esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Chaotic-Based PRNG for Secure Cryptography Applications
## 5 Results and Analysis
### Encryption Quality Analysis
#### 5.1.4 Histogram Analysis  
The distribution of color intensities in an image can be visualized through a histogram. The more robust encryption scheme should result in an encrypted image with a more uniform histogram, even if the original image had a weak intensity distribution. The histograms of the original images have unique intensity distributions that characteristically represent the images as shown in Figure 8. In contrast, the histograms of all the encrypted images exhibit a uniform shape, showing no unique features for potential decryption purposes. This indicates that the proposed algorithm can effectively resist statistical attacks.
A reconfigurable software-defined acoustic modem (SDAM) based on the multicore processor, AM5728 [11], is proposed in this paper. AM5728 is a floating-point, high-performance processor with dual DSP C66x and dual ARM Cortex-A15 clocked at 750 MHz (DSP) and 1.5 GHz (ARM). In our proposed solution, DSP cores host the signal processing for physical layers such as modulation, demodulation, and AD/DA conversion. ARM cores host the networking protocols and extended peripheral drivers (GPS system, remote wireless communication module, etc. ), so the platform can run the complete NS3 based on the LINUX system with external memory. NS3 is one of the most common software simulation platforms for networking protocols. Network protocols developed in software simulation platforms (e.g., NS3 platform) can be seamlessly migrated to hardware platforms without modification. Moreover, a new physical-layer module associated with a real acoustic channel is developed, such that a data packet generated from the application layer will be transmitted through a real acoustic channel. The proposed SDAM platform has high integrity and high scalability, and the multiple peripheral devices can be accessed to ARM core to expand the platform functionality.
# Construction and Application of an Ecological Quality Evaluation System Based on a PIE-Engine +
## 4 Conclusions  
Based on the PIE-Engine, the spatial and temporal changes in the ecological quality of the Taihu Basin over the past 20 years from 2001 to 2020 were analyzed in terms of three dimensions: ecological function, ecological stability, and ecological threat. The main conclusions are as follows:  
1. The overall average EFI of the Taihu Basin exhibited a trend of an initial decrease followed by an increase between 2001 and 2020. Significant spatial differences in EFI were observed, with the highest EFI appearing in the western and southwestern areas of the Taihu basin, characterized by forest and grassland cover, while the densely urbanized northeastern part of the basin exhibited relatively lower EFI values;
2. The mean ESI of the Taihu Basin exhibited a trend of initial decrease followed by an increase, with the rate of increase surpassing the rate of decrease. The ESI was higher in the southwest, while it was relatively lower in the southeastern, southern, and western regions;
3. The ETI of the Taihu Basin exhibited a fluctuating trend, initially decreasing and then increasing. The rate of increase was higher than the rate of decrease, indicating that the risk of ecosystem quality degradation is still rising. Land use change is the most important threat factor for habitat quality, with grassland reduction and urban expansion being the primary contributors to the increase in the ETI.  
Land use and land cover changes caused by rapid economic development and urbanization seriously threaten the ecological quality of the Taihu Basin. Over the past decade, the policy of \"Returning Cropland to Forests, Grassland, and Lakes\" has contributed significantly to ecological restoration. However, in the process of steady ecological protection  
Figure 6: Interannual variation in ETI in the Taihu basin.  
and restoration in the future, it remains essential to optimize the industrial structure layout in a coordinated manner, aiming to achieve a balance between a green economy and sustainable development.  
P.L.: Writing--original draft, Methodology, Software, Resources, Data curation, Supervision, Formal Analysis, Visualization; C.Y.: Writing--original draft, Methodology, Software, Resources, Visualization; L.L.: Methodology, Software, Resources, Visualization; J.J.: Conceptualization, Writing--original draft, Methodology, Investigation, Writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflict of interest.
# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 3 Target Tracking with IMM Filtering
### Measurement Gating and Data Association  
Measurement gating is a pre-process of data association that reduces the number of candidate measurements. Let \(Z(k)\) be a set of measurement vectors detected at frame \(k\):  
\[Z(k)=\Big{\{}\mathbf{z}_{1}(k),\ldots,\mathbf{z}_{m(k)}(k)\Big{\}}, \tag{17}\]  
where \(m(k)\) is the number of measurements at frame \(k\). The measurement gating is chi-squared hypothesis testing, assuming the Gaussian measurement residuals. Thus, a set of valid measurements for target \(t\) and mode \(j\) is obtained as:  
\[Z^{t}_{j}(k)=\Big{\{}\mathbf{z}_{m}(k)|\mathbf{v}^{t}_{mj}(k)^{T}[S^{t}_{j}(k )]^{-1}\mathbf{v}^{t}_{mj}(k)\leq\gamma\Big{\}}, \tag{18}\]  
\[\mathbf{v}^{t}_{mj}(k)=\mathbf{z}_{m}(k)-H\hat{\mathbf{x}}^{t}_{j}(k|k-1),\ \ m=1, \ldots,m(k), \tag{19}\]  
where \(\gamma\) is the gating size. The NN rule is adopted to associate a measurement with a track by minimizing the norm of the residual as:  
\[\hat{m}^{t}_{j}(k)=\operatorname*{arg\,min}_{m=1,\ldots,m^{t}_{j}(k)}\| \mathbf{v}^{t}_{mj}(k)^{T}[S^{t}_{j}(k)]^{-1}\mathbf{v}^{t}_{mj}(k)\|, \tag{20}\]  
and \(m^{t}_{j}(k)\) is the number of candidate measurements which falls in the validation region for target \(t\) and mode \(j\).
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
## 4 Results Analysis
### Analysis of Simulation Results
#### 4.2.2 Flow Field  
Figure 8 shows the velocity and streamline distribution of axial plane under different radial heights at \(\sigma=0.6\). For the separate inducer, a strong backflow area distributing intensively at the leading edge of the inlet can be seen on account of tip leakage and blade rounding. For the inducer with helical static blades, the backflow in the whole flow field is enhanced. Because of the work of the inducer, the liquid passes from inlet to outlet and its pressure increases. The pressure difference between inlet and outlet causes some liquid at the outlet to flow back to the inlet along the channel of the helical static blade, and this liquid then enters into the mainstream. Therefore, backflow is the main flow mode in the channel of helical static blades, accompanied with a large number of vortexes. On the one hand, the backflow causes the additional loss of efficiency; on the other hand, the fluid from the channel of helical static blade has a smaller attack angle because of the opposite turning direction of blades, resulting in the improvement of head to some extent. As the radial height increases, the fluid passing through the channel of helical static blade increases, so that the fluid with a smaller attack angle increases, and the head increases, but the loss of efficiency increases as well. In addition, the installment of a helical static blade will affect the inlet backflow vortex at the same time. When the radial height \(k=0.05\), the inlet backflow vortex is mainly concentrated around the channel of the helical static blade. With the increase in radial height, the scale of the inlet backflow and the velocity of the tip vortex continues to increase, which aggravates the instability of the flow field at the inlet of the inducer.  
Figure 9 is the static pressure distribution of suction surface and pressure surface at \(\sigma=0.6\). Owing to the pressurization of inducer, the pressure along the flow direction gradually increases; the low-pressure zone appears in the leading edge of the suction surface; the high-pressure zone appears in the trailing edge of the pressure surface. As the radial height of helical static blade increases, the area of the low-pressure zone at the inlet expands, while the pressure of the high-pressure zone at the outlet rises. The expansion of the low-pressure zone adversely affects the cavitation performance of the inducer, but the rise in the outlet pressure shows that the cavitation is limited to the inlet area. Hence, further research about the effect of the cavitation evolution process on the cavitation performance of inducers is needed, and it is presented in the rest of this section.
# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
## 3 Materials and Method
### B Value and D Value Calculation  
The b value of an earthquake indicates the average intensity of earthquakes occurring in an earthquake area during a measurement period. The d value, on the other hand, indicates the total intensity of earthquakes occurring in an earthquake area [28].  
