esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Simulation Results
### Application of Dynamic Time-Of-Use Pricing Strategy for EVs
#### 5.2.2 The Economic Research in Three Scenarios
The work of this paper is compared with the recently published reference [32] to verify the feasibility and rationality of the proposed strategy. When the EV number is set to be 2000 in the case, the charging cost of EVs under proposed method and under the peak-valley price in [32] are shown in Table 5.  
It can be seen from the above table that the charging cost of EVs using this method is slightly lower than that under peak-valley price, which is due to the fact that the number of EVs participating in the dispatching is few in the two cases. Since the total charging capacity of EVs will increase dramatically with the increase of the number of EVs in the future. Therefore, the charging cost of EVs using this method will be markedly lower than that under peak-valley price. Secondly, the implementation of dynamic time-of-use pricing strategy for EVs can reduce the electricity purchased from external power \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Charging** & **Unit** & **Decreased** & **Variable** & **Decreased** \\
**Scenarios** & **Cost/Yuan** & **Percentage** & **Costs/Yuan** & **Percentage** \\ \hline Uncoordinated charging & 0.82 & / & 69,468.89 & / \\ Peak-valley price & 0.45 & 45.12\% & 39,534.45 & 43.09\% \\ Dynamic price & 0.43 & 47.56\% & 21,345.95 & 69.27\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The unit charging cost of EV users and overall operation costs of the micro-grid. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Charging** & **Excess** & **Decreased** & **Purchased** & **Decreased** \\
**Scenarios** & **Generation/kWh** & **Percentage** & **Electricity/kWh** & **Percentage** \\ \hline Uncoordinated charging & 35,650 & / & 37,930 & / \\ Peak-valley price & 14,270 & 59.9\% & 36,080 & 4.9\% \\ Dynamic price & 0 & 100\% & 21,820 & 42.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Excess generation for distributed generators and electricity purchased form the external power system in different scenarios. \begin{table}
\begin{tabular}{c c} \hline \hline
**Strategy** & **The Charging Cost of EV Users/Yuan** \\ \hline The dynamic time-of-use price & 14,035.40 \\ The peak-valley price & 14,400.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of the charging cost of EV between the two strategies. system, so as to improve the economic operation of the micro-grid. Meanwhile, the dynamic price of EVs obtained in this paper adopted the peak-valley price as the upper limit, and the generation and transmission cost of distributed generation as the lower limit. The obtained dynamic charging price of this paper is 0.38 yuan/kWh-1 yuan/kWh, which is close to 0.5 yuan/kWh-0.8 yuan/kWh in the reference. Therefore, the pricing strategy of this paper is feasible and rational.
## 2 Materials and Methods
### Accelerated Stress Test  
Reliability tests for memory packages are typically conducted under harsh environments, including high temperature, high humidity, and voltage stress. However, since direct heat application to the package is not feasible in an immersion environment, this study focused on the high-voltage stress test (HVST). It is an accelerated life evaluation method that applies a voltage exceeding the standard operating voltage to assess device stability under overvoltage conditions [16].  
To proceed with the HVST, a high DC voltage was applied to the power supply pins (VDD/VSS) of the semiconductor package for an extended duration using a DC power supply (UTP3305-II, UNI-T, China). Subsequently, electrical characteristics were measured using a probe station (mst4000A, MSTECH, Hwaseong, Republic of Korea) and a precision I-V analyzer (E5270B, KEYSIGHT, Santa Rosa, CA, USA) as shown in Figure 2a.  
Figure 1: Memory package: (**a**) top view and (**b**) bottom view; (**c**) cross-sectional image showing the chip, solder balls, and EMC; (**d**) schematic of leakage current in the MOSFET structure.  
Because of the small size of the solder balls, jumper wires could be applied only to a single VDD/VSS pair (No. 1) [Figure 2b]. To ensure that the selected pair effectively delivered voltage across the package, I-V curves of four different VDD/VSS pairs were measured using a probe station [Figure 2c]. The measurement results demonstrated that all pairs exhibited identical electrical characteristics in fresh state [Figure 2d]. Based on this, it was concluded that the selected pair could reliably represent the entire set of VDD/VSS pairs, as all pairs exhibited consistent electrical behavior.  
