esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 2 Materials and Methods
### Overall Prediction Framework for the Proposed Method: A Real Case
\[R^{2}=1-\frac{\sum_{i=1}^{N}(\hat{p}_{i}-p_{i})^{2}}{\sum_{i=1}^{N}(\overline{p}-p _{i})^{2}} \tag{20}\]  
\(p_{i}\) and \(\widehat{p_{i}}\) are the actual and predicted values of wind power at time \(i\), \(\overline{p}\) is the average value, and \(N\) is the total number of predicted values. The smaller the error indicators of _MAE_, _MSE_, and _RMSE_ are, the better the performance of the model is, while \(R^{2}\) value is the opposite. \begin{table}
\begin{tabular}{c c} \hline
**Abbreviation** & **Specific Description** \\ \hline NWP & Numerical weather prediction \\ ARMA & Autoregressive moving average \\ ARIMA & Autoregressive integrated moving average \\ ANN & Artificial neural network \\ SVM & Support vector machine \\ RNN & Recurrent neural network \\ LSTM & Long short-term memory \\ GRU & Gated recurrent unit \\ CNN & Convolutional neural network \\ BiLSTM & Bidirectional Long Short-Term Memory \\ KDE & Kernel density estimation \\ VMD & Variational mode decomposition \\ WPE & Weighted permutation entropy \\ MCNN & Multi-convolutional neural networks \\ SBiLSTM & Stacked bidirectional long short-term memory \\ AM & Attention mechanism \\ MAE & Mean absolute error \\ MSE & Mean squared error \\ RMSE & Root mean squared error \\ R\({}^{2}\) & Coefficient of determination \\ \hline \end{tabular}
\end{table} 
Table 1: Nomenclature. \begin{table}
\begin{tabular}{c c} \hline
**Model** & **Specific Description** \\ \hline ANN & layer = 1; hidden neurons = 16 \\ \hline CNN & filters = 32; kernel size = 2; pooling kernel = 2; stride = 1 \\ \hline LSTM & layer = 1; hidden neurons = 128 \\ \hline BiLSTM & layer = 1; hidden neurons = 128 \\ \hline MCNN & Conv1 & filters = 32; kernel size = 2; stride = 1 \\  & Pooling1 & kernel size = 2; stride = 1 \\  & Conv2 & filters = 64; kernel size = 2; stride = 1 \\  & Pooling2 & kernel size = 2; stride = 1 \\  & Conv3 & filters = 32; kernel size = 3; stride = 1 \\  & Pooling3 & kernel size = 2; stride = 1 \\  & Conv4 & filters = 64; kernel size = 3; stride = 1 \\  & Pooling4 & kernel size = 2; stride = 1 \\  & Conv5 & filters = 32; kernel size = 5; stride = 1 \\  & Pooling5 & kernel size = 2; stride = 1 \\  & Conv6 & filters = 64; kernel size = 5; stride = 1 \\  & Pooling6 & kernel size = 2; stride = 1 \\ \hline SBiLSTM & layers = 3; hidden neurons = 128 \\ \hline dropout & 0.01 \\ \hline \end{tabular}
\end{table} 
Table 2: All model network parameters’ selection.
# Target Detection of Passive Bistatic Radar under the Condition of Impure Reference Signal
###### Abstract  
This paper addresses the issue of the reference signal being contaminated by target echoes due to factors such as wide beamwidths, beam pointing errors, or site errors in passive bistatic radar (PBR). Such contamination can lead to reference signal impurity and subsequently degrade the performance of traditional object detection methods. To overcome this challenge, a novel target detection method centered around the reference signal's purification is presented. The proposed method involves the modeling, analysis, localization, and purification of the impure reference signal. First, a new signal model incorporating the impure reference signal is established, followed by a comprehensive analysis of its impact on target detection. Then, leveraging the valuable information hidden in the impure reference signal, target echoes within the impure reference signal are localized and reconstructed. Finally, the reconstructed target echoes are subtracted from the original impure reference signal to purify the reference signal. Based on the purified reference signal, traditional target detection is performed to detect the expected targets. The core advantage of this method lies in its efficient reconstruction and removal of the impure terms within the reference signal. The effectiveness of the proposed method is demonstrated through a series of simulation results.  
