esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
### Processing Chain  
To estimate the future coastal evolution, we combined the SL variations predicted by MED16 with projections of the vertical ground motion based on the available EGMS data. The model SL was spatially averaged in the test regions and translated to the coastal area using a simple \"bathtub\" approach [113; 114]. On the other hand, the available 6 years of EGMS data were used to compute present time average velocities of vertical displacement in the five areas of interest, which were then assumed to remain constant during the timespan of the scenario. This is a limitation of the analysis because future geological events could modify the present rates of displacement. However, since the dataset will be constantly updated, uncertainty will eventually reduce in time.  
The MED16 SL data and the EGMS displacements were both spread on the high-resolution DTM to obtain the overall movement yearly from 2010 to 2099. The area of interest (AoI) was extended for 10 Km inland from the coastline, and CORINE was then used to study the impacts of the SLR on the exposed infrastructure and natural environments in the test areas. A general workflow of the methodology is represented in Figure 4.  
The GIS procedure we implemented makes use of the open-source software QGIS 3.22 and later. The Atlas function present in the \"Layout\" section of QGIS was used to create the custom maps with dynamic data. To create the diagrams, the QGIS plugin \"Data Plotly\" was used, which allows dynamic charts to be inserted both within the GIS project and in the \"Layout\" section with customized plugins. All the input data were managed in a GIS project and tailored to the UTM WGS84 coordinate system, ETRS89-extended Lambert Azimuthal Equal Area (LAEA) Europe. Using the graphic modeller available in QGIS, a specific and flexible tool was implemented that collects the input data, allows data processing, and produces inundation maps for the areas of interest. The computational steps involved are reported in Figure 5.  
Figure 3: Coastal Zones Land Cover: example of classification and standard colours.  
The GIS procedure was used to compute the maximum extension of inundated areas, by using the highest SL during the periods 2010-2040, 2040-2070, and 2040-2099 for the inundation scenarios.  
Figure 4: Implementation workflow.  
Figure 5: Input data, steps of the analysis tool, and OUTPUT files in the graphic representation of the GIS tool.  
When raster images of first-level flood maps are available (basemap is provided by OpenStreetMap of the OSM Foundation and bathymetry of ISPRA), Corine Land Cover service provides a LC/LU dataset for areas along the marine coastline of the EEA39 countries. So, the area of inundation considers the classes of CZ. In the present study, we focused on the impact of inundation scenarios by calculating the typology of terrain under sea level by the end of this century (time interval 2070-2099).
# Analysis of Vulnerabilities That Can Occur When Generating One-Time Password
## 5 Analysis of OTP Vulnerabilities
### Vulnerabilities in OTP Extraction
* Use of static or dynamic algorithm: Rather than using an improved algorithm that can cause vulnerabilities, using a static or dynamic algorithm is suitable for information security by supplementing the randomness and unpredictability of OTP. * Increases the number of LSB digits used as extraction information, and increases the length of the output bit string accordingly: Since 16 offsets can be selected using LSB 4-bit extraction information, the length of the output bit stream is used as 20-bytes \((=16+4)\). If this is used as LSB 5-bit or 6-bit, the offset that can be selected increases to 32 or 64, so the length of the output bit string is 36-bytes\((=32+4)\) and 68-bytes\((=64+4)\). In this case, the probability of offset overlapping decreases by \(\frac{1}{2}\) each time the bit is increased by one. (For example, _Pr_[All extraction information is duplicated] for 5-bit \(=\frac{1}{32}\), _Pr_ for 6-bit \(=\frac{1}{64}\))
* Increase the amount of extraction to 4-byte: When the amount of extraction is increased from 3-byte to 4-byte, the range is [0, 16777215] (i.e., \([0,2^{24}-1]\)) to [0, 4294967295] (i.e., \([0,2^{32}-1]\), About 4 billion cases). Therefore, OTP can be used up to 7 and 8 digits instead of 6 digits. In this case, since \([0,10^{7}-1]\) and \([0,10^{8}-1]\) can be used, the predictable probability decreases by \(1/10\) each time by one digit increment.
# Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize
## 2 Materials and Methods
### Experimental Equipment  
The hyperspectral imaging system was used to acquire spectral images of different varieties of maize grains. The system includes an Imaging spectrometer (ImSpector N17E, 900-1700 nm, Spectral Imaging Ltd., Oulu, Finland), 14-bits 1600 \(\times\) 1200 pixel CCD camera (Bobcat ICL-1410, Boca Raton, FL, USA), bilateral 150 W halogen linear light source (IT3900, Illumination Technologies, Liverpool, NY, USA), one-dimensional displacement platform (IRCP-0076-400, Isuzu Optics Corp., Taiwan), etc. The spectral resolution of the system is 5 nm, and the image resolution is 320 \(\times\) 256 pixels. The whole system is packaged in a dark box to avoid the interference of ambient light. The optical imaging system and the control parameters of the displacement stage need to be adjusted before acquisition.  
Before hyperspectral image acquisition, it is necessary to adjust the object distance, exposure time, and moving speed of the system to ensure the collected maize seed image is clear and accurate in shape and that the spectrum of each pixel is as far as possible without saturation. After many tests, the setting parameters were as follows: the distance from the sample to the edge of the lens was 43 cm, the exposure time was 10 ms, and the moving speed of the platform was 5 mm\(\cdot\)s\({}^{-1}\).  
The spectral images taken by the system need to be calibrated to reduce or eliminate the dark current of the machine and the noise interference of charge-coupled devices in the acquisition process [21; 22]. The image correction formula is shown as follows:  
\[\mathrm{R}=\frac{\mathrm{I_{raw}-I_{dark}}}{\mathrm{I_{white}-I_{dark}}} \tag{1}\]  
where R is the corrected image, \(\mathrm{I_{raw}}\) is the original image, \(\mathrm{I_{white}}\) is the standard whiteboard image, and \(\mathrm{I_{dark}}\) is the dark field image.
## 2 Materials and Methods
### DEM Data  
For elevation data, we employed processed Shuttle Radar Topography Mission (STRM) data version 4.1 downloaded from International Center for Tropical Agriculture Consortium for Spatial Information SRTM (CIAT-CSI SRTM) [53]. The original dataset was derived from the United States Geological Survey (USGS)/NASA SRTM, and its no-data regions have been filled using interpolation methods described by [PERSON] et al. [54]. CIAT provides seamless, continuous topography surfaces at 250 m spatial resolution, and we resampled it to 0.05\({}^{\circ}\) to match the spatial resolution of _SIF_ datasets. Considering the natural distribution of meadows in the Tibetan Plateau and possible misclassified regions in the vegetation map, regions above 5500 m and below 3200 m were discarded in the subsequent analysis. DEM values were binned at 100 m intervals.
## 2 Materials and Methods  
### Microalgae Strain and Maintenance Medium  
The microalga _C. vulgaris_ (CCAP 211/11B) was obtained from the Culture Collection of Algae and Protozoa, United Kingdom. The strain was maintained on modified Organization for Economic Co-operation and Development (OECD) culture medium [14], with the following composition (mg L\({}^{-1}\)): 119 KNO\({}_{3}\); 12 MgCl\({}_{2}\)2H\({}_{2}\)O; 18 CaCl\({}_{2}\)2H\({}_{2}\)O; 15 MgSO\({}_{4}\)-7H\({}_{2}\)O; 20 KH\({}_{2}\)PO\({}_{4}\); 0.08 FeCl\({}_{3}\)-6H\({}_{2}\)O;0.1 Na\({}_{2}\)EDTA-2H\({}_{2}\)O;0.185 H\({}_{3}\)BO\({}_{3}\);0.415 MnCl\({}_{2}\)-4H\({}_{2}\)O;0.003 ZnCl\({}_{2}\);0.0015 CoCl\({}_{2}\)-6H\({}_{2}\)O; 0.0001 CuCl\({}_{2}\)-2H\({}_{2}\)O; 0.007 Na\({}_{2}\)MoO\({}_{4}\)-2H\({}_{2}\)O and 100 Na\({}_{2}\)CO\({}_{3}\).
