esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Reporting Technique-Based Clinical-Trial Service Platform for Real-Time Arrhythmia Detection
## 4 Discussion
### Clinical Potential  
The clinical potential of this platform is not limited to clinical trials. This platform can be used in telemedicine. Because patients can report their symptoms and communicate with clinicians while a certain type of disease is identified, real-time medical consultations and treatments can be possible. This platform is capable of monitoring the medical conditions of patients with chronic illnesses over the long term, and enables quick responses during emergencies. This merit is highly appreciated in the COVID-19 pandemic. To prevent the spread of the disease, noncontact medical treatments using telemedicine technologies should be encouraged. In Korea, telemedicine services are not allowed by law but are exceptionally allowed in living and treatment support centers (LTSCs). LTSCs are temporary quarantine-treatment facilities for healthy COVID-19 patients with mild or no symptoms [34]. During quarantine, wearable devices are used to continuously acquire the vital signs from the admitted patients, and the acquired data are transmitted to a nearby tertiary hospital [35; 36]. This platform can be used to monitor cardiac disorders along with the conventional vital signs of patients in LTSCs. Other biosignals and health information can also be measured by adding new devices to this platform wirelessly.  
Another clinical potential of this platform is its wide applicability to other research. ECG monitoring and arrhythmia detection are examples that demonstrate how this platform can be exploited. This platform can be easily tailored to different types of diseases and medical data. If wireless connections with medical devices and the diagnosis algorithm are prepared, other diseases can be automatically diagnosed. Trials of drugs can also be feasible because medication history is managed. For example, cardiovascular disorders related to side effects of drugs can be efficiently monitored. Moreover, medical images and video data can be securely forwarded to clinicians using the SFU [28]. The applicability of this platform for other purposes should be considered further.
## 3 Nonlinear Modeling of the Semi-Active Energy Regenerative Suspension with an EHA  
### Dynamic Model of a Two Degrees of Freedom Suspension System for a Quarter-Vehicle  
In this paper, a quarter-car model with two degrees of freedom was exactly established [17], which is shown in Figure 3.  
According to Figure 3, the equations of motion are obtained by using [PERSON]'s laws of motion:  
\[\left\{\begin{array}{l}m_{s}\ddot{x}_{2}+k_{s}(x_{2}-x_{1})+c_{s}\big{(} \dot{x}_{2}-\dot{x}_{1}\big{)}=F\\ m_{u}\ddot{x}_{1}-k_{s}(x_{2}-x_{1})-c_{s}\big{(}\dot{x}_{2}-\dot{x}_{1}\big{)} +k_{t}(x_{1}-z)=-F.\end{array}\right. \tag{1}\]  
The state vector and output vector are selected as follows:  
\[\mathbf{X}=\big{[}x_{2}-x_{1}\quad\dot{x}_{2}\quad x_{1}-z\quad\dot{x}_{1}\big{]}^ {\mathsf{T}}\]  
\[\mathbf{Y}=\big{[}\ddot{x}_{2}\quad x_{2}-x_{1}\quad k_{t}(x_{1}-z)\quad\dot{x}_{1 }\big{]}^{\mathsf{T}}\]  
where \(\ddot{x}_{2}\) is the sprung mass acceleration, \(x_{2}-x_{1}\) is the suspension deflection response, \(k_{t}(x_{1}-x_{0})\) is the tire load response, and \(\dot{z}(t)\) is the speed excitation of the road input. In this way, the state equation of the suspension can be obtained as follows:  
\[\left\{\begin{array}{l}\ddot{\mathbf{X}}=\mathbf{A}\mathbf{X}+\mathbf{B}\mathbf{U}\\ \mathbf{Y}=\mathbf{C}\mathbf{X}+\mathbf{D}\mathbf{U}\end{array}\right. \tag{2}\]  
where \(\mathbf{A}\) is the state matrix, \(\mathbf{B}\) is the input matrix, \(\mathbf{C}\) is the output matrix, and \(\mathbf{D}\) is the transfer matrix. When the control input force \(F\) is \(0\), it becomes a passive suspension.  
