esa-sceva/satcom-chunk-collection huggingface dataset:
# Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)
### Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Contribution** & **Key Findings** & **Limitation(s)** \\ \hline \multirow{4}{*}{[1]} & \multirow{4}{*}{Automated hate speech detection and the problem of offensive language} & Logistic regression, naive bayes, decision trees, random forests, and SVM are tested using 5-fold cross-validation & The definition of hate speech in this research is limited to language that threatens or incites violence which excludes a large proportion of hate speech. Lexical methods used are inaccurate at identifying hate speech, and only a small percentage of tweets flagged by hate base lexicon are considered hate speech. \\ \hline \multirow{4}{*}{[2]} & Detection of offensive content and identification of potential offensive users & Lexical syntactical feature (LSF) framework & Comparison of existing text-mining methods in detecting offensive contents with LSF framework in not detailed and lacks scientific validation. \\ \hline \multirow{4}{*}{[3]} & A feature attribution method for explainability & Necessity and sufficiency explained in detailed in the context of hate speech & The analysis is limited by limitation of the existing dataset used which lacks variety of demographic groups. \\ \hline \multirow{4}{*}{[4]} & Detecting bullying and aggressive behavior on Twitter & Random forest classifier using WEKA tool, 10-fold cross-validation & Results obtained with random forest classifier are only presented with respect to training time and performance due to limited space. \\ \hline \multirow{4}{*}{[5]} & A unified deep learning architecture for abuse detection & Deep learning architecture for detection of abuse online & Network-related metadata are not considered in the dataset due to time limitations as it takes a significant amount of computation to crawl Twitter data due to Twitter API rate limits. \\ \hline \multirow{4}{*}{[6]} & A unified approach to explaining complex ML models & SHAP (Shapley additive explanations) framework for the explanation of complex, ensemble and deep learning models & SHAP model is not consistent with human intuition in some cases, which can lead to false positives or false negatives; a different approach is not considered in such cases. \\ \hline \multirow{4}{*}{[7]} & Explanation of RNN predictions in sentiment analysis & Propagation rule for growing connections in recurrent neural networks (RNN) architectures & Gradient-based sensitivity analysis used with approach is not able to get accurate relevance score when a sentiment is decomposed into words. \\ \hline \multirow{4}{*}{[8]} & Intuitive explainability along with using various deep learning techniques & LIME explanation with individual examples & Some misclassification is observed in the case of nontoxic comments. \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table} 
Table 1: Summary of literature.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 3 Communication System Model
### Theory
#### 3.1.1 Transmitter Model  
The transmitter sends data in blocks with a duration of \(T_{D}\). The input bit stream for each block is mapped into \(M\) symbols using a QPSK constellation scheme (in this study, \(M=4\)). The data stream is then converted from serial to parallel, forming parallel symbol streams, which are transmitted on orthogonal carriers centered at different subcarrier frequencies. Suppose the original data rate is \(R\); the parallel stream rate is \(\frac{R}{R}\), where \(K\) is the number of parallel streams.  
The data streams are loaded onto the subcarriers using IFFT. The symbol duration on each subcarrier increases by a factor of \(K\). The number of subcarriers \(K\) is chosen such that the symbol time on each subcarrier is much greater than the channel delay spread, or  
Figure 2: Composition of the UWA OFDM system based on ZoomFFT.  
the subcarrier bandwidth is less than the channel coherence bandwidth. The modulation scheme modulates only the odd subcarriers in the first half of the \(N\) subcarriers to meet the Hermitian symmetry requirement.  
Let \(X_{p}=[X_{0},X_{1},...,X_{N-1}]\) be the QPSK modulated information sequence, where \(N\) is the number of subcarriers. Zeros are inserted at the carrier positions, and the modulated information sequence is loaded onto the corresponding subcarriers. Suppose the sequence after zero insertion is \(X_{i}[k]\); this step can be expressed by Equation (5).  
\[X_{i}[k]=\begin{cases}X_{p}[k],&\text{if }k\in C_{D}\\ 0,&\text{else}\end{cases} \tag{5}\]  
where \(C_{D}\) is the data set composed of the positions of the information sequence.  