To investigate the thermal and electrical characteristics of DRAM under voltage stress in air and immersion cooling environments, DC voltages of 2.5 V, 2.8 V, 3.0 V, and 3.1 V were applied to the semiconductor chip for up to 1 h [Figure 2e]. However, when 3.1 V was applied, excessive heat generation caused detachment at the junction between the jumper wire and the solder ball, limiting the test duration to a maximum of 30 min. An I-V sweep was performed using a probe station in order to analyze electrical changes after voltage stress application. The sweep voltage started from 0 V and went up to 5 V, and the maximum measurable current was 100 mA.
## 4 Open Issues and Challenges
### Trajectory Planning of the UAV
#### 4.3.3 Summary and Insight  
Through reviewing the static and dynamic path planning of mobile vehicles (as presented in Table 7), we notice that few algorithms use extra hardware to plan the trajectory. Most of the existing algorithms use col-linearity principle [72] of mobile vehicles. A single mobile vehicle would be cheaper. However, it may bring more col-linearity or longer localization time issues. Multiple mobile vehicles could help to reduce the localization time, especially in 3-dimension scenarios.  
Compared to the dynamic case, static path planning algorithms fail to fully utilize the real-time information that obtained. In practical applications, the environment where the sensors are deployed is full of uncertainties, and therefore, dynamic path planning performs better than pre-defined one because it can consider more real-time information. The main issues in dynamic path planning are adaptability and computational complexity. In the existing dynamic path planning algorithms, which were applied in obstructed scenarios, it is assumed that the nodes are equipped with hardware (such as cameras, radar, infra-red, sonar, etc) for detecting obstacles. Based on these, the dynamic algorithms are recognized to fully use the real-time information and they are more accepted for areas with uncertainties.
## 3 Methods
### Validation  
Two sets of data are used for validation and further analysis, depending on the pixel-level completeness of time-series Landsat NDVIS at 25-m pixel resolution across the growing season (_i:_ 8-19) between April (DOY 113) and October (DOY 304) (Figure S3, Supplementary Materials). Firstly, the coordinates of complete time-series Landsat pixels are used to obtain the resampled MODIS, L-M blended, and observed yield data for validation at the 25-m resolution as the first dataset. Secondly, these complete time-series Landsat pixels are upscaled at 250 m pixel size to extract the MODIS and the L-M blended data with the same pixel size for the validation at the moderate resolution. Thirdly, the pixel-level yield predictions are aggregated for each respective field by averaging the yield values of the pixels within, for field-level validation. Fourthly, and finally, the predicted yields are evaluated using the model coefficient of determination (\(R^{2}\)), the root-mean-square error (\(RMSE\)), and the mean bias error (\(MBE\)). The \(R^{2}\) describes the proportion of the response variable that can be explained by the model. \(RMSE\) gives more weight to the largest errors, and the \(MBE\) indicates the systematic error of the model to under or overestimate. These procedures are then repeated for the second dataset createdaccording to the coordinates of incomplete time-series Landsat NDVIs at the 25-m pixel resolution. As C-Crop requires a time series of NDVI, the results are assessed without the incomplete time-series pixels of Landsat.
# An Omnidirectional Near-Field Comprehensive Damage Detection Method for Composite Structures
## 2 Ultrasonic Phased-Array Damage Imaging Method  
### Definition of Near Field and Far Field  
In the traditional sonar theory, the far-field region is defined by [14]:  
\[R_{far}>\frac{2D^{2}}{\lambda} \tag{1}\]  
where \(D\) is the length of a piezoelectric array, and \(\lambda\) is the wavelength of a Lamb wave generated by the excitation signal. In the ultrasonic phased-array field, the far-field can be regarded as a region at a much larger distance from the phased-array than the array length, \(D\). On the one hand, when a target is in the far-field region, the excitation signal wave of the one-dimensional array elements is approximately a parallel wave, and the propagation direction of every piezoelectric element wave is approximately parallel to that of the received Lamb wave signal; then, beam synthesis can be conducted by using the parallel theory of an ultrasonic phased-array.  
