esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Inactivation Modeling  
Inactivation models were fitted in R (version 3.6.1) [24], using the nlsMicrobio package [25]. As inactivation data are frequently nonlinear, for example, often exhibiting either a tail or a shoulder, a Weibull model and a Weibull + tail models were compared to describe the microbial inactivation curve [26; 27; 28]. For the Weibull model, the concentration of microorganisms at time \(t\), \(N_{t}\) is given by:  
\[\log_{10}N_{t}=\ \log_{10}(N_{0})-\left(\frac{t}{\delta}\right)^{p} \tag{1}\]For the Weibull + tail model is:  
\[\log_{10}N_{t}=\ \log_{10}\Big{[}(10^{log_{10}N_{0}}-10^{log_{10}N_{res}})\times 10^{( \frac{-1}{\delta})^{p}}+10^{log_{10}N_{res}}\Big{]} \tag{2}\]  
where, \(N_{0}\) is the initial concentration of microorganisms, and \(N_{res}\) is the residual microbial cell density at the end of the study period while \(p\) and \(\delta\) are related to the shape and scale of the curve, respectively. Here \(\delta\) is the time for the first decimal reduction, and \(p\) gives the shape of the curve, where \(p=1\) indicates a straight line, \(p<1\) corresponds to an upward concave survival curve, and \(p>1\) indicates a downward concave curve. Thus, in the context of microbial inactivation, \(p<1\) indicates that the rate of inactivation is slowing over time, as the remaining cells are potentially harder to kill, whereas \(p>1\) suggests that the remaining cells are be seemingly increasing susceptible ([PERSON], 2002).  
The fit of the two models (Weibull and Weibull + tail) was compared using leave one out cross-validation. That is, each observation was removed from the dataset in turn, and both models were fitted to the remaining data and used to predict the value for the removed observation. The predictive ability of the two models was compared using root mean square error and R-squared values (Table 1).  
Separate inactivation curves were fitted for each combination of the treatment types, that is, for the two bacteria (_L. monocytogenes_ and _Salmonella_), the distances from the plasma source (10 or 20 mm), and the cleanliness of the steel (clean or protein-soiled). For each treatment combination, the final concentration at the end of the study period (i.e., after 300 s) was predicted under both models (Equations (1) and (2), respectively) to enable the log reduction to be calculated. Parameter estimates and the log reduction achieved during the study period were compared between the treatments.
## 4 Numerical Results and Discussions
### Spectral Efficiency
#### 4.2.2 Spectral Efficiency in S\({}_{2}\)  
In Figure 15, we portrait SE performance of NU in S\({}_{2}\) for \(M\)-PSK/QAM modulation with respect to SNR, where SNR holds values between 0 and 30 dB. The term in legend of the figure denote \(M\)-PSK/QAM modulation order used by BS to transmit data to NU, the number of transmit antennas at BS (A\({}_{\text{BS}}\)), and the number of receive antennas at NU (A\({}_{\text{NU}}\)). It is noticed that the system achieves lower SE employing higher order of modulation at low SNR compared to the ones using lower order of modulation with identical antenna configuration at BS and NU. We noted that SE performance with QAM modulation is better than that with PSK modulation for the same modulation order. However, in each case, the SE curves approach the theoretical number of data bits with increasing SNR because of the same reason as mentioned before.  
Figure 16 depicts SE performance of FU in S\({}_{2}\) with respect to SNR, where SNR ranges between 0 and 30 dB. The term in legend represents the parameters as mentioned earlier in Figure 9. Here, the SE curves lead to theoretical number of data bits with increasing SNR which is expected. It is duly noted that an increase in the number of antennas at FU improves SE performance of the system with the same antenna configuration at BS and D\({}_{1}\), and same order of modulation used by D\({}_{1}\).  
