esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Characteristics Comparison  
Table 1 provides a comprehensive comparison of some major characteristics of prominent CP-ABE schemes and SP-MAACS. The comparison involves important features such as multi-authority setting, access policy structure and its expressiveness, privacy preservation, and the security settings of the schemes. The schemes [25; 38; 39; 48] were designed for healthcare systems. From this comparison, we can see that the adaptively secure privacy-preserving schemes [28; 38; 48] are single-authority schemes. The multi-authority CP-ABE schemes [16; 23; 25; 26] are adaptively secure but do not offer privacy preservation of the access policy. The schemes [39; 35] are multi-authority and privacy-preserving schemes, but their security is only evidenced in weaker selective models. As far as our survey goes, the SP-MAACS scheme is the only one that preserves privacy while also offering adaptive security in a multi-authority setting.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scheme** & **CP/KP** & \
\begin{tabular}{c} **Multi-** \\ **Authority** \\ \end{tabular} & **Privacy-Aware** & **Expressiveness** & **Security** &
\begin{tabular}{c} **Group** \\ **Order** \\ \end{tabular} & **Security Model** \\ \hline \multicolumn{6}{c}{**Single-Authority Privacy Preserving Schemes with Adaptive Security in Standard Model**} \\ \hline
[28] & CP & \(\times\) & \(\surd\) (Partially hidden policy) & LSSS & Adaptive & Composite & Standard \\ \hline
[38] & CP & \(\times\) & \(\surd\) (Partially hidden policy) & LSSS & Adaptive & Composite & Standard \\ \hline
[48] & CP & \(\times\) & \(\surd\) (Partially hidden policy) & LSSS & Adaptive & Composite & Standard \\ \hline \hline \end{tabular}
\end{table}
Table 1: Prominent ABE schemes vs. SP-MAACS scheme.
###### Abstract  
The quality and reliability of wind turbine blades, as core components of wind turbines, are crucial for the operational safety of the entire system. Carbon fiber is the primary material for wind turbine blades. However, during the manufacturing process, manual intervention inevitably introduces minor defects, which can lead to crack propagation under complex working conditions. Due to limited understanding and measurement capabilities of the input variables of structural systems, the distribution parameters of these variables often exhibit uncertainty. Therefore, it is essential to assess the impact of distribution parameter uncertainty on the fatigue performance of carbon-fiber structures with initial cracks and quickly identify the key distribution parameters affecting their reliability through global sensitivity analysis. This paper proposes a sensitivity analysis method based on surrogate sampling and the Kriging model to address the computational challenges and engineering application difficulties in distribution parameter sensitivity analysis. First, fatigue tests were conducted on carbon-fiber structures with initial cracks to study the dispersion of their fatigue life under different initial crack lengths. Next, based on the Hashin fatigue failure criterion, a simulation analysis method for the fatigue cumulative damage life of cracked carbon-fiber structures was proposed. By introducing uncertainty parameters into the simulation model, a training sample set was obtained, and a Kriging model describing the relationship between distribution parameters and fatigue life was established. Finally, an efficient input variable sampling method using the surrogate sampling probability density function was introduced, and a Sobol sensitivity analysis method based on surrogate sampling and the Kriging model was proposed. The results show that this method significantly reduces the computational burden of distribution parameter sensitivity analysis while ensuring computational accuracy.  
Keywords:initial crack; carbon fiber structure; distribution parameter uncertainty; surrogate model; sensitivity analysis; surrogate sampling probability density function; fatigue life +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Water Sorption and Solubility Parameters  
The weight percentage data of water sorbed by the synthesized dental nanocomposite resins at 37 \({}^{\circ}\)C after their storage for 7 days are given in Table 3. It is well-accepted that the presence of fillers may have a strong impact on the sorption characteristics of the composite resin [69]. It can be seen that the composite filled with ACPS silanized nanosilica showed the highest sorption value (2.68 \(\upmu\)g/mm\({}^{3}\)) relative to the other tested materials, maybe as a result of its strongest hydrophobicity, which is responsible for hydrogen bonding between ester groups of the methacrylate moiety and water molecules. Furthermore, both pure MPS and MPS blends containing composites exhibited lower water sorption in comparison with all ACPS counterparts, probably due to the presence of the additional methyl- group in the methacrylate segment of MPS, which renders it more hydrophobic. This susceptibility of the nanocomposites with ACPS and ACPS blends may also be ascribed to the occurrence of some microvoids detected on their surface by means of SEM (Figure 3d,f,g). As was expected, the OTMS-composite exhibited the highest water resistance (2.29 \(\upmu\)g/mm\({}^{3}\)) owing to its non-polar nature. Regarding the dental nanocomposites reinforced with silane blends, they exhibited an intermediate water sorption performance in comparison with composites loaded only with the starting silanes. The water uptake for the composite containing neat silica (2.58 \(\upmu\)g/mm\({}^{3}\)) is due to the free surface silanols of the filler, which easily absorb water molecules through hydrogen bonding.  
