esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
### Shape Factor of the Voids  
Using the pore length and the volume of each individual void, it is possible to obtain its shape factor, defined as the ratio between the volume of the void and the volume of the sphere circumscribed to it (Equation (1)).  
\[SF=\frac{V_{p}}{\frac{1}{6}\cdot\pi\cdot L_{p}{}^{3}} \tag{1}\]  
where \(V_{p}\) is the volume of the void, and \(L_{p}\) is the pore length. Figure 10 shows the histograms of the shape factor of the different mixtures and the different curing conditions.  
Figure 10 show some interesting results. Firstly, it can be observed that, in all cases, pores are far from a perfect sphere, since, in all cases, the shape factors are far from 1.0.  
Fiber-reinforced concretes show smaller shape factors, showing a mode between 0.10 and 0.15 and with more than 90% of the voids with a shape factor below 0.3. On the contrary, plain concretes show a slightly larger shape factor, showing a mode between 0.10 and 0.25, and with more than 90% of the voids with a shape factor below 0.4.  
Voids, which are the less-dense and the less-stiff component of the concrete matrix, tend to occupy the space that the rest of the components are not able to occupy. These spaces are not rounded, but elongated.  
Figure 10: Histogram of the shape factor.  
In the case of fiber-reinforced concretes, spaces become more elongated because of the presence of the fibers, and, in consequence, the voids are more elongated too.  
In the case of fiber-reinforced concretes, there is almost no difference in the histograms of shape factor for air-cured and moist-cured concretes. On the contrary, in the case of plain concretes, there is a significant difference in the histograms. Moist-cured concrete shows a histogram of shape factor moved to the right, with means that voids tend to be a bit more rounded. This means that curing conditions affect the pore morphology.  
Pore morphology shows relevant variations along the depth. Figure 11 shows the histograms of shape factor along the depth for the different mixtures and curing conditions.  
Figure 11 shows that, in general, the histograms of shape factor vary with the depth, in such a way that, when the depth increases, the histogram moves to the right.  
This means that the voids tend to be more rounded. This phenomenon is observed in all concretes, although it is clearer in moist-cured concretes, both in plain and fiber-reinforced concretes. This is because of the increase of the hydrostatic pressure with the depth when concrete paste is fresh, which favors the creation of voids with a more efficient geometry, i.e., closer to the sphere.  
In the case of plain concrete, the influence of the horizontal construction joint is shown as a greater percentage of stretched voids.
## 6 Discussion, a Perspective, and an Outlook for Future Research  
Organisations benefit from a framework that directs the mitigation efforts for business disruptions, especially during the BCP design phase. It is acknowledged that there are gaps in assessing the capacity or maturity of achieving these activities; such assessments help to gauge the level of accomplishment compared to the desired functionality for each BC activity.  
The SLR presented here was developed with the invaluable help of powerful research databases, with adequate functions and options to search and export the results. The Parsifal software is decisive in registering the protocol and developing and carrying out the quality assessment and data extraction processes. This paper aimed to return studies that provide strategic guidelines for implementing a BCP, considering the initiation constraints and the gaps identified in the ISFs. Therefore, these ISFs are relevant for the SLR studies analysis.  
According to all the 1297 publications, 949 studies use the PICOC keywords (BCP or DRP) in the abstract, and 429 studies use them in the subject terms, topic prominence, or author keywords. Considering the combination with the other intervention keywords (guidelines and strategy), the number decreases to 26 studies which mention these in the abstract. No studies use these keywords in author keywords, subject terms, or topic prominence. Furthermore, by combining the intervention (BCP, DRP, strategy, and guidelines) with all ISFs mentioned in this paper, the number of studies is four, with only one since 2018.  
We will introduce some areas of research that could be future research paths, either because of a lack of studies or by being innovative or prominent themes. Nevertheless, it is relevant to give a perspective of the results and point to the path forward. We have selected some identified components of the BCM as a perspective of the results.
# Detection Model for 5G Core PFCP DDoS Attacks Based on Sin-Cos-bIAVOa
## 4 Experimental Results and Analysis
### Experimental Dataset  
[PERSON] et al. [32] introduced the 5G Core PFCP Intrusion Detection Dataset. This resource comprises a collection of network flow statistics designed for AI-based detection models to identify cyberattacks aimed at the PFCP. PFCP facilitates communication on the N4 interface between the SMF and the UPF within the 5G core. The public datasets have been released by IEEE Dataport.  
