esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Impact of Synthesis Temperature  
The influence of synthesis temperatures ranging from 140 to 180 \({}^{\circ}\)C was evaluated, while tightness results revealed that lower temperatures (140 and 150 \({}^{\circ}\)C) were insufficient for the uniform formation of a selective layer, as evidenced by N\({}_{2}\) permeance through the selective layer of 19.01 and 0.03 \(\times\) 10\({}^{-7}\) [mol/(m\({}^{2}\)sPa)], separately (Table S9). Elevated hydrothermal temperatures led to a decrease in N\({}_{2}\) permeance, signifying the enhanced quality of the SSZ-13 layer. Figure 3c illustrates a decline in CO\({}_{2}\)/CH\({}_{4}\) and H\({}_{2}\)/SF\({}_{6}\) permeability from 122 to 45 and from 144 to 51, separately, as well as a decline in CO\({}_{2}\) permeance from 30 to 12 \(\times\) 10\({}^{-7}\) [mol/(m\({}^{2}\)sPa)]) with increasing synthesis temperature from 160 \({}^{\circ}\)C to 180 \({}^{\circ}\)C. Furthermore, SF\({}_{6}\) permeance increases with rising temperature and is similar to extended synthesis time, leading to thicker membranes, often accompanied by increased defect formation and potential second phase incorporation. This can be described such that at higher synthesis temperatures, the crystal growth rate will be enhanced (thicker membrane layer) and these intergrown structures will produce more flaws during calcination. In addition, elevated temperatures may cause second phases or impurities, resulting in low separation performance.
## 3 Experimental Results  
Since the maintenance of photovoltaic solar plants does not require the daily presence of an operator, in many cases, these facilities are neglected, operating in a non-optimal way. In this sense, INSPECTORMAP allows for detecting risks that may negatively affect the operation of the photovoltaic installation, which allows for optimizing the movements of maintenance workers. Thus, the operator in charge of checking the state of the solar plant surroundings will only appear before a real risk due to the growth of vegetation or the presence of water masses. The presence of large bodies of water or high levels of humidity in the soil can negatively affect the foundations of the facilities. In addition, the presence of water bodies can make accessibility to the plant difficult. This problem also occurs due to the existence of large vegetation in the vicinity. However, the presence of vegetation not only affects the accessibility, but also the performance of the installation itself. Large vegetation creates shadows on the solar panels, preventing solar radiation from striking the photovoltaic panels. In addition, the presence of vegetation is an attraction for animals and insects, which increases the presence of excrement on the solar panels, reducing their performance.  
To verify the correct operation of the SDI INSPECTORMAP and to corroborate its usefulness, six photovoltaic plants located in Spain subjected to different conditions have been analyzed (Figure 8).
# Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment
## 3 Results
### Real Data
#### 3.2.6 Comparison to the Earlier Solution  
In order to establish a comparison between the newly developed marker-based method and the approach of [10], we ran corresponding data through their method that consists of estimating a 3D representation of the scene by stereo matching and aligning it to the reference CAD model using an iterative closest point (ICP) algorithm. To obtain corresponding data, we captured the same sequence of robot poses with the same scene object, but with different lighting conditions. Since the stereo reconstruction relies on Lambertian reflectivity of the target object, it requires more light than the new marker-based approach.  
The earlier approach relies on a number of parameters, which should be tuned to the current scene properties. This is needed to avoid falling into local minima and converging on false position estimates. To avoid the time-consuming manual tuning step to adapt to this particular setup, we gave an initial estimate to the algorithm based on the known true target position with an added offset. For the objective of measuring the accuracy (not the  
Figure 19: Error in each of the X and Y axis for the estimation provided by the monocular (**a**) and stereoscopic (**b**) approaches. The colours represent the horizontal and vertical deviation from the from the central position.  
Figure 20: Error in each of the X and Y axis for the estimation provided by the monocular (**a**) and stereoscopic (**b**) approaches. The colours represent the distance between the camera and target.  
robustness) of the earlier solutions, this approach is justified for the sake of a comparison. We found that adding up to 25 mm of position error and five degrees of angular error still results in the algorithm converging as close to the correct position as the data allow (Figure 21, left side). Larger amounts (such as 50 mm and 10 degrees) make it difficult for ICP to find the correct neighborhood to converge, and the pose estimates start having large amounts of outliers (Figure 21, right side).
