esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Program Representation Accommodated for Direct Hardware Synthesis
### The Control and Data Flow Graph  
A user control program is executed as one of the items in the closed loop. The system assures the repeated execution of the user program in the loop and is responsible for signal exchanges. The control program computes the current value of process variables to work out the new controlled variables state. This limits the control program to statements and conditional choices of the execution path. Waiting for the input change using the loop statement is prohibited. The following limitation allows for representing the processing, using a data flow graph that does not contain cycles.  
For program representation, the directed data flow graph is used. It must be able to represent logic, arithmetic operations, and conditional processing flow. To improve analysis and optimizations, the edges with attributes have been proposed. The enhanced flow graph (EFG) is defined as follows:  
\[\begin{split} EFG=\langle V,E,X,O,A\rangle\\ v=\langle o,XV\rangle:v\in V,o\in O,XV\subset X\\ e=\langle v_{S},v_{D},AE\rangle:v_{S},v_{D}\in V,AE\subseteq A \end{split} \tag{5}\]  
where: \(V\) is the set of vertices, \(E\) is the set of directed edges, \(X\) is the set of variables, \(O\) is the set of operations (that can be implemented by the graph vertices), and \(A\) is the set of attributes.  
The EFG application for representing processing is shown in Figure 3. Cases A and B show the EFG for \(y=a\overline{b}c+de\). The graph initially obtained from the statement analysis is shown in Case A. Next, it is a subject of [PERSON]'s law application that enables the merging of nodes (Case B). The pointing end of the edge depicts the attribute, assigned  
Figure 2: The fast LD translation method illustration: input ladder diagram (**A**) and the equivalent logic circuit implementation (**B**).  
like a logic inversion or a sign change. Attributed edges simplify the implementation of logic function manipulation, e.g., the application of De Morgan laws or checking argument in simple and inverted form. Similarly, Case C depicts EFG constructed for the arithmetic formula: \(y=(a-b)\cdot(c-d-e)\), obtained directly from the statement analysis. The subtraction is represented by the attributed edge of the sign change. This allows for the merging of addition and subtraction in a single node. The merged graph is depicted in Case D. Finally, the conditional processing flow is illustrated in Case E. The node, shown as a multiplexer, enables a choice between inputs of processed data. The conditional choice node requires an ordered connection of edges. The proposed implementation of a data flow graph minimizes the number of different operations and is able to represent operations and specific results (e.g., adder carry line) using edge attributes.
# In Silico Modeling the Impact of Cartilage Stiffness on Bone Tissue Stress
## 2 Materials and Methods
### Problem Formulation  
In order to determine the mechanical behavior of the model, the nonlinear theory of elasticity was applied [15]. In dynamic analysis, the equilibrium equations of the system at time step \(t+\Delta t\) are  
\[[M]^{t+\Delta t}\{U^{\prime\prime}\}^{(i)}+[C]^{t+\Delta t}\{U^{\prime}\}^{(i )}+^{t+\Delta t}[K]^{(i)t+\Delta t}\{\Delta U\}^{(i)}={}^{t+\Delta t}\{R\}-{}^ {t+\Delta t}\{F\}^{(i-1)} \tag{3}\]  
where \([M]\) is the mass matrix; \([C]\) is the damping matrix; \({}^{t+\Delta t}[K]^{(i)}\) is the stiffness matrix; \({}^{t+\Delta t}[R]\) is the vector of external loads; \({}^{t+\Delta t}[F]^{(i-1)}\) is the vector of internal forces at iteration \((i-1)\); \({}^{t+\Delta t}[\Delta U]^{(i)}\) is the vector of incremental displacements at iteration \((i)\); \({}^{t+\Delta t}[U^{\prime}]^{(i)}\) is the vector of total velocities at iteration \((i)\); and \([M]^{t+\Delta t}[U^{\prime\prime}]^{(i)}\) is the vector of total accelerations at iteration \((i)\), where damping matrix \([C]\) was neglected, \([C]=0\).  
