esa-sceva/satcom-chunk-collection huggingface dataset:
# Elastoplastic Constitutive Model for Energy Dissipation and Crack Evolution in Rocks
## 4 Construction and Algorithm Implementation of the Theoretical Model
\begin{table}
\begin{tabular}{c c} \hline
**Model Parameter** & **Parameter Equation** \\ \hline Poisson\({}^{\prime}\)s ratio \(\mu\) & Equation (2) \\ Elastic modulus \(E\) & Equation (2) \\ Yield function \(f\) & Equation (3) \\ Internal friction angle \(\varphi\) & Equation (3) \\ Cohesion \(c\) & Equation (3) \\ Plastic shear strain \(\gamma_{p}\) & Equation (8) \\ Potential function \(g\) & Equation (9) \\ Dilatancy angle \(\psi\) & Equation (9) \\ Elastic strain energy stored during the deviatoric loading phase \(U^{e}-U_{0}\) & Equations (20) and (22) \\ Axial and circumferential work \(U_{1}+U_{3}\) & Equations (23) and (24) \\ Dissipated energy \(U^{d}\) & Equation (25) \\ Volumetric strain \(\varepsilon_{V}\) & Equation (27) \\ Elastic volumetric strain \(\varepsilon_{V}^{e}\) & Equation (28) \\ \hline \end{tabular}
\end{table} 
Table 1: Theoretical elastoplastic constitutive model. Substituting Equations (11) and (31) into Equation (39), we obtain the following:  
\[ \begin{cases}\sigma_{1}^{N}=\sigma_{1}^{I}-\lambda\big{(}\alpha_{1}-\alpha_{2}N_ {\psi}\big{)}\\ \sigma_{2}^{N}=\sigma_{2}^{I}-\lambda\big{(}\alpha_{2}-\alpha_{2}N_{\psi}\big{)} \\ \sigma_{3}^{N}=\sigma_{3}^{I}-\lambda\big{(}\alpha_{2}-\alpha_{1}N_{\psi}\big{)} \end{cases}  \tag{40}\]  
At this point, \(\sigma_{n}^{N}\) is on the Mohr-Coulomb yield surface, and according to Equation (3):  
\[f\Big{(}\sigma_{n}^{N}\Big{)}=0 \tag{41}\]  
Equation (39) can be written as the complete differential form of the yield function \(f\):  
\[f\Big{(}\sigma_{n}^{I}\Big{)}-f\Big{(}\sigma_{n}^{N}\Big{)}=\frac{\partial f} {\partial\sigma_{n}}\mathrm{S}_{n}\Big{(}\lambda\frac{\partial g}{\partial \sigma_{i}}\Big{)} \tag{42}\]  
Substituting Equations (3), (11), (31), and (41) into Equation (42), we obtain the plastic multiplier \(\lambda\):  
\[\lambda=\frac{f\big{(}\sigma_{n}^{I}\big{)}-f\big{(}\sigma_{n}^{N}\big{)}}{f \Big{[}\mathrm{S}_{n}\Big{(}\frac{\partial g}{\partial\sigma_{i}}\Big{)} \Big{]}-f(0)}=\frac{f\big{(}\sigma_{n}^{I}\big{)}}{\big{(}\alpha_{1}-\alpha_{2 }N_{\psi}\big{)}+\big{(}\alpha_{1}N_{\psi}-\alpha_{2}\big{)}N_{\psi}} \tag{43}\]  
Thirdly, the elastic strain energy stored during the deviatoric loading phase \(U^{e}-U_{0}\), the axial and circumferential work \(U_{1}+U_{3}\), and the dissipated energy \(U^{d}\) are calculated according to Equations (20) and (22)-(25). The volumetric strain \(\varepsilon_{V}\), elastic volumetric strain \(\varepsilon_{V}^{e}\), and crack volumetric strain \(\varepsilon_{V}^{e}\) are determined according to Equations (27)-(29). The detailed computational workflow of the theoretical model is illustrated in Figure 2.  
Figure 2: Detailed computational workflow of the theoretical model in FLAC\({}^{3\text{D}}\).
