esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Grid-Based Position Calculation Method for Satellite-Ground Communication Links
## 5 Conclusions  
Combined with constellation groups such as BeiDou satellites, this paper introduces the GeoSOT model and applies it within the aerospace field to explore a spatially fast calculation method of satellite links. By using the GeoSOT model to discretize the Earth's space, the GeoSOT-based grid calculation model for satellite-ground position model is constructed, and the angular calculation of longitude and latitude floating point numbers is replaced by the number calculation between grids. A grid data judgment model in terms of the longest service time of satellites is formed. We start with the grid coding of satellite trajectories, then calculate the grid distance between satellites and ground using grid coding algebra, rank and classify the optional satellites and then verify the feasibility and accuracy of the experimental results via STK simulation. On the basis of verifying the correctness of the model, the accuracy of the grid calculation method was verified and the time efficiency of the traditional latitude and longitude calculation method was compared with that of the grid calculation method. It is about 82% faster than the traditional latitude and longitude calculation method on the basis of a single constellation calculation. In future research, we plan to add improvements and undertake further experiments. Firstly, the current environment of our test is relatively simple; the real situation will be more complex, so more variables and constraints will be added in future research. Secondly, we will also conduct more levels of comparison with other studies to further explore the advantages and disadvantages of grid computing. Finally, there are thousands of satellites in the low-orbit constellation, and the subsequent research will analyze the scheduling applications of this full constellation of satellites in specific application scenarios.  
Conceptualization, C.C. and B.C.; Data curation, Q.L. Formal analysis, L.A. and Q.L.; Funding acquisition, C.C.; Investigation, L.A.; Methodology, B.C.; Project administration, B.C.; Software, L.A.; Supervision, C.C. and T.Q.; Validation, L.A.; Visualization, L.A.; Writing--original draft, L.A.; Writing--review & editing, Q.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Projects (2018 YFB0505300).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Mathematical Background
### Unified Mathematical Formulation  
The table presented as Table 1 outlines the differential PA and PD instructions for BEC over \(GF(2^{m})\). These instructions are comprised of 7 complex steps and require a memory unit capable of storing initial, intermediate, and final results, with a total memory requirement of \(11\times m\), where 11 represents the number of memory locations and \(m\) represents the width of each location. The BEC curve parameters in the table, denoted as \(e_{1},e_{2}\), and \(w\), are computed based on a rational function for an elliptic curve \(E\) over \(GF\) and the values of \(d_{1}\) and \(d_{2}\). The initial projective points are represented by \(W_{1}\), \(Z_{1}\), \(W_{2}\), and \(Z_{2}\), while the final points are represented by \(Z_{a}\), \(Z_{d}\), \(W_{a}\), and \(W_{d}\). Intermediate values are stored in \(A\), \(B\), and \(C\).
# Antidiabetic, Anticolesterol, and Antioxidant Activity of
## 3 Results and Discussion
### Anticolosterol Activity  
The HMGRI activity of fermented GB is shown in Table 2. The HMGRI activity of all GB fermented with a starter culture was significantly higher than that of CON (2.91%). In particular, HAGB showed significantly strong HMGRI activity (120.89%), even more than that of atorvastatin (111.24%), which was used as the positive control, and LPGB (103.24%). SKGB (45.50%) and SEGB (37.90%) showed similar HMGRI activity. The MKSK-J1 had no HMGRI activity (Table A1), but after GB fermentation, it increased to 45.5%. This was thought to be caused by various bioactive substances produced by interactions between GB and the starter during the fermentation process. Several studies have reported that some phenolic compounds, polyglutamic acids, or viscous substances produced during fermentation may exhibit HMGRI activity [31; 32]. On the other hand, MKHA15, which had 65.81% of HMGRI activity, increased to 120.89% after GB fermentation. Several researchers have reported that during fermentation, some organic acids such as ferulic acid and propionic acid, or peptides inhibit HMGRI activity [33; 34]. Therefore, in our results, it was predicted that HAGB showed significantly high HMGRI activity as the combined effect from various active substances such as organic acids and peptides increased during fermentation as well as the enzyme inhibitory activity of the strain itself.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **HMG-CoA Reductase Inhibitory Activity (\%)** \\ \hline CON & 2.91 \(\pm\) 6.02  \\ SKGB & 45.50 \(\pm\) 5.99  \\ HAGB & 120.89 \(\pm\) 0.42  \\ Atorvastatin (100 mM) & 111.24 \(\pm\) 8.60 [FOOTNOTE:]Footnote 2: Values are mean \(\pm\) standard error (\(n=2\)). Means with different superscripts in a column (\({}^{\text{a}-\text{d}}\)) are significantly different in terms of [PERSON]’s least significant difference test (\(p<0.05\)).[ENDFOOTNOTE] \\ \hline \hline \end{tabular}
\end{table}
Table 2: 3-Hydroxy-methyl glutaryl-coenzyme A (HMG-CoA) reductase inhibitory activity of fermented GB.  
