esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
#### 3.1.3 Heavy-Duty Transport  
Although the use of H\({}_{2}\) as fuel for HD transport overcomes the main drawbacks of BEVs related to limited range and charging infrastructure requirements, the impact of its internal production on the energy system response must be evaluated. A parametric study varying the H\({}_{2}\) share as HD transport fuel was performed to illustrate how the energy system structure must evolve to supply the demand. The same five criteria described in Section 3.1.2 have been followed to carry out the simulations.  
Figure 6 shows the additional RE and EES capacity requirements to reach full decarbonisation of the HDV fleet with an internal energy supply as a function of the H\({}_{2}\) penetration as fuel. Readily apparent from Figure 6 is the lesser RE and EES capacity requirements with the H\({}_{2}\) share. The different origin of the additional energy required to charge (BEVs) or fill the tanks (FCEVs) explain this behaviour. With respect to BEVs, large electricity demands located during overnight hours significantly stress the grid system. With no direct support of PV, the additional grid demand must be covered by EES (limiting the energy available to support RE intermittency) and H\({}_{2}\) turbines that must operate for longer periods. This behaviour increases the proportion of the low efficient Power-to-H\({}_{2}\)-to-Power path. As a result, the RE capacity must be increased to reach 100% autonomy.  
The opposite behaviour is observed by introducing higher shares of H\({}_{2}\) fuel. Although the system must generate the H\({}_{2}\) to feed the HDV fleet, the electrolysers' dispatch approach prioritises an excess of RE, turning into a better use of the available energy. Therefore, for energy systems similar to the one examined in this study, the use of H\({}_{2}\) requires less power-generation infrastructure and improves the overall system efficiency, quite the opposite of only taking the vehicle efficiency into account to carry out an analysis.  
Regardless of the type of technology employed to propel the HDV fleet, the predicted additional power and storage capacity to be deployed are significant, with 600-900 MW and 2470-2750 MWh of RE and ESS capacities, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Current** & **Power Generation** & **LDV** \\ \hline Wind On-shore [MW] & 222.60 & 1530 & 1700 \\ Wind Off-shore [MW] & - & 455 & 961 \\ PV On-shore [MW] & 107.60 & 1485 & 1980 \\ PV Off-shore [MW] & - & 24 & 32 \\ PV Self-Consumption [MW] & 38 & 746 & 995 \\ \hline EES–User [GW/GWh] & - & 2.4/2.2 & 3.2/3.0 \\ EES–Distributed [GW/GWh] & - & 1.1/1.1 & 1.3/1.3 \\ EES–Large-Scale [MW/GWh] & - & 313/5.2 & 313/5.2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Current structure and RE and EES capacities required to reach autonomous supply in power-generation sector and light-duty vehicle transport.
## 4 Experiments
### Performance Analysis
#### 4.4.1 Performance for Dog Detection  
The results of the dog detection experiment are listed in Table 11. Since the experimental images were taken from the video on the camera, there may be more than two dogs in one picture. Therefore, the number of images in the table will be less than the number of dogs. The detection rate of the TestSet1 data-set was 97.62%; in total, 199 dogs were undetected. The reasons for the detection errors were the obstruction of the facial features of the dog, the breed of the dog, and the obstruction or cropping of the body of the dog (Figure 13). Another factor contributing to the detection error rate may have been the training data-set, which accounted for too many object categories and contained too few dog samples. The detection rate of the TestSet2 data-set was 98.39%; in total, 357 dogs were undetected. In addition to the aforementioned factors contributing to the detection error rate, some detection errors in the experiment conducted using the TestSet2 data-set were attributable to incomplete dog regions, as illustrated in Figure 14. In the future, training data-sets that contain higher numbers of dog images and that account for the types of detection errors identified in this study should be used to improve the detection rate of the proposed system.
