esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 3 Method
### Feature Extraction
#### 3.3.3 Signal-to-Noise Ratio Assessment  
To enhance the precision of photoplethysmography (PPG) measurements and assess the signal quality obtained for different regions, we subdivided the entire facial area into five distinct regions, excluding the eyes, nose, mouth, and forehead, as illustrated in Figure 8 below.  
Following the conversion of heart rate estimates obtained through remote photoplethysmography into a frequency spectrum, the maximum peak within the heart rate bandwidth range of 0.7 to 3 Hz was designated as the first peak. The second peak was set at a frequency point twice that of the first peak.  
The function \(U_{t}(f)\) denotes the heart rate per minute signal within the first bandwidth, which includes a margin of \((-\alpha)\) and \((+\alpha)\) to the left and right, centered around the first peak. Similarly, the function \(S(f)\) represents the power spectrum density corresponding to heart rate per minute within the second bandwidth, including a margin of \((-\beta)\) and \((+\beta)\) to the left and right, centered around the second peak.  
To calculate the signal-to-noise ratio (\(SNR\)) for each facial area, which represents the ratio of the heart rate signal-to-noise, the following equation was utilized [22].  
\[SNR=10 log_{10}\left(\frac{\sum_{\text{42}}^{180}\left(U_{t}(f)\hat{S}(f) \right)^{2}}{\sum_{\text{42}}^{180}\left((1-U_{t}(f))\hat{S}(f)\right)^{2}}\right) \tag{6}\]
## 3 Results and Discussions
### Performance Characterization of Microcapsules
#### 3.2.4 Thermal Stability  
TGA curve analysis shows that the presence of the shell material significantly reduces the decomposition rate of the core material, providing the prepared microcapsules with good thermal stability. As shown in Figure 9, the decomposition rate of the core material increases significantly at 240 \({}^{\circ}\)C, with a remaining mass of 97.27 wt%. The decomposition rate slows down at 422 \({}^{\circ}\)C, and the remaining mass decreases to 17.27 wt%. In contrast, the shell material's decomposition rate only starts to increase at 386 \({}^{\circ}\)C, with a remaining mass of 90.67 wt%. The rate of loss slows down at 473 \({}^{\circ}\)C, and the remaining mass decreases to 11.51 wt%, indicating that the thermal stability of the core material is relatively low,  
Figure 7: FTIR spectra of MMA, DVB, and crosslinked polymer shell material.  
making it prone to decomposition at high temperatures, resulting in significant quality loss, while the thermal stability of the wall material is significantly higher than that of the core material. For the microcapsules, due to the presence of residual moisture that has not been fully dried, the mass decreases by 3.36 wt% during the initial heating stage. At 346 \({}^{\circ}\)C, the decomposition rate begins to accelerate, with the remaining mass at 89.67 wt%. At 451 \({}^{\circ}\)C, the loss rate slows down, and the remaining mass decreases to 16.81 wt%.  
Figure 8: FTIR spectra of core material, shell material, and microcapsules.  
Figure 9: TGA curves of core material, shell material, and microcapsules.  
As shown by the dashed line in Figure 9, Comparing the remaining mass at the same temperature, it is observed that when the temperature reaches 386 \({}^{\circ}\)C, the mass loss rate of the shell material begins to increase, with a remaining mass of 90.67 wt%. At this temperature, the remaining mass of the core material is only 41.01 wt%, while the microcapsules have 81.46 wt%, indicating that their thermal stability is between that of the core material and the shell material. Further comparison at the same mass loss rate shows that at a 40 wt% loss, the corresponding temperatures are 366 \({}^{\circ}\)C for the core material, 435 \({}^{\circ}\)C for the shell material, and 418 \({}^{\circ}\)C for the microcapsules. At the end of the test, the residue of the core material, shell material, and microcapsules are 12.7 wt%, 7 wt%, and 9.2 wt%, respectively, which further confirms the accuracy of the experimental data.
