esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion of the Results
### Influence on the Bending Strength  
The bending strength results for both water/cement ratios are shown in Figure 7. For the 0.4 w/c ratio, the bending strength varied by \(-8.8\%\), \(+3.5\%\), and 0.3% for HS replacement percentages of 2.5%, 5%, and 10%, respectively, compared to the standard specimens. These results indicate that the strength tended to remain stable or even slightly increase as the HS replacement percentage increased.  
However, for the 0.5 w/c ratio, a progressive decrease in the strength was observed as the HS replacement percentage increased. This was consistent with the strong inverse correlation (\(r_{s}=-0.9\)) presented in Table 6, indicating that the bending strength decreased as the HS replacement percentage increased. These findings are in agreement with the results presented in Figure 7.  
The compressive strength results for a w/c ratio of 0.4 were found to be higher than those for a w/c ratio of 0.5. In the case of the latter, a strong inverse correlation was observed between the strength results and the docility of the mixture (indicated by a slump test), as well as the air content of the mixture. Both of these correlations were characterized by a strong negative [PERSON]'s correlation coefficient (\(r_{s}\)=\(-0.8\)), as shown in Table 6.
## 10 Simulation Results and Discussion
### Electricity Cost with Renewable Sources  
Figure 13 shows the electricity cost comparison of both the WDO and PSO algorithms and their variants with the RE source. In our proposed work, we assume that we have RE storage, and EMC can use either RE or grid energy. During each time interval, if the RE is greater than the aggregated energy required to complete a task, the scheduler utilizes RE energy only. Otherwise, if the required energy is greater than the available RE capacity, EMC will use both the RE, as well as the grid's energy. Therefore, the electricity cost using the RE source is comparatively less.
## 4 Remote Sensing Platforms Used in Peatland Research
### UAV-Based Remote Sensing
#### 4.3.3 Assessing Vegetation Structure and Disturbance Impacts  
Understanding vegetation structure and the impacts of disturbances is crucial for effective peatland management and conservation. [PERSON] et al. (2018) present a methodology for analysing vegetation structure in bog-healthland mosaics using UAV-based imagery [48]. Similarly, the study by [PERSON] and [PERSON] (2019) uses UAV-derived digital surface models to assist in understanding vegetation patterning in recovering blanket peatlands. Their study highlights the value of UAV remote sensing in characterizing complex wetland habitats [121]. [PERSON] et al. (2018) evaluate the impacts of low-impact seismic lines on hydrology, methane emissions, and surface morphology in boreal treed bogs. They emphasise the significant impacts of disturbances on peatland ecosystems [32].
## 3 Materials and Methods
### Satellite Data and Processing
For some of our models, we created additional bands through post-processing:
* Haze-optimized transformation (HOT), a linear combination of the blue and red bands: \[\text{HOT}=\text{blue}-0.5*\text{red}\]
* Whiteness [44], which consists of: \[\text{whiteness}=\frac{|\text{blue}-\text{visible}|+|\text{green}-\text{visible }|+|\text{red}-\text{visible}|}{\text{visible}}\] where \(\text{visible}=\frac{1}{3}(\text{blue}+\text{green}+\text{red})\)
* Normalized difference vegetation index (NDVI), a ratio between the red and near-infrared (NIR) bands, \[\text{NDVI}=\frac{\text{NIR}-\text{red}}{\text{NIR}+\text{red}}\]
* Normalized difference shortwave infrared, a ratio between the shortwave infrared (SWIR) bands, \[\text{normalized difference SWIR}=\frac{\text{SWIR2}-\text{SWIR1}}{\text{ SWIR2}+\text{SWIR1}}\]
* Normalized difference thermal infrared, a ratio between the shortwave infrared (TIR) bands, \[\text{normalized difference TIR}=\frac{\text{TIR2}-\text{TIR1}}{\text{ TIR2}+\text{TIR1}}\]  
These were generated for the satellites that have these available bands, i.e., normalized difference SWIR for Landsat 8 and Sentinel-2 but not for Planet. The band combinations provided additional beneficial features for some of our models. For example, HOT and whiteness acted as a basic plume mask. Our gradient-boosted tree models used all these bands, while the neural networks were more limited because of transfer learning, as described in Section 3.5. Additional uses of these synthetic bands for image selection will be detailed in Section 3.4.