###### Abstract  
The quality of reconstructed images in relation to the bit depth of holograms formed by wavelength-selective phase-shifting digital holography was investigated. Wavelength-selective phase-shifting digital holography is a technique to obtain multiwavelength three-dimensional (3D) images with a full space-bandwidth product of an image sensor from wavelength-multiplexed phase-shifted holograms and has been proposed since 2013. The bit resolution required to obtain a multiwavelength holographic image was quantitatively and experimentally evaluated, and the relationship between wavelength resolution and dynamic range of an image sensor was numerically simulated. The results indicate that two-bit resolution per wavelength is required to conduct color 3D imaging.  
Keywords:three-dimensional imaging; digital holography; multiwavelength digital holography; color holography; phase-shifting interferometry; phase-shifting digital holography +
Footnote †: journal: Applied Sciences
# EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure
## 3 Methodology  
Vibration-based SHM has become an important topic in civil and structural engineering because vibrations have been demonstrated to be sensitive to changes produced by damage in civil structures, even when that damage is located in internal areas. Hence, vibration analysis is considered a powerful tool for assessing the integrity of civil structures.  
Figure 1 shows a schematic diagram of the proposed methodology for evaluating structure conditions in order to identify if the structure is healthy or presents damage produced by corrosion. It is based on four steps. In step 1, the vibrational response of the structure produced by a forced excitation (electrodynamic shaker) is analyzed or decomposed by three EMD methods, i.e., the EMD, EEMD, and CEEMD methods. In step 2, the decompositions or frequency bands (IMFs) estimated by EMD methods are processed by SEI, a nonlinear measure, to identify features to be used for damage detection. In step 3, the calculated SEI values for each IMF of the three EMD methods are evaluated using the ANOVA (analysis of variance) method, a statistical analysis, in order to determine the most discriminating values for assessing structural conditions. In step 4, the selected SEI values are classified using a decision tree classifier (DTC) in order to assess the condition of the bridge in an automatic way.
## 3 Proposed Method
### Cross-Attention Feature Fusion Module (CaFF)
\[Out_{2}=\beta\cdot V_{2}\cdot AM_{2}^{T}+R_{1} \tag{12}\]  
After the previous cross-branch attention calculations, the updated feature maps are used in the third round of computation to further strengthen the feature relationship between branches. In this stage, the updated query vector \(Q_{4}\) is multiplied with key vector \(K_{3}\) and pooled to generate the third attention matrix, \(AM_{3}\), resulting in deeper weighted features, \(Out_{3}\). \[AM_{3}=\text{Softmax}(K_{3}\times Q_{4}^{T}) \tag{13}\]  
\[Out_{3}=\gamma\cdot V_{3}\cdot AM_{3}^{T}+Out_{1} \tag{14}\]  
In this process, the features generated via the two branches are complementarily fused through multi-stage cross-branch attention calculations. This results in a more expressive feature output. Finally, the three weighted feature matrices are processed through a convolution layer to produce the fused output feature map. \[F_{CaFF}=\text{Concat}((\alpha(AM_{1}^{T}\times V_{1})+S_{1}),(\beta(AM_{2}^{T }\times V_{2})+R_{1}),(\gamma(AM_{3}^{T}\times V_{3})+Out_{1})) \tag{15}\]  
\[S_{1}=low\_Conv(Input_{Map1}) \tag{7}\]0  
The low_conv module is designed to refine low-level features and enable effective feature fusion. It uses a \(1\times 1\) convolutional kernel for feature transformation and dimensionality reduction, adjusting the depth of feature maps without altering their spatial dimensions. Batch normalization follows to stabilize training and speed up convergence. With stride = 1 and padding = 0, the spatial dimensions of the feature maps are preserved, facilitating direct feature fusion across layers. Overall, the module enhances feature refinement and fusion while maintaining computational efficiency. The core of the entire process is the application of the cross-attention mechanism. The features generated via the Swin Transformer and ResNet50 branches are fused through multi-stage cross-branch attention calculations, achieving the complementary fusion of weld seam features. This results in a more expressive feature output. The three rounds of cross-attention calculations ensure a deep integration of features from both branches. This results in output features with richer contextual information and more comprehensive semantic representations.
