggesting that the MVF has no redundant data and makes the interface simpler. The MVF provides a unified baseline for all attributes, which is more intuitive. The VR immersion environment can give an overview angle in the MVF, decreasing the need to remember the data from the visual field. Moreover, users can observe details without tremendous body and head movements.
# Fault Detection of Bearing Systems through EEMD and Optimization Algorithm
## 5 Results and Discussions  
This section discusses the results of employing PSO to find the optimized weights of damage-sensitive feature vectors using the bearing testing data. We trained independent data sets of time-series of the bearing system to find optimized weights for the feature vector. Having finished training, the weights were applied to the new data set to validate the performance and feasibility of the proposed method. Compared to the case of all-unity-weights, the converged set of weights from training data showed better performance or smaller variance (see Figure 14). Figure 15a shows the Isomap results from using the characteristic feature vectors of training data sets without optimization, while Figure 15b shows the results of feature vectors of training data sets after optimization by PSO. Figure 15a shows that the case of defective bearing D15, i.e., having the least severe indentation, appeared very close to the group of healthy bearings. This means that the vibration pattern of D15 was very similar to that of the healthy bearing condition, due to the small size of the defect. Thus, it is difficult to monitor the bearing integrity in the early stage, given equal weights on the IMF-based parameter vector. On the other hand, Figure 15b shows that the results from the optimally adjusted weights through PSO showing fault signals of D15, D30, D45 states and Healthy conditions were well separated.  
Figure 14: PSO-based optimization results of the weightings of parameter vectors extracted from various time-segments having an inner-race defect in the bearing testbed.  
Figure 13: First four EEMD-decomposed IMFs of actual vibration signals: (**a**) healthy bearing condition (H); (**b**) low-level damage (D15); (**c**) middle-level damage (D30); and (**d**) high-level damage (D45).  
Having finished the training process for choosing optimally weighted parameter vectors, we tried the test using different data sets. Again, the Isomap results of parameter vectors using the testing data sets, D15, and the healthy state are relatively close to each other. However, the Isomap results of Figure 16b using the optimized characteristic vector weights using D15, D30, and D45 signals among the testing data sets show that they are each clearly distinguishable from the others. It appears to be valid to use the optimized weights acquired from the training data sets for other arbitrary bearing data sets. This means that optimization of the weighting of the feature vectors using the PSO is effective in the experimental results.
## 6 Numerical Simulation Analysis of the Dual-Electrode Excitation Method
### Analysis of Influencing Factors on the Distribution of the Magnetic Induction Intensity Formation
#### 6.4.5 Analysis of Influencing Factors on Formation Resistivity  
The formation resistivity \(R_{f}\) is adjusted, and numerical simulations are conducted using values of 0.1 \(\Omega\cdot\)m, 1 \(\Omega\cdot\)m, 10 \(\Omega\cdot\)m, 100 \(\Omega\cdot\)m, and 1000 \(\Omega\cdot\)m, respectively.  
Figure 9 illustrates that, as the formation resistivity increases, the casing current also increases before rapidly decaying. The maximum current amplitude is achieved at approximately 1 \(\Omega\cdot\)m, with corresponding maximum casing currents of 0.91 A, 0.998 A, 0.96 A, 0.78 A, and 0.27 A. The results indicate that both the higher and lower formation resistivities result in a reduction in the casing current amplitude for the following reasons: Smaller formation resistivities lead to more current being injected into the formation through the internal loop, thereby reducing flow into the casing. Larger formation resistivities strengthen the leakage effect of the mud channels with most of the current returning through these channels in the drilling well. These effects ultimately lead to a corresponding reduction in casing currents.  
Figure 9 illustrates the impact of varying formation resistivities on the induced magnetic field intensity along the axis of the drilling well. The attenuation characteristics of \(B_{c}\) are similar to those of \(I_{c}\). The maximum magnetic field intensity corresponding to different formation resistivity values is as follows: 7.6 nT, 8.4 nT, 7.4 nT, 5.8 nT, and 2.9 nT. Additionally, at the position of the magnetic field measurement (z\({}^{\prime}\) = \(\pm\)2.5 m), the magnetic  
Figure 9: The amplitude of the casing current and the intensities of the induced magnetic field on the axis of the drilling well with different formation resistivities.  
field intensities are recorded as 7.4 nT, 8.2 nT, 7.2 nT, 5.6 nT, and 2.8 nT for the varying formation resistivity values of interest.