esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Redundancy  
The most basic concept in error control mechanisms is redundancy. When the digital data are transmitted in the network, a few redundant bits are added to the original data during the encoding process. These redundant bits are used to detect and correct the errors that have occurred during transmission. These additional bits are added on the sending side, which are then removed at the receiving side after the transmission is complete. Redundant bits are also known as _parity bits_ or _check bits_. Parity bits add checksums in the data that enable the receiving device to check the occurrence of errors. A parity check counts the number of 1s in the dataword. There are two methods to add redundant bits on the basis of a parity check: even parity and odd parity. In _even parity_, the parity bit is assigned a value of 1 if the number of bits having 1s is odd; if the number of bits having 1s is even, then 0 is assigned to the parity bit. So the total number of 1s will be even to maintain even parity. In _odd parity_, if the number of 1s is even, the parity bit is set to 1 to make it odd; if the number of 1s is odd, 0 is assigned to make odd parity.
## 4 Egocentric Betweenness Measure in VANETs
### Assumptions  
In order to compute the egocentric betweenness score, first we need to make some assumptions:  
1. Each vehicle has bidirectional communication links among neighbour vehicles within transmission range. The link breaks if the distance between vehicles is greater than the transmission range;
2. All vehicles have the same transmission range;
3. The propagation model employed is two-ray interference path loss.
# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 4 Results
### Comparison with the Existing Methods  
To further validate the performance of PAM-UNet, this paper compares the proposed segmentation algorithm with several typical algorithms. The comparison results are shown in Tables 4 and 5. From Table 4, it can be seen that on the DRIVE dataset, PAM-UNet achieves evaluation results of 97.15% for Acc, 83.16% for Se, 98.45% for Sp, 83.15% for F1, and 98.66% for AUC, which are significantly better than for other models. As shown in Table 5, on the CHASE_DB1 dataset, PAM-UNet achieves evaluation results of 97.64% for Acc, 85.82% for Se, 98.46% for Sp, 82.56% for F1, and 98.95% for AUC, with the Sp result being only 0.03% lower than the optimal result. PAM-UNet achieves the best results in terms of Acc, Se, F1, and AUC on both datasets, demonstrating the effectiveness of the model.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Acc/\%** & **Se/\%** & **Sp/\%** & **F1/\%** & **AUC/\%** \\ \hline UNet [13] & 95.31 & 75.37 & 98.20 & 81.42 & 97.55 \\ R2U-Net [16] & 95.56 & 77.92 & 98.13 & 81.71 & 97.84 \\ LadderNet [48] & 95.61 & 78.56 & 98.10 & 82.02 & 97.93 \\ [PERSON]’s model [49] & 95.68 & 79.21 & 98.10 & - & 98.06 \\ SCS-Net [21] & 96.97 & 82.89 & 98.38 & 81.89 & 98.37 \\ ResDo-UNet [23] & 95.61 & 79.85 & 97.91 & 82.29 & - \\ NAUNet [50] & 96.71 & 79.99 & 98.31 & - & 98.31 \\ SegR-Net [51] & - & 82.06 & 98.14 & 80.97 & - \\ LUVS-Net [52] & 96.92 & 82.58 & 98.30 & - & 82.44 \\ OCE-Net [25] & 95.81 & 80.18 & 98.26 & 83.02 & 98.21 \\ MU-Net [27] & 96.90 & 81.97 & 98.33 & - & 98.53 \\ PAM-UNet & **97.15** & **83.16** & **98.45** & **83.15** & **98.66** \\ \hline \hline \end{tabular}  
* The bold font indicates the optimal value.  
\end{table}
Table 4: Comparison of PAM-UNet with other algorithms on DRIVE dataset.  
Figure 13: CHASE_DB1 dataset segmentation details.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 8 Conclusions  
Aiming at privacy leakage, identity theft and wireless DoS attacks that Sybil attacks may bring, this paper proposes a detection system that can detect Sybil attacks based on fine-grained physical layer channel state information. Moreover, in this system, we combine the self-adaptive MUSIC algorithm with the CSI amplitude for static attacks and propose a detailed feature extraction method with DBSCAN to detect dynamic attacks. Experiments show that the system can achieve a high detection rate for both static attacks and dynamic attacks. According to our experimental results, the average detection rate of the static algorithm is 98.5%, and the average detection rate of the dynamic algorithm is 99.5% as long as no more than five clients are moving. However, the dynamic detection algorithm is greatly disturbed by the environment. When people without WiFi devices are moving in WLAN, this will affect the acquisition of data and the detection accuracy. At present, we cannot effectively distinguish the movement without the WiFi device, which will be carried out in our future work.  