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 2 Materials and Methods
### Cygnss Data
#### 2.1.3 Quality Control  
Through Equation (2), we obtain the CYGNSS reflectivity. In order to better apply CYGNSS data to flood monitoring, several corrections are performed to control the quality of the data. Firstly, CYGNSS reflectivity is a left-handed circular polarization reflectivity which is related to incidence angles [19]. Based on the soil moisture work of [PERSON] et al. in 2020, we removed the observations with incidence angles greater than 65\({}^{\circ}\). In addition, we do not consider the observations that the signal-to-noise ratio is less than 2 dB and the receiver antenna gain is less than 5 dB [21].
# Crosslinked Bifunctional Particles for the Removal of Bilirubin in Hyperbilirubin Cases
## 2 Materials and Methods
### Characterization of Polymers  
#### 2.3.1 Scanning Electron Microscopy  
The morphology of the synthesized particles was observed by using a Quanta 250 scanning electron microscope (FEI Company, Hillsboro, OR, USA).
## 4 CFD Modeling
### Grid Sensitivity Analysis  
In a FDS application, the accuracy of the results and the calculation time depend above all on the mesh fineness. In this respect, the grid resolution is evaluated by the non-dimensional expression \(\mathrm{D}^{*}/\delta_{\mathrm{x}}\)[16], in which \(\delta_{\mathrm{x}}\) is the nominal size grid [m], while the characteristic length scale \(\mathrm{D}^{*}\) [m] is expressed as follows:  
\[\mathrm{D}^{*}\ =\ \left(\frac{\mathrm{Q}}{\rho_{\infty}\mathrm{c}_{\mathrm{p}} \mathrm{T}_{\infty}\sqrt{\mathrm{g}}}\right)^{\frac{2}{5}} \tag{1}\]  
where Q is the HRR [W], \(\rho_{\infty}\) is the air density [kg/m\({}^{3}\)], \(\mathrm{c}_{\mathrm{p}}\) is the specific heat of the air [J/kg/K], \(\mathrm{T}_{\infty}\) is the ambient temperature [K], and g is the gravity acceleration [m/s\({}^{2}\)]. According to the FDS user guide [16], an adequate resolution is obtained when \(\mathrm{D}^{*}/\delta_{\mathrm{x}}\) is between 4 and 16. In this regard, it was found that: \(\mathrm{D}^{*}\) = 2.21 for two cars, \(\mathrm{D}^{*}\) = 3.75 for bus, \(\mathrm{D}^{*}\) = 4.60 for HGV (50 MW), and \(\mathrm{D}^{*}\) = 6.07 for HGV (100 MW). Therefore, for each scenario investigated, the size of the cell side should be in the range of 0.14-0.55 m for two cars, 0.23-0.94 m for bus, 0.29-1.15 m for HGV (50 MW), and 0.38-1.52 m for HGV (100 MW). It is to be said that only parallelepiped cells (preferably cubic) can be employed in the FDS.  
In Table 2 are reported the results of the grid sensitivity analysis in terms of temperature as a function of the cell size. These outcomes are related to points located at the tunnel ceiling; in particular, at a height of 5.5 m from the road pavement, and longitudinal distances equal to 10 m both downstream and upstream from the fire center.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Temperature Values 5.5 m from the Road Pavement**} \\ \hline \multirow{3}{*}{**Cell Size**} & \multicolumn{2}{c}{**Cross-Section –10 m**} & \multicolumn{2}{c}{**Cross-Section +10 m**} \\ \cline{2-5}  & **T** & **Error** & **T** & **Error** \\  & [\({}^{\circ}\)C] & **[\%]** & [\({}^{\circ}\)C] & **[\%]** \\ \hline
0.2 & 588 & - & 754 & - \\
0.4 & 583 & 0.85 & 751 & 0.40 \\
0.5 & 567 & 3.57 & 739 & 2.39 \\
0.8 & 484 & 17.69 & 703 & 6.76 \\
1.0 & 449 & 23.64 & 674 & 10.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grid sensitivity analysis: temperature values at driving lane center 5.5 m from the road pavement.  
