esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Geochemical Map on the 19-Level Fixed-Value Method
In summary, the geochemical maps of Sn and Li are similar and mainly in yellow and blue tones (corresponding to the high and low background regions, respectively). The geochemical map of Ni shows clear regions in black color and grey tone, which correspond to the mineralization regions of Ni as an associate and main economic metal, respectively. Except for two known Ni deposits located in the Ni mineralization regions, three other regions are predicted for the Ni resource potential.
# Real-Time Feedback in Swimming Exercises Using Embedded Devices in Water
## 2 Materials and Methods
### Computing and Data Transmission
DTW then identifies the optimal alignment path through this matrix--one that minimizes the cumulative distance--thus providing a similarity measure that is resilient to temporal shifts and speed variations. To facilitate data verification and visualization, all relevant information is transmitted to the master node (Node_4) via Wi-fi. Each slave node sends the following types of data:* The complete set of accelerometer and gyroscope values recorded during each iteration of the training phase;
* The averaged motion profile computed at the end of the training phase;
* All accelerometer and gyroscope values collected during each recognized voting movement, along with the _similarity percentage_ calculated by the Dynamic Time Warping (DTW) algorithm.
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 4 Results and Discussion
### Classification of ASD Using PWC FC Images  
In this experiment, we evaluate the performance of FC images generated from DFC patterns, plus SFC patterns separately for ASD classification using CNNs. It is important to highlight that the full ABIDE dataset is a combination from 17 sites, giving a total of 505 ASD subjects and 530 NC subjects. The 3L-CNNs are trained using 90% of the data, consisting of 454 ASDs and 477 NCs under the framework of 10-fold CV, whereas, the test dataset comprising the remaining 10% of the data contains 51 ASD subjects and 53 NC subjects. The test data (10%) are considered outside the training dataset to validate generalization of the trained classifier model on unseen data. Here, the CNN models play the role of feature extraction and classification of the FC images and are able to capture high-ranking time-frequency features of WC and PWC images, as well as the features of SFC images.  
For the DFC patterns, i.e., WC and PWC, 11 images of size \(224\times 224\times 3\) are generated from each subject for the BOLD signals of the node pairs listed in Table 10. The total number of FC images for training are 4994 for ASD and 5247 for NC. At the same time, 561 ASD images and 583 NC images are generated for the test dataset. Comparison is made with the conventional method of previous studies by using SFC images as input for the CNN [17; 18]. The best PCC matrix has 87 significant connections out of a total of 90, as identified by the ANOVA-SVM algorithm and listed in Table 3. As input to the 3L-CNN, the PCC matrices are converted to 2D images of size \(224\times 224\times 3\). Furthermore, the proposed CNN models are trained and tested on a computer equipped with an Nvidia GeForce RTX 2060 SUPER GPU. The model parameters were set as \(7\times 7\) CNN filter size, batch size of 8 and ADAM optimizer based on the experimental results tabulated in Table 11 in the Appendix B.
# Use of Smart Technology to Improve Management of Utility Tunnels
## 3 Smart System Design
### Monitoring Layer
#### 3.5.2 Utilities Monitoring  
The smart technology achieved a high level of maturity in utilities management. The use of this technology in utility tunnels is facilitated by the ease of access to utilities and favorable conditions for utilities monitoring. Figure 6 summarizes the smart monitoring of the following utilities: drinking water, water drainage, district heating, and electrical grid.  
The drinking water system is monitored by AMRs (automatic meter readings) measuring water debit, pressure cells to record the water pressure, and water quality devices for real-time control of the water quality (turbidity, pH, temperature, chlorine, conductivity). This system allows rapid detection of water leaks and water contamination.  
The drainage system is monitored by sensors following the water level and flow, as well as water quality (turbidity, temperature, pH, etc.) and pumping equipment. This monitoring system allows early detection of floods and faults in pumping equipment.  
The electrical grid is monitored by sensors measuring the electrical tension, current, and frequency, as well as switch status (on/off) and electrical wire temperature. This system allows early detection of faults in the electrical grid.  
The district heating system is monitored by sensors recording fluid temperature, pressure, and flow, as well as the state of valves (open/closed). This system allows early fault detection, as well as improvement of the system performance.  
Figure 5: Tunnel smart monitoring.
# COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning
## 4 Results
### Evaluation Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Accuracy (\(\pm\)std)** & **Precision (\(\pm\)std)** & **Sensitivity (\(\pm\)std)** & **Specificity (\(\pm\)std)** & **F1-Score (\(\pm\)std)** & **MCC (\(\pm\)std)** & **Balanced Accuracy (\(\pm\)std)** \\ \hline Baseline & 86.8\% & 83.1\% & 86.5\% & 87.0\% & 84.2\% & 73.4\% & 86.7\% \\ (Non-pretrained/Mendeley) & (\(\pm\)0.005) & (\(\pm\)0.058) & (\(\pm\)0.085) & (\(\pm\)0.059) & (\(\pm\)0.047) & (\(\pm\)0.004) & (\(\pm\)0.001) \\ \hline Proposed & 96.7\% & 96.2\% & 96.1\% & 97.2\% & 96.1\% & 93.2\% & 96.6\% \\ (Pretrained/MIMIC(PA)—Mendeley) & (\(\pm\)0.025) & (\(\pm\)0.016) & (\(\pm\)0.018) & (\(\pm\)0.092) & (\(\pm\)0.097) & (\(\pm\)0.003) & (\(\pm\)0.008) \\ \hline Model 1 & 95.8\% & 94.8\% & 95.4\% & 96.1\% & 95.1\% & 91.3\% & 95.8\% \\ (Pretrained/Mendeley) & (\(\pm\)0.061) & (\(\pm\)0.023) & (\(\pm\)0.024) & (\(\pm\)0.013) & (\(\pm\)0.104) & (\(\pm\)0.005) & (\(\pm\)0.013) \\ \hline Model 2 & 95.6\% & 94.4\% & 95.3\% & 95.9\% & 94.8\% & 90.1\% & 95.6\% \\ (Non-pretrained/MIMIC–Mendeley) & (\(\pm\)0.027) & (\(\pm\)0.014) & (\(\pm\)0.023) & (\(\pm\)0.037) & (\(\pm\)0.013) & (\(\pm\)0.002) & (\(\pm\)0.018) \\ \hline Model 3 & 95.6\% & 94.5\% & 95.0\% & 96.0\% & 94.8\% & 90.1\% & 95.5\% \\ (Non-pretrained/MIMIC(PA)-Mendeley) & (\(\pm\)0.023) & (\(\pm\)0.022) & (\(\pm\)0.018) & (\(\pm\)0.026) & (\(\pm\)0.019) & (\(\pm\)0.003) & (\(\pm\)0.014) \\ \hline Model 4 & 95.9\% & 95.8\% & 94.5\% & 97.0\% & 95.2\% & 91.2\% & 95.7\% \\ (Pretrained/MIMIC–Mendeley) & (\(\pm\)0.032) & (\(\pm\)0.023) & (\(\pm\)0.005) & (\(\pm\)0.005) & (\(\pm\)0.005) & (\(\pm\)0.002) & (\(\pm\)0.012) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of comparative experiments.
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
## 2 LTE-M Adjacent Channel Interference  
### Frequency Band Division  
Recently, rail transits have used a wide range of civil and private wireless communication systems. As their frequency and space localization are similar to the LTE-M, the adjacent channel interference exists between them. In addition, private wireless communication is principally based on the LTE-M system, which operates in the 1785-1805 MHz frequency range, whereas the civil communication systems of other operators are distributed in their adjacent frequency bands [34]. The specific distribution of LTE-M adjacent frequency bands is shown in Table 1.  
Table 1 reveals that the two upper adjacent channel interference sources are both immovable base stations. As opposed to the base station with a relatively fixed location, the lower adjacent channel interference source is user equipment, the distribution of which is fairly random, and the position can be changed at any time. Once the interfering user is very close to the LTE-M system, the interference will become severe. Therefore, this paper focuses on the interference between lower adjacent frequency bands based on the LTE FDD UE and LTE-M systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Interference Band** & **Frequency** & **Type of Interfering Link** & **Mode** & **Interference Mobility** \\ \hline Lower adjacent frequency & 1765–1785 MHz & Uplink signal & LTE FDD & Movable \\ \hline \multirow{2}{*}{Upper adjacent frequency} & 1805–1820 MHz & Downlink signal & LTE FDD & Immovable \\ \cline{2-5}  & 1820–1825 MHz & Downlink signal & GSM FDD & Immovable \\ \hline \hline \end{tabular}
\end{table}
Table 1: LTE-M band with upper and lower adjacent frequency range.
## 3 Quad-Port MIMO Antenna
### Radiation Pattern  
Figure 24a-d display the 3D radiation patterns pertaining to 4, 6.125, 9.185, and 10.5 GHz. The plots indicate nearly omnidirectional radiation with a gain ranging from 2.3 dB to a peak of 6.12 dB.  