Employing implicit time integration Newmark-Beta scheme and using [PERSON]'s iterative method, the above equations are expressed in the form:  
\[{}^{t+\Delta t}[K]^{(i)}\{\Delta U\}^{(i)}={}^{t+\Delta t}\{R\}^{(i)} \tag{4}\]  
where \({}^{t+\Delta t}\{R\}^{(i)}\) represents the effective load vector, and \({}^{t+\Delta t}[K]^{(i)}\) denotes the effective stiffness matrix. In order to perform calculations, the SolidWorks Simulation module is used.  
In order to verify the stressed state of model components, the [PERSON] stress criterion was applied. It is defined in Equation (5):  
\[\sigma_{y}=\sqrt{\frac{\left(\sigma_{1}-\sigma_{2}\right)^{2}+\left(\sigma_{2} -\sigma_{3}\right)^{2}+\left(\sigma_{3}-\sigma_{1}\right)^{2}}{2}}, \tag{5}\]  
where \(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) are the maximum, intermediate, and minimum principal stresses, respectively, and \(\sigma_{y}\) is a yield stress.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Stiffness** & \begin{tabular}{c} **\(C_{1}\)**, **MPa** \\ **(femur)** \\ \end{tabular} & \begin{tabular}{c} **\(C_{2}\)**, **MPa** \\ **(femur)** \\ \end{tabular} & \
\begin{tabular}{c} **\(C_{1}\)**, **MPa (Tibia)** \\ \end{tabular} &
\begin{tabular}{c} **\(C_{2}\)**, **MPa (Tibia)** \\ \end{tabular} \\ \hline Mild & 0.24 & 0.03 & 0.21 & 0.04 \\ Moderate & 0.66 & 0.32 & 0.67 & 0.18 \\ Severe & 1.08 & 0.61 & 1.13 & 0.32 \\ \hline \end{tabular}
\end{table}
Table 1: Elastic constants of femur and tibia cartilages.  
Figure 4: Stress–strain curve representing the mechanical properties of meniscus.
## 3 Results and Discussion  
During the calibration process, we continuously monitored the temperature of the payload and changes in the dark background. The UVS CCD has a specific area designated for measuring the dark background, so during data processing, the background of the current image was subtracted accordingly. The test data should be averaged based on the incident angle state, followed by subtraction of the dark background from the averaged data to obtain the 'incident angle--DN' dataset. The irradiance responsivity was determined using standard conversion values under various incident angle conditions. Per Formula 5, the BRDF of the NIST standard diffuser can be transformed into a set of \"\(\alpha\)-\(\beta\)-BRDF\" data corresponding to various incident angles. The BRDF is a function of wavelength (CCD column), viewing direction (CCD row), and the incident angles of the diffuser. For the results presented in this section, we focus on the normal azimuth (\(\beta\) = 26.45\({}^{\circ}\)) and elevation (\(\alpha\) = 0.0\({}^{\circ}\)) angles. Under thermal-vacuum conditions, the BRDF of the UVS instrument was calibrated for a subset of viewing angles ranging from \(-56^{\circ}\) to +56\({}^{\circ}\). Figures 5-7 depict the BRDF results for the UV1, UV2, and VIS channels at a viewing angle of 0\({}^{\circ}\) for the two onboard diffusers. Notably, these curves exhibit smooth variations across wavelengths. Figure 8 illustrates the BRDF for all pixels at the normal azimuth and elevation angles across the three channels. Figure 5: The measured UVS instrument UV1 BRDF for the two diffusers at the nadir viewing angle and the nominal azimuth and elevation angles. The curves depict the BRDF results for the diffusers. Figure 6: The measured UVS instrument UV2 BRDF for the two diffusers at the nadir viewing angle and normal azimuth and the elevation angle. Figure 7: The measured UVS instrument VIS BRDF for the two diffusers at the nadir viewing angle and the nominal azimuth and elevation angles. The data series for the \"\(\alpha\)-\(\beta\)-BRDF\" was fitted using a quadratic equation, as illustrated in Equation (6). In addition to satellite operations, the high correlation between the BRDF and the VIS is not shown. of UVS and azimuth and elevation angles must be considered.