# Improving Graph Neural Network Models in Link Prediction Task via A Policy-Based Training Method
## 4 Experiments
### Implementation Details  
The implementation details of experiments in this work are described as follows.  
In this work, all experiments are performed on a computer with a 2 GHz quad-core processor and 16 GB memory. To illustrate the effectiveness of the proposed PbTRM, we utilize three GNN models as the encoder, including GCN, GraphSAGE, and GAT. The PyTorch Geometric [25] is used to construct the GCN, GraphSAGE, and GAT in this work. For the GCN, we construct it with two graph convolutional layers, where the output sizes of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Nodes** & **Edges** & **Features** & **Train/Valid/Test Edges** \\ \hline
**Cora** & 2708 & 5429 & 1433 & 70\%/10\%/20\% \\
**CiteSeer** & 3327 & 4732 & 3703 & 70\%/10\%/20\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The split details of two datasets in this work.  
the first layer and second layer are 128 and 64, respectively. The GraphSAGE is constructed with two SAGE layers, whose output sizes of two layers are the same as the GCN. As for the GAT, its first layer is the graph attention layer that uses multi-headed attention with 4 heads, and the input size of the second graph attention layer is 512.  
The GCN, GraphSAGE, and GAT models in this work use the same decoder. In the decoder, the dot product of convolved features is used to calculate the edge probability. During the training procedure of GNN models, the binary cross entropy with logits backward is used as the loss function to calculate the loss of the predicted edge probability. All GNN models in this work are trained for 100 epochs.  
The policy network is implemented as a two-layer fully connected network, whose input and output sizes are 5 and 2, respectively.
# Multi-Lens Array Full-Field X-ray Microscopy
## 3 Results
### Simulation Results  
Between the individual images of the field of view, the background should be dark or show some stray light, because the FoV was stopped down. In the real measurements, however, clear structures can be seen between the individual images of the FoV. To understand these structures, beam tracking simulations were performed with the commercial software OpticStudio (Zemax LLC, Kirkland, WA, USA). The measurement setup was simulated true to scale taking into account the aperture, the grating as a sample, and the illumination with the polycapillary. The results are shown in Figure 6. There is an excellent agreement with the measured data. By tracing the path of individual rays through the multi-focus CRL, the origin of the structured background could be clarified. The background is caused by rays that have partially or completely passed through the lens material in between the individual CRLs on their way through the multi-focus CRL (Figure 7). Since the sample illumination is also at wide angles, some rays only partially pass through a CRL and therefore do not contribute to the desired image. The structures in the background are caused by the fact that there are rays that have passed through a significantly different number of vertical or horizontal single lens elements. Such rays provide an image of the sample that appears distorted in one direction in space, similar to imaging with an astigmatic lens. The structured background disappears when an absorbing wall with windows for each CRL is placed in front of and behind the multi-focus CRL in the simulation. In this case, all rays that have not passed through the optics correctly are blocked out.  
Figure 4: Multi full-field X-ray microscopy image of an absorption grating stopped down using a tilted 50 \(\upmu\)m \(\times\) 50 \(\upmu\)m aperture. The image is dark-field and flat-field corrected. The picture section in the middle includes four images of the sample. For increasing the image quality, twelve images of a 3 \(\times\) 4 array are summed up (right).  
Figure 5: Comparing the intensities (c) of one of the twelve images in Figure 4 ((**a**) blue line, right axis) with the overlay of the twelve images ((**b**) black line, left axis).  
When the intensities of the 25 individual images are superimposed and summed up in the correct position, a low-noise image of the sample is obtained as expected (Figure 6b). The average intensity in the summed image of the sample was 22.8 times higher than in the individual images. Theoretically one would have expected a factor of 25. The deviation is due to the fact that the four outermost images are clearly vignetted due to the illumination limited by the polycapillary and therefore contain less intensity. The intensity noise in the image is reduced by a factor of 4.8 in the summed image in the simulation compared to the individual images. This corresponds to the theoretical value, since the stochastic noise decreases with the root of the increase in intensity.
# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 4 Results and Discussion
### Results with Event Sentiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**With Sentiment** & \multicolumn{3}{c}{**Mean Absolute Error (MAE)**} & \multicolumn{3}{c}{**Root Mean Squared Error (RMSE)**} \\ \hline
**Final PAK** & \begin{tabular}{c} **Linear** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Support Vector** \\ **Regression** \\ \end{tabular} & \begin{tabular}{c} **Deep** \\ **Learning** \\ \end{tabular} & \begin{tabular}{c} **Linear** \\ **Regression** \\ \end{tabular} & \
\begin{tabular}{c} **Support Vector** \\ **Regression** \\ \end{tabular} &
\begin{tabular}{c} **Deep** \\ **Learning** \\ \end{tabular} \\ \hline Lahore blast 2016 & 8.024 \(\pm\) 5.041 & 7.785 \(\pm\) 7.215 & 11.466 \(\pm\) 6.677 & 9.476 \(\pm\) 0.000 & 10.615 \(\pm\) 0.000 & 13.268 \(\pm\) 0.000 \\ US election 2012 & 3.250 \(\pm\) 2.696 & 3.127 \(\pm\) 3.360 & 2.613 \(\pm\) 2.380 & 4.223 \(\pm\) 0.000 & 4.590 \(\pm\) 0.000 & 3.534 \(\pm\) 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results for Pakistan with event sentiments. Figure 5: The plot of 50 forecasted and actual values of the foreign exchange rate of Pakistan considering linear, support vector, and deep learning model with the sentiment of US election and Lahore blast. LR: Linear regression, SVR: Support vector regression, DL: Deep learning.
## 3 Mobility Management Architecture Using CoAP
### Mobility Management Procedure of CoMP
In order to consider the sleep mode operation of CoAP node, we use the PUT request/response messages for holding using H_Flag in CoMP. More specifically, the CoAP node exchanges the PUT request/response messages to perform the hold mode operation with WMMS before the CoAP client goes to sleep mode operation. The PUT request message for holding includes the H_Flag and sleep period to reflect the status of CoAP node. When the WMMS receives the PUT request message for  
Figure 4: Mobility management procedure of CoMP. holding, WMMS holds on the request message until expires the sleep period of CoAP node. In a result, the PUT request/response messages for holding can prevent this unnecessary network traffic overhead and packet loss due to the sleeping CoAP server.
## 4 Results and Discussion
### Influence of Vacuum Level on Temperature Distribution of EMU Surface and Fluid Field
Figure 5: Velocity contour of flow field on the symmetric plane of levitation electromagnets module after 60 min heating: (**a**) Vacuum level: 0.1 atm; and (**b**) Vacuum level: 1.0 atm. Figure 6: Distribution of temperature and temperature gradient in the fluid domain along the surface’s outer-normal direction of point \(\mathrm{P_{d}}\) at different vacuum levels: (**a**) Temperature diagram; (**b**) Temperature gradient diagram. For all cases investigated in this study, the maximum temperature gradient appears near the fluid-solid interface region. The maximum absolute value of temperature gradient under 0.1 atm is 0.59 K/mm. Compared with the maximum temperature gradient under 1.0 atm, which is 2.55 K/mm, the lowered environmental pressure has led to a 77% decrease of the absolute value of the temperature gradient. This indicates that the lowered pressure has also caused the decrease of convection buoyancy flow in the flow field, which further worsened the heat dissipation of the levitation electromagnet module. The low-vacuum environment has resulted in reduced convection heat flux and therefore leads to a faster heating process of the levitation electromagnet module. Figure 7 gives the temperature rise curve of the maximum temperature point on the EMU surface during the heating process under different vacuum levels. As the environmental pressure decreases, the time-temperature curve exhibits a higher increase rate. The 100 K temperature rise process under 0.1 atm takes approximately 51 min, which is 23% faster compared to the temperature rise time at 1.0 atm. The summarized temperature rise time under different vacuum levels is shown in Table 5. According to Table 5, it can be concluded that, if the existing levitation electromagnet module is operating without additional cooling devices or methods, the operation time of the vacuum maglev should be limited to avoid device overheating.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 7 Performance Comparison
The successful changes in pseudonyms of vehicles at different periods are shown in Figure 21. The efficient management of the pseudonym-changing process reduces the possibility of linking a vehicle pseudonym at various locations. The comparative results clearly show that the proposed scheme DGVP outperform CPS [10] and CLPS [18] in terms of successful pseudonym-changing. This is because of the cooperation of neighboring vehicles for the pseudonyms changing process. DGVP manages the grouping of vehicles in the vicinity, providing an environment for vehicles to change pseudo-identities successfully. The lack of cooperation among vehicles in the existing schemes CPS and CLPS reduces the change in pseudo-identities, which increases the possibility for pseudonyms linking attack.
