esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Simulation Results
#### 5.3.3 Case III: Link Failure with a High-Capacity Backup Path  
Here, we configure the SCN \(\mathcal{N}\) in such a way that there exists a backup path that has sufficient capacity to support the data rate generated at source node \(v_{1}\). Like the last  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Application & UDP (Packet Size = 50 KB, Data rate = 10 Kbps) \\ Simulation start at (s) & 0.001 \\ Simulation stop at (s) & 300 \\ Application start at (s) & 1 \\ Application stop at (s) & 299 \\ Point-to-point link fails at (s) & 50 \\ Point-to-point link restores at (s) & 110 \\ Point-to-point link fails at (s) & 140 \\ Point-to-point link restores at (s) & 210 \\ Point-to-point link fails at (s) & 240 \\ Point-to-point link restores at (s) & 280 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Some of the key simulation parameters for the SCN.  
case study, the backup path is considered to be through the Internet, which is modelled using network \(\mathcal{N}_{2}\). Node \(v_{1}\) is connected to the Internet through network \(\mathcal{N}_{3}\). Node \(v_{6}\) is connected to the Internet through network \(\mathcal{N}_{4}\). Thus, the backup path comprises networks \(\mathcal{N}_{2}\), \(\mathcal{N}_{3}\), and \(\mathcal{N}_{4}\). To configure a high-capacity backup path, networks \(\mathcal{N}_{2}\), \(\mathcal{N}_{3}\), and \(\mathcal{N}_{4}\) are configured with data rates \(R_{\mathcal{N}_{2}}\), \(R_{\mathcal{N}_{3}}\), and \(R_{\mathcal{N}_{4}}\), respectively, such that \(min\{R_{\mathcal{N}_{2}}\), \(R_{\mathcal{N}_{3}}\), \(R_{\mathcal{N}_{4}}\}\geq 2R_{v_{1}}\), where \(R_{v_{1}}\) is the data rate at \(v_{1}\). In particular, the bandwidth of the channel belonging to networks \(\mathcal{N}_{1}\), \(\mathcal{N}_{2}\), \(\mathcal{N}_{3}\), and \(\mathcal{N}_{5}\) is considered to be 2 Mbps. The bandwidth of network \(\mathcal{N}_{4}\) is also considered to be 2 Mbps. However, its effective available bandwidth is variable, subject to the applied background traffic. To elaborate, the effective available bandwidth for the PMU data rate becomes only 1 Mbps when network \(\mathcal{N}_{4}\) is subjected to 50% background traffic. In a nutshell, PointToPointHelper and Csma-Helper syntax in ns-3 are used to configure different attributes related to the dedicated and shared CSMA channels, respectively. After configuring all network parameters, network \(\mathcal{N}_{1}\) is disabled for 240 s to 280 s. Moreover, the SCN is simulated for 300 \(s\).
## 2 The Proposed Method
### Automatic Template Extraction
#### 2.1.1 Template Size Estimation
Here, \(c_{i}=(x_{i},y_{i})\) denotes the center of the \(i\)-th object, and \(1\leq i\leq N_{\text{Obj}}\) for a total of \(N_{\text{Obj}}\) objects obtained from \(R_{\text{B}}\). A set of displacement tuples is found by considering every possible pair of \((i,j)\), for \(1\leq j\leq N_{\text{Obj}}\) and \(j<i\leq N_{\text{Obj}}\). \[\mathcal{D}=\left\{ \begin{array}{c}d_{i,j}=\left(\left|x_{i}-x_{j}\right|, \left|y_{i}-y_{j}\right|\right)\mid\forall\;i>j\end{array} \right\} \tag{3}\]  
Here, we only count under the condition satisfying \(i>j\) because \(d_{i,j}\) is equal to \(d_{j,i}\). Every displacement vector \(d_{i,j}\) contributes to a voting space \(\mathcal{V}(p,q)\) as follows:  
\[\mathcal{V}\left(\left|x_{i}-x_{j}\right|,\left|y_{i}-y_{j}\right|\right) \leftarrow\mathcal{V}\left(\left|x_{i}-x_{j}\right|,\left|y_{i}-y_{j}\right| \right)+1 \tag{4}\]  
Similar to the voting technique used in Hough transform, we accumulate all displacement vectors in the voting space \(\mathcal{V}\) to determine the parameters (width and height) of the template. Similar to steps 7-9, the centroid of every local peak is found in this voting space, and the centroid \(c^{*}=(p^{*},q^{*})\) that is nearest to the origin of \(\mathcal{V}\) is then localized. Therefore, the template size is estimated as follows:  
\[w^{*}_{\text{Tpl}}=p^{*}\text{ and }h^{*}_{\text{Tpl}}=q^{*} \tag{5}\]
# Ultra-Precision Cutting Mechanism of KDP Crystal in Microplastic Region via Heating Assistance
## 2 Theory of Ductile/Brittle Transition
As shown in Figure 2, the surface was flat without defects, such as pits or cracks. Six points at different positions on the surface of the KDP crystal were selected for the nanoindentation experiments. Data measurements and the related analysis are discussed in Section 5. The experimental data were recorded and averaged to obtain parameters, such as the elastic modulus, fracture toughness, and hardness of the KDP crystals. The surface morphology was checked using an atomic force microscope, and the result is presented in Figure 3.  
