esa-sceva/satcom-chunk-collection huggingface dataset:
# Precedence Table Construction Algorithm for CFGs Regardless of Being OPGs
## 5 Examples
### Example 3
#### 5.3.5 Case Studies  
For Algorithm 5, the application of some precedence relations is exhibited.  
1. For the delimiter symbol $\mathfrak{s}$: \[\text{(a)}\quad\mathfrak{S}\ll\mathcal{L}(S)\to\text{Rule (A.1)}.\\ \bullet\quad\mathfrak{S}\ll a\\ \bullet\quad\mathfrak{S}\ll b\\ \bullet\quad\mathfrak{S}\ll d\\ \bullet\quad\mathfrak{S}\ll c\end{array}\] (a) \[\begin{array}{l}\text{(b)}\quad\mathfrak{R}(S)\gg\mathfrak{S}\to \text{Rule (A.2)}.\\ \bullet\quad c\gg\mathfrak{S}\end{array}\]
2. For \(S\longrightarrow ABC\) (a) \[\begin{array}{l}\text{(a)}\quad X=A,\ Y=B\ \to\text{Rule (E.1)}.\\ \quad\mathcal{R}(A)\ \gg\mathcal{L}_{m}(B)\\ \quad\mathfrak{a}\gg b\\ \quad\mathfrak{b}\end{array}\] (b) \[\begin{array}{l}\text{(b)}\quad X=B,\ Y=C\ \to\text{Rule (E.1)}.\\ \quad\mathcal{R}(B)\ >\ \mathcal{L}_{m}(C)\\ \quad\mathfrak{b}\gg c\end{array}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{2}{l}{Leftmost(C)} & \multicolumn{3}{l}{For \(C\longrightarrow CDc\)} \\ \hline \multicolumn{2}{l}{For \(C\longrightarrow CDc\)} & \multicolumn{3}{l}{For \(C\longrightarrow c\)} \\ \hline \multirow{3}{*}{1. Evaluating the **first**} & \(A=C\) & & \(A=C\) \\  & \(\beta=\varepsilon\) & & \(\beta=\varepsilon\) \\  & \(B=C\) & & \(a=c\) \\  & \(\gamma=Dc\) & & \(\gamma=\varepsilon\) \\  & \(\mathcal{L}_{m}(C)\supseteq\mathcal{L}_{m}(C)\) & & \(\mathcal{L}_{m}(C)=\{c\}\) \\ \hline \hline \multicolumn{2}{l}{Leftmost(\(D\))} & & & \\ \hline \multicolumn{2}{l}{For \(D\longrightarrow d\)} & & & \\ \hline \multirow{3}{*}{1. Evaluating the **second**} & \(A=D\) & & \\  & \(\beta=\varepsilon\) & & \\ \cline{1-1}  & \(a=d\) & & \\ \cline{1-1}  & \(\gamma=\varepsilon\) & & \\ \cline{1-1}  & \(\mathcal{L}_{m}(D)=\{d\}\) & & \\ \hline \end{tabular}
\end{table}
Table 15: _Cont._\(A\in l\
eq[\ ]\ \rightarrow\) Rule (E.3). \(\mathcal{R}(A)\ \gtrdot\ \mathcal{L}_{m}(\mathbb{C})\) \(a\ \gtrdot\ c\) \(u=null\), \(l=[\ ]\)
3. For \(A\ \longrightarrow aA\) (a) \(X=a\), \(Y=A\ \rightarrow\) Rule (C). \(a\ \lldot\ \mathcal{L}(A)\) \(a\ \lldot\ a\) \(u=a\), \(l=[\ ]\)
4. For \(B\ \longrightarrow bB\) (a) \(X=b\), \(Y=B\ \rightarrow\) Rule (C). \(b\ \lldot\ \mathcal{L}(B)\) \(b\ \lldot\ b\) \(u=b\), \(l=[\ ]\)
5. For \(C\ \longrightarrow CDc\) (a) \(X=C\), \(Y=D\ \rightarrow\) Rule (E.1). \(\mathcal{R}(C)\ \gtrdot\ \mathcal{L}_{m}(D)\) \(c\ \gtrdot\ d\) \(u=null\), \(l=[\ ]\) (b) \(X=D\), \(Y=c\ \rightarrow\) Rule (D.1). \(\mathcal{R}(D)\ \gtrdot\ c\) \(u=null\), \(l=[\ ]\)
6. In the following cases, the form \(XY\) is not fulfilled in the right side part of the production; therefore, no rule can be applied: (a) \(A\ \longrightarrow a\) (b) \(B\ \longrightarrow b\) (c) \(B\ \longrightarrow\varepsilon\) (d) \(C\ \longrightarrow c\) (e) \(D\ \longrightarrow d\)  
The remaining precedence relations are derived using the same method as the previously demonstrated cases.
