esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modelling and Optimization
### Capacitor Voltage Balancing  
For capacitor voltage balance, the capacitor voltage is compared to the desired reference voltage. The charging and discharging occurs because of the switching action of \(S_{3}\) and \(S_{4}\), as depicted in Figure 4. Charging occurs when switches \(S_{4}\) and \(S_{3^{\prime}}\) are turned on and charging occurs when \(S_{3}\) and \(S_{4^{\prime}}\) are turned on, as shown in Figure 4a,b, respectively. It also depends on the current flowing through the capacitor and is taken to realise a mathematical model of capacitor voltage balance. The voltage of the capacitor (working as source in the H\({}_{2}\) bridge) is represented by adding it as one more term to the CF. To predict capacitor voltage (\(V_{\text{c}}\)) for the next sampling instant, the relation between current and voltage variation can be written in Equations (16) and (17) as follows:  
\[\int\frac{i(S_{4}-S_{3})}{C}dt=\int dV_{\text{c}} \tag{16}\] \[\frac{i(S_{4}-S_{3})}{C}T_{\text{S}}=V_{k+1}-V_{k} \tag{17}\]  
\(V_{\text{k+1}}\) and \(V_{\text{k}}\) are the capacitor voltages at time instants \((k+1)T_{\text{S}}\) and \(kT_{\text{S}}\), respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Without Voltage Vector Selection** & **With Voltage Vector Selection** \\ \hline \(1.0435\times 10^{-5}\) & \(3.3102\times 10^{-6}\) \\ \(1.0557\times 10^{-5}\) & \(3.1408\times 10^{-6}\) \\ \(1.0732\times 10^{-5}\) & \(3.2107\times 10^{-6}\) \\ \(1.0615\times 10^{-5}\) & \(3.1326\times 10^{-6}\) \\ \(2.0162\times 10^{-5}\) & \(3.6402\times 10^{-6}\) \\ Average & Average \\ time \((\text{s})=1.0775\times 10^{-5}\) & time \((\text{s})=3.2435\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of modification in MPC control scheme.  
Figure 4: Capacitor voltage balancing: (**a**) charging intervals, and (**b**) discharging intervals.  
By rearranging the predicted capacitor voltage, \(V_{c}(k+1)\) can be written in (18) as:  
\[V_{c}(k+1)=V_{c}(k)+[(S_{3}-S_{4})\times(\frac{i\cdot T_{S}}{C})] \tag{18}\]  
The predicted capacitor is compared with the reference value of the capacitor (33% of the DC voltage source) by adding the modelled term in the CF (\(G_{2}\)) in (19) as follows:  
\[G_{2}=W_{2}.\left(0.33\times V_{DC}-\left(V_{k}-\frac{i(S_{3}-S_{4})}{C}\right) \right)^{2} \tag{19}\]  
An additional term in Equation (19) needs the switching vectors responsible for the charging and discharging of the capacitor. The adjacent voltage selection reduces the maximum iteration to only three vectors.
## 1 Introduction
We argue that the image gradient feature, knowing the position (region) that corresponds to an edge, texture or smoothness, is beneficial for recovering high-frequency image details. By incorporated gradient guidance in the feed forward network, the network can recover more image high-frequency details. Our main contributions to MR image super-resolution are summarized as follows:  
1. We design a gradient-guided residual network for solving the single contrast MRI image super-resolution problem. The proposed network exploits the mutual relation of the super-resolution and the image gradient priors. Thus, the network employs image gradient information for image super-resolution intentionally. 2. With a suitable model, image gradient is exploited for MR image super-resolution to supply the clues regarding the high-frequency details. Under the guidance of gradient, the forward super-resolution process reconstructs HR image explicitly, thereby leading a more accurate HR image. Figure 1: The results of different super-resolution methods on real data in NAMIC with an upscaling factor of 4: (**a**) the ground-truth; (**b**) the result of SCSR [13], which tends to blur the high-frequency details; (**c**) the result of the residual network (ResNet); and (**d**) the result of our method, which is guided by gradient information during image reconstruction. More realistic results are obtained via the gradient-guided methods (zoom in for better view). 3. The experimental results of three public databases show that the gradient-guided CNN outperforms the conventional feed-forward architecture CNNs in MRI image super-resolution. The proposed approach provides a flexible model of employing image prior for CNN-based super-resolution.
