esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid EDA Solution for the GC-CRSN Problem
### Iterative Greedy Scheme and Constraint Satisfaction for Source Power (IGS-CSSP)  
Now, we will describe IGS-CSSP. We denote by \(\widetilde{p}_{s,k}=p_{i}^{j},\forall i=L+1,L+2,\cdots,L+K\), the source power level at the \(k\)th user band in the \(j\)th sample drawn by the EDA. We denote by \(\Gamma_{s}\), a vector that will be used for user indices, \(\Gamma_{k}\), a set of users, and \(\delta\), a power control factor. The power control factor is used for source power adjustment. This adjustment will be done iteratively until all the interference constraints related to the source power are satisfied. The proposed IGS-CSSP is also a two-stage algorithm.  
Stage 1: In the first stage, users are ranked according to their channel conditions. Similar to the IGS-CSRA, the maximum profit to loss ratio criterion is used to rank the users. The profit is secondary users channel gain, _i.e._, \(|h_{s,i}||h_{i,k}|\), and loss is the maximum channel gain from the secondary user to the primary users, _i.e._, \(\text{max}(|g_{i,1}|^{2},|g_{i,2}|^{2},\cdots,|g_{i,M}|^{2})\). Mathematically:  
\[\Gamma_{s}\left(i\right)=\underset{k\in\Gamma_{k}}{\text{arg max}}\frac{|h_{s,i}||h_{i,k}|}{ \text{max}\left(|g_{i,1}|^{2},|g_{i,2}|^{2},\cdots,|g_{i,M}|^{2}\right)}\]  
In the next stage, the power of each user will adjusted according to their ranks.  
Stage 2: At the start of the second stage, the algorithm starts adjusting the source power levels if there is a violation of the primary users' interference constraint, using the expression:  
\[\widetilde{p}_{s,k}=\text{min}\left(\widetilde{p}_{s,k},\frac{I_{1,k}^{max}}{ |g_{s,1}|^{2}},\frac{I_{2,k}^{max}}{|g_{s,2}|^{2}},\cdots,\frac{I_{M,k}^{max} }{|g_{s,M}|^{2}}\right),\forall k\]  
After source power adjustment with the interference constraint, the algorithm verifies the power constraint. If the source power constraint is not satisfied, then the algorithm adjusts the source power using power control factor, \(\delta\), till the constraint is satisfied. The user with the worst channel condition is reduced first using the power control factor, \(\delta\). This process will be executed for all users till we get a feasible solution.  
At the end of the algorithm, we shall have a feasible solution. Since, we are using the half-duplex amplify and forward protocol, both IGS-CSRA and IGS-CSSP will execute independently.
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 5 Simulation Results  
### Experiment Settings  
In our experiments, we measure the performance of charging time and overflow energy. The number of devices varies from 30 to 40 to 50. The minimum distance between user and AP is 1 m, and the maximum distance is \(5\sqrt{2}\) m. Meanwhile, we set three different transmission-direction offsets, of 10, 5, and 3 degrees, and three transmission angles, of 90, 60, 30 degrees. The transmission power of the AP is 5 Watts, the battery capacity is 5 Joules, and the conversion efficiency is 60%. The parameters of our experiments are listed in Table 2.  
We show the performance of the proposed greedy and DE algorithms. We use the Python-based scikit-opt for the implementation of DE [30]. Our algorithm is compared with three methods, namely Random, Round Robin, and Fix. These methods are described below.  
1. Random (RAN): The AP randomly chooses any direction to transfer energy for one second until all devices are fully charged.
2. Round Robin (RR): The AP transmits energy in counterclockwise order for one second until all devices are fully charged.
3. Fix: The AP has one antenna for one fixed direction. The number of the sectors is equal to the number of antennas.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline Topology & Uniform \\ Number of Devices & \([30,40,50]\) \\ The minimum device distance & 1 m \\ The maximum device distance & \(5\sqrt{2}\) m \\ Offset value & \([3,5,10]\) degree \\ Transmission angle & \([30,60,90]\) degree \\ \(P_{t}\) & 5 W \\ \(B_{max}\) & 5 J \\ Conversion rate \(\mu\) & 0.6 \\ Population size NP & 50 \\ Scaling factor F & 0.3 \\ Hybridization probability CR & 0.8 \\ Upper bound & \(t_{max}\) \\ Lower bound & 0 \\ Generation & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.