In Figure 17, we plot SE of different users in S\({}_{2}\) against SNR for a comparative analysis. The legend terms show the corresponding parameters as mentioned earlier and additionally includes the transmitted number of data bits. As seen in the previous two figures, the SE curves here also lead to theoretical number of data bits eventually. We observed that NU achieves higher SE performance than other two users (D\({}_{1}\) and FU) while SE of FU is lower than others for the transmission of equal number of data bits with a similar antenna configuration at the nodes involved in transmission.  
Figure 14: SE comparison among the users in S\({}_{1}\).  
Figure 15: SE of NU in S\({}_{2}\).
## 3 Results and Analysis
### Analysis of the Average RSEI in the Study Area
The combined area with light and significant improvements accounted for 48.86%, which was substantially higher than the areas with light (1.08%) and significant (0%) degradation. When examined in stages, from 1990 to 2005, the proportion of the study area with light and significant degradation reached 10.86%, exceeding the 7.43% of areas that experienced light and significant improvements during the same period. In contrast, from 2005 to 2020, the proportion of areas with light and significant improvements significantly increased to 51.86%, and the light and significant degradation areas decreased to 174.46 km\({}^{2}\), making a reduction of 97.24%. This trend demonstrates that over the past 30 years, the ecological environment quality in the study area initially declined and then improved markedly. Furthermore, an analysis of ecological environment quality grade changes over seven periods indicates that the most substantial degradation occurred from 1990 to 1995, while the most significant improvement occurred from 2005 to 2010, aligning with the previously described two stages of the environmental quality trajectory. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Significant**} & **Light** & \multirow{2}{*}{**Unchanged**} & **Light** & **Significant** \\  & & **Degradation** & & & **Improvements** & **Improvements** \\ \hline \multirow{3}{*}{1990–1995} & Area & 0.39 & 5547.91 & 49,594.64 & 3056.91 & 0.15 \\  & Proportion & 0 & 9.53 & 85.21 & 5.25 & 0.00 \\  & Area & 1.7 & 3863.03 & 49,093.32 & 5241.91 & 0.03 \\  & Proportion & 0 & 6.64 & 84.35 & 9.01 & 0.00 \\  & Area & 1.45 & 4838.81 & 49,456.02 & 3903.7 & 0.02 \\  & Proportion & 0 & 8.31 & 84.98 & 6.71 & 0.00 \\  & Area & 0.14 & 363.24 & 43,145.34 & 14,691.28 & 0.01 \\  & Proportion & 0 & 0.62 & 74.13 & 25.24 & 0.00 \\  & Area & 0.43 & 1678.03 & 47,923.79 & 8597.44 & 0.32 \\  & Proportion & 0 & 2.88 & 82.34 & 14.77 & 0.00 \\  & Area & 0.09 & 1506.27 & 45,648.81 & 11,044.79 & 0.04 \\  & Proportion & 0 & 2.59 & 78.43 & 18.98 & 0.00 \\  & Area & 5.57 & 6319.89 & 47,550.74 & 4323.61 & 0.19 \\  & Proportion & 0 & 10.86 & 81.70 & 7.43 & 0.00 \\  & Area & 0.06 & 174.16 & 27,843.06 & 30,180.53 & 2.19 \\  & Proportion & 0 & 0.30 & 47.84 & 51.86 & 0.00 \\  & Area & 0.31 & 626.25 & 29,137.82 & 28,414.82 & 20.8 \\  & Proportion & 0 & 1.08 & 50.06 & 48.82 & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of ecological grade changes in the study area.
