esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Cryptanalysis of Some RSA Variants
## 4 Comparison with the Existing Attacks
### Comparison with [PERSON] et al.'s Attack  
In [12], [PERSON] et al. proposed an attack against the RSA variants based on the key equation  
\[ed-k(p^{2}-1)(q^{2}-1)=1.\]  
They demonstrated that the system is vulnerable when \(e=N^{\alpha}\), \(d=N^{\delta}\), and \(\delta<2-\sqrt{\alpha}\). This represents the optimal bound for attacks targeting small private exponents in such schemes. This bound can be obtained by taking \(\gamma=0\) and \(\sigma=\frac{1}{2}\). As a consequence, our attack can be seen as an extension of the attack of [PERSON] et al.  
### Comparison with [PERSON] et al.'s Attack  
In [13], [PERSON] et al. introduced an attack on cryptographic systems defined by the key equation \(ed-k(p^{2}-1)(q^{2}-1)=1\). They showed that for a modulus \(N=pq\), where \(e=N^{\alpha}\), \(d<N^{\delta}\), and \(|p-\bar{p}|=N^{\sigma}\) with \(\bar{p}\) being an approximation of \(p\), the scheme becomes susceptible to attack when \(\delta<2-\sqrt{2\alpha\sigma}\). This vulnerability threshold is derived by setting \(\gamma=0\) in Theorem 3, highlighting that the approach of [PERSON] et al. is a particular case within the broader framework presented in our methodology.  
Additionally, the number of exponents \(e\) of size \(N^{\alpha}\) that are prone to the attack of [PERSON] et al. can be approximated by  
\[\#\Big{\{}d\mid d<N^{2-\sqrt{2\alpha}},\;\gcd(d,\psi(N))=1\Big{\}}=\mathcal{O }\Big{(}N^{2-\sqrt{2\sigma\alpha}-\epsilon}\Big{)},\]  
where \(\epsilon\) is a small positive parameter representing the exponents that are not coprime with \(\psi(N)=(p^{2}-1)(q^{2}-1)\).  
Alternatively, the quantity of the weak exponents \(e\) of size \(N^{\alpha}\) associated with our attack can be expressed as  
\[\mathcal{Q}=\#\bigg{\{}(u,z)\,\Big{|}\,|u|\leq N^{\delta},\,|z|\leq N^{\gamma},\,\delta+\frac{1}{2}\gamma<2-\sqrt{2\sigma\alpha},\;\gcd(u,\psi(N))=1\bigg{\}}.\]  
Hence,  
\[\mathcal{Q}\leq\#\bigg{\{}(u,z)\,\Big{|}\,|u|\leq N^{\delta},\,|z|\leq N^{ \gamma},\,|u|\sqrt{|z|}<N^{2-\sqrt{2\sigma\alpha}},\;\gcd(u,\psi(N))=1\bigg{\}}.\]  
By setting \(\rho=2-\sqrt{2\sigma\alpha}\), and observing \(1\leq|z|<\frac{N^{2\rho}}{|u|^{2}}\), where \(1\leq|u|<N^{\rho}\), an upper bound for this quantity is determined by summing the possible values of \(|z|\) within the interval \(\Big{[}1,\frac{N^{2\rho}}{|u|^{2}}\Big{]}\) for each \(u\) satisfying \(1\leq|u|<N^{\rho}\). This results in  
\[\mathcal{Q}\leq 4\sum_{u=1}^{N^{\rho}}\sum_{z=1}^{\frac{N^{2\rho}}{|u|^{2}}}1=4 \sum_{u=1}^{N^{\rho}}\frac{N^{2\rho}}{|u|^{2}}=4N^{2\rho}\sum_{u=1}^{N^{\rho} }\frac{1}{|u|^{2}}\leq\frac{2\pi^{2}}{3}N^{2\rho},\]  
where we utilized the established result \(\sum_{k=1}^{+\infty}\frac{1}{k^{2}}=\frac{\pi^{2}}{6}\). Thus,  
\[\mathcal{Q}=\mathcal{O}\Big{(}N^{2\left(2-\sqrt{2\sigma\alpha}\right)-\epsilon }\Big{)},\]  
where \(\epsilon\) is a small positive constant representing the integers \(u\) that are not coprime with \(\psi(N)\). This bound greatly exceeds the number of exponents \(e\) of size \(N^{\alpha}\) that are susceptible to the attack introduced by [PERSON] et al. [13].