Another point to note about the result of HMGRI activity is that there was no inhibitory activity for CON. Even in CON, unknown _Bacillus_ strains proliferated during the 24 h of fermentation, although it was difficult to achieve the desired functionality through natural fermentation.
###### Abstract  
The vehicle-track interaction generates forces and consequently vibrations in the environment. The interaction has been analysed by the simultaneous measurements of vehicle, track and ground vibrations during test runs with varied train speeds. The special effects of the passage over a bridge and through a tunnel are studied and compared with the measurements on a conventional ballasted surface line. The maximum amplitudes, narrow band and one-third octave band spectra are presented for the axle-box accelerations and for the track, bridge and ground vibrations. The different frequencies and frequency bands are related to wheel out-of-roundness, track alignment errors, the sleeper passage and the wheelset-track resonance. An axle impulse component has been observed at the track, at the near-field soil and as a scattered version in the far field. Specific results can be found for the bridge track, where clearly speed-dependent bridge resonances occur due to the axle sequence of the train, and for the tunnel track where soft rail pads are responsible for a strong amplification around the wheelset-track resonance. On the other hand, the axle impulses are strongly reduced by the tunnel track, and the scattered axle impulse component is not as relevant as for the surface track. As a consequence, a strong mid-frequency amplitude reduction of the tunnel compared to the surface line has been measured for low and high train speeds by the Federal Institute of Material Research and Testing (BAM) and by other institutes.  
vehicle-track interaction; ground vibration; tunnel-to-surface reduction; bridge resonance; axle sequence Article  
The Dynamic Train-Track Interaction on a Bridge and in a Tunnel Compared with the Simultaneous Vehicle, Track and Ground Vibration Measurements on a Surface Line. _Appl. Sci._**2023**, 13, 10992. [[https://doi.org/10.3390/appl1319092](https://doi.org/10.3390/appl1319092)]([https://doi.org/10.3390/appl1319092](https://doi.org/10.3390/appl1319092))  
Academic Editor  
[PERSON]  
Received: 18 August 2023  
Revised: 30 September 2023  
Accepted: 3 October 2023  
Published: 5 October 2023
## 1 Introduction
Based on camera response and weighted least squares strategies, a details enhancement approach was proposed by [PERSON] et al. [26] to enhance low-light images. In order to reduce the loss of detailed information during image enhancement in low light environments, [PERSON] et al. [27] proposed an image enhancement algorithm based on improved multi-scale Retinex and artificial bee colony. However, over-enhancement may occur in areas of the image that have salient information, and the visual effect of enhanced coal mine images still needs to be improved. Existing low-illumination image enhancement algorithms inevitably cause an enhanced image to be over- or under-enhanced images after enhancement, resulting in color distortion, so some measures are needed to correct the images. [PERSON] et al. [28] proposed a simple but effective low-light image enhancement algorithm based on gamma function correction to address uneven brightness. Using a fractional-order mask and the fusion framework to enhance the low-light image, a novel fractional-order fusion model (FFM) is presented by [PERSON] et al. [29] for low-light image enhancement, which extracts more invisible contents in dark areas. Aiming to increase the information content and enhance the details of an image, [PERSON] et al. [30] presented an image contrast enhancement algorithm, which uses an adaptive gamma correction technique aided by particle swarm optimization. However, it is a challenge to effectively combine the advantages of the above image enhancement methods. In this paper, a gamma transformation and fractional order image enhancement algorithm based on particle swarm optimization are proposed, which removes noise and enhances the texture information of low-light images. Firstly, by analyzing the characteristics of underground coal mine images, a median filter is selected for image denoising. Moreover, gamma transformation is introduced to correct images with unbalanced grayscale, and facial detail features are enhanced by the fractional order operator. Experimental results show that the image enhancement performance indexes processed by the proposed algorithm, such as average local standard deviation, average gradient, information entropy and contrast, are superior to those of other classical image enhancement algorithms. Lastly, the validity of the proposed algorithm is verified by an image enhancement test in the simulated coal mine environment. The remainder of this paper is organized as follows. In Section 2, the proposed improved particle swarm optimization algorithm is presented. The comparison experiments of image enhancement algorithms are carried out, and the effectiveness of the proposed improved algorithm is verified in Section 3. In Section 4, a face recognition system is built, and an occluded face recognition experiment is carried out in the simulated coal mine environment. Conclusions and future works are summarized in Section 5.
