esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fracture Simulation Based on the XFEM  
All the elements in the FE model are set to the enriched regions in the fracture simulation based on the XFEM. The subroutine UDMIGINI in the ABAQUS software is initially programmed to determine the element damage initiation in compression or tension [25]. The damage initiation criterion is that the maximum or minimum principal strain in the element reaches the critical tensile or compressive failure strain of cortical bone material. The damage evolution law describes the rate at which the element stiffness is degraded once the corresponding damage initiation criterion is reached. The damage evolution law in the element is based on the traction-separation criterion that is embedded in the ABAQUS software. The available traction-separation model in Abaqus assumes initially linear elastic behavior followed by the initiation and evolution of damage. The elastic behavior is written in terms of an elastic constitutive matrix that relates the nominal stresses to the nominal strains across the crack interface, as shown in Equation (4). With crack propagation, the cohesive stiffness of the damaged element at the crack interface gradually decreases, leading to the reduced traction. When the cohesive stiffness degrades to 0, the traction would thoroughly disappear, which results in the apparent fracture [26].  
\[\mathbf{t}=\left\{\begin{matrix}\mathbf{t_{n}}\\ \mathbf{t_{s}}\\ \mathbf{t_{t}}\end{matrix}\right\}=\mathbf{E}\left\{\begin{matrix}\varepsilon _{\mathrm{n}}\\ \varepsilon_{\mathrm{s}}\\ \varepsilon_{\mathrm{t}}\end{matrix}\right\} \tag{4}\]  
where \(\mathbf{t}\) is the nominal traction stress vector in the element of the finite element model, consists of three components, \(\mathbf{t_{n}}\) represents the normal traction, \(\mathbf{t_{s}}\) and \(\mathbf{t_{t}}\) represent the shear tractions. \(\mathbf{E}\) is the elasticity matrix tensor in the element of the finite element model. \(\varepsilon_{\mathrm{n}}\) represents the normal strain, and \(\varepsilon_{\mathrm{s}}\) and \(\varepsilon_{\mathrm{t}}\) represent the shear strains.
# Automatic Correction of Indonesian Grammatical Errors Based on Transformer
## 6 Result and Discussion
### Discussion  
The proposed IGEC framework in this paper demonstrated that is an effective system for addressing the challenge of a lack of training data for the Indonesian language. Furthermore, it was able to correct all types of errors, and it was not restricted to specific errors. The experimental results for over 100,000 sentences demonstrated that SAN-GEC with a copy mechanism and BPEChar performed the best with a BLEU score of 78.00, making it our best model. SAN-GEC achieved remarkable improvement in GEC, perceiving the entire input sequence simultaneously regardless of each word's position, while the copy mechanism increased the accuracy of the name entities usually identified as UNK words during training in the classical neural-based GEC systems. Furthermore, BPEChar addressed spelling errors more effectively compared with the copy mechanism, which is one of the main challenges of the automatic GEC models. However, compared with the different settings of BPEChar, the 30,000-word vocabulary size caught the syntax and features from the word level more efficiently, while the 10,000-word vocabulary size was better for correcting spelling and UNK words and 1000-word size was the best. In the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** & **BLEU** \\ \hline SAN-GEC + copy mechanism (UNK words) & 64.85 & 60.64 & 62.67 & 66.12 \\ SAN-GEC + copy mechanism (non-UNK words) & 61.25 & 59.31 & 60.26 & 64.32 \\ SAN-GEC + copy mechanism + BPEChar 30k & 64.11 & 64.09 & 64.09 & 67.19 \\ SAN-GEC + copy mechanism + BPEChar 10k & 67.21 & 65.29 & 66.23 & 71.17 \\ SAN-GEC + copy mechanism + BPEChar 1k & 70.42 & 69.33 & 69.87 & 77.21 \\ SAN-GEC + copy mechanism + BPEChar 1k + Fine-tuning & 71.14 & 72.76 & **71.94** & **78.13** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The impact of the copy mechanism, different settings for BPEChar and fine-tuning the IGEC framework, the bold refers to state-of-the-art values.  