The difference between the b and d values is that while the former represents the number of earthquakes occurring in an earthquake zone, the latter represents the total intensity of those earthquakes. If the number of earthquakes in an earthquake zone is high, the b value may be high and the d value may be low. In this case, earthquakes of low intensity are frequent in that earthquake zone. If the total intensity of earthquakes occurring in an earthquake zone is high, the d value may be high, while the b value may be low [29]. In this case, rare but high-intensity earthquakes may occur in that earthquake zone.  
The b value of an earthquake is a parameter used to measure the ratio between the number of higher and lower magnitude events. The b value is calculated using the formula in Equation (4) [30].  
\[\text{b}=\text{log}10\ (\text{N}+\text{C}) \tag{4}\]  
where N is the number of earthquakes occurring in an earthquake zone and C is a constant value. A high b value means that frequent earthquakes of high intensity occur in the earthquake zone. A low b value means that earthquakes of low intensity occur in the earthquake zone.  
To calculate the b value, the number of earthquakes (N) occurring in the earthquake zone was determined first. Then, the constant value (C) was added, and the b value was calculated using the aforementioned formula. All the calculated b values were included in this study as a dataset.  
To calculate the d value, the formula in Equation (5) [31] was used.  
\[\text{d}=\text{log}10\ (\text{D}+\text{C}) \tag{5}\]  
where D is the total intensity of earthquakes occurring in an earthquake zone and C is a constant value. A high d value means that earthquakes occurring in the earthquake zone are generally of high intensity. A low d value means that earthquakes occurring in the earthquake zone are generally of low intensity.  
To calculate the \"d\" value using the formula, d = log10 (D + C), one needs to obtain specific values for D (distance) and C (constant). The formula that we have used herehighlights a logarithmic relationship between the distance value (D) and the constant value (C).  
In earthquake seismology, the Richter scale or other such scales are commonly used to measure earthquakes' magnitude. These scales take into account factors such as the amplitude of seismic waves recorded with seismographs.  
To calculate the d values, the total intensity (D) of the earthquakes occurring in a zone is first calculated. Then, the constant value (C) is added and the d value is calculated using the aforementioned formula. The calculated d values were added to this study as a dataset.
## 5 Results
### Simulation 1: Signal with 11 Harmonic Components Reduced to 5 Components
In this second phase of Simulation 1, it is demonstrated how the Kalman filter can produce erroneous results when the variables to be estimated do not correspond to the actual variables of the input signal (Figures 16-18). It has been shown how the developed method addresses this issue by recalculating the DFT of the input signal and, through this method, determining which DFT values are real and which are not. With this information, the method appropriately implements the equations constituting the Kalman filter (5-17), causing the filter's output to converge to the true value (Figure 19), thereby minimizing the error between the actual and estimated signals (Figures 21 and 22). Figure 23 illustrates  
Figure 21: LKF estimated signal error. Figure 22: Detail of the error made in the signal estimation. how the method enables the estimation of the value of each harmonic component present in the input signal. In this case, the 22 Hz component is shown. The same applies to the other components, which are not displayed to avoid redundancy. Figure 24 shows that another piece of information provided by the method is the temporal evolution of the frequency value of each spectral component forming the input signal. In summary, these results have demonstrated how the method resolves the problem that occurs when using the LKF in measuring spectral components that vary over time (Figures 17-19). The simulation has also shown the necessity of determining, among the output values of the DFT (Figure 10), which values correspond to components that really exist and shows how the presented method is suitable for identification.
## 7 Results and Discussion
### Processing and Analysis of Samples in 2-s Windows  
By dividing each of the tests into 2-s subsamples and obtaining their statistical values, a new dataset is generated. It contains all the tests performed with 34,137 rows and 21 columns, with which the RF, KNN, and SVM algorithms are trained and evaluated.  
The obtained performance of the KNN classifier with the aforementioned approach was quite poor (57.3% test accuracy), a dimensional reduction was performed in the same way that it was done for the set of 476 samples. Then, the behavior of the three algorithms could be checked again with this new reduced dimensionality dataset. After performing the steps described in Section 7.1, dimensions are reduced from 21 to 8, as shown in Figure 12, to reduce processing time and to evaluate the classification score again.  