## 5 Simulation Study
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Components** \\ \end{tabular} } & \multirow{2}{*}{**Sample Size (per** **Component)**} & \multirow{2}{*}{\begin{tabular}{c} **k=1(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **k=2(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **k=3(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **k=4(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **k=5(\%)** \\ \end{tabular} } \\ \hline
1 & 2 & 125 & 0 & 100 & 0 & 0 & 0 \\
2 & 2 & 250 & 0 & 100 & 0 & 0 & 0 \\
3 & 2 & 500 & 0 & 100 & 0 & 0 & 0 \\
4 & 2 & 1000 & 0 & 100 & 0 & 0 & 0 \\
5 & 3 & 125 & 0.8 & 4.2 & 95 & 0 & 0 \\
6 & 3 & 250 & 0 & 0 & 100 & 0 & 0 \\
7 & 3 & 500 & 0 & 0 & 100 & 0 & 0 \\
8 & 3 & 1000 & 0 & 0 & 100 & 0 & 0 \\
9 & 4 & 125 & 0 & 0 & 3.2 & 96.8 & 0 \\
10 & 4 & 250 & 0 & 0 & 0 & 100 & 0 \\
11 & 4 & 500 & 0 & 0 & 0 & 100 & 0 \\
12 & 4 & 1000 & 0 & 0 & 0 & 100 & 0 \\
13 & 5 & 125 & 0 & 0 & 0 & 2.4 & 97.6 \\
14 & 5 & 250 & 0 & 0 & 0 & 0 & 100 \\
15 & 5 & 500 & 0 & 0 & 0 & 0 & 100 \\
16 & 5 & 1000 & 0 & 0 & 0 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the results of grouping the EV-I mixture distribution with 200,000 sweeps and replicated 500 times. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{ \begin{tabular}{c} **Number of** \\ **Components** \\ \end{tabular}  } & \multirow{2}{*}{**Component**} & \multirow{2}{*}{ \begin{tabular}{c} **Number of** \\ **Generated Data** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **\mu** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **\sigma** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **w** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **\hat{\rho}** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **\hat{\sigma}** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **\hat{w}** \\ \end{tabular}  } \\ \hline
1 & 2 & 1 st & 125 & \(-2\) & 1 & 0.5 & \(-1.9483\) & 1.019 & 0.498 \\ \cline{2-9}  & & 2 nd & 125 & 0 & 0.5 & \(0.5\) & \(-0.2771\) & 0.5077 & 0.502 \\ \hline
2 & 2 & 1 st & 250 & \(-2  
Besides displaying the results of grouping in Table 2 and parameter estimation results in Table 3, we also provide an overview of the histogram and predictive density for each scenario. Histograms and predictive densities for the first to fourth scenarios can be seen in Figure 2a-d, the fifth to eighth scenarios in Figure 3a-d, the ninth to twelfth scenarios in Figure 4a-d, and the thirteenth to sixteenth scenarios in Figure 5a-d.
###### Abstract  
Hydrothermal carbonization (HTC) technology can convert various types of waste biomass into a carbon-rich product referred to as hydrochar. In order to verify the potential of hydrochar produced from stabilized sewage sludge to be an adsorbent for bacterial pathogen removal in water treatment, the _Escherichia coli_'s removal efficiency was determined by using 10 cm sand columns loaded with 1.5% (\(w/w\)) hydrochar. Furthermore, the removal of _E. coli_ based on intermittent operation in larger columns of 50 cm was measured for 30 days. Since the removal of _E. coli_ was not sufficient when the sand columns were supplemented with raw hydrochar, an additional cold-alkali activation of the hydrochar using potassium hydroxide was applied. This enabled more than 90% of _E. coli_ removal in both the 10 cm and 50 cm column experiments. The enhancement of the _E. coli_ removal efficiency could be attributed to the more hydrophobic surface of the KOH pre-treated hydrochar. The idle time during the intermittent flushing experiments in the sand-only columns without the hydrochar supplement had a significant effect on the _E. coli_ removal (\(p<0.05\)), resulting in a removal efficiency of 55.2%. This research suggested the possible utilization of hydrochar produced from sewage sludge as an adsorbent in water treatment for the removal of bacterial contaminants.  
Escherichia coli; bacterial removal; sewage sludge; chloride tracer; hydrothermal carbonization; hydrochar +
Footnote †: journal: Applied Chemistry and Chemical Physics
## 3 Results
### Relationships between Satellite-Based SIF and GPP for Different PFTs  
To evaluate the performance of SIF, in terms of estimating GPP for different PFTs, we compared the relationships between TROPOMI SIF and FLUXCOM GPP using the selected samples detailed in Table 1 and Figure 3. We also explored the effect of temperature on the SIF-GPP relationship for TeC3 GRA, C4 GRA, and C3 CRO, which had both group A and group B samples, using a linear regression approach with no intercept. The selected samples in group A experienced low-temperature stress (less than 10 \({}^{\circ}\)C), while those in group B did not.  