The sequence after zero insertion is transformed into a time-domain signal through IFFT, which can be expressed as  
\[x_{p}(t)=\frac{1}{N}\sum_{k=0}^{N-1}X_{i}[k]e^{j2\pi\frac{t}{t}t} \tag{6}\]  
where \(x_{p}(t)\) is the time-domain signal, and \(N\) is the number of IFFT points.  
The baseband signal \(x_{p}(t)\) is upsampled (using an upsampling factor \(D\)), resulting in the refined frequency bands. These bands are then frequency-shifted to their actual frequencies and passed through a bandpass filter to prevent aliasing, and that can be expressed as  
\[x_{D}(t)=x_{p}(\frac{t}{D})e^{-j\omega_{0}t}*h_{t}(t) \tag{7}\]  
where \(h_{t}(t)\) is the impulse response of the low-pass filter, and \(\omega_{0}\) is the frequency shift parameter.  
A CP is inserted at the front end of the signal to eliminate ISI. The signal with the CP added is then converted from parallel to serial, and upconverted to the carrier frequency \(f_{c}\). After up-conversion, the transmitted signal \(x(t)\) and its spectrum \(X(\omega)\) can be described as follows:  
\[x(t)=2\mathcal{R}((x_{p}(\frac{t}{D})e^{-j\omega_{0}t}*h_{t}(t))e^{-j2\pi f_{c }t}) \tag{8}\]  
\[X(\omega)=X_{p}((\omega+\omega_{0})D+2\pi f_{c})H_{T}(\omega+2\pi f_{c}) \tag{9}\]
## 5 Influence of the Laser Peak Intensity  
It is also worth noting that the Rabi period in the present simulations, as mentioned above, is \(\simeq\)180 fs, which means that during the interaction times that we have considered (\(\leq\)90 fs) the system is in the exciting half of the Rabi cycle. We have performed simulations by doubling the amplitude of the interacting pulse so that the Rabi period becomes of \(\simeq\)90 fs. We would expect a response of the system to the fact that most of the interaction is produced during a whole Rabi cycle, but the results that we obtain are qualitatively the same as those described for a Rabi period of \(\simeq\)180 fs. The only difference that we observe is quantitative. We obtain a higher enhancement factor of \(\simeq\)3.2 for the population in \(\rho_{11}+\rho_{22}\). The maximum enhancement of \(\mid\!\rho_{12}\!\mid\) results in \(\simeq\)1.6, and for \(\rho_{33}\)+ \(\rho_{44}\) is \(\simeq\)2.1. Of relevance, however, is the maximum enhancement factor in \(\mid\!\rho_{34}\!\mid\), which becomes in this case \(\simeq\)38.5. This large value is observed close to the resonance of the first vibrational upper state level, with \(\widetilde{v}\simeq\) 19,365 cm\({}^{-1}\) and for two almost symmetric chirp values, namely \(\phi^{\prime\prime}\simeq-210\) fs\({}^{2}\) and \(\phi^{\prime\prime}\simeq 220\) fs\({}^{2}\) (see Figure 6).  
Figure 5: Enhancement/suppression factors of (**a,b**) population in the ground state; (**c,d**) vibrational coherence in the ground state; (**e,f**) population in the upper state; and (**g,h**) vibrational coherence in the upper state. The right hand panels are for a laser pulse of 30 fs and the right hand panels for 17 fs, as indicated. The colored lines represent the different excitation frequencies: 18,800 cm\({}^{-1}\) (black line), 19,400 cm\({}^{-1}\) (red line), 19,700 cm\({}^{-1}\) (blue line), 20,000 cm\({}^{-1}\) (dark cyan line), and 20,600 cm\({}^{-1}\) (magenta line). The electronic dephasing time is 100 fs for all cases. A bold (blue) line has been chosen for the excitation wavenumber of 19,700 cm\({}^{-1}\), in order to clarify the comparisons with Figure 7.