Solubility results reflecting the amount of residual monomer extracted by water after 7 days aging at 37 \({}^{\circ}\)C are also provided in Table 3. During the polymerization process, the unreacted monomer is entrapped inside the microvoids formed between the macromolecular chains and remains intact in the cross-linked network. The nanocomposites with OTMS and OTMS coupling agents indicated the highest monomer release (0.39-0.56 \(\upmu\)g/mm\({}^{3}\)), maybe due to the absence of methacrylate groups, which could be copolymerized with the dimethacrylated monomers, thus eliminating the amount of available monomers to be released in the aqueous environment. The observed tendency MPS composite \(<\) MPS-OTMS composite \(<\) OTMS composite was in accordance with the results found by [PERSON] et al. [27]. On the other hand, the reactive MPS, ACPS agents either alone or in the form of blend, can copolymerize with the dimethacrylate monomers and, hence, reduce the  
Figure 7: Time dependence of polymerization shrinkage strain of the synthesized dental nanocomposite resins containing 60 wt% of diverse types of untreated and organically modified nanosilica.  
solubility of the composite in water. The relatively high DC of the composite filled with untreated silica may account for the controlled solubility to a slight level.
## 3 Beamforming Antenna and Backhaul Line of Millimeter
### Passive Reflective Array Antenna  
The static millimeter wave backhaul link implemented herein is based on the use of passive reflection facility [21; 22]. The reflective array includes an array of specially designed dipole antenna elements on a printed circuit board (PCB) and a metallization layer on the opposite side of the ground. The dipole elements can be polarization-dependent, thus allowing the reflective array to use different focal points for orthogonal polarization. The shape of the collimated beam can be controlled by phase configuration.  
In this paper, an integrated millimeter-wave transceiver is combined with an on-chip antenna in a reflective array structure to increase the antenna gain to achieve a longer link distance. The integrated transceiver can operate at 60 GHz based on the IEEE 802.11 ad standard. The RF(Radio Frequency) feed structure and its associated losses can be minimized because the on-chip antenna requires only a lower gain. Installation costs can be reduced by implementing mechanical fine-tuning.
## 5 Heuristic Algorithm Design
### LEO-First Topology Abstraction-Based Routing Algorithm
```
Input:\(K(t_{i})\), \(R(t_{i})\), \(V_{\rm g}\), \(V_{\rm l}\), \(V_{\rm h}\), \(P(t_{i})\), \(E_{\rm c}\), \(E_{\rm c}\)

Output:\(SPT(t_{i})\), \(AHT(t_{i})\), \(TKT(t_{i})\), \(P(t_{i+1})\), and solutions for the requests

1: Initialize \(SPT(t_{i})\)-\(\alpha\), \(AHT(t_{i})\) = 0, \(\alpha(m,n,t_{i})\) \(\leftarrow\) 0, vs. \(\leftarrow\)\(V_{\rm g}\)\(\cup\)\(V_{\rm l}\), \(V_{\rm c}\)\(\leftarrow\)\(V_{\rm g}\)\(\cup\)\(V_{\rm h}\);
2: Execute lines 2-25 in GF-TA algorithm;
3:\(f_{\alpha}(m,n,t_{i})\)\(\leftarrow\) - \(h_{\rm r}\), \(f_{\alpha}(m,n,t_{i})\)\(\leftarrow\) - \(\Delta\), \(E_{\rm c}\)\(\leftarrow\)\(E_{\rm c}\)\(\cup\)\(E_{\rm G}\);
4:for(\(s_{\rm c},d_{\rm r},k_{\rm r},t_{i}\)) do
