esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Architecture and Operation Principles
The peak group delay for a simple APR can be derived from Equations (5) and (12) as  
\[\frac{\tau_{g,\ peak}}{\tau_{0}}=\frac{a_{1}(1-r)^{2}}{(a_{1}-r)(1-a_{1}r)}\text { for a simple APR}. \tag{14}\]  
It is clear from Equation (14) that the peak group delay depends on the coupling coefficient. Moreover, the peak group delay increases as the coupling coefficient is chosen to approach the critical coupling condition, i.e., \(a_{1}=r\)[24; 31]. For tunable racetrack resonators, the peak group delay occurs when \(\cos(\phi_{1}+\psi^{\prime})=1\). Under such a resonant condition,  
\[\cos(\phi_{1}+\phi_{2}-\psi)=\cos(\phi_{2}-\psi-\psi^{\prime})=Re\left[e^{i \psi}e^{i\psi^{\prime}}e^{-i\phi_{2}}\right]=a_{0}^{2}\cos(\phi_{2}). \tag{15}\]  
The last equality of Equation (15) is obtained by replacing \(e^{i\psi}\) and \(e^{i\psi^{\prime}}\) with Equations (7) and (8), respectively. From Equations (13) and (15), the peak group delay is expressed as  
\[\frac{\tau_{g,\ peak}}{\tau_{0}}=\frac{\sigma^{2}-\rho a_{0}^{2}\cos(\phi_{2})} {\rho^{2}+\sigma^{2}-2\rho a_{0}^{2}\cos(\phi_{2})}+\frac{\rho a_{1}}{1-\rho a _{1}}\text{ for a tunable racetrack resonator}. \tag{16}\]  
Note that \(\rho\) is also a function of \(\phi_{2}\). Therefore, the peak group delay can be adjusted by simply tuning \(\phi_{2}\). It can be easily shown that Equation (16) becomes Equation (14) for a simple APR if \(\phi_{2}=0\) and \(a_{0}=1\). With the analytic formula, Equation (16), it can be clearly seen that the peak group delay can be tuned by changing \(\phi_{2}\) but is insensitive to \(\phi_{1}\). However, the peak wavelength also changes when tuning \(\phi_{2}\). Thus, the stabilization of the peak wavelength can be done by tuning \(\phi_{1}\) without affecting the peak group delay value. We will demonstrate by simulating the tuning of the group delay with a stable peak wavelength by realizing the tunable ODL on a SOI platform. The derived analytic formula of our design stated in Equation (13) is calculated by using python. The parameters \(\sigma\), \(a_{0}\), and \(a_{1}\) are calculated to be \(\sigma=0.984\), \(a_{0}=0.996\), and \(a_{1}=0.9916\) by assuming a _1 dB/cm_ propagation loss for all the passive waveguide sections in the tunable delay line, \(L_{2}=173.832\)\(\mu m\), and \(L_{1}=336.664\)\(\mu m\). The coupling coefficient of the MMI coupler _k = 0.5_. The 3D plots for the calculated normalized group delay versus \(\phi_{1}\) and \(\phi_{2}\) are depicted in \[r^{\prime}=a_{0}\left[-k+(1-k)e^{i\phi_{2}}\right]\equiv\rho e^{i\psi^{\prime }}, \tag{8}\]0 The normalized group delay can be tuned by changing \(\phi_{2}\). The tuning of group delay is more pronounced near the resonant condition of which the wavelength will change by tuning \(\phi_{2}\). The peak normalized group delay happens at a \(\pi\) phase angle of \(\phi_{2}\). \[r^{\prime}=a_{0}\left[-k+(1-k)e^{i\phi_{2}}\right]\equiv\rho e^{i\psi^{\prime }}, \tag{8}\]1 3D plots of the normalized group delay for a racetrack resonator with tunable coupling as a function of \(\phi_{1}\) and \(\phi_{2}\).
## 2 Materials and Methods
### Sample Preparation  
Rhodamine 6G (Sigma-Aldrich Co.) and pseudoisocyanine chloride (Hayashibara Co.) were prepared as organic molecular samples and dissolved in deionized water (San-ei Chemical Co.). As for the R6G aqueous solution sample, 0.0021 g of R6G was measured with an electronic balance dissolved in 6 mL of deionized water for its molecular concentration to be 0.75 mmol/L. As for the PIC aqueous solution sample, we measured 0.0041 g of PIC-Cl and dissolved it in 6 mL of deionized water for a high-concentration sample of 1.88 mmol/L. In our experiment, we diluted this PIC aqueous solution to 75 mmol/L for an experiment sample. The prepared aqueous samples were preserved in sample vials. In this experiment, the aqueous solutions were made to flow in order to avoid decolorization similarly to the preceding reports [38; 39].  