On the other hand, the near-field region is defined by:  
\[0.62\sqrt{\frac{D^{3}}{\lambda}}<R_{near}\leq\frac{2D^{2}}{\lambda} \tag{2}\]  
In the region with the diameter of \(R\leq 0.62\sqrt{\frac{D^{3}}{\lambda}}\), the phased-array theory does not hold. Additionally, when a target is in the near-field region, as shown in Figure 1, the propagating wavefront is curved (a circular wavefront), and the wave propagation direction depends on the position of each element of a one-dimensional array.  
In the near-field region, if the damage is located by using the parallel wave algorithm intended for the far-field, the result will either contain a large error or the damage will not even be able to be identified. This is because a one-dimensional phased-array ultrasound suffers from near-field blindness.
## 3 Results
### Statistical Analysis  
Based on the statistical analysis results, weight, history of peptic ulcer disease, the third heart sound, BMI, DBP, and valve-related features (including aortic valve insufficiency, mitral valve stenosis, valvular heart disease, mitral regurgitation grade) were not significant (\(p\geq 0.01\)) between the positive and negative group during the varied cardiovascular events and treatments predictions (Case I). Further, in the PCI treatment predictions (Case II), weight, history of peptic ulcer disease, BMI, DBP, maximum stenosis of the left main artery, aortic valve insufficiency, aortic valve stenosis, mitral valve stenosis, and mitral regurgitation grade were not statistically significant (\(p\geq 0.01\)). In the CABG treatment predictions (Case III), weight, history of peptic ulcer disease, history of smoking, the third heart sound, BSA, BMI, DBP, aortic valve insufficiency, aortic valve stenosis, mitral valve stenosis, and valvular heart disease were not found statistically significant (\(p\geq 0.01\)). Among the patient samples, the number of VS cases is the least compared to the other treatments, and VS has been excluded as the criterion in the PCI and CABG predictions. Therefore, the conventional statistical results found that heart valve-related features from the CR were not found to be significant between the positive and negative groups. BMI and DBP were also not significant in all three prediction models. Compared to the importance  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model 1** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **AUROC (\%)** \\ \hline Risk-RFC & 88.17 & 89.72 & 86.98 & 91.68 \\ PCI-GBC & 89.21 & 90.20 & 88.74 & 94.16 \\ CABG-GBC & 93.86 & 77.57 & 96.23 & 96.47 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 7: Performance of models trained after feature selections.  
of features, most of these insignificant features (e.g., BMI, DBP) were used among these three prediction scenarios as important features. However, these features were classified as weak features in the applied conventional statistical techniques (_t_-test (on continuous data), [PERSON]'s exact tests (on Boolean data), and chi-square tests (on categorical data)). In this work, ensemble methods, RFC and GBC, produced an optimal predictive output by combining several weak learning models. The interactions/relations between features were examined, and the weak features were efficiently considered. As shown in Tables 2, 4 and 6, the addition of the non-CR features improves the model performance.
## 3 Results
### Significance of the Detrended Cross-Correlation  
Figure 4 depicts the computational time and model performance regarding the cross-validation Pearson correlation of the 20 individual models, computed over the lag range of \(-250\) ms to \(0\) ms (baseline model) and \(-100\) ms to \(500\) ms (trained model), for the detrended cross-correlation and ridge regression.  
In comparison with ridge regression, the detrended cross-correlation function required significantly less computational time to train both the univariate (\(p<0.05\)) and multivariate (\(p<0.01\)) forward models (Figure 4A). In terms of cross-validation performance, the trained models of both the detrended cross-correlation and ridge regression significantly outperformed the corresponding baseline models (\(p<0.01\); Figure 4B). No significant difference was observed in the cross-validation Pearson correlation between the two models.  