\[\mathbf{A}=\left[\begin{array}{cccc}0&1&0&-1\\ -\dfrac{k_{s}}{m_{s}}&-\dfrac{c_{s}}{m_{s}}&0&\dfrac{c_{s}}{m_{s}}\\ 0&0&0&1\\ \dfrac{k_{s}}{m_{u}}&\dfrac{c_{s}}{m_{u}}&-\dfrac{k_{t}}{m_{u}}&-\dfrac{c_{s} }{m_{u}}\\ \end{array}\right]\mathbf{B}=\left[\begin{array}{cccc}0&0\\ 0&\dfrac{1}{m_{s}}\\ -1&0\\ 0&-\dfrac{1}{m_{u}}\end{array}\right]\]  
The road surface input model adopts filtered white noise [18] as follows:  
\[\dot{z}(t)=-2\pi f_{0}z(t)+2\pi\sqrt{G_{0}}u\omega(t) \tag{3}\]  
where \(z\) is the input displacement of the road, \(G_{0}\) is the road irregularities coefficient, \(f_{0}\) is the lower cutoff frequency, \(u_{0}\) is the vehicle speed, and \(\omega(t)\) is unit white noise.  
Figure 3: The schematic diagram of the quarter-car model.
## 4 Simulation Results and Discussion
However, it can also be seen that the compensation effect deteriorates when the shape of the cumulative dispersion profile of the antipodal symmetric configuration changes with respect to the midway OPC; that is, the overall profile gradually decreases and then increases and takes on an overall negative value, as can be seen in Figures 5c,d and 6c,d. When the optical pulse propagates in the section with the positive cumulative dispersion amount, its width becomes wider (expansion), while it narrows (compression) in  
Figure 6: Five dispersion maps with the best/worst compensation based on TJ evaluation. (**a**) the best compensation cases for varying DCF length, (**b**) the best compensation cases for varying SMF length, (**c**) the worst compensation cases for varying DCF length, and (**d**) the worst compensation cases for varying SMF length. the section with the negative sign. Expansion and compression increase as the cumulative amount of dispersion increases. As the width of the optical pulse increases, the intensity of the optical pulse becomes weaker, and as a result, it is less affected by the nonlinear Kerr effect. However, if the positive cumulative dispersion amount continues to increase, inter-symbol interference (ISI) occurs due to overlap with adjacent pulses due to the large broadening of the optical pulse width. Looking at the dispersion map configurations that greatly enhance the compensation effect, it is desirable to gradually increase the amount of positive cumulative dispersion to reduce the distortion of optical pulses due to nonlinear effects in the front part of the transmission link (approximately 1/4 of the entire link) and then to gradually decrease the amount of cumulative dispersion for minimizing the occurrence of ISI in the remaining sections up to the midway OPC. To take advantage of the impact of the MSSI system, the cumulative dispersion profile of the second half can be inverted from the first half.
## 3 Results
### Effects of Ultrasonic Vibration on the Fluidity of Cement Paste after CO\({}_{2}\) Absorption
#### 3.3.2 The Most Available Geometric Diameter of Pore Distribution  
[PERSON] divided the concrete pore into four types according to influences of pore size on the durability of concretes: Harmless pores (<20 nm), slightly harmful pores (20-100 nm), harmful pores (100-200 nm), and multi-harmful pores (>200 nm). Influences of ultrasonic vibration on pore distribution in the hardened cement paste after CO2 absorption are shown in Figure 7. Clearly, the percentages of harmless pores are 4%, 5%, 6%, 8%, 9%, and 9% under mechanical agitation when the CO2 AA increases from 0% to 2.20% at a rate of 0.44%. Meanwhile, the percentages of slightly harmful pores are 58%, 60%, 61%, 63%, 65%, and 67%, respectively. The percentages of harmful pores are 34%, 31%, 29%, 26%, 23%, and 21%, respectively. The percentages of multi-harmful pores are 4%, 4%, 4%, 3%, 3%, and 3%. Under ultrasonic vibrating agitation, the percentages of harmless pores are 6%, 6%, 8%, 9%, 10%, and 10% when the CO2 AA increases from 0% to 2.20% at a rate of 0.44%. Meanwhile, the percentages of slightly harmful pores are 70%, 72%, 74%, 77%, 77%, and 78%, respectively. The percentages of harmful pores are 21%, 19%, 15%, 12%, 11%, and 10%, respectively. The percentages of multi-harmful pores are 3%, 3%, 3%, 2%, and 2%, respectively.  