A comprehensive comparison between the existing designs and the presented antenna with emphasis on diversity metrics, bandwidth, and gain is presented in Table 5. Additionally, the computed diversity parameters of the presented UWB antenna across different frequencies are listed in Table 6.  
Figure 19: (**a**) \(\text{S}_{\text{XX}}\) vs. frequency (**b**) \(\text{S}_{\text{XY}}\) vs. frequency.  
Figure 21: Directive gain (DG) comparison.  
Figure 20: Simulated vs. measured ECC comparison.  
Figure 23: Simulated vs. measured MEG comparison.  
Figure 22: Total active reflection coefficient (TARC) comparison.  
Figure 24: Three-dimensional (3D) radiation patterns at (**a**) 4.0 GHz; (**b**) 6.125 GHz; (**c**) 9.185 GHz; (**d**) 10.5 GHz.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref** & **Size** & **UWB** & **Isolation** & **ECCC** & **DG** & **TARC** & **MEG** & **Gain** & **No. of** \\
**Year** & **(mm\({}^{3}\))** & **Bandwidth** & **(GHz)** & **ECC** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **Points** \\ \hline
[34]/2024 & 40 \(\times\) 40 \(\times\) 1.6 & 2.51–18 & 16 & \textless{}0.1 & \(\approx\)10 & NR \({}^{*}\) & NR & 3.5 & 4 \\ \hline
[35]/2022 & 60 \(\times\) 60 \(\times\) 1.6 & 3.0–11.0 & 20 & \textless{}0.02 & 9.98 & \(\textless{}\)\(-\)10 & NR & 3.4 & 4 \\ \hline
[36]/2022 & 45 \(\times\) 45 \(\times\) 1.6 & 3.1–13.1 & 17 & \textless{}0.02 & 9.998 & \(\textless{}\)\(-\)25 & NR & 4 & 4 \\ \hline
[37]/2020 & 80 \(\times\) 80 \(\times\) 1.6 & 2.1–20 & 25 & \textless{}0.02 & 9.99 & NR & NR & 5.8 & 4 \\ \hline
[38]/2019 & 58 \(\times\) 58 \(\times\) 1.6 & 3.0–13.5 & 22 & \textless{}0.008 & \(\approx\)10 & NR & NR & 2.9 & 4 \\ \hline
[39]/2018 & 75.19 \(\times\) 75.19 \(\times\) 1.6 & 3.1–17.3 & 13 & \textless{}0.1 & NR & NR & NR & 5.5 & 4 \\ \hline
[40]/2014 & 60 \(\times\) 60 \(\times\) 1.6 & 2.7–10.6 & 15 & \textless{}0.063 & NR & NR & NR & 3.5 & 4 \\ \hline P.A. \({}^{1}\) & 60 \(\times\) 41 \(\times\) 1.6 & 2.6–10.8 & 15 & \textless{}0.05 & 9.65 & \(\textless{}\)\(-\)10 & 0.3 & 6.12 & 4 \\ \hline \hline \end{tabular} \({}^{*}\) NR–Not Reported \({}^{1}\) P.A.—Proposed Antenna.  
\end{table}
Table 5: Comparison of antenna characteristics: proposed design vs. existing literature.
