esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion of IHSSAO Performance Verification
### Experiment II: IEEE CEC2017 Test Functions
#### 5.1.2 Threat Constraints  
The main threats faced by UAVs in their missions include radar threats, electromagnetic threats, and missile threats. In order to simplify the UAV mission environment model, the cylindrical area with radius \(R_{i}\) is used to represent the threat area, whose radius size determines the coverage of the threat area.  
Based on the above conditions, we model the simulation scene of the UAV path planning workspace as depicted in Figure 9.  
Figure 9: UAV path planning workspace in complex terrain.
## 3 Results and Discussion  
### Mixing Process Tracking  
#### 3.1.1 Determination of the Concentration  
The calibration curve of each component and measurement line is shown in Figure 3. The curves present a linear tendency. The linear regression equations, correlation coefficients (\(r^{2}\)), and the molar absorptivity of PFK5 and HFO3E were calculated, and they are collected in Table 4. The results show that the sensitivity of the measuring system for HFO3E is considerably lower than the one of PFK5, due to the great difference in the molar absorptivity of both gases.  
The maximum absorption of the component HFO3E is located at 196 nm in the UV-Vis spectrum, where it is common to find noise when measuring the absorbance since this wavelength shows very poor selectivity. Because of this, the concentration, velocity and molar flux profiles of HFO3E have more deviations and are less accurate.  
Figure 3: Experimental concentration values (C\({}_{exp}\), dots) and calibration curves (dashed lines) of the components of the gas mixtures. T (Top, \(\odot\)), MT (Medium Top, \(\triangle\)), MB (Medium Bottom, \(\square\)), and B (Bottom, \(\diamond\)) correspond to the different heights of the measurement lines.
# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
## 2 Problem Formulation and Objective Function
### Estimated Objective Function  
Minimizing the root mean square error (\(RMSE\)) of the PV characteristics between the estimated parameters and the experimental results is an important objective function to be considered. Therefore, the decision variables (\(X\)) are extracted in each run of the optimizer. The mathematical formula to compute \(RMSE\) can be formulated as follows:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(J(V,I,X)^{2}\right.} \tag{7}\]  
\[J(V,I,X)=I-I_{\text{exp}} \tag{8}\]  
where \(I_{exp}\) is the experimental current, \(N\) is the reading data number, \(V\) is the experimental voltage, \(I\) is the estimated current, and \(X\) is the decision variables that are calculated as follows:  
For SDM, \(X=\Big{\{}\Big{(}I_{ph},\ I_{s1},\ a_{1},\ R_{s}\,\ R_{sh}\,\ I_{s2}\,\ a_{2}\ \Big{)}\Big{\}}\).  
For TDM, \(X=\Big{\{}\Big{(}I_{ph},\ I_{s1},\ a_{1},\ R_{s}\,\ R_{sh}\,\ I_{s2}\,\ a_{2}\,\ I_{s3}\,\ a_{3}\Big{)}\Big{\}}\).
# Transmission Performance Analysis of RV Reducers Influenced by Profile Modification and Load
## 3 Effects of Modification Coefficients and Load
### Influence of Load under Case 3 Modification Method  
Compared with the previous two traditional compound modification methods, the load effect on the transmission performance using the case 3 method is specific. As can be seen from Figure 14a, the changing trend is basically the same as under both the rated load and thrice the rated load. However, the TPM will cause an opposite changing trend at twice the rated load. Under the rated load torque, the contact area is the largest when the modification coefficient is 101. With the increase in the TPM, the meshing area is significantly increased at twice the rated load. For large load conditions, such as twice and thrice the rated load, the TPM has little effect on the transmission efficiency, as shown in Figure 14b. However, the TPM slightly reduces the transmission efficiency at the rated load. It can be seen from Figure 14c,d that, similar to the case 1 and case 2 methods, the TPM increases the maximum contact force and friction. As can be seen from Figure 14e, the influence of the TPM on the mean value of the TE is linear, as is the influence of the load. However, when the modification coefficient is 86 at the rated torque, the amplitude of the TE is minimized, as shown in Figure 14f. The TPM causes the TE amplitude to increase slightly at twice the rated load. In addition, the TE amplitude is slightly reduced at three times the rated load.  
