esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methodology  
### Study Area  
The southwest railway, as shown in Figure 1, is situated on the margin of Qinghai-Tibet Plateau and crosses various geomorphic units, such as a high mountain and gorge area, western Sichuan high mountain and plain area, and the core area of the Hengduan Mountains high mountain and gorge. The section starts from Ya'an and passes through Kangding, Litang, Dege, and other places before reaching Qamdo. The length of the railway is about 700 km, and the altitude varies greatly along the way, as shown in Figure 2 (the elevation data used come from [[https://www.earthdata.nasa.gov/](https://www.earthdata.nasa.gov/)]([https://www.earthdata.nasa.gov/](https://www.earthdata.nasa.gov/)) (accessed on 2 June 2023)). The complex terrain results in climatic features along the railway, and the spatial distribution of meteorological elements is significantly different.
# Biological Production of 3-Hydroxpropionic Acid: An Update on the Current Status
## 2 Production of 3 HP from Glycerol
### Production Using Genes That Are Part of the Pdu Operon  
The second operon that can be used for the construction of a strain capable of converting glycerol to 3 HP is the _pdu_ operon. In nature, the _pdu_ operon is required for microbial growth on 1,2-propanediol and it can be found in _Salmonella_ and _Lactobacillus_ species [21; 22]. First, 1,2-propanediol is converted to propionaldehyde by the action of an AdoCbl-dependent diol dehydatase; this is further converted to  
Figure 1: Conversion of glycerol to 3-hydroxypropionic acid (3 HP) employing genes from the _dha_ operon.  
propionic acid and propanol by the action of the enzymes CoA-dependent aldehyde dehydrogenase, phosphothransacylase, propionate kinase, and alcohol dehydrogenase [22]. Regarding the conversion of glycerol to 3 HP by the enzymes encoded in the _pdu_ operon, more intermediate steps are required than when using the _dha_ operon. Initially, glycerol is converted to 3-hydroxypropionaldehyde by the action of the diol dehydatase, which is followed by its transformation to 3-hydroxypropionyl-CoA by the action of propionaldehyde dehydrogenase. The third step consists of phosphorylation of 3-hydroxypropionyl-CoA to 3-hydroxypropionyl phosphate by phosphate propanoyltransferase. Finally, dephosphorylation of 3-hydroxypropionyl phosphate, by the enzyme propionate kinase, leads to the formation of 3 HP (Figure 2).  
Similarly to the _dha_ pathway, the use of the _pdu_ pathway presents some challenges that should be taken into account when intending to use it for industrial production of 3 HP. As NAD\({}^{+}\) is required during the second step of the pathway (conversion of 3-hydroxypropionaldehyde to 3-hydroxypropionyl-CoA), an adequate supply of NAD\({}^{+}\) is necessary. Similar strategies and considerations as described before apply here also, with the co-production of 1,3-propanediol serving  
Figure 2: Conversion of glycerol to 3 HP employing genes from the _pdu_ operon.  
as a promising solution, as the action of 1,3-propanediol oxidoreductase requires NADH, and therefore, NAD\({}^{+}\) is regenerated [23; 24], maintaining the equilibrium between NAD\({}^{+}\) and NADH in the process.
# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 3 Results and Discussion
### Comparing Different Dimension Reduction Methods  
The classification results of different winter wheat types can be influenced by the dimension reduction method. Table 1 shows the classification accuracy of different dimensionality reduction methods. For both MLE and SVM (Linear) classification methods, data obtained by the MNF dimension method has the best classification results, followed by PCA and ICA. The results indicate that for dimension reduction of UHD185 hyperspectral data, MNF is better in the fine classification of different varieties of winter wheat. The reason could be that the MNF method considers the original hyperspectral data as both signal and noise, and it separates them before performing the transform to improve image features. Also, when compared with PCA, MNF can involve two or more separate PCA rotations to detect critical information. [PERSON] used both PCA and MNF to identify apple bruises, and, according to the correct detection result, MNF could be better at reaching acceptable results [43]. Compared with hyperspectral data after dimension reduction, data without dimensionality reduction have the poorest classification results. This finding is consistent with the opinion of [PERSON] et al. that the higher spectral dimension does not equal the higher classification accuracy, and it is necessary to effectively extract critical information, and reduce redundant information and storage space during hyperspectral data processing [28]. However, useful spectral information and noise cannot be completely separated by using these methods. From Figure 10, it seems that for data processed with PCA and MNF, the longitudinal field bank between wheat cannot be classified as clear as data processed with ICA, probably because useful information about field bank might be considered as noises and abandoned during the feature extraction process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Methods** & **Dimensionality Reduction Methods** & **The Overall Accuracy** & **Kappa Coefficient** \\ \hline \multirow{4}{*}{MLE} & Original (without dimensionality reduction) & 49.51 & 0.4101 \\  & PCA & 72.86 & 0.6831 \\  & MNF & 77.23 & 0.7339 \\  & ICA & 64.74 & 0.5888 \\ \hline \multirow{4}{*}{SVM (Linear)} & Original (without dimensionality reduction) & 46.88 & 0.3800 \\  & PCA & 74.15 & 0.6989 \\ \cline{1-1}  & MNF & 78.11 & 0.7449 \\ \cline{1-1}  & ICA & 74.14 & 0.6887 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The influence of different dimensionality reduction methods on the classification accuracy (unit: %).  
Figure 9: Spectral curves of different winter wheat cultivars.
# Sensor Fusion Architecture for Fault Diagnosis with a Predefined-Time Observer
## 2 Sensor Fusion Architecture
### Sensor Fusion Architecture
#### 2.1.3 Filter  
The sensor fusion stage generates more reliable signals. These signals are filtered to reduce their noise even more. To this end, a classical second-order low pass filter (LPF) is used:  
\[F_{\Sigma^{i}}(s)=\frac{\omega_{n_{i}}^{2}}{s^{2}+2\zeta_{i}\omega_{n_{i}}s+ \omega_{n_{i}}^{2}} \tag{6}\]  
The parameters \(\zeta_{i}\) and \(\omega_{n_{i}}\) are chosen such that a desired transient and noise reduction are achieved.
## 4 Numerical Results
### Influence of System Parameters
#### 4.1.4 Effect of the Node Density
The graph (**a**) of Figure 11 considers \(B_{\min}=0.001\), \(N=10\) and \(B_{\max}=0.1\), i.e., \(D\propto 100\), whereas the graph (**b**) considers \(N=100\) and \(B_{\max}=1\), also yielding \(D\propto 100\). From these results, the scalability of our method is demonstrated, and it is illustrated that the network lifetime increases as the number of nodes is increased or their energy consumptions are decreased. To plot the results in Figure 11, we have used two independent realizations of the sensor nodes consumptions during the corresponding interval of analysis.
## 2 Background  
### The Baihetan Underground Powerhouse  
The Baihetan hydropower station is located on the Jinsha River in southwest China and has the largest underground powerhouse in the world in the mountain on both sides of the river (Figure 2a,b). The left bank underground powerhouse consists of pressure piping, the main powerhouse, omnibus bar caves, a transformer chamber, a draft tube bulkhead gate chamber, tailrace surge chambers and tailrace tunnels (Figure 2c). In addition, the left bank underground powerhouse also includes many auxiliary caverns such as drainage corridors, anchorage observation tunnels, ventilation shafts, access tunnels and construction adits. The main powerhouse is 438 m long and 88.7 m high; its span of 34 m is the largest in the world. The transformer chamber, which is 59.15 m away from the main powerhouse, has a length of 368 m, a span of 21 m and a height of 39.5 m. The span of the draft tube bulkhead gate chamber is 12-15 m, the height is 129.5 m and the length is 374.5 m. The axis of the four tailrace surge chambers is parallel to the main powerhouse and the transformer chamber. The distance between the axis and the centerline of the unit is 220 m and the distance between the axis and the centerline of the transformer chamber is 130.5 m. The diameter of the tailrace surge chambers #1-4 is 44.5-48 m and the height is 124-127.91 m. Generally, the Baihetan underground powerhouse (BUP) features a large-scale and complex structural layout. The main caverns have a large span and high sidewalls. The cavern group has numerous different chambers and tunnels and these caverns are connecting or intersecting with each other. As reflected in Figure 3b, the main powerhouse was excavated in ten layers from May 2014 to June 2018; the transformer chamber was excavated in four layers.  
