esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Findings from the Literature Review
### Topic 1--Chemical Context  
The predominant role in this topic is played by chemical plants, which are the focus of research in various ways. This is not surprising, as the literature shows that chemical processes tend to be the ones with the most time-varying characteristics. Interestingly, in the chemical context, clustering helped several applications, such as the identification of fault subclasses in contexts where faults exhibit time-varying characteristics [29]. Similarly, in penicillin production processes, clustering has been used to identify the specific production stage in the batch process [30]. This contribution also ties in with the keywords in topic 2, in which phase partitioning is applied with clustering. In this contribution [30], it is highlighted that the analyzed process belongs to the world of chemical processes, while in topic 2 the focus is on phase recognition, and the production process assumes a pure case study. Moreover, this contribution [30] is the only one applying the AdTsPCA. As further analysis of the chemical context, more specifically petrochemicals, clustering allows the identification of the operating modes present in the process, based on a process variable highlighting the change, to develop specific models for the operating modes identified [31]. Entering into the field of water distribution and treatment, clustering again showed interesting performance. In general, water distribution systems are assimilated to chemical plants because of the frequent use of chemical substances to ensure water potability. These systems are fragile due to their high number of components. There are several flow regimes, and clustering allows their modeling and recognition, through self-organizing maps and the analysis of pressure [32]. Similarly, wastewater treatment processes have also seen the application of clustering applied to predict the trend of the data pattern, specifically the fuzzy c-means [33]. Considering instead the bearing analyses attributed to this cluster, they are mainly based on the analysis of impulsive vibration signals. In this case, clustering is a generalization of k-means, to support the separation of the impulsive components of the vibration signals [34]. Finally, the fault diagnosis of gearboxes is treated by considering vibrations [35]. It is to mention the proposal of the innovative AdTsPCA technique [30] in this topic. The last two concepts available are the recursiveness of some of the proposed algorithms and methods [29; 31; 33] and the use of multifractal spectrum parameters as fault features [35]. The issue of non-linearity is transversal to the various topics and not specific to this one, suggesting a future research developments.
## 3 Results
To confirm this observation in a quantitative way, we calculated Pearson's correlation coefficient \(R\) according to the standard definition:  
\[R=\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sqrt{\sum_{i= 1}^{n}(x_{i}-\overline{x})^{2}}\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}}\]  
where \(x_{i}\) and \(y_{i}\) are the \(n\) acquired data and \(\overline{x}\) and \(\overline{y}\) are their mean value. Figure 7: (a) SO\({}_{2}\) flux; (b) CO\({}_{2}\) flux; (c) VLP time series extracted from LGS bulletins in 2019–2021. A scatter plot for SO\({}_{2}\) and CO\({}_{2}\) data is reported in Figure 9, with the corresponding calculated correlation coefficient being \(R_{\text{CO}_{2}\text{SO}_{2}}=0.3036\), thus indicating a positive correlation between the two variables. In a real sample (with a finite number of datasets), \(R\) can be non-zero even if the variables are not correlated. By increasing the population, if the variables are not correlated, \(R\) will approach to zero. To understand if the value obtained for \(R_{\text{CO}_{2}\text{SO}_{2}}\) is different from zero because of either a reduced population number or the variables are correlated, we performed a statistic simulation. We generated \(n=358\) (equal to the number of acquired data) pairs (CO\({}_{2}\), SO\({}_{2}\)) randomly extracted from two Gaussian distributions having the same mean and standard deviation of the acquired data. Using the 358 extracted pairs, we calculated the corresponding correlation coefficient \(R_{\text{CO}_{2}\text{SO}_{2}}\). The simulation (358 pairs extraction and calculation of corresponding \(R_{\text{CO}_{2}\text{SO}_{2}}\)) has been repeated 100,000 times. The histogram representing the obtained correlation is shown in Figure 10.  