5: Execute lines 27-48 in GF-TAR algorithm;
6:if flag = 0 then
7:for (\(m,n\)) \(\in\)\(E_{\rm c}\)do
8:if\((m,n)\) \(\in\)\(E_{\rm c}\)do
9:if\(v(m,n)\) - \(f_{\beta}(m,n,t_{i})\) \(\leftarrow\)\(\min(K_{\rm s})\)do
10:\(E_{\rm c}\)\(\leftarrow\)\(E_{\rm c}\)\(-\left\{(m,n)\right\}\);
11:endif
12:else
13:if\(v(m,n)\) - \(f_{\alpha}(m,n,t_{i})\) \(\leftarrow\)\(\min(K_{\rm s})\)do
14:\(E_{\rm c}\)\(\leftarrow\)\(E_{\rm c}\)\(-\left\{(m,n)\right\}\);
15:endif
16:endif
17:\(P_{\rm d}\)\(\leftarrow\) routing from \(s_{\rm r}\) to \(d_{\rm r}\), in \(G_{\rm c}(V_{\rm c},E_{\rm c})\) with Dijkstra algorithm;
18:if\(P_{\rm d}\)\(\
eq\)\(\varnothing\)do
19:\(\xi\) = 1;
20:for(\(m,n\)) \(\in\)\(P_{\rm d}\)do
21:if\((m,n)\) \(\in\)\(E_{\rm c}\)& & \(\left(f_{\alpha}(m,n,t_{i})\) - \(k_{\rm r}/v(m,n,t_{i})\right)\) \(<\) 0 do
22:\(\xi\)\(\leftarrow\) 0;
23:elseif\((m,n)\) \(\in\)\(E_{\rm G}\)& & \(\left(f_{\beta}(m,n,t_{i})\) - \(k_{\rm r}/v(m,n,t_{i})\right)\) \(<\) 0 do
24:\(\xi\)\(\leftarrow\) 0;
25:endif
26:endif
27:if\(\xi\) = 1 do
28:\(TKT(t_{i})\) \(\leftarrow\)\(TKT(t_{i})\) + \(k_{\rm r}\), \(SPT(t_{i})\) \(\leftarrow\)\(SPT(t_{i})\) + \(1\), \(AHT(t_{i})\) \(\leftarrow\)\(AHT(t_{i})\) + \(|P_{\rm d}|\), and update the \(f_{\alpha}(m,n,t_{i})\) and \(f_{\beta}(m,n,t_{i})\) to all the \((m,n)\)\(\in\)\(P_{\rm d}\);
29: Record the solutions for the request \(t(s_{\rm r},d_{\rm r},k_{\rm r},t_{i})\);
30:endif
31:endif
32:endif
33:endfor
34:return\(AHT(t_{i})\) \(\leftarrow\)\(AHT(t_{i})\)/\(SPT(t_{i})\), \(SPT(t_{i})\)\(\leftarrow\)\(SPT(t_{i})\)/\(|R(t_{i})|\), \(P(t_{i+1})\)\(\leftarrow\)\(P(t_{i})\), \(TKT(t_{i})\), and solutions for the requests set \(R(t_{i})\). ```  
**Algorithm 2**LF-TAR algorithm. The time complexities of lines 2 and 5 have been evaluated in the GF-TAR algorithm. The time complexity in lines 7-31 is \(O(|V_{\rm g}\cup V_{\rm h}|_{2})\). The overall time complexity of the LF-TAR algorithm in a time slot is \(O(\left|R(t_{i})\right|\cdot(|V_{\rm g}\cup V_{\rm h}|_{2}+|V_{\rm g}\cup V_{ \rm h}|_{2})+(1_{\rm g}+l_{\rm b})\cdot|V_{\rm l}|)\).
## 4 Validation and Comparison
### Comparative Testing of Baseline Methods
#### 4.4.7 Path Loss Prediction Method Based on GA--Optimized Ensemble Model  
Genetic Algorithm is employed to optimize the output weights of the multi-neural network ensemble model for predicting path loss in high-speed train carriages. This method aims to improve the prediction accuracy by identifying the optimal combination of weights for the ensemble model. Table 12 shows the optimal combination of parameters for the GA-optimized output weights.
## 5 Conclusions  
In this study, the dust-removal efficiency of PM2.5 in a high-temperature wire-plate ESP under different working conditions in a magnetic field is numerically simulated. The simulated data are used as training data for the prediction model, and the PSO-SVM and the BPNN models are used to predict the magnetic field effect under different working conditions. The conclusions are as follows:  
1. Regardless of the particle size and flue gas velocity, the dust-removal efficiency of PM2.5 in the wire-plate ESP shows a nonlinear decreasing trend with the increase in temperature, and it gradually slows down.