## 3 Results and Discussion
### Volatile Aroma Compounds
#### 3.2.5 Alcohols
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Co.** & **Volatile Aroma Compounds** & **ID** & **LRI\({}_{\text{exp}}\)** & **LRI\({}_{\text{lit}}\)** & **F-Ratio** & **Treatment** \\ \hline  & & & & & \multicolumn{1}{c}{\(SCE\)} & \(LEV\) \\ \hline AL1 & 2-Heptanol & S, MS, LRI & 1319 & 1312 & 1693.390 & 9.17 \(\pm\) 0.05 a  & 2.12 \(\pm\) 0.29 b  \\ AL2 & 2-Nonanol & S, MS, LRI & 1520 & 1518 & 1015.857 & 69.54 \(\pm\) 2.12 a  & 15.87 \(\pm\) 2.01 b  \\ AL3 & 2-Undecanol & MS, LRI & 1722 & 1723 & 756.958 & 5.27 \(\pm\) 0.17 a  & 1.22 \(\pm\) 0.19 b  \\ AL4 & 3-Ethoxy-1-propanol & MS, LRI & 1377 & 1379 & 194.427 & 23.99 \(\pm\) 2.87 a  & 0.74 \(\pm\) 0.29 b  \\ AL5 & 1-Heptanol & MS, LRI & 1456 & 1457 & 192.084 & 16.58 \(\pm\) 0.54 a  & 10.08 \(\pm\) 0.61 b  \\ AL6 & Isobutanol (mg/L) a  & S, MS, LRI & 1090 & 1098 & 167.389 & 14.49 \(\pm\) 0.13 b  & 26.13 \(\pm\) 1.55 a  \\ AL7 & 3-Methylpentanol & S, MS, LRI & 1329 & 1322 & 132.272 & 144.17 \(\pm\) 16.1 a  & 35.9 \(\pm\) 3.1 b  \\ AL8 & 2-Phenyllethanol (mg/L) t  & S, MS, LRI & 1891 & 1893 & 106.218 & 34.61 \(\pm\) 2.05 a  & 20.84 \(\pm\) 1.08 b  \\ AL9 & 1-Propanol (mg/L) o  & S & - & 1035 & 103.811 & 23.53 \(\pm\) 0.31 a  & 18.50 \(\pm\) 0.80 b  \\ AL10 & 4-Methylpentanol & MS, LRI & 1314 & 1309 & 100.639 & 54.87 \(\pm\) 7.33 a  & 12.27 \(\pm\) 0.60 b  \\ AL11 & Isoamyl alcohol (mg/L) a  & S, MS, LRI & 1229 & 1229 & 93.326 & 164.9 \(\pm\) 1.3 a  & 134.11 \(\pm\) 5.4 b  \\ AL12 & 1-Octanol & MS, LRI & 1553 & 1558 & 67.072 & 34.09 \(\pm\) 1.61 a  & 23.33 \(\pm\) 1.61 b  \\ AL13 & _cis_-3-Octen-3-ol & MS & 1450 & 1452 & 34.675 & 21.70 \(\pm\) 0.44 a  & 17.27 \(\pm\) 1.23 b  \\ AL14 & _cis_-6-Nonen-1-ol & MS, LRI & 1716 & 1714 & 18.124 & 0.89 \(\pm\) 0.04 b  & 1.11 \(\pm\) 0.08 a  \\ AL15 & 2-Methyl-5-nonanol & MS & 1575 & - & 15.989 & 0.436 \(\pm\) 0.015 b  & 0.497 \(\pm\) 0.022 a  \\ \hline \end{tabular}
\end{table} 
Table 6: Concentrations (\(\mu\)g/L, if not otherwise indicated) of alcohols found in Malvazija istarska white wines produced using different yeasts determined by targeted gas chromatography with flame-ionization detection (GC/FID) o, targeted one-dimensional gas chromatography/mass spectrometry (GC/MS) t, and untargeted two-dimensional gas chromatography with time-of-flight mass spectrometry (GC\(\times\)GC/TOF-MS) sorted by decreasing Fisher’s \(F\)-ratio.
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Day**} & **Federal Agency and** & **Natural Resources** & **European Space** & **GeofScrushingZentrum** & **d\(\uparrow\)Eudes Spatiales** & **IGS** \\  & **Category (BKG) (\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
349 & 43.7 & 37.9 & 53.3 & 44.8 & 50.8 & 49.1 \\
350 & 23.3 & 23.1 & 14.2 & 28.0 & 35.1 & 28.3 \\
351 & 37.5 & 28.4 & 30.7 & 44.4 & 51.8 & 33.2 \\
352 & 10.3 & 6.7 & 7.9 & 6.0 & 7.5 & 6.9 \\
353 & 7.9 & 4.8 & 5.5 & 4.6 & 5.7 & 5.5 \\
354 & 17.3 & 15.5 & 16.1 & 63.4 & 15.4 & 15.9 \\
355 & 13.8 & 13.2 & 14.1 & 47.9 & 17.0 & 15.2 \\
356 & 20.9 & 12.3 & 24.2 & 81.8 & 21.0 & 10.5 \\
358 & 15.6 & 15.8 & 17.5 & 3.3 & 15.7 & 15.8 \\ \hline \end{tabular}
\end{table} 
Table 1: Loss rate of real-time clock products in each analysis center. \begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Satellite Type**} & **Clock Type** & **Pseudo Random Noise (PRN)** \\ \hline Block IIR & Rb & 2, 11, 13, 14, 16, 18, 19, 20, 21, 22, 23, 28 \\ Block IIR-M & Rb & 5, 7, 12, 15, 17, 29, 31 \\ Block IIF & Rb & 1, 3, 6, 9, 10, 25, 26, 27, 30, 32 \\  & Cs & 8, 24 \\ \hline \end{tabular}
\end{table} 
Table 2: The type of satellite and atomic clock on 1 May 2016.  
