esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Engineering Background and Main Parameters Affecting Support Structure Design  
### Major Geological Conditions of the Typical Tunnel  
The engineering area is characterized by complex tectonic settings and high stress levels. Multiple water diversion tunnels are arranged in parallel within the region, with a maximum burial depth approaching 2000 m. Based on existing in situ stress measurements, the maximum magnitude of the principal stress is inferred to range between 85.00 and 102.00 MPa. Taking a segment of the tunnel as an example, the initial section is predominantly composed of quartz schist, quartzite, mica schist, and metamorphic basalt. The rock mass is primarily classified as hard to moderately hard, with some softer zones. The rock mass is relatively intact and slightly weathered, with no regional faults identified, and is subjected to medium to high in situ stress. The latter section of the tunnel is mainly composed of banded migmatite, biotite monzonitic speis, and granodiorite. It is dominated by moderately hard to hard rocks, with a relatively intact rock mass and medium to high in situ stress. The distribution of surrounding rock grades is as follows: approximately 10% is Grade II, 55% is Grade III, 20% is Grade IV, and 15% is Grade V.
# Elucidating the Mechanical Energy for Cyclization of a DNA Origami Tile
## 3 Results
### Mechanical Energy Induced Cyclization
Thus, 11 nm on average was used as the criterion for the distance between the boundaries. Overall, a total of 10 states were used in the simulations of cyclization. Under spring forces, the origami tile gradually rolled up and deformed into a cylindrical shape. If a very high potential is given at once (e.g., 400 kcal/mol), the tile can cyclize immediately in a single computation (from the initial to the final states). However, such simulations will neither reveal the minimum energy needed for cyclization, nor depict the details of the process. Instead, we used quasi-equilibrium deformations in the simulations. In the early states for cyclization (state 1 through 3), only the dsDNA bundles near the boundaries responded to the pulling forces (Figure 4a). The folding of the entire origami was not strong. In state 4, all the bundles start to twist and bend. This trend of deformation became more distinct in states 5 through 9. The global response to the forces suggests that the deformation in the origami propagates gradually. The structure in the final state did not change too much visually compared with that in state 9. The noticeable change is the alignment of dsDNA bundles in parallel positions. For all states from 1 to 9, the two boundaries are not parallel to each other, exhibiting a degree of the global twisting. Reaching the final state, they become aligned, indicating that the curvature is fully overcome and the tile cyclizes into a tube as anticipated. In Figure 4b, the lines from one state to the next indicate the energy minimization process, while the vertical lines indicate the increase in the potential energy by introducing new sets of stronger spring forces. All the potential energy changes are added together, and the energy needed for the full cyclization is calculated as \(\sim\)33.1 kcal/mol. To quantitatively illustrate the cyclization, the averaged distance between the cut site of the linkers on the upper and lower boundaries is plotted in Figure 4c. The distance starts at approximately 84 nm, which is consistent with the designed height (\(\sim\)88 nm). As the tile cyclizes, the distance reduces steadily and reaches below 11 nm in the final state. State 10 has \(\sim\)63 nm in width and \(\sim\)25 nm in diameter, which is comparable to the previous measurement of a cyclized tile (\(\sim\)65 \(\times\) 45 nm for a flattened tube). [27] This confirms the validity of our approach for cyclization in MD simulations.
# On the Entropy of Oscillator-Based True Random Number Generators under Ionizing Radiation
## 3 Experimental Section
### TID Setup  
The main source in accordance with the standard test method [21] to perform Total Ionizing Dose tests in space industry is the electromagnetic radiation (photons) from Co-60. For this experiment it was provided by the Gamma Radiation Laboratory (RadLab) of the Centro Nacional de Aceleradores (CNA), in Seville. This is a joint center of the Universidad de Sevilla (Spain), Junta de Andalucia and CSIC.  
The Co-60 radioactive source available at CNA is installed in a Gammabeam X200 irradiator from Best Theratronics [22; 23]. The photon energies are 1.17 MeV and 1.33 MeV.  