From Table 2 one can see that the cell size smaller than 0.4 m did not provide significant differences in temperature predictions. In fact, the percentage errors between the temperature values were found to be with cells size of 0.4 m and 0.2 m less than 1%. Since the computation time increases as the mesh becomes finer, the tunnel volume is assumed to be divided into cubic cells of 0.4 m side in the present paper. A total number of 1,095,255 cells were used in our study.  
Figure 1 provides a view of the tunnel simulated by means of FDS.
# Achievable Rate Maximization for Multi-Relay AF Cooperative SWIPT Systems with a Nonlinear EH Model
## 2 System Model
### Asymptotic Analysis for the Maximum Achievable End-to-End Rate  
In this section, in order to get more intuitive insight about the effect of nonlinear EH model on achievable information rate maximization for the system with TS and PS schemes, we analyze the asymptotic maximum achievable information rate for the system with nonlinear EH model in the region of low and high input SNRs, say \(P_{s}\to 0\) and \(P_{s}\rightarrow\infty\) with fixed noise power \(N_{0}\) and saturation harvested power \(M_{l}\), which is then compared with traditional linear EH model so as to further investigate the impact of the resource allocation mismatch caused by using a traditional linear EH model.
# Tracking of Moving Targets Through Asynchronous Measures
## 4 Numerical Results
### Velocity  
In this section, we have simulated the algorithms for different target velocities, with the following assumptions.  
1. The altitude of the target is \(h=100\,\mathrm{m}\);
2. The radius of the orbit is \(r=1000\,\mathrm{m}\);
3. The RMSE of range measurements is \(\sigma_{\rho}=0.1\,\mathrm{m}\);
4. For all the anchors the LoS probability is \(p=100\%\).  
As expected, we can observe in Figure 5 that the proposed algorithm performs similarly or slightly better than the Kalman filter at low velocities, while the performance gap gets larger as the velocity increases. This is due to the fact that the proposed algorithm updates the estimate as soon as an anchor measure is received, while the Kalman filter must collect all the measures before updating the position estimate, and during the time required to receive all the anchors, the target moves significantly when the velocity is high enough.  
Figure 4: Positioning root mean square error of the vertical (V) and horizontal (H) components with \(h=100\,\mathrm{m}\), \(v=0.1\,\mathrm{m}/\mathrm{s}\), \(r=1000\,\mathrm{m}\) and \(p=100\%\).
# The Optimization of the Geometry of the Centrifugal Fan at Different Design Points
## 4 Summary and Discussion  
As it mentioned before, the aim of impeller geometry optimization is to maximize full efficiency by changing the values of the leading and trailing angles of the blade. Considering the results and to represent a summary of research, the following key findings are presented:  
* It is determined that at the point of maximum power (1 st DP), the highest achievable value of the static efficiency of the optimized fan, which is in the medium flow-rate zone (56.7-61.4% of the maximum amount of blown air at the tested speeds), is 3.2-4.4% higher compared to the fan at the second DP when the test objects run at the same speed.
* The results collected during the virtual tests show that the test object, the geometry parameters of which are selected for the second DP, exhibits up to 12.5% higher static efficiency than the fan at the first DP operating with a low amount of blown air (11.1-35% of the maximum flow) and in the zone of high static system pressure gain (95.2-99.4% of the maximum sustained system resistance gain).
* Comparing the performance curves of the fans results in a 0.56-2.45% higher airflow rate for the fan optimized at the first DP compared to the maximum flow rate of the fan at the second DP in the absence of system static resistance.
* Reviewing the established operating characteristics at the same fan speeds, it is observed that the fan optimized at the first DP supports 1.4-4.5% higher static system pressure increases than the test object designed at the second DP.
* Based on the results of this research, we recommend selecting the operating modes of the fan in the stable zone.  
To represent future work, a brief discussion can be additionally provided. Although the optimization of the geometry of a centrifugal fan at different design points is reported in this work, there still exist unsolved problems. One problem is the improvement of numerical investigation; the possible use of the Navier-Stokes equation can be considered to solve this problem [8; 9; 10]. Another problem is fan research from the physical implementation perspective. In the Introduction, many problems were mentioned. Considering physical implementation, as well as the problems related to fan reliability, wear and failure research, stability and control, and vibration, could be the next research step in this field.
