esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Intermittent Jumping Coupled Map Lattice Based on Multiple Chaotic Maps
## 3 Analysis of Dynamic Behaviors
### Information Entropy Analysis  
Information entropy is a frequently-used criterion to measure the degree of confusion of a dynamical system. Following the quantization procedure previously applied in [13; 14; 16], we uniformly quantize a state variable \(x_{n}(l)\) to an integer between 0 and 9. Specifically, the quantization procedure takes the form \(\lfloor x_{n}(l)\cdot 10\rfloor\). The information entropy of a specific lattice is defined by:  
\[H(\mathbf{c})=-\sum_{c=0}^{9}p(c)log_{2}p(c), \tag{15}\]  
where \(c=\{c|c=0,\,1,\,\,\cdots,\,9\}\) denotes the possible levels of quantization, and \(p(c)\) represents the probability of occurrence of \(c\) in a quantized sequence. Since we consider ten levels in the course of quantizing, the maximum value of \(H(\mathbf{c})\) equals \(log_{2}10\approx 3.3219\). It is desirable to achieve a higher value of \(H(\mathbf{c})\), which implies better pseudo-randomness of the quantized sequence.  
Figure 6 shows the results for the information entropy analysis. We conduct the simulation study under the setting \(3\leq\mu<4\) and \(0\leq e\leq 1\), and calculate the average value of \(H(\mathbf{c})\) over all \(L\) lattices.  
Figure 6: Information entropy analysis for (**a**) the CML system, (**b**) the NCML system, (**c**) the DCML system, (**d**) the NDCML system, and (**e**) the proposed IJCML system, respectively. The symbol ‘max’ on the vertical axis signifies the maximum value of the average information entropy, namely 3.3219.  
We see that, for the baseline systems, the average values of \(H(\textbf{c})\) form a stair-stepped upward trend as \(\mu\) increases, and the two notable steps appear at \(\mu=3.02\) and \(3.48\), respectively. When \(\mu>3.48\), the DCML system has a smooth surface, in which the average values are independent of \(e\). For the NDCML system, two straight ravines that appear at \(\mu=3.74\) and \(3.84\), respectively, go through the surface. This phenomenon is consistent with the observation in Section 3.3, demonstrating that there still exist defects in the NDCML system even when \(\mu\) is set to a higher value. For the CML system, a L-shaped valley with larger area is embedded into the surface. This analysis provides a warning that one should carefully select the parameters before using a CML-based image cryptosystem. Equipped with the non-adjacent coupling mechanism, the NCML system reduces the area of the valley, but fails to construct a smooth surface. For the IJCML system, the average value of \(H(\textbf{c})\) increases along the axes of \(\mu\) and \(e\), and the leaf-like surface covers a larger parameter space of spatiotemporal chaos. These results verify that the IJCML system is superior to the baseline ones in terms of information entropy.
# Lightning Protection of Photovoltaic Systems: Computation of the Developed Potentials
## 2 Protection of PV Systems against Lightning Overvoltages  
As it is described in [4; 19], PV modules are more vulnerable to direct lightning strikes than conventional low-voltage power distribution systems, due to installation on roofs, facades of buildings, and, in general, on unsheltered areas. For this, a risk assessment must be taken into account [10] and if it is deemed necessary, a thorough study of the LPS of PV park must be conducted [7]. Lightning and surge protective measures are described in IEC 62305 Standard (Parts 1 to 3) for conventional low-voltage distribution systems. Part 1 [21] lists damages due to lightning, protection needs and measures and basic criteria for protection of structures. Part 2 [22] provides the risk management procedure in order to evaluate the need of an LPS. Part 3 [23] concerns the lightning protection system (LPS).  
A necessary requirement for the efficient design of an LPS is to perform the previous stated risk assessment using the procedures and data stated in [22]. The most susceptible parts of the PV installation should be taken into consideration and the consequences of a direct or indirect lighting flash should be studied. In general, lightning strikes result in breakdown of the insulation, serious damage of the electrical equipment, and rise of the grounding potential [15]; the severity of the consequences are strongly influenced by the geometry of the PV system, the LPS, the peak current [8], and the duration of the lightning flash and the lightning strike position [24].  