## 3 The Vectorized Earth Observation Retrieval Classifier
### Feature Selection
Whenever adding new features does not bring any improvement to the kappa value, the forward feature selection procedure converges and the feature subset with the highest class separability skills is returned. The sequence in which the optimal features are selected further corresponds to the 8-bit positions within the 64-bit ID value. Features derived at the beginning of the forward feature selection procedure occupy more significant bit ranges than features derived later. Furthermore, VEOR ranks not only the separation skill of single features but also the separation skill of feature combinations, which greatly facilitates the understanding of the acquired classification results. The last part of the VEOR algorithm training involves the oversampling and compaction of the LUV derived for the optimal feature subset. Firstly, oversampling involves reconstruction of the classification probability using the optimal \(K\)-nearest grid cell number for the user-defined number (default \(10^{6}\)) of random cells located around the grid cells filled with samples within an Euclidean distance selected from the normal distribution with the user-defined scale (default 10). Secondly, the oversampling involves reconstruction of the classification probability using the \(5^{D}\) kernel around each grid cell filled with samples, where \(D\) is the number of dimensions (features). Consequently, classification probability is predicted for grid cells not populated with samples during algorithm training (marked in white in Figures 1 and 2). This is achieved by means of the inverse distance interpolation between the grid cells populated with data, which essentially extrapolates a classification probability to grid cells without any data. Such a prediction of classification probability within a multidimensional percentile grid is performed only once, which significantly increases the numerical performance of the VEOR classification. Ultimately, compaction of LUV involves the removal of entries that are redundant from the perspective of a binary search algorithm. The LUV with ID is sorted, and therefore if several consecutive ID have exactly the same classification probability (which happens often if the classes are clearly separable), then only the IDs at the sequence edges are required for the binary search algorithm. This procedure reduces the number of entries within LUV several times, which further reduces the computational effort of the VEOR classification process. Figure 3: The VEOR algorithm workflow.
## 3 Methodology
### Data Preprocessing
#### 3.1.2 Sentinel-2 Images  
On Sentinel-2 L1C images, the Sen2 Cor algorithm was first applied for atmosphere calibration to generate bottom-of-atmosphere data where images acquired over time and space shared the same reflectance scale, thereby enhancing crop mapping when monitoring large-scale areas over time [34]. Four spectral bands (bands 2, 3, 4, and 8) with a spatial resolution of 10 m and six spectral bands (bands 5, 6, 8A, 11, and 12) with a spatial resolution of 20 m were produced for each Sentinel-2 image.  
A quality scene classification (SLC) band, which labels pixels obscured by clouds and shadows, was created using the Sen2 Cor algorithm in the meantime. Misclassification in the SLC band was further corrected through expert visual interpretation, particularly at the cloud and shadow edges. Then, the SLC band was reclassified into a binary classification to generate a final masking band, one indicating clean pixels and the other for contaminated pixels (including cloudy and shadowy regions and no data value regions). Finally, masked images were generated by overlaying the masking band on Sentinel-2 images and setting the spectral reflectance values of pixels in masked regions to a default masking value (0 in our experiments).
## 9 Conclusions and Prospect  
### Conclusions  
The existing literature on machine learning design pattern detection largely adopts traditional machine learning algorithms like the KNN, decision trees, ANN, SVM, and logistic regression. No scholarly attempt has been made to use deep learning to recognize design patterns. It is a daunting task to find the most suitable and effective features to address the design pattern detection problem. Deep learning is mainly suitable for locally correlated, dense continuous data such as images, texts, and voices, and cannot be directly applied to address the design pattern detection problem. In this research project, a colored UML model is proposed by adding colors, line types, and extending shapes for the purpose of extending graph information with elements like the traditional UML classes, operations, the relationship between classes, and call relationship between methods, thereby converting the design pattern detection problem into a graph recognition problem. On the basis of converting positive and negative samples of collected instances and the system to be recognized into graph-style colored UML model, this paper leverages deep learning technology with advantages like powerful automatic feature extraction in combination with the SVM excelling at binary classification problems to realize the end-to-end design pattern detection, producing more favorable detection effect than other methods.  
Compared with other methods, the biggest contribution of this paper is to transform the design pattern detection problem into an image classification problem and realize end-to-end design pattern detection based on deep learning technology. This provides a completely new and effective way of thinking and direction for the design pattern detection problem.