passive bistatic radar (PBR); signal purification; signal localization; signal reconstruction Article
## 4 Application of Hyperchaotic Sequence in Secure Communication
### Transmitter Structure  
The transmission signal structure in RM-DCSK is depicted in Figure 6, where a frame signal is made up with two neighboring slots. The detailed structure of the sender of the RM-DCSK is plotted in Figure 7. Take the \(k\) frame signal for example, where the chaotic sequence \(x_{k}\) is sent on the basis of information bit \(b_{2k}\in\{-1,+1\}\) in the first slot. However, the signal of the second time slot is the sum of two parts, the first part is the modulation of information bit \(b_{2k+1}\in\{-1,+1\}\) and the transmission signal of the first time slot; the second part is the chaotic sequence of this slot. We set the length of chaotic sequence to \(M\), and the mathematical expression of the \(k\)-frame signal \(s_{i}\) is as follows  
\[s_{i}=\begin{cases}b_{2k}x_{i},2 kM<i\leq(2k+1)M\\ b_{2k+1}b_{2k}x_{i-M}+x_{i},(2k+1)M<i\leq 2(k+1)M\end{cases} \tag{15}\]  
where \(x_{i}\) satisfies the following conditions  
\[x_{(2k+1)M+m}=x_{2(k+1)M+m},0<m\leq M;k\in\mathbb{Z}. \tag{16}\]  
Figure 5: The hyperchaotic sequence produced by three models: (**a**) 2D-DMLM; (**b**) 2D-DMSM; (**c**) 2D-DMTM.  
Figure 6: Structure of the transmission signal in RM-DCSK.
# Web3 DGIS-Based System for Reservoir Landslide Monitoring and Early Warning
## 4 Key Technologies  
### Multi-Data Organization and Management  
Landslide monitoring and early warning systems involves a large volume of data from different sources and of various types, and with complex relationships between them. They include both static and dynamic real-time monitoring data, lattice and arrow data, and non-spatial data on charts, texts, photos, and videos. In order to guarantee system functionality, a reasonable data model was needed, in which data were effectively organized and managed. This required a number of key technologies to be applied to the construction of the landslide special data pool, and to the organization, transport, and expression of three-dimensional scene data.
# A Method for Ferulic Acid Production from Rice Bran Oil Soapstock Using a Homogenous System
###### Abstract  
Ferulic acid (FA) is widely used as an antioxidant, e.g., as a Ultraviolet (UV) protectant in cosmetics and in various medical applications. It has been produced by the hydrolysis of \(\gamma\)-oryzanol found in rice bran oil soapstock. In this study, the base-catalyzed, homogenous hydrolysis of \(\gamma\)-oryzanol was conducted using various ratios of potassium hydroxide (KOH) to \(\gamma\)-oryzanol, initial concentrations of \(\gamma\)-oryzanol in the reaction mixture, and ratios of ethanol (EtOH) (as cosolvent)/ethyl acetate (EtOAc) (\(\gamma\)-oryzanol solution). Acceleration of the reaction using a planar type of ultrasound sonicator (78 and 130 kHz) at different reaction temperatures was explored. By using a heating method, the 80% yield of FA was attained at 75 \({}^{\circ}\)C in 4 h under homogeneous conditions (initial concentration of \(\gamma\)-oryzanol 12 mg/mL, the KOH/\(\gamma\)-oryzanol ratio (_wt/wt_) 10/1, and EtOH/EtOAc ratio (_v/v_) 5/1). With the assistance of 78 and 130 kHz irradiation, the yields reached 90%. The heating method was applied for the \(\gamma\)-oryzanol-containing extract prepared from rice bran oil soapstock. From soapstock, the 74.3% yield of FA was obtained, but 20% of the _trans_-FA in the reaction mixture was transformed into _cis_-form within one month.  