LPSs are divided into external and internal. Basic components of an external LPS are the air termination system, the down conductors, and the ground system. The air termination system captures the lightning discharge current and dissipates it harmlessly to earth via the down conductor and the ground termination system. Thermal and explosive effects caused at the occasion of a lightning strike and the consequences to the installation must be a guide in choosing the type of air termination system (isolated or non-isolated) [23] (Figure 1).  
The earth termination system has a vital significance for the dispersion of the lightning current safely and effectively into the ground. According to [14], grounding resistance lower than 10 \(\Omega\) is recommended. To avoid galvanic corrosion, earth electrode material and connection clamps should be chosen carefully, taking into account PV frame and support structure materials. Open-field PV installation electrode arrangements are used for lightning protection. Moreover, the fundamental role of the internal LPS is to ensure avoidance of dangerous sparking due to the lightning current flowing through the external LPS or other conductive parts of the installation. The electrical interconnection can be achieved by equipotential bonding and surge protective devices, in the case that the direct connection with bonding conductors is not suitable.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Link Faults
#### 3.2.3 ROCOC Algorithm  
In the depicted scenario, Figure 10(a) shows a pole-to-pole fault in link L34. This is reflected in the results concerning the fault detection time, the relay, which is closer to the fault, taking 0.06 ms less to detect the fault than the one that is furthest away.  
In Figure 10(b), a pole-to-ground fault is simulated in the 0% of link L24, which means that the fault is located 0 km away from relay R24 and 12 km from relay R42. As the length  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fault at Link L34**} & \multicolumn{6}{c}{**Detection Time at R34 (ms)**} & \multicolumn{6}{c}{**Detection Time at R43 (ms)**} \\ \cline{2-13}  & \multicolumn{3}{c}{**P2P**} & \multicolumn{3}{c}{**P2G+**} & \multicolumn{3}{c}{**P2P**} & \multicolumn{3}{c}{**P2G+**} \\ \cline{3-13}  & **0\%** & **50\%** & **100\%** & **0\%** & **50\%** & **100\%** & **100\%** & **50\%** & **0\%** & **100\%** & **50\%** & **0\%** \\ \hline \multirow{3}{*}{UV} & R = 0.01 \(\Omega\) & 0.010 & 0.155 & 0.445 & 0.010 & 0.155 & 0.470 & 0.445 & 0.155 & 0.010 & 0.470 & 0.155 & 0.010 \\ \cline{2-13}  & R = 2 \(\Omega\) & 0.010 & 0.155 & 0.455 & 0.010 & 0.165 & 0.505 & 0.455 & 0.155 & 0.010 & 0.505 & 0.165 & 0.010 \\ \cline{2-13}  & R = 20 \(\Omega\) & 0.670 & 0.265 & 0.590 & 5.705 & 5.645 & 5.495 & 0.620 & 0.265 & 0.670 & 5.660 & 5.790 & 4.850 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Fault at link L34, detection times at R34 and R43, for the UV algorithm.  
Figure 11: ROCOC: (a) pole-to-pole fault L34 link 100%, and (b) pole-to-ground fault L24 link 0%.  
Figure 10: DC voltages: (a) pole-to-pole fault L24 link 0%, and (b) pole-to-ground fault L34 link 50%.  
of link L24 is shorter than the length of link L34, it directly affects the fault detection time, unlike the fault resistance, which has no effect on the detection time. So the main factor that affects the fault detection time is the distance of the measurement relay from the fault, as can be seen in Tables 11 and 12. The results are considered valid for DC protection.
## 3 Results
### Community Diversity  
The variations of the Shannon-Wiener diversity index, the Simpson diversity index, the Pielou's evenness index, and the Margalef richness index values in relation to the enclosure years are shown in Figure 4. The Shannon-Wiener, Simpson, and Margalef indices inside the enclosure were all observed higher than their values outside the enclosure in years F3, F5, and F7. The inside-outside difference of these indices was tested statistically significant in year F7, while significance was not found for the difference in other years. Moreover, only slight inside-outside difference was observed in the [PERSON]'s evenness index values in years F3 and F5 (Figure 4d). In year F9, all the measurements, regardless of diversity, evenness, or richness, showed lower values inside the enclosure than outside the enclosure, albeit not significant.  
Figure 4: _Cont_.  