## 3 Results
### Correlation Analysis of the CWUE on Drought Events  
In this study, we investigated the response of the CWUE under drought conditions, according to cultivated land vegetation types. MODIS GPP and ET were used to calculate the CWUE (gC kg\({}^{-1}\) H\({}_{2}\)O) [47]. For each 1 km grid cell, the monthly CWUE was calculated by combining the GPP and ET data for every eight days from 2001 to 2020. At the ecosystem level, the WUE is a comprehensive indicator of terrestrial biological and physical processes.  
From 2001 to 2020, the trend analysis of the CWUE was carried out based on the Theil-Sen Median and Mann-Kendall trend test method [48]. It can be seen from Figure 9 that the CWUE did not change significantly in most areas, and some coastal areas of the Shandong Peninsula increased significantly. By comparing Figures 7 and 9, it can be seen that in the HHHP, the significant decrease in the mISDI was more consistent with the increase in CWUE, but according to Table 8, most of the areas where the CWUE increases were insignificant. From Figures 2 and 9, it can be found that the areas with a significantly increased CWUE were mainly distributed in the eastern hilly area of the HHHP. Therefore, drought had an enhanced effect on the CWUE of the HHHP, and the enhancement of the CWUE in the eastern hilly area was more significant.  
mainly irrigated, most of the cultivated land will be irrigated when drought occurs, so the [PERSON]'s correlation analysis with time lag was selected. By comparing the lag period of one month to four months (Figure 10a-e), it was found that the correlation between drought and the CWUE was the strongest and positively correlated when the lag period was three months (Figures 10d and 11). According to Figure 10f, the left plain area had the highest significance (\(p<0.01\)). This is consistent with that of [PERSON] et al. [49], who concluded that there is a positive correlation between drought and the CWUE in the HHHP.  
Figure 11: Analysis of the proportions of pixel values of \(\leq\)\(-0.5\) (negative correlation) and pixel values of \(\geq\)0.5 (positive correlation) under different lags.  
Figure 10: (**a**) The correlation analysis without lag; (**b**) the correlation analysis with a lag of 1 month; (**c**) the correlation analysis with a lag of 2 months; (**d**) the correlation analysis with a lag of 3 months; (**e**) the correlation analysis with a lag of 4 months; and (**f**) a significant test with a lag of 3 months.
## 3 The Proposed EFD Framework Based on Convolutional GAN and History-State Ensemble
### Overall Framework  
With the pre-processing of dimensional reduction, the original high-dimensional AE signals can be processed by the proposed ensembled convolutional GAN. The general procedures of the proposed EFD framework are summarized as follows.  
**Step 1: Data acquisition.** AE signals are acquired at fixed time intervals from sensors mounted on the test machine. The signals received at the initial health stage of the experiment are treated as the training set, and the remaining serve as the test set.  
**Step 2: Dimension reduction** The MVW is first applied to the raw signals to reduce the dimension.  
**Step 3: Model set-up.**  
1. Offline training stage: the pre-processed training data are fed into the convolutional GAN. During the training phase, the history states are recorded at fixed training epochs. Thus, \(N\) base models are obtained.
2. Setup threshold: the training set is fed into the Discriminator only, and \(N\) scores are generated by the base models for each sample. The \(EHI\) and the threshold are calculated by Equations (17) and (18).  
**Step 4: Online test stage.** The samples in the test set are sequentially fed into the Discriminator to calculate the \(EHI\). The \(EHI\) values exceeding the threshold are considered a fault alarm.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 5 Discussion
### CNN Approach  
Another approach tested was the convolutional neural network (CNN). The CNN applied directly to the four S1 derived temporal series showed a remarkable capability to classify irrigated/non-irrigated areas. It is clearly observed that the CNN-based classification approach has a better performance than the classical RF machine learning using either the PC dimensions or WT coefficients. The CNN approach shows higher overall accuracy (94.1%) than the PC-RF (90.7%) and the WT-RF (89.1%). In terms of the F-score and the Kappa coefficient, the CNN is also superior to the RF classifier. The gain in the performance offered by the CNN is clearly visible on the irrigated class where the precision, recall and F-score are higher for CNN than that for the RF classifier. For the non-irrigated class, the obtained accuracies by the two approaches are quite similar. Thus, the increase of the overall accuracy when using the CNN approach is mainly caused by better detection of irrigated plots.
