esa-sceva/satcom-chunk-collection huggingface dataset:
# Chalcogenide Thin Films--Holographic Media for Augmented Reality Devices
## 4 Experimental Results and Discussion
### Chalcogenide Glasses with Optimized Compositions  
#### 4.2.1 The First Series of ChGs Samples  
To increase blue color transmission through CHG, the first series of new samples with variety of compositions were produced: As\({}_{15}\)S\({}_{85}\), As\({}_{12}\)S\({}_{88}\), As\({}_{10}\)S\({}_{90}\); Ge\({}_{20}\)S\({}_{80}\), and Ge\({}_{24}\)S\({}_{76}\). One As\({}_{2}\)S\({}_{3}\) sample was used as reference one. Thickness of all samples was 700 nm;  
Figure 6: ChG sample with surface grating recorded.  
Figure 7: Red image provided through the recorded periscope system (this image is composed with three separate sub-images, illustrating, correspondingly, transmission of left, central, and right FOV parts through the waveguiding system).  
substrate glass thickness, 0.5 mm; substrate glass refractive index, 1.6. ChG film refractive index was \(\geq\)2.2. The photo of all samples is presented in Figure 8.  
Transmission spectra of these samples are shown in Figure 9.  
As it seen from Figure 9, new samples have better transparency in blue range than the ChGs described in the previous section. Ge-S type compositions absorb light shorter than 450-440 nm; thus, such compositions should transmit blue light without significant loss and are suitable for AR purposes.  
To check the photosensitivity of the suitable Ge-S samples, they were uniformly illuminated by 457-nm laser. After that, spectra of illuminated parts were measured again and compared with the initial spectra (Figure 10). Measured spectra reveal that absorption edge of Ge-S type compositions shifts for about 25 nm to the shorter wavelengths after illumination, and illuminated regions became mostly transparent for human eye.  
Figure 8: Photo of the ChGs samples from the first optimization step.  
Figure 9: UV-VIS spectra of the ChGs samples from the first optimization step.  
Using these newly created two Ge-S compositions, we recorded two volume holograms to form a periscope system. The recording parameters were as follows: recording wavelength, 457 nm; linear polarized beams; average power density, 100 mW/cm\({}^{2}\); exposure time, 15-30 min; and period of the recorded gratings was set to 360 nm for symmetrically transmission the green part of the FOV through the periscope system Both the compositions of Ge-S type demonstrated similar dynamics and maximum diffraction efficiency of 1%.  
Observing the image obtained, it is seen that blue light is well transmitted through the periscope system, and FOV is wide and limited only by substrate glass refractive index (Figure 11). One of the main issues of the recorded periscope is its low efficiency. Several steps can be made to increase it, such as using shorter recording wavelength (i.e., 405 nm), slanted holographic gratings recording, sample thickness increase (up to several \(\upmu\)m), and relief holograms recording.
###### Abstract  
Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.  
