esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 NTF Derivation and Application  
### NTF Derivation  
Although the data rejection criteria are clearly given for the met mast wind data in IEC 61400-12-2, no data rejection criteria are available for the nacelle LIDAR measurements. Thus, the following nacelle LIDAR measurements and the concurrent data of the other sources were discarded according to suggestions from previous studies [28; 29; 30; 34; 35]:  
* data without a measurement sector;
* data with nacelle LIDAR measurement availability less than 80%;
* data with a carrier-to-noise ratio less than \(-23\) dB;
* data in the case of the nacelle LIDAR or the test wind turbine in abnormal operation.  
Using the table method given in IEC 61400-12-2, the nacelle anemometer wind speed can be corrected to free-stream wind speed, V\({}_{\text{free}}\). The selected wind data after the data rejection were binned  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Description** \\ \hline Model & Wind Iris (2 nd ed.) \\ Measurement range & 80 \(\sim\) 400 m \\ Data sampling rate & 1 \(\sim\) 2.5 Hz \\ Laser source & Fibre-pulsed laser (Four beam) \\ Speed accuracy & 0.1 m/s \\ Direction accuracy & \(\pm\)0.5 deg \\ Measurement height & 80 m (Hub height) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specification of the nacelle LIDAR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor (Height)** & **Model** & **Accuracy** \\ \hline Anemometer (80 m) & Thies first class advanced & 0.2 m/s \\ Wind vane (78.5 m) & Thies first class advanced & \(\pm\)0.5\({}^{\circ}\) \\ Thermometer (78.5 m) & Rotornic hyromer & 0.3 K \\ Hydrometer (78.5 m) & PT 100 class A & \(\pm\)2\% \\ Barometer (78.5 m) & P-GE 6/11 & \(\pm\)0.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specification of sensors on the met mast.  
in 0.5-m/s intervals, and then, linear interpolation was performed between the bins using the following equation to obtain \(\mathrm{V_{free}}\):  
\[\mathrm{V_{free}}=\frac{\mathrm{V_{free,i+1}-V_{free,i}}}{\mathrm{V_{nacelle,i+1}- V_{nacelle,i}}}\times(\mathrm{V_{nacelle}}-\mathrm{V_{nacelle,i}})+\mathrm{V_{free,i}} \tag{3}\]  
where \(\mathrm{V_{free,i}}\) and \(\mathrm{V_{free,i+1}}\) are the bin-averaged free-stream wind speeds in bins i and i + 1, respectively, \(\mathrm{V_{nacelle,i}}\) and \(\mathrm{V_{nacelle,i+1}}\) are the bin-averaged wind speeds measured by the nacelle anemometer in bins i and i + 1, respectively. \(\mathrm{V_{nacelle}}\) is the wind speed measured by the nacelle anemometer.  
NTFNL and NTFcup were computed using the nacelle wind speeds of the test wind turbines with free-stream wind speeds measured by the nacelle LIDAR and the cup anemometer of the met mast, respectively. Figure 3 shows the relationship between the nacelle LIDAR and the nacelle anemometer wind speeds for turbine no. 1, from which NTFNL was calculated using the table method. The relationship was close to the slope of one, which means that the correlation between the nacelle LIDAR and nacelle anemometer wind speeds was very high and consequently, NTFNL was considered to be derived properly.
# Exchange Rate Analysis for Ultra High Bypass Ratio Geared Turbofan Engines
## 5 SFC Exchange Rate Analysis  
After analyzing the engine performance, exchange rates analysis was conducted to explore the relationship between changes in component performance levels and their resulting impact on engine parameters. Different cases were examined to assess the effect of bypass pressure loss and air mass extraction on the engine SFC. Both DP exchange rate and synthesis exchange rate analysis were investigated. The engine performance simulation code (Turbomatch) was used to assess engine performance. Preliminary calculations were done to estimate the effect of adding heat and increasing the fuel temperature as well. The scenarios are as follow:  
Figure 11: HPC running line at cruise.  
Figure 10: IPC running line at cruise.  
* Design point (DP) exchange rates: in this scenario, the engine geometry is changed, and the cycle is fixed at cruise conditions by running the simulation as design point steps. The objective is to study the impact of a small change in leading component performance levels on engine DP performance parameters.