The DUT and the PCB where it is soldered were placed into a filter box of 12 cm \(\times\) 17 cm to be submitted to radiation, according to [21]. This container has 2 mm of aluminum and 1.5 mm of lead in the outer layer, and a front cover of 5 mm of PMMA (poly-methilmetacrilate) to achieve the charged-particle equilibrium. The distance between the filter box and the Co-60 was 302 cm. Figure 3 shows the DUT inside the filter box and its placement in the radiation facility.  
The dose rate was measured in the four corners of the filter box (inside) using two Farmer ionization chambers connected to a MULTIDOS electrometer, all of them by PTW. First air kerma rate was obtained, then the dose rate in silicon (Si) was finally calculated considering the conversion factors. The dose rate uniformity calculated from the four dose rates previously mentioned was 98.2% (a minimum of 90% is required by [21]), and the corresponding average dose rate was 217.0 rad(Si)/h. The total accumulated dose of the test was 45 krad(Si).
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 3 Experimental Results
### Results
#### 3.3.1 Evaluation of FRCNN  
To analyze the evaluation power of FRCNN, we evaluated it with other techniques like RCNN [56], and SPPnet [44], which use a similar pre-trained framework and bbox regression. SPPnet uses five scales in training and testing while FRCNN uses single scale testing and training. The RCNN technique achieves good performance by using a deep ConvNet, however, it has some limitations: i.e., training is expensive in time and space, ConvNet to SVMs to bbox regressors and the detection of objects is slow. We used FRCNN, that utilizes the ideas from SPP-net and RCNN and fixes the key issue in SPPnet by sharing computation of Conv layers among various proposals and exchanging the order of producing region proposals and running the CNN. FRCNN utilizes a backpropagation estimation, also added the bbox regression and classification head, and trained the model with a multi-task loss. FRCNN approach has a large improvement in mAP because of the fine-tuning process of Conv layers and reduces the disk storage due to no need for cache features. The proposed method achieves the precise localization of disease regions with mAP of 0.94 (in Table 3) in comparison with other approaches.
## 4 Analysis of the Simulation Signal
### Signal-to-Noise Separation with Fuzzy Entropy  
Entropy serves as a metric for assessing the complexity of a signal. with higher entropy indicating greater complexity. Using VMD decomposition, the AE signal is separated into several components with distinct characteristic frequencies. A higher entropy value signifies a more chaotic nature of the signal. In comparison to the effective signal, the AE noise signal exhibits heightened complexity and uncertainty, resulting in higher entropy. Consequently, in this section, fuzzy entropy is employed to eliminate noise components from the AE signals.  
The fuzzy entropy of different modes obtained using EMD, EVMD, and FCI-VMD is computed and illustrated in Figure 6. From Figure 6, it can be observed that EMD yields larger fuzzy entropy values for IMF1 and IMF2. VMD, on the other hand, produces a larger fuzzy entropy value for the IMF1 component. Notably, EVMD introduces more noise components due to signal over-decomposition. To remove noise, modes with fuzzy entropy exceeding 0.2 are eliminated, while the remaining IMF components are retained as optimal components. The denoised simulation AE signal is subsequently reconstructed based on these optimal components.  
Figure 7 depicts the denoising results obtained with the EMD method applied to a noise-contaminated analog AE signal. It can be observed that the 110 kHz frequency signal is filtered out, leading to waveform distortion. This indicates that filtering the complex  
Figure 5: The modes obtained using EVMD. The number of modes, \(k\), is determined by the number of layers of EMD decomposition. (**a**) The number of modes, \(k\), is determined by the number of layers of EMD decomposition. (**b**) Spectrum of different IMF components.  
simulation AE signal using EMD decomposition compromises the integrity of the effective signal. Conversely, upon decomposing the simulation AE signal using FCI-VMD, the desirable frequencies of 40 kHz, 70 kHz, and 110 kHz are successfully extracted, allowing for the separation of noise and effective signals.  
Figure 6: Fuzzy entropy for different IMFs.  
Figure 7: Waveform and spectrum of the simulated AE signal with noise after denoising. (**a**) Distribution of different waveform components. (**b**) Spectrum of different waveform components.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 3 Experimental Results and Discussion  
In this section, a constructed platform of the traffic state perception is demonstrated, based on which we will describe the intersection scenario selected for the experiment. The datasets collected by ourselves are introduced. Then the parameter settings and experimental conditions of the neural network model are also clarified. Finally, experimental results based on the proposed predicted model are analyzed and discussed. In addition, the comparisons between our method and other methods are evaluated.
# Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region
## 4 Results and Discussion  
### Analysis of Temporal Variables Used for Classification  
In order to use temporal variables to distinguish winter wheat, the temporal changes of each land cover class were analyzed from the temporal variables derived from optical and SAR images. Then, the separability of the land cover classes was also explored.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Simple Code in This Study** & **Descriptions of Inputs** \\ \hline A & VH & All six Sentinel-1A images (VH) \\ \hline B & VV & All six Sentinel-1A images (VV) \\ \hline C & VV + VH & Dual polarization (VV + VH) of all six Sentinel-1A images \\ \hline D & T & Textures of all six Sentinel-1A images \\ \hline E & C & Coherence values of all six Sentinel-1A images \\ \hline F & VV + VH + T & Textures and dual polarization (VV + VH) of all six Sentinel-1A images \\ \hline G & VV + VH + C & Coherence values and dual polarization (VV + VH) of all six Sentinel-1A images \\ \hline H & C + T & Textures and coherence values of all six Sentinel-1A images \\ \hline I & VV + VH + C + T & Combination of dual polarization (VV + VH), textures, and coherence values of all six Sentinel-1A images \\ \hline J & S + O & Combination of all six Sentinel-1A images (dual polarization (VV + VH), textures, and coherence values) and Landsat-8 image on 28 March 2016 \\ \hline \hline \end{tabular}  
* Notes: VH means VH polarization; VV means VV polarization; C means coherence values; T means textures; S means SAR data; O means optical data.  
\end{table}
Table 3: Different combinations of SAR variables and optical images for winter wheat classification.
## 7 Conclusions
\begin{tabular}{l l} \(RQ\) & Research question \\ \(MI_{orig.}\) & Original Maintainability Index \\ \(MI_{orig.}\) & Original Maintainability Index with comment part \\ \(MI_{impr.}\) & Improved Maintainability Index \\ \(MI_{impr.}\) (\(CM\)) & Improved Maintainability Index with comment part \\ \(MI_{LOC}\) & Maintainability Index using Lines Of Code only \\ \(MI_{JH}\) & JHawk Maintainability Index \\ \(MI_{JH(CM)}\) & JHawk Maintainability Index with comment part \\ \(MI_{VS}\) & Visual Studio Maintainability Index \\ \(aveE\) & Average Halstead's Effort per module \\ \(aveV\) & Average Halstead's Volume per module \\ \(aveV(g)\) & Average Cyclomatic Complexity per module \\ \(aveLOC\) & Average Lines of Code per module \\ \(aveNOS\) & Average Number of Statements per module \\ \(perCM\) & Average Percent of Lines of Comments per module \\ \(S\) & Software system \\ \(CS\) & Case study \\ \(Avg.\) & Average \\ \(Med.\) & Median \\ \(Std.\) & Standard deviation \\ \(Min.\) & Minimum \\ \(Max.\) & Maximum \\ \(Skew.\) & Skewness \\ \(Kurt.\) & Kurtosis \\ \(C1\) & Confidence interval \\ \(SE\) & Standard error \\ \(SW\) & Shapiro-Wilk Test \\ \(MDS\) & Multidimensional scaling \\ \end{tabular}   
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Case Study** & **Release** & \(\mathbf{MI_{orig. }}\) & \(\mathbf{MI_{orig. (CM)}}\) & \(\mathbf{MI_{impr. }}\) & \(\mathbf{MI_{impr. (CM)}}\) & \(\mathbf{MI_{LOC}}\) & \(\mathbf{MI_{IH}}\) & \(\mathbf{MI_{IH(CM)}}\) \\ \hline _CS2_. Caffeine & 1.0 & 69.38 & 115.10 & 62.99 & 108.71 & 61.63 & 68.65 & 114.37 \\ _CS2_. Caffeine & 1.0.1 & 70.11 & 116.11 & 63.88 & 109.88 & 62.48 & 69.47 & 115.46 \\ _CS2_. Caffeine & 1.1.0 & 70.39 & 116.34 & 64.19 & 110.14 & 62.80 & 69.76 & 115.71 \\ _CS2_. Caffeine & 1.2.0 & 70.39 & 116.22 & 64.18 & 110.01 & 62.79 & \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Case Study** & **Release** & \(\mathbf{MI_{orig.}}\) & \(\mathbf{MI_{orig.(CM)}}\) & \(\mathbf{MI_{input.}}\) & \(\mathbf{MI_{impr.(CM)}}\) & \(\mathbf{MI_{LOC}}\) & \(\mathbf{MI_{IH}}\) & \(\mathbf{MI_{IH(CM)}}\) \\ \hline _CS2._ Caffeine & 3.0.6 & 69.75 & 113.75 & 63.36 & 107.37 & 61.80 & 68.47 & 112.47 \\ _CS2._ Caffeine & 3.1.0 & 69.37 & 113.26 & 63.06 & 106.95 & 61.50 & 68.22 & 112.11 \\ _CS2._ Caffeine & 3.1.1 & 69.49 & 113.37 & 63.18 & 107.06 & 61.66 & 68.26 & 112.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Index variant values for _CS3_. Joda-Time (RQ2.2).