## 3 Results and Discussion
### Validation of HS-SPME and DI-SPME Optimized Methods Coupled to GC/MS  
The optimized HS- and DI-SPME conditions were tested with linear regression analysis of peak area versus analyte concentration in culture medium, utilizing standard solutions of suitable concentration. The linear ranges and the equations of the calibration curves, together with limits of detection (LOD) and quantification (LOQ), calculated for the selected compounds with both the approaches, are reported in Table 2. The regression lines obtained by HS-SPME showed higher slopes than those obtained by DI-SPME only for benzaldehyde and octanal; opposite results were observed in the case of toluene, while comparable results were observed for the remaining analytes. This trend was also reflected in the LOD and LOQ values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & **Equation** & **R2** & **Linear Range** & **LOD \({}^{*}\)** & **LOQ \({}^{*}\)** \\  & & **(mg/mL)** & **(mg/mL)** & **(mg/mL)** \\ \hline \multicolumn{5}{c}{HS-SPME-GC/MS-SIM} \\ \hline toluene & y = 5110x + 225 & 0.8681 & 0.05–100 & 0.02 & 0.05 \\ \hline ethylbenzene & y = 2436x + 262 & 0.9941 & 0.14–100 & 0.04 & 0.14 \\ \hline benzaldehyde & y = 18762x + 327 & 0.9531 & 0.09–100 & 0.03 & 0.09 \\ \hline octanal & y = 11427x + 1283 & 0.8974 & 0.08–100 & 0.02 & 0.08 \\ \hline benzoic acid & y = 194091x + 5000 & 0.9891 & 0.01–100 & \(3\times 10^{-3}\) & \(9\times 10^{-3}\) \\ \hline dodecane & y = 1732x + 306 & 0.8717 & 1.40–100 & 0.40 & 1.40 \\ \hline tetradecane & y = 17293x\(-\)5202 & 0.9883 & 0.10–100 & 0.03 & 0.10 \\ \hline \multicolumn{5}{c}{DI-SPME-GC/MS-SIM} \\ \hline toluene & y = 18135x + 1617 & 0.9987 & 0.02–1000 & \(6\times 10^{-3}\) & 0.02 \\ \hline ethylbenzene & y = 2270x + 3031 & 0.9977 & 0.04–1000 & 0.04 & 0.14 \\ \hline benzaldehyde & y = 1704x + 384 & 0.9995 & 0.90–1000 & 0.27 & 0.90 \\ \hline octanal & y = 1990x + 648 & 0.9997 & 0.40–1000 & 0.12 & 0.40 \\ \hline benzoic acid & y = 242162x + 4204 & 0.9974 & \(7\times 10^{-3}\)–100 & \(2\times 10^{-3}\) & \(7\times 10^{-3}\) \\ \hline dodecane & y = 1245x \(-\) 930 & 0.9996 & 2.00–1000 & 0.60 & 2.00 \\ \hline tetradecane & y = 13800x + 1127 & 0.9969 & 0.17–1000 & 0.05 & 0.17 \\ \hline \hline \end{tabular} \(\Upsilon\) = peak area counts (a.u.), \(x\) = VOCs concentration in culture medium (ug/mL). \({}^{*}\) LOD and LOQ were three and ten times the signal to noise ratio, respectively.  
\end{table}
Table 2: Linear ranges, equations of the calibration curves, LOD and LOQ calculated for the analytes.  
Figure 5: DI extraction time profiles obtained with the CAR/DVB/PDMS fiber at room temperature in the presence of 30% NaClThe within day (\(n=3\)) and day-to-day (\(n=3\), over 7 days) relative standard deviations (RSD%) of the HS- and DI-SPME procedures were investigated on standard solutions of the analytes at concentration levels equal to 5, 10 and 20 times the LOQs. In the case of the HS procedure, RSD% values \(\leq\)10.5 (headspace) and \(\leq\)9.9% (direct immersion) were always obtained for all the analytes at different concentrations levels.