According to the test results, slightly harmful pores and harmful pores change mostly in the pore distribution in hardened cement paste after CO2 absorption under ultrasonic vibrating agitation. Specifically, the proportion of slightly harmful pores increases significantly, while the proportion of harmful pores decreases dramatically. When the CO2 AA are 0%, 0.44%, 0.88%, 1.32%, 1.76%, and 2.20%, the percentages of slightly harmful pores are increased by 20.7%, 20%, 21.3%, 22.2%, 18.8%, and 16.4%, respectively, and the percentages of harmful pores are decreased by 38.2%, 38.7%, 48.3%, 53.8%, 52.2%, and 52.4%, respectively.  
It can be obtained from Figure 7, with an increase in CO2 AA, that harmless pores and slightly harmful pores of the hardened carbonized cement paste will increase, while harmful pores and multi-harmful pores will decrease. However, the increment of harmless pores and slightly harmful pores and the decrement of harmful pores and multi-harmful pores are more significant when ultrasonic agitation is adopted.  
Figure 7: Pore distributions in hardened cement paste under different CO2 AA.
## 2 Adaptive Streaming Algorithm for Internet of Autonomous Vehicles
### Real-Time Streaming Bandwidth Allocation at the RSU
Since bandwidth is relayed from base stations located in the less congested outlying areas of the metropolitan region to autonomous vehicles navigating congested road segments in the central metropolitan region, the maximum distance for bandwidth relay along a path is determined by the longest distance a vehicle travels between the two farthest points in the outlying areas of the metropolitan region.
## 3 The Objective Electromagnetic Environmental Effects Assessment
### Grey Theory and the Analytical Hierarchy Process  
Besides the basic indicators, the analytical hierarchy process (AHP) and grey theory [29] are also used. Although the electromagnetic environment is complex and uncertain, we can still discover some rules in the messy electromagnetic environmental data. Therefore, most researchers employ the grey theory in electromagnetic environmental complexity assessments. Additionally, the AHP is also adopted. The research design for the general electromagnetic environmental complexity based on this method is depicted in Figure 3. AHP is used to model the evaluation system, the weightings are determined based on the expert system and scale method, and the comprehensive evaluation result is obtained using grey theory. Figure 2: The research design of the four-domain method’s electromagnetic environmental effect evaluation. [PERSON] [30] adopted electromagnetic signal density, frequency coincidence, electromagnetic signal types, direction coincidence, and background signal intensity as basic evaluation indicators. They used the fuzzy AHP to build the fuzzy complementary judgment matrix and obtain the corresponding evaluation index weighting. Subsequently, a comprehensive quantitative evaluation model of electromagnetic environmental complexity is constructed. [PERSON] et al. [31] selected four basic evaluation indexes of the frequency coincidence and frequency occupancy degrees and background and electromagnetic signal densities. Except for the basic signal indexes of the four domains, they also involved the electromagnetic
## 4 Simulation Model Input Parameters  
### Emissivity and Reflectivity  
In practice, the uncertainty of emissivity varies and is very much case-specific. If a traceable emissivity measurement is required, spectral emissivity must be evaluated. Spectral emissivity of an object's surface is a material property, specific to the surface finish, angle of observation, wavelength and temperature. The majority of emissivity uncertainties in this research assume a uniform distribution.  