To compare the light-induced absorption spectra and distinguish molecular aggregation from other effects such as absorption saturation, we prepared the sample of dispersed molecules in polyvinyl alcohol (PVA) film on microscope slides. For R6G, we measured 0.0022 g of R6G and 0.0856 g of PVA to be dissolved in 2 mL of deionized water while heating them. The prepared R6G/PVA solution was dropped on a microscope slide and spin-coated. For PIC, we measured 5.5 mg of PIC-Cl and 6.31 g of PVA to be dissolved in 13 mL of deionized water and spin-coated similarly to R6G. In the PVA film samples, the molecules are fixed at specific positions and cannot move under the optical gradient force. Hence, the absorbance change due to aggregation-state change should be absent.
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### Sap Flow Data and Transpiration Estimation  
#### 3.3.1 Sap Flow Time Series  
Figure 9 shows the results of the sap flow collected by one WSN node (i.e., node 2084) the week between 20 and 27 July 2016. Figure 9a shows the raw voltage measurements between 0 and 1000 mV from the sap flow probes collected by the same node; however, measurement noise produced raw voltage readings as high as 1500 mV (not shown).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{
\begin{tabular}{c} **Depth** \\ **(cm)** \\ \end{tabular} } & \multirow{2}{*}{\(\theta_{s}\)} & \multirow{2}{*}{**b**} & \multirow{2}{*}{\(\psi_{s}\) **(kPa)**} & \multirow{2}{*}{\(\psi_{s}\) **(kPa)**} & \multirow{2}{*}{**NSE** \\  & & & & & **(Clapp-Hornberger)** & & & **(Genuchten)** \\ \hline DL1 & 10 & 0.32 & 9.63 & 0.045 & 0.914 & 1.093 & 62.34 & 0.929 \\ DL1 & 30 & 0.44 & 10.69 & 0.00066 & 0.800 & 1.081 & 10814.0 & 0.821 \\ DL2 & 10 & 0.31 & 4.49 & 0.658 & 0.921 & 1.215 & 1.808 & 0.922 \\ DL2 & 30 & 0.42 & 5.87 & 0.521 & 0.801 & 1.154 & 3.51 & 0.812 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil parameter calibration results for the Clapp and Hornberger, and Van Genuchten PTFs.  
Figure 8: Estimation of the soil hydraulic parameters from data at the location close to the middle of the hill in site 2 (i.e., Nodes 2282 and 2292, and data logger DL2). Wetness, \(s\), in blue; Estimated wetness from measured matric water potential (WP) using the Clapp and Hornberger, and van Genuchten equations, in green and red, respectively. (**a**) Comparison of the wetness, \(s\), time series, at a depth of 10 cm; (**b**) Same as part a, for a depth of 30 cm; (**c**) Relationship between the soil wetness, \(s\), and the absolute value of the WP, in kPa, \(\mid\psi\mid\) at a depth of 10 cm. The fitted Clapp and Hornberger equation is shown in green and the fitted van Genuchten equation in red; (**d**) Same as part c, for a depth of 30 cm.  
To perform an accurate estimation of the voltages for each probe, a robust weighted local regression [86] is used. The robust weighted local regression smooths the raw data and it is not affected by a relatively small number of outliers. The smoothed results are shown as red and blue lines in Figure 9a for the heater probe (HP) and the temperature probe (TP), respectively. Figure 9b shows the temperature conversions from the smoothed raw measurements based on the individual calibrations for the HP and TP in red and blue, respectively. The difference in temperature between the HP and TP (i.e., \(\Delta\mathrm{T}=\mathrm{HP}-\mathrm{TP}\)) in Celsius degrees is shown in Figure 9b, in magenta. Finally, Figure 9c shows the resulting sap velocity based on Equation (4).
## 2 Materials and Methods
### Data Analysis  
The normality of the variables under study was tested using the Shapiro-Wilk test. Assuming normality of the variables, the homogeneity and homoscedasticity of the randomly formed groups (CG and EG) were tested by applying a Student's \(t\)-test for the independent data and [PERSON]'s test. A descriptive analysis of the groups was performed by calculating the measures of central tendency (mean and standard deviation) of the quantitative variables and the percentages of the qualitative variables. The effect of the ReViPark program compared with the cycling program without virtual reality was evaluated by applying parametric tests (intragroup Student's \(t\)-test and \(2\times 2\) intergroup ANOVA (Anova Applied Electronics, Inc., San Francisco, CA, USA)). All analyses were performed globally and by stratifying the sample by gender. The data obtained were processed with IBM-SPSS statistical software v.25. The value of statistical significance was set at \(p<0.05\).  