From Figure 6, we can see that the SIF showed a strong correlation with GPP in almost all PFTs. In addition, Figure 7 shows the statistical metrics of GPP, SIF, and their ratio for different PFTs. The results indicate that, although the magnitude of SIF across PFTs was consistent with the GPP (Figure 7a,b), the GPP/SIF ratio differed for different PFTs (Figure 7c). Notably, the GPP/SIF ratio for forest types was generally higher than that of grass and crop types.  
Figure 5: Seasonal patterns of air temperature (Ta), photosynthetic active radiation (PAR), and vapor pressure deficit (VPD) for each PFT in the Northern Hemisphere (2018–2020). The lines correspond to the means of the corresponding data, with different colors for Ta (red), PAR (dark), and VPD (blue). The shaded area is the standard deviation.  
Noticeably, the SIF-GPP relationship in group A (with a temperature below 10 \({}^{\circ}\)C) showed a lower slope than that in group B (with a temperature above 10 \({}^{\circ}\)C throughout the year). For example, considering TeC3 GRA, the slope between SIF and GPP for group A (slope = 7.466) was lower than that for group B (slope = 8.199); see Figure 8. Similar results can be observed in C4 GRA and C3 GRO. The relatively lower slope between SIF and GPP for group A of TeC3 GRA, C4 GRA, and C3 GRO than that for group B may be due to the differing temperature responses of SIF and GPP. Therefore, the SIF-GPP relationship seems to be affected by the temperature.  
Figure 6: Relationship between SIF and GPP for different plant functional types (2018–2020). A and B represent different groups. Each panel also lists the slope and determination coefficient obtained in the linear regression with no intercept. The color bar represents the fraction of point density, normalized by the maximum point density.  
Figure 7: SIF **(a)**, GPP **(b)**, and the ratio of GPP to SIF **(c)** for different PFTs. The bars represent the mean values, while the error bar indicates the standard deviation.
## 3 Results
### Attitude to Use (ATU)  
With respect to ATU, the result of the Wilcoxon-signed rank test indicated that users' ATU toward the interactive interface is affected by the use of hand gesture recognition and joystick control (Z = \(-\)2.591, \(p<0.05\)). Further examination of the result suggests that users' attitudes toward the joystick control (gesture recognition: M = 8.06, SD = 2.24; joystick control: M = 10.91, SD = 3.086) are better than their attitudes toward hand gesture recognition (see Figure 6).  
Regarding the task completion time in the conditions of joystick control, being non-normal distribution data, the Kruskal-Wallis test indicated a significant effect on ATU (H = 6.067, \(p<0.05\)). The post analysis of the data suggests that using the joystick control for more than 20 min and within 15 min has a statistically significant effect on ATU toward the interactive interface (Kruskal-Wallis H = 8.896, \(p<0.05\)). Specifically, users who use joystick control for more than 20 min have a more positive attitude (M = 11.93, SD = 2.401; see Figure 7). No significant effect was found from factors like gender and college major.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 5 Simulation  
Firstly, the IA-VMD method is used to identify the constituent broadband oscillation signal. It has been verified that the IA-VMD method has better identification accuracy than other methods for broadband oscillation. Then, the actual LFO events in the Hunan power grid in June 2018 are used to further verify the effect of the IA-VMD method. Before the LFO divergence, the phasor measurement unit (PMU) captured a small amplitude of power fluctuation, which is the measured ambient signal. Because the amplitude of the signal fluctuation was too small, the PMU did not provide a warning. Through the study of this LFO event, this paper proves that the measured ambient signal can be analyzed and it can provide a reference for the early warning of LFOs in the system.
## 1 Introduction
This integration enhances the diagnostic effectiveness of ADSs for missiles in complex and dynamic environments, such as random target movements and sensor noises, thus ensuring the reliability of missile missions. The main contributions of this article are summarized as follows:  
1. Based on the idea of integrating model and data methods, after establishing the unscented Kalman filter model for the air data system, data that reflect fault information, including innovations and residual sequences, were extracted and input into the neural network. This approach effectively overcomes the limitations of model-based and data-based methods, allowing faults to be diagnosed more easily and accurately. 2. A neural network model for fault diagnosis based on Inception V3 architecture was constructed, incorporating the lightweight attention mechanism known as ECA. Compared to traditional neural networks, this network is able to learn features of data at multiple scales and in a selective manner while ensuring higher computational efficiency and requiring fewer computational resources. The structure of this paper is arranged as follows: Section 2 introduces the model of the air data system and describes the main problems in fault diagnosis; Section 3 provides a detailed description of the proposed method; in Section 4, the experimental procedure and results are provided; and Section 5 summarizes the work of this paper.