Using Formula (1), the depth, \(H\), of the ductile/brittle transition on the test surface of the sample was obtained as 38 nm. On the KDP crystal surface, the processing depth was controlled within 38 nm. The material was in the plastic zone; thus, the removal occurred via plastic slip, and the surface quality was high without surface defects. When the cutting depth exceeded 38 nm, the material became brittle and then was removed by brittle fracture; the surface quality was poor. The critical depth of the ductile/brittle transition of KDP crystals is small, and the cutting depth is difficult to control in the plastic region. Presently, KDP crystals are processed using ultraprecision diamond fly cutting (UPDFC) tools. During KDP crystal processing, the cutting depth of the cutter fluctuates because of the interfering environment, temperature, machine stability, etc. The cutting depth of the tool cannot be controlled in the plastic region, and brittle removal easily occurs. The analysis of the ductile/brittle transition and the cutting process of KDP crystal demonstrates that the surface quality of KDP crystals can be enhanced by increasing the depth of the ductile/brittle transition. Therefore, a heating-assisted method was developed to reduce the strength and hardness of the cutting-layer material by introducing high-density external energy and changing the internal stress field; the cutting performance  
Figure 3: Morphology of nanoindentation points on KDP crystal surfaces. Figure 2: KDP crystal sized 80 mm \(\times\) 40 mm \(\times\) 5 mm. changes at high temperatures, improving the plasticity of the material. When the yield strength of the materials is reduced below the fracture strength, the material removal mode changes from brittle fracture to plastic slip, achieving a high-quality surface.
# Dictionary Encoding Based on Tagged Sentential Decision Diagrams
## 1 Introduction
[7], the researchers introduced the zero-suppressed variant of the SDD, known as the ZSDD, which also utilizes structured decomposition and applies zero-suppressed trimming rules instead of the standard trimming rules used in SDDs. ZSDDs offer a more compact representation for sparse Boolean functions compared to SDDs, while SDDs are better suited for homogeneous Boolean functions. To leverage the strengths of both SDDs and ZSDDs, ref. [8] devised a new decision diagram, the TSDD, which combines the standard and zero-suppressed trimming rules. The contributions of our paper mainly include the following: (1) We propose an algorithm for encoding dictionaries into decision diagrams. We first transform a dictionary into a well-known data structure: the trie. With the help of tries, our algorithm can encode a dictionary into a decision diagram more efficiently than the method without tries. (2) We encoded 14 dictionaries into seven decision diagrams, i.e., BDDs, ZDDs, CBDDs, CDDs, ZSDDs, and TSDDs, in four ways, and we believe that TSDDs are the most suitable for representing dictionaries among all decision diagrams. We adopted TSDDs to represent dictionaries, and the experimental results show that TSDDs are more compact representations compared to other decision diagrams. (3) We also designed an algorithm that decodes a decision diagram and obtains the original dictionary. Our algorithm recursively restores each word in the dictionary and then saves these words together to obtain the original dictionary. Moreover, our algorithm can quickly complete the decoding process in most cases. The rest of this paper is organized as follows. We first introduce the syntax and semantics of SDDs and ZSDDs. Section 3 introduces the syntax of TSDDs and the binary operation on TSDDs. In this section, we mainly introduce how to use a TSDD to denote a Boolean function and the trimming rules of TSDDs. Ref. [8] proposed a related definition of TSDDs. However, the researchers used three different semantics to explain SDDs, ZSDDs, and TSDDs. In fact, we believe that the difference in these decision diagrams mainly lies in the different trimming rules. These three decision diagrams can be explained with the same semantics. In this way, we can have a more intuitive understanding of these three decision diagrams and theoretically understand why TSDDs are more effective than SDDs and ZSDDs. Section 4 introduces how to use a Boolean function to represent a dictionary, the process of encoding a dictionary with the help of tries, and the decoding algorithm. An experimental evaluation comparing TSDDs with other decision diagrams appears in Section 5. Finally, Section 6 concludes this paper.