In the previous simulation of uncorrelated pairs (CO\({}_{2}\), SO\({}_{2}\)), we calculated, by numerical integration, the probability \(p\) that the correlation coefficient \(R_{\text{CO}_{2}\text{SO}_{2}}\) is 0.3036 (or larger), obtaining \(p<\)1%. Consequently, we can assume that the obtained value of \(R_{\text{CO}_{2}\text{SO}_{2}}=0.3036\) is different from zero because of the variables correlation and not due to the sample shortage. A similar analysis carried out on (CO\({}_{2}\), VLP) and on (SO\({}_{2}\), VLP) variables produced \(R_{\text{CO}_{2}\text{,VLP}}=0.2592\) and \(R_{\text{SO}_{2}\text{,VLP}}=0.3095\), respectively. Even in these cases, the probabilities of obtaining a value of correlation coefficient larger than \(R_{\text{CO}_{2}\text{,VLP}}=0.2592\) and \(R_{\text{SO}_{2}\text{,VLP}}=0.3095\) in a sample of 358 pairs of uncorrelated variables is lower than 1%, thus confirming a correlation also for (CO\({}_{2}\), VLP) and (SO\({}_{2}\), VLP) value pairs. Figure 8: SO\({}_{2}\) and CO\({}_{2}\) trend over time. Figure 9: Correlation between SO\({}_{2}\) and CO\({}_{2}\).
## 3 Results  
In the overall assessment of the four parameters, the K coefficient showed excellent inter-reader concordance (k = 0.86) in the analysis of PAN.  
True positives, false positives, true negatives, false negatives, sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of PAN for each of the four parameters (deep caries, root remnants, stage III periodontal disease, and AP lesions) are shown in Tables 1 and 2.  
No false positive or false negative was found in the assessment of deep caries, root remnants, and stage III periodontal disease, whereas false positives were very few (2.6%) (Figure 2) and false negatives accounted for just over half of the cases (53.2%) for AP lesions (Figure 3).  
The agreement between PAN images in relation to the reference standard represented by MSCT was moderate in the analysis of AP (k = 0.45) and excellent in the analysis of caries, root remnants, and stage III periodontal disease (k = 0.93 to 0.96). PAN showed sensitivity, negative predictive value, and diagnostic accuracy of 100% for deep caries, root remnants, and stage III periodontal disease, whereas they were 46.8%, 64.7%, and 72.1% for AP lesions, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**PAN**} & \multicolumn{3}{c}{**Multi Slice CT**} \\ \cline{2-4}  & **Diseased** & **Healthy** & **Total** \\ \hline Deep caries & & & \\ \hline Positive & 65 (100\%) & 0 & 65 \\ \hline Negative & 0 & 65 (100\%) & 65 \\ \hline Total & 65 & 65 & 130 \\ \hline Root remnants & & & \\ \hline Positive & 37 (100\%) & 0 & 37 \\ \hline Negative & 0 & 37 (100\%) & 37 \\ \hline Total & 37 & 37 & 74 \\ \hline Stage III periodontal disease & & & \\ \hline Positive & 143 (100\%) & 0 & 143 \\ \hline Negative & 0 & 143 (100\%) & 143 \\ \hline Total & 143 & 143 & 286 \\ \hline Apical periodontitis & & & \\ \hline Positive & 36 (46.8\%) & 2 (2.6\%) & 38 \\ \hline Negative & 41 (53.2\%) & 75 (97.4\%) & 116 \\ \hline Total & 77 & 77 & 154 \\ \hline \hline \end{tabular}
\end{table}
Table 1: True Positives, False Positives, True Negatives, and False Negatives for Panoramic Radiography (PAN) in Relation to Multislice Spiral Computed Tomography (MSCT).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **SEN** & **SPE** & **PPV** & **NPV** & **ACC** & **K** \\ \hline Deep caries & 100\% & 100\% & 100\% & 100\% & 100\% & 0.96 \\ Root remnants & 100\% & 100\% & 100\% & 100\% & 100\% & 0.94 \\ Stage III periodontal disease & 100\% & 100\% & 100\% & 100\% & 100\% & 0.93 \\ Apical periodontitis & 46.8\% & 97.4\% & 94.7\% & 64.7\% & 72.1\% & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), diagnostic accuracy (ACC) and Kappa Value for Panoramic Radiography in Relation to Multislice Spiral Computed Tomography.