## Appendix A  
The DC active power equation in incremental form is shown in Equation (A1),  
\[\Delta\mathbf{P} =B_{0}\Delta\mathbf{\theta}\] (A1) \[\Delta\mathbf{\theta} =\mathbf{X}\Delta\mathbf{P}\]  
where \(B_{0}\) is the matrix based on \(1/x\) (\(x\) is the reactance of the branch); \(\mathbf{X}\) is the inverse of \(B_{0}\)--that is, the reactance matrix; \(\Delta\mathbf{P}\) is the column vector of the APCV of the node in the power grid; and \(\Delta\mathbf{\theta}\) is the column vector of the change value of the node voltage phase angle.  
Let the two end nodes of the branch \(l\) be \(a\) and \(b\), respectively. Assuming that the active power injected into each node before and after the removal of branch \(l\) is unchanged, Then both \(\mathbf{B}_{0}\) and \(\Delta\mathbf{P}\) will change after the branch is removed. Assuming that \(\mathbf{B}_{0}\) and \(\Delta\mathbf{P}\) become \(\widetilde{\mathbf{B}}_{0}\) and \(\Delta\widetilde{\mathbf{P}}\) after branch \(l\) is removed, respectively, Equation (A2) can be obtained from the compensation principle.  
\[\widetilde{\mathbf{B}}_{0}=\mathbf{B}_{0}-\mathbf{M}_{l}x_{l}^{-1}\mathbf{M}_{l}^{\text{T}}\] \[\Delta\widetilde{\mathbf{P}}=[0\ \cdots\ \ \ \ \ \ P_{l}^{0}\ \cdots\ \ \ \ -P_{l}^{0}\ \cdots\ \ 0]^{\text{T}}_{l}=\mathbf{M}_{l}\text{P}_{l}^{0}\] (A2)where \(M_{l}\) is the node-branch correlation vector of branch \(l\) with only two non-zero elements, \(+1\) and \(-1\), at the corresponding positions of nodes \(a\) and \(b\) at both ends, and the remaining elements are zero. The voltage phase angle change value caused by the removal of branch \(l\) is shown in Equation (A3).  
\[\Delta\theta=\widetilde{\textbf{B}}_{0}^{-1}\Delta\widetilde{\textbf{P}}=( \textbf{B}_{0}-M_{l}x_{l}^{-1}M_{l}^{\text{T}})^{-1}M_{l}\textbf{P}_{l}^{0}\] (A3)  
where \(x_{l}\) is the reactance of branch \(l\).  
Based on Equation (A3), Equation (A4) can be obtained by using the matrix inversion lemma shown in Appendix B.  
\[\Delta\theta=\Big{(}X-\eta_{l}c_{l}\eta_{l}^{\text{T}}\Big{)}M_{l}\textbf{P}_{ l}^{0}\] (A4)  
where  
\[\eta_{l}=X\textbf{M}_{l},\;c_{l}=(-x_{l}+X_{l-l})^{-1},\;X_{l-l}=M_{l}^{\text {T}}\eta_{l}\] (A5)  
where \(X_{l-l}\) is the self-impedance of port \(l\). The mutual impedance between port \(k\) and port \(l\) is defined as shown in Equation (A6).  
\[X_{k-l}=M_{k}^{\text{T}}\eta_{l}\] (A6)  
where \(M_{k}\) is the node-branch correlation vector of branch \(k\).  
The APCV caused by the removal of branch \(l\) on branch \(k\) (\(k\
eq l\)) is shown as follows.  
\[\Delta\textbf{P}_{k}^{l}=\frac{M_{k}^{\text{T}}\Delta\theta}{x_{k}}=\frac{M_{ k}^{\text{T}}\big{(}X-\eta_{l}c_{l}\eta_{l}^{\text{T}}\big{)}M_{l}\textbf{P}_{l}^{0} }{x_{k}}=d_{k-l}\textbf{P}_{l}^{0}\] (A7)  
where \(x_{k}\) is the reactance of branch \(k\).