## 3 Results
### Results of Multi-Variant Surveys Performed Using Smart Station Equipment  
In the case of multi-variant surveys, the differences of coordinates were calculated too. The differences between the reference coordinates from model static GNSS occupation and the surveys performed using smart station equipment were calculated. The number of maximum differences in positioning (for plus or minus) after applying GNSS static occupation and with the use of the individual type of corrections in multi-variant measurements (single reference station SRS, master-auxiliary concept MAC, or virtual reference station VRS). The number of extreme differences (NoED) of coordinates between the reference coordinates from model static GNSS occupation and surveys performed using the smart station are present below in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Point**} & \multirow{2}{*}{**D [m]**} & \multicolumn{4}{c}{**Accuracy Assessing by Uncertainty [m]**} \\ \cline{3-6}  & & **u(Hz)** & **u(V)** & **U\({}_{68}\)(3D)** & **U\({}_{99}\)(3D)** \\ \hline
1000 & 1325.375 & 0.0037 & 0.0057 & 0.007 & 0.021 \\ \hline
18970 & 1252.585 & 0.0036 & 0.0056 & 0.007 & 0.021 \\ \hline
10010 & 1197.575 & 0.0036 & 0.0056 & 0.007 & 0.021 \\ \hline
10020 & 1102.643 & 0.0036 & 0.0056 & 0.007 & 0.021 \\ \hline
10030 & 1051.773 & 0.0035 & 0.0055 & 0.007 & 0.021 \\ \hline
10040 & 963,924 & 0.0035 & 0.0055 & 0.007 & 0.021 \\ \hline
18966 & 901.319 & 0.0035 & 0.0055 & 0.007 & 0.021 \\ \hline
10070 & 739.494 & 0.0034 & 0.0054 & 0.006 & 0.018 \\ \hline
10080 & 632.409 & 0.0033 & 0.0053 & 0.006 & 0.018 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Expected accuracy of rapid static GNSS surveys.  
Figure 4: Number of maximum differences in positioning after applying GNSS static occupation and with use the individual type of corrections in multi-variant measurements.  
For classical accuracy estimation of the multi-variant data acquisition for reference point stationing, rigorous adjustment was realized. The parameters and results of the adjustment of terrestrial and satellite surveys performed using smart station equipment are presented in Table 4.  
The obtained RMSE from adjustment in the horizontal and vertical plane (shown in the last two rows of Table 4) are close to the differences present in Table 3. The characteristics for the terrestrial measurements obtained a posteriori are approximate to the assumed errors a priori. It is different for GNSS vectors surveying in fast static procedure, where the average errors were calculated from a variance-covariance matrix. It has been previously found in the literature [41] that, in the case of GPS (today GNSS), the rapid static positioning of the formal variance-covariance matrix output using processing software is of little use.