**Acknowledgments:** Our work was supported by the Foundation of the Educational Commission of Tianjin, China (Grant No. 20130801), the General Project of Tianjin Municipal Science and Technology Commission under Grant No. 15 LYBIC15600, the Major Project of Tianjin Municipal Science and Technology Commission under Grant No. 15 ZXDSGX00030, NSFC: The United Foundation of General Technology and Fundamental Research (No. U1536212), the National Natural Science Foundation of China (No. 616 ZX900) and the National Natural Science Foundation of Tianjin (No. 16 JCYBJC15500).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived of and designed the study. [PERSON], [PERSON] and [PERSON] conceived of and designed the experiments. [PERSON], [PERSON] and [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] analysed the  
Figure 13: Dynamic Sybil attack detection rates for different mobile clients.  
data. [PERSON] wrote the paper. [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.
## 3 Mathematical Modeling of the System Components
### Photovoltaic Module Modelling  
The output characteristic of a photovoltaic array is determined by cell temperature, solar irradiation, and output voltage. The operation mode of a PV or solar cell refers to the PN junction diode operation, which converts light energy into electricity by the photovoltaic effect [39]. Figure 4 represents the configuration of a single PV cell.  
Figure 4: A single equivalent circuit model of a PV cell [40].  
Figure 3: Block diagram of the developed system.  
According to this circuit, the solar irradiance is described through a current source \(I_{ph}\), and diode current \(I_{D}\), output current \(I_{pv}\), series resistance \(R_{s}\), parallel resistance \(R_{p}\), and output voltage \(V_{pv}\) represent the remaining circuit parameters of the configuration. The output current is determined by the following formula:  
\[I=\mathrm{N_{p}}\left[I_{ph}-\mathrm{I_{rs}}\left(exp\frac{q(V+IR_{S})}{akTN_{s }}-1\right)\right] \tag{1}\]  
\[\mathrm{I_{rs}}=\mathrm{I_{rr}}\left(\frac{T}{T_{ref}}\right)^{3}\mathrm{e}^{ \frac{E_{B}}{K}(\tau_{ref}^{1}-\frac{1}{L})} \tag{2}\]  
where \(\mathrm{N_{p}}\) and \(\mathrm{N_{s}}\) are the numbers of cells connected in parallel and series, \(\mathrm{k}\) is [PERSON]'s constant, \(1.380658\times 10^{-23}\) J/K, \(\mathrm{q}\) is the charge of an electron equal to \(1.6\times 10^{-19}\) (\(C\)), \(a\) is the ideal diode factor between 1 and 5, \(\mathrm{I_{rs}}\) represents the reverse saturation current of the cell, \(\mathrm{I_{rr}}\) represents the reverse saturation current at \(\mathrm{T_{r}}\), \(\mathrm{T_{ref}}\) is the reference temperature (K), Eg is the energy in eV (\(1.602176634\times 10^{-19}\)J), and T is the absolute temperature of the junction.  
\[I_{ph}=\left[\mathrm{I_{scr}}+\mathrm{K_{i}}(T-\mathrm{T_{r}})\frac{S}{100}\right] \tag{3}\]  
where \(\mathrm{I_{scr}}\) represents the short-circuit current at a reference temperature of the cell, \(\mathrm{K_{i}}\) is the coefficient of the short-circuit temperature (A/m\({}^{2}\)K),\(\mathrm{T_{r}}\) refers to cell temperature, and \(S/100\) represents the solar radiation in W/cm\({}^{2}\)[41]. According to this configuration, the shunt resistance in parallel with the ideal shunt diode and the I-V characteristics are described by the following equation:  
\[I_{pv}=I_{ph}-I_{D} \tag{4}\]  
The PV output power is a function of the solar irradiation and the area of PV module, as shown in the following equation:  
\[\mathrm{P_{solar}}=\mathrm{\eta_{g}i_{r}A}. \tag{5}\]  
where \(\mathrm{\eta_{g}}\) is the production efficiency, \(\mathrm{i_{r}}\) is the radiation of solar in W/m\({}^{2}\), and A is the area.