Figure 2: The Baihetan hydropower station and underground powerhouse: (**a**) the location, (**b**) an aerial view at the dam site and (**c**) a three-dimensional layout of the left bank underground powerhouse.
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 4 Results
### Data Calibration  
Data calibration in this research is based on the findings of the data validation. In the calibration process, a linear regression equation is used to improve the aerial data quality and reduce its distance from ground-based data. In the following sections, we discuss the calibration experiments to investigate the possibility of improving the aerial data quality. The three experiments performed in the data calibration process to improve aerial humidity and temperature data, infrared data, and spectrometer data are discussed in the following section.
## 4 Wind Loading and Vibration Mitigation
### Considered Load Cases
#### 4.2.2 Oscillations in Smooth Flow  
Fluctuations in the flow can either exist without the presence of the structure, such as the case in the previous scenario, or additional ones can arise due to a body being immersed into the flow. The CAARC-B is a bluff body that will facilitate vortex creation around it. Of particular interest is the case of a smooth flow, which triggers dominant shedding around the building. The critical situation is when the vortex shedding frequency \(f_{sh}\) matches one of the lower eigenmodes of the structure. We planned a setup to trigger this mechanism for the weak bending mode.  
At a particular angle of attack, this occurs for a given streamwise velocity. This is described in detail by [41]. It leads to a practically constant along-wind force accompanied by a close-to-harmonic cross-wind component. If the narrow-banded excitation force [44] exactly matches the underlying structural frequency, we are led to the situation of mechanical resonance. However, in the case of (wind) flow, vortex-shedding can trigger a feedback mechanism, also called _lock-in_[38]. This means that not only is the exact overlap of the frequencies dangerous, but also the slight shifts might need considering, as the oscillating systems could synchronize. This leads to the necessity of the approach labeled as Two-Way Coupling (TWC). In the numerical context, this means that forces simulated by CFD trigger deformations in the CSD model, which, in turn, will require an update of the shape in the flow field.
## 3 Results and Discussion
### Structural Characterization and Degree of Mixing
In the \({}^{13}\)C CP/MAS spectra, recorded with a short contact time of 0.2 ms (Figure 2c,d), signals arising from rigid domains are enhanced, showing broader lines with respect to the DE spectra. In particular, the signals of resin carbons are favored compared to those of SBR, as can be seen from the higher intensity of the spinning sidebands. The isotropic signal and the corresponding sidebands of aromatic tertiary carbons of Kristalex(tm) 5140 were assigned on the basis of the \({}^{1}\)H-\({}^{13}\)C CP spectrum recorded for the resin under the same experimental conditions (Figure 2e). Interestingly, the observation of spinning sidebands for Kristalex(tm) 5140 in the spectra of vSBR_k indicates that the resin maintains a rigid character even when blended with the polymer, in agreement with the results from \({}^{1}\)H FID analysis. The CP dynamics was then studied for the tertiary aromatic carbons in pure Kristalex(tm) 5140 and in vSBR_k and compared with that of aromatic/alkene carbons in vSBR and vSBR_k (see Supplementary Materials). Interestingly, the \({}^{1}\)H \(T_{1\text{o}}\) of the aromatic protons of the resin in pure Kristalex(tm) 5140 (-9 ms) is significantly longer than that measured in both vSBR_k and vSBR (0.6-1 ms) (Table S1). This seems to be ascribable to the proton spin diffusion between resin and polymer domains occurring in vSBR_k and suggests that in this sample the resin is intimately mixed with the polymer. To this regard, further information could be obtained by the low-resolution measurement of \({}^{1}\)H \(T_{1}\): mono-exponential trends for the relaxation curves were obtained for all the samples and \({}^{1}\)H \(T_{1}\) values of 114, 66 and 74 ms were measured for pure Kristalex, vSBR and vSBR_k, respectively. Although these values are quite similar, these results seem to confirm an intimate mixing (on a tens of nm scale) between resin and polymer in vSBR_k.