## 3 Results and Discussion
### Multivariate Statistical Analysis
#### 3.4.3 Analysis of Key Differential Volatile Components  
Based on the principles of VIP \(>1\), \(p<0.05\), and rOAV \(>1\), the abovementioned \(11\) differential volatile components with rOAV \(>1\) were selected as the key differential volatile components of Rucheng Baimao black tea in different seasons. It is speculated that the differences in the distribution of these key differential volatile components in Rucheng Baimao black tea in different seasons play an important role in the formation of the aroma quality of black tea in the corresponding seasons. To better analyze the impact of seasons on the key differential volatile components, this section will specifically analyze the distribution of their relative contents in Rucheng Baimao black tea in spring, summer, and autumn. As shown in Figure 6, the relative contents of geraniol, methyl salicate, nonanal, and (\(E\))-citral in spring black tea were significantly higher than those in summer and autumn (Figure 6A-D). Geraniol can be formed by the hydrolysis of glycosidic aroma precursors during tea processing [54]. [PERSON] et al. proposed that monoterpene alcohol glycosides, especially geraniol glycosides, have a high content in spring tea [66]. This may be the reason for the high relative content of geraniol in Rucheng Baimao spring black tea. Methyl salicate exhibits a fresh and minty aroma, and nonanal shows a floral and fresh aroma. Moreover, methyl salicate was considered to be related to the fresh aroma quality of Rucheng Baimao black tea in previous studies [7]. This implies that the high contents of methyl salicate and nonanal in Rucheng Baimao spring black tea may contribute to the formation of a fresher aroma quality. (\(E\))-citral has a fresh lemon-like aroma and has been identified as a key aroma component of black tea [67], which may also contribute to the formation of the freshness of Rucheng Baimao spring black tea. In addition, the relative contents of lanalool, phenylacetaldehyde, benzaldehyde, phenethyl alcohol, benzyl alcohol, and \(\beta\)-ionnone in summer black tea were significantly higher than those in spring and autumn (Figure 6E-J). Except for the relative content of lanalool in spring black tea being significantly lower than that in autumn black tea (Figure 6E), there were no significant differences in the remaining volatile components between spring and autumn black tea. [PERSON] et al. proposed that spring tea contains more volatile components with fresh tea and floral aromas such as nonanal, lanalool, and geraniol, while summer and autumn teas contain more floral components such as phenethyl alcohol and phenylacetaldehyde [57]. This is basically consistent with the results of this study. The
# Impact of Post-Earthquake Seismic Waves on the Terrestrial Environment
## 3 Gas Anomalies in the CCSD-LOS during the Magnitude-9 Earthquake in Sumatra
However, seismic waves of the earthquake came about 17 min after the earthquake, consistent with the CCSD high Ar concentration anomalies. The fact indicates that these gas anomalies were triggered by the seismic waves triggered by this earthquake. Based on a theory we know, a sudden drop of the stress field in the seismic zones stimulates the crustal fluid upwelling and causes geoelectric, geomagnetic and other anomalies [18; 19; 20]. However, there have been no detailed studies on the long-distance effects of a strong earthquake, which cover thousands of kilometers from the epicenter. The surface waves from a big earthquake are relatively strong and slow to decay, and can spread in the global  
Figure 3: (**a**) A 3-component record of Sumatra earthquake observed. (**b**) Real-time gas anomalies observed during the CCSD drilling; the vertical ordinate is the date of observation; columns from the left to right are the seismic magnitude, records of helium, argon, argon/helium, and argon/nitrogen, respectively. crust for several days. The energy of the seismic surface waves loosens the pore fissures exist in underground strata and promotes pore fracture connectivity, causing fluid release from the deep crust. The records of Figure 3 prove that there was a crustal gas-releasing effect after the strong earthquake occurred. If the post-earthquake seismic waves cause the corresponding degassing effect and fluid upwelling, it may have an impact on the terrestrial environment. However, the occurrence of earthquakes as strong as Sumatra is unusual; on average, this occurs once every a few years. Moderate earthquakes have much higher frequency of occurrence. If the post-earthquake seismic waves of a moderate earthquake also produce large-scale crustal effects and fluid movement, its impact on the living environment cannot be ignored. The question is, does a moderate earthquake also produce the large-scale crustal effects of fluid motion? Let us see once more a set of records of a typical moderate-intensity earthquake observed at the CCSD-LOS.