# Efficient Lattice-Based Cryptosystems with Key Dependent Message Security
## 2 Preliminaries  
### Basic Notation  
In this paper, we use the following notation and lemmas. We will use a ring \(R\). In our concrete instantiations, we prefer to use either \(R=\mathbb{Z}\) (the integers) or the polynomial ring \(R=\mathbb{Z}[x]/\left(x^{d}+1\right)\), where \(d\) is a power of 2. For integer \(q\), we use \(R_{q}\) to denote \(R/qR\). Sometimes we will use abuse notation and use \(R_{2}\) to denote the set of \(R\)-elements with binary coefficients, when \(R=\mathbb{Z}\), \(R_{2}\) may denote \(\{0,1\}\), and when \(R\) is a polynomial ring, \(R_{2}\) may denote those polynomials that have \(0/1\) coefficients. For \(a\in R\), we use the notation \(\left[a\right]_{q}\) to refer to \(a\mod q\), with coefficients reduced into the range \((-q/2,\;q/2]\).  
For the security parameter \(\lambda\), denote a negligible function \(\mathrm{negl}(\lambda)\). For some distribution \(\chi\), writing \(e\leftarrow\chi\) means that \(e\) is distributed according to \(\chi\), the error distribution \(\chi\) is the discrete gaussian distribution \(D_{\mathbb{Z}^{n},\sigma}\) for some \(\sigma>0\). The usual norm \(\ell_{1}(s)\) over the reals equals \(\sum\limits_{i=1}^{n}|s_{i}|\). The \(\ell_{\infty}(s)\) norm is defined as \(\max\{|s_{1}|,|s_{2}|,\ldots,|s_{n}|\}\).  
**Lemma 1**.: (see [19]). _Let \(n\in\mathbb{N}\). For any real number \(\sigma=\omega\big{(}\sqrt{\log n}\big{)}\), we have_  
\[\Pr_{x\leftarrow\mathbb{D}_{\mathbb{Z}^{n},\sigma}}[\|x\|>\sigma\sqrt{n}]\leq 2 ^{-n+1}. \tag{1}\]  
**Lemma 2**.: (see [20]). _Let \(n\in\mathbb{N}\). For any real number \(\sigma=\omega\big{(}\sqrt{\log n}\big{)}\),and any \(c\in\mathbb{Z}^{n}\), the statistical distance between the distributions \(\mathrm{D}_{\mathbb{Z}^{n},\sigma}\) and \(D_{\mathbb{Z}^{n},\sigma,c}\) is at most \(\|c\|/\sigma\)._  
**Lemma 3**.: (see [21]). _Let \(n\in\mathbb{N}\). \(m=2n\), and let \(f(x)=\Phi_{m}(x)=x^{n}+1\) and let \(R=\mathbb{Z}[x]/\left(\Phi_{m}(x)\right)\). For any \(s,t\in R\), \(\|s\cdot t(\text{mod }\Phi_{m}(x))\|\leq\sqrt{n}\cdot\|s\|\cdot\|t\|\) and \(\|s\cdot t(\text{mod }\Phi_{m}(x))\|_{\infty}\)\(\leq n\cdot\|s\|_{\infty}\cdot\|t\|_{\infty}\)._
# The Basis for Reliability-Based Mechanical Properties of Structural Aluminium Alloys
## 5 Summary of Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Property**} & **Alloy** & **Mean Value** & **Distribution** & **Reference** & **Lower** \\  & **EN AW** & **\(X_{\text{m}}\)** & **CofV** & **Type** & **Value** & **Value** \\  & & & & & \(X_{\text{5\%}}\) & \(X_{\text{0.1\%}}\) \\ \hline  & 5754 & H111 & 1.56 \(R_{\text{p0.2,min}}\) & 11\% & LogN. & 1.30 \(R_{\text{p0.2,min}}\) & 1.12 \(R_{\text{p0.2,min}}\) \\  & 6005A & T6 & 1.18 \(R_{\text{p0.2,min}}\) & 4.7\% & Norm. & 1.09 \(R_{\text{p0.2,min}}\) & 1.01 \(R_{\text{p0.2,min}}\) \\  & 6060 & T6 & 1.25 \(R_{\text{p0.2,min}}\) & 7.0\% & LogN. & 1.11 \(R_{\text{p0.2,min}}\) & 1.01 \(R_{\text{p0.2,min}}\) \\ \(f_{\text{o}}\) & 6060 & T66 & 1.28 \(R_{\text{p0.2,min}}\) & 8.8\% & LogN. & 1.11 \(R_{\text{p0.2,min}}\) & 0.98 \(R_{\text{p0.2,min}}\) \\  & 6063 & T6 & 1.26 \(R_{\text{p0.2,min}}\) & 4.4\% & Norm. & 1.17 \(R_{\text{p0.2,min}}\) & 1.09 \(R_{\text{p0.2,min}}\) \\  & 6063 & T66 & 1.19 \(R_{\text{p0.2,min}}\) & 4.1\% & Norm. & 1.11 \(R_{\text{p0.2,min}}\) & 1.04 \(R_{\text{p0.2,min}}\) \\  & 6082 & T6 & 1.20 \(R_{\text{p0.2,min}}\) & 6.2\% & Norm. & 1.08 \(R_{\text{p0.2,min}}\) & 0.98 \(R_{\text{p0.2,min}}\) \\ \hline  & 5754 & H111 & 1.14 \(R_{\text{m,min}}\) & 3.4\% & LogN. & 1.08 \(R_{\text{m,min}}\) & 1.03 \(R_{\text{m,min}}\) \\  & 6005A & T6 & 1.07 \(R_{\text{m,min}}\) & 2.8\% & LogN. & 1.03 \(R_{\text{m,min}}\) & 0.99 \(R_{\text{m,min}}\) \\  & 6060 & T6 & 1.15 \(R_{\text{m,min}}\) & 5.3\% & LogN. & 1.06 \(R_{\text{m,min}}\) & 0.98 \(R_{\text{m,min}}\) \\ \(f_{\text{u}}\) & 6060 & T66 & 1.07 \(R_{\text{m,min}}\) & 6.5\% & LogN. & 0.96 \(R_{\text{m,min}}\) & 0.88 \(R_{\text{m,min}}\) \\  & 6063 & T6 & 1.09 \(R_{\text{m,min}}\) & 3.0\% & LogN. & 1.04 \(R_{\text{m,min}}\) & 1.00 \(R_{\text{m,min}}\) \\  & 6063 & T66 & 1.07 \(R_{\text{m,min}}\) & 3.3\% & LogN. & 1.01 \(R_{\text{m,min}}\) & 0.97 \(R_{\text{m,min}}\) \\  & 6082 & T6 & 1.12 \(R_{\text{m,min}}\) & 5.0\% & Norm. & 1.03 \(R_{\text{m,min}}\) & 0.95 \(R_{\text{m,min}}\) \\ \hline \hline \end{tabular} \(f_{\text{o}}\)—0.2% proof strength, \(f_{\text{u}}\)—ultimate tensile strength, \(E\)—modulus of elasticity. \(R_{\text{p0.2,min}}\) and \(R_{\text{m,min}}\) are the minimum yield strength \(R_{\text{p0.2}}\) and the lower bound of the ultimate tensile strength \(R_{\text{m}}\), according to the applicable product standard, e.g., of the EN 755 or EN 485 series.  
\end{table} 
Table 8: Variability of available mechanical properties. each registered alloy has its tolerance in chemical composition amplified by treatments (tempering). This again is reflected in upper and lower reference values as they are connected to the product standard values as well as to the individual CofV. The scatter of multipliers indicates that certain adjustments are needed, i.e., the scatter can be minimised with the tuning of standard values.
## 4 Results and Discussion
### Velocity Analysis  
Figure 9 shows the wave velocities obtained at each stress state, which is another parameter reflecting the mechanical state of concrete between two SA's. It is evident that the entire curve can be divided into three stages.  