queue length; shockwave; DNN; automated vehicles; IoT +
Footnote †: journal: Applied Sciences
## 4 Conclusions
** & **F1** \\ \hline bamital & 98.74\% & 100.00\% & 99.37\% & padcrypt & 59.22\% & 91.16\% & 71.80\% \\ banjori & 92.67\% & 96.46\% & 94.53\% & phorpiex & 40.18\% & 89.06\% & 55.38\% \\ bazarloader & 99.68\% & 99.46\% & 99.57\% & pitou & 50.94\% & 98.41\% & 67.13\% \\ chinal & 79.26\% & 93.17\% & 85.65\% & proslikefan & 4.78\% & 34.60\% & 8.40\% \\ conticker & 14.81\% & 28.18\% & 19.41\% & pseudoman & 19.29\% & 70.44\% & 30.28\% \\ corebot & 92.41\% & 92.78\% & 92.60\% & pushdo & 74.06\% & 71.93\% & 72.98\% \\ cryptolocker & 13.26\% & 23.24\% & 16.88\% & pushdotid & 40.08\% & 63.31\% & 49.08\% \\ darkshell & 88.49\% & 100.00\% & 93.90\% & pykspa2s & 91.56\% & 84.07\% & 87.65\% \\ darkwatchman & 98.36\% & 100.00\% & 99.17\% & qadars & 58.89\% & 89.06\% & 70.90\% \\ dircrypt & 1.14\% & 21.11\% & 2.17\% & qakbot & 35.32\% & 12.29\% & 18.24\% \\ dyre & 99.82\% & 100.00\% & 99.91\% & qsnatch & 96.44\% & 89.77\% & 92.98\% \\ ebury & 66.11\% & 99.33\% & 79.38\% & ramdo & 96.99\% & 100.00\% & 98.47\% \\ emotet & 25.59\% & 94.53\% & 40.28\% & rammit & 12.66\% & 17.32\% & 14.63\% \\ flubot & 57.15\% & 58.62\% & 57.88\% & ranbyus & 45.23\% & 47.75\% & 46.46\% \\ fobber & 6.62\% & 43.12\% & 11.48\% & rovnix & 58.39\% & 94.70\% & 72.24\% \\ gameover & 99.64\% & 99.38\% & 99.51\% & shiftu & 37.94\% & 75.04\% & 50.40\% \\ gamceiver\_p2p & 16.06\% & 67.43\% & 25.94\% & simda & 88.08\% & 83.40\% & 85.68\% \\ gozi & 16.05\% & 68.82\% & 26.03\% & sphix & 6.07\% & 55.49\% & 10.95\% \\ granderoiren & 69.28\% & 100.00\% & 81.85\% & suppobox & 68.61\% & 86.03\% & 76.34\% \\ locky & 19.04\% & 15.03\% & 16.80\% & symmi & 82.34\% & 95.25\% & 88.33\% \\ m0 ytdd & 43.85\% & 75.06\% & ¡ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Family** & **Prec.** & **Rec.** & **F1** & **Family** & **Prec.** & **Rec.** & **F1** \\ \hline  & 37.72\% & 94.51\% & 53.92\% & vawtrak & 30.07\% & 65.78\% & 41.27\% \\  & 85.82\% & 36.71\% & 51.42\% & virut & 98.73\% & 95.42\% & 97.05\% \\  & 63.86\% & 84.47\% & 72.74\% & xchhex & 96.79\% & 99.70\% & 98.22\% \\  & 5.10\% & 15.72\% & 7.70\% & zloader & 55.74\% & 80.92\% & 66.01\% \\  & 99.65\% & 62.24\% & 60.92\% & metasealer & 95.78\% & 100.00\% & 97.84\% \\  & 62.32\% & 99.54\% & 76.65\% & Average & 59.19\% & 73.96\% & 62.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Class Incremental Method, 1 st incremental train with Suppobox examples precision, recall, and F1 score of each DGA family.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 3 MarCO  
The MarCO (Mars CubeSat One) mission [2] is composed of two twin CubeSat designed to perform a Mars flyby and to relay data during the Entry, Descent and Landing (EDL) phase for the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission. The two CubeSats were, like ASTERIA, designed, assembled, and operated by JPL. The mission was launched on 5 May 2018, together with the JPL mission InSight. Immediately after launch, MarCO-A and MarCO-B separated from the Atlas V launch vehicle and traveled along their own trajectories to Mars. Once there, they supported the InSight mission by receiving the UHF signal from the lander, by decoding it and re-encoding it into an X-Band signal that was then forwarded directly to the Deep Space Network (DSN) antennas during the EDL phase (26 November 2018). After the EDL phase, both MarCO spacecraft completed a flyby of Mars and remained active until some time in January 2019, when it was reported that they both lost contact [3].
# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 3 Methodology
### Generalized Mathematical Model for Criticality Assessment
#### 3.3.2 General Criticality Formula  
The criticality of an asset (\(i\)) can be modeled mathematically as follows:  
\[\text{CR}_{i}=\text{FF}_{i}(\text{A}_{i})\cdot\sum_{j=1}^{k}w_{j}\cdot\text{C} _{ij}(\text{A}_{i})\] where * \(\text{FF}_{i}(\text{A}_{i})\) is the failure frequency of the asset (\(i\)) depending on operational characteristics \((\text{A}_{i})\), * \(\text{C}_{ij}(\text{A}_{i})\) is the value of the consequence of the failure in the dimension (\(j\)) depending on operational characteristics \((\text{A}_{i})\) (e.g., safety, environment, etc.), * \((w_{j})\) is a weight assigned to each dimension (\(j\)) to reflect its relative importance in the criticality analysis, * \((k)\) is the number of dimensions considered (in this case, 4: safety, environment, service, costs). The weights (\(w\)) can be adjusted according to the strategic importance of each dimension for the railway undertaking; the methodology for determining these weights is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Original Data Type** & **Data Source** & **Transformation Applied** & **Transformed Data** \\ \hline
**Asset Inventory** & SQL Database & Normalization of units and reference & Standardized asset attributes \\
**Maintenance Records** & Excel Files & Filtering for relevant maintenance records & Relevant maintenance history \\
**Real-Time Sensor Data** & IoT Devices & Conversion to standard metrics and aggregation & Normalized sensor readings (e.g., wear rate) \\
**Geolocation Data** & GIS & Mapping to asset identifiers and classification & Location-based asset classification \\
**Failure Reports** & Incident Reports & Coding and categorization by failure type and severity and location reference & Structured failure history and frequency \\ \hline \hline \end{tabular}
\end{table}
Table 4: Examples of Original and Transformed Data Types in the ETL Process.  
explained in Section 3.3.4. For example, for a company with a strong focus on safety, the weight will be higher than for the other dimensions. (\(w_{\text{safety}}\)) will be greater than that of the other dimensions.
## 2 Method  
### Heat Production Performance Optimization Problem Formulation  
As a high temperature geothermal resource, HDR is primarily used for power production. The capacity of heat generation power must be addressed when developing a geothermal system. As a result, heat generating capacity is taken as an assessment indicator to evaluate heat production performance. The following equation is widely used to compute heat generating power according to the First Law of Thermodynamics:  
\[W_{\rm e}=0.45q(h_{pro}-h_{inj})(1-T_{o}/T_{pro}),q=\rho\
u{\rm A} \tag{1}\]  
where \(W_{\rm e}\) stands for the heat generation power, MW; \(q\) represents the circulation mass flow rate, kg/s; \(h_{pro}\) and \(h_{inj}\) stand for the enthalpy of the producing water and injection specific enthalpy, J/kg, respectively; \(T_{0}\) is the injection temperature, while \(T_{pro}\) is the outlet temperature; and 0.45 is the thermal energy conversion coefficient. The mass flow rate \(q\) could be calculated according to \(q=\rho\
u{\rm A}\), where \(\rho\) stands for the density of the circulating working fluid, kg/m\({}^{3}\); \(\
u\) represents the injection velocity of circulating working fluid, m/s; while \(A\) denotes the injection pipe's cross sectional area, m\({}^{2}\).  
Therefore, the calculation formula of total power generation \(Q\) within a certain time is as follows:  
\[Q=\int_{0}^{t}0.45q(h_{pro}-h_{inj})(1-T_{o}/T_{pro}){\rm d}t \tag{2}\]  
The total power generation Q is employed to reflect the heat production performance, which would be optimized to obtain the best performance. The production specific enthalpy \(h_{pro}\) and outlet temperature \(T_{pro}\) are obtained through the computer simulations described in Section 2.3, which is a black-box issue. A Kriging model is employed to replace the production specific enthalpy \(h_{pro}\) and outlet temperature \(T_{pro}\) in the optimization problem to benefit the whole optimization procedure with easy implementation and much less computational burden. In this work, the fracture space \(W\), mass flow rate of injected circulating working fluid \(q\), and well space \(L\) are taken as the design parameters to gain the maximum heat generation power. The well spacing \(L\) can be decomposed into \(L_{1}\) and \(L_{2}\) in the vertical and horizontal directions.  