2. Under the same working conditions, the introduction of a magnetic field significantly enhances the particle grade efficiency compared with that in the absence of a magnetic field; further, the magnetic effect has a more obvious improvement in the dust-removal efficiency of fine particle sizes. At high temperatures and high flue gas flow velocities, the effect of an external magnetic field on improving the dust-removal efficiency of PM2.5 particles is more evident.
3. Both the BPNN and the PSO-SVM models are capable of describing the variation law of PM2.5 dust-removal efficiency under different working conditions; moreover, the predicted curves are generally close to the simulated points.
4. Compared with the BPNN model, the predicted values of the PSO-SVM model are more consistent with the simulated data, and the PSO-SVM model also has a higher prediction accuracy, showing a better prediction ability and adaptability to dust-removal efficiency under various working conditions.  
All authors contributed to the study conception and design. J.Z.: conceptualization, methodology, supervision, project administration, funding acquisition, and resources; L.Z.: methodology, data curation, and writing--original draft and editing. All authors have read and agreed to the published version of the manuscript.  
Figure 16: Comparison and relative error between the predicted values and the simulated data of grade efficiency.  
**Funding:** This work is sponsored by the National Natural Science Foundation of China (12172228, 11572187), Natural Science Foundation of Shanghai (22 ZR1444400), the Program of Foundation of Science and Technology Commission of Shanghai Municipality (22 dz1204202, 22 dz1206005), and Shanghai Professional Technical Service Platform for Intelligent Operation and Maintenance of Renewable Energy (22 DZ2291800).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results Analysis
### Noise Pattern Analysis
Specifically, Cluster 5 has an Leq of 83.01 dB, a dynamic range of 65.99 dB, and a main frequency component of 17.18 Hz, while Cluster 7 has an Leq of 82.05 dB, a dynamic range of 87.82 dB, and a main frequency component of 20.13 Hz. These clusters exhibit fluctuations in Leq and relatively low intensity, consistent with the low-frequency echo characteristics produced by the tunnel reflection effect when the train is running at low speeds [33]. The above results indicate that the sources of noise in the driver's cab are diverse and have substantial disparities in their impact on the acoustic environment, which provides a basis for developing tailored noise control strategies.
# Wideband Ring-Monopole Flexible Antenna with Stub for WLAN/C-Band/X-Band Applications
## 4 Conclusions  
In this paper, we proposed a miniaturized wideband flexible antenna that could be used in various applications. The proposed antenna can resonate in a wider band using a stub, step structure, and t-strip line based on a ring monopole. The bandwidth of the antenna is 15.68% (2.36 GHz to 2.87 GHz) at 2.615 GHz, and 111.69% (3.4 GHz to 12 GHz) at 7.7 GHz. The antenna uses 0.2 mm thick polyimide as the substrate so that it can be bent sufficiently. In addition, the bending characteristics of the antenna were analyzed by measuring the S-parameter according to the bending degree and bending direction of the antenna. It was confirmed that the antenna was suitable as a flexible antenna because it resonated similarly when the antenna was bent and when the antenna was flat. The size of the antenna is 0.256 \(\lambda\)\(\times\) 0.32 \(\lambda\)\(\times\) 0.0016 \(\lambda\) (\(32\times 40\times 0.2\) mm\({}^{3}\)). The proposed antenna can be used for wearable devices operating at WLAN/C-band/X-band.  
Conceptualization, H.L. and Y.B.P.; methodology, H.L.; software, H.L.; validation, H.L.; formal analysis, H.L.; investigation, H.L.; resources, H.L.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L. and Y.B.P.; visualization, H.L.; supervision, Y.B.P.; project administration, Y.B.P.; funding acquisition, Y.B.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Electric Power Corporation, Grant Number R21X001-12.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 17: S-parameter when the antenna is bent. (**a**) When bent horizontally, S-parameter according to the radius of the bend; (**b**) when bent vertically, S-parameter according to the radius of the bend.
# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 4 Experiments and Results
### Evaluation Metrics  
The performance of our model is reported using weighted standard classification metrics, including accuracy, recall, precision, and F1-score, to consider the class imbalance. These metrics can be calculated based on the confusion matrix. The confusion matrix for the binary classification is represented in true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN).  
* Accuracy (A) is the average between the number of samples classified accurately and the overall number of samples. \[A=\frac{(TP+TN)}{(TP+TN+FP+FN)}\] (1)
* Precision (P) is the average between the number of positive samples classified accurately and the overall number of samples classified as positive. \[P=\frac{TP}{(TP+FP)}\] (2)
* Recall (R) is the average between the number of positive samples classified accurately and the overall number of samples that should be classified as positive. \[R=\frac{TP}{(TP+FN)}\] (3)
* F1-score (F1) is the harmonic mean between the precision and recall scores. \[F1=2*\frac{(P*R)}{(P+R)}\] (4)  
For each experiment, we calculated the average for each metric obtained from the five-fold test sets.