# Blister Defect Detection Based on Convolutional Neural Network for Polymer Lithium-Ion Battery
## 3 Results and Discussions
### Real-Time  
All tests were performed on a computer with 32G RAM, Intel Xeon E5-2620 CPU, and NVIDIA GeForce 1080 Ti GPU. Each sample test took no more than 0.3 s. The total processing time including capturing images of two sides and transportation on the production line for each PLB sheet was less than 10 s. If parallel processing were used, the processing speed could be improved. In the actual production line, parallel pipeline processing was adopted. The image acquisition and detection on both sides of the PLB were performed simultaneously, the time was shortened to half of the original, and the efficiency was increased to twice the manual processing. The proposed CNN based defect detection method was fast enough for real-time industry application.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Proposed Method with FPA** & **Proposed Method without FPA** \\ \hline Recall & 0.982 & 0.971 \\ Precision & 0.993 & 0.988 \\ Accuracy & 0.986 & 0.977 \\ Specificity & 0.991 & 0.985 \\ F1-score & 0.988 & 0.979 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification performance comparison on the proposed FPA optimization.  
Figure 9: Confusion matrices of different methods.
# Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm
## 7 Numerical Experiment  
We performed simulation experiments on the multiprocessor fair scheduling problem, with the same experimental environment as the performance testing of the improved slime mold algorithm. Assuming there were 1000 tasks and 10 processors with varying efficiencies, we randomly initialized a matrix \(P_{ij}\) with dimensions 1000 rows by 10 columns. The elements of the matrix were set to values between 1 and 1000. The value at the \(i\)th row and \(j\)th column corresponded to the execution time of the \(i\)th task when executed on the \(j\)th processor. We used the IMSMA to solve the multiprocessor fair scheduling problem. The swarm size of the slime mold was fixed to 30, and the dimension was fixed to \(n\times m\), which corresponded to the size of matrix \(P_{ij}\).  
Experiments were carried out on a range of problem sizes with a 100-iteration setting. The results for the objective values obtained by each algorithm are presented in Table 4.  
The convergence curves of various algorithms for solving problems of different scales are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Number of Experiments** & \(n\) & \(m\) & **IMSMA** & **SMA** & **WOA** & **BOA** & **SSA** \\ \hline Experiment 1 & 500 & 10 & **3378** & 3434 & 3534 & 4639 & 4293 \\ Experiment 2 & 500 & 20 & **6544** & 6797 & 6854 & 9352 & 8753 \\ Experiment 3 & 500 & 30 & **9915** & 10,409 & 10,173 & 13,966 & 13,155 \\ Experiment 4 & 1000 & 10 & **3761** & 3953 & 3935 & 4804 & 4679 \\ Experiment 5 & 1000 & 20 & **7593** & 7932 & 7925 & 9483 & 9428 \\ Experiment 6 & 1000 & 30 & **11,634** & 11,709 & 11,858 & 14,483 & 13,878 \\ Experiment 7 & 1500 & 10 & **4070** & 4092 & 4114 & 4788 & 4746 \\ Experiment 8 & 1500 & 20 & **8092** & 8145 & 8235 & 9713 & 9519 \\ Experiment 9 & 1500 & 30 & **12,038** & 12,205 & 12,152 & 14,321 & 14,398 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of objective values obtained by different algorithms for various problem sizes.  