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
###### Abstract  
This paper presents a new output power smoothing control strategy for a wind farm based on the allocation of wind turbines. The wind turbines in the wind farm are divided into control wind turbines (CWT) and power wind turbines (PWT), separately. The PWTs are expected to output as much power as possible and a maximum power point tracking (MPPT) control strategy combining the rotor inertia based power smoothing method is adopted. The CWTs are in charge of the output power smoothing for the whole wind farm by giving the calculated appropriate power. The battery energy storage system (BESS) with small capacity is installed to be the support and its charge and discharge times are greatly reduced comparing with the traditional ESSs based power smoothing strategies. The simulation model of the permanent magnet synchronous generators (PMSG) based wind farm by considering the wake effect is built in Matlab/Simulink to test the proposed power smoothing method. Three different working modes of the wind farm are given in the simulation and the simulation results verify the effectiveness of the proposed power smoothing control strategy.  
power smoothing; wind farm; wind turbine allocation; rotor inertia; power wind turbine; control wind turbine; battery energy storage system; wake effect +
Footnote †: journal: Applied Sciences
## 2 Epitaxial Growth on InP-on-Insulator Substrate
### Integration Method of III-V Photonic Devices on Si  
Figure 1 shows the process flow of fabricating membrane photonic devices on Si using epitaxial growth technique on III-V-on-insulator platform. The process begins with the epitaxial growth of a III-V epitaxial layer including InP membrane layer and InGaAs etch-stop layer on standard InP substrate. Next, the epitaxial InP wafer and a Si wafer are bonded. Then, the InP membrane layer is left on the SiO\({}_{2}\) layer by removing the InP substrate and InGaAs etch-stop layer (InP-on-insulator substrate). After that, a III-V active layer such as InGaAsP/InGaAsP- or InGaAs/InGaAlAs-MQW double-heterostructure (DH) sandwiched between the InP layer is grown on InP-on-insulator substrate as an active region for photonic devices. The MQW-DH is then etched by dry- and selective wet-etching, which leaves an InP layer except for under the SiO\({}_{2}\) mask (mesa-stripe). After that, undoped InP is selectively grown on the InP membrane to fabricate the BH, which  
enables high optical- and carrier-confinement into the active region. Finally, formation of the lateral _p-i-n_ or _p-n_ junction and metallization are carried out to fabricate the membrane photonic devices.
## Appendix B Reference Data Quality Framework  
Our reference data quality framework, by [PERSON] et al. [6], is hierarchically structured into three levels: dimensions, domains, and indicators. The four dimensions of the framework are defined in [6; 29] as follows:  
_Integrity:_ The degree to which the data conforms to structural and technical requirements.  
_Completeness:_ The degree to which expected data values are present.  
_Consistency:_ The degree to which data values are free of breaks in conventions or contradictions.  
_Accuracy:_ The degree of agreement between observed and expected distributions and associations.  
The integrity dimension entails three domains:  
_Structural dataset error:_ The observed structure of a dataset differs from the expected structure.  
_Dataset combination error:_ The observed correspondence between different datasets differs from the expected correspondence.  
_Value format error:_ The technical representation of data values within a dataset does not conform to the expected representation.  
The completeness dimension consists of two domains:  
_Crude missingness:_ Metrics of missing data values that ignore the underlying reasons for missing data.  
_Qualified missingness:_ Metrics of missing data values that use reasons underlying missing data.  
The consistency dimension entails the following two domains:  
_Range and value violations:_ Observed data values do not comply with admissible data values or value ranges.  
_Contradictions:_ Observed data values appear in impossible or improbable combinations.  
Lastly, the accuracy dimension contains three domains:  
_Unexpected distribution:_ Observed distributional characteristics differ from expected distributional characteristics.  
_Unexpected association:_ Observed associations differ from expected associations.  
_Disagreement of repeated measurements:_ Disagreement between repeated measurements of the same or similar objects under specified conditions.  
Table 1 summarizes the structure of the data quality framework and provides links to the definitions of the indicators.