## 3 Materials and Methods
### Shadow Compensation Model Based on Wallis Filter and LCC Model
\[g^{c}(i,j)=\overline{m}_{\text{NSD}}+(g(i,j)-\overline{m}_{\text{SD}}).\frac{ \sigma_{\text{NSD}}}{\sigma_{\text{SD}}} \tag{10}\]  
Thus, in this study, a new shadow compensation model based on Wallis filter and LCC model was designed by acquiring the target value from the non-shadow area adjacent to the shadow area and adding an intensity coefficient \(\alpha\) and a stretching coefficient \(\beta\), as shown in Equation (11):  
\[g^{c}(i,j)=\alpha\Bigg{(}\overline{m}_{\text{NSD}}+\frac{g(i,j)-\overline{m}_{ \text{SD}}}{\beta\cdot r_{1}}-r_{0}\cdot r_{1}\Bigg{)} \tag{11}\]  
where \(\alpha\) represents the compensation intensity coefficient, \(\beta\) represents the stretching coefficient, \(\overline{m}_{\text{NSD}}\) and \(\overline{m}_{\text{SD}}\) represent the mean values of the Non-Shadow area (NSD) and the Shadow area (SD), \(r_{1}\) and \(r_{0}\) are the multiplicative coefficient and the additive coefficient in the Wallis model. It is worth noting that this model can capture the target information from NSD areas more precisely as well as increase the average value and gradients of the shadow component more precisely. \(\alpha\) is more useful for reinforcing the average brightness, while \(\beta\) is effective for enhancing the average gradient (contrast). With the help of the specific parameters, the compensation model can be more effective in heightening the information in brightness and contrast.
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 5 Performance Evaluations
### Results and Discussion
#### 5.2.3 E2E-Delay Calculation  
In order to reduce the delay, this paper developed a cross-layer approach in our proposed work, which is a combination of GSO-based clustering and cooperative MAC. Both ideas were used to reduce the delay by reducing the energy consumption and network congestion. The graphical representation for the calculation of the E2E delay is shown below. From the results, it was proven that using the technique involved in the proposed. We achieved a very low E2E delay when compared with the earlier works. Figure 10 represents the calculation of the E2E delay of the CLEA-AODV protocol and the other comparative works. In our protocol, due to the usage of the MAC model in the cross-layer approach, the delay of the network was greatly reduced when compared with the other  
Figure 8: The TH with time.  
Figure 9: The TH with the number of UAVs.  
works. Here, the proposed work produced an overall delay of up to 186 ms, whereas the earlier AODV, SOC-GSO, and EENFC-MRP protocols reached 281 ms, 256 ms, and 231 ms, respectively.  
Figure 11 represents the E2E delay calculation of the network with various scenarios in terms of the number of UAVs. The delay produced by the CLEA-AODV protocol was very low in all scenarios such as 30, 60, 90, 120, 150, and 180 UAVs compared with the earlier approaches. The delay produced by the produced work ranged from 25 to 45 ms on average in the proposed work, whereas the earlier works ranged from 45 ms to 55 ms, respectively.
# Machine Learning-Assisted Secure Random Communication System  
###### Abstract  
Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random communication system (ML-RCS). It comprises a pretrained decision tree (DT)-based receiver that extracts binary information from the transmitted random noise carrier signals. The ML-RCS employs skewed alpha-stable (\(\alpha\)-stable) noise as a random carrier to encode the incoming binary bits securely. The DT model is pretrained on an extensively developed dataset encompassing all the selected parameter combinations to generate and detect the \(\alpha\)-stable noise signals. The legitimate receiver leverages the pretrained DT and a predetermined key, specifically the pulse length of a single binary information bit, to securely decode the hidden binary bits. The performance evaluations included the single-bit transmission, confusion matrices, and a bit error rate (BER) analysis via Monte Carlo simulations. The fact that the BER reached \(10^{-3}\) confirms the ability of the proposed system to establish successful secure communication between a transmitter and legitimate receiver. Additionally, the ML-RCS provides an increased data rate compared to previous random communication systems. From the perspective of security, the confusion matrices and computed false negative rate of 50.2% demonstrate the failure of an eavesdropper to decode the binary bits without access to the predetermined key and the private dataset. These findings highlight the potential ability of unconventional ML-RCSs to promote the development of secure next-generation communication devices with built-in PLSs.  