# Fluidization Dynamics of Hydrophobic Nanosilica with Velocity Step Changes
## 3 Results and Discussion
### Incipient Fluidization and Terminal Velocity  
Figure 11 shows the overall pressure drop, which was normalized by dividing the actual pressure drop by its theoretical value obtained using Equation (2). The experimental data for the defluidization cycle were used for evaluating the U\({}_{\text{mf}}\), which is approximately 4 mm/s here for the hydrophobic nanosilica, as seen in the figure. On the other hand, the U\({}_{\text{mf}}\) of hydrophilic nanosilica was approximately 22 mm/s, almost five times higher than the value obtained here [12]. This shows that the surface treatment of the nanosilica can significantly affect its dry dispersion characteristics. Moreover, given that the U\({}_{\text{mf}}\) depends on the particle size, a lower U\({}_{\text{mf}}\) indicated smaller agglomerates in the present case. Besides the minimum fluidization velocity, there was another aspect of the fluidization hydrodynamic, which was noteworthy as well. The normalized pressure drop for the  
Figure 10: Effect of velocity on the bed expansion (**a**) Bed height; (**b**) Bulk density.  
fully fluidized bed was 0.72. This means that the actual pressure drop was significantly lower than the theoretical pressure drop, despite the particulate nature of the fluidization, which rules out the occurrence of gas by-passing and other similar non-homogeneities in the bed.  
Next, the dependence of the bed expansion on the velocity was investigated here by using the Richardson-Zaki correlation in conjunction with the total mass balance considerations as follows [5]:  
\[U_{0}^{1/n}=U_{t}^{1/n}-U_{t}^{1/n}\Big{(}1-\varepsilon_{g0}\Big{)}\frac{H_{0}} {H} \tag{3}\]  
where \(U_{t}\) is the terminal velocity of the agglomerate, \(H_{0}\) and \(H\) are initial bed height and the bed height at velocity \(U_{0}\), respectively. \(\varepsilon_{g0}\) is the inter-agglomerate bed void fraction at the initial bed height conditions, and n is the Richardson-Zaki exponent. The above equation describes the linear relationship between the \(U_{0}^{1/n}\) (\(y\)-axis) and the \(H_{0}/H\) (\(x\)-axis), thereby yielding \(U_{t}^{1/n}\) as the y-intercept and \(U_{t}^{1/n}\Big{(}1-\varepsilon_{g0}\Big{)}\) as the slope. The results are shown in Figure 12 using \(n=5\). Previous studies pointed out that the above equation's parameters were relatively independent of the value of \(n\)[5]. From the figure, the terminal velocity of the hydrophobic nanosilica was found to be 21.6 mm/s. Using the Stokes law and the terminal velocity data, the values of the agglomerate diameter and the terminal Reynolds number was found to be 87 \(\upmu\)m, and 0.12, respectively.  
Figure 11: Minimum fluidization velocity from the overall pressure drop profile of the defluidization cycle.  
Figure 12: Evaluation of the terminal velocity from the dependence of the bed expansion on the gas velocity.
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 2 Early Endosome-to-Golgi Transport of STx and STx1
### Cholesterol and Membrane Microdomains  
Cholesterol also plays an important role in the retrograde transport of STxB/STx1B (Table 1). In HeLa cells, STxB/STx1B is associated with cholesterol-rich membrane microdomains, and disruption of these microdomains by cholesterol extraction strongly inhibits the early endosome-to-Golgi transport of STxB/STx1B [28]. Interestingly, it is not yet clear whether STxB/STx1B remains bound to globotriasolceramide, the cell-surface receptor for STxB/STx1B [16], in early endosomes and the Golgi, but globotriasolceramide is also enriched in cholesterol-rich microdomains [29] and could contribute  
Figure 2: Golgi phosphoprotein 4 (GPP130) is the endosomal receptor for STx/STx1. (**A**) Under control conditions, STxB/STx1B binds the luminal domain of GPP130, which allows the toxin to traffic to the Golgi from early endosomes. (**B**) Manganese treatment induces lysosomal degradation of GPP130. Under these conditions, STxB/STx1B fails to exit early endosomes and is also degraded in lysosomes. Mn, manganese; ER, endoplasmic reticulum.  
to the retention of STxB/STx1B in these regions (note that globotriaosylceramide is the cell-surface receptor while GPP130 is the endosomal receptor for STxB/STx1B).