## 3 Results and Discussion
### Comparison of Volatile Components of Rice Grains Stored under Different Conditions

Table 2: _Cont._Partial Least Squares Discriminant Analysis (PLS-DA) is a supervised discriminant analysis method that establishes a relationship model between metabolite expression and sample categories through Partial Least Squares regression [40]. It is used to identify and classify patterns or differences between groups in a dataset. PLS-DA is particularly useful when analyzing high-dimensional data where the number of variables is larger than the number of samples. It has applications in various fields, including metabolomics, chemometrics, and bioinformatics. To identify the specific volatile components in rice grains under various storage conditions, we performed a PLS-DA analysis on these components. The score plot showed that the similarity of the six experimental samples was within the 95% confidence interval (Figure A1). In this model, R2 (X) = 0.651, R2 (Y) = 0.979, and Q2 = 0.908, indicating that this model has the advantages of reliability, stability, and good performance. In addition, the rice grain samples were significantly distinguished according to storage conditions in the score plot (Figure A1). A total of 35 characteristic volatile components were detected among three sample groups (Figure A2). Among these volatile compounds, 2,6,10-trimethyl-dodecane (RA: 7.94 \(\pm\) 1.74 \(\upmu\)g/kg, RB: 8.30 \(\pm\) 1.93 \(\upmu\)g/kg, RC: 22.25 \(\pm\) 1.96 \(\upmu\)g/kg), tetradecane (RA: 14.71 \(\pm\) 1.09 \(\upmu\)g/kg, RB: 8.30 \(\pm\) 1.93 \(\upmu\)g/kg, RC: 22.25 \(\pm\) 1.96 \(\upmu\)g/kg), and 2-methyl-tridecane (RA: 9.97 \(\pm\) 0.86 \(\upmu\)g/kg, RB: 12.58 \(\pm\) 2.74 \(\upmu\)g/kg, RC: 11.77 \(\pm\) 1.35 \(\upmu\)g/kg) were detected in the rice grains stored under three different storage conditions, with varying concentrations (Table A1 and Figure A2).
## 4 Implementation and Result
### System Operation Verification
The performance of the system was significantly improved after applying a neural network model additionally trained according to the transfer learning technique. The weights of the convolutional base remained unchanged, only the fully-connected layer was additionally fine-tuned. Despite the small learning dataset used for transfer learning, classification accuracy increased to almost 30% when only one form was indicated and 60% when three forms were indicated, meaning that among the correct decisions of the system, more than half were already indicated as the first verification hypothesis (the first AM form). Errors in victim verification were most common for people for whom a small number of photos were attached (one or two photos in the PM form). The indicated mistaken identity hypotheses, however, also pointed to visually similar people--same skin color, hair color, similar hairstyle. Errors were observed more frequently in the identification of women compared to men, potentially due to challenges posed by variations in hairstyles and the presence or absence of makeup. Additionally, the model demonstrated reduced accuracy when recognizing non-white individuals, which can be attributed to the significant overrepresentation of white subjects in the LFW dataset used for training. These findings highlight critical limitations in the model, underscoring the need for further fine-tuning and the incorporation of more diverse and representative training data to enhance performance and mitigate biases. The conducted tests of the neural network module confirmed the effectiveness of the proposed concept of applying deep metric learning and utilizing transfer learning for model fine-tuning. These methods significantly improved the system's accuracy and demonstrated the feasibility of our approach in the context of disaster victim identification. In the final implementation of the solution, cyclic fine-tuning based on real-world data (from real-world disasters) is envisaged to continuously improve the performance of the model. This iterative approach will allow the system to adapt to the diversity and variability of real-world conditions, ensuring its continuous improvement and reliability in operational scenarios.
# Potential Imaging Capability of Optical Coherence Tomography as Dental Optical Probe: A Mini-Review
## 2 Search Strategy  
Online searches were performed on PubMed and SPIE digital library databases. The combinations of search terms were constructed from the following: \"optical coherence tomography\", \"OCT\", \"SD-OCT\", \"SS-OCT\", \"dental\", \"periodontal\", \"peri-implant\", \"gingiva\" and \"subgingival\". This was followed by a manual screening of references listed in relevant studies. Due to explorative purposes and limited number of relevant reports, all of the studies that attempted to visualize the subgingival area using OCT regardless of the study design were reviewed. As a result, 12 studies investigated the visibility of periodontal tissue, peri-implant condition, and marginal evaluation of dental prosthesis were reviewed, including in vitro, in vivo, and ex vivo studies performed in animal and human subjects.