Figure 3: Variations in plant community characteristics observed both inside and outside the fenced enclosure in the third, fifth, seventh, and ninth years of the enclosure. Observed community characteristics include (**a**) species height in centimeters, (**b**) species’ relative ground coverage in percentage, and (**c**) plant density in terms of count of plant individuals per square meter.  
It was notably observed that the Shannon-Wiener and the Simpson diversity indices, together with the Margalef richness index, showed a synchronous pattern whereby these indices tended to increase first and then decrease. All three indices reached a maximum level in year F5 which acted as a turning point. It is interesting to indicate that the Pielou's evenness index showed an opposite trend. The Pielou's index decreased first and then increased, with a turning point also in year F5.
## 3 Experiments on the 3D Quartz Tuning-Fork Probe  
### Probe System  
A 3D resonant positioning trigger probe using a quartz tuning fork is fabricated to verify the reliability of the micromechanism of the dynamic contact model and the feasibility of the positioning trigger method mentioned. The quartz tuning fork has the advantage of a stable mechanical resonant characteristic, especially a high-quality factor, which is approximately 10\({}^{3}\)-10\({}^{5}\)[39]. The higher quality factor may contribute to the sensitivity of the device. Therefore, the material has been used to fabricate scanning probes for 2D measurement [24]. However, in the current study, the probe based on the quartz tuning fork is further used for 3D triggering. While the quartz tuning fork vibrates in its resonant state, its resonant vibrating parameters (resonant vibrating amplitude and resonant frequency) are extremely sensitive to external microforces. Figure 6 shows a typical frequency spectra of the 3D trigger probe in air. The horizontal axis represents the excitation frequency of the microsphere, and the longitudinal axis represents the amplitude voltage of the microsphere. The figure shows that the quality factor is about 3200, which is deemed very high.  
Figure 7 shows the structure of the probe system. The base of the quartz tuning fork is fixed steadily on the probe frame to avoid additional energy loss when the probe vibrates. One of the quartz tuning fork arms serves as a piezoelectric driver, and the other serves as a piezoelectric sensor to output the electrical signal. A home-made integrated fiber microstem with a microsphere tip is mounted on the free end of the sensing arm. The length of the stem is about 1 mm, and the diameter of the microsphere is 80 \(\upmu\)m [40]. During the operation, the probe is driven in resonant state by a sinusoidal signal. When the microsphere tip approaches the sample surface, the amplitude attenuates, and the resonant frequency shifts significantly as soon as the tip-sample interaction comes into the nanocontact region. As a result, the electrical output signal is changed. This signal can be used as the trigger signal, but it cannot distinguish the contact direction. For micro/nano CMM, the unknown contact directions may bring ambiguities when reconstructing a surface. However, in most cases, the basic profiles of the samples are already known, according to the blueprints or observation. So, the contact directions can be predicted when measuring these objects.  
Figure 6: Typical frequency spectra of the 3D trigger probe in air.
# Tension Force Detection of Short Suspender by Using Laser Doppler Vibrometer
## 3 Results
### Quadratic Regression Relationships for Vibration Frequency and Tension Force for Short Suspenders  
It can be found from the experimental results in Figure 5 that there is a high degree of quadratic regression fitting between vibration frequency and tension force for short suspenders with different aspect ratios. Based on this result, the quadratic regression considering the influence of aspect ratios can be used to establish the calculation model between vibration frequency and tension force for short suspenders, as shown in Figure 5. Equation (4) is used to calculate the relative errors between the experimental and calculated values of tension force. It can be observed that when the applied tension force is more than 10 kN, the relative error between the experimental and calculated values of vibration frequency is less than 5%, as demonstrated in Figure 6. Meanwhile, with the increase of tension force, the error between the experimental and calculated values obtained by quadratic regression fitting for vibration frequency presents a declining trend, indicating the accuracy improvement of calculation.  
\[\delta_{T}=\left|\frac{T_{A}-T_{C}}{T_{A}}\right|\times 100\% \tag{4}\]  
where \(\delta_{T}\) is the relative error between the experimental and calculated values of tension force, \(T_{A}\) is the experimental tension force, and \(T_{C}\) is the calculated tension force based on quadratic regression fitting.  
It is worthwhile to note that the established quadratic regression relationships between the frequency and tension force was only used to illustrate that the tension force state of steel rod-type short suspenders can be judged according to vibration frequency without considering the influence of mass, stiffness, and constrained type. Meanwhile, different quadratic regression formulas can be used for different types of short suspenders. For example, the quadratic regression formulas for short suspenders with specific features can  
Figure 5: Fitting analysis between vibration frequency and tension force.  