# Covert Surveillance Video Transmission with QAM Constellations Modulation
## 3 Performance Analysis of Cover Surveillance Video Transmission  
In previous works [40; 43], the performance metrics of covert transmission, i.e., KL divergence and mutual information, have been approximated using intricate integration calculations or less stringent constraints. As depicted in Figure 3, in this section, we introduce a precise approximation method based on the Taylor series expansion theory. This method transforms intricate integration computations into straightforward summations, thereby reducing computational complexity while ensuring the accuracy of results.
## 5 Results and Discussion
### High-Density Polyethylene (HDPE)
Results for \(\alpha\)-A\({}_{\rm o}\) are plotted in Figure 17c. Observed data points followed the calculated \(\alpha\)-A\({}_{\rm o}\) curve to 110 kHz. Three datasets were plotted, two of which used pulse excitation (+ and triangles), and the third used 50-kHz sine pulse (dots). The velocity range of these datasets was from 1.1 to 0.6 mm/\(\upmu\)s. For these asymmetric tests, data fitted better to the calculated curve than for the symmetric case above (Figure 17a), and the deviation was comparable to that of PS. The deviation statistics for this HDPE plate are given below. \begin{tabular}{c c c c} \hline Wave Mode & Deviation (dB/m) & Percent Deviation (\%) & Data Count \\ \hline Symmetric case: & 8.0 & 24.7 & 28 \\ \hline Asymmetric case: & 4.6 & 13.0 & 53 \\ \hline \end{tabular}   
This HDPE plate showed the same general trend in Lamb-wave attenuation behavior as those found in PS, PMMA, and PVC, even though these four materials have different bulk-wave attenuation coefficients. In terms of general trend in Lamb-wave attenuation, Al-1100 and glass also matched with theory. However, the data on Al-6061, Al-7475, and stainless steel 410 were limited, and the only conclusion that can be drawn is that the data on these three alloys were generally consistent with theoretical prediction.
# Conditional Dependencies and Position Fixing
## 6 Membership Functions and Uncertainty  
Equation (4) specifies the conversion of histograms featuring uncertainty. In the simplest case, the doubt is related to the histogram cell widths. As previously mentioned, the vertical variety of the cells also contributes to the overall uncertainty. Structures with the same cell heights are extremely unreliable. The case is rare, but it should be considered during analyses. In this situation, the resulting MF should take the form of a straight line that is parallel to the x-axis. In most practical cases, one can operate with \(\alpha\)-cuts (assuming \(\alpha\) equals 0.5) as the basic feature of the membership diagram. I present the basic issues that are important in the discussed field in Figure 6, which contains two histograms and uncertainty-dependent functions that reference the doubt in the cells of the inserted structural features (\(\Theta_{R1}\) and \(\Theta_{R2}\)). The figure also includes the alpha-cut range.  
The expansions of the function diagram are correlated with the uncertainty. Greater doubt means a wider MF range. The credibility of the evidence increases once its membership is closer to the singleton. According to the fuzzy-system-principle equation:  
\[\int_{-\infty}^{+\infty}f_{1}(x)dx*g_{H1}=\int_{-\infty}^{+\infty}f_{2}(x)dx*g _{H2}=S_{2}=S_{1}, \tag{5}\]  
which is valid and can be used to derive the next expression:  
\[\frac{d_{2}}{d_{1}}=\frac{\theta_{R2}}{\theta_{R1}}. \tag{6}\]  
Figure 6: Uncertainty-dependent membership functions for example case, where \(\Theta_{R2}>\Theta_{R1}\). \(f_{1}\) is the membership function for the \(\text{S}_{\text{i}}\) set feature of the \(i\)-th uncertainty level; \(d_{\text{i}}\) is the width of the 0.5 cut for the \(i\)-th membership function; \(\text{S}_{\text{i}}\) is the underlying crisp limited histogram of the \(i\)-th cell.  