## 2 Dataset, Models and Method
### Dataset
#### 2.1.1 Initial and Boundary Conditions  
The initial and lateral boundary conditions were derived from the NCEP data. These NCEP-FNL (Final) operational global analysis data are available as a \(1^{\circ}\times 1^{\circ}\) grid at 6-hourly intervals. To obtain the corresponding initial and boundary conditions required to run the mesoscale model, analysis data available at 26 pressure levels were used. Variables (e.g., pressure, geopotential height, temperature, relative humidity, and u and v winds) required for the mesoscale model simulations were interpolated from the pressure levels.
## 4 Cooling System Design and Cooling Simulation Based on UG and Moldflow
### Cooling Process Simulation and Analysis
#### 4.3.3 Analysis and Comparison of Cooling Results
Figure 14 shows the distinct time required for various sections of the plastic parts to reach ejection temperature. Table 5 displays the statistical data for a randomly chosen point on the surface of the simulated part. The data indicate that when utilizing conformal cooling channels, the product temperatures are significantly lower compared to conventional ducts, with a temperature reduction ranging from 11 to 28 degrees Celsius. Additionally, the average time required to attain the ejection temperature of the inner corner of the plastic part is 32.29% lower with the use of conformal cooling channels as compared to conventional channels. Thus, we can calculate the cooling efficiency of the conformal cooling channel to be more than 30%. Figure 12: Time to reach ejection temperature of the part with conventional cooling channels. Figure 13: Time to reach ejection temperature of the part with conformal cooling channels. Figure 14: The distinct time needed for various sections of the plastic parts to reach the ejection temperature. (**a**) Specific time of the part with conventional cooling channels. (**b**) Specific time of the part with conformal cooling channels. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Conventional** & **Conformal** & **Type** & **Conventional** & **Conformal** \\  & **Cooling Channel** & **Cooling Channel** & **Cooling Channel** & **Cooling Channel** \\ \hline  & 75.11 & 52.29 & & 19.14 & 14.39 \\  & 78.23 & 60.79 & & 20.29 & 13.72 \\  & 83.80 & 60.42 & & 21.15 & 14.53 \\  & 90.02 & 68.09 & & 22.34 & 14.66 \\  & 93.72 & 68.09 & & 19.33 & 15.01 \\  & 59.39 & 60.73 & & 20.23 & 14.81 \\  & 78.50 & 58.86 & & 23.39 & 14.72 \\  & 90.84 & 69.29 & & 24.21 & 14.43 \\  & 69.96 & 58.89 & & 15.89 & 14.17 \\ Temperature & 98.88 & 60.98 & Time to reach ejection & 19.89 & 13.99 \\ of Part (\({}^{\circ}\)C) & 70.26 & 59.62 & temperature (s) & 22.08 & 14.27 \\  & 77.23 & 57.73 & & 25.89 & 14.23 \\  & 90.02 & 69.62 & & 19.33 & 14.48 \\  & 91.04 & 69.66 & & 20.10 & 14.69 \\  & 99.49 & 70.63 & & 23.24 & 14.36 \\  & 75.24 & 58.22 & & 25.06 & 14.45 \\  & 78.59 & 58.11 & & 19.75 & 14.78 \\  & 85.00 & 64.28 & & 20.69 & 14.65 \\  & 89.19 & 65.27 & & 21.85 & 14.67 \\  & 91.34 & 68.89 & & 22.35 & 14.04 \\ \hline Average Temp & & & & & \\ (\({}^{\circ}\)C) & 83.30 & 63.04 & Average Time (s) & 21.31 & 14.41 \\ \hline Reduction & 11\(\sim\)28 \({}^{\circ}\)C & & Reduction & 32.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of specific values of part temperature distribution and time to reach ejector temperature for conventional cooling channels and conformal cooling channels.