## 2 Materials and Methods
### Antibiofilm Activity  
Different inoculums of the standard and clinical strains of _E. faecalis_ were prepared and standardized in saline solution (0.9% NaCl) at a concentration of 1 \(\times\) 10\({}^{8}\) CFU/mL using a spectrophotometer. Then, 100 \(\upmu\)L/well of the corresponding inoculum and 100 \(\upmu\)L/well of BHI broth were added in 96-well plates. The plates were kept at 37 \({}^{\circ}\)C for 48 h to form biofilms, in which the BHI broth was changed after 24 h [14].  
After biofilm formation, the _N. officinal_ and _P. guajava_ hydroethanolic extracts were placed in contact with the biofilms, and the plates were incubated again at 37 \({}^{\circ}\)C for 24 h. Saline solution (Eurofarma, Sao Paulo, SP, Brazil) and 2.5% sodium hypochlorite (NaOCl) (Biodinamica, Ibiproa, PR, Brazil) were used as the control groups. Each experimental group consisted of n = 10.  
The experimental groups were evaluated by introducing 100 \(\upmu\)L of MTT solution (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolzolium bromide) (Sigma-Aldrich, St. Louis, MO, USA). The samples were incubated at 37 \({}^{\circ}\)C for 1 h, shielded from light. After incubation, the MTT solution was discarded, and 100 \(\upmu\)L of dimethyl sulfoxide (DMSO) (Sigma-Aldrich, St. Louis, MO, USA) was added. The plates underwent a second incubation at 37 \({}^{\circ}\)C for 10 min, followed by agitation on a shaker for an additional 10 min. Absorbance was then recorded at 570 nm using a microplate reader (BIO-TEK Instruments, Highland Park, Winooski, VT, USA). The resulting optical density (OD) values were used to determine microbial cell viability percentages [13].  
\(\%\) Viability = (OD Treated Group \(\times\) 100)/(Mean OD Control Group)
# Reliability of Automated Intracranial Volume Measurements by Synthetic Brain MRI in Children
## 5 Conclusions  
Automated SyMRI hydrocephalus analyses could be a helpful supplement to conventional MRI of the brain in children. The SyMRI evaluations presented demonstrated sufficient accuracy and reliability, but the automated measurements should still undergo validation from human experts as artifacts could impair the results.  
Conceptualization, S.T. and V.W.; methodology, N.V., S.T. and V.W.; software, N.V. and S.T.; validation, A.D., N.S., E.N. and S.T.; formal analysis, A.C.-H. and E.N.; investigation, A.C.-H., M.S., N.S., N.V. and V.W.; resources, S.T.; data curation, A.C.-H., N.S., N.V., V.W. and A.D.; writing--original draft preparation, N.V.; writing--review and editing, A.C.-H. and V.W.; visualization, M.S. and S.T.; supervision, S.T.; project administration, S.T.; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the Medical University of Graz (IRB00002556) (protocol code 35-147 ex 22/23).  
Patient consent was waived due to the retrospective study design.  