## 5 Results and Discussion
### The DOE Analysis  
Design Expert 7 software was used to investigate the impact of the simulated runs on the responses. Numerical data revealed that the percentage of the signal is varied between 0.38 and 1. Table 3 shows that the value of total determination \(R^{2}\) is 0.8808% (close to 1), which indicates that the quadratic model reasonably fits the numerical data and can represent signal percentage in terms of the independent parameters [18]. The significance of each parameter was determined (\(p<0.05\)) using the \(p\)-value.  
Table 3 shows that the analysis of variance (ANOVA) of the independent parameters, depth of the defect, and width of the defect were significant (\(p<0.05\)). Additionally, the type of material, the interaction impact of depth of defect with the type of material, width of the defect with the type of material, depth of the defect with width of the defect, quadratic terms of the depth of the defect, and quadratic terms of the width of the defect were not significant, given that the \(p\)-values are 0.0004,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Depth of Defect** **(mm)** & \multicolumn{2}{c}{**The Width of Defect (mm)**} & \multicolumn{2}{c}{**Signal Display**} & \multicolumn{1}{c}{**Percentage Signal**} \\ \hline
1 & 0.2 & & 56\% \\ \hline
1.5 & 0.4 & & 72\% \\ \hline
2 & 0.6 & & 86\% \\ \hline
2.5 & 0.8 & & 92\% \\ \hline
1.5 & 1 & & 58\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results for mild steel.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Square** & **Df** & **Mean Square** & **F Value** & **\(p\)-Value Prob \(>F\)** \\ \hline Model & 1.39 & 4 & 0.35 & 101.58 & \textless{}0.0001 \\ A-material & 0.032 & 1 & 0.032 & 9.33 & 0.0035 \\ B-depth & 1.14 & 1 & 1.14 & 332.66 & \textless{}0.0001 \\ C-width & 0.16 & 1 & 0.16 & 46.72 & \textless{}0.0001 \\ A2 & 0.060 & 1 & 0.086 & 25.15 & \textless{}0.0001 \\ Residual & 0.19 & 55 & \(3.422\times 10^{-3}\) & & \\ Cor Total & 1.58 & 59 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: ANOVA for response surface quadratic model of the signal percentage.  
0.6257, 0.0254, 0.0020, 0.0002, and 0.3040, respectively. Thus, non-significant terms were eliminated, and the optimization process was repeated until all terms become significant.  
The implementation of RSM provides the following regression equation, which can be considered an empirical relationship between the percentage of the signal and the independent parameters.  
\[y=+0.29064-8.69189\times 10^{-3}\times X_{1}+0.24640\times X_{2}+0.18250\times X _{3}+7.45897\times 10^{-5}\times{X_{1}}^{2} \tag{4}\]  
where \(y\) is the percentage of the signal, \(X_{1}\) is the type of material, \(X_{2}\) is the depth of the defect, and \(X_{3}\) is the width of the defect.
## 3 Experimental Activities
### Investigation of the Fragmentation Process
#### 3.3.1 Aluminum and CFRP Plates  
The investigation of aluminum thin plates aimed to evaluate the effect of impact velocity and target thickness on fragment generation. Four tests were performed on aluminum plates of two different thicknesses (2 and 5 mm). Aluminum spherical projectiles of 2.9 mm in diameter were launched at velocities between 3.5 and 4.8 km/s. The fragments generated by the impact were collected, catalogued, and analyzed to obtain characteristic length cumulative distributions (see Figure 7) and shape histograms [45]. The results showed that characteristic length distributions can be modelled by a power law similar to the NASA SBM and that shape distributions are independent from target thickness and impact velocity.  