## 3 Broadband Stator Model for Data Storage
### High-Frequency Modeling of PMSM  
Classical electrical equivalent circuit (EEC) models of electrical machines, either sound [38,39] or tailored to the study of interturn short-circuit faults [40], are external machine models. They can also be described as \"low-frequency\", since high-frequency phenomena, which are of less importance for the purpose of the modeling, are generally neglected. As mentioned in the previous section and recalled in [41], the HF behavior is particularly sensitive to \"parasitic\" effects linked to the quality of the conductor insulation, which means that it must be properly modeled to monitor the evolution of the winding state of health. There are several approaches in the literature to model and/or characterize the HF behavior of a PMSM. These have interesting properties for identifying behavioral deviations associated with insulator aging. They can be divided into two categories: those based on a physical model of the machine phases and those based on an experimental frequency response of the machine behavior. The most popular are HF EEC models [19,25,42,43,44,45,42,43]. There are many such methods in the literature, mainly because they can be used to evaluate either overvoltages at the terminals of inverter-fed machines [46], CM currents circulating through bearings [47], or electromagnetic interference phenomena within power conversion chains [35]. The accuracy of these methods depends on their complexity, i.e., the number of parameters to be evaluated. Depending on the purpose of the model and the accuracy required, these parameters are calculated using hybrid methods that combine analytical and numerical calculations with experimental characterization of the machine behavior [48].  
There are also transfer function approaches to the impedance frequency response of PMSM, as presented in [17,35]. Finally, there is an HF approach based on the physical design of the machine phases, which takes into account capacitive coupling phenomena between coils and with the machine ground [13].  
The next parts will describe these different HF modeling approaches in detail.
## 4 Results and Discussion
### J48 Classifier Results  
The J48 algorithm has two important parameters: The first is C (the confidence factor) which is the level used to define the confidence interval. A lower C value will give a wider interval, meaning a more negative estimate, which will result in heavier pruning. The confidence factor value is the parameter basis for pruning after the decision tree has been established. The smaller the value, the more extensive the pruning. The second is the M parameter which is the minimum number of instances in the two most popular branches. The classifier displays the results in the text box next to the selection button, and shows (J48-C 0.05-M 2), (J48-C 0.25-M 2), (J48-C 0.4-M 2), (J48-C 0.05-M 50), (J 48-C 0.25-M 50), (J 48-C 0.4-M 50), (J 48-C 0.55-M 500), (J 48-C 0.25-M 500), (J 48-C 0.4-M 500) as the parameter settings. The results in Table 4 show that the calculation speed tends to be fast, but the recognition rate is generally poor, and the data after dimension reduction (J48-C 0.05 to 0.4-M 500) had the best recognition rate, reaching 86.02%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Classifier**} & \multirow{2}{*}{**Data**} & **First Group** & **Second Group** & **Third Group** \\  & & **(Correct Rate/Time)** & **(Correct Rate/Time)** & **(Correct Rate/Time)** \\ \hline \multirow{3}{*}{J48(C = 0.05, M = 2)} & Original data & 59.30\% & 51.08\% & 44.20\% \\  & & 0.19 (s) & 0.2 (s) & 0.14 (s) \\ \cline{2-5}  & Data after Dimension & 48.32\% & 50.94\% & 51.08\% \\  & Reduction & 0.13 (s) & 0.14 (s) & 0.11 (s) \\ \hline \multirow{3}{*}{J48(C = 0.25, M = 2)} & Original data & 59.30\% & 51.08\% & 44.20\% \\  & Reduction & 0.16 (s) & 0.2 (s) & 0.17 (s) \\ \cline{2-5}  & Data after Dimension & 48.32\% & 53.02\% & 50.22\% \\  & Reduction & 0.13 (s) & 0.14 (s) & 0.11 (s) \\ \hline \multirow{3}{*}{J48(C = 0.4, M = 2)} & Original data & 52.30\% & 51.62\% & 44.70\% \\  & Reduction & 0.16 (s) & 0.16 (s) & 0.17 (s) \\ \hline \multirow{3}{*}{J48(C = 0.05, M = 50)} & Original data & 49.22\% & 52.64\% & 53.24\% \\  & Reduction & 0.13 (s) & 0.14(s) & 0.11(s) \\ \hline \hline \end{tabular}
\end{table}