## 4 Discussion
### Comparison with Previous Studies
#### 4.1.1 Comparison with Previous Studies Based on Bonn Database  
For Bonn database, method comparisons on two classification tasks, namely, \"normal versus ictal\" and \"interictal versus ictal,\" are presented in Tables 4 and 5 respectively. Because the data in Bonn database are balanced between each category, classification accuracy was expected to be a good metric to characterize method performance. Obviously, the proposed RR-DTDWT method outperformed most other latest methods, achieving an accuracy of 100% for both cases. As previously mentioned, Bonn database contains artifact-free EEG signals. The artifacts due to muscle activity and eye movements were pre-removed through visual examination. Accordingly, Bonn database cannot simulate the challenging practical application scenarios, although it can be utilized for \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **RR-DTDWT** & **RR-DWT** & **DTDWT** & **DWT** & **DTDWT + Fisher** & **DWT + Fisher** \\ \hline Sensitivity & 100\% & 100\% & 99\% & 99\% & 100\% & 100\% \\ Specificity & 100\% & 99\% & 97\% & 97\% & 98\% & 99\% \\ F1 Score & 1 & 0.9950 & 0.9802 & 0.9802 & 0.9901 & 0.9950 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of different methods on Bonn database (interictal versus ictal). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **RR-DTDWT** & **RR-DWT** & **DTDWT** & **DWT** & **DTDWT + Fisher** & **DWT + Fisher** \\ \hline Sensitivity & 96.69\% & 92.27\% & 92.82\% & 92.82\% & 93.92\% & 91.71\% \\ Specificity & 99.63\% & 98.35\% & 98.90\% & 99.27\% & 98.17\% & 98.17\% \\ Fl Score & 0.9813 & 0.9516 & 0.9573 & 0.9591 & 0.9596 & 0.9477 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of different methods on CHB-MIT database. method comparison. Some previous studies have also reported a 100% accuracy. For example, [PERSON] et al. [41] employed tunable-Q wavelet transform to develop a seizure detector which also achieved an accuracy of 100% for \"normal versus ictal\" problem. The model of [PERSON] et al. [4] can discriminate normal data from ictal data with a 100% accuracy and also discriminate interictal data from ictal data with a very low error rate. Methods proposed by [PERSON] et al. [42] yielded excellent performance for both classification tasks on Bonn database. Moreover, previous studies also proposed wavelet-based methods [43; 44] and achieved a 100% or almost 100% classification accuracy. [PERSON] et al. [45] proposed a wavelet-based deep learning approach in which no manual feature extraction was required. This method can achieve a high accuracy on small EEG database. All these studies made great contributions to the epilepsy monitoring field. Although our method achieved a 100% classification accuracy and outperformed most previous studies on Bonn database, its superior performance compared with other studies on the practical and challenging CHB-MIT database is described next.
## 2 Materials and Methods
### LGE Quantification  
After ROI delineation was concluded, the image and the corresponding ROIs were exported to MATLAB (RRID:SCR_001622), version R2021a, as an 8-bit grayscale image and binary mask, respectively. Through pixel-to-pixel multiplication, we obtained ROI matrices in which the elements differed from zero only within the ROIs (Figure 3).  
We calculated the LGE extent for the 3\(\sigma\) and FWHM methods separately using the ROI matrices. In both methods, we found pixels associated with myocarditis pathology according to a corresponding mathematical condition, and then we calculated the LGE extent as their percent share of the total ROI\({}_{\text{myoc}}\) area, for which the area was quantified with an exact number of pixels included. In the 3\(\sigma\) method, we found pixels brighter than three \(\sigma\) above the mean value of pixel brightness within ROI\({}_{\text{ref}3\sigma}\). In the FWHM method, we found pathological pixels brighter than 50% of maximal pixel brightness within ROI\({}_{\text{ref}\text{FWHM}}\).  
Figure 2: Clinical specialists manually segmented the LGE-CMR images into regions of interest (ROIs): myocardial region, or ROI\({}_{\text{myoc}}\); reference region, or ROI\({}_{\text{ref}}\); and pathology-free region, or ROI\({}_{\text{excl}}\). ROIs were then used in the pathology extent calculation with the 3\(\sigma\) method (**left**) (pathology threshold set to three standard deviations (\(\sigma\)) in reference), and with the full width at half maximum (FWHM) method (**right**) (pathology threshold set to 50% of maximum signal in reference). The selection of ROI\({}_{\text{myoc}}\) is independent of the mathematical quantification method used; that is, ROI\({}_{\text{myoc}3\sigma}=\text{ROI}_{\text{myoc}\text{FWHM}}=\text{ROI}_{\text{myoc}}\).  
