esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modelling and Simulation
### Mode #1 Harmonic Supply and Linear R-L Load
#### 3.1.3 2-Phase Network and Linear RL Load  
Parameters for steady states at \(t\in\langle 0.0;0.1\rangle\) and at \(t\in\langle 0.1;0.2\rangle\) are given in the Table 12:  
Power components average values, classical calculus, given in Table 13:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time** & \(\mathbf{U_{a,rms}}\) **[V]** & \(\mathbf{U_{g,rms}}\) **[V]** & \(\mathbf{I_{a,rms}}\) **[A]** & \(\mathbf{I_{g,rms}}\) **[A]** & \(\mathbf{A_{1}}\) **[A]** & \(\mathbf{\varphi_{1}}\) **[\({}^{\circ}\)el.]** & **cos \(\mathbf{\varphi_{1}}\) **[\({}^{\circ}\)el.]** \\ \hline t = 0.1 s & 229.8 & 229.8 & 9.991 & 9.991 & 14.13 & 36.88 & 0.7947 \\ t = 0.2 s & 229.8 & 229.8 & 13.85 & 13.85 & 19.60 & 56.31 & 0.5547 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Simulation results before and after the change (at steady states).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time** & \(\mathbf{S_{av}}\) **[VA]** & \(\mathbf{P_{av}}\) **[W]** & \(\mathbf{Q_{av}}\) **[VAr]** & \(\mathbf{D_{av}}\) **[VAd]** & \(\mathbf{PF}\) **[\({}^{-}\)]** & \(\mathbf{THD_{i}}\) **[\%]** & \(\mathbf{P_{1 av}}\) **[A]** & \(\mathbf{P_{0 are}}\) **[W]** \\ \hline t = 0.1 s & 11,479.8 & 9182.8 & 6689.3 & 0 & 0.7999 & 0.0 & 9123.2 & 0 \\ t = 0.2 s & 15,916.7 & 8828.7 & 13,243.7 & 0 & 0.5547 & 0.0 & 8833.2 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Simulation results before and after the change (at steady states).  
Figure 9: Phase voltages (**a**) and currents (**b**) in \(a,b,c,d,e\)—coordinates.  
As can be seen, the calculated and simulated values of power components are nearly the same. Network voltages and currents in \(a,b\) (\(\alpha,\beta\))- coordinates during transient, Figure 10a,b.
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 2 Related Work
### End-to-End Delay  
In many applications, energy effect and end-to-end delay are related. In order to increase the lifetime of a battery-provided sensor, it should be close the wireless transceiver; otherwise, the data cannot arrive at the objective node in time, which can increase the end-to-end time. To increase the network lifetime, we should use a high-efficiency duty-cycled factor, which is bound to influence the  
Figure 1: Topological structure based on ETX.  
Figure 2: Network topology constructed according to the residual energy.  
system delay. High-efficiency means an increase in communication time. It will increase the single-hop delay. Under the circumstance of multi-hop, the end-to-end delay will be increased.  
In [26], the authors study the problem of network delay with low duty cycle and compare it with the RPL based on ETX. At the same time, it expands the duty cycle of ContikiMAC (Media Access Control of Contiki) to support different sleep modes. However, it only uses a simple delay metric, but does not consider the quality of the link and failing to build an efficient energy metric mode. It fails to improve the remaining performance of the network and is not verified in a real environment. In [27], the authors use the residual energy and transmission delay as the basis of the next hop choice. In some ways, it considers the network lifetime and end-to-end delay and optimizes the network by ant colony optimization. However, the author did not consider the multipath support. Some nodes still may fail as a result of too much energy consumption. In [28], the authors use fuzzy logic to design objective function and consider node and link measurement, namely, end-to-end delay, hops, ETX and LQI (Link Quality Indicator). This algorithm supports the quality of service in a static and dynamic network environment, and improves the reliability and average delay of the network. The author ignores the node energy consumption, not considering the network lifetime. In [29], opportunistic routing is proposed, which will probably obtain better load balance. The data forwarding decision depends on the receiver nodes, rather than the sender nodes.