## 6 Discussion
### Monitoring the Condition of the Grid's Cyber Systems to Achieve Both Physical and Cyber Resilience  
The parts of the power grid must be monitored, analyzed, and controlled by a cyber-system for the grid in order to function effectively and consistently. The resilience of these cyber systems has received little focus. In addition, the numerous control systems of an interconnected power grid operate under a variety of jurisdictions, necessitating close coordination for design and operation to ensure seamless and prompt information transmission in real time and coordinated response actions.  
To share data on the health of both physical and cyber systems, TSOs must create standardized data definitions, communication protocols, and industrial control system designs. Standardizing data definitions and communication protocols would help all concerned transmission operators and distribution operators to better understand the operational state of all connected power systems.
## 4 Assessment of Adjacent Building Damage with Ground Movements
### Determination of the Building Damage Pattern  
The ground settlement profile behind the excavation supported by the IVFRW has a concave shape. Figure 15 illustrates that buildings at different locations from the excavation are affected by different settlement patterns. According to the inflection point of the curve, the effect of the settlement profile on building damage should be taken into account [4]. Generally, when comparing buildings with the same DPI, the damage caused by the hogging pattern is more severe than that caused by the sagging pattern. This is because, in the hogging pattern, tensile cracks develop earlier and faster in the upper part of the building [30; 31].  
The inflection point of the curve can be determined through the settlement profile. A large number of numerical analyses show that the distance from the inflection point to the retaining wall is 2.8 times that of the maximum settlement to the retaining wall. However, according to Equations (5) and (6), because of the difference in settlement curves at different depths behind the excavation, the position of the inflection point will change. Adjacent building damage can be assessed by the assessment criteria proposed by [PERSON] et al. [30].  
However, for any complex process, it is essential to evaluate the uncertainty of the model. Due to this model uncertainty, the assessment of whether the building damage exceeds a specified condition cannot be \"certain\". Therefore, the results of the assessment can be described with a probability.
## 3 Results and Discussion
### Evaluation of the 30-Year Trends in NDVI and Modeled GPP
In this region, there was a consistent discrepancy between the three model simulations run with only climate data (_i.e._, BIOME-BGC, LPJ and TRIFFID) _versus_ the NDVI data and the BEAMS model simulation that incorporates effects of satellite-based observation. In addition, the BIOME-BGC simulations had anomalously high spikes in 2007 for all temperate regions. The disagreement between these data should be analyzed in the future to identify the potential limitations of each methodology. Disagreements between the models and data for tropical regions are evident in the current study. Possible explanations may relate to both the satellite-based observations and the terrestrial biosphere models. In principle, monitoring tropical forests using shortwave radiations from satellite-based observations is difficult because of atmospheric effects, for example, severe cloud contamination and heavy aerosol loading in tropical regions [68]. Moreover, simulating the terrestrial carbon cycle using terrestrial biosphere models is difficult in tropical forests. For example, a site-level model comparison study showed that the model performance in simulating carbon fluxes was poor in tropical forests among various ecosystems in Asia [69]. The difficulty in modeling GPP in tropical forest is partly due to complex processes involving water and solar radiation controls on seasonal GPP variations and the requirement for appropriate rooting depth settings [70].
## 2 Materials and Methods
### Chromatographic System and Measurements  
**SFC system:** both retention and adsorption isotherm measurements were carried out with Waters Acquity UPC\({}^{2}\) supercritical fluid chromatography system equipped with a photodiode array (PDA) detector (Waters Corporation, Milford, CT, USA).  