## 4 Fast Automatic Adjustment Algorithm of Imaging Waveform
### Automatic Judgment of Measurement Mode  
The actual working state of the dispensing robot alternated between dynamic and static. First, the dispensing robot moved to the target position at a high speed in the dynamic state, and then measured and dispensed in the static state.  
Therefore, we set the dynamic centroid difference threshold between two adjacent frames of imaging waveform to distinguish the dynamic and static states and adopted different algorithm strategies. When it was the static working mode, the peak intensity was strictly controlled in an interval close to the ideal value (520), and therefore reduced the interference caused by different measured objects. Then, 64 times of smooth filter was applied to improve the measuring stability. In the dynamic mode, considering that the sharp fluctuation of the peak intensity of the dynamic waveform decreased the dynamic response speed of the sensor, we enlarged the peak intensity control interval. Different from the static mode, we did not filter the measured value in dynamic mode.  
In the above algorithm, the dynamic centroid difference threshold and the peak intensity control interval of different working modes were identified as two important parameters.
## 4 Testbed Development
### Testbed Architecture
#### 4.2.3 Unknown IP Addresses Because of CGNAT  
Starlink applies Carrier Grade Network Address Translation (CGNAT) to efficiently manage public IP addresses [55]. This means that user terminals are not directly assigned a  
Figure 7: Testbed architecture. Note that WireGuard creates a virtual network with _Machine (A)_, _VM_, and _Machine (B)_, and that _User terminal_ remains behind a CGNAT configuration.  
public IP address, hiding the internal LAN from the Internet. In our testbed, this technology presents an obstacle, as the gNB and the CU must bind their public IP address in the N2/N3 and F1 interfaces, respectively.  
As CGNAT impedes the previous configuration, we decided to set up a WireGuard Virtual Private Network (VPN) centralized on _VM_. By doing so, we were able to ensure that the IP address of _Machine (A)_ is known in the testbed, enabling the correct configuration of the interfaces. WireGuard also enabled bidirectional access between _Machine (A)_, _VM_, and _Machine (B)_, regardless of the CGNAT. The usage of WireGuard in the testbed is dependent on the specific transparent architecture to be evaluated, as it is not always strictly required (as explained in Section 5).  
To measure the impact of the VPN, we conducted the same iPerf3 tests as for the characterization of Starlink's service performance. Again, the test campaigns were conducted in the same setup and under the same weather conditions as before. The results, depicted in Figure 8, show a decrease in performance compared to those obtained in Figure 4. In uplink conditions, we observe smoother dispersion, especially in UDP. While the mean bitrate remains similar in UDP, around 42 Mbps, it is notably reduced in TCP. The performance of 10 streams is decreased by 27% (from 33 Mbps to 24 Mbps) and the performance of 1 stream by 23% (from 13 Mbps to 10 Mbps). In downlink conditions, there are also noticeable bitrate reductions, except for UDP, which only drops 15 Mbps. The performance of 10 streams is decreased by 22% (from 237 Mbps to 185 Mbps) and the performance of 1 stream by 41% (from 56 Mbps to 33 Mbps). These serious impacts on TCP may be caused by the specific technology of WireGuard, as it implements UDP at the transport layer. Regardless of this, the results conclude that WireGuard was considerably affecting the bitrate performance of the testbed.