# Single-Shot Time-Lapse Target-Oriented Velocity Inversion Using Machine Learning
## 2 Methodology  
### Synthetic Modeling  
The proposed inversion methodology was tested and validated with synthetic seismic data. We used a realistic 20 km wide, 7.5 km deep P-wave velocity model (Figure 1) estimated from a pre-salt oil field (named Gato do Mato) located at the Santos basin, off a shore in Brazil [20; 21]. The work of [22] provides more details about the geology of this area. Figure 1 also shows the acquisition geometry, formed of 1 shot and 49 ocean bottom nodes (OBNs). The source is located at an 8 m depth and at the center of the analysis region in the lateral direction, and the receivers are at a depth of 2 km with 400 m spacing. Also, the source wavelet is a Ricker with peak energy at 5 Hz frequency (15 Hz maximum bandwidth); this frequency range covers the basic frequency band employed in seismic exploration. The reservoir region is located at a depth of about 6 km, as indicated in Figure 1; the depth of the reservoir region is usual in a pre-salt formation.  
The above parameters correspond to the baseline survey. We also considered 5000 monitor surveys, which differ from the baseline by the added velocity perturbations in the reservoir region to represent the time-lapse effect of production-induced fluid substitution. Different velocity perturbations were applied to the monitor surveys to simulate a number of possible fluid movements in the reservoir region due to oil and gas production. The velocity perturbations representing reservoir changes are described in more detail in Section 2.2.  
The dataset is composed of 5001 shot gathers in total, one associated to the baseline and the others to each of the monitor surveys. The seismograms were obtained by calculating the finite differences' approximation of the acoustic wave equation in 2D for constant-density and isotropic media. The main numerical parameters were a time step of 4 milliseconds, a total recording time of 10 s, and a regular grid spacing of 20 m. In addition, to avoid reflections at the borders, we used 25 convolutional perfectly matched layers (CPMLs) as the absorbing boundary condition added around the domain of analysis. Surface-related multiples were not modeled.
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
## 4 The Research Case Study, Gabel El-Zeit Wind Farm Complex (580 MW)  
Egypt leads the MENA region in wind and solar energy production, boasting a capacity of 3.5 GW, with ambitions to expand this to 6.8 GW by 2025. The Gabel El-Zeit wind farm complex, located in the Gulf of Suez in Egypt, serves as the data source for this paper. This wind farm complex is the largest in the MENA region, with a total installed capacity of 580 MW. It is divided into three phases: Gulf of Zeit (1), with a capacity of 240 MW; Gulf of Zeit (2), with 220 MW; and Gulf of Zeit (3), with 120 MW. The turbine rotation speed reaches 16 rpm, and the actual wind speed at 100 m height above ground level reaches 12-33 m/s. The production rate of the wind farm reached a standard number, which is 52% of the station's total capacity. To maintain the birds' safety, the wind farm contains a radar monitoring system for bird migration to save more than 300,000 birds annually.  
The onshore Gabel El-Zeit wind farm (580 MW) was chosen due to its significance and capacity, providing a comprehensive dataset for the models under development [62]. The strategic placement of renewable wind energy stations in proximity to the Suez Canal, Egypt, Jordan, and NEOM offers several significant advantages as shown in Figure 5. European investments and Saudi Arabian in the Gabel El-Zeit wind farm underscore a strategic partnership aimed at bolstering renewable energy capacities and environmental sustainability. Saudi Arabia's interest, particularly in alignment with its Vision 2030 for energy diversification, complements European expertise and commitment to reducing carbon footprints, as demonstrated through various green initiatives and funding mechanisms [63; 64]. The critical strengths at Gabel El-Zeit and the Gulf of Suez are as follows:  
1. The region's arid climate and consistent winds provide ideal conditions for wind power generation.
2. The wind power potential reaches 1300 W/m\({}^{2}\) onshore and 1700 W/m\({}^{2}\) offshore.
3. The wind speed reaches 11.5 m/s onshore and 12.5 m/s m/s offshore. Therefore, Gabel El-Zeit and the Gulf of Suez have the highest wind energy potential in the MENA region.
4. The location's proximity to major trade routes, including the Suez Canal and 12% of the world trade passing route, presents opportunities for energy export and regional cooperation, especially with existing electrical grid infrastructure.
5. By harnessing wind energy, these countries can reduce their reliance on fossil fuels, mitigate climate change impacts, and contribute to global energy security.
6. Developing wind energy infrastructure can stimulate economic growth, create jobs, and foster technological advancement in the region.  
Figure 5: The location of the Gabel El Zeit wind farm.