## 4 Reliable and Secure Probability Analyses
### Overall Reliable-and-Security of Probability
Since \(\overline{\gamma}_{\text{S-UR}_{1}}=\overline{\gamma}_{\text{S-UR}_{2}}=aE_{T}\), \(\overline{\gamma}_{\text{UR}_{1}\text{-D}  
\[P_{3} =\Pr\Big{\{}0<R_{\text{UR}_{1}}^{\mathscr{D}}<H(x_{\text{UR}_{1}}|x_{ \text{UR}_{2}}),R_{\text{UR}_{2}}^{\mathscr{D}}>1\Big{\}}\] \[=\int_{0}^{2^{\text{UR}_{2}\text{UR}_{1}\text{-D}^{H(\text{UR}_{1} \text{-U}_{2})}-1}}f\big{(}\gamma_{\text{UR}_{1}\text{-D}}\big{)}d\gamma_{\text{UR }_{1}\text{-D}}\int_{2^{\text{UR}_{2}\text{-D}^{-1}}}^{\infty}f\big{(}\gamma_{ \text{UR}_{2}\text{-D}}\big{)}d\gamma_{\text{UR}_{2}\text{-D}}\] \[=\frac{\gamma\Bigg{\{}m_{\text{UR}_{1}\text{-D}},\frac{m_{\text{ UR}_{1}\text{-D}}\Big{[}^{2^{\text{UR}_{1}\text{-D}^{H(x_{\text{UR}_{1}}|x_{ \text{UR}_{2}})}-1}}{(1-a)bE_{T}}\Big{]}}\Bigg{\}}}{\Gamma(m_{\text{UR}_{1} \text{-D}})}\Bigg{\}}1-\frac{\gamma\Bigg{[}m_{\text{UR}_{2}\text{-D}},\frac{m _{\text{UR}_{2}\text{-D}}\Big{(}2^{\text{UR}_{2}\text{-D}-1}\Big{)}}{(1-a)bE_ {T}}\Bigg{]}}{\Gamma(m_{\text{UR}_{2}\text{-D}})}\Bigg{\}}, \tag{37}\]  
and  
\[P_{\text{out}}^{\text{D}}=1-\sum_{i=1}^{4}P_{i}. \tag{28}\]0 \[P_{\text{out}}^{\text{D}}=1-\sum_{i=1}^{4}P_{i}. \tag{28}\]1 \[P_{\text{out}}^{\text{D}}=1-\sum_{i=1}^{4}P_{i}. \tag{28}\]2  
The primary challenge lies in selecting an optimal value for \(a\) and \(b\) that maximizes the RSP of the system. This entails enhancing the destination's ability to accurately recover the message sent by the source while concurrently minimizing the untrusted relay's potential to intercept and decipher the original message. The objective is to optimize the system's security posture by balancing these two aspects effectively, as formally expressed in the maximization of the RSP metric. The optimization problem can be formulated as follows:  
\[ \begin{array}{ll}a^{*},b^{*}=&\underset{a,b}{\text{arg max}}&P_{\text{RSP}}(a, b)\\ &\text{subject to:}&a-1\leq 0,\quad-a\leq 0\\ &&b-1\leq 0,\quad-b\leq 0\\ &&-E_{\text{T}}<0.\end{array}  \tag{39}\]  
Theoretically, by taking the partial derivatives of \(P_{\text{RSP}}\) with respect to \(a\) and \(b\), the extreme point can be determined by evaluating the determinant and eigenvalues of the Hessian matrix formed from these partial derivatives. Since the explicit expression of \(P_{\text{RSP}}\) is complicated to derive, we use a numerical approach to search for optimal power allocations.