## Appendix A
### Proof of \(df_{r}(t)/dt<0\) for \(\zeta=1\) Case  
It is concluded in Section 3.2 that \(df_{r}(t)/dt<0\) can be derived from (19). This conclusion will be proved by contradiction. Assuming that \(df_{r}(t)/dt\geq 0\) is valid and substituting this inequality into (19), the following inequality can be obtained:  
\[G_{0}\big{[}e^{-\omega_{n}t}-\omega_{n}te^{-\omega_{n}t}+z_{1}te^{-\omega_{n} t}\big{]}\geq 0 \tag{18}\]  
Since \(G_{0}<0\), inequality (18) can be simplified into  
\[1+(z_{1}-\omega_{n})t\leq 0 \tag{19}\]  
In order to solve (19), the relationship between \(\omega_{n}\) and \(z_{1}\) should be derived. Substituting \(\zeta=1\) into the fourth equation of (2), and then it becomes:  
\[\frac{(2H+DT_{G})}{4 HT_{G}}\sqrt{\frac{2 HRT_{G}}{DR+1}}=1\Rightarrow\frac{(2 H+DT_{G})}{4 HT_{G}}=\omega_{n}\Rightarrow\omega_{n}=\frac{1}{2T_{G}}+\frac{D}{4H} \Rightarrow\omega_{n}=\frac{z_{1}}{2}+\frac{D}{4H} \tag{20}\]Considering the prerequisite that \(H>DT_{G}/2=D/2z_{1}\), the following relationship between \(\omega_{n}\) and \(z_{1}\) is obtained:  
\[\omega_{n}<\frac{z_{1}}{2}+\frac{z_{1}}{2}=z_{1} \tag{10}\]  
Substituting (10) into (11), the satisfactory region of time \(t\) can be derived as  
\[t\leq\frac{-1}{(z_{1}-\omega_{n})}<0 \tag{11}\]  
In practice, the value of time \(t\) should always be positive. Therefore, the assumption that \(df_{r}(t)/dt\geq 0\) is incorrect, and hence, \(df_{r}(t)/dt<0\) must be satisfied.
## 3 Results
### Binary Protein Mixture Ultrafiltration at pH 3 in Concentration Mode  
As already mentioned in the Materials and Methods section, the ultrafiltration of binary protein mixtures was carried out in concentration mode. A constant flux as a function of time was observed when the ultrafiltration process was carried out using initial protein concentrations of 0.5 or 2 g\(\cdot\)L\({}^{-1}\) (Figure 3). In particular, using an initial protein concentration of 0.5 g\(\cdot\)L\({}^{-1}\) and applying a TMP of 0.2 bar, a steady-state flux of 68 (\(\pm\)5) L\(\cdot\)h\({}^{-1}\)\(\cdot\)m\({}^{-2}\) was obtained, while using an initial protein concentration of 2 g\(\cdot\)L\({}^{-1}\) and applying a TMP of 0.1 bar, a steady-state flux of 30 (\(\pm\)2) L\(\cdot\)h\({}^{-1}\)\(\cdot\)m \({}^{-2}\) was obtained. The similar flux obtained operating in concentration mode (Figure 3) or at constant feed volume (Table 2) is a further confirmation that no significant fouling is observed, since just reversible fouling is obtained, which can be easily removed by washing steps. The TMP values were selected according to the results of the critical pressure study. Furthermore, in this series of experiments, the initial pure water permeance (6.70 \(\times\) 10\({}^{-8}\) (\(\pm\)1.68 \(\times\) 10\({}^{-9}\)) mPa\({}^{-1}\)\(\cdot\)s\({}^{-1}\) was also completely restored after UF with protein solutions (6.65 \(\times\) 10\({}^{-8}\) (\(\pm\)2.52 \(\times\) 10\({}^{-9}\)) mPa\({}^{-1}\)\(\cdot\)s\({}^{-1}\)). In Figure 4, the electrophoretic profile of samples analyzed as a function of ultrafiltration time were reported together with the MW standards of used proteins (Figure 4a). Analyzing the electrophoresis of permeates collected as a function of time (Figure 4b,c), it is possible to note that both proteins can pass through the membrane; however, ALA (that at this pH value has a lower charge density) is less rejected by the positively charged membrane. This is clear evidence that at pH 3 the membrane cannot be used to purify one protein with respect to the other, since both can pass through the membrane. Similar results were obtained for both initial protein concentrations used (Figure 4b,c).  