## 3 Two-Stage Stochastic Optimization Problem
### Energy Balance Constraint  
In the hydrogen hybrid UAV energy system, the primary focus is on the electrical load demand. During system operation, the electrical energy obtained by the system should be greater than or equal to the energy consumption. The devices that generate electrical energy in the system include hydrogen fuel cells and lithium batteries. The devices that consume electrical energy include DC motors, fans, communication equipment, and lithium batteries. The energy balance is represented by the following equation:  
\[E_{FC}+E_{BT}^{out}\geq E_{M}+E_{F}+E_{CD}+E_{BT}^{in} \tag{18}\]  
where \(E_{FC}\) represents the electrical energy provided by the hydrogen fuel cell, \(E_{BT}^{out}\) represents the electrical energy output by the lithium battery, \(E_{M}\) represents the electrical energy consumed by the DC motor, \(E_{F}\) represents the electrical energy consumed by the fan, \(E_{CD}\) represents the electrical energy consumed by the communication equipment, and \(E_{BT}^{in}\) represents the electrical energy input to the lithium battery.  
Capacity constraint: For the hydrogen hybrid UAV system, the output power of each device should not exceed its designed capacity. Furthermore, for the hydrogen fuel cell, its minimum operating power should not be lower than 30% of its capacity to avoid operating under excessively low load. Therefore, the capacity constraints for the hydrogen fuel cell and fan are as follows:  
\[Y_{FC,t}\times 0.3 Cap_{FC}\leq E_{FC}\leq Y_{FC,t}\times Cap_{FC} \tag{19}\]  
\[0\leq E_{F}\leq Y_{F,t}\times Cap_{F} \tag{20}\]  
In the equation, \(Y_{FC,t}\) represents the Boolean variable used to determine whether the hydrogen fuel cell is operating, with a value of 0 or 1; \(Cap_{FC}\) represents the maximum output power of the hydrogen fuel cell; \(Y_{F,t}\) represents the Boolean variable used to determine whether the fan is operating, with a value of 0 or 1; \(Cap_{F}\) represents the maximum output power of the fan.
# Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
## 2 Algorithm Description
### Interface Definition
#### 2.2.2 Equalization and Truncation  
The 3-bit truncation applied by the Observable Generation block can be configured by means of the \(\delta_{p}\) input variable. \(\delta_{p}\) is a scaling factor used during truncation, defined as:  
\[\delta_{p}=\frac{2^{N_{bits}-1}-1}{A_{clip}}=\frac{2^{N_{bits}-1}-1}{\delta \cdot\sqrt{2}}, \tag{1}\]  
where the number of quantization bits is \(N_{bits}\) = 3, and the scaling factor is \(\delta\) = 2 [23]. In principle, \(\delta\) must be selected so that the clipping effects are negligible. This factor is applied to the signal prior to truncation as:  
\[Xr =\Re(X)\cdot\delta_{p} \tag{2}\] \[Xi =\Im(X)\cdot\delta_{p}\] (3) \[Yr =\Re(Y)\cdot\delta_{p}\] (4) \[Yi =\Im(Y)\cdot\delta_{p}, \tag{5}\]  
and it controls the clipping point for the truncation. A deeper study on the effects of the \(\delta\) parameter can be found in Appendix A of [17].
# Determining the AMSR-E SST Footprint from Co-Located MODIS SSTs
## 4 Results  
### Simulation Study  
Simulations were undertaken to analyze the performance of the estimation algorithm. The results of these simulations were used to choose \(R\), the number of bootstrap samples, and \(n\), the number of matchu-ps in each sample, when the algorithm was applied to the actual dataset. A simple (but realistic) AMSR-E footprint (referred to as the imposed footprint hereafter) was used to generate simulated microwave measurements from 250,000 real high-resolution MODIS patches according to Equation (3). The 250,000 patches were randomly selected from the approximately four million matchu-ps discussed in Section 3.1, with the actual AMSR-E values being replaced by simulated values. For the imposed footprint, we used an elliptical Gaussian function with its major axis rotated 45\({}^{\circ}\) clockwise, about the vertical-axis with an aspect ratio of 1.74 (\(\approx\)75/43) approximating the aspect ratio of the 6.93 GHz AMSR-E channel. The footprint is normalized according to Equations (5) and (6). Microwave measurements were simulated with Equation (3), using MODIS data as the ground truth, to which we added Gaussian white noise with a standard-deviation of 0.2 K. 0.05 K Gaussian white noise was added to the MODIS data in order to simulate the infrared instrument noise; i.e., the original MODIS field was taken as the true SST field and the simulated AMSR-E and MODIS fields were obtained from the 'true' field. The footprint was then estimated using the method described in Section 3.2. Footprints were retrieved for three different sets of parameters \((R;n)=\) (1; 250,000), (1; 2000), (2000; 2000). Figure 5a shows the imposed footprint, which we aim to reconstruct, used to generate the AMSR-E samples from the MODIS data. The retrieved footprints without bootstrap sampling (i.e., \(R=\) 1; Figure 5b,c) were dominated by noise. The results of regularization (bootstrapping procedure; Figure 5d), showed a dramatic decrease in the noise of the retrieved footprint with the rotated Gaussian shape of the imposed footprint emerging clearly. Smoothing the retrieved footprint with a 4 \(\times\) 4 moving average further reduced the noise yielding a close facsimile to the imposed footprint (Figures 5e and 6).
# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 6 Discussion
### Applicability of the BCPM-UII Model to Small-Scale Vector Maps  
In small-scale vector map datasets, typically comprising limited data such as essential geographic features like roads, water bodies, and buildings, the representation tends to be less detailed [50]. These datasets are commonly utilized in straightforward applications like route planning and location positioning. In this paper, we discovered that the BCPM-UII model not only suits large-scale vector map datasets with high precision but also proves effective for small-scale ones.  
In this section, we conducted experiments using local vector map datasets from Chongqing and Xi'an to showcase the algorithm's adaptability to small-scale datasets. The Chongqing dataset encompasses 450 features, while the Xi'an dataset includes 1012 features, as depicted in Figure 14. The constructed unique identifiers have a length of 1500.  
Figure 15 exhibits the BER of the model under various types of attacks. Notably, even in the face of varying degrees of RST, object deletion, and cropping attacks, the BER values  
Figure 14: Vector map dataset with small data volume: (**a**) Chongqing dataset; (**b**) Xi’an dataset.  
Figure 13: Experimental results of robustness with different unique identification length: (**a**) cropping attack; (**b**) layer-delete attack; (**c**) object-delete attack.  
remain below the 0.10 threshold. These experimental findings underscore the significant applicability of the BCPM-UI model in handling small-scale vector map datasets. This offers valuable insights into exploring and researching watermark algorithms tailored for such datasets and lays a theoretical groundwork for developing more sophisticated and efficient copyright protection models for small-scale vector map datasets in the future.
# Spectral Correction of CPV Modules Equipped with GaInP/GaInAs/Ge Solar Cells and Fresnel Lenses
## 2 Materials and Methods
### Experimental Campaign  
The experimental validation of the proposed equations was carried out at the CEAEMA of the University of Jaen [13]. The Centre is equipped with a weather station and irradiance sensors such as pyrheliometers and pyranometers that measure different components of the irradiance (i.e., global horizontal and normal, direct normal and diffuse horizontal), see Figure 1-left.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(SF\) & \(f(AM)\) & \(f(AOD\ coeff.)\) & \(f(PW\ coeff.)\) \\ \hline top & & \(\mathrm{aln(x)+b\ ^{*}}\) & \(\mathrm{aln(x)+b\ ^{*}}\) \\ middle & \(\mathrm{ax^{4}+bx^{3}+cx^{2}+dx+e\ ^{*}}\) & \(\mathrm{ax^{2}+bx+c\ ^{*}}\) & \(\mathrm{Ax+b\ ^{*}}\) \\ bottom & & \(\mathrm{aln(x)+b\ ^{*}}\) & \(\mathrm{aln(x)+b\ ^{*}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed spectral corrections for each subcell. \(SF\): spectral factor; \(AM\): air mass; \(AOD\ coeff.\): aerosol optical depth coefficients; \(PW\ coeff.\): precipitable water coefficients.  
cells (or isotypes) from Black Photon and a solar spectral irradiance metre (SolarSIM-D2) from Spectrafy Inc. are used to characterise the spectral composition and also measure the spectrally resolved direct normal irradiance and the atmospheric parameters that influence the spectrum, i.e., \(AOD\) at 500 nm and \(PW\). The \(AM\) is also calculated from the sun's zenith angle.  
It is important to note that all the sensors are within the calibration period and are cleaned once a week and also after rainy days to ensure quality of measurements.  