Table 4: J48 training and detection results  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Types of Classifier**} & \multirow{2}{*}{**Data**} & **First Group** & **Second Group** & **Third Group** \\  & & **(Correct Rate/Time)** & **(Correct Rate/Time)** & **(Correct Rate/Time)** \\ \hline \multirow{4}{*}{Naive Bayes} & Original data & 90.00\% & 81.28\% & 80.90\% \\  & & 0.67 (s) & 0.61 (s) & 0.61 (s) \\ \cline{2-5}  & Data after dimension & 91.54\% & 79.94\% & 79.46\% \\ \cline{2-5}  & reduction & 0.44 (s) & 0.64 (s) & 0.47 (s) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training and detection results for naive Bayes
## 2 Study Area and Datasets
### Data Collection  
To observe the ground deformation rate of the Weihe Fault, the SAR, GNSS, and leveling data were collected. For the SAR data, 26 Sentinel-1A Interferometric Wide Swath (IW) single-look complexes (SLC) spanning from 2015 to 2019 were downloaded from the ESA's Sentinels Scientific Data Hub (\"[[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu))\"(18 December 2022)). The SAR data were processed by the stacking-InSAR technique to obtain the high-spatial-resolution LOS deformation rate from 2015 to 2019. Table 1 shows the parameters of these SAR images. The shuttle radar topography mission (SRTM) digital elevation model (DEM), which had a spatial resolution of 30 m, was acquired as an external DEM to remove the topographic phase from the differential interferograms. The GNSS-derived horizontal deformation rates, with a temporal span of 1999 to 2016, were acquired from the Crustal Movement Observation Network of China (CMONOC) [23], as shown by the blue arrows in Figure 1. The longer time span of GNSS data enabled more accurate velocity estimates. The SAR-derived and GNSS-derived deformation rates were further combined to produce the three-dimensional deformation rates. The leveling-derived deformation rates in the vertical direction were mainly from the network of leveling benchmarks used for seismic applications across the major active faults in China and those surveyed by the China Earthquake Administration (CEA) [24], as shown by the gray arrows in Figure 1. In this study, the leveling deformation rates were mainly used for evaluating the vertical deformation rates produced by the integration of SAR and GNSS.
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 4 Conclusions  
In this work, we propose an AM-MCNN-SBiLSTM prediction model, which utilizes feature weighting and ensemble modeling, for efficient and accurate wind power forecasting. The attention mechanism is used to assign weights to each input feature, effectively addressing the issue where the model fails to discern differences in the importance of input data. The weighted reconstructed feature sequence facilitates the model to extract more key information. By utilizing an MCNN with three types of convolutional kernels and stacking three layers of BiLSTM (SBiLSTM), the model fully explores the multi-scale information of the feature sequence and its long-term trends. Experiments are conducted using actual operational data of wind turbines and compared with the prediction performance of other models. It is demonstrated that the model proposed in this paper exhibits higher predictive accuracy. It demonstrates stronger robustness in experiments with different time steps and longer time ranges and is better able to handle the actual fluctuations of wind power. Thus, this approach can offer more dependable short-term wind power prediction, serving as a reliable point of reference for consistent operation and power allocation in wind farms.  
Although the above methods have good predictive performance, their training is complex. We conducted numerous experiments in this study to identify the optimal model parameters, which consumed a significant amount of time. Intelligent algorithms can improve the efficiency of model training. In future research, we plan to use different optimization algorithms to optimize the parameters of the prediction model and to continue to explore the ability of different combination models to extract features, in order to improve prediction accuracy.  
Conceptualization, L.Z. and D.Y.; methodology, L.Z.; software, L.Z.; validation, L.Z. and K.Z.; formal analysis, L.Z. and D.Y.; investigation, L.Z. and K.Z.; data curation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z. and D.Y.; supervision, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant SJCX24_1668.  
Not applicable.