machine learning; \(\alpha\)-stable distributions; decision tree; covert communication; random communication system +
Footnote †: footnote]Footnote : [[https://doi.org/10.3390/e27080815](https://doi.org/10.3390/e27080815)]([https://doi.org/10.3390/e27080815](https://doi.org/10.3390/e27080815))
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 4 Resource Allocation  
Lately, resource management literature has reported diverse NSL resource allocation approaches. Those approaches have applied techniques such as linear programming [139; 140; 141; 142], (meta)heuristics [143; 144; 145], and game theory [147; 10; 148] to assign resources to slices in environments involving mainly a unique tenant. Figure 4 presents an architecture using RL and DRL to make resource allocation decisions in a multitenant environment. This architecture operates when the resource allocation module allocates RAN, CN, and edge network resources to preadmitted NSLRs (received from the admission control module) by employing a RL/DRL agent, a RAN resource allocator, an edge network resource allocator, and a CN resource allocator. The agent determines normalized weight values for each preadmitted NSLR belonging to a 5G/6G use case. Those values determine the allocation priority and the number of resources to assign per preadmitted NSLR and should lead to achieving a goal, for instance, obtain the minimum delay and maximum reliability. For example, in the raised example, the agent selects an action that, if taken, minimizes the delay and maximizes the reliability. The agent learns to select actions that decrease delay and increase reliability by considering the information on states and rewards from interaction with the environment by using, for example, policy gradient or DDQN. In the example, the allocators use the agent's outputs to assign resources according to the priority and resources available in the network substrate.
# Optimization of DG-LRG Water Extraction Algorithm Considering Polarization and Texture Information
## 3 Optimized Local Region Growth Algorithm Considering Polarization and Texture Information
### Optimization of Image Segmentation Results  
The results obtained above take into account the characteristics of GF-3 polarization and water texture information, which can effectively reduce the impact of noise on the results and also reduce false extraction situations (e.g., misclassification of smooth road surfaces, mountain shadows, and airport runways). However, in flooded areas, the water environment is relatively complex. Extreme weather is often accompanied by high wind speeds, and the submerged vegetation, crops, and turbidity in a water body can increase its surface roughness, affecting the back-scatter coefficients of radar signals. This can lead to isolated pixels in the water extraction results, resulting in burrs and voids [23]. In addition, although the DG-LRG method proposed in this article can reduce the impact of mountain shadows to some extent, it is difficult to distinguish between these shadows and water bodies located in a mountainous area, especially when they are connected in an image. Based on this, morphological methods and external DEM data were introduced in the experiment, in order to optimize the water extraction results.  
Morphology defines four basic operations: expansion, corrosion, opening, and closing. Assuming that the structural element is A and the target image is B, common edge detection operators are defined as follows:  
\[\mathrm{A}\odot\mathrm{B}=(\mathrm{A}\odot\mathrm{B})\oplus\mathrm{B}, \tag{14}\]  
\[\mathrm{A}\bullet\mathrm{B}=(\mathrm{A}\oplus\mathrm{B})\odot\mathrm{B} \tag{15}\]  
where A\(\odot\)B is an open operation, A\(\bullet\)B is a closed operation, \(\oplus\) is an expansion operation, and \(\oplus\) is a corrosion operation. In order to remove isolated pixels from the water extraction results and fill in voids, we used an open-then-close operation to optimize the results.  
For the mountain shadow areas that still exist in the water extraction results, external DEM data are used as auxiliary information for their removal. The specific method is to register the SAR images with DEM data, calculate the radar signal overlay shadow area based on the satellite incidence angle and flight azimuth angle, as well as the slope and aspect generated by the DEM, and define the slope threshold. Areas with slope values greater than \(15^{\circ}\) are defined as the final mountain shadow areas, Filtering out this portion of pixels from the extracted water pixels yields the optimized water extraction result.