We performed all calculations with our custom-made program in MATLAB (RRID: SCR_001622). Together with the open-source program 3D Slicer (RRID: SCR_005619), it forms a functional unit as an alternative to commercial LGE-CMR image analysis software. To calibrate this, we compared MATLAB (RRID: SCR_001622)-provided results of LGE extent with the results provided by the trial version of the commercially available software Circle cvi42, version 5.10, Circle Cardiovascular Imaging Inc. (Calgary, AB, Canada). During the segmentation process, we ensured that the ROIs were delineated similarly when using different tools. The results differed by less than 1%.
# Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling
## 3 Neural Network Modeling
### Model Prediction Accuracy  
The selected excellent sample set of the same equipment state and steel grade is divided into two parts: training set and test set. The training set is used to train the limit gauge rolling neural network, build the mapping relationship between process parameters and rolling stability, and establish the mapping model of limit gauge rolling. Then, the test set is brought in to predict the rolling stability score, and the fitting of the prediction results is shown in Figure 4.  
Figure 2: Neural network model of limit gauge rolling.  
The results show that the neural network has a high prediction accuracy, and can well predict the mapping relationship between the input process parameters (width, thickness, rolling rhythm, intermediate slab thickness, final rolling temperature, rolling sequence in furnace time and roll change cycle) and the output rolling stability score. Therefore, it can be used for the optimization of limit specification rolling process parameters.
## 3 Results  
### Volatile Components of NCS  
The volatile components of the NCS were greatly affected by the evaporation temperature (Table 1). Forty-seven compounds were identified in the NCS obtained using SPME-GC-MS analysis, and the total volatile component concentrations were significantly increased as the final heating temperature increased (10.37, 30.87, and 86.29 \(\upmu\)g/100 g at 120, 130, and 140 \({}^{\circ}\)C, respectively; \(p<0.05\)). The increases were generally observed in 10 volatile component groups, including Maillard reaction products such as pyrazine,furan, furanone, pyridine, pyrrole, and pyranone. The most noticeable increases were observed for 2-acetylfuran, 5-methyl-2-furanaldehyde, 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one, furufural, dihydro-2(3H)-furanone, 2-acetylpyrrole, dihydro-2-methyl-3(2H)-furanone, 2,3,5-trimethylpyrazine, 5-methyl-2-furanmethanol, and 2-furanmethanol (>90% increase from 120 to 140 \({}^{\circ}\)C evaporation temperature). Among them, the predominant compounds in NCS obtained at 140 \({}^{\circ}\)C were 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one, furufural, and 2-furanmethanol (21.05, 14.92, and 5.98 \(\upmu\)g/100 g, respectively). Other major compounds included 2-methylpyrazine, acetic acid, 2,5-dimethylpyrazine, and 2,6-dimethylpyrazine, although the content increases from 120 to 140 \({}^{\circ}\)C were lower than 90%. On the contrary, the content of two aldehydes (hexanal and heptanal), 2-pentylfuran, [R-(R*,R*)]-2,3-butanediol, and benzeneacetaldehyde significantly decreased as the evaporation temperature rose, whereas the chromatographic peaks of hexanal, heptanal, and 2-pentylfuran in NCS obtained at 140 \({}^{\circ}\)C were detected at trace levels. In contrast, 2-ethenylpyrazine, 2-furanmethyl acetate, 2-acetylpyridine, 2-acetyl-5-methylpyrazine, pyrazinamide, 2-ethenylbenzofuran, 3-phenylfuran, 2-phenyl-2-butenal, 2,5-dimethylfuran-3,4(2H,5H)-dione, and 5-hydroxymethylfurfural were not detected or found at trace levels in the NCS obtained at 120 \({}^{\circ}\)C.