## 1 Introduction
However, the thinnest cable which satisfies the cable's current carrying capacity was selected as the optimized cable, and thus the impact of the power losses on the layout formulation was actually not taken into account. In the OWFCCLP, a crossed cable connection layout is not desired. Firstly, it will increase the installation cost [16]; secondly, it incurs thermal perturbation, which decreases the cable's current carrying capacity. In addition, crossed cables are easily damaged which, endangers the safe operation of the entire offshore wind farm. Considering the huge costs maintenance [17], a method was proposed in [17] to solve the OWFCCLP while ensuring an uncrossed cable connection layout using mathematical programming. However, the impact of power loss on the layout formulation is taken into account. Despite the power loss cost (PLC) being considered in the objective function in [13; 14; 15], its impact on the cable sectional area was not modeled in [13; 14], while reference [15] neglects the influence of PLC on the cable selection by using cables with the same sectional area. In addition, the uncrossed cable connection layout was studied in [17], whereas the PLC's influence was neglected. Through the literature study, it can be seen that the power loss impacts on the cable sectional area selection and cable connection layout formulation were not well studied. It would be beneficial if the impact of the power losses and the cable crossing to the cable connection layout formulation are considered at the same time, thus delivering a better offshore wind farm electrical system design. Thus, an OWFCCLP model was proposed in this paper. Compared to the above works, the PLC's impact on the cable connection layout formulation as well as the cable sectional area selection were both considered in the optimization model, and an uncrossed cable connection layout was ensured during the layout formulation process. The internal array cable and its laying costs are considered as the CAPEX while the revenue losses due to power losses are regarded as the operational cost (OPEX). To solve the problem, an updated bat algorithm (BA) is adopted, and the proposed model is validated via a case study. The paper is organized as follows: Section 2 describes the OWFCCLP which is derived based on the open multiple traveling salesman problem. Section 3 provides the modified bat algorithm to solve the problem. An irregularly shaped wind farm is chosen as the study case to demonstrate the proposed method in Section 4. Section 5 draws conclusions and proposes further work.
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
###### Abstract
Figure 1: Rollplan side view [18] and angle measurement principle. For clarity, the SCM itself is not shown. The “passline” is an imaginary line, which represents the contour of the strand backside under ideal alignment conditions. This line passes through the contact points between the outer rolls and the strands outer surface. However, the tangential angles provided by an SCM are not immediately useful to a CCM operator [24]. To determine the impact of the rolls on the strand, their absolute positions with respect to the machines coordinate system must be known. Therefore, the angles must be converted into the x-y-positions of the rolls. Since the angle measurement is conducted in a rough environment, measurement errors are not entirely avoidable. Thus, the conversion from angles to positions must be as robust as possible against errors within the angle measurements. Developing and evaluating algorithms that robustly and precisely convert the set of \(n-1\) angle measurements into \(n\) roll positions represents the contribution of this work. In related work, some researchers have approached this problem. However, none of them aimed to compute all the roll positions in the global coordinate system of the CCM. Rather they usually only consider three consecutive rolls at a time. [PERSON] et al. [9] compute the \"local radius\" or \"local curvature\" using three consecutive rolls and relate it to the \"preset radius\". The local radius is then used to estimate stress inside the strand material. [PERSON] [12] uses an SCM to determine the tangential angles as described above. Applying trigonometry on the two angles measured between three consecutive rolls, they calculate the position deviation of the middle roll. This approach assumes, however, that the two outer rolls are perefectly aligned. The position of the middle roll will only be calculated relative to the outer rolls and not to the global coordinate system. The authors of [25], a manufacturer of SCM, use the same approach. They argue that roll positions are \"double-checked\" because the relative position to the rear and front roll is considered. Because this resolves the problem of calculation error accumulation (which will be discussed in detail in Section 4), they consider this the best approach. While it does not suffer from the error accumulation, it introduces the aforementioned problem of only producing a relative position result. This paper presents a holistic solution to this challenge that results in absolute values for the roll positions while significantly reducing the effect of calculation error accumulation. Three different approaches are described in Section 3 and evaluated and compared in Section 4. However, some preliminary assumptions and considerations must be taken into account beforehand.