f 2017 7 796 10.3390/app7080796
## 3 Results and Discussions  
The results of this study and valuable discussions are presented. The results are divided into four parts in line with the objectives of the study. The first part concentrates on the quantification and analysis of the measured path loss in comparison with the standard path loss models. The second part presents the enhanced prediction using the developed hybrid Random Forest and Particle Swarm (RF-PS) optimization method over the standard path loss optimization methods. The third part provides detailed results using the developed (RF-PS) optimization method for path loss prediction in other study locations used for model validation.  
There is a need to quantify and compare measured path loss in comparison with the standard path loss models before employing the proposed RF-PS method for path loss model optimization. Thus, this first part concentrates on the quantification and analysis of the measured path loss in comparison with the standard path loss models such as the COST 231 Hata and ITU-R M.2412-0 models. As shown in Figures 3-6, the graphs reveal the level of practical measured signal path loss as a function of measurement distances across the four study locations. The locations include: Asaba, Ontisha, Awka, and Agbor. In each of the aforementioned study locations, one eNodeB site each was engaged to obtain the path loss data. From the graphs, the levels of the measured path loss in Asaba, Ontisha, Awka, and Agbor vary between: 90-144 dB, 100-150 dB, 110-140 dB and 106-120 dB, respectively. For the standard path loss models, the COST 231 Hata and ITU-R M.2412-0 models vary between 200 and 240 dB and between 171 and 175 dB, respectively. From the results, it is noticeably clear that loss values attained by the standard models are quite higher than the ones obtained through field measurements. In terms of accuracy, the results also imply that the standard COST 231 Hata and ITU-R M.2412-0 models over predicted the measured path loss values across the four study locations. As a case in point, the COST 231 model achieved 78.27, 83.75, 78.27 and 82.77 dB RMSE values, and the ITU-R M.2412-0 model gives 27.29, 34.39, 64, 28, 32.76 dB values, respectively. The higher path loss values produced by the standard models may be ascribed to the physical terrain and topographical differences between the actual locations where the measurement was conducted and the terrain characterization where the models were initially developed. Thus, the need to fine-tune the existing standard models for reliable and improved prediction of the measured path loss data is self-evident and worthy of investigation.  
Figure 4: Measured Path loss values versus distance and Prediction comparison with standard COST 231 and ITU-R M.2412-0 model in site 1 Location 2.  
Figure 3: Measured Path loss values versus distance and Prediction comparison with standard COST 231 and ITU-R M.2412-0 model in site 1 Location 1.
## 4 Conclusions  
The use of chirped pulses in hollow-fiber pulse compression is proposed as a means to further increase the energy and intensity of few-cycle pulses. In this paper, we derived analytical expressions for chirped-pulse SPM, focusing on the relationship between the initial chirp and the dispersion remaining in the output pulse. The results show that a strongly chirped pulse with large initial GDD retains the initial TOD. On the other hand, the initial TOD decreases in a FTL or weakly chirped pulse. Such characteristics are convenient for dispersion compensation in the SPM-based pulse compression technique. It has generally been considered that pre-compensation of the chirp in an optical system including nonlinear processes such as SPM is difficult. However, the present result shows that dispersion can be roughly compensated for by a pair of prisms or chirped mirrors after a hollow fiber, while the fine-adjustment can be carried out with a programmable phase modulator prior to the hollow fiber, or even before a chirped-pulse amplifier.
# Line Structure Extraction from LiDAR Point Cloud Based on the Persistence of Tensor Feature
## 3 Methodology
### The Algorithm of the Line Structure Extraction Framework  
The line structure extraction process is consisted of 4 steps. First, compute the initial tensor using the tangent space and decompose geometric features in different dimensions. Second, revote the tensor to get refined results and construct the _LCS_. Third, calculate the Morse-Smale complex for the _LCS_ and get persistent features. Finally, extract the persistent line structure based on connection relations of critical points. The algorithm is depicted in Algorithm 1.  