This dataset contains network traffic and traffic statistics for each entity involved. Specifically, each 7z/zip file includes the following: pcap files, TCP/IP network traffic  
Figure 9: Principle of the 5G core PFCP DDoS attack detection model.  
statistics, PFCP flow statistics, and statistics using different timeout values (e.g., 15, 20, 60, 120, and 240 s).  
Based on the previous remarks, the dataset was divided into six distinct sub-datasets: PFCP Session Deletion DoS Attack, PFCP Session Establishment DoS Attack, two variants of PFCP Session Modification DoS Attack (DROP and DUPL), Balanced PFCP APP Layer, and Balanced TCP-IP Layer. For training and testing, the data were split at a ratio of 70% to 30%, respectively, with a stratified approach ensuring that each subset contained the same class proportions.
## Appendix C Damping  
In the main text, the dependence of velocity on strain and the dependence on temperature of nonlinear parameters have been analyzed (see Figures 3 and 5, respectively). This approach also allows us to estimate damping. The data for concrete are unreliable due to a high degree of noise (and will not be discussed here). For Berea sandstone, for damping, a similar behavior is observed as for velocity, as shown in Figure 12.  
In particular, as shown in the left subplot, damping depends on strain following a sigmoidal-like curve, as does velocity, which fits well using Equation (6).Furthermore, for both materials, fast contributions to nonlinearity seem independent on temperature (right subplot). Recall that nonlinear velocity variations were found to increase with increasing temperature.
## 3 Result and Discussion
### Analysis of Maximum Pressure and Maximum Pressure Rise Rate
In the Z-axis, the maximum MPRR of 2.20 bar/s was observed at the lower location (1.1 m), whereas the minimum value of 1.90 bar/s occurred at 4.1 m. This trend may have resulted from interplay between the vertical pressure gradients and fluid dynamics that are specific to the upper and lower regions of the enclosure. Figure 8: Peak pressure–time ratio at different ignition positions. (**a**) Maximum pressure rise rate along the X-axis. (**b**) Maximum pressure rise rate along the Y-axis. (**c**) Maximum pressure rise rate ratio along the Z-axis. Floor-level ignition creates a confined, low-space environment in which the flame rapidly propagates upward. This leads to a sharp and rapid pressure rise in the early phase of combustion, and resulted in the highest MPRR among all positions. In explosion safety evaluations, the MPRR is a significant indicator because it reflects the initial intensity of the combustion and the dynamic loading imposed on the structure. Unlike the peak pressure, which reflects the cumulative energy, the MPRR better captures the combustion-induced pressure shock, which can compromise the structural integrity of the area. Therefore, in this study, the MPRR was adopted as a risk marker to emphasize early-stage hazard prediction, particularly in confined enclosures. This approach aligns with recent findings that highlight the importance of the flame acceleration and the fuel depletion rate as key drivers of explosion severity [41; 42]. In conclusion, the spatial centre in the X- and Y-directions yielded the highest maximum pressure and MPRR. In the Z-direction, the ceiling region exhibited the highest pressure, whereas the floor region showed the steepest pressure increase. These findings underscore the unique vertical behaviour of flame dynamics, where ceiling ignition intensifies the peak pressure accumulation and floor ignition accelerates the combustion rate. These observations indicate that the ignition position critically influences the characteristics of explosions. Thus, meticulous control of the ignition location could play a pivotal role in improving explosion safety during the design of ammonia FPRs.