Figure 1: Study design: CONSORT 2010 flow diagram. Static cycling program in immersive virtual environments (ReViPark program) versus static cycling program in patients diagnosed with Parkinson’s.
## 4 Case Study
### Comparison and Analysis  
The results evaluated by the three methods are showed in Figure 7, on condition that the confidence level is 95%.  
From Figure 7, it can be found that if all the fault data are regarded as a whole to fit, the point estimation of MTBF will be less than MTBF of the observed value. The main reason is that the failure data are too focused on the early time of the reliability test. The evaluation result by the Monte Carlo method is close to the observed value. The interval estimation of Monte Carlo is the least of the three methods, that is to say that the evaluation result is in one smaller interval below a certain confidence level than the other two methods. The smaller the interval estimation range, the bigger the reference to engineering. It can be concluded that the accuracy of the reliability evaluation of the Monte Carlo method is high and it has important reference engineering significance to the reliability design.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 2 Review Objectives and Methodology  
To ensure a systematic approach to finding and selecting relevant material, this study adopted the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. PRISMA is a widely used method for collating empirical evidence that fits pre-defined selection criteria to answer specific research questions. The main aim of this survey article was to explore research trends and challenges in product routing and loading approaches within the Industry 4.0 context [1]. More specifically, we aimed to answer the following research questions:  
**RQ1:**: How do volumetric techniques contribute to the optimisation of product routing and loading processes in the context of Industry 4.0?
**RQ2:**: What are the Industry-4.0-driven real-world applications that benefit from the utilisation of 3D Bin Packing and volumetric techniques in product routing and loading?
**RQ3:**: In the context of Industry 4.0, how can volumetric-based approaches be enhanced to further improve the efficiency of product packing and routing, while minimising computational demands
**RQ4:**: What Industry-4.0-specific challenges and constraints are associated with the application of volumetric methods in 3D Bin Packing, and what strategies can be employed to address these challenges effectively?  
Figure 1 presents the PRISMA workflow followed for identification, screening, and selection of the reviewed sources.
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 3 Experiments and Results
### Computational Cost Comparison  
Table 9 lists the average feature extraction cost of an image in LIVE database for all competing algorithms. All the results were measured in seconds with Matlab2014b implementation on a desktop computer with 2.7 GHz Intel Core i7 CPU and 16 GB RAM. As shown in Table 9, the three most efficient algorithms were BRISQUE [15], GMLOG [19], and the proposed BIQA-SC, while DIIVINE [12] and BLIINDS-II [13] had the highest cost. The main reason for such big differences in computational cost is that the image transformation and feature extraction strategies adopted by the competing algorithms are quite different. In DIIVINE [12], the scale-space-orientation decomposition of the distorted image and the GGD fitting procedure for each wavelet subband slow down the approach greatly. The block-based method BLIINDS-II [13] needs to perform local discrete cosine transformation and complex feature extraction strategies for each block, which will inevitably lead to high computational complexity. Different from DIIVINE [12] and BLIINDS-II [13], BRISQUE [15] is based on spatial domain, thus it does not need to perform complex image transformation. Similarly, GM-LOG [19] only employs gradient and Laplacian Gaussian features, which costs very short computational time. Compared with BRISQUE and GM-LOG algorithms, the proposed BIQA-SC that extracts statistics from multiple NFMs, DFMs, and color space needs a little more computational time, but the performance of  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithm** & **TID2013** & **CSIQ** & **MD1** & **MD2** & **WIQCD** \\ \hline DIIVINE [12] & 0.414 & **0.569** & **0.772** & **0.655** & **0.357** \\ BLIINDS-II [13] & 0.319 & **0.535** & 0.731 & 0.355 & 0.143 \\ BRISQUE [15] & 0.457 & 0.472 & 0.733 & 0.224 & **0.332** \\ GMLOG [19] & **0.468** & 0.547 & 0.563 & 0.309 & 0.218 \\ BIQA-SC & **0.469** & 0.497 & **0.741** & **0.451** & 0.323 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The SROCC of the competing algorithms when trained on LIVE database [28]. The best two algorithms are highlighted in boldface.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithm** & **LIVE** & **CSIQ** & **MD1** & **MD2** & **WIQCD** \\ \hline DIIVINE [12] & 0.680 & 0.456 & **0.679** & 0.447 & **0.170** \\ BLIINDS-II [13] & 0.681 & 0.426 & 0.660 & 0.098 & 0.085 \\ BRISQUE [15] & 0.740 & 0.499 & 0.521 & **0.472** & **0.280** \\ GMLOG [19] & **0.812** & **0.591** & 0.541 & 0.019 & 0.121 \\ BIQA-SC & **0.748** & **0.584** & **0.719** & **0.475** & 0.160 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The SROCC of the competing algorithms when trained on TID2013 database [39]. The best two algorithms are highlighted in boldface.  