With this new data set of 34,137 rows and eight dimensions, the RF, KNN, and SVM algorithms were trained and evaluated again; the results are presented in Table 5, where it can be seen that the performance of the k nearest neighbors improved when considering  
Figure 11: Benchmark of 14 different classification algorithms for the same dataset.  
Figure 12: Importance of pre-selected features obtained with help of a decision tree classifier.  
only six neighbors. On the contrary, the SVM decreased its performance according to the precision, recall, and F1-score metrics. However, its accuracy in the test was not highly affected. Random Forest scores also decreased when reducing the features dimensionality.  
The Figure 13 table presents the resultant confusion matrices of the three classification algorithms and an example of classification area graphs created by them for a pair of features.  
Figure 13: (**A**) Confusion matrices for KNN, SVM:RBF, and RF. (**B**) Class zones examples created by each classifier for two dimensions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **KNN k = 19** & **SVM (RBF)** & **Random Forest** \\ \hline Test accuracy & 93.2\% & 88.9\% & 90.7\% \\ Precision & 93\% & 89\% & 91\% \\ Recall & 94\% & 89\% & 91\% \\ F1-score & 93\% & 89\% & 91\% \\ Support samples & 8535 & 8535 & 8535 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation metrics for KNN and SVM after dimensional reduction.  
The separation is more visible at higher dimensions (Figure 14). Considering that the selected features were eight, the high-dimensional classification allows us to achieve better classification scores.
# A Novel Magnetic Coupling for Miniaturized Bandpass Filters in Embedded Coaxial SIW
###### Abstract  
In this paper, embedded coaxial substrate integrated waveguide (CSIW) filters with innovative magnetic couplings are presented and studied. By creating the loading capacitance of a combine topology using inner layers of a low-temperature co-fired ceramic (LTCC) stack-up, it is possible to achieve resonator miniaturization while improving the spurious-free band and providing full-packaged solutions. Moreover, a new magnetic coupling scheme consisting of short-ended stripline probes is proposed and analyzed in detail, both for direct and external couplings. An in-line three-pole filter at L-band is designed, manufactured, and measured proving how the proposed approach can be used for designing wideband bandpass filter (BPF) with extremely compact size. The designed BPF is centered at 1.5 GHz with 10% fractional bandwidth (FBW), while the layout size is just 35 \(\times\) 9.5 mm\({}^{2}\). The experimental results validate the coaxial SIW technology that allows for, at the same time, easy integration, compact size, flexible design, and enhanced stop-band performance.  
Keywords:bandpass filters; coaxial-cavity filters; planar circuit integration; substrate integrated waveguide +
Footnote †: journal: Applied Physics Letters
# Decision-Maker's Preference-Driven Dynamic Multi-Objective Optimization
## 3 Experimental Setup
### Performance Measures  
Each of the performance measures were calculated immediately before a change in the environment occurred. This was performed for thirty runs. An average of the values of the thirty runs was then calculated for each measure in each environment.  
The following traditional DMOO performance measures were used in this study:  
* Accuracy (acc) measures how accurately a DMOA is able to approximate the true POF of a DMOP [1;37;38]. The lower the value of acc, the better the performance of the DMOA.
* Stability (stab) quantifies the effect of environment changes on the accuracy measure value [1;37;38;47]. The lower the value of this measure, the better the DMOA's performance.
* Hypervolume Ratio (hvr) [48] measures the proportion of the objective space that is covered by a non-dominated set without suffering from the bias of a convex region as seen with the hypervolume measure [49]. The higher the value of this measure, the better the DMOA's performance.
* Reactivity (react) [50] measures how long it takes a DMOA to recover after a change in environment occurred, i.e., the length of time it takes to reach a specified accuracy threshold after the change occurred [38]. The lower the value of this measure, the better the DMOA's performance.  
The following newly proposed measures were used in this study:  
* Number of Non-Violating Decisions (nNVD) measures the number of decisions that fall within the DM's preference set. The higher the value of this measure, the better a DMOA's performance.
