esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimisation of Volatiles Extraction from Pineapple By-Products by HS-SPME  
#### 3.2.1 Model Fitting  
The actual and predicted results of volatile compounds extraction as a response to the different extraction conditions (temperature, time, and NaCl concentration) are tabulated in Table 3. The actual values were like those predicted by the fitted model.  
The results of the ANOVA are summarized in Table 4. The determination coefficient (R\({}^{2}\)) of the quadratic regression model was 0.7868, higher than 0.75, which indicates the high suitability of the model. The significance of the model was corroborated by obtaining \(p\)-value < 0.05. In addition, a significant value of lack of fit (\(p\) > 0.05) was obtained indicating a reliable model that enough explains the response taking into consideration the variations that can occur within the system.  
According to Table 4, the final second-order polynomial equation is detailed in Equation (2), where A, B, C and D represent extraction temperature, extraction time, and NaCl concentration, respectively:  
\[\begin{array}{l}\text{Sum of areas = 1.37 \times 10^{9}-3.46\times 10^{7}*A+7.35\times 10^{6}*B-3.18 \times 10^{8}*C+}\\ 242177*A^{2}-922.35*A^{*}B+3.76\times 10^{6}*A*C-37262*B^{2}-909211*B^{*}C+ 2.97\times 10^{7}*C^{2}\end{array} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pineapple** & **TPC** & **DPPH** & **ABTS** & **FRAP** \\ \hline  & mg GAE 100 g\({}^{-1}\) & μmol TE 100 g\({}^{-1}\) & μmol TE 100 g\({}^{-1}\) & μmol TE 100 g\({}^{-1}\) \\ \hline Core S1 & 66 \(\pm\) 5 c & 183 \(\pm\) 10 b & 153 \(\pm\) 3 b & 127 \(\pm\) 11 b \\ Core S2 & 54 \(\pm\) 4 b & 156 \(\pm\) 19 a & 126 \(\pm\) 9 a & 131 \(\pm\) 6 b \\ Core S3 & 22 \(\pm\) 2 a & 166 \(\pm\) 15 b & 124 \(\pm\) 8 a & 112 \(\pm\) 5 a \\ \hline Peel S1 & 15 \(\pm\) 1 a & 138 \(\pm\) 14 a & 124 \(\pm\) 8 a & 115 \(\pm\) 7 a \\ Peel S2 & 41 \(\pm\) 9 b & 243 \(\pm\) 17 b & 158 \(\pm\) 8 b & 138 \(\pm\) 3 b \\ Peel S3 & 18 \(\pm\) 4 a & 163 \(\pm\) 27 b & 125 \(\pm\) 6 a & 108 \(\pm\) 9 a \\ \hline \hline \end{tabular} Values are mean \(\pm\) standard deviation per 100 g FW (\(n\) = 3). GAE (galic acid equivalents) and umol TE 100 g\({}^{-1}\) (umol TROLOX equivalent 100 g\({}^{-1}\) FW). Values for core or peel samples within the same column with a different superscript letter are significantly different from the others at a \(p\) < 0.05.  
\end{table}
Table 2: Total phenol content and DPPH, ABTS, and FRAP antioxidant capacity values obtained for the pineapple core and peel of three samples.
# High-Resolution Digital-to-Time Converter Implemented in an FPGA Chip
## 2 Principle of the Proposed DTC
### Vernier Delay Line Realized by PDL  
Herein, we use the programmable delay line (PDL) to realize the Vernier delay line. Figure 3a shows the schematic diagram of a PDL, which is a fully controllable, voltage-controlled, wrap-around delay line with a calibrated tap resolution. The control module is used to quantize a period of the reference clock and to ensure the time delay of the delay taps. If there are \(N\) taps in the delay line, the time delay of a single tap can be obtained from Equation (4). A control module can calibrate all the PDL modules within its clock region.  