###### Abstract  
To improve the rutting resistance and anti-aging performance of bio-asphalt, the composite modifier of rock asphalt and montmorillonite is used to modify the bio-asphalt. The optimum content of each component was determined by orthogonal tests based on the results from penetration, softening point, ductility and viscosity tests. The rheological properties and anti-aging performance of rock asphalt and montmorillonite composite-modified bio-asphalt (RAMB) with the optimum content were evaluated as compared to those of matrix asphalt (MA), untreated/treated bio-asphalt (UBA/TBA) and rock asphalt-/montmorillonite-modified bio-asphalt (RMB/MMB). The test results illustrated that the optimum content of each component in the rock asphalt/montmorillonite composite-modified bio-asphalt--as determined by orthogonal experimental design and penetration, softening point, ductility and viscosity tests--was 7% bio-oil treated by thermostatic water bath, 5% rock asphalt and 30% montmorillonite. The high-temperature performance, low-temperature performance and anti-aging performance of RAMB were studied by comparison to those of matrix asphalt, UBA, TBA, RMB and MMB. Additionally, the composite modification mechanism was studied by Fourier transform infrared spectroscopy (FTIR). The results suggested that the high-temperature of TBA was obviously improved compared with UBA. The reason, as seen from infrared spectrum tests, was that the amount of ester compounds decreased after water bath treatment. The light components and soluble substances in bio-oil decreased. Compared to UBA, the unrecoverable creep compliance (Jnr) of RAMB decreased by 66.6% and the recovery rate (R) increased by 75.9% at 0.1 KPa. The stiffness modulus (S) of RAMB was 0.87 times that of matrix asphalt and the creep rate (m) was 1.03 times that of base asphalt. Compared to single-modified asphalt, the high- and low-temperature performance of RAMB was good. Meanwhile, the complex modulus aging index (CMA) and stiffness modulus aging index (SAI) of RAMB were lower than all other asphalt studied, while the phase angle aging index (PAAI) and creep rate aging index (mAI) of RAMB were the largest. The results of infrared spectroscopy also suggest that the mixing of rock asphalt, montmorillonite, bio-oil and matrix asphalt is a physical blending process. During the process, no functional groups are formed. Pretreatment and addition of rock asphalt and montmorillonite can improve high-temperature performance, low-temperature performance and anti-aging performance of the bio-asphalt.  