Therefore, the optimization problem could be formulated as follows:  
\[\begin{array}{ll}max&Q\\ s.t.&6\leq q\leq 12,\text{kg}/\text{s},\ \ 40\leq W\leq 120,\text{m}\\ &600\leq L\leq 1000,\text{m},\ T_{pro}\geq 393.15\ \text{K}\end{array} \tag{3}\]  
The optimization objective function is to maximize the total power generation \(Q\). The fracture space \(W\), mass flow rate of injected circulating working fluid \(q\), and well space \(L\) are the variables, which are employed in the numerical simulation model to obtain the production specific enthalpy. There is a cut-off outlet temperature constraint (presented as \(T_{pro}\geq 393.15\ \text{K}\)) considering the system lifetime; that is, an outlet temperature less than \(393.15\ \text{K}\) is considered to have no development and utilization value.
## 4 Numerical Experiments
### Korea Network
#### 4.2.1 Convergence Characteristics  
Figure 8 illustrates the convergence characteristics of the route-based GP method used to solve the multi-class TAP with mixed modes. As can be seen, the method was capable of achieving convergence with a relative gap (RG) of \(1\times 10^{-6}\). The computational effort required to achieve a relative gap of \(1\times 10^{-6}\) was around 3600 s. Based on the results, computational CPU times were reduced as the available roads for CAVs increased. This was expected since adding available roads for CAVs to the network resulted in a reduced congestion level with increased capacity and the algorithm required less computational efforts to equilibrate the path flows of all vehicle classes.  
Figure 8: Convergence characteristics.  
Figure 7: Korea network and analysis scenarios. (**a**) Test network; (**b**) network characteristics; (**c**) available roads for CAVs.
# Optimization of Number of GCPs and Placement Strategy for UAV-Based Orthophoto Production
## 3 Methodology
### Development of Scenarios  
Scenarios were developed for experimental analysis based on the data acquired from the aerial photography in the previous stage. The locations of the GCPs and CPs within the study site are shown in Figure 2.  
Figure 1: Locations of GCPs number and CPs.  
Figure 2: Different numbers and distributions of GCPs for each scenario.  
Based on the image data acquired through the drone photography, the scenarios required for experimental analysis were developed. Based on the four GCPs that were installed, Scenarios 1 to 5 were set as shown in Table 2 by varying the number of selected GCPs in each scenario. Additional scenarios were created by changing the placement of GCPs included in each scenario. The number of all possible cases that could be generated was \(\sum_{\mathrm{x=0}}^{4}\mathrm{c_{x}}\ =\ 16\); a total of 16 cases were generated, as shown in Table 3. Therefore, the final number of models is 16.  
Based on the formulated scenarios, the selected GCPs were placed as shown in Figure 2 and modeling was performed with PIX4D Mapper. An orthophoto was created, the resulting values for the geolocation of GCPs were produced, and the data were analyzed.
## 4 Performance Analysis
### Simulation Results  
All results are summarised in Table 2, which represents a static network with 100 sensor nodes after running 300 s of simulation time. In such a scenario, there was one sink node, while the remaining nodes acted as sensor nodes to collect sensing data.