\[\tau=\frac{1}{N\times 2\times F_{\mathrm{REF}}}. \tag{4}\]  
The delay taps in the delay line are voltage-controlled delay taps. In the control module, a phase detector (PD) is used to compare the output phase of the delay line with the reference clock. If the phase difference is not equal to half a period of the reference clock, the output of the phase detector changes the control voltage of the voltage-controlled oscillator (VCO), and the time delay of delay taps until the phase difference becomes coincident. The close-loop feedback mechanism reduces the influence from the voltage and temperature variations on the delay line. If variations of the voltage and the temperature lead to changes in the time delay of delay taps, a phase difference will be detected, and the close-loop structure will compensate the changes.  
The time delay of delay taps in a PDL is determined by the frequency of the reference clock. For an \(N\)-tap delay line, the time delay of a tap is \(\tau_{A}\) when the frequency of the reference clock is \(f_{A}\), and the time delay of a tap is \(\tau_{B}\) when the frequency of the reference clock is \(f_{B}\). Therefore, the resolution of a Vernier delay line built by PDLs can be calculated from Equation (5). The schematic diagram of a Vernier delay line built by PDLs is shown in Figure 3b. The delay of a Vernier delay line built by PDLs can be programmed in two ways. First, the tap delay of PDLs can be programmed by changing the frequency of REFCLK A and REFCLK B. Second, the number of working delay taps can be specified by TAPCNT and the bit width, which is 5-bit (0-31). For the programmability, we call the delay line the programmable delay line.  
\[\Delta\tau=\tau_{B}-\tau_{A}. \tag{5}\]  
Figure 2: Schematic of a digital-to-time converter (DTC) based on a Vernier delay line.
# Reinforcement Learning for Semi-Active Vertical Dynamics Control with Real-World Tests
## 4 Training the Controller  
Two different kinds of excitations stimulate the vertical dynamics of a vehicle: The vertical excitation induced by the road profile and the coupled pitch and roll excitation caused by the horizontal vehicle motion. The latter is mainly induced by steering, acceleration, and braking inputs, which all can be measured. This allows to predict the horizontal vehicle motion reasonably well. Thus, this impact on the vertical dynamics can also be estimated by utilizing a simple pitch and roll model of the vehicle together with some auxiliary measurements. Consequently, a prediction-based feed-forward controller can be applied to compensate for undesired pitch and roll motion.  
In contrast, it is less reasonable to apply a model-based feed-forward control for road-induced disturbances. Since it is inconvenient to measure the uncertain road excitation early and accurately enough, it is challenging to obtain an accurate prediction. Considering that the pitch and roll dynamics of the vehicle can be handled by feed-forward control as described above, we applied the RL-based control solely to the vertical dynamics part excited by the road profile.  
As described in Section 1, model-based or model-derived vertical dynamics controllers are often designed based on different assumptions or modeling simplifications. Additionally, it is non-trivial to tune the controller parameters to account for different road excitations and vehicle speeds. RL algorithms optimize a control law based on iterative interaction with the to-be-controlled system or a simulation model of the system. By utilizing sophisticated training models and training on a wide range of road excitations, the RL-based training process could adopt a more performant control law operating on different road excitations. Because of the generic characteristics of RL methods, the toolchain can be automatized for different vehicle types with little additional effort.  
Figure 6: Comparison of AFM’s frequency response from (**a**) road displacement \(z_{road}\) to the acceleration of the vehicle body \(a_{body}\) for different constant damper currents and (**b**) from road displacement \(z_{road}\) to the dynamic wheel load \(F_{2}\) for different constant damper currents for the rear left side of the vehicle. Each subplot visualizes the measurement data, the data obtained from an optimized simple QVM model as well as the resulting data obtained from the optimized best QVM model structure.  