Figure 13: Effect of case 2 modification method on the transmission performance of RV reducer: (**a**) contact ratio, (**b**) transmission efficiency, (**c**) max contact force, (**d**) max friction, (**e**) mean TE value, and (**f**) TE amplitude.
## 3 Results
### Comparison with Other Mapping Approaches  
At the Amazon Basin scale, RadWet-L applied over the time period 2019-2023 estimated a maximum inundation extent of 655,788 km\({}^{2}\) (95 th Conf Int: 622,996-639,350 km\({}^{2}\)), where open water accounts for 170,129 km\({}^{2}\) (95 th Conf Int: 161,622-179,913 km\({}^{2}\)) and inundated forest contributes 485,659 km\({}^{2}\) (95 th Conf Int: 461,374-513,588 km\({}^{2}\)). This overall extent is comparable with those found in previous studies, including [PERSON] et al. [11] (overall inundation extent: 629,453 km\({}^{2}\), 1996), [PERSON] et al. [12] (overall inundation extent: 568,064 km\({}^{2}\), 2006-2010), and [PERSON] [9] (overall inundation extent: 538,699 km\({}^{2}\), 2014-2017) (Table 4). Both [PERSON] et al. [11] and [PERSON] et al. [9] include a Submerged Vegetation class, defined as vegetation that is fully submerged during a part of the hydrological year [9]. These landcovers were not considered an independent class in this study and would likely be classified as open water.  
Comparing RadWet-L to other commonly used global wetland products such as the Global Lakes and Wetland Database [61; 62], or global landcover classifications such as the European Space Agency GlobCover [42], RadWet-L shows good agreement. However, it is able to map wetland features at a finer spatial scale and identifies areas which both these maps miss, as shown in Figure 16.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Study Period** & \multirow{2}{*}{**Extent**} & **Inundated Forest (km\({}^{2}\))** & **Submerged (km\({}^{2}\))** & **Open Water (km\({}^{2}\))** & **Total Wetled Area (km\({}^{2}\))** \\ \hline [PERSON] et al. & 1995 & Min & 175,814 & 53,188 & 56,018 & 285,020 \\
[11] & 1996 & Max & 437,553 & 119,836 & 72,047 & 629,436 \\ \hline [PERSON] et al. [12] & 2006–2010 & Min & 48,984 & N/A & 44,510 & 93,494 \\  & & Max & 405,368 & N/A & 162,696 & 568,064 \\ \hline [PERSON] et al. [9] & 2014–2017 & Min & 4982 & 0 & 52,287 & 57,270 \\  & & Max & 418,219 & 56,464 & 64,016 & 538,699 \\ \hline \multirow{2}{*}{This Study} & \multirow{2}{*}{2019–2023} & Min & 8779 & \multirow{2}{*}{N/A} & 84,107 & 92,886 \\  & & 95 th Conf Int & (8340–10,390) & & (81,412–88,944) & (89,752–99,334) \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{Max} & 485,659 & \multirow{2}{*}{N/A} & 170,129 & 655,788 \\  & & 95 th Conf Int & (461,374–513,588) & & (161,622–179,913) & (622,996–639,350) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between RadWet-L-classified maximum and minimum Open Water, Inundated Forest and Total Wetted Area and other comparable studies mapping inundation within the Amazon River Basin.  
Figure 15: In situ measurements of river stage height at 5 gauging stations within the Amazon River Basin between 2019 and 2023, with RadWet—L total wetted [59] area.