# A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
## 4 Experiment and Results
### Baseline  
We selected seven baseline models to compare the performance of KANR on three datasets, including three recommendation algorithms combining knowledge graphs and two classic recommendation algorithms. The baseline model was as follows:  
Wide&Deep [29] proposed a recommendation algorithm that combines a deep model and a shallow wide model for fusion training. The embedding vector dimensions of users and items in Wide&Deep are unified to 64, and a double-layer deep channel with dimensions of 100 and 50 and a wide channel are set at the same time.  
CKE [14] proposed a unified recommendation framework to embed multimodal data such as knowledge graphs, text information, and picture information into the recommendation task. This paper sets the embedding vector dimensions of CKE users and items in three datasets. The dimensions are 64, 128, and 32, and the entity's vector embedding dimension is uniformly set to 32.  
LibFM [30] is a feature-based factorization model widely used for CTR prediction. In this paper, TransR is used to train the initial user and item vectors.  
RippleNet [5] is a hybrid method that uses the knowledge graph structure to assist with making recommendations. It completes personalized recommendation tasks by exploring the user's potential interest characteristics on the knowledge graph. This paper uses the TransE [14] algorithm to learn the embedding vectors of users and items.  
KGCN [19] is a recommendation algorithm that uses knowledge graphs to convolve item features. The graph convolution operation greatly increases the utilization efficiency of the knowledge graph's network structure information. This paper sets the entity embedding vector dimensions of KGCN in the three datasets to be 32, 64 and 16, and the number of neighbor nodes is 8.  
[PERSON] [10] proposed a multi-task feature learning method combining knowledge embedding and recommendation tasks. By combining the knowledge graph embedding algorithm and the recommendation system module, the potential information of the recommended scene and the knowledge graph can be exchanged.  
[PERSON] [31] proposed a collaborative filtering model based on neural network graph matching. By modeling and aggregating the attribute interaction in the graph matching structure, two types of attribute interaction are effectively captured.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **\#Users** & **\#Items** & **\#Triples** &
\begin{tabular}{c} **\#Interaction** \\ **Types** \\ \end{tabular} & **D** & **N** & **Batch Size** \\ \hline Movie-1M & 6036 & 2347 & 20,195 & 5 & 64 & 4 & 512 \\ Last-FM & 1872 & 3846 & 15,518 & 12 & 16 & 5 & 128 \\ Book-Crossings & 17,860 & 14,910 & 19,793 & 10 & 16 & 4 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic data statistics of the dataset and hyperparameter settings.
## 3 The PFAC Measurements and the FRM Uncertainty Analysis
### Uncertainty Budget for Sea-Surface Calibration  
Apart from the uncertainty constituent in the determination of the absolute coordinates for the Cal/Val site, presented in Section 3.1, other major contributing sources of uncertainty in sea-surface calibration are: (1) determination of the sea-surface height at the Cal/Val location, (2) transfer of sea-surface height from the Cal/Val site to open sea, and (3) processing errors. Estimations of the uncertainties at the PFAC are explained using indicative examples.
## 2 Multilayered MM-Based Effective Medium Approach  
Herein, it is aimed to derive the effective medium permittivities for an anisotropic multilayered MM. The technique is based on the generalized Maxwell Garnett method [19], which seeks to derive analytical formulas describing the material in different directions, i.e. parallel (\(\varepsilon_{\parallel}\)) and perpendicular (\(\varepsilon_{\perp}\)). Based on the mentioned methodology, a sufficiently dilute material is considered such that a hierarchy of macroscopic and microscopic fields can be assumed. The schematic view of a structure composed of metallic and dielectric layers making a multilayered MM is outlined in Figure 1.  
Figure 1: A view of a multilayered metamaterial (MM).  
The permittivities of the metallic and dielectric layers are \(\epsilon_{m}\) and \(\epsilon_{d}\), correspondingly. Additionally, the fill ratio of the total width of metal in the composite to the overall width of the MM can be defined as follows:  
\[\rho=\frac{d_{m}}{d_{m}+d_{d}} \tag{1}\]  
where \(d_{m}\) is summation of the widths of metallic sheets in the composite and \(d_{d}\) is summation of the widths of the dielectric sheets.