* Synthesis exchange rates: in this scenario, the engine geometry is fixed (i.e., fixed components defined for the DP) and engine cycle changes. (T/O is set as design point, and then simulations are run at off-design cruise condition). The objective is to study the real effect of making only the subject change with the rest of the engine hardware unchanged. In both scenarios, the following cases were examined:
* Effect of bypass duct pressure loss 1% to 4%
* Effect of bypass duct mass flow loss 1% to 4%
* Effect of core mass flow into bypass 1% to 4%  
For the synthesis scenario, an additional case was also examined to consider the effect of power offtake from HPT by extracting 20 kW, 30 kW, 40 kW, and 50 kW on top of the 37.5 kW (DP value).
# A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting
## 2 Current Status of Research on Grouting Materials
### Existing Diffusion Theories  
In the field of single-fissure grouting, researchers have conducted in-depth studies. [PERSON] et al. [51] studied the grouting process of rapidly solidified slurry in a single horizontal fissure. They proposed a step-by-step calculation method to address the uneven distribution of slurry viscosity. [PERSON] et al. [52] developed a 3D rough single-fissure hydro-mechanical (HM) linked seepage flow model and investigated various fissure seepage properties and the factors influencing them. [PERSON] et al. [53] examined the mechanism of slurry diffusion in a single fissure under moving water conditions, established a mathematical model for slurry diffusion, and obtained theoretical solutions for diffusion parameters. [PERSON] et al. [54] used the finite element method to simulate the diffusion process of cement slurry in a single rough fissure and analyzed the diffusion form of cement slurry and its influencing factors in a hydraulic environment. [PERSON] et al. [55] conducted cement-sodium silicate grouting experiments under dynamic water conditions in a single-plate fissure-grouting model and then established a transient equation to characterize the diffusion trajectory of slurry. It was found that under dynamic water conditions, slurry diffusion presents an asymmetric elliptical shape, and the ratio of slurry-water velocity has a significant correlation with the diffusion distance of slurry (against water and with water) and aperture. [PERSON] et al. [56] conducted chemical slurry grouting experiments under dynamic water conditions in a single-plate fissure-grouting model and found that the larger the velocity of dynamic water, the smaller the diffusion distance of slurry in the opposite water and vertical directions, and the smaller the area of slurry solidification.  
In the field of network fissure grouting, [PERSON] et al. [57] investigated the permeation behavior of slurry in fissures using a two-dimensional orthogonal fissure network diffusion model. They analyzed the influence of grouting parameters, including fissure aperture, slurry velocity, and viscosity, on the diffusion distance of the slurry. [PERSON] et al. [58] proposed an extended two-phase flow model to study the behavior of yielding power-law fluids, especially cement slurry, in water-saturated fissure networks. The model was validated using experimental data, and numerical simulations were conducted to evaluate the effect of rheological parameters and time-varying rheological properties on the propagation process. In the context of pore-fissure grouting, [PERSON] et al. [59] conducted modification and reinforcement tests using nano-silica sol as an alternative to standard cement slurry. They investigated the injectability of slurry, gel impermeability, and macroscopic and micro scopic pore closure properties in solidified mudstone. The study established fundamental principles for siliconsol grouting and mudstone anti-seepage reinforcement.
## 5 Discussion
### Difference Between the Proposed Approach and the Traditional Approach  
Because the experimental results shown in Section 4.3 proved that using the CWT and a CNN achieved better performance than using RF and the original time series in identifying the vineyard with the Sentinel-2 time series, it is meaningful to discuss the difference between these two approaches and the corresponding reasons for the purpose of further study and application. Feeding RF with directly stacked original, multi-temporal images results in two adverse outcomes: (i) the direct stacking process of multi-temporal images lost time information and RF considered each feature as independent rather than time-related; and (ii) the same or similar amplitude of features at different times enhanced the redundancy problem, which had a negative impact on the RF classifier. Additionally, the similarity between the \"spectrums\" of the original time series confused RF in terms of distinguishing vineyards from other crops. For example, vineyards with a higher canopy cover were similar to peach trees, so RF misclassified them as non-vineyard. Conversely, applying the CWT to the reconstructed time series in the time-frequency domain better observed the local characteristics of the original time series and simultaneously observed the time and frequency information. As a result of the CWT, the scalogram represented time-related frequency (scale) information and formed a unique distribution pattern for each type of planting condition (Figure 6). The convolution and max pooling process of the CNN captured the difference of the distribution pattern in the scalogram and then distinguished the vineyard from other classes. Compared with the traditional approach, the proposed approach has two advantages: (i) time information that remained in the time series of the VIs was reserved and used in the identification process; (ii) the excellent ability of the CNN in image identification was fully exploited. Therefore, as confirmed by this study, the proposed approach was comprehensively superior to the traditional approach in terms of identifying vineyards using the Sentinel-2 time series.