# Signal-to-Noise Ratio Analysis of Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy
However, this method is also irrational. The reason is that although the photon noise obeys the Poisson distribution as a whole, it also has the characteristics of random distribution, that is, the distribution of each point only obeys the Poisson distribution, but its true value cannot be known and can only be estimated. In addition, the interferogram of each OPD is the superposition of all spectra, so the photon noise value generated by each spectrum is also unknown. In other words, there is no one-to-one correspondence between its distribution at each wave number of the reconstructed spectrum and its distribution at each OPD of the interferogram. After Fourier transform, the noise corresponding to each wave number of the reconstructed spectrum cannot be accurately obtained. Therefore, finding a reasonable and effective SNR analysis method has also become a problem that needs to be focused on in this technical field. However, it is generally believed that the average signal-to-noise ratio of the reconstructed spectrum has a correlation among the average SNR of the interferogram, the number of sampling points of the interferogram, and the number of spectral segments of the reconstructed spectrum. Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy (BPS-FTS) is a type of traditional Fourier Transform Spectroscopy (FTS) [9; 10; 11; 12; 13; 14; 15; 16]. However, the FTS uses Nyquist sampling, while BPS-FTS uses bandpass sampling. In comparison, the number of BPS-FTS sampling points is greatly reduced, and the sampling time is correspondingly increased, which will also affect the SNR of the reconstructed spectrum. The number of sampling points, sampling interval, and integration time are the key links that determine the quality of the signal obtained by BPS-FTS, but there is still a blank in this area. In view of this, this paper will combine the theory of radiative energy transfer and, based on [PERSON]'s energy conservation theorem, model, analyze, and estimate the SNR of the interferogram and the reconstructed spectrum of BPS-FTS and conduct simulation verification to provide theoretical and data support for the performance evaluation of such technology in practical applications.
## 5 Conclusions  
In this paper we have proposed an energy-efficient coverage-preserving algorithm, called CoCMA, which encompasses optimization for initial deployments using a MA and a wake-up scheme. The CoCMA turns off the redundant nodes according to the MA-based schedule for nodes in order to save energy. It also used the wake-up scheme to awaken some sensor nodes in a sleeping mode through an optimization strategy to preserve the sensing coverage of the CWSN. The experimental results show that the proposed CoCMA can be successfully applied to the CWSNs. It minimizes the number of active nodes during the operation period of the CWSN, so the sensing coverage is better preserved comparing with the LEACH, LEACH-Coverage-U, PEGASIS and EGDG. Moreover, the performance of the CoCMA could be significantly improved if the positions of the sink are chosen and the nodes are chosen appropriately. With the promising results demonstrated in this paper, the proposed CoCMA might be applied to other WSNs with different structures, and we reserve this as future work.  
Figure 13: Spatial distribution of the remaining energy for the CWSN, under a same uniform deployment but with previously mentioned case for a uniform deployment, except for a different position of the sink which is changed to (50, 50).
## 3 Results and Discussion  
### Traditional Analysis of GPN Protein Gels at Different Concentrations  
Lanes 5 to 9 of Figure 2A reveal an image of polyacrylamide gel untreated with pure recombinant GPN protein. These lanes show the protein concentration at different values (see Figure 2B). An image of polyacrylamide gel untreated with GPN protein in various concentrations is presented in Figure 4A. The thicker bands (highlighted in red) correspond to the GPN protein with higher concentration.  
Images of the SDS-PAGE gels, revealed with Coomassieie blue, were analyzed using the intensity profile as shown in Figure 4. The gel image in Figure 4A includes samples of GPN protein at different concentrations (lanes 2 to 11 enclosed in a red box). Preliminary analyses were performed on the image to verify whether the intensity profile plots can detect the presence of the protein and its overexpression. Figure 4B shows the intensity profile of the molecular weight control or ladder (lane 1, Figure 4A). The minimum values of the graph coincide with the position of the protein bands of lane one or the control sample, which is used to determine the molecular weight of the samples analyzed in the rest of the gel. Figure 4C shows the intensity profile of lane 4, where the minimum enclosed in a red box indicates the presence of the protein with the highest expression or concentration within the lane. Finally, Figure 4D shows the intensity profile of the region containing the GPN protein at different concentrations (graph corresponding to the proteins enclosed in the red box in Figure 4A, lanes 2 to 11). The background noise generated by the proteins in the total extract and the different concentrations of GPN in the samples can be seen. The multiple maxima in the graph cannot be related to each different expression of the GPN protein.