Figure 6: Illustration of the performance of the proposed model with BPEChar and fine-tuning.  
final analysis, we compared the performance of our model with existing Indonesian GEC systems. The performance of the proposed framework was compared with the recent Indonesian GEC systems. [PERSON] et al. [42] introduced a GEC system reexamining LMs limited to correct a range of errors that include prepositions, conjunctions and indefinite pronouns errors. which reported 41.60 as its F1 score. [PERSON] et al. [4] proposed an Indonesian GEC model based on POS tags trained using 10,000 sentences and used to correct 10 types of grammatical errors, which posted an F1 score of 55.10. To the best of our knowledge, our work is the first work to employ end-to-end neural-based GEC with three different neural-based models. Aside from that, our semi-supervised confusion method constructed massive training data for the Indonesian GEC task.
## 2 Materials and Methods  
### Study Sites and Measurements  
#### 2.1.1 Study Sites  
We selected three typical plantation forests, Chinese cork oak, poplar, and arborvitae, located in warm temperate monsoon climate zone of northern China, for field observation. Chinese cork oak plantation (35\({}^{\circ}\)01\({}^{\prime}\)45\({}^{\prime\prime}\)N, 112\({}^{\circ}\)28\({}^{\prime}\)08\({}^{\prime\prime}\)E) and arborvitae plantation (35\({}^{\circ}\)11\({}^{\prime}\)24\({}^{\prime\prime}\)N, 112\({}^{\circ}\)35\({}^{\prime}\)24\({}^{\prime\prime}\)E) are located in Jiyuan City, Henan Province. The soil is primarily composed of brown soil and limestone weathered eluvial brown soil with a high content of gravel. The forests' ages are 49 years and 33 years, respectively, with average heights of 10.5 m and 8 m. _Grewia biloba_ G. and _Vitex negundo_ L. dominate the under canopy of the Chinese cork oak plantation, while _Vitex negundo_ L. primarily makes up the understory of the arborvitae plantation. In two sites, the litter layer is relatively thick, and herbaceous plants are seldom found. The poplar (34\({}^{\circ}\)43\({}^{\prime}\)55\({}^{\prime\prime}\)N, 115\({}^{\circ}\)04\({}^{\prime}\)48\({}^{\prime\prime}\)E) plantation is located in Shangqiu City, Henan Province, on the old course of the Yellow River, and has sandy, thick soil. The age of poplar plantation is 20 years, the trees' height averaging 20 m. There are no shrubs in the under canopy of this site, and the herbaceous layer is primarily dominated by _Semna tora_ L.
## 3 Problem Formulation and Proposed Solution
### The RPSO Algorithm
We call this modified PSO algorithm as the RPSO algorithm. Algorithm 1 displays the pseudocode of the RPSO algorithm. ```
0: Size of the particle swarm \(m\); Maximum number of iterations \(T\);
0: The historically optimal position of the entire particle swarm \(X_{\text{all,best}}\); The historically optimal fitness of the particle swarm;
1:Initialization: The first particle's position \(\mathbf{X}_{1}=\mathbf{F}_{0}\);
2: Other particles' position \(\mathbf{X}_{i}=\mathbf{F}_{i}\), \(2\leq i\leq m\);
3: The velocity of each particle \(\mathbf{V}_{i}\), \(1\leq i\leq m\);
4: The \(i\)-th particle's historically optimal position \(\mathbf{X}_{i,best}\), \(1\leq i\leq m\);
5: The particle swarm's historically optimal position \(\mathbf{X}_{\text{all,best}}\);
6: All the initial value are satisfied with the constraints in (17);
7:While\(t\leq T\)
8:For one of the particles
9:Refresh: The velocity and position of the particle based on (20) and (21);
10:Calculate: The corresponding fitness according to the objective function in (17);
11:If the fitness of \(X_{i}^{t+1}\) is larger than the fitness of \(X_{i,best}^{t}\)
12:Choose:\(X_{i}^{t+1}\) as the the \(i\)-th particle's historically optimal position;
13:EndIf
14:If the fitness of \(X_{i,best}^{t}\) is larger than the fitness of \(X_{\text{all,best}}\)
15:Choose:\(X_{i,best}^{t}\) as the particle swarm's historically optimal position;