## 4 Data Analysis
### Performance of the Model  
The additional columns at the end of the dataset produced by the anomaly detection algorithm classified data with anomaly labels. We trained different classification models and obtained their Accuracy, Precision, Recall, and F1 score, to evaluate their performance using the compare_model() function on the resulting labelled dataset. The clustering-based local outlier algorithm produced the best results when we compared its performance to the other commonly used neighbour based (knn) and classification-based (iforest) anomaly detection algorithms. Moreover, local anomalies are crucial in smartwatch data, as anomalies at any point in time may influence neighbouring readings. The time instances just before and after an anomaly were critical and significant in any medical condition [14]. A clustering-based local outlier algorithm was used because it detected local anomalies [14]. Additionally, the clustering-based local outlier approach had a faster calculation time and was effective for big datasets [40]. The results of the performance analysis are provided in Supplementary File S2.
## 3 Results and Discussion  
### Identity Assignment and Confirmation of the Components in PJ, PM, and PZ  
In this study, the UHPLC-Q-Exactive/HRMS technique was utilized to rapidly separate and comprehensively identify the major compounds in PJ, PM, and PZ. To separate most of the compounds and achieve the most sensitive detection in a short time, the chromatographic and spectral conditions were optimized. Because the negative mode was more sensitive than the positive mode for detecting ginsenosides in the pre-experiment, heated electrospray ionization of the chemical compounds was performed in the negative ion mode. In addition, formic acid, when added to the mobile phase, not only improved the chromatographic peaks, but also easily generated formic acid adductions [M + HCOO]\({}^{-}\), which made it easier to detect and confirm the molecular ion. After optimizing the experimental conditions, the base peak chromatograms were obtained as shown in Figure 2. The details for the identified ginsenosides, such as the retention time (tR), the molecular formula, the theoretical molecular mass, the experimental molecular mass, and MS/MS (fragment ion) information, are summarized in Table 3. These provide abundant information that can be used as a basis for identifying the constituents in the three Panax herbs. The mass error for molecular ions in all identified ginsenosides was within 10 ppm, indicating that the experimental molecular formula well-matched with the quasimolecular ions, theoretical molecular ions, and fragment ions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Analyte** & **TR (min)** & **Precursor Ion (_ml/z_)** & **Product Ion (_ml/z_)** & **DP/V** & **CE/V** \\ \hline
8 & G-Rb1 & 13.86 & 1107.2 & 945.1 & 115 & 65 \\
9 & G-Ro & 14.68 & 955.1 & 793.0 & 110 & 60 \\
10 & G-Rc & 14.72 & 1077.2 & 783.2 & 105 & 55 \\
11 & G-Rb2 & 15.70 & 1077.2 & 945.2 & 105 & 50 \\
12 & G-Rb3 & 16.01 & 1077.2 & 783.1 & 100 & 55 \\
13 & CS-IV & 16.31 & 925.1 & 569.1 & 50 & 45 \\
14 & CS-IVa & 17.48 & 793.2 & 569.2 & 35 & 45 \\
15 & G-Rd & 17.51 & 945.2 & 621.2 & 105 & 60 \\
16 & G-Rg3 & 20.68 & 783.2 & 621.2 & 100 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
\begin{table}
\begin{tabular}{r r r r r r r r} \hline \hline
**No.
## 5 Numerical Results and Discussion
### System Outage Performance  
In this section, due to the long simulation time involved, only the Monte-Carlo simulation results up to \(P_{\text{out}}=10^{-8}\) are included. The scintillation indexes for the three selected Argo nodes are 0.553, 1.102, and 1.670, respectively, when the link length is 26 m. For a QAM order \(m\) of 8, 16, or 32, respectively, and an increase in the received aperture diameter \(D_{\text{rx}}\), we separately verified the effects of the normalized pointing error coefficient \(\sigma_{\text{p}}/a\) and the turbulence strength on the system outage performance. The numerical results are shown in Figure 9. It can be seen from Figure 9 that the outage probability increases gradually with increases in \(\sigma_{\text{p}}/a\), the QAM order, and the turbulence strength. Moreover, the outage probability decreases with the increase in \(D_{\text{rx}}\). In brief, the effect of turbulence strength on the outage probability is greater than that of \(\sigma_{\text{p}}/a\). For example, based on the numerical results shown in Figure 9, if the system is designed to satisfy \(P_{\text{out}}=10^{-3}\), then the \(D_{\text{rx}}\) that can be reliably transmitted over this channel is 8 mm.  