# Characteristics of BeiDou-3 Experimental Satellite Clocks
## 4 Stability Analysis  
The stability of the on-broad clock is critical to the quality of the GNSS measurements, influencing the application of navigation system. To this end, the OADEV is used to characterize the stability of the new PHM and the improved Rb AFS operated on the BeiDou-3 [27]. We refer to the computing strategy proposed in the literature [28], so that it can better reflect the stability of the clock sequence with the missing data. In terms of the phase data \(x_{i}\), it can be estimated from a set of N time measurements, as follows:  
\[\sigma_{y}(\tau)=\sqrt{\frac{1}{2(N-2m)\tau^{2}}\sum_{i=1}^{N-2m}[x_{i+2m}-2x_ {i+m}+x_{i}]^{2}}\,\ \ \ x_{i+2m}\
eq 0,\ x_{i+m}\
eq 0,\ x_{i}\
eq 0 \tag{5}\]  
where \(\tau=m\tau_{0}\) is the averaging time interval, \(m\) is the averaging factor, and \(\tau_{0}\) is the basic interval. All of these clock offset sequences have been detrended by removing a daily quadratic trend. In the following discussion, we evaluated the stability of the BeiDou-3 experimental satellites. Then, we analyzed the estimated clock offsets based on the BeiDou-2 global stations and regional stations. After that, the OADEVs of GPS, GLONASS, and Galileo were compared in order to evaluate the frequency stability of the new AFS carried on the BeiDou-3.
# Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning
## 3 Results and Discussions
### Screening Results Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Formula** & \(\mathbf{\tau}\) & **Stability** & **Formula** & \(\mathbf{\tau}\) & **Stability** & **Formula** & \(\mathbf{\tau}\) & **Stability** \\ \hline NiPtO\({}_{3}\) & \(-\)58.1089 & \(-\)0.729 & RbPuO\({}_{3}\) & 3.4805 & \(-\)0.103 & CeRhO\({}_{3}\) & 1.3071 & \(-\)0.061 \\ KPaO\({}_{3}\) & 4.0629 & \(-\)0.567 & EuMoO\({}_{3}\) & 3.2674 & \(-\)0.101 & SmVO\({}_{3}\) & 2.9774 & \(-\)0.057 \\ RbPaO\({}_{3}\) & 3.6251 & \(-\)0.543 & KPuO\({}_{3}\) & 3.78 & \(-\)0.1 & CsNpO\({}_{3}\) & 3.3106 & \(-\)0.056 \\ LiPaO\({}_{3}\) & \(-\)5.7739 & \(-\)0.311 & SrTcO\({}_{3}\) & 3.8734 & \(-\)0.097 & SmAlO\({}_{3}\) & 1.9819 & \(-\)0.054 \\ CsPaO\({}_{3}\) & 3.3524 & \(-\)0.298 & BaZrO\({}_{3}\) & 3.7699 & \(-\)0.096 & EuNbO\({}_{3}\) & 4.09 & \(-\)0.053 \\ BaHfO\({}_{3}\) & 3.7284 & \(-\)0.247 & EuVO\({}_{3}\) & 3.3069 & \(-\)0.093 & BaSnO\({}_{3}\) & 3.6543 & \(-\)0.052 \\ KNbO\({}_{3}\) & 3.5401 & \(-\)0.221 & AcTiO\({}_{3}\) & 3.8113 & \(-\)0.092 & NpTiO\({}_{3}\) & 3.6701 & \(-\)0.051 \\ EuGeO\({}_{3}\) & 3.1904 & \(-\)0.212 & CeAlO\({}_{3}\) & \(-\)0.5164 & \(-\)0.086 & KTcO\({}_{3}\) & 3.555 & \(-\)0.048 \\ EuTcO\({}_{3}\) & 2.8001 & \(-\)0.15 & EuAsO\({}_{3}\) & 2.0406 & \(-\)0.081 & LiOsO\({}_{3}\) & \(-\)2.1229 & \(-\)0.044 \\ RbNpO\({}_{3}\) & 3.5353 & \(-\)0.15 & CeMnO\({}_{3}\) & \(-\)0.9994 & \(-\)0.079 & CeGaO\({}_{3}\) & 1.8339 & \(-\)0.043 \\ EuOsO\({}_{3}\) & \(-\)65.0247 & \(-\)0.143 & AcCuO\({}_{3}\) & 3.2679 & \(-\)0.077 & EuIrO\({}_{3}\) & 3.1159 & \(-\)0.043 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ninety-eight ABO\({}_{3}\) perovskites calculated by DFT in the literature and our calculated \(\tau\) value (stability less than 0.025, indicating that ABO\({}_{3}\) perovskite is stable). Figure 8: Formation energy distribution of hypothetical perovskite materials predicted by M2 model. In general, extensive literature inspection shows that our model made reasonable predictions and can be used for discovery of new perovskite materials. The top 200 predicted new perovskites are listed in Table 6. The complete set of predicted perovskites is provided in the supplementary file. The remaining non-reported perovskite materials are also promising.
# A Method to Estimate Dynamic Pore Water Pressure Growth of Saturated Sand-Gravel Materials
## 3 Analysis of Dynamic Triaxial Test Results of Saturated Sand-Gravel Material
### Relationship between the Strain and the Cyclic Numbers  
The dislocation and slip between the saturated grains of sand and gravel generate an axial strain after cyclic loading, which increases and changes with the periodic change in the loading. The amplitude of the axial strain is small during the initial stages of loading but increases gradually with an increase in the number of dynamic cycles. This is because the pore water pressure accumulates during the dynamic loading process, which reduces the mean effective stress of the sample and reduces the dynamic shear modulus of the material.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Number** & \(\mathbf{K_{c}}\) & \(\mathbf{\sigma_{3c}}\) **(kPa)** & \(\mathbf{u_{d}}\) **(kPa)** & \(\mathbf{N_{L}}\) \\ \hline S1 & & 50 & 49.93 & 174.4 \\ S2 & & 100 & 99.95 & 65.4 \\ S3 & & 100.01 & 147.5 \\ S4 & & & 197.1 & 42.35 \\ S5 & 1.5 & 200 & 198.54 & 74.3 \\ S6 & & & 199.79 & 153.3 \\ S7 & & & 295.87 & 30.37 \\ S8 & & 300 & 298.07 & 43.35 \\ S9 & & & 299.94 & 84.3 \\ \hline S10 & & 50 & 49.24 & 145.3 \\ S11 & & & 100 & 95.3 & 44.3 \\ S12 & & & 95.06 & 105.25 \\ S13 & & & 187.77 & 23.3 \\ S14 & 2.0 & 200 & 193.42 & 35.25 \\ S15 & & & 195.93 & 78.25 \\ S16 & & & 280.62 & 18.25 \\ S17 & & & 282.71 & 32.2 \\ S18 & & & 281.03 & 73.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results for the \(N_{L}\) and \(u_{d}\) tests on saturated sand-gravel materials.  
The dynamic strain of the material, therefore, increases accordingly under the same cyclic loading conditions. Under the same confining pressure and cyclic stress conditions, samples with a higher initial consolidation stress ratio and a smaller dynamic strain amplitude experience less pore water pressure growth when the consolidation stress is relatively high. The deviatoric consolidation also causes a significant axial residual deformation of the soil sample, as shown in Figure 7. The black and red lines denote the axial strain of the sand-gravel material before and after reaching initial liquefaction, respectively.  