16:EndIf
17:Endfor
18:EndWhile
19:return\(X_{\text{all,best}}\) and the corresponding historically optimal fitness of the particle swarm
```  
**Algorithm 1** The RPSO algorithm for Asymptotic Capacity Maximization
## 3 Results and Discussion
### Phase I: Greenhouse Column Study
#### 3.2.4 Mass Balance of Cu in Greenhouse Column Study
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Roots** & **Stem** & **Leaves** & **Pods** & **Seeds** & **Total Plant** & **Total Cu** & **Total Cu** \\  & **(mg)** & **(mg)** & **(mg)** & **(mg)** & **(mg)** & **(me)** & **(Leachates; mg)** & **(Soil; mg)** \\ \hline Control (No plant) & - & - & - & - & - & - & 0.05 & 1757 \\ Camelina & & & & & & & & \\ Compost–2.5\% & 0.02 & 0.06 & 0.01 & 0.01 & 0.01 & 0.11 & 8.69 & 1733 \\ Compost–5\% & 0.04 & 0.06 & 0.12 & 0.01 & 0.003 & 0.23 & 20.0 & 1709 \\ Compost–10\% & 0.02 & 0.08 & 0.14 & 0.02 & 0.02 & 0.28 & 43.3 & 1671 \\ Compost–20\% & 0.07 & 0.17 & 0.34 & 0.01 & 0.01 & 0.60 & 131 & 1550 \\ Field Pennycress & & & & & & & \\ Compost–2.5\% & 0.02 & 0.01 & 0.07 & 0.04 & 0.01 & 0.15 & 5.78 & 1719 \\ Compost–5\% & 0.03 & 0.02 & 0.19 & 0.04 & 0.01 & 0.29 & 14.4 & 1697 \\ Compost–10\% & 0.08 & 0.02 & 0.16 & 0.05 & 0.02 & 0.33 & 37.4 & 1654 \\ Compost–20\% & 0.15 & 0.03 & 0.37 & 0.04 & 0.03 & 0.62 & 106 & 1570 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mass balance of Cu for the greenhouse column study. [PERSON] et al. [25] showed that while biochar and green waste compost added to acidic soil increased the mobilization of Cu, the leaching of Cu gradually decreased with age. However, if the pH of the soil mixture increased with the addition of the amendment, the leaching of heavy metals decreased [25]. Our results show that Cu concentration in leachate increased with increasing compost levels in both amelina and field pennycress planted columns (Table 3). Uptake by amelina and field pennycress plants were low, most of which remained in the leaves, with a negligible amount of Cu in the seeds. However, most of the Cu remained in the stamp sand. For the six-month experimental period, out of the total Cu in the stamp sand, 8% and 6.7% Cu leached out of the amelina and field pennycress columns, respectively, at the 20% compost amendment rate, whereas at the 10% rate, the corresponding amounts were 2.6% and 2.3%, respectively (Table 3). The total percentage of Cu taken up by amelina and field pennycress were 0.04% of the total Cu at 20% compost rate and 0.02% at the compost 10% rate.
## 3 Proposed Method
### Concatenated YOLOv3-SPP
#### 3.3.2 LCat: Long-Skip Concatenation Model  
LCat merges the feature maps with longer skip-connection, employing shallower layers than the baseline model. While YOLOv3-SPP brings the local feature maps from layers 61 and 36, we draw lower-level features from layers 55 and 24 with one more concatenation layer from layer 8, as illustrated in Figure 9.
## 3 Results and Discussion
### Compressibility Curve
#### 3.2.2 Unsaturated Condition  
In unsaturated soils, various factors significantly influence their behavior, such as matric suction, capillary tension, water content, and the degree of saturation, among others, which notably alter the soil's basic structure [37; 38; 39]. Consequently, the results presented in this section and later may vary, especially due to differences in initial saturation.  
In Figure 9a, the models show behavior like saturated conditions, with a progressive decrease in the void ratio as the load increases, though to a lesser extent, consistent with the experimental response recorded by [PERSON] et al. (2023) [13]. The curves overlap throughout the loading range and diverge during the unloading phase. The Soft Soil model shows a 5% decrease in the final void ratio compared to the experimental model, while the Hardening Soil model shows a 6% increase compared to the experimental model.  