As mentioned in the introduction, the following CPV configurations are considered in this study:  
* LM: a device based on a lattice-matched GaInP/GaInAs/Ge solar cell
* LM + PMMA: a device based on a LM solar cell and a poly(methyl methacrylate) (PMMA) Fresnel lens
* MM: a device based on a metamorphic GaInP/GaInAs/Ge solar cell
* MM + SoG: a device based on a MM solar cell and a silicone-on-glass (SoG) Fresnel lens.  
Further information about the external quantum efficiency (\(EQE\)) of the MJ solar cells and transmittance of the Fresnel lenses can be found in [14].
# Development of Rock Embedded Drilled Shaft Resistance Factors in Korea based on Field Tests
## 2 Evaluation of Resistance for the Drilled Shaft
### Measured Resistance from the Load Test Results  
A load transfer analysis was conducted to measure the shaft and base resistance based on the drilled shaft load test results. The strain data were utilized to calculate the axial loads at each depth along the test shaft. In addition, the f-w curves, describing the unit shaft displacement for each depth, and the q-w curves, describing the unit displacement at the base of the drilled shafts, were established using the calculated axial loads. Then, according to [PERSON] and [PERSON] [21], the ultimate shaft and base resistance values were determined when the displacement of the shaft was equal to 1% and 5% of the shaft diameter, respectively. If the load tests were terminated before the applied loads reached these ultimate resistance values, the f-w and q-w curves were extrapolated using the hyperbolic method [22], as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Shaft No.**} & \multicolumn{2}{c}{**Calibrated Elastic Modulus for**} & \multicolumn{2}{c}{**Calibrated Load Ratio for the**} \\  & **Load Test Transfer Analysis** & **Equivalent Load-Displacement Curve** \\ \cline{2-4}  & **E\({}_{50}\) (GPa)** & **E\({}_{\text{Fellenius (GPa)}}\)** & **Load Ratio** \\ \hline TP1 & 32.29 & 32.91\(-\)0.0172\(\mathrm{\SIUnitSymbolMicro c}\) & 1.00 \\ \hline TP2 & 34.67 & 29.19\(-\)0.0258\(\mathrm{\SIUnitSymbolMicro c}\) & 3.82 \\ \hline TP3 & 37.97 & 35.71\(-\)0.0177\(\mathrm{\SIUnitSymbolMicro c}\) & 1.00 \\ \hline TP4 & 37.74 & 37.78\(-\)0.0007\(\mathrm{\SIUnitSymbolMicro c}\) & 1.00 \\ \hline TP5 & 33.13 & 41.26\(-\)0.0374\(\mathrm{\SIUnitSymbolMicro c}\) & 4.44 \\ \hline TP6 & 39.72 & 35.66\(-\)0.0108\(\mathrm{\SIUnitSymbolMicro c}\) & 4.31 \\ \hline TP7 & 38.15 & 40.81\(-\)0.0160\(\mathrm{\SIUnitSymbolMicro c}\) & 1.22 \\ \hline TP8 & 36.39 & 42.02\(-\)0.0214\(\mathrm{\SIUnitSymbolMicro c}\) & 1.42 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of calibrated elastic modulus and load ratio.  
Figure 7: Equivalent load-displacement curve of TP 2.  
To determine the ultimate total resistance values, equivalent load-displacement curves were applied using the ASCE 20-96 method [23]. In this method, the ultimate total resistance is defined as the load at which the displacement of the shaft is equal to the elastic displacement + 3.81 + D/100 (mm). If a load test was terminated before the applied load reached this ultimate resistance value, the equivalent load-displacement curve was extrapolated using the hyperbolic method shown in Figure 9.
## 3 Model Architecture
### Construction of the Spatial-Temporal Graph Based on the Skeleton
#### 3.2.2 Spatial-Temporal Graph Model Based on Human Skeleton Nodes  
The spatial-temporal Sequence of key points of the human skeleton obtained from the video sequence was used as a three-dimensional lattice to implement Delaunay three-dimensional triangulation. Three vertices of each triangle were taken as adjacency points to form a graph. Since adjacent triangles have common edges, the subgraphs constructed according to each triangle have common vertices, thus forming a stable graph structure. It can better describe the three-dimensional spatial structure information of a key point sequence and improve the identification degree of the action recognition model.  
The resulting graph model is shown in Figure 5.