## 5 Conclusions  
As a new communication technology, high-speed power line carrier communication offers the advantages of fast transmission speed, wide coverage, and low cost. However, with the continuous development of communication technologies, information security has become a critical concern. Therefore, we study a secure transmission method for high-speed power line carrier communication based on generalized two-dimensional polynomial chaotic mapping. Using generalized two-dimensional polynomial chaotic mapping for information encryption can effectively enhance the security performance of power line carrier communication and ensure that information remains protected against leakage or tampering during transmission, which is crucial for ensuring the safe and stable operation of power systems. The experimental results demonstrate that the proposed method exhibits significant advantages in terms of bit error rate and security performance, making it suitable for various communication scenarios with high security requirements. Specifically, the carrier devices numbered 38, 16, 17, 28, 31, and 37 are selected as cluster head nodes, thereby improving the stability and reliability of the high-speed power line carrier communication network. This method significantly enhances the security performance of high-speed power line carrier communication and improves the protection of encrypted data. The proposed method achieves comparable bit error rates, with a maximum of 0.051 and a minimum of 0.010, demonstrating its ability to effectively resist power line noise interference and signal attenuation while improving stability.  
Conceptualization, J.Y.; Methodology, Z.N. and Z.G.; Software, Z.N.; Formal analysis, Z.N.; Investigation, Z.N.; Data curation, Z.N.; Writing--original draft, Z.N.; Writing--review & editing, Z.N., Z.G. and J.Y.; Visualization, Z.N.; Supervision, Z.G.; Project administration, Z.G. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Cooperation Special Project of Shijiazhuang grant number SJZZXB23005.  
Not applicable.  
Figure 9: Secure transmission effect of high-speed power line carrier communication. Among them, Bouzinis, PS+2023 is the reference method [6]’s method, and Vishal Sharad H+2022 is the reference method [7]’s method.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to privacy reasons.  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest to report regarding the present study.
###### Abstract
Another so-called inverse-Heusler structure, also known as the _XA_ structure, is where there are more valence electrons in the Y atom than the corresponding X atom. Moreover, the atomic position of B is swapped with C, and, thus, the atomic arrangement is X-X-Y-Z along the diagonal direction of the cubic cell, as shown in Figure 1b. The detailed atomic positions for the regular and _XA_ Heusler compounds in the conventional unit cell are shown in Figure 1.  
Figure 1: \(L2_{1}\) crystal structure for the (**a**) regular, and (**b**) inverse, (_XA_) Heusler alloys. These are \(L2_{1}\) crystal structures where the atomic arrangement along the diagonal direction differs. The anomalous Hall effect (AHE) was discovered by [PERSON] in 1881. It describes the anomaly spin-related transport phenomenon in magnetic conductors. It has recently received much attention both in experimental and theoretical studies. In half-metallic ferromagnets, electrons with different spin orientations have completely different conduction behaviors. Therefore, these HMs represent very promising materials for spin-dependent devices. Nonetheless, first-principles investigations of the AHE, especially in half-metallic ferromagnetics, have been few. Discussion of the spin polarization of the transverse Hall current induced by the Hall effect is even more sparse. In this paper, we study the anomalous Hall effect (AHE) and spin polarization of the Hall current in Co-based full-Heusler compounds Co\({}_{3-x}\)Cr\({}_{x}\)Al (x = 0, 1, 2, 3) by ab initio calculations of intrinsic anomalous and spin Hall conductivities. Our study focuses on the magnetic properties related to Cr, as well as the regular and \(XA\) structures from Co\({}_{3}\)Al to Cr\({}_{3}\)Al. The results may assist with an experimental search for Heusler compounds with a large AHE, for applications such as magnetization sensors.
## 6 More Comparisons with Alternative Approaches
### Practical Application and Experiences by Physicians
At this point, each physician gave a point for each characteristic with the following scale: too bad (1); bad (2); normal (3); good (4); and very good (5). The obtained findings with mean points are given briefly in Table 16.  