bio-asphalt; composite modification; rheological performance; anti-aging; modification mechanism +
Footnote †: journal: Applied Sciences
## 3 Results
### Experimental Results
Figures 9 and 10 show the confusion matrix of the proposed method on the AID with training set ratios of 20% and 50%. For the training ratio of 20%, it can be seen from the confusion evidence in the above figure that most of the categories achieved an accuracy of more than 95%. Among them, the accuracy of the resort category is low. According to the confusion matrix, the reason for this is that the proposed method misjudges some resort scenes as park scenes. For a training ratio of 50%, it can be seen from the confusion matrix in the figure above that the accuracy rate of most categories is higher than 95%, and the accuracy rates of the beach, mountain, pond, and viaduct categories all reach 100%. When 20% of the images are divided into the training set, the proposed method has low accuracy in the resort category because some samples are misjudged as belonging to the park category. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Overall Accuracy**} \\ \cline{2-3}  & **10\% Training Ratio** & **20\% Training Ratio** \\ \hline (Fine-tuning) GoogleNet [39] & 83.44 \(\pm\) 0.40 & 86.39 \(\pm\) 0.55 \\ (Fine-tuning) VGG-16 [40] & 86.59 \(\pm\) 0.29 & 89.64 \(\pm\) 0.36 \\ (Fine-tuning) ResNet50 [16] & 92.57 \(\pm\) 0.21 & 95.96 \(\pm\) 0.17 \\ D-CNN [41] & 90.82 \(\pm\) 0.16 & 96.89 \(\pm\) 0.10 \\ MG-CAP [42] & 93.34 \(\pm\) 0.18 & 96.12 \(\pm\) 0.12 \\ ACGLNet [47] & 94.44 \(\pm\) 0.09 & 96.10 \(\pm\) 0.10 \\ BMDF-LCNN [43] & 94.46 \(\pm\) 0.15 & 96.76 \(\pm\) 0.18 \\ SCCov [44] & 93.12 \(\pm\) 0.25 & 96.89 \(\pm\) 0.10 \\ MLFF [45] & 92.73 \(\pm\) 0.12 & 95.06 \(\pm\) 0.33 \\ T-CNN [46] & 94.55 \(\pm\) 0.27 & 96.72 \(\pm\) 0.23 \\ Ours & 94.96 \(\pm\) 0.13 & 96.72 \(\pm\) 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overall accuracy (%) of different methods with the training ratios of 50% and 20% on AID. Figure 10: Confusion matrix of the proposed method on the AID with a training ratio of 50%. The content of the matrix is the accuracy of the predicted category within each category. Figure 9: Confusion matrix of the proposed method on the AID with a training ratio of 20%. The content of the matrix is the accuracy of the predicted category within each category.
# A Distributed Hybrid Community Detection Methodology for Social Networks
## 3 Proposed Methodology
Empirically, after carefully evaluating the overall performance for the various experiments conducted and presented in the following section,both loose similarity and local edge betweenness depths have been practically proven to be strongly related to the social graph's diameter, where specifically not deeper than the eighth depth neighbors were required at the worst case. It is worth pointing out, that both the local network topology properties calculations can inherently performed in a distributed fashion. Then, the inter-connection label assignment for each of the existing edges occurs. Specifically, an edge would be considered inter-connected should at least one of the following cases occur:  
Figure 1: The proposed methodology flowchart. * The corresponding LS-th depth loose similarity is less than a predefined LS threshold;
* The corresponding EB-th depth local edge betweenness measure is more than a predefined EB threshold; and
* The corresponding node attribute cosine similarity is less than a predefined NA threshold. If no edge removal is applied or fewer edges than a certain threshold are removed, the community extraction process is terminated and the generated structure at this point is considered final. Otherwise, the iteration step is re-executed. At this point, it should be noted that the network topology related need to be recalculated at each iteration step. It is more than obvious that after an edge removal occurs, the already calculated LS-th depth loose similarity and EB-th depth local edge betweenness measures are no longer reflect the true network structure. Therefore, in order to proceed to meaningful and qualitative network division, the network topology measures recalculation is considered mandatory. Indisputably, in terms of efficiency and scalability, the application of a classic modularity measure that would require the repetitive process of the entire network is considered impracticable. Hence, the values of the above-mentioned thresholds play a substantially vital role to the methodology's overall performance, affecting at the same time the number of iterations needed to converge to the final communities' hierarchy and the general quality of the generated community structure. Finally, even if the loose similarity implementation is consider trivial, the edge betweenness generally calculated with high polynomial solutions, outreaching the cubic bound in the average case. Fortunately, new algorithms and techniques have yet been proposed to speed up the edge betweenness calculation process [6; 34; 35]. Specifically, by using the fast matrix multiplication technique described in [6], the local edge betweenness calculation has been achieved in O (m x n) complexity running time in the worst case, where n and m factors denote the number of vertices and edges of the corresponding LS-depth subgraph.