Figure 6: Error analysis between the experimental and calculated values of tension force.  
be established in the design stage, and then this fitting model can be used to detect the tension force of short suspenders in the service stage.
## 4 Case Study of Data Collaboration
### Space-Time Travel Data Mining
#### 4.2.3 Mapping  
In this paper, the number and speed of cars on the whole road are calculated to find the average travel speed of a road, and according to different values, it is marked in different colours on the high-precision map. Since there are many abrupt changes in the instantaneous speed in our dataset, but the position data are relatively accurate, we choose the ratio of the total distance travelled by vehicles to the total time in the period to represent the average speed of the road section. The average travel speed is calculated as follows:  
\[\bar{v}=\frac{nL}{\sum_{i=1}^{n}t_{i}} \tag{5}\]  
where, \(\mathbf{L}\) is the length of the regional road section, which was calculated by the Haversine formula, \(\mathbf{t_{i}}\) is the time for vehicle \(\mathbf{i}\) to pass through the regional road section, and \(\mathbf{n}\) is the number of vehicles measured.  
According to the latest road traffic congestion evaluation method (GA/T 115-2020), the specific marking colour is divided into four levels, and the range is determined according to the road conditions. For example, on the urban main road with a speed limit of 60 km/h, if the average road travel speed is 35-60 km/h, it is marked in green; if the average road travel speed is 30-35 km/h, it is marked in yellow; 20-30 km/h, it is marked in orange; and 0-20 km/h, it is marked in red.  
Due to the unique coordinate marking method of GPS data, we first convert the WGS84 coordinate system to the GCJ02 coordinate system, and then correct the trajectory according to the trajectory correction algorithm [33], and finally match the high-precision map.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Id** & **Time** & **Car Speed** & **Longitude** & **Latitude** \\ \hline... &... &... &... &... \\
66899 & 27 July 2020 8:37 & 13.43901897 & 120.3296316 & 31.83989 \\
22645 & 27 July 2020 10:33 & 42.3437493 & 120.39129 & 31.8879616 \\
67063 & 27 July 2020 12:58 & 22.1819016 & 120.2446566 & 31.6393466 \\
08605 & 27 July 2020 22:23 & 13.4394087 & 120.48094 & 31.4990583 \\... &... &... &... &... \\ \hline \hline \end{tabular}
\end{table}
Table 3: Partial data of car speed.
# Privacy and Security Issues in Online Social Networks
## 3 Privacy and Security Threats in OSNs
### Classic Threats
#### 3.1.1 Malware  
Malware stands for malicious software. It is a generic term that refers to intrusive software. It is developed with the intention to log into someone's computer and access their private contents. A malware attack on social networks is easier as compared to other online services because of the structure of an OSN and the interactions among users. The worst malware case is to access users' credentials and impersonate them to send messages to their peers. For example, the Koobface malware was spread through OSNs such as MySpace, Facebook, and Twitter. It was used to collect login credentials and make the target-infected computer a part of a botnet [15]. An OSN has a vital role for various purposes, for example, marketing and entertainment. However, it has opened up its users to harmful activities. Committing fraud and propagating malware are criminal actions wherein users are engaged to access a URL and run a malicious code on the computer of an OSN user [16].
## 3 Experimental Results  
All the specimens failed in a ductile fracture mode, and Figure 4 shows the rupture modes of the tested specimens under monotonic tension. Two specimens, H30A85R0.5 and H40A75R0.5, failed prematurely due to unexpected manufacturing defects, which was due to the complicated configurations and small sizes of the specimens. Experimental results were obtained for the five well-manufactured specimens. Cracking first initiated at an edge of a re-entrant honeycomb unit, and propagated rapidly to the other neighboring honeycomb units. All the cracks occurred in the perforated segment, and were also away from the ends of the perforated segment. In most of the specimens, cracks propagated diagonally along the width direction except for in Specim H40A85R0.5, as shown in Figure 4. Figure 5 gives experimental load-displacement curves of the specimens. According to the load-displacement curves, all members reached the yield force and entered the plastic state under a tensile force of 106.91 kN to 110.32 kN, as listed in Table 2, indicating minor differences among the specimens. Loads of the specimens continued to increase after initial yielding, and the five specimens ruptured at maximum tensile forces ranging from 187.06 kN to 210.75 kN. The ultimate displacement of the five specimens ranged from 14.83 mm to 17.26 mm; the specimen with the maximum ultimate displacement was H40A80R0.5, and the minimum was H40A85R1. The maximum difference of the rupture displacement was about 14.07%, which was not apparent.  