## 3 Data Derivation System of Electric Field Modeling
### Multidimensional Array Modeling of Grading Ring  
Two voltage equalization rings are arranged at the top of the MMC valve, serving the dual functions of voltage equalization and providing connections for the incoming and outgoing lines of the MMC valve. The modeling parameters matric for the equalization rings are defined as follows:  
\[\left\{\begin{array}{l}\mathbf{Y}_{\text{ring1}}=\left[\begin{array}{c} \mathbf{E}(\text{ring})_{i}\\ \mathbf{G}(\text{ring})_{i}\\ \mathbf{SP}(\text{ring})_{i}\end{array}\right]\\ \mathbf{E}(\text{ring})_{i}=\left[\begin{array}{c}GV_{ring1}\\ MC_{\text{ring}}\end{array}\right]\\ GV_{ring1}=GV_{1}\\ GV_{ring2}=GV_{1}+N_{\text{sm}}\text{-u}_{\text{smd}}\end{array}\right. \tag{13}\]  
In Equation (13), \(\mathbf{Y}_{\text{ring1}}\) and \(\mathbf{Y}_{\text{ring2}}\) are the modeling parameters of the two-voltage equalization ring, \(GV_{\text{ring}}\) is electric potential of the ring, \(N_{\text{sm}}\) is voltage coefficient, \(GV_{1}\) is the electric potential of ring 1. The electric field modeling parameters of top voltage equalization rings are also composed of three sets of parameters.  
The voltage equalization rings at the top of the MMC valve are typically composed of two C-shaped tubular structures. The geometric parameters matrix and positional parameters matrix are defined as follows:  
\[\mathbf{G}(\text{ring})_{i}=EC(\text{ring})=\left[\begin{array}{c}Form\\ R\\ X_{r0}\\ Y_{r0}\\ Z_{r0}\\ ALR\end{array}\right]\text{and }\mathbf{SP}(\text{ring})_{i}=\left[\begin{array}{c}X_{\text{sm1}}+xrm_{i }\\ Y_{\text{sm1}}+yrm_{i}\\ Z_{\text{sm1}}+zrm_{i}\end{array}\right] \tag{14}\]  
In Equation (14), _EC_(ring) is a computational matrix for the geometric dimensions of the voltage equalization ring, _Form_ is the shape structure, \(xrm_{i}\), \(yrm_{i}\), and \(zrm_{i}\) are the displacement coordinates of voltage equalization ring \(i\) relative to another. The geometric parameters employs a database format. This limited variety makes it easier to establish a database.
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 2 Materials and Methods
### Machine Learning Algorithms
#### 2.4.5 Gaussian Process Regression  
Gaussian process regression (GPR) is a stochastic statistical technique that seeks a multivariate normal distribution within random variables from the feature space domain. GPR measures the similarity between points (the kernel function) to predict the value from training data.
## 1 Introduction
These converters enable direct control over grid voltage and frequency by emulating the dynamic behavior of conventional synchronous generators. This capability supports enhanced grid stability, particularly in low-inertia or weak grid environments. Figure 1: Future grids powered by RESs and electronics. This capability allows them to provide essential grid-supportive services, such as voltage regulation and frequency control, even in islanded or weak grid conditions, as discussed in [19]. Unlike grid-following systems, GFMCs do not require phase-locked loops for synchronization, which makes them capable of operating independently in islanded conditions or weak grids, offering a more robust solution for modern power systems [20, 21]. Despite their advantages, GFMCs have primarily focused on voltage and frequency regulation, while harmonic mitigation has not been fully integrated into their control strategies. This gap presents a significant challenge for modern power systems, as harmonic distortions remain a critical issue, especially in grids with high RES and nonlinear load penetration. This paper proposes a novel GFMC APF that integrates the voltage and frequency control capabilities of GFMCs with advanced harmonic filtering to address the limitations of conventional APFs. The GFMC APF utilizes harmonic extraction from the PCC to generate precise harmonic voltage components, which are then used to generate compensating currents that reduce harmonic distortion and minimize disruptions in the system. The innovative control method of the GFMC APF ensures fast frequency and voltage regulation while enhancing grid stability by improving inertia, damping, and short-circuit capacity. This makes the system particularly effective in low-inertia grids where maintaining stability is more challenging, ensuring that the grid can effectively support increasing RES penetration without compromising power quality. The major contributions of this work are as follows:  
1. Developing a GFMC APF control method that integrates grid-forming functionality with harmonic suppression to improve power quality in weak grids. 2. Proposing a precise harmonic extraction technique using PCC voltage measurements for accurate mitigation without affecting fundamental operations. 3. Designing a scalable and cost-effective control approach that eliminates complex parameter tuning, enabling seamless deployment in modern power systems. The structure of this paper is as follows: Section 2 presents the GFMC APF system configuration, detailing its integration into the grid and harmonic mitigation components. Section 3 describes the control structure, focusing on harmonic suppression and grid synchronization. Section 4 examines the stability analysis, focusing on controller tuning and its impact on system stability. Section 5 describes the harmonic compensation principle. Section 6 presents simulation results to demonstrate the effectiveness of the proposed method. Finally, Section 7 concludes the paper.