Figure 4A shows that the detrended cross-correlation outperformed ridge regression as the complexity increased. Table 1 summarizes the mean and standard deviation of the computational time (s) and validation of the Pearson's correlation coefficients for the trained models of the detrended cross-correlation function and ridge regression, as seen in Figure 4B. In the univariate forward model, the computational time for ridge regression was \(1.18\) times that required for detrended cross-correlation, while that number increased to \(5.82\) times in the case of the multivariate forward model. Regarding prediction accuracy, Figure 4 and Table 1 show that both approaches converged to a similar performance in EEG prediction for both the univariate and multivariate models (\(p>0.05\)). Figure 4C illustrates the correlation (using the Pearson correlation coefficient) between performance  
Figure 4: Comparison of the computational efficiency and EEG decoding performance between the detrended cross-correlation and ridge regression functions. (**A**) The distribution (n = 20) of the computational time for training the univariate and multivariate models using the detrended cross-correlation and ridge regression. (**B**) The Pearson correlation coefficient in validating performance of EEG prediction was compared for the univariate and multivariate models (n = 20). The notations of \"\", *, and n.s (not significant) denote \(p<0.01\), \(p<0.05\), and \(p>0.05\), respectively. (**C**) The relationship between performance of individual models (n = 20) calculated by the detrended cross-correlation and ridge regression functions. The Pearson correlation coefficient between model performance computed by the two methods was \(r_{\text{Perf,D-R}}\). Abbreviation: ‘detrended’ and ‘ridge’ in an abscissa label denote detrended cross-correlation and ridge regression, respectively.  
of individual models (n = 20) computed by the detrended cross-correlation (ordinate) and ridge regression (abscissa) functions. The Pearson correlation coefficients denoted as 0.98 and 0.95 are correlations between model performance computed by the detrended cross-correlation and ridge regression for the univariate and multivariate model, respectively.
# Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
## 2 Methods
### Peak Detection  
The detection of the sensors' signal variations and maximum values, commonly named peaks, is important for discretizing the different activities because activities with high intensity have more peaks with high values and low intensities have fewer peaks with low values [54].  
The detection of peaks may be performed with different methods. This study used a sequential method, smoothing the sensors' signal and saving only the values where the next and previous values are lower. The process must be executed several times until the iteration where the value of peaks is the minimum, but it retains at least five peaks.
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 5 Average Distance from Favorable Propagation  
The favorable propagation is an important metric which is defined as mutual orthogonality among the vector-valued channels to the terminals [26]. The measure is one of the key properties of the radio channel that is exploited in massive MIMO. From [26], we can further characterize favorable propagation as  
\[\Delta_{\text{C}}=\frac{K\mathbb{E}\left[\log_{2}(1+\rho M\beta_{k})\right]- \mathbb{E}\left[\log_{2}\lvert I_{K}+\rho\mathbf{G}^{H}\mathbf{G}\rvert \right]}{\mathbb{E}\left[\log_{2}\lvert I_{K}+\rho\mathbf{G}^{H}\mathbf{G} \rvert\right]}. \tag{35}\]  
As can be seen from Equation (35), when \(\Delta_{C}=0\), the channel offers favorable propagation. The measure defined in Equation (35) is an extension of the one presented in [1] for the ergodic capacity case assumed in this work.
# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 4 Results
During the investigated time periods, both test sites were erosion-prone areas: the diachronic shoreline analysis revealed a maximum retreat of 24 m from 2011 to 2013 in Test Site I (Figure 9a), and a shoreline erosion up to 20 m from 2009 to 2015 in Test Site II (Figure 9b). A comparison between SAR extracted shorelines and 2018 satellite maps (Figure 9a,b) reveals that beach erosion in both examined areas still occurs, attesting the need for a continuous coastal monitoring system for coastal risk assessment. Figure 8: Organic deposits of _Posidonia oceanica_ along the coastline during SAR acquisition. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Accuracy**} \\ \hline
**Test Site** & **\textless{}1 Pixel Distance** & **1–2 Pixel Distance** & **2–3 Pixel Distance** \\ \hline Test Site I & 87\% & 100\% & 100\% \\ Test Site II & 85\% & 98\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Algorithm performance for different neighborhood sizes.
## 5 Simulation
### Simulation Platform  
The platform consists of three parts, as Figures 8 and 9 illustrate: an intermediate-frequency (IF) signal generator, a software receiver, and a post-processing unit for multipath mitigation. The generator in Figure 8 generates two IF signals to simulate the received signals in RHCP and LHCP channels, respectively. At most four paths can be simulated, with one LOS signal, two static multipath signals and one programmable dynamic multipath signal. The IF signal generator could specify the received signal power and carrier phase for each path in both RHCP and LHCP channels. Pseudorandom Noise (PRN) Code 1 is assigned to the target channel interfered by multipath and PRN Code 2 is assigned to the reference multipath-free channel. The delays and the carrier phases of the LOS signals in both target and reference channels are synchronized. After the rectangular waves of the four signals are generated, the generator modulates them to the IF, superimposes the IF signal of each path to form a composite one, and adds white Gaussian noise to the composite signal. Finally, the oversampled IF signal goes through a band-pass filter and is down-sampled to meet the requirement of the software receiver.  