In the case of the spectral emissivity evaluation using a spectrometer, the uncertainty is reported relative, according to [PERSON] et al. [13] in the order of 1% of the measured value, ranging to 10% at low emissivity values.  
However, usual practices rely on nominal spectral parameter values with much higher uncertainties, usually sourced from general emissivity charts and/or tables from literature. Such practices simplify objects as (spectrally independent) grey bodies with increased emissivity uncertainty, accounting for the inhomogeneity of spectral emissivity.  
The emissivity of human skin is an important topic in medical non-contact temperature measurements. While consensus has never been made, most sources agree on the skin emissivity value 0.98 \(\pm\) 0.01, as evident from Table 1. Uniform (boundary) distribution has been assumed for this agreement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author [Source]** & **Year** & **Spectral Range** & **Skin Emissivity** \\ \hline [PERSON] [14], in [15] & 1934 & \(>\)2 μm & 0.989 \(\pm\) 0.01 \\ [PERSON] [16] & 1937 & 0.3–50 μm & 0.954 \(\pm\) 0.004 \\ [PERSON] [17] & 1960 & 0.8–12 μm & 0.939 \(\pm\) 0.003 \\ [PERSON] [16] & 1961 & 3–15 μm & within 0.99 and 1 \\ [PERSON] [16] & 1964 & unknown & 0.976 \(\pm\) 0.015 \\ [PERSON] et al. [17] & 1967 & \(\leq\)20 μm & within 0.995 and 1 \\ [PERSON] in Williams [16] & 1969 & 4–6 μm & 0.990 \(\pm\) 0.045 \\ [PERSON] in Williams [16] & 1969 & 6–18 μm & 0.972 \(\pm\) 0.041 \\ [PERSON] in Williams [16] & 1969 & 4–18 μm & 0.975 \(\pm\) 0.043 \\ [PERSON] [15] & 1973 & 3–14 μm & 0.98 \(\pm\) 0.01 \\ [PERSON] [16] & 1989 & 8–14 μm & 0.969 \(\pm\) 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of the sources of human skin emissivity value and uncertainty.  
Figure 6: Normal (**A**) and uniform (**B**) parameter probability distribution function.  
In specific cases, such as for laboratory calibrations, the spectral emissivity is geometrically enhanced by using cavities. In such cases, emissivity is less material dependent and generally more spectrally homogeneous. The scalar emissivity value is usually calculated analytically from cavity dimensions and the general surface emissivity value.
## 2 Preliminary Theory and Related Work
### Dynamic Mode Decomposition (DMD)  
Firstly, the matrix \(\mathbf{V}=\left[\begin{array}{cccc}v_{1}&v_{2}&\cdots&v_{r}\end{array} \right]^{T}\) is partitioned as:  
\[\mathbf{V}_{1}^{r-1}=\left[\begin{array}{cccc}v_{1}&v_{2}&\cdots&v_{r-1} \end{array}\right],\mathbf{V}_{2}^{r}=\left[\begin{array}{cccc}v_{1}&v_{2}& \cdots&v_{r}\end{array}\right]^{T}. \tag{17}\]  
The goal of DMD is to find a linear approximation matrix \(\widetilde{\mathbf{A}}\) that satisfies:  
\[\mathbf{V}_{2}^{T}=\widetilde{\mathbf{A}}\mathbf{V}_{1}^{T},\mathbf{v}_{i+1}= \widetilde{\mathbf{A}}\mathbf{v}_{i}. \tag{18}\]  
The previously mentioned SVD not only helps find Koopman modes but also calculates the Moore-Penrose pseudo-inverse for non-square or singular matrices, facilitating the solution of linear equations. Solving for \(\widetilde{\mathbf{A}}\) can be achieved by applying SVD to \(\mathbf{V}_{1}^{-1}\) (where the symbol \(\sim\) is used to indicate the separation into results of SVD):  
\[\mathbf{V}_{1}^{r-1}=\widetilde{\mathbf{U}}\widetilde{\mathbf{\Sigma}} \widetilde{\mathbf{V}}^{T}. \tag{19}\]  
Thus, \(\widetilde{\mathbf{A}}\) is determined as:  
\[\widetilde{\mathbf{A}}=\widetilde{\mathbf{U}}^{T}\mathbf{V}_{2}\widetilde{ \mathbf{V}}\widetilde{\mathbf{\Sigma}}^{-1}. \tag{20}\]By applying DMD to matrix (3), we aim to approximate the finite-dimensional Koopman operator. This linear mapping between columns of \(\mathbf{V}\) can then be analyzed via DMD to identify the system's underlying dynamics.