The first stage is rapidly growing stage (concrete age is before 36 days), in which the velocities rise from 3854 m/s (C1) and 3535 m/s (C2) sharply at early loading steps below 6 MPa stress state. In this stage, concrete is in its early age, when the cement hydration reaction takes place severely. It is obvious that the elastic modulus of concrete develops rapidly in the early-age and this leads to an increase in the velocity with the growth of elastic modulus, according to Equation (4) [33]. On the other hand, when the concrete is subjected to a step load, it undergoes elastic shortening and the subsequent creep strain. In this process, some of the pores or micro cracks perpendicular to step load are close, and the concrete becomes more compact, which is equivalent to the increase of modulus elasticity of concrete [18]. It can be concluded that both cement hydration and applied step load result in an increase in wave velocity, and this explains the reason of why the growth rate of velocity in first stage is larger than that of in second stage. The velocity is a function of elastic modulus, density, and Possion's ratio in the following form  
\[V_{p}=\sqrt{\frac{(1-v)E}{(1+v)(1-2v)\rho}} \tag{4}\]  
where \(V_{p}\), \(E\), \(\rho\), and \(v\) are the velocity of stress waves, elastic modulus, density, and Possion's ratio of concrete, respectively.  
The second stage is called slowly growing stage (concrete age is among 36 and 180 days), in which the elastic modulus approaches to near stability and the wave velocities are almost linearly proportional to the stress values with a relative small gradient until peaking at 4462 m/s (C1) and 4285 m/s (C2) with approximate half of the peak stress value. At this stage, the influence of cement hydration on monitoring signals is negligible, and linearly increment in wave velocity mainly dependents on load variation. It demonstrates that, for mature concrete, wave velocity can be employed as an index to evaluate the compressive stress level of concrete, which undergoes monotonic incremental uniaxial loads in elastic phase.  
Figure 9: Velocity development with different stress.  
Followed by the third stage, i.e., declining stage (concrete age is after 180 days), in which the wave velocities drop sharply to 3305 m/s (C1) and 3046 m/s (C2), which are smaller than the initial values. In this phase, the micro cracks in ITZ develop rapidly and some obvious damage occurs, which causes a sharp decrease of velocity for the wave transferring in concrete.
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 7 Challenges/Future Directions of CR-LPWAN
### PHY Layer Challenges
#### 7.1.4 Spectrum Mobility  
Spectrum mobility enables CR systems to migrate seamlessly from an occupied channel to a free band without disrupting ongoing communication sessions. It is often called spectrum hand-off [54]. One main challenge in spectrum mobility is the presence of temporary breaks during communication, which will greatly hamper the effectiveness of CR-LPWAN systems as well. Although glitches in communication are inevitable, nevertheless, it is essential to develop new methods for spectrum mobility that would reduce such degrees of discontinuities to the barest minimal.
## 4 Field Tests
### Electrical Resistivity Survey  
Electrical resistivity has been used as an indicator to represent the spatial and temporal variability of soil properties. The electrical resistivity is known to be affected by the porosity, salinity, clay content, and water content. Among the factors that control the electrical resistivity of bulk soil, the electrical conductivity of pore water and the water content are the main factors. An electrical resistivity survey performed on the ground surface has the advantage of providing the continuous resistivity profile of greater volumes of soil [46]. On the other hand, while the TDR method is applied to a position, it is more accurate in determining the water content of soil compared to the electrical resistivity survey.  
The electrical resistivity survey was conducted at the same site along the positions for the application of PTDR, as shown in Figure 3b. For the electrical resistivity measurement, twenty-one electrodes were used for a 20 m length at a 1 m spacing. The dipole-dipole array was selected to minimize the electromagnetic coupling between the current and potential circuits, and to  
Figure 8: Volumetric water content profiles estimated from using PTDR.  