## 3 Results
### Research Results and Analysis of Carbon Emission Risk of Land Use
#### 3.3.3 Spatial Aggregation Analysis of Carbon Emission Risk of Land Use  
Through a global spatial autocorrelation analysis [40; 41] of land use carbon emission risk in the study area, [PERSON]'s I index, Z value, and \(p\) value were calculated for each year. As shown in Table 6, the \(p\) value for carbon emission risk in this region for 2010, 2015, and 2020 is less than 0.1, indicating spatial autocorrelation at a 90% confidence interval. The global [PERSON]'s I index is greater than 0, showing a positive spatial correlation in carbon emission risk, while the Z value exceeds 1.65, suggesting that the carbon emission risk in various counties and districts exhibit an aggregated distribution.  
The global [PERSON]'s I index provides insight into the overall spatial differentiation of land use carbon emission risk in the study area, it does not capture the internal spatial clustering characteristics of these risk [42]. To address this, the study further explores the local spatial dynamics of land use carbon emission risk and their changes in the region, analyzing the hotspots and cold spots of carbon emission risk for the years 2010, 2015, and 2020 using ArcGIS10.1 software.  
Overall, during the study period, the hotspot pattern of land use carbon emission risk in the study area has changed significantly. Figure 6 shows that in 2010, hotspot areas were primarily concentrated in Wangdu County, while cold spot areas were found in Chengde County, Kuancheng Manchu Autonomous County, Longhua County, Pingquan County, and Fengning Manchu Autonomous County, where carbon emission risk was relatively low, creating a clear cold spot distribution. In 2015, Yixian County, Tang County, and Quyang County emerged as new hotspots for land use carbon emission risk, and the cold spots showed a diminishing trend. In 2020, although hotspots and cold spots continued to exist, the overall distribution pattern remained largely unchanged. This stability may indicate that policies and measures for managing land use carbon emission risk have achieved some degree of success. However, it remains essential to monitor the increasing risk in hotspot areas and strengthen sustainable management strategies in cold spots to foster more balanced regional development and effective environmental protection.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Year** & **Moran’s I** & **Z Value** & \(p\) **Value** \\ \hline
2010 & 0.219978 & 2.322809 & 0.020189 \\
2015 & 0.209909 & 2.221674 & 0.009783 \\
2020 & 0.205892 & 2.192888 & 0.026305 \\ \hline \end{tabular}
\end{table}
Table 6: Global Moran’s I index of carbon emission risk from land use.
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 4 Performance Analysis of the Proposed SCbSC
### SCbSC Hardware Metrics  
The hardware metrics of the SCbSC system are shown in Table 4, and as expected, they are similar to those of SPCNG.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & \(p\)**-Value** & **Proportion \%** \\ \hline Frequency test & 0.616 & 100 \\ Block-frequency test & 0.182 & 97 \\ Cumulative-sums test (2) & 0.825 & 99.5 \\ Runs test & 0.956 & 100 \\ Longest-run test & 0.868 & 100 \\ Rank test & 0.182 & 99 \\ FFT test & 0.868 & 99 \\ Nonperiodic-templates (148) & 0.507 & 98.912 \\ Overlapping-templates & 0.956 & 99 \\ Universal & 0.575 & 98 \\ Approximate Entropie & 0.658 & 99 \\ Random-excursions (8) & 0.511 & 99.432 \\ Random-excursion-variant(18) & 0.376 & 99.832 \\ Serial test (2) & 0.290 & 98 \\ Linear-complexity & 0.834 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(P\)-values and proportion results of NIST test.  
The comparison of the hardware metrics of several chaotic and non-chaotic systems (from eSTREAM project phase-2 focus hardware profile) is summarized in Table 5. This comparison is difficult to interpret due to the differences in characteristics of the FPGAs tested--particularly for the clock rate parameter. However, considering the clock rate of the FPGA board and the efficiency achieved, we can make this comparison. Thus, the SCbSc system presents competitive hardware metrics compared to those obtained from most other chaotic and non-chaotic systems, except the Trivium cipher. However, since 2007, different types of attacks have been applied to eSTREAM ciphers, thereby revealing some weaknesses, in particular on Trivium cipher [34; 35]. Indeed, in Trivium AND gates are the only nonlinear elements to prevent attacks that exploit, among other things, the linearity of linear feedback shift registers.