real-time clock correction prediction is important to guarantee the broadcast stability of real-time clock correction and improve real-time PPP accuracy. Many methods, such as polynomial, gray, autoregressive integrated moving average (ARIMA), and neural network models, can be used for clock error prediction [10; 11; 12]. The linear or quadratic polynomial model with periodic terms is used as the model of IGU-P products. [PERSON] corrected the original IGU-P clock model, and the accuracy of the improved model is better than that of IGU-P [13]. Several studies focused on short-term real-time clock correction prediction. [PERSON] proposed a polynomial model with periodic items and clock jumps [14]. [PERSON] used a polynomial model with periodic items to model the single-difference sequence of clock correction, which was obtained by conducting first-order difference processing on adjacent clock data. The accuracy of this method using 300 s to predict 60 s is better than 0.3 ns [15], which is insufficient for real-time PPP. This study focuses on short-term clock correction prediction and proposes a new model to improve prediction precision. The prediction residuals between linear sliding forecast and real-time clock product are analyzed and modeled through fast Fourier transform (FFT) [16]. Periodic signals can be eliminated by applying the additional residual model in the subsequent sliding forecast. The experimental results show that with the proposed model, the static PPP achieves precision better than 0.1 m and the kinematic PPP achieves precision better than 0.2 m. This improved method can solve the problems of broadcast delay and interruption in real-time applications.
## 4 Conclusions  
In this paper, a square wave is used as the excitation signal of the circuit under test, which provides another choice for the excitation signal in analog circuit fault diagnosis. EWT is improved based on the stimulus frequency to effectively extract each component of the response signal, thus providing a possibility for more accurate analysis of the influence of different CUT faults on the components. In addition, multi-input ResNet is applied to fault diagnosis of analog circuits, which is used for fault feature extraction and fault classification. This approach applies an ANN for fault diagnosis of analog circuits, and three analog circuits are used to verify the effectiveness of the proposed method.  
Conceptualization, Z.L. and X.L.; methodology, X.L.; software, X.L. and S.X.; validation, X.L., S.X. and J.W.; formal analysis, X.L., J.W. and X.Z.; investigation, X.L., J.W. and X.Z.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, Z.L. and X.L.; supervision, Z.L.; project administration, Z.L.; and funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grants No. U1830133 (NSFC) and Project of Sichuan Youth Science and Technology Innovation Team, China (Grant No. 2020 JDTD0008).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Accuracy (\%)**} \\ \hline \multirow{2}{*}{**Fault Code**} & **Proposed Method** & **Traditional EWT + Multi-Input ResNet** & [8] & [10] & [13] \\ \hline F0 & 100.00 & 96.00 & 96.00 & 96.00 & 100.00 \\ F1 & 100.00 & 100.00 & 92.00 & 100.00 & 100.00 \\ F2 & 100.00 & 100.00 & 94.00 & 80.00 & 100.00 \\ F3 & 100.00 & 92.00 & 70.00 & 64.00 & 100.00 \\ F4 & 90.00 & 76.00 & 68.00 & 72.00 & 80.00 \\ F5 & 100.00 & 98.00 & 98.00 & 94.00 & 100.00 \\ F6 & 100.00 & 96.00 & 86.00 & 94.00 & 100.00 \\ F7 & 100.00 & 100.00 & 96.00 & 98.00 & 100.00 \\ F8 & 100.00 & 100.00 & 100.00 & 94.00 & 100.00 \\ F9 & 96.00 & 92.00 & 70.00 & 58.00 & 100.00 \\ F10 & 86.00 & 74.00 & 64.00 & 70.00 & 78.00 \\ F11 & 100.00 & 100.00 & 100.00 & 96.00 & 100.00 \\ F12 & 100.00 & 98.00 & 100.00 & 84.00 & 100.00 \\ F13 & 98.00 & 98.00 & 100.00 & 96.00 & 100.00 \\ F14 & 96.00 & 96.00 & 96.00 & 92.00 & 100.00 \\ F15 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ F16 & 100.00 & 98.00 & 94.00 & 90.00 & 96.00 \\ F17 & 100.00 & 100.00 & 100.00 & 74.00 & 100.00 \\ F18 & 98.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Average accuracy & 98.11 & 95.47 & 90.74 & 86.95 & 97.58 \\ accuracy & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Fault diagnosis result of the leap-frog low-pass filter circuit.