BIQA-SC is greatly improved. In short, the experimental results in Tables 2-9 prove that BIQA-SC has the best performance on all the databases and has a relatively low computational cost, which makes BIQA-SC more suitable for practical application.
# Heat Triggered Release Behavior of Eugenol from Tobacco Leaf
## 3 Results
The eugenol release experiment was repeated for three times at each heating temperature, and the average value was taken as the release amount at the sampling point after quantification. Figure 3: Optimized configuration showing the adsorption of eugenol on (**A**) cellulose, (**B**) monosaccharide, (**C**) starch and (**D**) lignin. Figure 2: FTIR spectra of eugenol, tobacco leaf and tobacco leaf loaded with eugenol. According to the results above, the time-dependent release behavior of eugenol from tobacco can be plotted (Figure 5). Generally, the eugenol release amount firstly increased and then decreased with time. For example, the released eugenol was 7.89, 12.07, 13.69, 12.60, 10.65, 9.64, 9.37 and 8.76 \(\upmu\)g at 32, 64, 96, 128, 160, 192, 224 and 256 s, respectively at a heating temperature of 260 \({}^{\circ}\)C. From the comparison of the release profiles collected at different temperatures, it was observed that the amount of released eugenol increased with heating temperature until 250 \({}^{\circ}\)C. When the heating temperature increased to 260 \({}^{\circ}\)C, the amount of released eugenol dropped quickly. Depending on the state of the tobacco leaf substrate, the release of eugenol can follow a Fick or non-Fick process. To further understand the changes in the release behavior of eugenol at different temperatures, SEM images of the pristine tobacco leaf and tobacco leaf after release experiments were collected and compared. It was seen that the pristine tobacco leaf had a smooth surface morphology with micro-sized particles (\(\sim\)20 \(\upmu\)m) scattered on the surface of the tobacco leaf (Figure 6A). The surface morphology of tobacco leaves after heating at 220 \({}^{\circ}\)C and 250 \({}^{\circ}\)C remained similar (Figure 6B,C). In contrast, the microparticles on the tobacco leaf after release experiments at 260 \({}^{\circ}\)C showed slit pores on the surface (outlined with red circles, Figure 6D). The emergence of these pores could be caused by the internal  
Figure 4: Thermal gravity analysis of eugenol, tobacco and tobacco leaf loaded with eugenol. Figure 5: The time-laps release behavior of eugenol from tobacco leaf (the error bar represents the standard deviation of the measured value from three individual experiments). stress due to an abrupt increase in the heat release of additives from the internal tobacco leaf at 260 \({}^{\circ}\)C. The increased release of tobacco leaf additives and the changes in tobacco leaf morphology could be key factors that influence the release of eugenol at 260 \({}^{\circ}\)C.
###### Abstract  
To monitor variations of blood samples effectively, it is required to quantify static and dynamic properties simultaneously. With previous approaches, the viscosity and elasticity of blood samples are obtained for static and transient flows with two syringe pumps. In this study, simultaneous measurement of pressure and equivalent compliance is suggested by analyzing the velocity fields of blood flows, where a blood sample is delivered in a periodic on-off fashion with a single syringe pump. The microfluidic device is composed of a main channel (mc) for quantifying the equivalent compliance and a pressure channel (pc) for measuring the blood pressure. Based on the mathematical relation, blood pressure at junction (\(P_{x}\)) is expressed as \(P_{x}=k\beta\). Here, \(\beta\) is calculated by integrating the averaged velocity in the pressure channel (\(<\)\(U_{pc}\)\(>\)). The equivalent compliance (\(C_{eq}\)) is then quantified as \(C_{eq}=\lambda_{off}\cdot Q_{0}/P_{x}\) with a discrete fluidic model. The time constant (\(\lambda_{off}\)) is obtained from the transient behavior of the averaged blood velocity in the main channel (\(<\)\(U_{mc}\)\(>\)). According to results, \(P_{x}\) and \(C_{eq}\) varied considerably with respect to the hematocrit and flow rate. The present method (i.e., blood pressure, compliance) shows a strong correlation with the previous method (i.e., blood viscosity, elasticity). In conclusion, the present method can be considered as a potential tool for monitoring the mechanical properties of blood samples supplied periodically from a single syringe pump.  