Comparing the specimens before and after the test, it appeared that the re-entrant honeycomb elements in the specimens had significant shape changes, mainly axial stretch, and the concave angles were stretched. Searching for the occurrence of structural failure, most of the fractures appeared in the middle of the opening holes, and no fractures occurred in the corner end. The fractures were generally 45\({}^{\circ}\) inclined. Testing piece H40A85R0.5 was a little special, and the fracture appeared to be horizontal.  
Correlation is a coefficient that indicates a statistical relationship between the FE analysis result and the experimental one, and it is defined by the following equation.  
\[\text{Correlation}(X,Y)=\frac{Cov(X,Y)}{\sqrt{Var[X]Var[Y]}} \tag{3}\]  
where \(Cov(X,Y)\) is the covariance of \(X\) and \(Y\), and \(Var[X]\) and \(Var[Y]\) are the variance of \(X\) and variance of \(Y\) in the statistics.  
Figure 3: MTS machine and setting of test pieces with (**a**) Coupon test and (**b**) NPR specimen.  
Figure 4: Failure modes of tested specimens.  
Figure 5: Load–displacement curves of tested specimens for experiments with (**a**) H40A85R0.5, (**b**) H50A85R0.5, (**c**) H40A80R0.5, (**d**) H40A85R0 and (**e**) H40A85R1.
# Astronomical Investigation to Verify the Calendar Theory of the Nasca Lines
## 3 Astronomical Investigations in the Nasca Project
### Statistical Studies on the Astronomical Alignment of the Geoglyphs
#### 3.3.2 Investigation of the Correlation of Straight Lines with the Sun  
All of the 2308 straight lines were used for the calculations. However, the structure of these lines is different. The length of the lines varies from a few meters to several kilometers and, very often, long lines are interrupted by destruction. The restriction to the line type \"straight lines\" means that figures, areas, zigzags and other types of lines were initially not considered; this will be the subject of a further separate investigation.  
The calculation of the azimuths of the rising and setting Sun in the Nasca period was carried out for respective appearances or phenomena of solstices and equinoxes as well as for the zenith passage. The anti-zenith was not considered. The center of the Sun served as the target point.  
In order to obtain a general overview of the frequency of certain azimuths of the Nasca Lines, a histogram was calculated in an interval of 10 degrees (see Figure 13).  
This histogram clearly shows one symmetry because the opposite direction of the lines (azimuths differ by 180\({}^{\circ}\)) has exactly the same values for their frequency.  
Within one year, the relevant azimuths in the Nasca region (mean latitude: \(-14.7\) degrees) for sunrise are between approximately 65.6\({}^{\circ}\) and 114.4\({}^{\circ}\) and for sunset, they are between 245.6\({}^{\circ}\) and 294.4\({}^{\circ}\); the histogram shows no noticeable frequency at these azimuths. Only 569 of the total 2308 straight lines fall into these windows.  
In Table 2 are the numbers of lines which directly correspond to the exact azimuths (\(\pm 2^{\circ}\)) at the solstices and equinoxes as well as the zenith passages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Azimuths in Direction of** & **June Solstice** & **Equinox** & **December Solstice** & **Zenith** & **Total** \\ \hline Sunrise/Sunset & 43/19 & 24/24 & 19/43 & 8/5 & 94/91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of all line azimuths with reference to the Sun.  
Figure 13: Frequency distribution of the azimuths of the Nasca Lines in intervals of 10 degrees.  
The lines oriented to the sunrise at the equinox are exactly the same as the sunset lines (overall 24 lines). Further, the lines oriented to the sunrise in June solstice are the same ones for the sunset during the December solstice and vice versa (all together 62 lines). The zenith passages (13 lines) in particular seem to be very random. Regardless of this, it means that only 99 lines have an astronomical orientation to the Sun on those events. This corresponds to around 4% of all lines of the type \"straight line\".