# Strain Transfer Analysis of a Clamped Fiber Bragg Grating Sensor
## 4 Theoretical Analysis Results
### Main Factors Affecting the Strain Transfer Rate
#### 4.2.2 Effect of the Interlayer Thickness  
Due to the shear deformation of the interlayer under external stress, interlayer thickness is an important influencing factor. Figure 6 shows the relationship between the interlayer thickness and the strain transfer rate of a clamped FBG sensor. Interlayer thickness is equal to the radius of the interlayer minus the radius of the optical fiber, that is, the interlayer thickness depends on the diameter of the steel pipe. When the interlayer thicknesses are 2.5 \(\upmu\)m, 7.5 \(\upmu\)m, 17.5 \(\upmu\)m, 27.5 \(\upmu\)m, 37.5 \(\upmu\)m, 337.5 \(\upmu\)m, 737.5 \(\upmu\)m, 1137.5 \(\upmu\)m, 1537.5 \(\upmu\)m and 1937.5 \(\upmu\)m, the strain transfer rates are 76.36%, 73.80%, 70.85%, 68.89%, 67.38%, 55%, 50.86%, 48.76%, 47.38% and 46.37%, respectively. With the increase of the interlayer thickness, the strain transfer rate decreases accordingly. This tendency becomes slower with a larger thickness. Reducing the thickness of the interlayer contributes to increasing the strain transfer rate. However, in the actual encapsulation processing, if the interlayer thickness is too small, it will be hard to inject adhesive and cement to bond the optical fiber. In the following experimental tests, the inner radius of the capillary tube is selected as 500 \(\upmu\)m and the interlayer thickness is 440 \(\upmu\)m.  
Figure 5: Gauge ratio-FBG strain transfer rate.
# Arabic Hate Speech Detection Using Deep Recurrent Neural Networks
## 5 Conclusions  
Many social media platforms prevent users from using hate speech, racism, cyberbullying, etc. In many countries, privacy laws prevent governments from monitoring the internet and posting the activities of individuals, even for national security reasons. However, there are many countries in which the government is allowed to monitor social media and other citizen activities for various reasons, including national security and the prevention of cyberbullying, hate speech, and racism. In this paper, a deep machine learning algorithm was proposed for the automatic classification and detection of Arabic hate speech. The availability of publicly published datasets is scarce, and those that are published do not contain the type of written comments that were targeted in this study. Therefore, one of the main contributions of this work was the development of a unique dataset that contained 4203 comments gathered from various social media platforms and labeled into one of the following seven classes: content against religion, racist content, content against gender equality, violent/offensive content, insulting/bullying content, normal positive comments, and normal negative comments.  
The dataset was subjected to a thorough preprocessing phase and the comments were labeled in a scientific manner that ensured the accuracy of the labeling and classification. In this work, the use of deep recurrent neural networks (RNNs) for the automatic classification and detection of Arabic hate speech was proposed. The proposed model, which was called  
Figure 6: Confusion matrix of the experimental results from the proposed deep recurrent neural network model-based classification method for seven classes of Arabic comments.  