Figure 8: The diagram of the IF signal generator.  
In Figure 9, the software receiver on the right side initiates two channels to track both target (PRN 1) and reference (PRN 2) signals simultaneously. It stores the tracking loop parameters (carrier phase/frequency, code phase/frequency) and the differential pseudorange each time when the tracking loop is updated. The differential pseudorange is obtained by subtracting the pseudorange of the reference channel from that of the target channel. The LOS signals in these two channels are synchronized, thus, the differential pseudorange is the multipath error added with doubled noise which is small enough to be ignored for the narrow bandwidth of the loop filter in the code delay lock loop. The post-processing unit on the left side of Figure 9 uses the tracking loop parameters to apply the signal compression (SC) technology for perfectly matched reception [36] (traditional receiver uses rectangular wave to correlate with the attenuated band-limited baseband signal. Any mismatch of the signal will bring new bias to the multipath error estimation especially when high-resolution algorithm is used). The \(r_{\mathrm{RHCP}}(t)\) or \(r_{\mathrm{LHCP}}(t)\) produced by SC technology has 2001 points within the range between \(-1\) to \(+1\) code chip so that we can ignore the TOA estimation error caused by the searching interval.  
The CNR of the signal is 42 dB-Hz, the bandwidth of the band-pass filter is 16 MHz, and the down-sampling rate of the IF I/Q complex value data is 25 MHz. The coherent integration time of the tracking loop in the software receiver is 100 ms, which is also the interval between the consecutive multipath mitigation algorithms. The relative carrier phase of multipath to the LOS signal is set to be a constant value for better comparison of the code multipath mitigation performance. All of the simulations afterwards will use these basic parameters.
## 3 An Integral Sliding Mode Compensation Method with Weakened Integrals
### Adding Weight Factors to Optimize Feedback Currents and Solve the Inductance Mismatch Problem  
The system will be unstable and the feedback current will fluctuate widely when the predictive model inductance is more than twice the actual motor value. Therefore, in this article, the feedback current was corrected by introducing weight factors in accordance with [23]:  
\[\overset{\bullet}{i}_{dq}(k)=xi_{dq}^{*}(k)+iy_{dq}(k) \tag{23}\]  
where \(x+y=1\), \(0<x<1\), and \(0<y<1\); \(\overset{\bullet}{i}_{dq}(k)\) denotes the corrected feedback current of the \(dq\)-axis; and \(i_{dq}(k)\) is the \(dq\)-axis feedback current before correction.  
In the case of a very short current control period, (23) and (8) are combined and simplified to obtain the following:  
\[\left\{\begin{array}{c}i_{d}(k+1)-i_{d}^{*}(k+1)=\frac{\Delta L\omega_{ \varepsilon}(k)T_{s}}{L_{0}(1-x)}\big{[}x\Delta i_{d}(k)+i_{q}(k)\big{]}\\ i_{q}(k+1)-i_{q}^{*}(k+1)=-\frac{\Delta L\omega_{\varepsilon}(k)T_{s}}{L_{0}(1 -x)}\big{[}x\Delta i_{d}(k)+i_{d}(k)\big{]}-\frac{\Delta y_{f}\omega_{ \varepsilon}(k)T_{s}}{L_{0}(1-x)}\end{array}\right. \tag{24}\]  
where \(\Delta i_{d}(k)=i_{d}^{*}(k)-i_{d}(k)\) and \(\Delta i_{q}(k)=i_{q}^{*}(k)-i_{q}(k)\).  
From (24), it can be seen that the problem of large current fluctuations due to the excessive inductance of the predictive model can be reduced, but the size of the weight factor also affects the \(dq\)-axis steady-state error, which can be compensated by the algorithm, as mentioned above. Figure 2 shows the structure of the optimized method according to which a weakened integral sliding mode is combined with robust control.  
Figure 2: Structure of method according to which a weakened integral sliding mode is combined with weighting factors.