## 2 Materials and Methods
### Statistical Evaluation of Satellite Precipitation Products
#### 2.5.1 Continuous Statistical Indices  
The assessment and comparison of the SPPs was conducted on the basis of the overall assessment (continuous statistical measures) and the precipitation detection capability (categorical statistical measures).  
In addition, the capability of the SPPs to measure different precipitation intensity classes and flood periods was evaluated as well (Table 1). Four statistical measures were selected: correlation coefficient (CC), root mean square error (RMSE), and bias (bias), which were calculated to statistically evaluate the two PPS products.  
Where N represents the number of samples; Si and \(\overline{5}\) are gauge observations and their average; Pi and \(\overline{5}\) represent satellite estimates and their average, respectively.  
Additionally, a denotes the number of rainfall events that were observed and detected; c, is the number of rainfall events that failed to be detected by the satellite; b, denotes the number of rainfall events detected by the satellite that did not occur; the threshold for identifying a precipitation event is 0.2 mm/day.
## 5 Results
### Preliminary Evaluation Results
#### 5.4.1 Per-Band Bias Errors
In the case of B3 (Figure 8c), the radiances are underestimated using both Pre-F and I0 gains. Nevertheless, a reduced percentage of the sample, concentrated between 0.45 and 0.6 of reflectance, presents overestimation after cross-calibration. Using Pre-F gains, the bias errors were in the range from 14.7-2.45 \(\mathrm{W\cdot m^{-2}\cdot sr^{-1}\cdot\mu m^{-1}}\); once I0 gain was used, the values were in the range from 14.7-2.45 \(\mathrm{W\cdot m^{-2}\cdot sr^{-1}\cdot\mu m^{-1}}\); once I0 gain was used, the values were in the range from 14.7-2.45 \(\mathrm{W\cdot m^{-2}\cdot sr^{-1}\cdot\mu m^{-1}}\) to 14.7-2.45 \(\mathrm{W\cdot m^{-2}\cdot sr^{-1}\cdot\mu m^{-1}}\). Figure 8: Comparison of radiance bias error (\(\mathrm{W\cdot m^{-2}\cdot sr^{-1}\cdot\mu m^{-1}}\)) per sample vs. sample TOA reflectance. The results of the pre-flight calibration are shown in gray. The results after cross-calibration (I0) are shown as: blue for B1 (**a**); green for B2 (**b**); red for B3 (**c**) and purple for B4 (**d**). \(-\)3.30-9.78 \(\mathrm{W\cdot m^{-2}\cdot sr^{-1}\cdot\upmu m^{-1}}\). For this band, the decrease in MBE can be considered as a clear improvement, particularly at higher reflectances. For B4 (Figure 8d), where noticeable differences existed among low and high reflectances before the application of the I0 coefficients, overestimation increased with reflectance, mainly at reflectances higher than 0.5. Before cross-calibration, the bias ranged from \(-\)22.76--0.23 \(\mathrm{W\cdot m^{-2}\cdot sr^{-1}\cdot\upmu m^{-1}}\), which after the procedure was significantly reduced to the interval from \(-\)2.88-9.4 \(\mathrm{W\cdot m^{-2}\cdot sr^{-1}\cdot\upmu m^{-1}}\). Although the bias was not completely removed, it considerably diminished after the cross-calibration against RapidEye.