Figure 3: Time course of the UF carried out in concentration mode through charged membrane, by using different initial binary protein mixture concentration; pH: 3, T: 25 \({}^{\circ}\)C, ionic strength: 0.1 M.
## 3 Results and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Static Load** [**kPa**]} & \multirow{2}{*}{**Zone Interface**} & \multicolumn{5}{c}{**Temperature [\({}^{\circ}\)C]**} \\ \cline{3-6}  & & \(-\)**20** & **0** & **+20** & **+40** \\ \hline \multirow{4}{*}{3.488} & \multirow{4}{*}{Direct coupling/amplification} & Frequency [Hz] & 39.326 & 36.0297 & 30.9477 & 18.9899 \\  & & Transmissibility & 1.1002 & 1.1033 & 1.1028 & 1.1012 \\  & & Damping ratio & 1.0897 & 1.0729 & 1.0753 & 1.0846 \\ \hline \multirow{4}{*}{3.488} & \multirow{4}{*}{Peak transmissibility} & Frequency [Hz] & 133.3350 & 114.5280 & 94.8137 & 62.6298 \\  & & Transmissibility & 7.9639 & 8.0985 & 7.3446 & 3.7688 \\  & & Damping ratio & 0.0628 & 0.0617 & 0.0681 & 0.1327 \\ \hline \multirow{4}{*}{Amplification/attenuation} & Frequency [Hz] & 222.36 & 188.808 & 148.241 & 130.9 \\  & & Transmissibility & 0.9948 & 0.9824 & 0.9992 & 0.9939 \\  & & Damping ratio & & & & \\ \hline \multirow{4}{*}{3.488} & \multirow{4}{*}{Peak transmissibility} & Frequency [Hz] & 36.7846 & 30.2429 & 25.9771 & 17.4785 \\  & & Transmissibility & 1.1030 & 1.1010 & 1.1025 & 1.1028 \\  & & Damping ratio & 1.0741 & 1.0852 & 1.0771 & 1.0754 \\ \hline \multirow{4}{*}{4.651} & \multirow{4}{*}{Peak transmissibility} & Frequency [Hz] & 124.7180 & 99.5134 & 75.1312 & 50.6682 \\  & & Transmissibility & 9.4144 & 8.7795 & 7.1330 & 4.1833 \\  & & Damping ratio & 0.0531 & 0.0570 & 0.0701 & 0.1195 \\ \hline \multirow{4}{*}{Amplification/attenuation} & Frequency [Hz] & 184.934 & 152.396 & 119.928 & 102.303 \\  & & Transmissibility & 0.9980 & 0.9945 & 0.9999 & 0.9959 \\  & & Damping ratio & & & & \\ \hline \multirow{4}{*}{6.976} & \multirow{4}{*}{Peak transmissibility} & Frequency [Hz] & 29.1484 & 24.1309 & 16.3866 & 14.9786 \\  & & Transmissibility & 1.1043 & 1.1011 & 1.1002 & 1.1006 \\  & & Damping ratio & 1.0671 & 1.0849 & 1.0900 & 1.0878 \\ \hline \multirow{4}{*}{6.976} & \multirow{4}{*}{Peak transmissibility} & Frequency [Hz] & 97.0233 & 78.3122 & 48.8345 & 39.8734 \\  & & Transmissibility & 8.5342 & 6.2941 & 4.9942 & 7.0873 \\  & & Damping ratio & 0.0586 & 0.0794 & 0.1001 & 0.0705 \\ \hline \multirow{4}{*}{Amplification/attenuation} & Frequency [Hz] & 158.849 & 128.806 & 82.0049 & 64.0894 \\  & & Transmissibility & 0.9968 & 0.9919 & 0.9977 & 0.9971 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Vibration transmissibility and damping ratio of XPE cushion material.