**Hold-up time and retention data:** Mobile phase was pumped at a flow rate of 2 mL \(\text{min}^{-1}\) with 0 to 30 vol.% methanol. Columns packed with porous materials were fixed in an insulated container equipped with a preheater enabling temperature control in increments of 0.1 \({}^{\circ}\) C. The pressure was controlled by back pressure regulators with an accuracy of 0.5 bar. The samples were injected into the SFC system and had volumes of 2 \(\mu\)L. The PDA detector signal was recorded for each compound at its specific working wavelength: phenol at 210 nm, toluene 240 nm, naproxen at 220 nm, nicotinamide at 210 nm, ketoprofen at 254 nm, rosmarinic acid at 330 nm, and nitrous oxide at 195 nm [16]. Empower(r) 3 software [17] was used to operate the SFC system.  
The time at which the maximum signal appears was taken as the retention time, \(t_{R}\). The retention time of nitrous oxide was employed as the hold-up time, \(t_{M}\). Retention and hold-up times of all solutes were measured at least in triplicate and averaged. The averaged values were used to calculate the retention factor, as in Equation (1):  
\[k=\frac{t_{R}-t_{M}}{t_{M}} \tag{1}\]  
The methanol concentration was varied from 0 to 30 vol.%. Retention factors were plotted against methanol concentration to generate a retention profile.  
**Determination of methanol adsorption isotherms:** Adsorption isotherms of methanol on all porous matrices in sc-CO2 were measured by frontal analysis chromatography with the staircase method. This method was used in several previous studies [11; 18].  
Breakthrough curves were measured in the SFC systems for a series of methanol concentrations for each silica material. By integrating the area above the breakthrough curves, the maximum loadings at specific methanol concentrations were determined. Obtained loadings were fitted to the Langmuir equation to estimate the equilibrium constant \(K_{eq}\). The complete experimental procedure and integration method are detailed in Supplementary Materials (Section S1, Figure S1, Table S1).
## 4 Field Experiment Evaluations  
In this section, we evaluate the proposed AFCCS and methods by conducting field experiments, focusing on (a) UAV flight with collision detection and avoidance experiments as described in Section 3.1 and (b) UAV formation flight experiments as described in Section 3.2. To provide practical and objective evaluations, the proposed AFCCS is implemented on a commercially available multi-purpose UAV. Table 3 presents some key features  
Figure 11: A flowchart of the formation flight in the considered experiments.  
of the UAV and Table 4 presents the common parameters used in our field experiments. For the sake of safety, all field experiments were conducted in an open airspace located in Saitama prefecture in Japan, which is far from the crowded center of Tokyo. The results of experiments (a) and (b) are described in detail in the following subsections.
# Helmholtz Coils Based WPT Coupling Analysis of Temporal Interference Electrical Stimulation System
## 3 Analysis of Calculation and Experimental Results
### Calculation of a Simplified Multi-Layer Human Tissue Model  
A simplified multi-layer human tissue model, as shown in Figure 6, was further developed to simulate the magnetic field distribution induced in the human body by the proposed system. Based on the actual dimensional parameters of the human body, the three tissues of the skin, fat, and muscle were represented by cylinders of 2 mm, 4 mm, and 16 mm thickness, respectively. Considering the dispersion of human tissues, whose electrical characteristics change with frequency, the electromagnetic characteristics parameters of human tissues at experimental frequencies of 490 kHz were introduced into the material. The corresponding model parameters for each tissue are shown in Table 3[22]. Simulating the actual human tissue structure makes the subsequent calculation of the magnetic field distribution closer to reality.  
The system's magnetic field distribution, after adding human tissue, is shown in Figure 7. The distribution of its magnetic field in the \(xoy\) section is shown in Figure 8. It can be seen that, although the human tissue is a lossy medium, the distribution of the magnetic field in the solution domain is essentially undistorted. This further validates the uniformity  
Figure 5: The distribution of the magnetic flux density in free space in the \(yoz\) plane.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Skin** & **Fat** & **Muscle** \\ \hline Electrical Conductivity (S/m) & 0.13 & 0.045 & 0.5 \\ Thickness (mm) & 2 & 4 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of the human tissue model in numerical calculations.  
Figure 6: Simplified multi-layered human tissue model.  
of the magnetic field of the Helmholtz coils WPT system proposed in this paper. It also offers the possibility of a stable and efficient wireless power transfer between the system and the receiver at the deep tissue level of the body.