## 3 Simulation Modeling and Verification
### Polishing Trajectory Space Optimization and Planning
#### 3.2.4 Polishing Trajectory Spacing Optimization Experimental Evaluation and Error Analysis  
The theoretical and actual removal profiles under the specified contact length (L) and tilt angle (\(\theta\)) are shown in Figure 11. It is evident that the experimental profile closely matches the theoretical profile, indicating that the material removal profile during robotic grinding and polishing can be effectively predicted by the theoretical profile.  
In the experimental evaluation of polishing trajectory spacing optimization, although we obtained contours that closely matched theoretical predictions, some errors still exist. These errors primarily arise from the following aspects: Firstly, the control of polishing force during the experiment may exhibit certain fluctuations, leading to discrepancies between the actual polishing effect and theoretical predictions. Secondly, the precision and measurement errors of the coordinate measuring machine used during the contour measurement process can also affect the results.  
To quantify these errors, we conducted the following analyses: Firstly, by comparing experimental results under different polishing force control strategies, we found that when the polishing force fluctuation is within \(\pm 5\%\), the difference in polishing effects is less than \(5\%\). Secondly, by averaging multiple measurements, we reduced the impact of measurement errors on the results. Nevertheless, it is still recommended to further optimize polishing force control and robot motion precision in future studies to enhance the accuracy of the experiments.  
Figure 10: Selection of polishing trajectory spaces. (**a**) Overly small polishing trajectory space. (**b**) Excessively large polishing trajectory space. (**c**) Optimum polishing trajectory space. (**d**) Relationship between optimum polishing trajectory space and curvature.
## 2 Materials and Methods
### Measurement Uncertainty  
Following uncertainty sources of material purity \(u(p_{u})\), temperature measurement \(u(T)\), weight measurement \(u(w)\), calibration \(u(c)\), and repeatability \(u(rep)\) were considered to evaluate combined standard uncertainty of density and viscosity measurements of aqueous amine mixtures.  
The specified standard uncertainties for the uncertainty of density measurement were \(u(p_{u})\) = 0.006, \((p_{u})\) = 0.006, \(u(T)\) = 0.012 K, \(u(w)\) = \(2\times 10^{-4}\) kg, \(u(c)\) = 0.01 kg\(\cdot\)m\({}^{-3}\), and \(u(rep)\) = 0.13 kg\(\cdot\)m\({}^{-3}\). The maximum gradient of density against temperature, \(\partial\rho/\partial T\), was found to be 1.2 kg\(\cdot\)m\({}^{-3}\cdot\)K\({}^{-1}\), and the corresponding uncertainty in \(\rho\), \((\partial\rho/\partial T)\cdot u(T)\), was determined to be 0.02 kg\(\cdot\)m\({}^{-3}\). The combined standard uncertainty for the density measurement was calculated as described in the Guide to the Expression of Uncertainty in Measurement [14; 15] by considering all mentioned uncertainty sources to be \(u(\rho)\) = 5.9 kg\(\cdot\)m\({}^{-3}\). Then, the combined expanded uncertainty of the density measurement \(U(\rho)\) was found to be 11.8 kg\(\cdot\)m\({}^{-3}\) (level of confidence = 0.95).  
For the uncertainty of viscosity measurement, specified standard uncertainties for the uncertainty sources were \(u(p_{u})\) = 0.006, \(u(T)\) = 0.012 K, \(u(w)\) = \(2\times 10^{-4}\) kg, \(u(c)\) = 0.065 mPa\(\cdot\)s, and \(u(rep)\) = 0.008 mPa\(\cdot\)s. Then the combined standard uncertainty for the viscosity measurement was calculated to  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Chemical Name** & **CAS No** & **Source** & **Purity 1 ** & **Purification** \\ \hline MDEA & 105-59-9 & Merck KGaA & \(\geq\)98.0 & no \\ DEEA & 100-37-8 & Sigma-Aldrich & \(\geq\)99.5 & no \\ DMEA & 108-01-0 & Alfa Aesar & \(\geq\)99.0 & no \\ \hline \end{tabular}
\end{table}
Table 2: Material description. 1 be \(u(\eta)=0.07\) mPa\(\cdot\)s. The combined expanded uncertainty of the viscosity measurement \(U(\eta)\) was found to be 0.14 mPa\(\cdot\)s (level of confidence = 0.95).