## 5 Computational Experiments
### Comparison of Each Case in Inverse Optimization  
The inverse optimization is applied to problems of each case. Figure 5 shows the weighting factor estimation for the small-scale problem (2 objectives). The initial value of the weighting factor is set to (\(w_{1}\), \(w_{2}\)) = (0.7, 0.3). The target weighting factor is (\(w_{1}\), \(w_{2}\)) = (0.2, 0.8). The target weighting factor is reached after the 10 th iteration.  
Table 10 shows the estimation error in MSE for each case. First, in the 2-objective case, small-scale and large-scale problems are compared. The accuracy of the large-scale problem was higher than that of the small-scale problem. It is thought that the change in the objective function value has something to do with this. In small-scale problems, the objective function value may not change even if the weighting factors change. In such a case, the estimation is completed before reaching the weighting factors to be estimated. Therefore, it is thought that the estimation accuracy becomes low. Next, the 2-objective and 3-objective cases are compared in the small-scale case. The 2-objective case is more accurate. In the 2-objective case, as the weighting factor increases, the corresponding objective function value decreases. But this is not necessarily the case for the 3-objective case.  
We consider an example of the weighting factor and objective function values for the case I. When the weighting factor is (\(\mathbf{w}\)) = (0.2, 0.3, 0.5), the objective function value is (390, 2982, 1241). On the other hand, when the weighting factor is (\(\mathbf{w}\)) = (0.5, 0.1, 0.4), the objective function values are (478, 3783, 754). The value of maximum completion time for the first objective function increases as the weighting factor increases. The value of sum of setup costs for the third objective function decreases as the weighting factor decreases. In this case, the weighting factors are not updated correctly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Small-Scale** & **Large-Scale** & **Small-Scale** & **Small-Scale** \\  & **Problems** & **Problems** & **Problems** & **Problems** \\  & **(2 Objective)** & **(2 Objective)** & **(3 Objective: I)** & **(3 Objective: II)** \\ \hline MSE & 2.418 \(\times\) 10\({}^{-3}\) & 4.446 \(\times\) 10\({}^{-4}\) & 1.667 \(\times\) 10\({}^{-2}\) & 1.704 \(\times\) 10\({}^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: MSE result of inverse optimization.  
Figure 5: The result of estimation (initial value is (\(w_{1}\), \(w_{2}\)) = (0.7, 0.3) target value is (\(w_{1}\), \(w_{2}\)) = (0.2, 0.8)).
## 5 Results
### Image Restoration
#### 5.3.2 Results of Quantitative Analysis  
Figure 16 shows the results of the two methods to recover the distorted image, and the comparison results are presented in Table 2. The results show that compared with [PERSON]'s method, the proposed method has a significant improvement in performance indicators such as PSNR, MSE and SSIM, which proves the effectiveness of the algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**SSIM (H)**} & \multicolumn{3}{c}{**MSE (L)**} & \multicolumn{3}{c}{**PSNR (H)**} \\ \cline{2-10}  & **Data 1** & **Data 2** & **Data 3** & **Data 1** & **Data 2** & **Data 3** & **Data 1** & **Data 2** & **Data 3** \\ \hline Distortion & 0.5584 & 0.6558 & 0.6140 & 0.1367 & 0.0653 & 0.0914 & 8.6414 & 11.8538 & 10.3925 \\ [PERSON] [23] & 0.6814 & 0.6784 & 0.6270 & 0.0520 & 0.0441 & 0.0514 & 12.8389 & 13.5571 & 12.8878 \\ Proposed method & 0.7630 & 0.7877 & 0.7434 & 0.0317 & 0.0461 & 0.0297 & 15.00 & 13.4651 & 15.2656 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of image quality metrics among the proposed method, the distorted image and [PERSON]’s method [23].  
Figure 15: (**a**) Ground-truth image; (**b**) an image distorted by a wavy WAI; (**c**) recovered image; (**d**) a scatter plot showing coordinates of the corner points of the color-coded square checkerboards in above three images.