## 2 The Development of Typical Rotating Motion Mechanisms
### Belt Drive
#### 2.2.2 Synchronous Belt

Table 4: Comparison of the performance of different types of belt drives. Figure 26: Basic configuration of a trapezoidal-toothed synchronous belt without matrix (The blue arrow represents the two-dimensional coordinate system). Traditional V-belt transmission has advantages such as strong vibration absorption capacity, large transmission ratio, overload protection, and high cost-effectiveness. However, it has disadvantages such as low reliability and a short lifespan. It is widely used in high load scenarios such as automotive and marine engines. The improved intelligent friction electric V-belt (TVB) achieves real-time status monitoring by integrating friction nanogenerators, significantly improving reliability and lifespan. However, it is limited by humid and hot environments and is suitable for conveyor belts and small transmission systems that require long term monitoring. In synchronous belt transmission, traditional noncircular synchronous belts can achieve non-uniform motion, but they are prone to relaxation and have poor stability, making them suitable for non-uniform transmission machinery. The new type of nonlinear synchronous belt improves the relaxation problem through a tension wheel compensation device, with high transmission stability and resistance to low lubrication environments. However, its complex structure requires additional tension wheels, making it suitable for long-distance, precise variable speed transmission. The 3D printing synchronous belt adopts moldless moulding technology, with high design freedom and support for rapid customisation. However, due to material strength and process defects (such as insufficient durability of PLA materials), it is only suitable for low power, non-high-strength scenarios such as educational robots. Belt drive has significant advantages in cost-effectiveness, flexible design, and environmental adaptability. However, it must still combine material and structural innovation to improve performance boundaries under high dynamic loads and long term, high-intensity working conditions.
## 2 Seawater Channel Modelling Elements
### Inherent Optical Properties  
Water's optical properties can be categorised into inherent and apparent ones. IOPs refer to water's optical properties that are entirely dependent on the water itself, without regard for external influences such as light source characterisation. The two primary IOPs are the absorption coefficient and the VSF, from which the scattering coefficient will be determined. Other IOPs include the index of refraction, the beam attenuation coefficient, and the single-scattering albedo. AOPs are the optical properties of water as observed from outside. The transmission medium and external factors such as illumination, water depth, bottom reflectance, and surface waves influence AOP. They exhibit enough regularity and stability to be suitable forms of the water body [10].  
If a collimated single-mode optical beam at wavelength \(\lambda\) with spectral radiant power \(P_{i}(\lambda)\) (W/nm) travels through a small volume \(\Delta V\) of water and thickness \(\Delta r\), as schematically shown in Figure 4. Some parts of the optical power \(P_{a}(\lambda)\) of \(P_{i}(\lambda)\) is absorbed within the \(\Delta V\). Other parts of \(P_{s}(\lambda,\,\theta,\Phi)\) are scattered out of the beam at any probable polar angle \(\theta\) and azimuth angle \(\Phi\), where these two angles define the new radial direction \(r\). The remaining power \(P_{t}(\lambda)\) is transmitted through the volume with no change in direction. Hence, \(P_{t}(\lambda)\) is equivalent to no events. Equation (4) does not specify the no-event. Furthermore, the primary assumption is that no inelastic scattering occurs, implying no wavelength broadening. Hence, by the conservation of energy [66],  
\[P_{i}(\lambda)=P_{a}(\lambda)+P_{s}(\lambda,\,\theta)+P_{t}(\lambda)\qquad \qquad\text{no inelastic scattering} \tag{6}\]  
From Equation (6) and Figure 3, we can conclude the following mathematical definitions of the spectral scattering coefficients in Equation (1):  
\[a(\lambda)=\lim_{\Delta r\to 0}\left[\begin{array}{c}\frac{P_{a}(\lambda)}{ \Delta r\ P_{i}(\lambda)}\\ \end{array}\right]\qquad\qquad\text{in }m^{-1} \tag{7}\]  
Figure 4: The geometrical representation of IOP impairments within a water volume of length \(\Delta r\) and \(\Delta V\) width, incident optical power \(P_{i}(\lambda)\), absorbed optical power \(P_{a}(\lambda)\), scattered optical power \(P_{s}(\lambda,\,\theta)\), and transmitted optical power \(P_{t}(\lambda)\).  
\[b(\lambda)=\lim_{\Delta r\to 0}\left[\begin{array}{c}P_{\text{s}}(\lambda,\ \theta)\\ \Delta r\;P_{\text{i}}(\lambda)\end{array}\right]\qquad\qquad\text{in }\ m^{-1}\ \text{at polar angle }\theta \tag{8}\]  
Similarly, we can conclude the transmittance coefficient as follows:  
\[t(\lambda)=\lim_{\Delta r\to 0}\left[\begin{array}{c}P_{\text{i}}(\lambda)\\ \Delta r\;P_{\text{i}}(\lambda)\end{array}\right]\qquad\qquad\qquad\qquad\text {in }\ m^{-1} \tag{9}\]  
In general, the IOP identifies the optical properties of water channels in a mathematical form appropriate to the RTE needs. IOP's impairment set includes the attenuation due to absorption and scattering Equation (1).