## 3 Results and Discussion
### Effects Graphs
#### 3.3.3 Effect of Chemical Coagulant FeCl\({}_{3}\)  
An increase in FeCl\({}_{3}\) dosage beyond 10 mL/L led to a rise in turbidity, UV 254, and phosphate removal, as shown in Figure 4a,c,d. However, for COD removal (Figure 4b), the residual COD increased when the FeCl\({}_{3}\) dosage exceeded 5.5 mL/L.  
At low coagulant doses, the double layer surrounding colloidal particles remained intact, preventing effective aggregation and floc formation. As the coagulant dosage increased, the neutralization of surface charges facilitated the binding of particles, leading to the rapid formation of visible flocs through sweep flocculation or emneshment in precipitate. However, beyond the optimal dosage, excess FeCl\({}_{3}\) resulted in charge reversal and the destabilization of formed flocs, reducing treatment efficiency. At higher dosages, the surplus coagulant introduced excessive positive charges, preventing proper particle aggregation and leading to colloidal restabilization. Consequently, this negatively impacted water quality [41]. Based on the study's findings, the optimal FeCl\({}_{3}\) dosage for achieving maximum reduction in turbidity, UV 254, and phosphate is 10 mL/L, whereas for COD removal, the optimal dose is 5.5 mL/L.
## 4 Results
### CNN Classification Results  
Considering the stacked scalograms of NDVI, EVI, and GCVI as the input of the CNN, with the learning processes of three convolution layers, three max pooling layers, and a dense layer, the last softmax layer of the CNN indicated the identification result. If the value of the first unit was greater than that of the second unit, then this denoted a non-vineyard; conversely, it denoted a vineyard. Figure 7b represents the identification results of the proposed approach, from which we determined that, in the entire study region, most vines were located in the northwest, with less planting in the northeast and southeast. Figure 7c illustrates the identification results of RF, which share a similar trend with the CNN in the overall distribution pattern. However, RF misclassified many non-vineyards as vineyards and omitted some vineyards that had high similarity with other classes (e.g., peach tree), and the result was that its total planting acreage of the vineyard was more than that of the CNN by 29.8% (Figure 7d). Furthermore, the confusion matrices shown in Table 4 quantitatively characterized the difference between the CNN and RF in vineyard identification. The overall accuracy of the CNN was 89.66%, which was 9.87% higher than that of RF (79.78%). In addition to the overall accuracy, the CNN was also superior to RF in terms of producer's accuracy and user's accuracy. For example, the producer's accuracy of vineyard was 92.90% for the CNN and 60.60% for RF, which was in accordance with RF's higher omission rate and further proved that the CNN had a better capability than RF to distinguish the vineyard from other classes, even if it was very similar to other classes. Note that the user's accuracy of the vineyard was far lower than that of the non-vineyard for both the CNN and RF. This is largely because of the imbalance of the testing sample size (5500 pixels for non-vineyard and 1000 pixels for vineyard). Although the user's accuracies of vineyard and non-vineyard seemed to be less comparable, the huge difference of 21% in the user's accuracy of vineyard between the CNN and RF verified that RF misclassified more non-vineyards as vineyards than the CNN. To summarize, both the spatial distribution map and confusion matrices proved that the approach that combined the WT and CNN was comprehensively superior to the approach that combined RF and the original time series in terms of identifying the vineyard using the Sentinel-2 time series.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Prediction**} \\ \cline{2-7}  & & \multicolumn{2}{c}{**CNN**} & \multicolumn{4}{c}{**RF**} \\ \cline{3-7}  & & **Non-vineyard** & **Vineyard** & **Pro’s Acc.** & **Non-vineyard** & **vineyard** & **Pro’s Acc.** \\ \hline \multirow{3}{*}{**Truth**} & Non-vineyard & 4899 & 601 & 89.07\% & 4580 & 920 & 83.27\% \\  & Vineyard & 71 & 929 & 92.90\% & 394 & 606 & 60.60\% \\ \cline{1-1}  & Usr’s Acc. & 98.57\% & 60.72\% & **89.66\%** & 92.08\% & 39.71\% & **79.78\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrices for the CNN and random forest (RF).