# Individual Differences in Signal Perception for Takeover Request in Autonomous Driving
## 4 Results
### Subjective Preferences for Signals  
For each modality, participants were asked to provide subjective ratings on the extent to which they perceived the modality as easily noticeable, comfortable, and appropriate to use as a TOR signal. This was accomplished by utilizing paired comparison analysis, where the frequency of the signal selected as the better of the comparison pair based on each person's response was added together and converted to a percentage. Tables and graphs of all results are available in the Supplementary Materials. Regarding auditory signals, participants rated the 80 dB intensity level as easily noticeable. However, they perceived it as less comfortable and less appropriate to use as a signal compared to the 70 dB intensity level. In terms of pitch, participants rated the 4000 Hz as easy to perceive. However, they found it less comfortable and less appropriate to use as a TOR signal compared to the 1000 Hz and 2000 Hz signals. In the case of tactile signals, participants evaluated that higher amplitudes and frequencies were perceived as more noticeable, comfortable, and appropriate to use as TOR signals. Regarding visual stimuli, participants rated larger visual signal as better in terms of noticeability, comfort, and appropriateness to use. However, in terms of brightness, the brightest stimuli were rated as less comfortable and less appropriate to use as TOR cues. Additionally, in terms of individual difference, older people reported being less comfortable with smaller vibrations than other age groups (Figure 5b). A trend was observed indicating that as age increased, participants tended to rate higher pitches of auditory signals as more comfortable and more suitable as TOR signals (Figure 5a).
## 5 Results and Discussions
### Parametric Analysis  
The influences of several key parameters on the thermodynamic and exergoeconomic performances of the overall system are revealed through parametric analysis. The parameters studied include the turbine inlet temperature (\(T_{5}\)), the compressor pressure ratio (\(PRc\)), the generator outlet temperature (\(T_{\rm{AS}}\)), and the evaporator outlet temperature (\(T_{\rm{A1}}\)). As the turbine outlet pressure is set as 7.4 MPa to keep the sCO\({}_{2}\) topping cycle operating in the supercritical state, the \(PRc\) also represents the turbine inlet pressure. Both turbine inlet temperature and pressure are the vital parameters of sCO\({}_{2}\) topping cycle that can influence the electric power generation significantly, while other two parameters are important to the AHP bottoming cycle that can greatly affect the heat production. The parametric analysis is carried out by changing only one parameter at a time with all other parameters fixed.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**State Points**} & \multirow{2}{*}{\(T\)} & \multirow{2}{*}{\(P\)} & \multirow{2}{*}{\(h\)} & \multirow{2}{*}{\(s\)} & \multirow{2}{*}{\(e_{\rm{ph}}\)} & \multirow{2}{*}{\(m\)} & \multirow{2}{*}{\(\dot{C}\)} & \multirow{2}{*}{\(c\)} \\ \cline{2-2} \cline{5-8}  & & & & & & & & & \\ \hline
1 & 35.00 & 7.40 & 402.40 & 1.663 & 216.60 & 2309.66 & 26,014.53 & 14.445 \\
2 & 110.06 & 20.72 & 444.87 & 1.680 & 254.09 & 2309.66 & 33,257.10 & 15.741 \\
3 & 245.43 & 20.72 & 656.60 & 2.159 & 322.95 & 3099.68 & 57,527.04 & 15.963 \\