Based on the data shown in Figure 4 and Table 4, it can be concluded that the IMSMA achieves the lowest objective function values and performs the best in solving the fair scheduling problem on multiple processors. The IMSMA effectively enhances the efficiency of solving the fair scheduling problem on multiple processors.
# Enhanced Scalability and Security in Blockchain-Based Transportation Systems for Mass Gatherings
## 4 Results and Findings
### Scalability Analysis  
Blockchain technology has further revolutionized transportation systems, which has improved scalability and, in particular, transaction rates scaled to the order of thousands of transactions per second, even in highly congested settings with high transaction rate demands [5,16]. In the baseline case, we demonstrate that our system outperforms current solutions in terms of throughput and latency, achieving 800 transactions per second (TPS) and a latency of 5 s, respectively. With State Channels, throughput is boosted to 2500 TPS with just 2 s latency, exceeding the 2200-2450 TPS and 2.7-3.3 s latency range, as reported in previous studies. Finally, a Rollup-based implementation is used to achieve improved performance, with a latency of 1.5 s, outperforming the references by achieving 3500 transactions per second (TPS).  
These improvements are crucial for transportation systems that support mass gatherings (e.g., concerts, sports events, emergency evacuations) where the need for fast, large-scale data processing arises. At the failure point, technology that relies on centralized infrastructures will become highly vulnerable; meanwhile, blockchain's self-contained functionality, which is not dependent on external infrastructure, will'spare the day' and survive extreme conditions. Both Rollups and off-chain transactions significantly increase transaction efficiency in terms of time and resources compared to the main blockchain while maintaining security.  
Additionally, blockchain fosters trust and transparency in such a large-scale system. Ticketing, toll collection, and access control automation reduce fraud and manual intervention through smart contracts. This involves real-time data sharing between stakeholders, such as traffic authorities and ride-hailing services, to optimize routing and minimize congestion. Blockchain's immutability guarantees the accuracy of the records, both retrospectively and prospectively, for future events. Therefore, the application of blockchain demonstrates the potential for transforming transportation networks with high efficiency [32].  
Table 2 provides a comparative overview of the throughput and latency improvements across different scalability scenarios.  
The above table effectively presents throughput performance enhancements owing to the incorporation of elitist scaling methods, such as State Channels and rollups, with the added advantage of decreased latency.
## 2 Material and Methods
### Methods of Pad Surface Characterisation
#### 2.2.2 Scanning Electron Microscope (SEM) and Micro-CT  
SEM and micro-CT were used to take additional measurements to resolve the uncertainty in the pad surface shape due to the limitations of the confocal microscope for taking measurements. Figure 4a is a set of top-view images taken by SEM, and Figure 4b is a set of cross-sectional images taken by micro-CT. In Figure 4a, the change in the pad surface according to temperature can be clearly seen. At the lower temperatures, it is observed that the asperities on the surface are inclined in a wavelike shape, and there is a space underneath them that is thought to be a pore. At the higher temperatures, on the other hand, the wave-shaped asperities, which were characteristic at the lower temperatures, are not visible, and the pores appear in complete form. From this, it can be inferred that the pores were covered by the conditioning process at low temperatures and were revealed by the change in the shapes of the asperities as the temperature increased. This inference is confirmed by the cross-sectional images taken by micro-CT (Figure 4b). From this cross-sectional observation, it was also possible to know that the asperities occurred with the increase of the pad temperature, and the appearance of the pores was also confirmed. Through this, unlike the change of the pad surface according to the temperature expected in Figure 3c, the actual pad surface was predicted to show a change in which the asperities that were laid out gradually rise as the temperature increases, as shown in Figure 4c. In order to analyze this phenomenon closely, detailed data of the asperity angle and result values such as the contact area and number of contact asperities at the top area were measured.  
Figure 3: (**a**) Confocal microscope images, (**b**) surface roughness values according to steady-state surface temperature of the pad and (**c**) changes in the pad surface depending on the temperature expected through confocal microscope measurement.