```
Line structure extraction framework \(\textbf{LSE}(P,r,\sigma,\kappa,\delta)\) INPUT: point cloud \(P\), searching distance for neighborhood \(r\), saliency thresholds \(\sigma\left\{\sigma_{surface},\sigma_{line},\sigma_{point}\right\}\), resampled grid \(\kappa\), persistence threshold for line segment \(\delta\) OUTPUT: line structure with connection relations \(\{\iota\}\)//lstep 1: compute the initial tensor and decompose geometric features in different dimensions FOREACH\(p\) in \(P\)//lfor each point in \(P\)\(\Omega\{p_{i}\in P|norm(p_{i}-p)\leq r\}=\text{Neighbor}(p,r)\); \(T^{p}=\text{TensorVoting}(p,\Omega)\);//compute initial tensor based on Equations (1)-(3) \(\left\{\frac{\delta}{\epsilon},\lambda\right\}^{p}=\text{GeoFeatureDec}(T^{p})\);//eigen decomposition for tensor \(T^{p}\) END //lstep 2: revote tensor and compute refined geometric feature FOREACH\(p\) in \(P\)//lfor each point in \(P\) FOREACH\(d\) in \(N\)//for each dimension in \(N\), based on Equations (4)-(7) \(s_{d}=\text{SaliencyInDim}(\left\{\begin{smallmatrix}\sigma\\ \epsilon,\lambda\end{smallmatrix}\right\}^{p})\);//compute dimensional saliency \(T_{d}=\text{TensorInDim}(\left\{\begin{smallmatrix}\sigma\\ \epsilon,\lambda\end{smallmatrix}\right\}^{p})\);//compute dimensional tensor END \(T^{p}=\text{AggTensor}\) (\(s_{d},T_{d}\));//aggregate tensor in each dimension based on Equation (8) \(T=\text{RevoteTensorFromNeig}(T^{p\in\Omega})\);//refine the voting result based on Equation (9) END //lstep 3: represent geometric feature and construct the LCS \(s\left\{s_{surface},s_{line},s_{point}\right\}=\text{DimSaliency}(T)\);//compute dimensional saliency for each point \(LCS=\text{LineCandidateSubset}(s,\sigma)\);//compute the LCS based on Equation (10) //lstep 4: extract line structure based on the discrete Morse theory. \(\left\{LCS^{\prime},S^{\prime}\right\}=\text{ResampleLCS}(P,\kappa,\) LCS);//example the LCS based on Equation (11) \(LinePer=\text{MorseSmaleComplex}(LCS^{\prime},S^{\prime})\);//compute line structure based on Equation (12) \(LineSeg[P_{critical},l_{connection}]=\text{LineExtract}(LinePer,\delta)\);//extract line segment using threshold \(LineStr[\{\iota\}=\text{BuildLineStructure}(LineSeg)]\);//build line structure RETURN Line Structure{\(\iota\}\)