This translates to volumetric recoveries of \(\varepsilon\
u=-19\%\) for Soft Soil and \(\varepsilon\
u=-17\%\) for Hardening Soil, as shown in Figure 9b.  
In the curve with an 80% degree of saturation, a reduction in volumetric deformation can be observed as the vertical load increases compared to the HS_60% models. This demonstrates that an increase in the initial saturation leads to a higher degree of deformation and, therefore, tends to be more compressible [1].  
This implies that the numerical model of copper filtered tailings under study, with an initial saturation degree of 80% and subjected to one-dimensional consolidation, experiences an 11% variation in its void ratio compared to the models with 60% saturation. Additionally, it exhibits greater volumetric deformation under lower stress levels, as shown in Figure 9b.
## 2 Research Methodology
### Content Transfer Protocols
#### 2.4.2. FCP (File Catalyst Protocol)  
FCP is designed to enable high-speed file transfer over IP networks, especially for large files and data sets. Unlike TCP-based protocols, FCP is a UDP-based protocol that operates in parallel with other network traffic. This allows it to achieve high transfer rates, while minimizing network latency and avoiding congestion. It also includes various features to ensure reliability and security of file transfers, such as error detection and correction, packet-level resends, and encryption. FCP is optimized for high-speed data transfer, and it operates in parallel with other network traffic, which allows it to maximize bandwidth utilization and minimize network latency and congestion. FCP uses a combination of techniques to optimize transfer speeds, including parallel packet transmission, dynamic congestion control, and adaptive packet sizing.
# Universal Behavior of the Image Resolution for Different Scanning Trajectories
## 2 Scanning Methods
### Scanning Trajectory Characteristics
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Trajectories**} & \multirow{2}{*}{**Mathematical Expression**} & \multicolumn{2}{c}{**Frequency**} & \multirow{2}{*}{**Advantages**} & \multirow{2}{*}{**Disadvantages**} \\  & & & & & & \\  & & & & & & \\ \hline Bidirectional & \(H_{x}(t)=\begin{cases}Asin(2\pi f_{x}t),\,t<\frac{T_{R}}{2}\\ Basin(2\pi f_{x}t+\phi),\,t\geq\frac{T_{R}}{2}\\ \end{cases}\) & (3) & & & & - & \begin{tabular}{c} Low resolution and \\ irregular compared to \\ \end{tabular} \\  & \(H_{y}(t)=\begin{cases}Asin(2\pi f_{x}t+\phi),\,t<\frac{T_{R}}{2}\\ Basin(2\pi f_{x}t),\,t\geq\frac{T_{R}}{2}\\ \end{cases}\) & (4) & \(T_{R}=\frac{N_{P}}{2}\) & - & \begin{tabular}{c} Lissajous trajectories \\ Carteasian family \\ \end{tabular} & - & \begin{tabular}{c} Lissajous trajectory \\ high-quality image \\ \end{tabular} \\ \hline Triangular & \(i_{x}=Asauretooth(2\pi f_{x}t)\) & (5) & \(f_{x}=\frac{N_{P}}{2N_{P}-1}f_{y}\) & - & \begin{tabular}{c} Lisotropic resolution \\ Uniform pattern \\ \end{tabular} & - & \begin{tabular}{c} Scanning wave form \\ is discontinuous \\ \end{tabular} \\  & \(i_{y}=Bsauretooth\left(2\pi f_{x}t+\phi\right)\) & (6) & \(T_{R}=\frac{N_{P}}{2}\) & - & Homogeneous resolution \\ \cline{2-5}  & \(i_{x}=Asin(2\pi f_{x}t)\) & (7) & \(f_{x}=\frac{N_{P}}{2N_{P}}f_{y}\) & - & \begin{tabular}{c} Isotropic resolution \\ Able to generate \\ \end{tabular} & - & \begin{tabular}{c} Irregular pattern \\ Lowest resolution in the \\ center FOV \\ \end{tabular} \\ \hline Sinusoidal & \(i_{y}=Bsin\left(2\pi f_{y}t+\phi\right)\) & (8) & \(T_{R}=\frac{N_{P}}{2}\) & - & \begin{tabular}{c} Isotropic resolution \\ High-quality images \\ \end{tabular} & - & \begin{tabular}{c} Only scan circular or \\ elliptic FOV \\ \end{tabular} \\ \hline Radial & \(i_{x}=Asin(2\pi f_{x}t)\,\,sin(2\pi f_{x}t)\) & (9) & \(f_{x}=\frac{N_{P}}{2N_{P}-1}f_{y}\) & - & \
\begin{tabular}{c} Isotropic resolution \\ Highly dense pattern in \\ the center FOV \\ \end{tabular} & - &
\begin{tabular}{c} Only scan circular or \\ elliptic FOV \\ \end{tabular} \\ \hline \hline \end{tabular} Here, \(f_{x}\) is the fundamental frequency in x-direction, \(f_{y}\) is the frequency in y-direction, \(N_{P}\) is the integer coefficient, \(T_{R}\) is the repetition time, \(\phi\) is the phase, A and B are the amplitudes, and \(t\) is time [24,27,28].  