A similar campaign of four tests was performed on CFRP panels (thickness of 4 mm), with spheres of 1.9 mm and 2.9 mm impacting at velocities between 3.5 and 5.1 km/s. Again, generated fragments were collected, catalogued, and analyzed to obtain size cumulative distributions (see Figure 8). It was observed that the distributions strongly differed from aluminum plates in terms of total number and shape, with a strong contribution, in size classes larger than 6 mm, of fragments generated by panel surface delamination. Results showed that the thickness to projectile diameter ratio and the impact velocity significantly affect the fragment size distributions; in particular, the number of fragments larger than 3 mm increases with these two parameters.
# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 3 Material and Methods
### Spatial and Temporal Frequency Domains  
This section deals with the formulas of time-domain, frequency-domain, autoregression, and wavelet coefficients because most of the articles explain these methods in detail. In our paper, the formulas are used from papers [28,44]. In the following, we explain the formulas briefly.  
These extracting features formulas from 36 to 53 and 54 to 57 belong to the time-domain and frequency-domain, and other formulas that belong to other methods are mentioned above. For wavelet coefficients, the MATLAB function is used. Based on algorithms, features are selecting from different formulas. Using algorithms, the features of some formulas are selected, and the rest are not selected.  
\[\text{Absolute values sum}=\sum\limits_{i=1}^{N}|x_{i}| \tag{36}\]  
\[\text{Mean absolute Value},\ MAV=\frac{1}{N}\sum\limits_{i=1}^{N}|x_{i}| \tag{37}\]  
\[\text{Modified mean absolute value type 1},\ MAV_{\text{Type 1}}=\frac{1}{N}\sum\limits_{i=1}^{N}w_{i}|x_{i}| \tag{38}\]  
\[w_{i}=\left\{\begin{array}{c}1\,\ if\ 0.25N\leq i\leq 0.75N\\ 0.5,\end{array}\right. \tag{39}\]  
\[\text{modified mean absolute value type 2},\ MAV_{\text{Type 2}}=\frac{1}{N}\sum\limits_{i=1}^{N}w_{i}|x_{i}| \tag{40}\]  
\[w_{i}=\left\{\begin{array}{c}1,\ if\ 0.25N\leq i\leq 0.75N\\ \frac{4}{N},\ slseif\ i<0.25N\\ \frac{4(i-N)}{N}\end{array}\right. \tag{41}\]  
\[\text{Simple square integral},\ SSI=\sum\limits_{i=1}^{N}x_{i}^{2} \tag{42}\]\[Variance,\ Var=\frac{1}{N-1}\sum_{i=1}^{N}x_{i}^{2} \tag{41}\]  
The absolute value of the 3 rd, 4 th, and 5 th temporal moment  
\[TM3=\left|\frac{1}{N}\sum_{i=1}^{N}x_{i}^{d}\right|,\ d=1,23. \tag{42}\]  
\[\text{Root mean square, }RMS=\sqrt{\frac{1}{N}\sum_{i=1}^{N}x_{i}^{2}} \tag{43}\]  
\[\text{Wave form length, }WL=\sum_{i=1}^{N-1}|x_{i+1}-x_{i}| \tag{44}\]  
\[\text{Average amplitude change, }AAC=\frac{1}{N}\sum_{i=1}^{N-1}|x_{i+1}-x_{i}| \tag{45}\]  
\[Difference\ absolute\ standard\ deviation\ value,\] \[DASDV=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N-1}(x_{i+1}-x_{i})^{2}} \tag{46}\]  
\[\text{Maximum value, }Max=max(x_{i}),\ i=1,2,\ldots,N \tag{47}\]  
\[\text{Minimum value, }Min=min(x_{i}),\ i=1,2,\ldots,N \tag{48}\]  
\[\text{Standard deviation, }SD=sd(x_{i}),\ i=1,2,\ldots,N \tag{49}\]  
\[\text{Mean\ frequency, }MDF=\frac{1}{2}\sum_{j=1}^{M}p_{j} \tag{50}\]  
\[\text{Peak\ frequency, }RKF=max\big{(}P_{j}\big{)} \tag{51}\]  
\[\text{Mean\ power, }MNP=\sum_{j=1}^{M}\frac{P_{j}}{M} \tag{52}\]  
\[\text{Total\ power, }TTP=\sum_{j=1}^{M}p_{j} \tag{53}\]  