Table 16 shows that the software system running ANN-ALO in the background is'very good' in terms of its usability, accuracy, and effectiveness. Although the physicians thought that the speed of the system was between the scale of normal and good, the novelty value of the system was around the level of 'good'. Figure 10 below also shows the prediction examples done for two different patients at the Hospital of SDU. As necessary, ethical permission was obtained from both patients, one of which was a 37-year old female with a possible epilepsy problem and the other one with normal EEG data was a 32-year old male. One prediction was for early epilepsy seizure diagnosis (Figure 10a) for the female patient and the other one was for the male patient with a normal EEG flow (no disease diagnosis) at the end (Figure 10b). The data flow in the blue color corresponds to predictions, and from Figure 10a, it can be seen that the ANN-ALO system was effective enough to predict even epilepsy seizures, which is a vital medical diagnosis issue. All the findings obtained through the explained evaluation stages and practical application indicate that the designed ANN-ALO hybrid system (with the ALO A\({}_{4}\) form) was successful and effective enough for predicting chaotic EEG time series, which means such healthcare-oriented data can be predicted by that system to support diagnosis and treatment-based tasks. On the other hand, the system has good potential to predict the future states of chaotic time series which are generally in the form of medical data or other data types related to natural occurrences and dynamics in real-life. More discussion is provided in the following section. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hospital** & **Physician** & **Usability** & **Accuracy** & **Speed** & **Effectiveness** & **Novelty** \\ \hline Isparta State Hospital & P1 & 5 & 5 & 4 & 5 & 5 \\ Isparta State Hospital & P2 & 4 & 5 & 3 & 4 & 4 \\ Hospital of Suleyman & P3 & 5 & 5 & 4 & 5 & 4 \\ Demirel University (SDU) & P4 & 4 & 4 & 5 & 5 & 4 \\ Meddem Hospital & P5 & 5 & 5 & 3 & 5 & 5 \\ Davraz Life Hospital & P6 & 5 & 4 & 4 & 4 & 4 \\ \hline \hline  & **Mean** & 4.67 & 4.67 & 3.83 & 4.67 & 4.33 \\ \hline \hline \end{tabular}  
* Scale: too bad (1); bad (2); normal (3); good (4); very good (5).  
\end{table} 
Table 16: Findings from the survey work done with the physicians using ANN–ALO for EEG prediction.
# FnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm
## 3 Proposal and Implementation of FnmOS-ELM
### Model Structure  
In the fnnmOS-ELM model, CNNs and RNNs are mixed with OS-ELM to make full use of their respective advantages. As illustrated in Figure 1, structures between the fnnmOS-ELM model (the data are drawn in batch form) and the traditional neural network is compared. It is assumed that the traditional CNNs and RNNs are composed of feature extraction layers and classifiers (only full connection (FC) layers are drawn here).  
Figure 1: Network structures of a traditional neural network and fnnmOS-ELM (online sequential extreme learning machine) models. (**a**) The OS-ELM is used as a classifier in the original network. (**b**) The OS-ELM replaces part of the network layers of the classifier in the original network. (**c**) The fnnmOS-ELM is used as the optimiser of the original network.  
In this study, the network structure of the fnnmOS-ELM is divided into three categories. As shown in Figure 1a, the OS-ELM completely replaces the classifier in the original network, which leads to fast and accurate classification. In Figure 1b, we replace part of the network layer of the classifier in the original network with the OS-ELM, and we can decide whether to use the network layer in the classifier depending on the effect of the classification. For example, we can use dropout to prevent over-fitting problems [39]. As shown in Figure 1c, the fnnmOS-ELM is used as the optimiser of the traditional neural network. The OS-ELM is connected to the traditional neural network structure, and we can significantly improve the accuracy. In this study, we will verify the classification performance of different network structures of fnnmOS-ELM through some popular datasets, and we will investigate in detail the hyper-parameters that affect the network performance. The results and analysis will be shown in Section 4.
## 3 Results and Discussion
### Finite Element Model Validation
Figure 6: _Cont._  
The deformation of the liner at points A, B, C and D was selected according to Figure 5 to compare the test with the numerical results as shown in Figure 6. As shown in Figure 6a, the liner appears to float up under hydrostatic pressure, and the outer surface of the liner tube contacts the inner surface of the host pipe, while the liner undergoes slight rotation and the inner surface of liner had tensile deformation at monitoring point No. 2 and monitoring point No. 8. Compressive deformation occurred at monitoring points No. 5 and No. 11. The finite element model ignored the possible rotation of the liner under the condition of uniform external pressure. The numerical results show that the tensile deformation occurs at the onset of the liner tube. Figure 6: Comparing the deformation status of buckling liners at (**a**) point A; (**b**) point B; (**c**) point C; (**d**) point D marked in Figure 5.  
at the invert of the inner surface of the liner. As shown in Figure 6b, the displacement of the liner was slightly elevated along the radial direction at monitoring point No. 2. With the continuous increase in hydrostatic pressure, the displacement of the liner at monitoring point No. 2 along the radial direction gradually increased. When the displacement of the buckling lobe reached point C, as shown in Figure 4, the deformation of the liner in the test and numerical result was shown in Figure 6c. After the pressure value reached the critical buckling pressure, the buckling lobe had deformation in the opposite direction at monitoring point No. 2 and began to move to the port along the axial direction. Finally, the liner failed, while the buckling lobe continued to deform after the pressure value reached the critical buckling pressure, as shown in Figure 6d. The reason for this difference was that the 2D finite element model ignored the deformation in the axial direction and only considered the deformation of the cross-section of the liner.