\end{table} 
Table 1: Mathematical expressions for all scanning trajectories used in this work [24].
## 4 Experimental Results
### Evaluation
#### 4.2.2 Evaluate under Noisy Conditions  
To evaluate the effectiveness of the algorithm under noisy conditions, we introduce Gaussian noise with a mean of 0 and a standard deviation of 5 to the LR depth images for the noisy experiment and retrain the network model. We select Bicubic, TGV [42], SRCNN [9], DGN [23], MFR-SR [24], and RDN-GDE [38] as comparison algorithms for noisy experiments. We list the RMSE results with upsampling factors of 2, 4, and 8 in the Middlebury datasets in Table 4, where the optimal results are marked in bold font and the suboptimal results are marked with underlines. From Table 4, the TGV method [42] exploits the global information and can achieve better performance while addressing the noise. It can be observed that deep learning-based methods outperform traditional methods in suppressing noise. Among them, color-guided reconstruction methods such as MFR-SR [24] and RDN-GDE [38] can obtain smaller RMSE values. This indicates that the guidance of color images is of great help to suppress image noise. Our method utilizes color-guided information that can not only effectively suppress noise but also restore clear and sharp edges, making the restored image closer to the ground truth.
# Multi-Label Emotion Recognition of Korean Speech Data Using Deep Fusion Models
## 4 Feature Extraction and Model Construction  
This section describes the extraction and utilization of three speech features from the preprocessed audio data to construct multi-label classification models. Specifically, Log-mel spectrogram, MFCCs, and VQFs are extracted from the preprocessed audio data. Then, three deep single models are constructed: one utilizing the Log-mel spectrogram with a Vision-Transformer (ViT), another utilizing the MFCCs with a 1D CNN-LSTM, and the other utilizing the VQFs with a DNN. Subsequently, four deep fusion models are constructed: three models are built through the fusion of two deep single models, and one model is built by the fusion of three deep single models. Finally, the performance of the constructed models are compared and validated. Figure 4 shows the overall process of constructing the deep single and fusion models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \cline{2-7}  & **\# of Data** & **Ratio** & **\# of Data** & **Ratio** & **\# of Data** & **Ratio** \\ \hline Disgust & 5151 & 7.2\% & 861 & 7.2\% & 767 & 6.0\% \\ \hline Anger and Disgust & 6178 & 8.7\% & 1136 & 9.5\% & 1085 & 8.4\% \\ \hline Happiness and Sadness & 5818 & 8.2\% & 780 & 6.5\% & 1317 & 10.2\% \\ \hline Sadness and Disgust & 5400 & 7.6\% & 1279 & 10.7\% & 796 & 6.2\% \\ \hline Happiness and Neutral & 5335 & 7.5\% & 920 & 7.7\% & 1114 & 8.7\% \\ \hline Sadness and Anger & 5274 & 7.4\% & 781 & 6.5\% & 982 & 7.6\% \\ \hline Sadness and Neutral & 5023 & 7.0\% & 700 & 5.8\% & 972 & 7.6\% \\ \hline Anger and Neutral & 4935 & 6.9\% & 1032 & 8.6\% & 799 & 6.2\% \\ \hline Neutral and Disgust & 4835 & 6.8\% & 1068 & 8.9\% & 790 & 6.1\% \\ \hline Sum & 71,340 & 100\% & 11,968 & 100\% & 12,849 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 4: Overview process of constructing the SER models.