Equation (6) expresses one of the most practical aspects of the introduced concept of the exploitation of the histogram uncertainty. Given the referential fuzzy object with its doubtfulness and MF range, the uncertainty attributed to the related structure allows us to deduce its membership diagram expansion.
## 4 Results and Discussion
was \(-15\)\({}^{\circ}\)C, the difference between the freezing point of the pure water droplet and the wall temperature was \(15\)\({}^{\circ}\)C, and the difference between the freezing point and the wall temperature of the \(S\) = 40 saline droplet was \(12.68\)\({}^{\circ}\)C. When the wall temperature decreased, the gap in the freezing point caused by the increase in salinity was smaller, so the influence of salinity became smaller. Therefore, when the wall temperature was \(-5\)\({}^{\circ}\)C, with the increase in salinity, the solidification time increased more and more. When the wall temperature was \(-15\)\({}^{\circ}\)C, with the increase in salinity, the increase in the solidification time changed less. The method of judging the sensitivity of the influence of uncertainties on the indicator was to calculate the sensitivity factor \(E\), as follows. \[|E|=|(\Delta A/A_{0})/(\Delta B/B_{0})| \tag{19}\]  
where \(\Delta A/A_{0}\) is the rate of change of the evaluation index, \(\Delta B/B_{0}\) is the rate of change of the uncertainty factor. The larger \(\mid\!E\!\mid\) is, the more obvious the impact of the change in the evaluation index on the uncertainty factor is; the smaller \(\mid\!E\!\mid\) is, the less obvious the impact is. Figure 9 shows the results of the sensitivity effect of \(T_{t}\) at different contact angles \(\theta\), different wall temperatures, and different salinity values \(S\) on the sensitivity of \(T_{t}\). According to the calculation of the sensitivity analysis, the sensitivity coefficient \(\mid\!E\!\mid\) was calculated, and the complete freezing time at a contact angle of \(\theta\) = \(76^{\circ}\), salinity \(S\) = 10, and wall temperature \(T_{w}\) = \(-5\)\({}^{\circ}\)C was selected as the reference value. It can be seen in the figure that the sensitivity coefficient of the salinity \(S\) varied from 0.12 to 0.50, the sensitivity coefficient of the wall temperature varied from 0.32 to 0.68, and the sensitivity coefficient of contact angle varied from 1.52 to 2.58. Compared to the salinity \(S\) and wall temperature \(T_{w}\), the change in the contact angle \(\theta\) had a sharper impact on the complete freezing time \(T_{t}\), and increasing the contact angle more effectively prolonged the freezing time of the droplets.
###### Abstract  
Due to the rapid development of shale gas, a system has been established that can utilize a considerable amount of data using the database system. As a result, many studies using various machine learning techniques were carried out to predict the productivity of shale gas reservoirs. In this study, a comprehensive analysis is performed for a machine learning method based on data-driven approaches that evaluates productivity for shale gas wells by using various parameters such as hydraulic fracturing and well completion in Eagle Ford shale gas field. Two techniques are used to improve the performance of the productivity prediction machine learning model developed in this study. First, the optimal input variables were selected by using the variables importance method (VIM). Second, cluster analysis was used to analyze the similarities in the datasets and recreate the machine learning models for each cluster to compare the training and test results. To predict productivity, we used random forest (RF), gradient boosting tree (GBM), and support vector machine (SVM) supervised learning models. Compared to other supervised learning models, RF, which is applied with the VIM, has the best prediction performance. The retraining model through cluster analysis has excellent predictive performance. The developed model and prediction workflow are considered useful for reservoir engineers planning of field development plan.  
shale gas; machine learning; data-driven; variables importance method; clustering analysis 2020