Figure 8: Coding gain with different link lengths in three Argo nodes: (**a**) the different pointing errors \(\sigma_{\text{p}}/a\) = 2 and 3; (**b**) QAM order \(m\) = 8, 16, and 32.  
Figure 9: System outage probability with different received aperture diameters: (**a**) \(z\) = 26 m, three Argo nodes, and \(\sigma_{\text{p}}/a\) = 2, 3, and 4 with 16 QAM; (**b**) QAM order \(m\) = 8, 16, and 32 and \(\sigma_{\text{p}}/a\) = 2, 3, and 4 for node 4 (\(\sigma_{1}^{2}=1.670\)).
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 4 Results and Discussion
### Multi-View Color Consistency Evaluation  
Generally speaking, distinct mosaic lines in the mosaicked image should not be visible, and the hue and brightness should be uniform in the mosaicked image. In our experiments, the quantitative and qualitative comparisons were conducted between the results of the proposed method and the results of the Wallis method [29, 32].
## 4 Simulation Results
### Applications of KC200 GT PV Module
#### 4.2.3 Case 3: The Effect of Varying the Weather on SD and DD Models
The temperature was kept constant at 25 \({}^{\circ}\)C. Furthermore, with a fixed irradiance of 1000 W/m\({}^{2}\), the KC200 GT can be obtained in three temperatures: 25 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 75 \({}^{\circ}\)C. The results of the optimal parameters obtained by the proposed MGA for the SD and DD models are presented in Tables 7 and 8, along with a comparison with the reported methodologies, including the EVO, GO, HTS, and FPA approaches. The better performance of the suggested GO approach is seen for all the weather scenarios examined for the SD and DD models. At irradiance of 200 W/m\({}^{2}\) at temperature of 25 \({}^{\circ}\)C, for the SD model, the MGA shows a 28.901% improvement in reducing the RMSE compared to the closest algorithm. For the DD model, the improvement is even more significant, with an 8.07% reduction in RMSE compared to the closest algorithm. At irradiance of 400 W/m\({}^{2}\) and a temperature of 25 \({}^{\circ}\)C, the MGA optimizer demonstrates a 34.681% improvement in RMSE for the SD model compared to the closest algorithm. However, for the DD model, the improvement is notably higher at 90.29%. At irradiance of 600 W/m\({}^{2}\) at temperature of 25 \({}^{\circ}\)C, the MGA optimizer shows a 24.099% improvement for the SD model and a 14.53% improvement for the DD model in reducing RMSE, compared to the closest algorithm. At an irradiance of 800 W/m\({}^{2}\) and a temperature of 25 \({}^{\circ}\)C, the MGA optimizer demonstrates a significant 86.925% improvement for the SD model and a 62.89% improvement for the DD model in reducing RMSE compared to the closest algorithm. These results suggest that the MGA optimizer excels in capturing the parameters accurately under high irradiance levels. The proposed MGA approach produced low RMSE values at various temperatures and irradiances, as shown in Figures 12 and 13. To gain a better understanding of the proposed MGA technique's capacity to determine unidentified PV parameters, Figure 13(a) displays the I-V data modeled by the technique at different temperatures with respect to 1000 W/m\({}^{2}\) irradiance, and Figure 13(b) shows the same data at 25 \({}^{\circ}\)C with respect to different irradiance. (b). The P-V data simulated by the suggested MGA technique at various temperatures--1000 W/m\({}^{2}\) irradiance at 25 \({}^{\circ}\)C and variable irradiance at other temperatures--are displayed in Figure 14(a),b.