RDNN-2, consisted of 10 layers with 32 batch sizes and performed the classification in 50 iterations. Another model consisting of five layers, which was called RDNN-1, was used in the binary classification experiment only. Using the proposed models, a recognition rate of 99.73% was achieved for binary classification, 95.38% for the three classes of Arabic comments, and 84.14% for the seven classes of Arabic comments. This was a high accuracy for the classification of a complex language, such as Arabic, into seven different classes. The achieved accuracy was higher than any similar method reported in the recent literature, whether for binary classification, three-class classification, or seven-class classification.  
Future work should include the continuous development of the dataset in order to increase its size and classes and develop a unified dataset for researchers in this field. In addition, future research should focus on the continuous development and modification of machine learning techniques to achieve better accuracies for the classification of hate speech in Arabic. In addition, the author of the current study will work to develop a prototype that can be applied within social media platforms for real-time testing and processing.  
This research received no external funding.
# Improving the Sensory Quality of Black Tea by Blending Varieties During Processing
## 2 Materials and Methods
### Quantification of Catechins, Gallic Acid, Caffeine, and Theaflavin  
The contents of the caffeine, catechins, gallic acid (GA), and theaflavins were determined using high-performance liquid chromatography (HPLC, Agilent 1100 VL, Agilent Technologies Inc., Santa Clara, CA, USA). Tea powder (0.2 g) was extracted using 5 mL of 70 \({}^{\circ}\)C methyl alcohol in a water bath maintained at 70 \({}^{\circ}\)C for 10 min, with shaking every 5 min. After cooling to room temperature, the mixture was centrifuged at 4000 rpm for 10 min. The residues were re-extracted using the same method, and supernatants were merged and diluted to 10 mL with methanol. The final extracted solutions were filtered through a 0.45 \(\upmu\)m filter. A C18 column (5 \(\upmu\)m, 4.6 mm \(\times\) 250 mm; Agilent Technologies Inc., Shanghai, China) was used to separate the chemicals at a flow rate of 1.0 mL/min, and maintained at 40 \({}^{\circ}\)C. The injection volume was 10 \(\upmu\)L, and the detector was set at 280 nm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Name** & **Treatments** & **Manufacture Methods** \\ \hline
1 & Fud & Single variety, without blending & Using the variety of _Fudingdabai_ manufactured black tea. \\
2 & JGY & Single variety, without blending & Using the variety of _linguanyin (oolong tea variety)_ manufactured black tea. \\
3 & JX & Single variety, without blending & Using the variety of _linkuan (oolong tea variety)_ manufactured black tea. \\
4 & FJG & Mixed varieties, fresh leaf blending & Fresh leaves were blended with a ratio of 85\% \\
5 & FJX & Mixed varieties, fresh leaf blending & _Fudingdabai_ and 15\% _linguanyin_. \\  & & & Fresh leaves were blended with a ratio of 85\% \\
6 & AFJG & Mixed varieties, finished tea blending & _Fudingdabai_ and 15\% _linkuan_. \\  & & & Fresh leaves were not blended; samples manufactured by blending with the ratio of 85\% \\
7 & AFJX & Mixed varieties, finished tea blending & Fresh leaves were not blended; samples manufactured by blending with the ratio of 85\% \\  & & & Fud and 15\% _lX_. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Samples’ preparation and their manufacture methods.  
The mobile phase consisted of A (100% acetonitrile) and B (2% acetic acid in water), with a gradient elution program as follows: mobile phase B was subjected to linear variation from 6.5 to 15% within 16 min, 15-25% at 16-25 min, 25-6.5% at 25-25.5 min, and maintained at 6.5% between 25.5 min and 30 min. For theflavin analysis, the detector was adjusted at 380 nm, using the same gradient solutions. Mobile phase B was subjected to linear variation from 20-25% within 35 min, maintained at 25% at 35-38 min, and then from 25% to 20% at 38-40 min.