# Investigation of the Maturity Changes of Cherry Tomato Using Magnetic Resonance Imaging
## 2 Materials and Methods  
### Materials  
To investigate the internal structure changes of cherry tomatoes, three different cultivars (Tiara, Tiara TY, and Unicorn) were used in the study. Figure 1 shows cross-cut-section photos of all samples after the MRI experiment. Tomatoes were harvested at six different maturity stages: green, breaker, turning, pink, light red, and red, based on their colors. Three samples were prepared for each cultivar and maturity stage; thereby, a total of 54 cherry tomato fruits were investigated. The cherry tomatoes were grown under greenhouse conditions at the experimental farm in Jeonbuk National University, Korea, and harvested at different maturity stages by a horticultural expert.
# A Dual-Gas Sensor Using Photoacoustic Spectroscopy Based on a Single Acoustic Resonator
## 3 Results
### Instrument Performance: Allan Deviation, Accuracy, Precision and Uncertainty  
The Allan-Werle variance method was utilized to evaluate the long-term stability of the instrument with a constant concentration of 46 ppm of CH\({}_{4}\) and 270 ppm of NH\({}_{3}\) in the PAS cell. Figure 9a,b show the continuous measurement of CH\({}_{4}\) and NH\({}_{3}\) for 4000 s with a time interval of 20 s, respectively. Figure 9c,d plot the Allan deviation of the estimated concentration versus the average time, which indicates an optimum performance of the PAS instrument: minimum Allan deviations (0.05 ppm) are obtained with an optimum averaging time of 700 s for CH\({}_{4}\) and 0.1 ppm with an integration time of 160 s for NH\({}_{3}\).  
Figure 9e,f show the distribution histogram of the retrieved CH\({}_{4}\) and NH\({}_{3}\) concentrations with a concentration of 46 ppm for CH\({}_{4}\) and 270 ppm for NH\({}_{3}\) in the PAS cell, accompanied with Gaussian profile fits. The value of the mean concentration of CH\({}_{4}\) (Figure 9e) obtained was 45.5 ppm, which results in a measurement accuracy of 0.5 ppm, while the measurement precision was deduced to be 3.3 ppm (with an average time of 20 s) from the full width half maximum (FWHM) of the fitted Gaussian profile. For NH\({}_{3}\) detection, a measurement accuracy of 0.3 ppm and a precision of 2.8 ppm were achieved from the mean concentration of 269.7 ppm and the FWHM of the fitted Gaussian profile in Figure 9f. The relative measurement uncertainty was determined by the ratio between the standard deviation and the mean value of concentration. Figure 9a shows that the standard deviation and the mean value of concentration for CH\({}_{4}\) were 1.5 and 44.5 ppm, respectively. For the NH\({}_{3}\) measurement in Figure 9b, the standard deviation and the mean value of concentration were 1.2 and 169.7 ppm, respectively. Thus, the relative measurement uncertainties of CH\({}_{4}\) and NH\({}_{3}\) were determined to be 3.3% (1.5 ppm/44.5 ppm) and 0.4% (1.2 ppm/169.7 ppm), respectively. The limit of detection (LoD) of the instrument was determined by the standard deviation of the background signal. By averaging five times of PAS signals, which indicates an integration time of 100 seconds, the standard deviations of background signals of NH\({}_{3}\) and CH\({}_{4}\) were found to be 0.3 and 0.1 ppm, respectively.  