## 3 Methods
### Decoder  
#### 3.3.1 Hierarchical Decoder  
After encoding the target features, the decoding stage needs to accurately restore the encoded features to the original image size. Using interpolation for decoding may result in the loss of detailed information, negatively affecting the segmentation accuracy. To solve the problem, we designed a hierarchical decoder that integrates decoding features with the encoded features of corresponding spatial sizes for decoding, thereby minimizing the loss of detailed information. The hierarchical decoder consists of four upsampling modules, and its structure is shown in Figure 4.  
In practice, the encoder feature map \(F_{18,34}^{(5)}\) was first processed with transposed convolution to increase its resolution to the feature space dimensions of the penultimate residual module in the Pseudo-Siamese fusion encoding network. Subsequently, the output was combined with the encoded feature maps \(F_{18}^{4}\) and \(F_{34}^{4}\) using a concatenation operation. Finally, the concatenated feature maps sequentially were processed through a convolutional layer with a kernel size of \(3\times 3\), a BN layer, and a ReLU function to produce the output \(F_{1}\) of the first upsampling module. Formula 6 can be used to represent the calculation carried out in the first upsampling module, and the processing method for the remaining upsampling modules is similar:  
Figure 3: The structure of atrous spatial pyramid pooling.  
\[F_{1}=ReLU(BN(f^{3\times 3}(C(T(F_{18,34}^{(5)}),F_{18}^{4},F_{34}^{4})))) \tag{6}\]  
where \(T(\cdot)\) denotes the transposed convolution operation.
# Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach
## 3 Results and Discussions
### Personalized Classification Models
This approach could be useful in case of having a large amount of data with a diversity of subjects especially in the absence of streaming data from new subjects. The second approach of LOO mimics the case of having a prior knowledge about the test subject through personally labelled data. The localized model in this approach is also searching for the most similar training points, which may include this subject personal \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configurations** & **Classes** & **Precision** & **Sensitivity** & **F1-Score** & **Accuracy** \\ \hline \multirow{3}{*}{Conf. 1} & Class 1 & 0.47 & 0.36 & 0.41 & \\ \cline{2-5}  & Class 2 & 0.84 & 0.90 & 0.87 & \\ \cline{2-5}  & Class 3 & 0.83 & 0.71 & 0.77 & \\ \hline \multirow{3}{*}{Conf. 2} & Class 1 & 0.84 & 0.95 & 0.89 & \\ \cline{2-5}  & Class 2 & 0.74 & 0.68 & 0.70 & \\ \cline{2-5}  & Class 3 & 0.84 & 0.83 & 0.83 & \\ \hline \multirow{3}{*}{Conf. 3} & Class 1 & 0.87 & 0.94 & 0.90 & \\ \cline{2-5}  & Class 2 & 0.88 & 0.89 & 0.88 & \\ \cline{1-1} \cline{2-5}  & Class 3 & 0.86 & 0.74 & 0.79 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The error performance (precision, sensitivity, F1-score, and accuracy) of the localized model KNN-LS-SVM model for the three different 3-class configurations based on leave-one-subject-out (LOSO) testing approach. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Configurations** & **Classes** & **Precision** & **Sensitivity** & **F1-Score** & **Accuracy** \\ \hline \multirow{3}{*}{Conf. 1} & Class 1 & 0.75 & 0.56 & 0.64 & \\ \cline{2-5}  & Class 2 & 0.87 & 0.93 \({}^{*}\) & 0.90 \({}^{*}\) & 0.86 \\ \cline{2-5}  & Class 3 & 0.86 & 0.78 & 0.82 & \\ \hline \multirow{3}{*}{Conf. 2} & Class 1 & 0.84 \({}^{*}\) & 0.95 \({}^{*}\) & 0.89 \({}^{*}\) & \\ \cline{2-5}  & Class 2 & 0.71 & 0.62 & 0.66 & 0.79 \\ \cline{2-5}  & Class 3 & 0.81 & 0.83 \({}^{*}\) & 0.82 & \\ \hline \multirow{3}{*}{Conf. 3} & Class 1 & 0.84 \({}^{*}\) & 0.91 & 0.87 & \\ \cline{2-5}  & Class 2 & 0.89 \({}^{*}\) & 0.88 & 0.88 & \\ \cline{1-1} \cline{2-5}  & Class 3 & 0.89 \({}^{*}\) & 0.80 & 0.84 \({}^{*}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The error performance (precision, sensitivity, F1-score, and accuracy) of the localized model KNN-LS-SVM model for the three different 3-class configurations based on leave-one-out (LOO) testing approach. data. This approach can be efficient in the presence of streaming personal data that is labelled by the test subject.