blood pressure; equivalent compliance; microfluidic-based compliance unit; time constant; blood velocity fields; regression analysis +
Footnote †: journal: Applied Soft. 2020, 10, 5273; doi:10.3390/app10155273
# Application of a Local Dynamic Model of Large Eddy Simulation to a Marine Propeller Wake
## 4 Results and Discussion  
### Time History of Force  
The time history results of thrust and torque coefficients at the design advance ratio are shown in Figure 9a,b. The force on propeller basically tends to a stable mode after about 3 revolutions. After about 10 revolutions, the changes of average thrust and torque are very small and can be ignored. At this time, the wake of the propeller can be considered stable periodically. The physical quantity after the tenth turn is used for phase average calculation. Obviously it can be seen that the force perturbation of the LES-LDKSGS model (black line) is much stronger than that of the RANS-kEpsilon model (red line). Note that the thrust coefficient of the propeller here is negative, indicating that the thrust direction is negative along the \(x\)-axis. The torque of the propeller is positive, indicating that its direction points towards the positive direction of the \(x\)-axis determined by the right-hand rule, which is opposite to the direction of the propeller's rotation. This part of the propeller torque is directly related to the selection of the engine that drives the shaft to rotate, which is very important in engineering applications. The pressure and viscous force components of the propeller are shown in Figure 9c,d, and the pressure force is two orders of magnitude higher than the viscous force. The thrust and torque coefficients at non-design advance ratio are shown in Figure 9e,f, which shows a wider perturbation band compared to the results at design conditions.
## 6 Case Study
### Comprehensive Evaluation of Compaction Quality
#### 6.3.1 Real-Time Physical and Mechanical Indices Analysis  
We can obtain the real-time discrete physical and mechanical indices using the prediction model described in Section 6.1. Based on Kriging interpolation [4], the physical and mechanical indices of the selected working face with full coverage can be acquired. The contour maps of these indices in the two test blocks are presented in Figure 8a-e. The results indicate that the mean physical and mechanical indices of Block 1 are larger than those of Block 2, which means better compaction quality of Block 1. Moreover, we can get the semivariogram of each index as shown in Figure 9a-e. Taking the compactness \(K\), for example, the sill \(C\) of Block 2 is 2.19 times larger than Block 1, and the range of Block 2 is 21% lower than that of Block 1. Similar phenomena are found in the other indices, which indicate the greater compaction uniformity and lower geospatial variability of Block 1. Besides, the similar trends indicate there is close correlation between these indices. It is noteworthy that the mean range \(a\) of \(E_{vd}\) is 2.303, which is much lower than the other indices. It can be attributed to the difference between effective depth and the test principle of different measuring methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quality Level** & **I (Good)** & **II (Fine)** & **III (Ordinary)** & **IV (Poor)** & **V (Bad)** \\ \hline \(K\)(\%) & \(>\)99 & 98–99 & 95–98 & 90–95 & \(<\)90 \\ \(\rho_{d}\)(\(\times 10^{3}\) kg/m\({}^{3}\)) & \(>\)2.27 & 2.25–2.27 & 2.23–2.25 & 2.21–2.23 & \(<\)2.21 \\ \(E_{vd}\) (MPa) & \(>\)9.7 & 9.5–9.7 & 9.3–9.5 & 9.1–9.3 & \(<\)9.1 \\ \(E_{0}\) (MPa) & \(>\)16.5 & 16–16.5 & 15.5–16 & 15–15.5 & \(<\)15 \\ \(K_{30}\) (MPa/m) & \(>\)29 & 27.5–29 & 26–27.5 & 24.5–26 & \(<\)24.5 \\ Dimensionless value & 50 & 40 & 30 & 20 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quality-level classification standard for the physical and mechanical indices in earth-rock dam compaction.  
Figure 8: Contour maps of evaluation indices of each block: (**a–e**) the physical and mechanical indices; (**f**) the hybrid index _HCI_.