\[\text{Autoregressive\ coefficients, }x_{i}=\sum_{i=1}^{p}a_{p}x_{i-p}+w_{i},\ p\text{ is order of }AR \tag{54}\]  
\[\text{Power\ Spectrum }S_{AA}(f)=\frac{FFT(A)*FFT^{\prime}(A)}{N} \tag{55}\]  
\[ps[m]=\sum_{n=1}^{N}r_{xx}[n]e^{-j\times 2\pi imN}N,\ m=0,1,2,\ldots,N \tag{56}\]  
\[r_{xx}(t)\text{ and }r_{xx}(n)\text{ are autocorrelation functions.} \tag{57}\]
## 3 Results and Discussion
### Chemical and Physical Properties of the NR Sheets  
Table 1 presents the chemical and physical properties of the NR sheets which were related to the type of coagulant material. The results of the average dirt and volatile contents of the NR sheets showed that the different types of wood vinegar coagulants were not significantly different from acetic acid. This result was due to acetic acid being the major component of wood vinegars. For the physical properties, Figure 2 shows that the tensile strength at break of the NR sheet samples changed during the drying process. Figure 2 presents that an increase in drying time had the influence of increasing the tensile strength at break for all samples. This result was due to the space between the rubber molecules being decreased after water molecule evaporation [50,51]. In comparing the tensile strength at break values between each experimental and reference result, it is shown that all the wood vinegar coagulating materials were not significantly different from the acetic acid. The results show that the average percentage of elongation at break and the 300% modulus were not significantly different.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Type of Coagulating Material**} \\ \cline{2-5}
**Type of NR Sheet** & \multicolumn{4}{c}{**Raw Wood Vinegars**} \\ \cline{2-5}  & **Para Rubber Wood** & **Bamboo** & **Coconut Shell** & **Acetic Acid** \\ \hline Before drying & & & & \\ Dirt content (\%\(w\)/\(w\)) & 0.048 & 0.055 & 0.052 & 0.054 \\ Volatile content (\%\(w\)/\(w\)) & 0.79 & 0.87 & 0.85 & 0.83 \\ After drying & & & & \\ Dirt content (\%\(w\)/\(w\)) & 0.037 & 0.043 & 0.039 & 0.042 \\ Volatile content (\%\(w\)/\(w\)) & 0.49 & 0.56 & 0.52 & 0.53 \\ Elongation at break (\%) & 669 & 682 & 678 & 671 \\
300\% modulus (MPa) & 1.0 & 1.0 & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical and physical characteristics of the NR sheet results related to the type of coagulating material.  
Figures 3 and 4 present the effect of the type of coagulating material on the Mooney viscosity and plasticity retention index for the sheet rubber, respectively. In comparing the phenolic group among the four coagulating materials (para rubber wood vinegar > coconut shell wood vinegar > bamboo wood vinegar > acetic acid), an increasing number of phenolic groups in the NR latex reacted with the amine groups in the rubber [52; 53], which increased the Mooney viscosity and PRI of the NR sheet. Therefore, the phenolic group in the NR latex had a strong influence on the Mooney viscosity and PRI properties of the sheet. This agreed with the results reported in [52; 53]. Note that the results indicated that the concentration of acids influenced the Mooney viscosity, PRI properties, and the manufacturing process more than the acid types.  
Figure 3: Mooney viscosity change during the drying process.  
Figure 2: Tensile strength at break change during the drying process.