Figure 9: Temperature profiles obtained by PTDR. The dotted lines are the monitored temperature data in the same site reported by [PERSON] et al. [36].  
detect the variance in horizontal electrical resistivity. Using the Sting R1 earth resistivity meter (Advanced Geosciences, Inc.), the electrical resistivity was manually measured to avoid recording the outlier from the dataset. Figure 10a shows the distribution of average apparent resistivity of a section obtained from the Dipro 2D inversion software [47]. The interpreted resistivity values obtained above the 2 m depth were generally lower than those obtained under the 2 m depth, as shown in Figure 10a. The high resistivity values of 3000 to 8000 \(\Omega\cdot\)m under the 2 m depth corresponded to the resistivity values of permafrost or rocks. Figure 10b shows the magnified resistivity tomogram within the 2 m depth with contour lines. Overall, the low resistivity values ranging from 600 to 1800 \(\Omega\cdot\)m within the 2 m depth were included in the range of the resistivity of tills, and the resistivity values around P1 were greater than those around P2 and P3. Considering that the subsurface temperatures of P2 and P3 exceeded 0 \({}^{\circ}\)C, it is clear that the values represent the apparent resistivity of the water and soils, excluding the possibility of the existence of ice. It should also be noted that the thaw depth of this site was estimated to be slightly deeper than 1 m, according to the soil temperature profile reported by the Alfred Wegener Institute for Polar and Marine Research (AWI) and the Polar Institute Paul Emile Victor (IPEV).
## 1 Introduction
Excessive sulfates in industrial sewage cause environmental and operational issues, like equipment corrosion and harm to aquatic ecosystems. Calcium sulfate-induced crystallization is a promising, cost-effective solution that achieves up to 60% sulfate removal. Fluidized-bed crystallizers provide superior control and continuous operation for industrial applications, though challenges remain in optimizing the process and managing by-products [24]. Addressing the presence of the ether extract, phenols, chlorides and sulfates in sewage requires a multifaceted approach involving pollution prevention, regulatory enforcement and technological innovation [25]. Collaboration between industries, government agencies and environmental organizations is essential for implementing effective mitigation strategies and safeguarding water quality for current and future generations. Analysing contaminants, such as ether extract, phenols, chlorides and sulfates, presents significant challenges in sewage treatment technologies. These substances were selected as indicators in this study due to their high prevalence in industrial sewage, their considerable environmental impact and their importance in regulatory frameworks. This research innovates by using commercial products to reduce struvite formation, improving sewage treatment efficiency. It addresses gaps in evaluating coagulant (polyaluminium chloride (PAC)) usage and employs unsupervised machine learning to identify phenol patterns and ether extract anomalies. Our primary objectives were as follows: (i) analyse the composition of sewage by comparing raw sewage, mechanically treated sewage and totally treated sewage samples in terms of ether extract, phenols, chlorides and sulfates; (ii) assess the factors influencing biogas production, specifically comparing compost and biomass materials during the waste treatment process; (iii) optimize the use of coagulants, particularly polyaluminium chloride and commercial products (Polystabil KWS) to improve treatment processes by reducing the ether extract load and the volume of discharged sewage sludge; (iv) support decision-making by identifying the most critical pollutants through multivariate analysis and clustering, utilizing unsupervised machine learning techniques to evaluate the ether extract, phenols, chlorides, and sulfates.
## 3 Proposed Methodology
### Data Annotation Process
#### 3.2.2 Data Annotation Evaluation  
The dataset comprised 2500 samples, with each sample being manually annotated following the process depicted in Figure 5. Initially, three annotators independently annotated each review, generating a total of 7500 judgments. In case of any disagreements among the annotators, a majority voting approach was employed to resolve them. As a result, the raw dataset included 1009 samples marked as vulgar and 1476 samples marked as non-vulgar. Additionally, 15 conflicting samples were identified during the annotation process, and after discussion with the annotators, these were excluded and discarded from the final dataset. An example of a conflict was, e.g., a sentence like (English translation: _\"Mangal shouajatra_ (Mass procession) is inauspicious\"). Three annotators gave three different judgments to this comment, i.e., the first judged this comment as non-vulgar, the second as judged this comment as vulgar, and the third could not reach a  
Figure 4: Examples from the dataset.  
decision. Three people have given three types of judgments on this comment by looking at the word **Mangal** from a different religious point of view. Since this was a more difficult problem, surpassing the notion of vulgarity, we decided to not include it in the study this time but will consider it for separate research in the future. Figure 6 displays some of the most typical vulgar words in the dataset.