The raw data supporting the conclusions of this article will be made available by the corresponding author on reasonable request.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BPV & Brain parenchyma volume \\ CI & Confidence interval \\ CSF & Cerebrospinal fluid \\ DICOM & Digital Imaging and Communications in Medicine \\ ICV & Intracranial volume \\ MDME & Multi-Dynamic Multi-Echo \\ MRI & Magnetic Resonance Imaging \\ NIFTI & Neuroimaging Informatics Technology Initiative \\ PACS & Picture Archiving and Communications System \\ \end{tabular}
## 5 Energy Consumption on Active Tilting System
### Energy Consumption in Typical Driving Modes
#### 5.2.2 Energy Consumption in Slalom  
In contrast to the steady circle, the pylon slalom with repeated tilting is a typical example of high energy consumption for tilting. We set the slalom course with 10 pylons at 18 m interval, as shown in Figure 32. The vehicle reaches 60 km/h before the slalom section and passes through the slalom section at a constant speed.  
IPGDriver prepares the \"corner cutting coefficient (ccc)\" as one of the model parameters of driver characteristics in virtual driving.ccc = 0 means driver runs middle of given course andccc = 1.0 means driver runs fully in-cut course. In other words,ccc = 1.0 is set to follow the easiest course. Under this condition, withccc = 0.4, the driver could not pass the given course because of overturning. Therefore, this course setting may be considered to be almost at limit condition.  
Although three levels ofccc were compared, which areccc = 0.5, 0.7 and 0.9, as shown in Figure 33, no major difference was found in the steering wheel angles as input and lateral vehicle accelerations as output. Therefore, we decided to proceed with this study of energy balance atccc = 0.7, which is considered to be a general driver characteristic.  
In the slalom, a large amount of energy is consumed to give a quick change in roll angle, as shown on the left of Figure 34. During this time, although energy saving caused by avoidance of cornering  
Figure 31: Consumed and saved energy on steady circle.  
Figure 32: Driving on slalom course.  
Figure 33: Influence of driver parameter is not significant.  
drag also occurs significantly, as shown on the right of Figure 34, this cannot offset energy consumption for tilting.
## 2 Materials and Methods
### Measurement Indicators and Methods
#### 2.3.4 Measurement of Photosynthetic Parameters  
Photosynthetic parameters were measured in the middle leaves of each halophyte under different salt solution treatments using the LI-6400 Convenient Photosynthesis Measurement System analyzer from LI-COR, Lincoln city, NE, USA, on a sunny day. Measurements were taken every 2 h from 07:00 to 19:00. The light intensity of the photosynthesis measurement system analyzer was set at 1000 \(\text{\mu mol}\cdot\text{m}^{-2}\cdot\text{s}^{-1}\), the carbon dioxide concentration was set at 400 \(\text{\mu mol}\cdot\text{mol}^{-1}\), the flow rate was set at 500 \(\text{\mu mol}\cdot\text{s}^{-1}\), the humidity was set at 20% to 30%, and the temperature of the control leaf chamber was set at 25 \({}^{\circ}\)C. The net photosynthetic rate (_Pn_), stomatal conductance (_G_s), intercellular carbon dioxide concentration (_Ci_), and transpiration rate (_Tr_) of leaves were measured. Also, the water use efficiency (_Wue_) of leaves was calculated using the following equation:  
\[\text{Wue}=Pn/Tr \tag{4}\]
# Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
## 3 Results
### Extracted Features  
From each ECG segment, 24 times and frequency domain features were extracted. Additionally, the wavelet scattering algorithm set with an invariance scale of 20 s subdivides each signal into 5 scattering windows (so there are 5 times more scattered windows than original samples) and extracts a vector of 210 scattering coefficients for each of the 5 scattered windows of each signal. The coefficients from each scattered window are used as separate inputs, assuming that each scattered window inherits the label of its original signal. After classification, the outputs for each of the 5 scattered windows are combined in an equal-weighted voting to classify the original window.
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 2 Related Works and Background Information
### Machine Learning and Neural Network Approaches to Smart Factory Emergency Detection  
The use of machine learning and neural networks for emergency detection systems has attracted research interests among industry and research experts [33; 34]. For example, authors in Reference [33] described how the approach could lead to early detection of machines or cell failures which allows preventive measures to be put in place as quick as possible before the problem is escalated. According to Reference [34], ML has assisted the industry in achieving sustainability of the manufacturing value chain. Reviewing about eighty-two articles from the web of science and SCOPUS database which, address the application of ML to smart-manufacturing, the authors of Reference [34] posited that ML not only help in addressing the issue of productivity but also allows for optimization of scarce resources (human and natural).  