## 2 Materials and Methods
### Fibers and Data Acquisition
#### 2.1.2 Category 2: Ge-Doped Fiber (FS)  
Category 2 FBGs differ from Category 1 FBGs in their inscription methods. UV FBGs are inscribed with the Type I method and rely on the photosensitivity of the Ge-dopant to cause modulations to the refractive index of the silica fiber. Category 2 FBGs are inscribed with the Type II method which is based on inducing structural changes in the silica matrix by means of femtosecond (FS) laser pulses in order to achieve a change in the refractive index [17]. Instead of a nanosecond or continuous wave inscription laser operating in the UV spectrum, FS gratings are inscribed with an infrared laser operating (\(\lambda\) = -800 nm) typically using pulse durations in the range of 50-500 femtoseconds. High light intensity levels are thus employed over short durations to achieve the controlled damage. FS FBGs can  
Figure 1: Fiber Bragg Grating (FBG) operating principle. For details, see text.  
maintain their optical integrity up to 1000 \({}^{\circ}\)C and can be inscribed in a variety of fiber types including common single-mode telecom fibers. As the formation of FS FBGs does not rely on the presence of the Ge-dopant, their sensitivity to ionizing radiation is weaker compared to UV FBGs [18].
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 10 Conclusions  
In conclusion, we have overviewed beam forming and steering with integrated OPAs. Principles, building blocks, and configurations are discussed. Integrated OPAs have been implemented on various platforms, mainly including Si photonics platforms, III-V platforms, and III-V/Si hybrid platforms. In terms of employed wavelengths, OPAs can be  
Figure 16: Potential alternatives of integrated OPAs. (**a**) A lens-based integrated 2D beam-steering device. Reprinted with permission from [157] © The Optical Society. (**b**) A beam switching optical scanner. Reprinted with permission from [158] © The Optical Society.  
used for different applications. Some important performance parameters are discussed in detail, such as FOV, beamwidth, sidelobe suppression, modulation speed, power consumption, and scalability. Some typical applications of integrated OPAs have been given, together with potential alternatives.  
Conceptualization, Y.G. ([PERSON]) and L.Z.; investigation, Y.G. ([PERSON]), Y.G. ([PERSON]), C.L., H.Z. and X.Z.; writing--original draft preparation, Y.G. ([PERSON]); writing--review and editing, X.Z., L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
The work was supported by National Key R&D Program of China under grant 2019 YFB2203902 and National Natural Science Foundation of China (NSFC) under Grant 61775164 and 61775165.  
We acknowledge support by the Advanced Integrated Optoelectronics Facility at Tianjin University.  
The authors declare no conflict of interest.
## 4 Algorithm Overview
### Lower-Level Decision  
#### 4.2.1 Emergency Vehicle Process Overview  
The EV receives updated traffic signal times and route priorities from the Traffic Management Center (TMC). EVs use this information to evaluate routes considering updated signal times, such as dedicated routes or green waves. The aim is to select routes that minimize travel time and maximize the use of priority routes. Once the optimal route is determined, the EV communicates it to the TMC for coordination and traffic-flow optimization.  
_Objective for EVs_: Maximize the utility function using Equation (2).  
\[\text{U}_{\text{EV}}=-\delta\times(\text{Delay}_{\text{EV}})+\zeta\times(\text {Traffic Rerouting Efficiency}) \tag{2}\]  
where:  
* \(\delta\) is the weight for minimizing the emergency vehicle's delay.
* \(\zeta\) captures the effectiveness of rerouting regular vehicles to clear the path for the EV.
## 3 Analysis Results
### Spatial Pattern of Community in IMF Networks  
From the spatial pattern of degree distribution of IMF networks, we could confirm the presence of some hub-based clusters (communities); however, there was no spatial information on nodes with low degrees. Nodes with low degrees also make a community. In this section, we explore the community structure of IMF networks (Figure 15); the hub-based clusters seen in Figure 9 are well consistent with the communities at the same location. For low-mode IMF networks (IMF-1 through IMF-5), the regions, such as EKB and SPF, characterized by high IE levels (Figure 4), are distinctly indiscernible. However, as the mode increases, those regions are clearly discernible by the community, especially for IMF-6 through IMF-8. Particularly in both the IMF-7 and IMF-8 networks, there appears to be a connection between the northern end channel of the EJS and the ESIW formation site. More interestingly, the pathway from the northern origin of ESIW to its formation site [31] is consistent with the 4 th community structure (IMF-8 network in Figure 15). Note that IMF-8 has a biennial timescale (2-3 years). There is another suggestion for the ESIW formation by [PERSON] and [PERSON] [49], in which the ESIW is formed from the flowing-in waters through the KTS. The first community of the IMF-8 network is consistent with the route determined by the drifter trajectories presented in [PERSON] and [PERSON] [49]. Further, it is noteworthy that the 7 th community of the IMF-7 network in Figure 15 shows a remote-connection feature.  