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 4 Experiments and Discussion
### Comparison between ML Classifiers
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Feature Selection** & **Feature Number** & **Gmean** & **ACC** & **AUC** \\ \hline \multirow{4}{*}{CN2} & F\_Score & 39 & 0.707 & 0.581 & 0.834 \\  & ReliefF & 33 & 0.707 & 0.580 & 0.836 \\  & **HMVFS** & **28** & **0.730** & **0.612** & **0.841** \\ \hline \multirow{4}{*}{LR} & F\_Score & 16 & 0.833 & 0.756 & 0.889 \\  & **ReliefF** & **15** & **0.833** & **0.756** & **0.896** \\  & HMVFS & 33 & 0.831 & 0.752 & 0.896 \\ \hline \multirow{4}{*}{KNN} & F\_Score & 14 & 0.838 & 0.764 & 0.891 \\  & ReliefF & 11 & 0.835 & 0.760 & 0.895 \\  & **HMVFS** & **7** & **0.848** & **0.778** & **0.909** \\ \hline \multirow{4}{*}{SVM} & F\_Score & 18 & 0.812 & 0.728 & 0.908 \\  & ReliefF & 18 & 0.837 & 0.761 & 0.906 \\  & **HMVFS** & **14** & **0.849** & **0.779** & **0.921** \\ \hline \multirow{4}{*}{ANN} & F\_Score & 18 & 0.837 & 0.761 & 0.891 \\  & **ReliefF** & **15** & **0.850** & **0.780** & **0.911** \\  & HMVFS & 36 & 0.842 & 0.769 & 0.915 \\ \hline \multirow{4}{*}{RF} & F\_Score & **27** & **0.878** & **0.821** & **0.926** \\  & ReliefF & 12 & 0.876 & 0.819 & 0.935 \\  & HMVFS & 9 & 0.875 & 0.817 & 0.935 \\ \hline \multirow{4}{*}{AdaBoost} & F\_Score & 36 & 0.781 & 0.684 & 0.797 \\  & ReliefF & 19 & 0.777 & 0.679 & 0.830 \\  & **HMVFS** & **14** & **0.784** & **0.690** & **0.846** \\ \hline \multirow{4}{*}{META-DES} & F\_Score & 19 & 0.876 & 0.816 & 0.930 \\  & ReliefF & 11 & 0.872 & 0.812 & 0.928 \\  & **HMVFS** & **12** & **0.881** & **0.824** & **0.937** \\ \hline \multirow{4}{*}{DES-Clustering} & F\_Score & 32 & 0.872 & 0.812 & 0.916 \\  & ReliefF & 13 & 0.875 & 0.817 & 0.932 \\  & **HMVFS** & **10** & **0.882** & **0.827** & **0.945** \\ \hline \multirow{4}{*}{KNORA-U} & F\_Score & 15 & 0.872 & 0.812 & 0.926 \\  & ReliefF & 14 & 0.870 & 0.809 & 0.932 \\  & **HMVFS** & **12** & **0.884** & **0.829** & **0.942** \\ \hline \multirow{4}{*}{Stacking} & F\_Score & 16 & 0.880 & 0.824 & 0.930 \\  & ReliefF & 13 & 0.874 & 0.814 & 0.936 \\ \cline{1-1}  & **HMVFS** & **11** & **0.886** & **0.831** & **0.939** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Best performance comparison with different ML classifiers. From the perspective of learners, the classification performance improves with the enhancement of model complexity. CN2 as a rule-based learner cannot cope with multiview features to achieve acceptable performance. Individual learners cannot achieve accuracies greater than 0.8, which are apparently inferior to most ensemble models. Among ensemble models, stacking ensemble realizes the best fault-cause identification in this study. The experimental results of ML classifiers indicate that HMVFS is more suitable for classifiers with high generalization and that ensemble models can bring significant improvement for fault-cause identification.
###### Abstract  
With the growth of the Internet of Things (IoT), security attacks are also rising gradually. Numerous centralized mechanisms have been introduced in the recent past for the detection of attacks in IoT, in which an attack recognition scheme is employed at the network's vital point, which gathers data from the network and categorizes it as \"Attack\" or \"Normal\". Nevertheless, these schemes were unsuccessful in achieving noteworthy results due to the diverse necessities of IoT devices such as distribution, scalability, lower latency, and resource limits. The present paper proposes a hybrid model for the detection of attacks in an IoT environment that involves three stages. Initially, the higher-order statistical features (kurtosis, variance, moments), mutual information (MI), symmetric uncertainty, information gain ratio (IGR), and relief-based features are extracted. Then, detection takes place using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (Bi-LSTM) to recognize the existence of network attacks. For improving the classification accuracy, the weights of Bi-LSTM are optimally tuned via a self-upgraded Cat and Mouse Optimizer (SU-CMO). The improvement of the employed scheme is established concerning a variety of metrics using two distinct datasets which comprise classification accuracy, and index, f-measure and MCC. In terms of all performance measures, the proposed model outperforms both traditional and state-of-the-art techniques.  
Bi-LSTM; GRU; IoT; optimization; security attack detection +
Footnote †: journal:  
_Future Internet_
## 4 Analysis of Results
### Optimization for Performance and Security
This is because the decoy state protocol is being configured to operate at its minimum threshold and is extremely sensitive to implementation non-idealities and performance variations to include Alice's ability to generate weak coherent pulses, losses in the quantum channel, physical disturbances, detector efficiency, and particularly the system's operational dark count rate.
# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 4 Probabilistic Wheel Re-association for Disassociated Datasets
### Probabilistic Wheel Re-association Algorithm
```
0:\(\mathcal{T}^{*}\)
0:\(\mathcal{T}^{\prime}\)
1:for each cluster \(C_{i}\) in \(\mathcal{T}^{*}\)do
2:\(C_{i}^{\prime}=R_{0_{C_{i}}}\)
3:for\(k=0\), \(k{++}\), while \(|C_{i}|-|C_{i}^{\prime}|>0\)do
4:\(C_{i}^{\prime}=C_{i}^{\prime}\cup\emptyset\)
5:endfor
6:for each record chunk \(R_{j_{C_{i}}}\) from cluster \(C_{i}\) such that \(j>0\)do
7:\(ReconstructedRecord=\emptyset\)
8:while\(\|R_{j_{C_{i}}}\|>0\)do
9:\(r_{0}=\textsc{SelectRecord}(C_{i}^{\prime})\)
10:\(r_{1}=\textsc{SelectRecord}(R_{j_{C_{i}}})\)
11:\(r_{0}=r_{0}\cup r_{1}\)
12:\(ReconstructedRecord=ReconstructedRecord\cup r_{0}\)
13:\(C_{i}^{\prime}=C_{i}^{\prime}\setminus r_{0}\)
14:\(R_{j_{C_{i}}}=R_{j_{C_{i}}}\setminus r_{1}\)
15:endwhile
16:\(C_{i}^{\prime}=C_{i}^{\prime}\cup ReconstructedRecord\)
17:endfor
18:\(\mathcal{T}^{\prime}=\mathcal{T}^{\prime}\cup C_{i}^{\prime}\)
19:endfor
```  
**Algorithm 5** Probabilistic wheel re-association. ```
1:functionSelectRecord(\(R_{C}\))
2: Assign counts \(count_{r}\) to the distinct records \(r\) in \(R_{i_{C}}\)
3: Construct array of cumulative probabilities of the records \(ACP\)
4: Generate a random number \(rn\) between \([1,100]\)
5: From \(ACP\) select the record \(r\) such that \(ACP(r)\geq rn\)
6: Return \(r\)
7:endfunction
```  
**Algorithm 6** SelectRecord Function
## 4 Proposed Network Model
### Network Architecture Designing
The reason for adopting a multi-hop clustering model in the cluster edge comes from the fact that synchronization of multi-hoptime in the cluster core produces a very high load and data transmission among nodes, causing more packet delay and packet loss, and therefore, quick energy depletion. In the same way, to make a network more efficient, the proposed CH election algorithm has avoided the mathematical complexity and many factors considered for precision in electing the optimal CH. They are commonly used, causing much more power consumption and delays. Therefore, the periodic method has been used for electing the cluster head, assuring the node is alive by checking the remaining power without making any accounting or comparing load with other nodes. Due to all these procedures, the network lightness and efficiency of the proposed protocol are guaranteed.
###### Abstract
Our contribution to this research is summarized in some points;  
* To the best of our knowledge, this is the first study that compares the common integration frameworks and provides an assessment of each framework by using a high-resolution satellite image dataset (worldview3) that has training and test areas, which could be a guideline for researchers in the field of the integration between deep learning and OBIA methods;
* A custom-made computational framework is developed in Python programming to combine the two main image processing frameworks i.e., (1) OBIA and (2) deep learning, which was necessary to avoid using multiple software that is considered complex and time-consuming. The remainder of this paper presents related studies (Section 2), a methodology that includes research data, assessed integration frameworks, and the details of each method (Section 3), results and discussions on the main findings (Section 4), and conclusions and recommendations for future works (Section 5).
## 5 Numerical Results
### Radar Detection Performance Analysis
#### 5.1.1 Integrated Output Response  
Assuming the initial parameters of the target as [60 m, 150 m/s, 30 m/s2], and considering complex Gaussian noise with zero mean, various simulation experiments are conducted to demonstrate the effectiveness of the proposed approach. Figure 5a illustrates the distribution of the echo energy among different range cells. Figure 5b depicts the output of intra-frame integration, showcasing the energy distribution within a single range cell. Figure 5c shows the inter-frame integration of one subfragment. Figure 5d,e display Doppler cell and range cell cross-section slices, respectively, providing a clearer visualization of the integration results compared to the theoretical results. The experiment underscores the close alignment of the proposed method with the theoretical curves.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **All Complexity (Flops)** \\ \hline MHI & 1.9 \(\times\) 10\({}^{4}\) Flops \\ HI & 6 \(\times\) 10\({}^{5}\) Flops \\ MTD & 1.2 \(\times\) 10\({}^{4}\) Flops \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison between MTD, HI, and MHI.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 3 GHz \\ Number of subcarriers & 512 \\ Elementary OFDM symbol duration & 0.1 ms \\ Subcarrier spacing & 10 kHz \\ Cyclic prefix length & 0.025 ms \\ Total OFDM symbol duration & 0.125 ms \\ Total signal bandwidth & 5.12 MHz \\ Radar range resolution & 30 m \\ Unambiguous range & 15 km \\ Unambiguous velocity & [\(-\)800 m/s, 800 m/s] \\ Velocity resolution & 1.55 m/s \\ Radar PRI & 100 \\ Subfragment length & 40 pulses \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter design.