In Reference [35], authors reviewed deep learning approaches for human-robot collaboration for smart manufacturing. The deep learning algorithms were deployed for movement detection, classification and recognition. The smart factory environment imposes an accuracy requirements in the range of 99%. This is made possible recently with the development of deep neural network algorithms, such as AlexNet and CNN. However, the result of Reference [35] gave an accuracy of 96%. Similarly, authors in Reference [36] proposed a long short term memory (LSTM) model to detect and classify abnormal vibrations for emergency detection. However, CNN has been shown to perform better for human action recognition data and most suitable to such classes/type of data [37]. Moreover, the accuracy of 97.39% achieved is below the acceptable threshold for future factory and mission critical systems [38].  
A human activity recognition algorithm was proposed by Reference [39], wherein they used random forest and hidden markov models for industrial human robot interaction situations. The result shows an average frame-wise accuracy of 74.82% for movement, 72.65% for gestures and 71.2% for object handling. However, this approach differs from the proposed idea in this work, where we attempted to classify and detect non-image data. Moreover, emerging research focus of smart factory is human-centered systems where the human safety and well-being of smart factory shop floor workers are part of the design consideration [40; 41]. To the best of our knowledge, this is a major attempt to develop a testbed to capture data for the purpose of detection and classification of movement, breathing and vibration in a smart factory shop floor for human safety.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Cobalt Sulfide Films  
The p-type CoS films were deposited onto different substrates (glass and single crystal Si (111) wafers) by using a cobalt (II) sulfate (CoSO\({}_{4}\)) and sodium sulfide (Na\({}_{2}\)S) solution [64]. The XRD studies revealed the amorphous phase and polycrystalline structure in the glass and silicon (111) wafers, respectively. Steel was used as a substrate during the deposition of films [65]. The optical conductivity and band gap were 1.77 \(\times\) 10\({}^{17}\) S\({}^{-1}\) and 1.9 eV, respectively. The XRD analysis confirmed the hexagonal structure with a crystallize size of about 27.5 nm. The cobalt sulfide films could be used in optoelectronic and energy storage applications due to some unique properties. These films indicated 80% charge capacity retention (even after 1000 deposition cycles) and showed a specific capacity (298.4 mAh/g) at 30 deposition cycles when the scan rate was 20 mV\(\cdot\)s. [PERSON] and co-workers [66] highlighted that the grayish-black color changed to dark black with increasing the deposition cycle (35 to 115 cycles) during the experiment. Further, they explained that films were peeled off from the substrate after 95 deposition cycles. Lastly, they observed that activation energy decreased (0.18 to 0.15 eV) when film thickness increased (201 to 513 nm).
# Towards a Real-Time Description of the Ionosphere:
## 8 Validation Results for _Imit_F2 Based on Radio Occultation Observations  
The full COSMIC dataset, comprising 1,791,602 electron density profiles measured from 2006 to 2018, was used to retrieve reliable COSMIC _Imit_F2 values for calculating the two-dimensional density plots of IRI-_Imit_F2 vs. COSMIC-_Imit_F2 and IRTAM-_Imit_F2 vs. COSMIC-_Imit_F2, along with the histograms of corresponding residuals, as shown in Figure 40. The figure shows that IRTAM slightly improves the _Imit_F2 modeling accuracy because _Res. Mean_IRTAM = 0.140 km is smaller than _Res. Mean_IRI = 3.728 km. The same cannot be said for the _Imm_F2 modeling precision. In fact, the comparisons \(RMSE_{\text{IRTAM}}=32.223\) km vs. \(RMSE_{\text{IRI}}=22.446\) km