Figure 6: Upper and lower envelope lines of saturated sand-gravel: (**a**) saturated sand-gravel upper envelope line; (**b**) saturated sand-gravel lower envelope line.  
Figure 7: Relationship between sand-gravel material strain and cyclic numbers. (**a**) S2 and S12; (**b**) S7 and S17.
## 3 Broadband PA Implementation  
According to the design specifications, process characteristics, and simulation evaluation, a cascaded two-stage common-source amplifier architecture is adopted, with a device periphery ratio of 1:2 to guarantee a sufficient driving capability. All transistors operate in class AB mode, biased at the recommended gate and drain voltages of \(-1\) V and 12 V, respectively, to achieve a compromise between efficiency and linearity while preserving  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(f_{1}\) (GHz) & \(Z_{\text{opt,s}}\)(\(f_{1}\)) (\(\Omega\)) & **PAE (\%)** & \(w_{1}\) \\ \hline
22 & \(15.5+\text{j}15.3\) & 54.02 & 0.115 \\
24 & \(14.0+\text{j}15.3\) & 52.58 & 0.133 \\
26 & \(13.4+\text{j}14.1\) & 51.34 & 0.151 \\
28 & \(11.9+\text{j}12.9\) & 50.03 & 0.172 \\
30 & \(11.5+\text{j}11.6\) & 48.73 & 0.196 \\
32 & \(10.3+\text{j}10.3\) & 46.97 & 0.234 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weight allocation for matching error based on PAE differences.  
Figure 3: A flowchart of the parameter optimization method for the OMN.  
high-frequency gain. In addition, an appropriate parallel RC network is connected in series at the gate of each transistor, which not only enhances the in-band stability and suppresses parasitic oscillations but also compensates for the gain roll-off to help extend the bandwidth of the PA and facilitates input impedance adjustments for improved matching with the preceding stage, thereby lowering the reflection coefficient. Based on load-pull simulations, \(C_{\text{out}}\) of the transistors in the power stage and the driver stage is extracted as 0.27 pF and 0.13 pF, respectively, with the corresponding optimal intrinsic load (\(R_{\text{opt}}\)) determined to be 26 \(\Omega\) and 75 \(\Omega\)[16]. Using the Bode-Fano criterion, the theoretical bandwidth limit can thus be derived. This section will mainly discuss the design of broadband MNs.
## 5 Simulation Results and Discussion  
In this section, we evaluate the performance of the proposed DE-ACSW-R framework for predictive AoA estimation and robust beamforming under dynamic mobility scenarios. The simulation emulates challenging high-mobility conditions in O-RAN deployments, where users may exhibit both smooth trajectories and abrupt direction changes, resulting in non-linear non-stationary AoA evolution.
## 2 Related Work
### ANN-Based Approach  
As noted in the introduction, another branch of feature recognition is the learning-based approach. The artificial neural network (ANN) is one of the most common and well-known learning-based approaches. The authors of [16] first introduced neural network technology into 3D feature recognition. They proposed a CAD data descriptor and a multi-layer perceptron feature recognizer with the data descriptor as input. The work of [33] introduced a 3D feature recognition system based on a two-hierarchy Hopfield network. In addition, Ref. [34] presented an ANN-based approach based on adaptive resonance theory (ART). First, a boundary representation (B-rep) solid model was converted to modified face score vectors, which depict the topological relationships between faces. Then, the vectors were fed into a self-organizing neural network to identify the category of machining feature. The authors of [35] proposed a hybrid feature recognition system that combined the characteristics of a neural network and a genetic algorithm, aiming at reducing the computational complexity of network. The most significant characteristic of the learning-based approach is that it can learn the patterns of machining features from the data, instead of relying on a human-designed algorithm [14]. However, early ANN-based approaches failed to solve the intersection features because they were limited by the scale of dataset, computer performance, and network architecture. Furthermore, these early studies attempted to convert CAD models to a specific format, such as face score vector, which resulted in the loss of geometric and topological information [19].