Training in simulation is fast, scalable, and safe. The training of the controller in the real world would be performed on the four-post test rig, which is necessary to excite the real vehicle with defined excitations and measure the dynamic wheel load. The application of RL to a new problem usually includes a trial-and-error process and can be time consuming. Additionally, equipping the vehicle on the test rig poses a significant overhead. Therefore, our approach relies on a simulation-based training strategy utilizing the advanced QVMs developed in Section 3.
## 4 Key Parameters of Tunnelling Method for the FT
### Mathematic Model of SAM  
According to the cross-section characteristic of the neighborhood tunnels, the AT and part I of the following tunnel (FT-I) are discussed as a double-connected domain problem in underground engineering. The stress and displacement at any point on the surrounding rock of the AT and FT-I section can be solved using the Schwarz alternating method (SAM). Based on the results of the analytical solution, the optimal value of \(d_{2}\) can be determined. However, the AT section outline and FT-I outline are non-circular, causing great difficulties in calculating the stress field by the SAM [26]. [PERSON] et al. [27] have concluded that the straight-wall arches, curved-wall arches, and three-center arch tunnels can be simplified into circles using the method of conformal transformation to equivalent circles. The equivalent circle radius (\(R\)) is given by  
\[R=(h+l)/4 \tag{1}\]  
where \(h\) is the section height, and \(l\) is the section width.  
The mathematic model is shown in Figure 9. Two rectangular coordinated systems \(o_{1}x_{1}y_{1}\) and \(o_{2}x_{2}y_{2}\) are established. Circle 1 and circle 2 are the equivalent circle of the AT section outline and FT-I outline. The distance between two circles center is \(c\), while the horizontal distance between the outline of the two circles is \(d\). As shown in Figure 9, \(d\) equals to the sum of the minimum thickness of the interlaid rock (\(d_{1}\)) and the width of the reserved vibration-cushioning layer (\(d_{2}\)). To solve the problem, the following assumptions are made:  
Figure 8: (**a**) Cross-section of the neighborhood tunnels. (**b**) Schematic diagram of calculation model.  
(1) The geomaterial is elastic, homogeneous, and isotropic in the plane of the tunnel surrounding (similar assumptions can be found in [PERSON] et al. [28] and [PERSON] et al. [29]). This paper focuses on the influence of the changed \(d\) on the stresses of the tunnels surrounding rock. Therefore, the support structures are not included in the mathematic model.  
(2) The external loads \(P_{1}\) and \(P_{2}\) are applied to the ground at infinity (similar assumptions can be found in [PERSON] et al. [23]). Based on the Chinese Highway Tunnel Design Rules, the neighborhood tunnels can be classified as the shallow tunnel. Therefore, the tectonic stress is not obvious. Here, \(P_{1}\) is the vertical crustal stress under gravity. \(P_{1}\) is given by  
\[P_{1}=pH \tag{2}\]  
where \(p\) is the average bulk density, and \(H\) is the average buried depth of the tunnel. \(P_{2}\) is the horizontal ground stress, which is equal to \(P_{1}\) multiplied by the lateral pressure coefficient \(\gamma\) (usually equal to 0.3).  
(3) For the conventional blasting pattern of tunnels with surrounding rock class III, the row spacing of functional blastholes is 0.6 m [30], which would be taken as the minimum and the tolerance of the series of \(d_{2}\). Moreover, the maximum of \(d_{2}\) has been defined by half of the FT-I section width: 5.9 m.  
(4) The tension is positive, and the pressure is negative.
# Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression
## 1 Introduction
A large database of color images comprising all kinds of objects is used for training the neural networks. The trained model can then be used to efficiently colorize grayscale images. However, this approach is computationally expensive, and the training is significantly slow [15]. In order to overcome these limitations, this study presents a new, fast learning-based technique for the colorization of grayscale aerial images that colorizes them without any user intervention. The algorithm uses one grayscale image as the input and one-color image for reference, both of which have similar seasonal features at the same location. Then, the reference color image is converted from the Red-Green-Blue (RGB) color space to a CIE L*a*b (Lab) color space in which luminance and two-dimensional (2D) color information are stored. Change detection between the input grayscale image and the luminance of the reference color image is performed, and the unchanged region is selected as training pixels, which allows for the extraction of meaningful training data. For colorization, the relationships are established through learning between features of the input grayscale image and the 2D color information of the reference color image based on training pixels. In other words, for the corresponding unchanged region, the color relationships between the two images are directly established, and the colors for the changed region are predicted. At this time, the study's main technical framework is random forest (RF) regression. Random forest is a data-mining method that has some advantages over most statistical modeling methods [20], including: the ability to model highly nonlinear dimensional relationships; resistance to overfitting; relative robustness with respect to the presence of noise in the data; and the capacity to determine the relevance of the variables used. The main contributions of this paper can be summarized as follows: (1) to the best of our knowledge, this is the first work that exploits RF regression for aerial imagery colorization, although it has been used for natural image colorization [21; 22; 23]; (2) this paper develops a novel algorithm that establishes color relationships based on unchanged regions, which predict the color values of the changed regions; (3) this paper establishes color relationships by directly mapping the features of the input grayscale image with the color information of the reference color image; and (4) this paper performs visual and quantitative analyses that show that our method outperforms the current state-of-the-art methods. The rest of this paper is organized as follows. Section 2 describes the materials used in detail, the background of RF regression, and the proposed algorithm. Section 3 presents the colorization results and a detailed comparison with other state-of-the-art colorization algorithms. Section 4 presents the conclusions of the study.
## 2 Materials and Methods
### Determining the Parameters of Filter Transfer Functions  
In this paper, we consider the operation of the compensator control system with the following types of high-frequency filters: Butterworth filter, direct Chebyshev filter, inverse Chebyshev filter, Bessel filter, and elliptic filter (Cauer filter). Since the active filter control systems of the compensator in the considered works used Butterworth filters of the fourth or fifth order, in this work all filters were designed from the fifth order. The nominal supply frequency of the induction motor was taken as the cut-off frequency f\({}_{\mathrm{cut}}\) = f\({}_{\mathrm{nom}}\) = 55.8 Hz. The calculation of the transfer function parameters is provided in Appendix A. In its general form, the transfer function of a fifth-order filter can be expressed as follows:  
\[\mathrm{H(s)}=\frac{\mathrm{b_{5}}\cdot\mathrm{s^{5}}+\mathrm{b_{4}}\cdot \mathrm{s^{4}
## Materials and Methods
### Study Design
#### 2.1.1 First Stage  
Using the original PSQ as the gold standard, in order to implement it using other anamnestic and sleep quality determinants of OSAS, a review of the other sleep screening questionnaires was performed. Therefore, fifteen specific items were selected. The questions were kept simple and concise and were divided by topic, namely, \"anamnestic\" and \"sleep quality\".  
The \"anamnestic\" dimension included 7 items investigating ethnicity, whether the child was born premature or full-term, the presence of previous illnesses, familiarity with sleep breathing problems, the presence of respiratory diseases and the presence or absence of adenoids.  
The \"sleep quality\" dimension included 8 items investigating particular behaviours of the child during the night that are considered useful for assessing the quality of sleep: the tendency to sweat and frequent arousal, the assumption of bizarre positions, confused awakenings, sleepwalking, the tendency to drool during sleep, nightmares, insomnia and, finally, grinding teeth.  