and \(NRMSE_{\text{IRTAM}}=11.609\%\) vs. \(NRMSE_{\text{IRI}}=8.087\%\) show that the IRTAM absolute and percentage errors are by far higher than those of IRI. Moreover, the IRTAM density plot appears much more scattered than that of IRI (\(R_{\text{IRTAM}}=0.733\) vs. \(R_{\text{IRI}}=0.881\)). The statistical results reported in Table 7 show that in some cases _Res. Mean_\({}_{\text{IRIAM}}\)\(<\)_Res. Mean_\({}_{\text{IRI}}\), while in many cases \(RMSE_{\text{IRTAM}}>RMSE_{\text{IRI}}\), \(NRMSE_{\text{IRTAM}}>NRMSE_{\text{IRI}}\), and \(R_{\text{IRTAM}}<R_{\text{IRI}}\), which means that the same considerations made for the full COSMIC _Imm_F2 dataset hold also when IRI and IRTAM performances are evaluated according to the procedures described in Section 4.2, considering the three levels of solar activity defined in Section 3.1. Figure 40: Density plots between measured (\(x\)-axis) and modeled (\(y\)-axis) _Imm_F2 values, for (**a**) IRI and (**c**) IRTAM. Measured values are those from the entire COSMIC dataset. The black solid lines represent the best linear fit. Corresponding slope and intercept values, along with the correlation coefficient, are reported in the upper left box of each plot. Statistical distributions of the resultant residuals for (**b**) IRI and (**d**) IRTAM. Corresponding _Res. Mean_, _RMSE_, and _NRMSE_ values, along with the total number of counts on which the statistics is based, are reported in the upper left box of each plot. Analogously to what was carried out in Section 6 for \(f\)0F2, a global picture of the IRI and IRTAM behavior relative to the COSMIC _hm_F2 dataset is shown in Figures 41-44.
# The Condition Evaluation of Bridges Based on Fuzzy BWM and Fuzzy Comprehensive Evaluation
## 4 Case Study
### Case Study 2
#### 4.2.2 Condition Rating of Jigongling Bridge  
The membership matrices for both the primary indicator and secondary indicator layers are presented in Table 12.  
Therefore, the comprehensive evaluation vectors \(E_{\text{C1}}\), \(E_{\text{C2}}\), \(E_{\text{C3}}\), and \(E_{\text{C4}}\) can then be obtained as follows:  
\[E_{\text{C1}}=V_{\text{D1-D5}}\times Q_{\text{C1}}=\left( \begin{array}{c}0.3034 \\ 0.2256\\ 0.2783\\ 0.1066\\ 0.0861\end{array} \right)^{T}\times\left( \begin{array}{cccc}0&0.3&0.7&0&0\\ 0&0.82&0.18&0&0\\ 0&0.12&0.88&0&0\\ 0&0.5&0.5&0&0\\ 0&0&0.65&0.35&0\end{array} \right)=\left( \begin{array}{cccc}0&0.36&0.61&0.03 &0\end{array} \right) \tag{36}\]  
\[E_{\text{C2}}=V_{\text{D6-D8}}\times Q_{\text{C2}}=\left( \begin{array}{c}0.39 98\\ 0.3145\\ 0.2857\end{array} \right)^{T}\times\left( \begin{array}{cccc}0&0.2&0.8&0&0\\ 0&0.26&0.74&0&0\\ 0&0.6&0.4&0&0\end{array} \right)=\left( \begin{array}{cccc}0&0.33&0.67&0&0 \end{array} \right) \tag{37}\]  
\[E_{\text{C3}}=V_{\text{D9}}\times Q_{\text{C3}}=\left(1\right)^{T}\times \left( \begin{array}{cccc}0&0.7&0.3&0&0\end{array} \right)=\left( \begin{array} []{cccc}0&0.70&0.30&0&0\end{array} \right) \tag{38}\]  
\[E_{\text{C4}}=V_{\text{D10}}\times Q_{\text{C4}}=\left(1\right)^{T}\times \left( \begin{array}{cccc}0&0&0.1&0.9&0\end{array} \right)=\left( \begin{array} []{cccc}0&0&0.10&0.90&0\end{array} \right) \tag{39}\]  
Following the process outlined above, the membership matrix and the comprehensive evaluation vector of the target layer \(A\) can be obtained as Equations (40) and (41). \[Q_{A}=\left( \begin{array}{cccc}0&0.40&0.46&0.14&0\\ 0.12&0.47&0.39&0.02&0\\ 0.03&0.44&0.53&0&0\\ 0&0.46&0.54&0&0\end{array} \right) \tag{40}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Criteria** & & & & & \\
**Layer** & & & & \\ \cline{  
\[E_{A}=V_{B1-B4}\times Q_{A}=\left( \begin{array}{c}0.4133\\ 0.3246\\ 0.1620\\ 0.1001\end{array} \right)^{T}\times\left( \begin{array}{ccccc}0&0.40&0.46&0.14&0 \\ 0.12&0.47&0.39&0.02&0\\ 0.03&0.44&0.53&0&0\\ 0&0.46&0.54&0&0\end{array} \right)=(0.04,0.42,0.47,0.07,0) \tag{41}\]  