# A Survey on Surface Defect Inspection Based on Generative Models in Manufacturing
## 3 Defect Inspection Based on Generative Models
### Defect Inspection Based on GANs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Year** & **Brief Description** & **GM** & **Objective** & **Task** & **LM** \\ \hline
[94] & 2022 &
\begin{tabular}{c} Use CycleGAN to generate dynamic templates with \\ image-level annotations \\ \end{tabular} & CycleGAN & DAGM & SEG & WSL \\
[95] & 2022 & \
\begin{tabular}{c} Combine the CycleGAN with attention mechanism for \\ synthesizing defect samples to enlarge the samples \\ volume \\ \end{tabular} & CycleGAN &
\begin{tabular}{c} DAGM; \\ KolektorSDD1 \\ \end{tabular} & CLS & USL \\
[96] & 2023 & \begin{tabular}{c} Integrate channel attention mechanism into SinGAN \\ Introduce style-related features to enhance the control \\ on the generated images \\ \end{tabular} & \
\begin{tabular}{c} SinGAN \\ StyleGAN \\ StyleGAN \\ \end{tabular} &
\begin{tabular}{c} NEU-DET \\ CLS \\ \end{tabular} & WSL \\
[97] & 2023 & \begin{tabular}{c} Introduce style-related features to enhance the control \\ on the generated images \\ \end{tabular} & \
\begin{tabular}{c} StyleGAN \\ StyleGAN \\ \end{tabular} &
\begin{tabular}{c} CLS \\ \end{tabular} & WSL \\
[98] & 2023 &
\begin{tabular}{c} Integrate self-attention mechanism into the pix2 pix \\ model for reconstructing defect regions with less \\ information \\ \end{tabular} & pix2 pix & Fabric & SEG & USL \\
[99] & 2023 & \
\begin{tabular}{c} Incorporate attention network to guide the training \\ process \\ \end{tabular} & FCGAN. &
\begin{tabular}{c} KolektorSDD1; \\ DAGM \\ \end{tabular} & CLS & WSL \\
[100] & 2023 &
\begin{tabular}{c} Integrate data augmentation and self-attention \\ mechanism modules into Pro-GAN \\ \end{tabular} & Pro-GAN & NEU-DET & WSL \\
[101] & 2024 &
\begin{tabular}{c} Use dual U-net networks to enhance defect-texture \\ fusion \\ \end{tabular} & Pix2 Pix & Fabric & DET & WSL \\
[102] & 2024 & \begin{tabular}{c} Extract defect edges of original image as well as the \\ background texture information, and use them as \\ network input through the spatially-adaptive \\ normalization module \\ \end{tabular} & pix2 pix & \
\begin{tabular}{c} Severstal; \\ MT \\ \end{tabular} &
\begin{tabular}{c} DET \\ SEG \\ \end{tabular} & WSL \\
[103] & 2024 &
\begin{tabular}{c} Design a production-and-elimination process to \\ improve generation quality \\ \end{tabular} & PE-GAN & NEU-CLS-64 & CLS & WSL \\ \hline \hline \end{tabular}  
* Notions: GM = generative model, CLS = classification, SEG = segmentation, LM = learning mode, USL = unsupervised learning, WSL = weakly-supervised learning.  
\end{table} 
Table 4: Summary of studies based on image-to-image translation GANs for surface defect inspection.
## 4 Results  
### Deterministic Ground Surface Settlement Analysis  
The groundwater drawdown curves and ground surface settlement curves at various stages of draining and excavating for the excavation are depicted in Figures 6 and 7. As shown in Figure 6, the groundwater level outside the excavation was higher than that inside for all draining stages, indicating that the suspended seepage barrier affected the hydraulic connection between the inner and outer sides, which thus controlled the surface settlement. As indicated in Figure 7, at each stage of the excavation draining and excavation, the surface settlement curve was groove-shaped with a single peak, which resembles the shape of the drawdown curve. The reason is that the draining increases the effective stress of the soil outside the pit, and the presence of head differences inside and outside the pit leads to the infiltration force downward in the active zone soil, resulting in a larger settlement. According to the data, the maximum surface settlement outside the excavation was 38.22 mm, 0.32% of the excavation depth. The location with the maximum settlement was 39.7 m from the center of the pit and 27.9 m from the seepage barrier, which was 2.33 times the excavation depth.
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 3 Test Analysis
### Test of Orbit Determination for BDS-3 Satellites and LEO Satellites  
#### 3.2.1 Strategies for Orbit Determination  
Based on the normal equations mentioned above, the strategies for orbit determination could be further refined, as shown in Table 2.  
Figure 8: Number of satellite-to-ground observation links.  
Figure 10: Number of satellite-to-ground and satellite-based observation links, with the support of 12 LEO satellites.  
Figure 9: Number of satellite-to-ground and satellite-based observation links, with the support of 6 LEO satellites.