The seasonality of community structures in IMF networks is shown in Figures 16-19. The first characteristic is that a small number of communities are formed in IMF-4 and IMF-5 networks only during summer (Figure 17), compared to other seasons. At those modes, the southern part of EJS has a large cluster of hubs (Figure 11). At IMF-8 networks (Figures 16-19), the community structure relating to the pathway between the ESIW formation site and its origins seems to be robustly preserved for all seasons, except for winter for the northern origin, and spring for the southern origin. The high IE spatial patterns of IMF-7 through IMF-9 over the tongue-shaped region from the EKB extending towards the SPF (Figures 3-7) are almost consistent with the community structure of the IMF-7 and IMF-8 networks only in spring. The community structure along the coasts also shows strong seasonality. As mentioned in Section 2.4.2, it should be noted that the community structure could be related to the causal range of any forcing with a timescale corresponding to a given IMF.  
Figure 14: Assortativity of IMF networks with respect to mode number.  
Figure 16: The same as Figure 15, except for spring.  
Figure 15: The map of communities of IMF networks with respect to the IMF mode number.  
Figure 17: The same as Figure 15, except for summer.  
Figure 18: The same as Figure 15, except for autumn.
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## Appendix A.2.4 Feature Recognition
### Inflation Layer Details  
The first layer height (\(h_{1}\)) is calculated to position the first cell within the viscous sublayer, ensuring precise wall shear stress and velocity gradient calculations. The height of the viscous sublayer is approximated for a flat plate using the Schlichting skin-friction correlation (Equation (A2)) for wall shear stress \(\tau_{w}\), which is valid for Reynolds numbers \(Re<10^{9}\), according to [PERSON] and [PERSON] [44].  
\[h_{1}=\frac{y^{+}\mu_{\infty}}{\rho_{\infty}u_{*}},\;\;\textit{where}\;\;\;\; \;u_{*}=\sqrt{\frac{C_{f}\frac{1}{2}\rho_{\infty}u_{\infty}^{2}}{\rho}}\] (A2)  
Note that, given the fact that flows over airfoils and wings encounter pressure gradients, the boundary layer thickness will vary in regard to the theoretical flat plate (zero pressure gradient) value. Therefore, the real height of the viscous sublayer can only be determined after the execution of a simulation. Thus, it can be used to check the validity of a given simulation, as discussed in more detail in Section 4.  
Another important aspect for the inflation layer is that its total height was designed to be larger than the total height of the boundary layer. This choice is guided by a well-established rationale [44; 48], ensuring a sufficient number of cells within this critical region for accurate simulation. The total boundary layer height (\(\delta\)) can be approximated using Equation (A3) according to [37]. This relationship applies only for a fully turbulent flow over a flat plate. However, it can serve as a reliable indication, given that a turbulent boundary layer is thicker than its laminar counterpart (e.g., provided that the pressure gradients are identical).  
\[\delta=0.37\cdot Re^{-1/\delta}\cdot\vec{c}\] (A3)
# A Review on Potential Antimutagenic Plants of Saudi Arabia
## 7 Which Are the Medicinal Plants of Saudi Arabia That Might Demonstrate Anti-Mutagenesis?
### Cleome droserifolia  
According to literature, the _Cleome_([PERSON], 1977) genus is the largest member of the family Cleomaceae. The plant is widely distributed in the dry and semi-arid regions of Saudi Arabia and other Middle Eastern countries. The plant is a perennial, aromatic shrub, popular among several tribes of the region [96]. Traditionally, the plant is used for the treatment of stomach aches, skin allergies and open wounds. Several important phytochemicals have been isolated and characterized. The plant is reported to contain high concentrations of flavonoids, terpenes, glucosinolates, anthocyanin alkaloids and polyphenols. Pharmacological screening indicated that extracts of the herb possess anti-diabetic, antioxidant, hepatoprotective and anti-cancer properties. These activities are linked to the phyto-components present in the plant. In vitro DPPH and ABTS tests were carried out to determine the antioxidant property. The important constituents identified were rutin, naringenin, kaempferol and p-coumaric acid [97].