So, the \(\overline{E}_{A}\) is expressed as:  
\[\overline{E}_{A}=\frac{0.04\times 1+0.42\times 2+0.47\times 3+0.07\times 4+0 \times 5}{0.04+0.42+0.47+0.07+0}=2.57 \tag{42}\]  
According to Equation (42), the comprehensive score of Jigongling Bridge is 2.57. This score falls within the third interval (2, 3), as detailed in Section 2.3, the Jigongling Bridge was classified as a Class _III_ bridge. The inspection and evaluation results for the Jigongling Bridge, provided in the \"Yichang City Ordinary Highway Bridge 'Three-Year Elimination of Hazards' Project, Xingshan County S457 High Water Line Jigongling Bridge Maintenance and Reinforcement Project\"of the _2020 Bridge Inspection and Evaluation Report_ (Highway Administration of Hubei Provincial Department of Transportation [38]) are presented in Table 13 below.
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 2 Numerical Modeling and Setup  
### Establishment and Configuration of the Slope Model  
This paper takes the profile of the landslide section at K26+130 on the Yuxi to Chuxiong section of the G8012 Meili to Chuxiong segment (TJ4 section) of the National Expressway Network as the calculation prototype for numerical simulation research. Using FLAC3D 5.0 software and based on the finite difference method, a numerical simulation model was established. According to regional geological data and borehole investigation results, the geological conditions of this section are complex. The soil layers primarily consist of interbedded moderately weathered argillaceous siltstone and moderately weathered conglomerate rock, with a thin surface layer of highly weathered argillaceous siltstone, accompanied by intensive joint-fracture zones. The bedding layers have a dip angle of approximately 22\({}^{\circ}\).  
To minimize the influence of extraneous factors, the numerical model was simplified. The modeling excludes the highly weathered argillaceous siltstone removed during slope excavation and disregards joint-fracture zones that are located far from the actual sliding surface. According to the recommendations in [28], the distance between the left boundary and slope toe (L1 in Figure 2), the distance between the slope crest and right boundary (L2 in Figure 2), and the total height of the model to 1.5H (H is the height of the slope and is defined as the corresponding Z coordinate difference between the slope crest and the slope toe), 2.5H and 2H.  
The Mohr-Coulomb model was selected as the primary constitutive model for geotechnical materials, as it effectively characterizes the stress-strain behavior of soil and rock under various loading conditions, making it suitable for slope stability analysis, underground excavation, and related engineering applications. During the rainy season, groundwater infiltrates through the pores and weathered fractures of conglomerate rock, often leading to the softening of contact zones between conglomerate and argillaceous siltstone, thereby forming weak sliding surfaces and causing structural dislocation. To better investigate the influence of interbedded conglomerate and siltstone on the overall stability of the slope, the Coulomb slip contact interface function in FLAC3D was utilized. Using the  
Figure 2: Simplified CAD diagram of the model.  
\"interface\" command, contact surfaces were established between the rock layers on the slope to simulate dislocation, slippage, and separation trends under different conditions.  
Additionally, to visually analyze the numerical simulation results and study the deformation and failure mechanisms at various locations within the high-slope structure, five monitoring points were set along the slope surface at equal elevation intervals based on field monitoring data. These points were used to monitor key mechanical response parameters such as stress, displacement, acceleration, and saturation. The final slope model is illustrated in Figure 3.