```  
**Algorithm 1** The algorithm of the line structure extraction framework.
## 3 Performance Evaluation Criterion  
In this study, to test the accuracy of the proposed approach, six evaluation indexes including MAE, MAPE, RMSE, WI, E\({}_{\text{NS}}\), and E\({}_{\text{LM}}\) are applied for evaluating forecasting accuracy. These indexes are shown as follows in Table 1:  
Respectively, where \(N\) is the total output samples; \(y_{i}\) represents the actual series; and \(yp_{i}\) stands for the prediction results. For WI, E\({}_{\text{NS}}\), and E\({}_{\text{LM}}\), when the values of them are closer to 1, the model achieves a higher performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Definition** & **Equation** \\ \hline MAE & The mean absolute error of \(N\) forecasting results & MAE = \(\frac{1}{N}\sum\limits_{i=1}^{N}|yp_{i}-y_{i}|\) \\ \hline MAPE & The average of \(N\) absolute percentage error & MAPE = \(\frac{1}{N}\sum\limits_{i=1}^{N}\left|\frac{yp_{i}-y_{i}}{y_{i}}\right|\times 1 00\%\) \\ \hline RMSE & The square root of the average of the error square & RMSE = \(\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(yp_{i}-y_{i})^{2}}\) \\ \hline WI & Willmott’s Index & WI = \(1-[\frac{\sum\limits_{i=1}^{N}(yp_{i}-y_{i})^{2}}{\sum\limits_{i=1}^{N}(yp_{i} -\overline{y}|+|y_{i}-\overline{y}|)^{2}}]\), \(0\leq\text{WI}\leq 1\) \\ \hline E\({}_{\text{NS}}\) & Nash-Sutcliffe coefficient & E\({}_{\text{NS}}=1-[\frac{[\frac{1}{N}}{\sum\limits_{i=1}^{N}(yp_{i}-y_{i})^{2}} ],\infty\leq\text{E}_{\text{NS}}\leq 1\) \\ \hline E\({}_{\text{LM}}\) & Legates and McCabe Index & E\({}_{\text{LM}}=1-[\frac{\sum\limits_{i=1}^{N}|y_{i}-yp_{i}|}{\sum\limits_{i=1}^{N}|y_{i}- \overline{y}|}]\), \(\infty\leq\text{E}_{\text{LM}}\leq 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The description of the error evaluation indexes.
# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 6 Discussion  
This study proposed a novel segmentation framework that integrates boundary-focused data augmentation, BAT, and Geometry-aware Loss to achieve precise delineation of OD and OC boundaries in fundus images. Each of the three components independently contributed to performance improvements, and when combined, they produced a synergistic effect that maximized overall performance.  
Experimental results showed that the proposed model consistently outperformed existing SOTA models across diverse datasets, including DRIONS-DB, Drishti-GS, REFUGE, G1020, and ORIGA. The model maintained high performance despite variations in ethnicity, imaging devices, resolution, and pathology levels across datasets, suggesting strong generalization capability against diverse boundary deformations and structural differences.  
The improvement in fine segmentation performance for small structures like the OC supports the effectiveness of the BAT module. BAT explicitly learns the structural inclusion relationship by independently generating the Query and Key from the OC and OD feature maps, respectively, enabling more precise boundary recognition compared to conventional CNN and Transformer-based models. Geometry-aware Loss further enhanced boundary precision by correcting fine errors near boundaries that conventional Dice Loss-based approaches tend to miss.  
While CNN-based models (U-Net, Attention U-Net, and DeepLabV3+) excel at local feature extraction, they struggle to explicitly model structural inclusion relationships. Transformer-based models (TransUNet and Swin-Unet) capture global context, but conventional self-attention is not optimized for distinguishing boundaries between different structures. In contrast, the BAT module provides fundamental differentiation by performing boundary-focused attention based on structural inclusion.  
Ablation study results showed that data augmentation alone improved the performance by 1.76%, adding Geometry-aware Loss resulted in an additional 2.19% improvement, and applying BAT led to a further 4.97% gain. The full model integrating all components achieved a total improvement of 7.15% (from 0.9177 to 0.9892). These resultsempirically confirm that each component yields meaningful improvements individually and produces a complementary synergistic effect when combined.  
Visual analysis also demonstrated that the proposed model generated smoother and more consistent boundary representations for the OD and OC compared to conventional models. Particularly in ROI-based analysis, BAT-based structural learning showed clear superiority in boundary recognition and fine local boundary refinement compared to existing approaches.