###### Abstract  
This study aimed to analyze the validity and sensitivity of two time-shortened Wingate anaerobic tests (WAnTs), by means of three phases. In Phase A, 40 participants performed a traditional 30 s WAnT, whereas the first 15 s (WAnT\({}_{15}\)) and 20 s (WAnT\({}_{20}\)) were used to elaborate two predictive models. In Phase B, another 30 s WAnT was performed by 15 different volunteers to examine the error of these models (cross-validation). Finally, in Phase C, a 30 s WAnT was registered before and after a 10-week velocity-based training conducted by 22 different participants (training group, TRAIN = 11; control group that fully refrained from any type of training, CONTROL = 11). Power changes (in Watts, W) after this training intervention were used to interpret the sensitivity of the time-shortened WAnT. Adjusted coefficient of determination (R\({}^{2}\)) was reported for each regression model, whereas the cross-validation analysis included the smallest detectable change (SDC) and bias. Close relationships were found between the traditional 30 s WAnT and both the WAnT\({}_{15}\) (R\({}^{2}\) = 0.98) and WAnT\({}_{20}\) (R\({}^{2}\) = 0.99). Cross-validation analysis showed a lower error and bias for WAnT\({}_{20}\) (SDC = 9.3 W, bias = \(-\)0.1 W) compared to WAnT\({}_{15}\) (SDC = 22.2 W, bias = 1.8 W). Lastly, sensitivity to identify individual changes was higher for WAnT\({}_{20}\) (TRAIN = 11/11 subjects, CONTROL = 9/11 subjects) than for WAnT\({}_{15}\) (TRAIN = 4/11 subjects, CONTROL = 2/11 subjects). These findings suggest that the WAnT\({}_{20}\) could become a valid and sensitive protocol to replace the traditional 30 s WAnT.  
**Keywords:** power; assessment; strength; performance; anaerobic capacity; resistance training
## 3 Results  
### Surface Characteristics  
#### 3.1.1 Surface Morphology  
Scanning electron microscopy observation and EDX examination were performed to detect the surface micromorphology and elemental composition. Figure 2 shows the SEM micrographs and EDX spectra of the substrate surface before the experiments. The SEM micrographs of Ti showed polishing marks on the surface of the substrate metal (Figure 2A,D), while the surfaces of CTO 700 and 800 were covered by CaTiO\({}_{3}\) film. The CaTiO\({}_{3}\) film surface showed a cauliflower-like texture; in particular, the surface of CTO 800 exhibited nanoparticle structures (Figure 2B,C,E,F). The EDX spectra of the CTO 700 and CTO 800 mainly included characteristic Ti metal, O, and Ca peaks (Figure 2G-I).  
Figure 2: Scanning electron micrographs and energy-dispersive X-ray (EDX) spectra of specimen surfaces. (**A**,**D**,**G**) Ti; (**B**,**E**,**H**) CTO 700; and (**C**,**F**,**I**) CTO 800. EDX spectra (**G–I**) indicate EDX spot elemental analysis sites shown in (**D–F**), respectively. Bars = 100 μm (**A–C**); 1 μm (**D–F**).
## 3 Results
### Summer/Autumn Trial
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Statistics** & \begin{tabular}{c} **VARMODED** \\ \(\times\)**5** \\ \(\times\)**(BELGUM)** \\ \end{tabular} & \begin{tabular}{c} **VARMODED** \(\times\)**5** \\ **(TIALY,IZST)** \\ \end{tabular} & \begin{tabular}{c} **VARMODED** \(\times\)**5** \\ **(SLOVENIA)** \\ \end{tabular} & \begin{tabular}{c} **VARMODED** \\ \(\times\)**(CROATIA)** \\ \end{tabular} & \begin{tabular}{c} **CONTROL** \\ **(BELGUM)** \\ \end{tabular} & \begin{tabular}{c} **CONTROL** \\ **(TIALY,IZST)** \\ \end{tabular} & \
\begin{tabular}{c} **CONTROL** \\ **(SLOVENIA)** \\ \end{tabular} &
\begin{tabular}{c} **CONTROL** \\ **(CROATIA)** \\ \end{tabular} \\ \hline Minimum & 7.5 & 11.5 & 15.3 & 69.7 & 92.4 & 56.4 & 10.9 & 53.3 \\ Maximum & 12.2 & 27.94 & 63.2 & 94.6 & 24.7 & 48.1 & 108.9 & 111.7 \\ Net Quantile & 23.1 & 55.2 & 18.5 & 76.5 & 152.3 & 61.9 & 23.7 & 61.2 \\ Median & 44.0 & 90.1 & 20.4 & 80.2 & 180.0 & 117.5 & 28.8 & 70.8 \\