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 5 Results and Discussion  
We predicted the preference states (pleasant or unpleasant) using different classification algorithms: DNN, RF, KNN, and SVM. We used different evaluation measurements: accuracy, recall, and precision. The accuracy was calculated as the average of the binary measurements in which the score of every class was weighted by its availability in the real data. Precision is the proportion of pleasant preference predictions that were actually correct. Recall is the proportion of actual pleasant preferences that were successfully predicted. To evaluate the performance of classification algorithms, we used different cross validation methods: holdout (train/test splitting), k-folds cross validation, and leave-one-out cross validation (LOOCV). Table 8 presents the accuracy results of DNN, RF, KNN, and SVM for each cross validation method. In LOOCV, RF reached the best accuracy results at 90% while DNN reached similar results to RF at 93% in the holdout validation method. In k-fold validation method, KNN achieved the best accuracy results at 90% and 91% when k was set to 10 and 20, respectively. Because the best accuracy results were achieved using the holdout validation, this method was chosen as the base validation for comparison and the tuning the DNN hyper parameter (loss function).  
The proposed DNN model was compared with three conventional classification algorithms for EEG signals: SVM, RF, and KNN. Table 9 presents the accuracy, recall, and precision results of RF, KNN, and DNN using three different loss functions in the DNN: the categorical cross-entropy function, binary cross-entropy function, and hinge function. The KNN classifier led to a better accuracy of 88% when K was set to 1. Although the RF achieved a high accuracy of 92%, the DNN reached the highest accuracy result of 94% with hinge cross-entropy function compared to the other conventional classification algorithms. To ensure that the DNN does not have over-fitting problem, we presented the  
Figure 6: Block diagram of the DNN classifier.  
loss per epoch for each cross-entropy function. The average loss per epoch DNN with the categorical, binary, and hinge function reached a value of 0.28, 0.24, and 0.23, respectively, as shown in Figure 7.
## 3 Results and Analysis of Influencing Factors
### The Impact of Drilling Fluid Resistivity on Signal Attenuation  
Due to the variations in the geological environment of different regions, the characteristics of the drilling fluids in water injection wells also differ significantly. Therefore, the resistivity of the drilling fluid is set between 0.1 \(\Omega\cdot\)m and 100 \(\Omega\cdot\)m, and the relative dielectric constant is set to 80, with other settings the same as in Section 3.2.  
As shown in Figure 5, Data analysis shows that when the resistivity of the drilling fluid is 0.1 \(\Omega\cdot\)m, the electromagnetic wave signal attenuates by 270.4 dB after traveling 100 m; when the resistivity increases to 1 \(\Omega\cdot\)m, the attenuation decreases to 133.1 dB; and further increasing to 10 \(\Omega\cdot\)m, the attenuation is only 41 dB. This reveals a trend: as the resistivity of the drilling fluid increases, the attenuation of the electromagnetic wave signal gradually decreases. However, it is worth noting that at shorter transmission distances, even though the electromagnetic wave signal attenuates more in low-resistivity drilling fluids, its transmission effect is actually better than that in high-resistivity drilling fluids. Therefore, in practical operations, the relationship between the intended transmission distance and the resistivity of the drilling fluid should be considered comprehensively to achieve the best transmission effect.
## 4 Results and Discussion  
PLIF fluorescence signals from the P\({}_{1}\)(7) and Q\({}_{2}\)(11) transition lines are recorded and the typical image is shown in the left image of Figure 4. The CARS system is vertical scanning to take the temperature at those black rectangular points. The temperature results from CARS are shown in the right graph in Figure 4, with a standard deviation (SD) of 28-58 K. Standard error (SE) of the CARS results is 6-13 K when averaged 20 times.  
Figure 1: Overall structure of the jet flame and self-luminescence photograph of the flame.  
Figure 3: Two-dimensional Scanning CARS experimental facility.  
Figure 2: Diagram of two-color PLIF temperature measurement.  