# PreNNsem: A Heterogeneous Ensemble Learning Framework for Vulnerability Detection in Software
## 2 Related Work
### Prior Similar Studies  
Pattern-based approach. [PERSON] et al. [13] generated vectors from code gadgets using Word2 vec, like us. They used Recurrent Neural Network (RNN)-based deep learning and SoftMax for learning classification. [PERSON] et al. [28] also used RNN for learning high-level representations of abstract syntax trees (ASTs). [PERSON] et al. [29] extracted semantic features while using code property graph (CPG), obtaining feature matrices by encoding the CPG. Finally, they used attention neural networks for learning classification. [PERSON] et al. [30] proposed a deep-learning-based framework with the capability of leveraging multiple heterogeneous vulnerability-relevant data sources for effectively learning latent vulnerable programming patterns.  
Similarity-based approach. [PERSON] et al. [31] used a Siamese network to identify the similarity and deep learning architectures to classify the domain name. [PERSON] et al. [32] encoded code control flow and data flow into a semantic matrix. They designed a new deep learning model that measures code functional similarity that is based on this representation. [PERSON], [PERSON] et al. [33] used a novel program slicing to extract vulnerability and patch signatures from the vulnerability function and its patched function at the syntactic and semantic levels. Subsequently, a target function was identified as potentially vulnerable if it matched the vulnerability signature but did not match the patch signature. [PERSON], [PERSON] et al. [34] examined the effectiveness of graph neural networks for estimating program similarity by analyzing the associated control flow graphs. In [35], they built a graph representation of programs called flow-augmented abstract syntax tree (FA-AST) and applied two different types of graph neural networks (GNNs) on FA-AST to measure the similarity of code pairs.  
When compared with any pattern-based approach, the similarity-based approach is sufficient for detecting the same vulnerability in target programs. However, it cannot detect vulnerabilities in some code clones, including deletion, insertion, and rearrangement of statements. PreNNsem is categorized as a pattern-based approach to vulnerability detection. The existing pattern-based approaches have two problems: first, the extracted information's granularity is rough; second, the data set used to learn vulnerability patterns is insufficient. In contrast to the studies reviewed above, PreNNsem has two advantages: first, it directly extracts features from code granularity in order to avoid the loss of information during feature abstraction. Second, by expanding the corpus, it can learn from common programming patterns and improve generalization capabilities.
## 1 Introduction
### The Magellanic Pulsars SXP 15.3 and SXP 305  
The Small Magellanic Cloud (SMC) is a nearby dwarf galaxy that is home to more than 100 HMXBs [25], about 70 of which are X-ray pulsars, and all but one are Be-XRBs [21]. The low metallicity of the SMC makes it a great environment for forming massive stars and studying their life cycles [26]. With a plethora of known HMXBs, the SMC offers unique opportunities to understand how these systems evolve. Be X-ray binaries in the SMC are particularly interesting because the distance (\(\sim\)62 kpc [27]) and properties of the galaxy are well-known, allowing precise measurements of their properties.  
In this work, we address the difficult problem of resolving point sources in _NuSTAR_ X-ray images, especially when there is no other major distinguishable spectral or timing feature, and the observer has to rely entirely on the imaging capability of the telescope. The particular case that we explore concerns the closely spaced pair of SXP 15.3 and SXP 305 [28; 29]. These two pulsars have widely different spin periods, and their X-ray outbursts allow us to study the extreme conditions that develop near their accreting NSs.  
SXP 15.3 [30; 31; 32] and SXP 305 [29] are both Be-HMXB pulsars [21; 26], and they are separated by only 7\({}^{\prime\prime}\) in the sky (see Figure 1 of [PERSON] et al. [29]). [PERSON] et al. [28] determined the pulse period of SXP 305 for the first time using a _NuSTAR_ observation (also used in this work), but the _NuSTAR_ image did not resolve the pair as the projected separation of the two sources is much smaller than the _NuSTAR_ HPD of 58\({}^{\prime\prime}\). The study of systems like SXP 15.3 and SXP 305 shows how important yet challenging it can be to distinguish closely residing X-ray sources. However, monitoring, detecting, and understanding these systems is crucial for gaining a broader understanding of how neutron stars interact with their companion stars, and to keep track of how they evolve.