All the selected items, with numbering assigned in order to be added to the original 22-question PSQ (i.e., from No. 23 to No. 37), are detailed in Table 1.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Dimension** & **N\({}^{\circ}\)** & **Items** \\ \hline \multirow{8}{*}{**ANAMNESIC ITEMS**} & 23 & What is the ethnicity of your child? (Caucasian vs. Non-Caucasian) \\ \cline{2-3}  & 24 & Was your child born premature? (Yes vs. No) \\ \cline{2-3}  & 25 & Does your child have the following already diagnosed diseases or syndromes (e.g., skeletal malformations, chromosomal disorders, neuromuscular disorders, genetic disorders, storage disorders)? (Yes vs. No) \\ \cline{2-3}  & 26 & Does the child’s father or mother have sleep breathing problems? (Yes vs. No) \\ \cline{2-3}  & 27 & Does your child suffer from seasonal allergies, asthma or nasal congestion? (Yes vs. No) \\ \cline{2-3}  & 28 & Does your child suffer from frequent ear or respiratory tract infections? (Yes vs. No) \\ \cline{2-3}  & 29 & Does your child still have adenoids or tonsils? (Yes vs. No) \\ \hline \end{tabular}
\end{table}
Table 1: Items added to the Paediatric Sleep Questionnaire (PSQ) and considered for paediatric obstructive sleep apnoea syndrome (OSAS) screening.
## 4 HFAJAYA for the Multi-Constraint OPF Problems
### OPF without Stochastic Renewable Energy
#### 4.1.4 Case 4  
This case aimed to reduce both fuel costs and network losses. The objective function may be written like this:  
\[J_{4}=\sum_{i=1}^{NG}a_{i}+b_{i}P_{Gi}+c_{i}P_{Gi}^{2}+\lambda p*P_{Loss} \tag{29}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimizer** & \
\begin{tabular}{c} **Fuel Cost** \\ **(USD/h)** \\ \end{tabular} & **Emission (t/h)** &
\begin{tabular}{c} **Power Losses** \\ **(MW)** \\ \end{tabular} & **V.D. (p.u.)** \\ \hline FA & 647.2612 & 0.2835 & 6.7302 & 0.8915 \\ \hline HFAJAYA & **646.5020** & 0.2835 & 6.7375 & 0.9041 \\ \hline LTLBO [18] & 647.4315 & 0.2835 & 6.9347 & 0.8896 \\ \hline MSA [9] & 646.8364 & 0.28352 & 6.8001 & 0.84479 \\ \hline MICA-TLA [61] & 647.1002 & - & 6.8945 & - \\ \hline MPSO-SFLA [48] & 647.55 & - & - & - \\ \hline MDE [64] & 647.846 & - & 7.095 & - \\ \hline GABC [67] & 647.03 & - & 6.8160 & 0.8010 \\ \hline FPA [9] & 651.3768 & 0.28083 & 7.2355 & 0.31259 \\ \hline SSO [39] & 663.3518 & - & - & - \\ \hline MFO [9] & 649.2727 & 0.28336 & 7.2293 & 0.47024 \\ \hline IEP [60] & 649.312 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: The obtained optimal results in the current works for Case 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimizer** & \
\begin{tabular}{c} **Fuel Cost** \\ **(USD/h)** \\ \end{tabular} & **Emission (t/h)** &
\begin{tabular}{c} **Power Losses** \\ **(MW)** \\ \end{tabular} & **V.D. (p.u.)** \\ \hline FA & 832.5596 & 0.4372 & 10.6823 & 0.8539 \\ \hline HFAJAYA & **832.1798** & 0.4378 & 10.6897 & 0.8578 \\ \hline SP-DE [57] & 832.4813 & 0.43651 & 10.6762 & 0.75042 \\ \hline PSO [10] & 832.6871 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: The obtained optimal results in the current works for Case 3.  
where the value of \(\lambda p\) is selected 40, similar to [9]. The power losses, \(P_{Loss}\), are defined as:  
\[P_{Loss}=\sum_{\begin{subarray}{c}k=1\\ k=(i,i)\end{subarray}}^{NTL}g_{k}(V_{i}^{2}+V_{j}^{2}-2V_{i}V_{j}\cos\delta_{ ij}) \tag{30}\]  
In (30), the overall transmission network active losses is \(P_{Loss}\), the conductance of branch \(k\) is \(g_{k}\), \(V_{i}\) and \(V_{j}\) express the voltages of \(i\)th and \(j\)th buses, respectively, \(NTL\) represents the value of transmission lines, \(NPQ\) shows the value of \(PQ\) buses, and \(\delta_{ij}\) phase shift of voltages between the \(i\)th and \(j\)th buses.  