Within the measuring space of 30-105 mm from the nozzle, the combustion jet can be divided into three areas: the unburned zone, the reaction zone, and the burnout zone. The unburned flow in the center of the jet is heated and the temperature increases with the height. In the reaction zone at a height of 55 mm, the maximum temperature of the flow was obtained. In the burnout zone, the temperature of the hot flow decreased with height for the heat exchange with the surrounding cold air.  
Based on the temperature results from the CARS technique, PLIF thermometry calibration was carried out. The temperature results from PLIF are compared with CARS at different heights of jet center axis to obtain the thermometry accuracy of those two calibration methods. The mean temperature and its standard error are obtained by vertical statistic over 49 pixels around the center axis, and the standard deviation and the standard error of the average temperature is shown as  
\[T_{SD}=\sqrt{\frac{\sum\limits_{i=1}^{49}\left(T_{i}-\overline{T}\right)}{48}} \tag{9}\]  
\[\overline{T}_{SE}=T_{SD}/\sqrt{49} \tag{10}\]  
According to the traditional method, the calibration process is carried out at point A in the left image of Figure 4, from which the calibration coefficient C can be obtained, and then the temperature of other points can be calculated, shown as a red line in Figure 5, with an SE 9-36 K. The average temperature results from PLIF are different from CARS results, especially at those regions where the flow temperature is distinctively different from the calibration point. In terms of the optimized two-point calibration method, the CARS thermometry results at positions A and B are used. Then the calibration parameters of \(A\) and \(B\) can be calculated, and the optimized temperature results from PLIF are shown as a green line in Figure 5 with an SE 10-37 K. The thermometry results from two-point calibration PLIF agree well with CARS results in the whole region of 1300-1800 K, and the maximum temperature deviation between the PLIF and CARS is only 10 K.  
Figure 4: Fluorescence image of OH and the measured temperature from CARS.  
Figure 5: Temperature results of jet flame from PLIF and CARS.
# Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites
## 3 Results and Discussion
### Single-Fiber Deflection Angle Measurement Results
#### 3.1.3 Comparison with the Microscopic Measurement Method  
The orientation of single fibers in the cross-section of the carbon-fiber-reinforced composite plates was measured using the method in this paper and compared with the image measuring instrument measurement results for analysis. Figure 4 demonstrates how the azimuth angle \(\varphi\) between the fiber and the side of the plate was measured. The azimuth angles of the single fibers in the five prepared composite plates were measured using both methods. The measured results are shown in Table 1.  
As seen from Table 1, the measurement results of this method are consistent with the measurement results of the image measuring instrument, with a maximum error of \(3.83^{\circ}\) and an average error of \(1.54^{\circ}\). There are several potential sources of error that could affect the accuracy of the results. The quality of the SEM images could vary depending on sample preparation, imaging conditions, and microscope settings. The image processing techniques used to extract fiber orientation information could also introduce misalignment, noise, or bias. According to Table 1, the measured azimuth angle of protruding fibers is closer to that of the image-measuring instrument than the result of the fiber pull-out cavity. The reason for this is that the fiber retains their cylindrical shape, which is important for determining their axial direction vectors. Because of poor fiber-matrix bonding, the extracted cavity is near-elliptical cylindrical. The data set has more noise or outlier points. The accuracy of the fiber azimuth angle obtained by detecting cavities is typically not as precise as that obtained by detecting prominent fibers azimuth angle. Measuring the fiber orientation distribution of the fiber yields more accuracy results than counting the fiber pull-out cavities on a single section.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**The Method Used in This Paper**} & \multicolumn{1}{c}{**Image Measurement**} \\ \cline{2-4}  & **Prominent Fiber** & **pull-Out Cavity** & **Results** \\ \hline
1 & 16.508 & 19.638 & 17.027 \\
2 & 33.491 & 35.275 & 32.519 \\
3 & 19.746 & 19.917 & 19.642 \\
4 & 9.468 & 8.013 & 9.735 \\
5 & 24.955 & 23.481 & 21.128 \\ \hline \end{tabular}
\end{table}
Table 1: Single-fiber azimuth angle measurement (\({}^{\circ}\)).