## 3 Results
In an ideal system, HV should be constant and proportional to the inverse square value of the diagonal (1/D2, \(n=2\)). Nevertheless, HV in real systems is dependent on the applied load, implying a deviation of the \(n\) value from the ideal one  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Bisco Ortho Bracket Paste LC (Bisco)** & \multicolumn{3}{c}{**Light-Cure Orthodontic Paste (Light)**} & \multicolumn{3}{c}{**Transbond XTM Light Cure Adhesive (Trans)**} \\ \hline
**Run** & **D (\(\mu\)m)** & **HV** & **Run** & **D (\(\mu\)m)** & **HV** & **Run** & **D (\(\mu\)m)** & **HV** \\ \hline
1 & 102.0 & 89.2 & 1 & 101.6 & 89.9 & 1 & 116.8 & 68.0 \\ \hline  
(\(n=2\)). The extent of this deviation can provide a measure of the dependence of HV on the applied load. A higher deviation of \(n\) value from the ideal value indicates a higher dependence of the HV on the applied load. Linear regression on each specimen's dataset allowed us to extrapolate their relative \(n\) values. The obtained results are reported in Table 3:  
The calculated \(n\) values indicate that the dependence of HV on the applied load follows this order: Trans XT > Bisco > Light.
## 4 Simulation Results and Security Evaluation
### Statistical Attack
#### 4.4.1 Analysis of Correlation Coefficients  
The statistical method used to assess the magnitude and direction of the relationship between two variables is referred to as the correlation through bivariate analysis. The correlation coefficient value ranges between \(-1\) and \(+1\) and indicates the strength of the association between the two variables. When the correlation coefficient approaches 0, it signifies a weaker relationship between the variables. A positive (+) sign indicates a positive relationship, while a negative (\(-\)) sign indicates a negative relationship. An effective image encryption scheme should produce a randomly distributed output with minimal correlation between adjacent pixels. Typically, the correlation of pixels in both plain and cipher images is evaluated in the horizontal, vertical, and diagonal directions. The correlation coefficient \(r_{xy}\) between the adjacent pixels is defined and measured in Equation (20):  
\[r_{xy}=\frac{\text{cov}(x,y)}{\sqrt{(x)}\sqrt{(y)}} \tag{20}\]  
where  
\[\text{var}(x)=\frac{1}{N}\sum_{i=1}^{N}\left(x_{i}-E(x)^{2}\right),\ \text{cov}( \text{x},\text{y})=\text{E}([x-E(X)][y-E(Y)]),E(x)=\frac{1}{N}\sum_{i=1}^{N}x _{i},E(y)=\frac{1}{N}\sum_{i=1}^{N}y_{i}.\]  
Here,\(x_{i}\) and \(y_{i}\) represent the gray level value of two adjacent pixels, where by \(E(X)\) represents the mean of \(x_{i}\) and \(E(Y)\) is the mean of \(y_{i}\). After implementing Equation (20) in the proposed scheme, we noted a significant difference between the original and encrypted images. Table 2 displays the correlation coefficient between the two sets of images, while Table 3 compares the proposed scheme's results to other similar studies. These findings demonstrate that the proposed scheme exhibits superior resistance to statistical attacks. Figure 12 illustrates the random distribution of both the original and encrypted Baboon images.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **Test Name** & **\(p\)-Value** & **Results** & **[PERSON] et al. [25]** & **Results** \\ \hline
1 & Universal & 0.2429 & Pass & 0.304126 & Pass \\
2 & Frequency & 0.7985 & Pass & 0.883171 & Pass \\
3 & Block Frequency & 0.2375 & Pass & 0.236810 & Pass \\
4 & Cumulative sums forward & 0.9876 & Pass & 0.437274 & Pass \\
5 & Cumulative sums reverse & 0.7654 & Pass & 0.437274 & Pass \\
6 & Runs & 0.8529 & Pass & 0.759756 & Pass \\
7 & Longest run & 0.2147 & Pass & 0.759756 & Pass \\
8 & Rank & 0.1756 & Pass & 0.145326 & Pass \\
9 & FFT & 0.6563 & Pass & 0.719747 & Pass \\
10 & Overlapping template & 0.5644 & Pass & 0.595549 & Pass \\
11 & Approximate entropy & 0.9343 & Pass & 0.867692 & Pass \\
12 & Serial & 0.2231 & Pass & 0.554420 & Pass \\
13 & Linear Complexity & 0.5281 & Pass & 0.534146 & Pass \\
14 & Random excursions & 0.3541 & Pass & 0.494392 & Pass \\
15 & Random excursions variant & 0.2781 & Pass & 0.236810 & Pass \\ \hline \end{tabular}
\end{table}
Table 1: NIST Test.