Figure 8: Simultaneous measurement: (**a**) Plot of fluctuating concentration of NH\({}_{3}\) while CH\({}_{4}\) remains constant. The dashed blue line is obtained without eliminating the crosstalk of NH\({}_{3}\). The blue line is obtained after elimination. (**b**) Plot of fluctuating concentration of CH\({}_{4}\) while NH\({}_{3}\) stays constant.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis
### Security Analysis of the Proposed Chaos-Based Cryptosystem
#### 3.3.2 Statistical Analysis  
To prove the robustness of the proposed chaos-based cryptosystem against statistical attacks, we performed the following experiments on various plain images: histogram, chi-square test, correlation, and entropy analysis.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**S-Box**} & \multicolumn{2}{c}{**Nonlinearity**} & \multicolumn{3}{c}{**SAC**} & \multicolumn{3}{c}{**BIC Nonlinearity**} & \multicolumn{1}{c}{**DP**} & \multicolumn{1}{c}{**LP**} \\ \cline{2-10}  & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** & & & & \\ \hline Proposed S-box & 102 & 104 & 108 & 0.4375 & 0.4948 & 0.5625 & 0.4991 & 103.4286 & 10 & 0.1094 \\ [PERSON] et al. [42] & 104 & 106 & 110 & 0.4218 & 0.5039 & 0.5937 & 0.5058 & 103.40 & 10 & 0.1406 \\ [PERSON] et al. [43] & 106 & 106.75 & 108 & - & 0.5034 & - & 0.5014 & 103.78 & 10 & 0.1328 \\ [PERSON] et al. [44] & 106 & 107.5 & 108 & - & 0.4943 & - & 0.4982 & 104.35 & 10 & 0.1250 \\ [PERSON] et al. [49] & 104 & 105 & 110 & 0.3906 & 0.5014 & 0.5937 & 0.5028 & 102.75 & 10 & 0.1250 \\ [PERSON] et al. [50] & 106 & 108.5 & 110 & - & 0.5017 & - & 0.5026 & 104 & 10 & 0.1094 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of different S-boxes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Test** & \(p\)**-Value** & **Proportion (\%)** \\ \hline Frequency test & 0.494 & 98.000 \\ Block-frequency test & 0.514 & 99.000 \\ Cumulative sums test & 0.504 & 98.000 \\ Runs test & 0.494 & 99.000 \\ Longest-run test & 0.262 & 97.000 \\ Rank test & 0.868 & 99.000 \\ FFT test & 0.384 & 99.000 \\ Nonperiodic templates & 0.482 & 99.061 \\ Overlapping templates & 0.596 & 98.000 \\ Universal & 0.946 & 99.000 \\ Approximate entropy & 0.419 & 100.000 \\ Random excursions & 0.439 & 98.214 \\ Random excursion variant & 0.488 & 98.651 \\ Serial test & 0.415 & 100.000 \\ Linear complexity & 0.817 & 97.000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: \(P\)-values and proportion results of NIST tests.
## 4 Results and discussion
### Effect of thermal stress on the fundamental frequency of predefined plates  
In Figures 11 and 12, we plot for \(\mu=0\) the non-dimensional fundamental frequency of the deformed plate versus \(\beta\) for two values of \(\lambda\). The natural frequency is normalized with respect to the value \(\bar{\omega}_{0}\) corresponding to \(\lambda=0\), \(\mu=0\) and \(\beta=0\) reported in Table 1. The value of \(\beta\) for which \(\omega_{0}=0\) corresponds to the buckling instability of the MEMS plate, and is found with the method explained in section 3.5. For \(\beta\leq\beta_{\rm B}\), the fundamental frequency is computed from equation (37) with \(\mu=0\), the constant value of \(\lambda\), and the corresponding \(\zeta\) satisfying equation (26).  
For \(\alpha=4\) (\(g_{0}/h=2\)) and \(\lambda\
eq 0\) the trend is non-monotonic due to the concurrent effects of the strain hardening (\(\alpha k_{3}\zeta^{3}\)) and the thermal softening (\(\beta k_{2}\zeta\)). For the case studied, when \(\alpha=1\) the thermal softening is dominant with respect to the strain hardening, and the fundamental frequency monotonically decreases. For \(\lambda=\mu=0\), the buckling is due to equal biaxial thermal stresses of \(\beta\simeq 53\) for both \(\alpha=1\) and \(4\). In the absence of the Coulomb and the van der Waals forces the thermal stress at the onset of buckling is unaffected by the initial gap between the two plates.  
Figure 11: For the square plate, fundamental frequency versus \(\beta\) for \(\mu=0\), \(\lambda=0\) (solid curve) and \(\lambda=150\) (dashed curve); (a) \(\alpha=1\) and (b) \(\alpha=4\).