## 2 Materials and Methods
### Online Monitoring of PD Using the AE Method  
#### 2.2.1 Detection of PD Using the AE Method  
The AE method relies on the detection and measurement of impulse pressure changes generated during PD events in the insulation system. Acoustic emission waves are mainly detected using contact piezoelectric transducers, which are installed on the outer wall of the transformer tank using magnetic holders. Some types of AE transducers are designed to be installed inside the transformer tank, which, in theory, is supposed to provide a higher sensitivity to PD detection. These are primarily optoacoustic sensors, active dielectric windows, or hydrophones. The AE method has the following advantages: (i) resistance to electromagnetic interference, (ii) measurements that are non-invasive (installation of piezoelectric AE sensors on the tank does not require switching off the transformer) and relatively easy to perform, and (iii) low price of the measurement system, especially in comparison to the UHF method and the conventional electrical method. The main disadvantage of the AE method is the lack of the possibility of calibrating the acoustic signal with respect to IEC 60270. The sensitivity of the AE method is directly influenced not only by the distance of the piezoelectric transducer from the PD source but also by the phenomenon of attenuation, diffraction, interference, and scattering of the acoustic wave. For this reason, PDs of low energy (\(<\)300 pC) or located in deep layers of the insulation system are difficult or impossible to detect [33; 34].
## 3 Simulation and Analysis
Figure 12b shows the relative position of the upper layer and the water droplets with a radius of 1 mm, in which the number of radial sections of the droplets was 20, each radial portion included 12 droplets, and the total number of drops in the continuous oil was 240. Figure 12c shows the 2D model of the XOY section under the DW/O&DO/W flow pattern. Figure 4:0 The static responses of the CCS, CYS, and COS for the O/W flow pattern. Figure 4:1 Mesh model of the CCS for the dispersion of water in oil and oil in water (DW/O&DO/W) flow pattern (**a**) 3D with relative position of oil layer and oil droplet; (**b**) 3D with relative position of water layer and water droplet; (**c**) XOY section. The static responses of the CCS, CYS, and COS for the DW/O&DO/W flow pattern were simulation calculated as shown in Figure 4:2 As observed from the figure, the static response values of the CCS, CYS, and COS decreased with the increase of the water cut. The downward trend of the CCS simulation results were more consistent with that of the CYS, while the slope of the response curve of the COS was larger, which indicates that the COS has better resolution ability for DW/O&DO/W flow patterns. Furthermore, the CCS also has a certain resolution for DW/O&DO/W flow patterns. Figure 14 shows the mesh model of the CCS for the W/O flow pattern with the water cut of 0.1759. In order to realize the uniform distribution of water droplets in the continuous oil phase, the isometric water droplets were placed in the axial direction. The water cut was changed by changing the radius of the water droplet. Figure 14a shows the relative position of the water droplet and the center electrode, in which eight water droplets were placed in different radial positions in the same axial position and 20 oil droplets in different axial positions in the same radial position. Figure 14b shows the 2D model of the XOY section under the W/O flow pattern. The static responses of the CCS, CYS, and COS for the W/O flow pattern under different water cuts are shown in Figure 4:3 It can be seen from the figure that with the increase of the water cut, the static response values of the CCS, CYS, and COS decreased. The variation trend of the CCS and the  
Figure 4:4 Mesh model of the CCS for the water in oil emulsion (W/O) flow pattern (**a**) 3D; (**b**) XOY section. Figure 4:5 The static responses of the CCS, CYS, and COS for the DW/O&DO/W flow pattern. COS was consistent, and the slope of the CYS response curve was smaller, which indicates that the CCS and the COS have a better resolution ability compared with the CYS for the W/O flow pattern under different water cuts. In summary, for the six flow patterns of horizontal oil-water two-phase flow, although the COS shows a good resolution in some flow patterns, it has no resolution when the water cut is larger than 0.7 under ST and ST&MI flow patterns. Thus, the COS has difficulty meeting the conditions of low-yield horizontal wells. The CCS shows a good resolution under six flow patterns and is superior to the CYS. Therefore, the CCS combines the advantages of the CYS and the COS.