## 4 Conclusions  
In conclusion, we performed XRD, SEM-EDS, and FT-IR experiments to characterize layered double hydroxides (LDHs) modified with EDTA and to identify the prepared LDH-NO\({}_{3}\) (L1) and LDH-EDTA (L2). In addition, based on these experimental results, the adsorption capabilities and mechanisms of REEs ions on L1 and L2 were experimentally accessed. It was shown that both La(III) and Eu(III) could rapidly be adsorbed, and that the adsorption capacity of L2 was larger than that of L1 no matter whether the concept of adsorption capacity or that of the PC was used. In addition, comparing with other adsorbents, the PC of both L2-Eu and L2-La are relatively larger. It indicates that LDH intercalated by EDTA has superior adsorption performance. The complexation of REEs ions with EDTA (i.e., L2) brought about a lower zeta potential, which indicates that the former plays the role more dominant than the latter and that the latter is just a secondary result stemming from the former. In accord with this, quantum chemistry calculations showed that the complexation actually leads to the negatively charged species being adsorbed on LDHs, which promotes the adsorption of the complex compared with REEs without complexation. They also showed that the species in the presence of EDTA is more stable than that in its absence and that the intercalation of EDTA does indeed improve the stability of the adsorbed species. Finally, from our numerical results of adsorption and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Target Ions**} & \multirow{2}{*}{**Materials**} & **Final** & **adsorption** & **Partition** & \\  & & **Concentration** & **Capacity** & **Coefficient** & **Reference** \\  & & **(ug\(\cdot\)L\({}^{-1}\))** & **(ug\(\cdot\)g\({}^{-1}\))** & **(ug\(\cdot\)g\({}^{-1}\)\(\cdot\)uM\({}^{-1}\))** & \\ \hline \multirow{5}{*}{Eu} & Activated carbon & 61.2 & 92.7 & 230 & [45] \\  & Buccinum tenuissimum & 34.7 & 98 & 429 & [8] \\  & Sargassum hemiphyllum & 20.7 & 119 & 746 & [7] \\  & Fe\({}_{3}\)O\({}_{4}\)@MnOX & \(28.1\times 10^{3}\) & \(138\times 10^{3}\) & 745 & [46] \\  & ZSM-5 zeolite & 30.4 & 24.2 & 121 & [47] \\  & L1 (ZnAl-NO\({}_{3}\)) & 83.4 & 24.9 & 45.5 & This study \\  & L2 (ZnAl-EDTA) & 26.4 & 145 & 834 & This study \\ \hline \multirow{5}{*}{La} & Activated carbon & 52.7 & 71.0 & 205 & [45] \\  & Buccinum tenuissimum & 34.0 & 99.0 & 401 & [8] \\  & Sargassum hemiphyllum & 35.1 & 97.3 & 421 & [7] \\  & Schizymenia dubyi & 39.7 & 90.5 & 392 & [7] \\  & Lanthanide MOF & \(62.0\times 10^{3}\) & \(73.0\times 10^{3}\) & 179 & [48] \\  & L1 (ZnAl-NO\({}_{3}\)) & 86.1 & 20.8 & 33.6 & This study \\  & L2 (ZnAl-EDTA) & 17.7 & 124 & 422 & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison of adsorption properties of various of adsorbent. desorption rates, it was suggested that the recovery efficiency is large if the rate of desorption is large. Both the quantum chemistry calculation results and the adsorption and desorption rate results are consistent with the experimental ones. The LDH-EDTA synthesized in this work have a high affinity for removing REEs ions.
# Contrast-Enhanced Ultrasound Imaging Based on Bubble Region Detection
## 2 Materials and Methods
### Spatial Domain
#### 2.2.3 Implementation of Bubble Region Detection  
The calculation procedure of the bubble region detection method can be summarized as follows:  
1. BWT was first conducted to enhance the UCA signal, and the received RF signal was replaced by the wavelet coefficients under the optimal scale factor.
2. The covariance matrix was computed according to Equation (4).
3. The MV weight was obtained by minimizing the array noise output energy, which can be expressed as: \[W_{MV}=\frac{R^{-1}d}{d^{H}R^{-1}d}\] (7) where \(d\) is the direction vector; and \(w\) is the weight vector.
4. The eigen-decomposition of the covariance matrix was required: \[R=U\Lambda U^{H}=U_{S}\Lambda_{S}U_{S}^{H}+U_{P}\Lambda_{P}U_{P}^{H}=R_{S}+R_{P}\] (8)  
Figure 4: The maximum eigenspace-based coherence factor (ESBCF) value index curve of different depths.  
where subscript \(S\) denotes the signal and subscript \(P\) is the noise subspace. The signal subspace is comprised of eigenvectors corresponding to the largest few eigenvalues (\(\alpha\) times greater than \(\lambda_{I}\) or \(\beta\) times greater than \(\lambda_{L}\)).
5. Based on the last step, the maximum eigenvalue and ESBCF for each imaging point was obtained. Following that, we found the maximum ESBCF index.
6. We set the maximum eigenvalue and ESBCF index threshold to determine whether it is the UCA region or not.
7. The ESBMV weight comes to: \[W_{ESBMV}=U_{SU}U_{SU}^{H}W_{MV}\] (9) where \(U_{SU}\) is the eigenvectors of the signal subspace of the detected UCA region.
8. The region detection output is given by: \[S(k)=\frac{1}{M-L+1}\sum_{\mathrm{p}=1}^{M-L+1}W_{ESBMV}^{H}x_{d}^{p}(k)\] (10)
9. The final image can be formed after the signals are enveloped and the logarithmic transformation is applied.