## 4 Results and Discussion
### Long-Term (3 h) Measurements Stability  
In order to fully evaluate the long-term measurements stability of the sensor, measurements of standard CH\({}_{4}\) gas with the concentration of 1000 ppm were performed for 3 h with 1 s sampling intervals. Every 10 s, data were averaged to form a total of 1080 data. The concentration data were collected by the sensor under the DAS mode with the white noise perturbation and the WMS mode with the white noise perturbation, as shown in Figure 9. The results indicated that the concentration measurement in WMS mode was more stable than DAS mode. The largest measurement errors for WMS mode and DAS mode were 20 ppm and 40 ppm, respectively. The measured concentration had shifted with the long-term operation of the sensor. It might have been the system temperature rising.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & **SNR** & **MDL** & **Curve Fitting** \\ \hline DA-OA-ICOS & 16.2 & 2.2 ppm & 0.99954 \\ WM-OA-ICOS & 113.9 & 1.2 ppm & 0.99983 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of the two measurement modes.  
Figure 8: (**a**) Raw concentration data collected by the sensor under the original signal (black) and the white noise perturbation of 30% modulation depth (red); (**b**) the Allan deviation of the original signal (black) and the white noise perturbation (red).
## 4 Discussion and Conclusions
We must also mention that handling these large quantities of statistical yearbook data is laborious. If using other natural and socioeconomic data sources, dealing with data will be simpler and easier. It is also challenging to apply a model at a provincial scale (Henan Province) because previous studies mostly applied their models to a single basin that only occupied a small area. Applying a model to Henan Province required larger data sources and an increased workload. However, with the help of ArcGIS, most of the work was done in a graphical workspace. In addition to quantitative analysis, we conducted a sensitivity analysis on the dissolved pollution and the absorbed pollution. [PERSON]'s method, a global sensitivity analysis method based on variance, is capable of effectively handling nonlinear functions within models. Using [PERSON]'s method, the most sensitive parameters affecting the targeted output variable were identified. For dissolved pollution, the most influential parameters were the standard runoff coefficient \(\epsilon_{0}\), natural factor correction coefficient \(\text{N}_{\text{i}}\), newly produced TN pollutants per area \(\text{Q}_{\text{IN}}\), and runoff coefficient \(\varepsilon\). These four parameters had the most significant effect on the dissolved pollution. Moreover, the analysis indicates that the runoff process had the greatest influence on the uncertainty of dissolved pollution, as runoff was the main driving force of NPSP. Furthermore, FVC was the second-most important parameter, as a high vegetation cover intensity helped prevent dissolved pollution. For the absorbed pollution, the most influential parameters were the rainfall erosion factor R, water and soil conservation factor P, slope degree factor S, and slope length factor L. From the analysis results, it is apparent that rainfall erosion was strongly related to the absorbed pollution, as the erosion of soil by rainfall led to the occurrence of absorbed pollution. Additionally, the water and soil conservation factor significantly influenced both the soil erosion process and the absorbed pollution. Lastly, S and L were also related to the absorbed pollution, as they represented the shape of the soil cover. In this article, DPeRS was used to estimate NPSP in Henan Province, though there are other similar models that could be used, such as SWAT [48]. The DPeRS model utilizes remote sensing data to improve the spatial resolution of classification results, and thereby significantly increases the understanding of how NPSP disperses across the entire watershed. Moreover, the DPeRS model is easy to implement via programming, and it offers advantages in computer visualization over SWAT and other models. It simplifies the hydrological modeling process using easily accessible data sources. Although, unlike SWAT, the DPeRS model is not entirely mechanistic, it still performs modeling at the watershed scale. SWAT uses the HRU as its modeling unit, while DPeRS utilizes pixels as its unit. In the future, studies should focus on NPSP estimation at the pixel level and on improving the accuracy of basin-scale NPSP estimation.
# RMS Modeling and Control of a Grid-Forming E-STATCOM for Power System Stability in Isolated Grids
## 5 Results and Discussion
ensures sufficient damping without introducing excessive compensation [39]. These parameter values have been validated in previous studies and adapted to the characteristics of the Fuerteventura-Lanzarote grid to achieve an optimal trade-off between stability and responsiveness.