# Minkowski Island and Crossbar Fractal Microstrip Antennas for Broadband Applications
## 2 The Conventional Reference Antenna  
A rectangular microstrip patch antenna was designed to serve as a reference. The patch is 12 \(\times\) 16 mm\({}^{2}\), and the feeding line is 2.46 \(\times\) 8 mm\({}^{2}\) (Figure 1a). Note the asymmetrical geometry of the feeding line. Such configuration was mainly intended for the antenna with double-fractal layers presented in Figure 6. Nevertheless, all antennas analyzed below have the same asymmetrical geometry to ensure the same feeding condition. The simulated and measured values of reflection coefficient (S11 [dB]) of the conventional reference antenna are presented in Figure 1b.  
The S11 drawing of conventional microstrip patch antenna (Figure 1a) shows a very limited bandwidth of 225 MHz (Figure 1b).  
Figure 1: (**a**) Geometry of conventional microstrip antenna; (**b**) simulated and measured S11 of the conventional microstrip antenna.
## 3 Results
### Assessing NDM and FDM Based Laplacian Estimates for Real Life LIIRD TCRE on Human ECG Data  
Figure 7 shows 5 s of ECG signals recorded at CMV1. The bipolar signals (\(U_{2}-U_{1}\) and \(U_{4}-U_{1}\)) needed for the computation of the Laplacian estimates for emulation of LIIRD TCRE are shown in black in the top and middle panels. Laplacian estimates based on NDM (dotted line) and FDM (dashed line) models are shown in the bottom panel. Signals are of low amplitude (tens of microvolts). The P- and T-waves and the QRS complex can be clearly observed in all panels. In fact, the P1- and P2-waves of left and right atrial activity can be identified. As expected based on the Equations (16) and (17), Laplacian estimates based on NDM and FDM are virtually identical.  
Laplacian estimate signal metrics are summarized in Table 3. Estimates based on NDM and FDM presented very similar results with no statistically significant differences (Wilcoxon test following Lilliefors test to assess normality, \(p>>0.05\)). The mean QRS amplitude is low (18.6 uV) and presents high variability (28.4 uV). A mean signal-to-noise ratio of around 20 dB was obtained for both signals. Mean normalized P-wave amplitude was equal to 0.256 and 0.253 for NDM and FDM based Laplacian estimates respectively. The normalized amplitude for T-wave was also similar (0.37 and0.36) for both estimates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(\Delta v_{0}\)} & **Amplitude QRS** & **Normalized Amplitude P-Wave** & **Normalized Amplitude T-Wave** & **Signal-to-Noise Ratio (dB)** \\ \hline NDM & 18.59 \(\pm\) 28.40 & 0.256 \(\pm\) 0.145 & 0.367 \(\pm\) 0.260 & 20.05 \(\pm\) 8.29 \\ FDM & 18.60 \(\pm\) 28.40 & 0.253 \(\pm\) 0.141 & 0.362 \(\pm\) 0.264 & 19.94 \(\pm\) 8.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Laplacian estimate signal metrics (mean \(\pm\) standard deviation, \(N=20\)).  
Figure 7: ECG signals: recorded bipolar signals \(U_{2}-U_{1}\) and \(U_{4}-U_{1}\) (panels (**A**) and (**B**) respectively) and Laplacian estimates based on NDM and FDM (panel (**C**)).