Based on the findings in Table 6, it can be concluded that the suggested HFAJAYA outperformed the original FA and the two other approaches.
# Performance Evaluation of a Full-Duplex Relaying-Enabled Satellite Sensor Network
## 3 Performance Evaluation
### Ergodic Capacity  
The ergodic capacity is defined as the expectation of the instantaneous mutual information between the end-to-end SINR, which can be expressed as [29; 30]  
\[C=\mathrm{E}\left[\log_{2}\left(1+\gamma\right)\right], \tag{25}\]  
where the pre-log factor equals to 1 due to no spectral loss in FD mode. By utilizing (9) into (25) and denoting \(\tilde{\gamma}_{1}=\frac{\tilde{\gamma}_{1}}{\tilde{\gamma}_{1}+1}\), \(C\) can be written as  
\[C =\mathrm{E}\left[\log_{2}\left(1+\frac{\gamma_{1}\gamma_{2}}{ \tilde{\gamma}_{1}+\left(\gamma_{2}+1\right)\left(\tilde{\gamma}_{I}+1\right) }\right)\right]\] \[=\mathrm{E}\left[\log_{2}\left(\frac{\left(1+\tilde{\gamma}_{1} \right)\left(1+\gamma_{2}\right)}{\tilde{\gamma}_{1}+\gamma_{2}+1}\right)\right]\] \[=C_{1}+C_{2}-C_{3}, \tag{26}\]  
where \(C_{1}\\[C_{2}=\frac{1}{\ln 2}\int_{0}^{\infty}\ln\left(1+x\right)f_{\gamma_{2}}\left(x \right)dx. \tag{29}\]  
Then, using (21) into (28) and (29), and applying ([31], Equation (11)) to express \(\ln\left(1+x\right)\) in terms of Meijer-G function as  
\[\ln\left(1+x\right)=\mathrm{G}_{22}^{1,2}\left[x\left| \begin{array}{c}1,1\\ 1,0\end{array} \right.\right], \tag{30}\]  
we get the analytical results of \(C_{1}\) and \(C_{2}\) as  
\[C_{1}=\frac{\alpha_{1}}{\ln 2}\sum_{k_{1}=0}^{m_{1}-1}\frac{\Lambda\left(k_{1 },\tilde{\gamma}_{1}\right)}{\left(\mathcal{AC}\right)^{k_{1}+1}}\mathrm{G}_{2,2}^{1,2}\left[\mathcal{AC}\left| \begin{array}{c}-k_{1},1,1\\ 1,0\end{array} \right.\right], \tag{31}\]  
and  
\[C_{2}=\frac{\alpha_{2}}{\ln 2}\sum_{k_{2}=0}^{m_{2}-1}\frac{\Lambda\left(k_{2 },\gamma_{2}\right)}{\mathcal{B}^{k_{2}+1}}\mathrm{G}_{2,2}^{1,2}\left[ \mathcal{B}\left| \begin{array}{c}-k_{2},1,1\\ 1,0\end{array} \right.\right]. \tag{32}\]  
In deriving (30) and (31), we have applied ([27], Equation (7.813.1)). Due to the fact that the closed-form PDF expression of \(\tilde{\gamma}_{1}+\gamma_{2}\) is mathematically intractable, we employ an alternative approach based on the moment generating function (MGF) to derive \(C_{3}\) as  
\[C_{3}=\frac{1}{\ln 2}\int_{0}^{\infty}Ei\left(-s\right)M_{\gamma_{3}}^{\left(1 \right)}\left(s\right)ds, \tag{33}\]  
where \(M_{\gamma_{3}}^{\left(1\right)}\left(s\right)\) denotes the first-order derivation with respect to \(s\).