4 & 393.02 & 20.72 & 841.06 & 2.473 & 413.97 & 3099.68 & 73,115.84 & 15.828 \\
5 & 550.00 & 20.72 & 1034.63 & 2.734 & 529.77 & 3099.68 & 84,177.77 & 14.240 \\
6 & 433.11 & 7.40 & 906.74 & 2.763 & 392.97 & 3099.68 & 62,441.98 & 14.240 \\
7 & 271.71 & 7.40 & 722.28 & 2.467 & 296.85 & 3099.68 & 47,168.25 & 14.240 \\
8 & 132.69 & 7.40 & 564.52 & 2.132 & 238.88 & 3099.68 & 37,957.41 & 14.240 \\
10 & 50.00 & 7.40 & 447.38 & 1.806 & 218.94 & 2309.66 & 25,921.82 & 14.240 \\
11 & 25.00 & 0.1013 & 104.92 & 0.367 & 0.00 & 8957.55 & 0.00 & 0.000 \\
12 & 35.00 & 0.1013 & 146.72 & 0.505 & 0.00 & 8957.55 & 0.00 & 0.000 \\ H4 & 45.00 & 1.0 & 189.30 & 0.638 & 3.58 & 4314.5 & 1775.3 & 31.890 \\ H6 & 60.00 & 1.0 & 252.00 & 0.831 & 8.87 & 4314.5 & 4393.2 & 31.890 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Thermodynamic properties and costs of exergy streams for the sCO\({}_{2}\)/DH system.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**State Points**} & \multirow{2}{*}{\(T\)} & \multirow{2}{*}{\(P\)} & \multirow{2}{*}{\(h\)} & \multirow{2}{*}{\(s\)} & \multirow{2}{*}{\(m\)} & \multirow{2}{*}{\(e_{\rm{ph}}\)} & \multirow{2}{*}{\(e_{\rm{ch}}\)} & \multirow{2}{*}{\(\dot{C}\)} & \multirow{2}{*}{\(c\)} \\ \cline{2-2} \cline{5-8}  & & & & & & & & & \\
## 4 Applied Strategies for Textile Dye Wastewater Treatment
### Physical Treatment Process
#### 4.1.1 Adsorption
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Textile Dyes** & **Treatment Methods** & **Type of the Treatment Methods** & **References** \\ \hline \multirow{8}{*}{Basic fuchsin} & Adsorption by graphene & \multirow{8}{*}{[111]} \\  & oxide/zinc oxide & & \\  & (GO/ZnO) nanocomposite & & \\ \cline{2-3}  & Adsorption by bottom ash and & & \\  & deoiled soya & & \\ \cline{2-3}  & Adsorption on & & \\ \cline{2-3}  & alkali-activated diatomic & & \\ \cline{2-3}  & Adsorption with mussel shell & & \\ \cline{2-3}  & biomass waste & & \\ \cline{2-3}  & Electrochemical oxidation & Chemical & [115] \\ \hline \multirow{8}{*}{Amido black 10B} & _Planerochaete chrysosporium_ & \multirow{8}{*}{Biological} & [116] \\ \cline{2-3}  & _Leptothrix_ sp. & & [117] \\ \cline{2-3}  & Fenton oxidation & & [118] \\ \cline{2-3}  & Adsorption with zeolite & & [119] \\ \cline{2-3}  & Adsorption with polyaniline/iron & & [120] \\ \cline{2-3}  &oxide composite & & \\ \cline{2-3}  & Adsorption & & \\ \cline{2-3}  & usingpolyaniline/SiO2 & & [121] \\ \cline{2-3}  & nanocomposite & & \\ \hline Direct red 28 & Electrocoagulated sludge & Chemical & [122] \\ \hline Direct red & Oxidation with photo-Fenton & & [123] \\ \hline Direct red 23 & Adsorption with PAN/PVDF & \multirow{2}{*}{Physical} & [124] \\ \cline{2-3}  & composite anaerfibers & & \\ \hline Direct red 31 and Direct orange 26 & Biosorption by rice husk & Physical & [125] \\ \hline Direct red 89 and Reactive green 12 & Biosorption & Physical & [126] \\ \hline Direct blue 1 and Direct Red 128 & Biosorption using & \multirow{2}{*}{Biological} & [127] \\ \cline{2-3}  & _Trametes versicolor_ & & \\ \hline \multirow{4}{*}{4-nitroaniline} & _Acinetobacter_ sp. AVLB2 & \multirow{2}{*}{Biological} & [128] \\ \cline{2-3}  & _Candidia_ sp. AVGB4 & & [129] \\ \cline{2-3}  & RGO-Ni nanocomposite & Physical & [130] \\ \hline Acid Blue 92 & Coagulation/Flocculation & Chemical & [131] \\ \cline{2-3}  & Ozone based oxidation & Physical & [132] \\ \hline Acid Blue 113 & Electrocoagulation & Chemical & [133] \\ \hline \multirow{8}{*}{Congo red} & Nanofliltration & \multirow{2}{*}{Physical} & [134] \\ \cline{2-3}  & Adsorption by clay materials & & [135] \\ \cline{2-3}  & Ozone & Chemical & [136] \\ \cline{2-3}  & _Apergillus niger_ & Biological & [137] \\ \hline Congo red & _Bacillus colniti_ & Biological & [138] \\ \hline Remazol orange & _Pseudomonas aeruginosa_ & Biological & [139] \\ \hline Remazol brilliant orange 3R & CdO–ZnO nanoffibers & Physical & [140] \\ \hline Reactive blue 13 & _Pseudomonas_ sp. & Biological & [141] \\ \hline Reactive orange 16 & Ozone oxidation & Physical & [142] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Data Processing and Image Elaboration Procedures
### Reference Absolute Contrast
Figure 11: (**a**) Thermal profiles on multi-stringer component on right side of impact and (**b**) absolute contrast curve for delaminations with respect to intact area, using heating time of 7 s.  
Figure 10: (**a**) Thermal profiles on multi-stringer component on left side of impact and (**b**) absolute contrast curve for delaminations with respect to intact area, using heating time of 7 s.  
The second Table compares results for rectangular and circular shape zones (diameter \(\mathcal{O}\) 33 pixel for Defect 1 and diameter \(\mathcal{O}\) 9 pixel for Defect 2), as seen in Figure 12, showing higher contrast values in the case of rectangular shape with respect to circular shapes for defect 1 in any zone size and higher contrast values in the case of circular shape zones for Defect 2. These Tables illustrate the variation contrast results with the operator choice of both shapes and size for reference and damaged areas. In the Tables, we can notice the intact zone's choice significantly influences the analytical determination of the thermal contrast curve, used to characterize the defect intensity. From the previous images, sometimes heat is not uniform on the specimen surface and the choice of intact areas is a determining factor in the contrast computation. Simple circular shape is chosen because optimal for artificial circular defects, instead rectangular shape is considered firstly because very different with respect to circular shape and capable to detect delaminations in structural squared real elements. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{\(C_{A\_MAX}\) (\({}^{\circ}\)C)} \\ \hline \multicolumn{2}{c}{**Rectangular Area (px*px)**} & \multicolumn{4}{c}{**Circular Area (\(\mathcal{O}\) px)**} \\ \hline \multirow{2}{*}{**Intact Zone Size**} & **Def. 1** & **Def. 2** & **Intact Zone** & **Def. 1** & **Def. 2** \\  & **20 \(\times\) 30** & **8 \(\times\) 14** & **Size** & **\(\mathcal{O}\) 33** & **\(\mathcal{O}\) 9** \\ \hline
**14 \(\times\) 12** & 4.02 & 0.9 & **14** & 1.27 & 1.81 \\
**21 \(\times\) 30** & 4.87 & 1.74 & **30** & 0.91 & 1.49 \\
**14 \(\times\) 20** & 3.73 & 0.62 & **20** & 1.11 & 1.68 \\
**19 \(\times\) 20** & 3.41 & 0.37 & **16** & 2.38 & 2.90 \\
**13 \(\times\) 20** & 3.84 & 0.75 & **18** & 1.69 & 2.12 \\ \hline
**Mean Value** & 3.97 & 0.87 & & 1.47 & 2.00 \\
**Standard Deviation** & 0.55 & 0.52 & & 0.58 & 0.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Maximum absolute contrast value for rectangular and circular computation shape zone. Figure 12: Raw thermal image of Plate I with circular reference zones and defect’s relative Absolute contrast examples.