# Infrared Image Deblurring via High-Order Total Variation and Lp-Pseudonorm Shrinkage
## 2 Traditional Anisotropic Total Variation (ATV) Model  
The ATV deblurring model [38] is described as follows:  
\[\mathbf{F}=\underset{\mathbf{F}}{\text{argmin}}\frac{1}{2}\|\mathbf{H}{*}\mathbf{F}-\mathbf{G}\|_{ 2}^{2}+uR_{\text{ATV}}(\mathbf{F}). \tag{1}\]  
The Gaussian blurring kernel \(\mathbf{H}\in\mathbf{R}^{N\times N}\) is represented by a point spread function [5], \(\mathbf{F}\in\mathbf{R}^{N\times N}\) represents the original image, and \(\mathbf{G}\in\mathbf{R}^{N\times N}\) represents the observed image with noise. \(\frac{1}{2}\|\mathbf{H}{*}\mathbf{F}-\mathbf{G}\|_{2}^{2}\) is the fidelity term, \(R_{\text{ATV}}(\mathbf{F})\) is the prior term, and the balance factor \(u\) is used to balance the prior and fidelity terms.  
\[R_{\text{ATV}}(\mathbf{F})=\|\mathbf{K}_{h}{*}\mathbf{F}\|_{1}+\|\mathbf{K}_{v}{*}\mathbf{F}\|_{1}. \tag{2}\]  
where \(\mathbf{K}_{h}=[-1,1],\mathbf{K}_{v}=\left[\begin{array}{c}-1\\ 1\end{array}\right]\) represents the two-dimensional convolution kernels, \(\mathbf{K}_{h}{*}\mathbf{F}\) describes information in the horizontal direction of the image, while \(\mathbf{K}_{v}{*}\mathbf{F}\) describes the information in the vertical direction of the image.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments
### Case Study  
The case study evaluates the MMCL (Multi-Model Collaborative Learning) framework's ability to harness the complementary strengths of VSLNet and 2D-TAN models across various video temporal grounding scenarios, including short and long videos, complex temporal relationships, and fast-changing scenes.  
In scenarios with short videos (Figure 9, left), where precise boundary detection is critical, MMCL improved 2D-TAN's performance by allowing it to learn from VSLNet's accuracy in temporal boundary prediction. Conversely, in long video scenarios (Figure 9, right), which require a broader temporal context, MMCL enhanced VSLNet's performance by incorporating 2D-TAN's strength in extended temporal modeling. This demonstrates the framework's capacity to facilitate effective mutual learning between models with distinct advantages. In Figure 10, the performance on \"difficult\" samples was examined through complex temporal scenarios and fast-changing scenes. Difficult samples in our study refer to scenarios that require a deep understanding of extended temporal dependencies or rapid visual changes. The task demands not only temporal segmentation but also an  
Figure 7: Effect of the number of video frames and the similarity threshold on performance in the CLIP-guided module.  
understanding of the sequential order of events. VSLNet, typically challenged by such scenarios, showed notable improvement under the MMCL framework, demonstrating enhanced temporal reasoning by leveraging 2D-TAN's strengths. Conversely, in fast-changing scenarios like the BMX race video, which required precise alignment between fast-paced visual cues and the query, MMCL (2D-TAN) showed a significant performance gain. This result indicates that 2D-TAN, through bidirectional knowledge transfer, learned from VSLNet's dynamic attention mechanism, which is particularly effective in fast-changing environments.  
In specific challenging samples (Figures 11 and 12), the standalone models struggled with accurate predictions, whereas the MMCL-enhanced models succeeded. The MMCL (2D-TAN) model adapted better to fast-changing scenarios by learning from VSLNet's strengths, while MMCL (VSLNet) demonstrated improved performance in complex temporal scenarios by leveraging 2D-TAN's proficiency in modeling long-range temporal dependencies. These results confirm that the MMCL framework not only preserves each model's inherent strengths but also facilitates adaptive knowledge transfer, leading to improved performance on difficult samples and demonstrating the intended collaborative learning effect.  
Figure 8: Qualitative results of MMCL(VSLNet) and MMCL(2D-TAN).  
Figure 11: Improved temporal grounding in fast-changing scenarios with MMCL (2D-TAN).  
Figure 10: MMCL framework in fast-changing vs. complex temporal scenarios.  
Figure 9: Performance of MMCL framework in short vs. long video scenarios.