## 5 Results and Discussion
### Models Performance
* To determine the percentage of incorrect predictions, we calculate the hamming loss. As shown in Figure 6, XGBoost recorded the lowest hamming loss without FHE, indicating excellent predictive performance in plaintext. The Decision Classifier exhibited a noteworthy reduction in loss with FHE, ranking lowest among all three models. This enabled it to sustain a balanced performance during encryption. However, without FHE, its accuracy declined. Table 5 shows a detailed comparison of the results. * In the case of XGBoost and Random Forest without FHE, the RMSE is lower, showing high predictive accuracy. In a Random Forest with FHE, the RMSE increased, resulting in less accuracy under encryption. As shown in Figure 7, the RMSE of the Decision Classifier with FHE is lower than that of the Random Forest and XGBoost classifiers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **XGBoost** & **XGBoost with FHE** & **Random Forest** & **Random Forest with FHE** & **Decision Classifier** & **Decision Classifier with FHE** \\ \hline Accuracy & 98.00\% & 94.40\% & 98.35\% & 92.20\% & 96.73\% & 98.00\% \\ \  
Figure 4: Accuracy, precision, recall, and F1 Score comparison of XGBoost, Decision Classifier, and Random Forest with FHE and without encryption. Figure 5: Execution Time of XGBoost, Random Forest, and Decision Classifier with and without FHE. \[\text{Precision}=\frac{\text{True Positives}}{\text{True Positives + False Positives}} \tag{2}\]0 Loss of XGBoost, Random Forest, and Decision Classifier with and without FHE.
# A Review of Sustainable pavement Aggregates
## 4 Waste in Civil Engineering
### Recycled Concrete Aggregate (RCA)  
With urban growth, waste concrete is generated (Figure 4) which adds up from 35% to 50% of the solid waste of the construction industry [84]. Aggregates occupy the largest volume of concrete, between 60% and 75%, causing a demand that accounts for up to 48.3 billion tons a year of natural resources, therefore, recycled concrete aggregate (RCA) is a globally accepted option in the sustainable construction industry with the responsibility of verifying its mechanical properties and its percentage of replacement [85]. The hydraulic pavement that replaces 40% of its coarse aggregates with RCA generally achieves compressive strengths greater than 29 N/mm\({}^{2}\) at 28 days, reporting good durability of the concrete [86]. Hydraulic pavement concrete achieves a compressive strength of 50 MPa at 28 days and a bending strength equivalent to 4.72 MPa with 50% replacement of its conventional aggregates by recycled concrete aggregate [65]. When the aggregates of the semi-dense asphalt mixtures (SDA) are replaced at 60% by recycled concrete aggregate  
Figure 3: RAP fragments by milling.  
(RCA), a fracture energy of 12 kPa is reached through the indirect tensile strength (ITS) test, favoring the resistance to the formation of grooves, but with a potential propagation of cracking once initiated [87]. Marshall stability increases for hot mix asphalt (HMA), incorporating RCA in their coarse fraction due to the hexahedral and angular shape of this aggregate, however, the optimal asphalt content also increases [88]. The resilient modulus of samples with RCA compacted as unbound granular pavement layers reaches up to 247 MPa [89]. Partial replacement of coarse aggregates for warm mix asphalt (WMA) by RCA in a range of 15% to 45% directly increases the optimal asphalt binder content by 2.3% to 18.2%, related to negative environmental impact [90]. However, warm mix asphalt (WMA), incorporating RCA, reduces the consumption of natural aggregates and decreases the thermal energy for their elaboration, presenting a low yield and an increase of the optimal asphalt content [91]. An optimal replacement by industrial sludge requires 4% fine aggregate and 10% cement, allowing 100% coarse aggregate to be replaced by RCA with compressive strength at 28 days, which is equal to 18.9 MPa [92].