## 3 Results
### Thickness and Barrier Properties  
Table 2 shows the values of thickness (\(\upmu\)m), water vapour, and oxygen permeability after one week of storage at 53% RH and 25 \({}^{\circ}\)C. Thickness is an essential characteristic of coatings because it affects barrier properties such as WVP and other gases. It is observed that when adding essential oils to the starch-based matrix, the thickness decreased significantly (\(p<0.05\)). The values of the thicknesses of the formulations containing essential oils were statistically similar.  
The highest value of WVP was for the F1 film with 19.2 (g mm kPa\({}^{-1}\) h\({}^{-1}\) m\({}^{-2}\)), while the film F3 fe obtained the lowest value. The behaviour of WVP is significantly influenced (\(p<0.05\)) with the addition of essential oils to the films. The oxygen permeability data ranged between 2.4 and 3.2 (\(\times 10^{15}\) cm\({}^{3}\) m\({}^{-1}\) s\({}^{-1}\) Pa\({}^{-1}\)). Regarding WVP, adding a hydrophobic substance causes a decrease in this parameter. This phenomenon is because higher tortuosity is created inside the polymeric matrix as the water vapour molecules pass, slowing down the permeation [29]. An improvement in WVP of active biodegradable films based on cassava starch and pumpkin residue extract and oregano oil was reported by [PERSON] et al., 2018 The incorporation of natural compounds decreased WVP, reducing the water absorption by the films since these substances are complex and highly hydrophobic mixtures [30]. In the case of OP, an opposite effect is created; the presence of hydrophobic substances slightly accelerates the permeation of oxygen molecules. As is well-known, the oxygen permeability of starch films depends highly on plasticizer content and relative humidity. In this regard, the essential oil, acting as a plasticizer, could increase oxygen permeability [31]. [PERSON] et al., 2013 reported similar results in corn starch-based films containing Zataria multiflora Boiss or Mentha pulegium [32]. Adding 3% (\(v\)/\(v\)) of either essential oil to the films significantly increased the OP values. This behaviour was attributed to the swelling of the starch polymer matrix because of volatile oils. In this way, oxygen may penetrate through oil/starch interfaces, providing oxygen-penetration channels when higher essential oil concentrations are added to the films.
###### Abstract  
Nonlinear distortion for single-sideband (SSB) signals will significantly reduce the performance of Kramers-Kronig (KK) receiver-based optical transmission. In this work, we present a proof-of-concept study of stimulated Brillouin scattering (SBS)-induced nonlinear distortion for 10 Gbaud and 28 Gbaud SSB QAM16 transmission over 80 km standard single mode fiber (SSMF) based on a KK receiver. Significantly reduced bit error rate (BER) has been experimentally observed due to the SBS and the threshold of SBS at about 7 dBm is detected for such an 80 km SSMF link. With left sideband (LSB) modulation of SSB, together with optical filtering, reduced SBS nonlinear distortion has been achieved with \(\sim\)2 dB power tolerance improvement. The results reveal an important issue of SBS-induced nonlinear distortion, which would be of great significance for KK receiver-based optical transmission applications.  
nonlinear distortion; Kramers-Kronig receiver; stimulated Brillouin scattering 2022222227287. [[https://doi.org/10.3390/s22197287](https://doi.org/10.3390/s22197287)]([https://doi.org/10.3390/s22197287](https://doi.org/10.3390/s22197287))
## 3 Proposed Methodology
### Classification
#### 3.6.1 Logistic Regression  
A supervised machine learning algorithm called logistic regression (LR) is employed for binary classification tasks [63]. By fitting a logistic function, it models the association between a dependent binary variable and one or more independent variables. The logistic function converts the input features into probabilities, which are then used to categorize instances into one of the two classes. LR is widely used for a variety of applications, including cyberbullying detection [59], text classification [73; 74], and sentiment analysis [75] because of its simplicity, interpretability, and efficiency.