3 rd Quantile & 63.7 & 121.2 & 38.4 & 85.2 & 208.2 & 126.4 & 48.5 & 78.1 \\ Mean & 49.6 & 94.2 & 30.4 & 80.9 & 179.7 & 137.8 & 42.5 & 72.1 \\ Variance (\(\mathrm{n^{-1}}\)) & 15.3 & 37.0 & 3.5 & 0.5 & 25.1 & 147.1 & 12.7 & 2.9 \\ Standard & 39.1 & 60.8 & 18.8 & 7.3 & 50.1 & 121.3 & 35.6 & 16.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Variation of adult honey bee coverage in all groups recorded during the summer/autumn trial compared to the beginning of the treatment (%). Mean brood area compared to the control group decreased after VarroMed treatment in Italy and Slovenia (\(-71.1\%\) and \(-3.0\%\), respectively) and increased in Croatia (+24.7\(\%\)), but all variations were not statistically significant (Table 4; Figure 6). Brood area was not recorded in Belgium, as bees started clustering and weather conditions were prohibitive to check brood coverage properly.
###### Abstract
China's research on liquid-medium ultrasonic nozzles started late, but many scholars have performed extensive experimental and numerical simulation studies. [PERSON] et al. (2007, 2010) analyzed the principle and characteristics of liquid-medium ultrasonic atomization and examined the variation law of atomization quality with operating parameters and structural parameters [36]. [PERSON] (2004) experimentally investigated the variation of acoustic parameters with the structure parameters of nozzles and established relevant empirical formulas [37]. [PERSON] et al. (2002) analyzed the ultrasonic atomization performance of water through orthogonal experiments, and the results showed that the number of droplets with the size smaller than 50 \(\upmu\)m can be used as an index for the optimal parameter of the nozzle [38]. Using this index, the amount of water became the main influencing factor. In addition, a mathematical model for ultrasonic atomization performance was established through regression analysis of experimental data. Meanwhile, some scholars also used the fluid dynamics software ANSY FLUENT to simulate the internal flow field, external flow field, and atomization performance of the fluid-medium ultrasonic atomizing nozzle, and to analyze the pressure and velocity vector distribution of the droplet field [39; 40]. [PERSON] et al. (2017, 2018) and [PERSON] et al. (2017) designed an ultrasonic atomizing nozzle with the new structure, analyzed the influence of the structure parameters of the resonant cavity and operating parameters of the nozzle on the internal flow field, and atomizing performance of the ultrasonic nozzle, and calculated the relationship between the cavity structure and sound pressure [41; 42; 43]. In summary, in the previous studies, scholars have focused on the flow field mode in the resonant cavity and ultrasonic frequency, and thoroughly studied the atomization mechanism of the liquid-medium ultrasonic nozzle. Meanwhile, some scholars have investigated the droplet size of this type of nozzle, and obtained simple prediction mathematical models for the droplet size. However, in the existing mathematical models, the influencing factorsfor the droplet size were not completely considered, accordingly, the established droplet prediction model cannot be applied to the program development in engineering sites. In order to fill the gaps in existing research, this study analyzed the changes of the SMD of the nozzle with three parameters, i.e., air pressure, water pressure and outlet diameter using the orthogonal experimental design method. A Malvern real-time high-speed spray particle size analyzer, an intelligent electromagnetic flow meter, an air mass flow meter, and a high-performance camera were utilized in the study. Then, a mathematical model for predicting the SMD of the nozzle was constructed using the multivariate nonlinear regression analysis method, which can prove to be an effective tool for the parameter prediction for ultrasonic atomizing nozzle.