* Spread of Non-Violating Decisions (sNVD) measures the spread of decisions within the preference set. A high value indicates a good DMOA performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**S/N** & **DMOP** & \(\mathbf{\tau_{t}}\) & \(\mathbf{n_{t}}\) & **Iterations** & **c(f(x))** & \(\mathbf{\sigma(runs)}\) \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 3: Benchmark function configurations.  
* Number of Violating Decisions (nVD) measures the number of violating decisions in the archive. These are decisions that do not lie within the preference set. The lower the value of this measure, the better the performance of the DMOA.
* Total Deviation of Violating Decisions (dVD) measures the total deviation from the preference set for all violating decisions in the archive. It is calculated based on the steps that are highlighted in Section 2.2.1. The lower the value of this measure, the better the DMOA's performance.  
The four new performance measures proposed in this article specifically measure the performance of a DMOA with regards to DM preference constraints, and thus facilitate the comparative analysis of DMOAs in the context of a DM's preferences.
# PV-Powered Charging Station for Electric Vehicles: Power Management with Integrated V2G
## Abbreviations  
DC & Direct Current  
EV & Electric Vehicle  
EVs & Electric Vehicles  
ICDA & Immediate Charge/Discharge Algorithm  
G2V & Grid-to-Vehicle  
PV & Photovoltaic  
SPVA & Searching Peak and Valley Algorithm  
STC & Standard Test Conditions  
V2G & Vehicle-to-Grid  
**Nomenclature**  
\(c_{PV}\) & The coefficient of PV power  
\(c_{EV}\) & The coefficient of the energy that EVs can release  
\(c_{PV}\) & The coefficient of PV power  
\(C_{ERF}\) & The capacity of the storage (Ah)  
\(D_{cp}\) & The completion degree of EVs  
\(D_{cp}(1)\) & The completion degree of EVs under ICDA  
\(D_{cp}(2)\) & The completion degree of EVs under SPVA  
\(E_{discharge}\) & The maximum energy that EVs can discharge (kW)  
\(E_{charge}\) & The maximum energy that EVs can be charged (kW)  
\(I_{S\_MAX}\) & The storage current limitation (A)  
\(J_{EV\_ERF}\) & The battery energy of EV (kWh)  
\(k\) & Distribution coefficient  
\(Ordinal_{EV}\) & The ordinal number of EVs  
\(pi_{j}\_{ext\_G}\) & The power injected into the public grid (kW)  
\(P_{PV\_STC}\) & The estimated PV power under standard test conditions (kW)  
\(p_{PV}\) & PV power (kW)  
\(P_{EVs}\) & Total power of EVs (kW)  
\(P_{EV\_total}\) & The power that EVs can output (kW)  
\(P_{FAST\_MAX}\) & Maximal charging power demanded by fast mode (kW)  
\(P_{AVER\_MAX}\) & Maximal charging power demanded by average mode (kW)  
\(P_{SLOW\_MAX}\) & Maximal charging power demanded by slow mode (kW)  
\(P_{DIS}\) & The constant discharge power (kW)  
\(soc_{EV}\) & State of charge  
\(SOC_{MIN}\) & The \(soc\) lower limit  
\(SOC_{MAX}\) & The \(soc\) upper limit  
\(SOC_{EV\_in}\) & Initial state of charge  
\(SOC_{EV\_final\_lim}\) & The state of charge limit at the time of departure  
\(t_{EV\_charge\_1}\) & Parking periods of EVs connected to charger 1(s)  
\(t_{EV\_operate}\) & The start operating time of EVs (s)  
\(t_{EV\_arrive}\) & The arrival time of EVs (s)  
\(t_{EV\_depart}\) & The departure time of EVs (s)  
\(t_{EV\_best\_st}\) & The optimal start operating time (s)  
\(t_{EV\_latest\_st}\) & The estimated latest operating time (s)  
\(T_{arrive}\) & The time each EV arrives at the charging station (s)  
\(T_{depart}\) & The time each EV leaves the charging station (s)  
\(v_{C\_REF}\) & The rated voltage of DC bus (V)