esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
It has since been used in nearly every sector, including electrical load dispatch problems, business modelling, forecasting, search optimization, structural engineering design, etc. Main algorithms under this category include ABC algorithm [23], FA [24], DA [25], FPA [4], SSA [26], CS [27], fitness-dependent optimizer (FDO) [28], GWO [10], and others. Inspired by the collective nature of the community of interacting scientists, the Community of Scientists Optimization (CoSO) algorithm [29] was proposed. The solutions were updated based on the interactions of a group of scientists pursuing their research outcomes by raising finances to continue their research. In addition to the above-mentioned algorithms, there are some recent algorithms, such as Aquila optimizer (AO) [30], arithmetic optimization algorithm (AOA) [31], and hybridized optimization algorithms, such as adaptive SSA (ASSA) [32], adaptive FPA (AFPA) [33], CS version 1.0 (CV 1.0) [34], and NMRA version 3.0 (NMRV 3.0) [35] that are employed in every field of research, such as mathematics, medical imaging, robotics, antenna arrays, image segmentation, artificial immune systems, business management, and others. Furthermore, such algorithms are easier to implement and demonstrate a major contribution to basic optimization techniques. However, there are certain downsides to using nature-inspired algorithms. They may be appropriate for specific problems but not for others. The NFL theorem [9] confirmed this. Many scholars are inspired by this notion to examine and develop novel algorithms for tackling optimization problems. The inherent shortcomings of existing algorithms, such as inadequacy in exploring, stagnation, and premature convergence of population-based algorithms, are another motivation to create new algorithms. As a result, one must evaluate, hybridize, and propose more powerful algorithms to address optimization issues to eliminate these flaws. Compared to prior NIAs, such as FA, CS, GWO, and others, NMRA is a newly presented approach that offers extremely dependable results [35]. However, compared to upgraded variants or hybrid algorithms, the NMRA was proved to produce less accurate results. This is primarily due to NMRA's inadequate exploration and exploitation capabilities. A closer examination of these notions revealed that because the worker phase contains less randomness in the search space, the algorithm was more likely to get stuck in a local optimum, resulting in lower performance. This can be addressed by introducing new potential equations in the worker and breeder phases, significantly improving the algorithm's global and local searching capabilities.
## 4 Conclusions  
A molecular dynamics approach was used to simulate the deformation behavior and mechanical properties of a Cu/graphene nanocomposite composed of a graphene network and Cu nanoparticles. How the potential used to represent the atomic interactions affected the final composite structure and deformation behavior was analyzed. BOP, EAM-Morse, and Morse potentials were investigated in the present study.  
One of the purposes of the present study was to determine the most suitable potential for the simulation of the interaction between Cu and graphene in the composites and to identify which potential would make the simulation easier, more efficient, and more accurate. Although the BOP potential was able to accurately reproduce the interatomic interaction between C and Cu, it was found to be more suitable for the simulation of graphene growth on a Cu substrate than for the study of Cu/graphene composites. It was found that the Cu nanoparticles did not melt during high-temperature compression, while the melting temperature of Cu nanoparticles was quite low. Moreover, the EAM-Morse and Morse potentials gave approximately the same results for a much lower simulation time in comparison with the BOP potential. The same results were obtained for Ni nanoparticles in a graphene network where the complex ReaxFF potential was compared with the Morse potential [22]. Therefore, for the simulation of a large number of atoms, it is better to use a simple Morse potential.  
Conceptualization, J.A.B.; methodology, L.R.S.; formal analysis, E.A.R.; resources, R.T.M.; investigation, L.R.S.; writing--original draft preparation, L.R.S., J.A.B. and R.T.M. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON], R.T.M. and E.A.R. is supported by the State Assignment of IMSP RAS (Young Scientist Laboratory). The work of L.R.S. is supported by a grant for young scientists provided by the Republic of Bashkortostan.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the work is not finished yet.  
The authors declare no conflict of interest.  
Figure 4: Copper atoms (orange color) and graphene network (black color) are shown separately before and after uniaxial tension: (**a**) BOP; (**b**) EAM-Morse; and (**c**) Morse. Colors as in Figure 1.
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
## 3 Proposed Method  
In this study, we have proposed a novel multi-equation and user-attribute-based cryptography technique to improve security in video communication. The proposed method uses RSA and AES methods to achieve hybrid encryption and decryption. Then, we use a set of equations and randomisation to improve the complexity and, hence, better security.
## 4 Enhancements to ShinyAnonymizer Hashing Techniques
### Implementation of Advanced Techniques such as Hashing
#### 4.1.2 SHA512  
* Description: This method is implemented when the dataset is anonymized using unique or specific data. For instance, to delete all identities, account numbers, and other private data from a database.
* Application: SHA-512 builds on the SHA-2 family, generating a hash value of 512 bits. This shows up as a hexadecimal integer with 128 characters. It aims to be more dependable than MD5, offering better protection against collisions and standing up better to attacks on its cryptography [20].
## Materials and Methods
### Chemistry
#### 2.1.4 Ethyl 4-(prop-2-yn-1-ylamino)-1\(H\)-indole-6-carboxylate (**2**)  
To a solution of **6** (30 mg, 0.147 mmol), KI (2.4 mg, 14.7 \(\mathrm{\SIUnitSymbolMicro mol}\)) and K\({}_{2}\)CO\({}_{3}\) (44.6 mg, 0.323 mmol) in DMF (1.5 mL) was added 24.1 mg (0.162 mmol) of 80 w/w% solution of propargyl bromide in toluene at ambient temperature. The reaction mixture was stirred for 72 h at 65 \({}^{\circ}\)C, cooled to ambient temperature, and then diluted with water and EtOAc. The organic layer was washed with water and brine, dried over MgSO\({}_{4}\), and concentrated in vacuo. Purification of residue via column chromatography on silica gel (EtOAc: \(n\)-hexane = 1:4-1:1) afforded _N_-propargyl aminobenzoate **2** (21.8 mg, 61%) as a dark-yellow oil along with 4.2 mg of starting material **6** (71% yield brsm). 1\({}^{\mathrm{1}}\)H-NMR (400 MHz, CDCl\({}_{3}\)) 8 8.47 (s, 1H), 7.71 (d, 1H, \(J\) = 0.9 Hz), 7.25 (m, 2H), 7.09 (d, 1H, \(J\) = 0.9 Hz), 6.52 (m, 1H), 4.38 (q, 2H, \(J\) = 7.1 Hz), 4.15 (d, 2H, \(J\) = 2.5 Hz), 2.26 (t, 1H), \(J\) = 2.4 Hz), 1.40 (t, 3H, \(J\) = 7.1 Hz); 1\({}^{\mathrm{3}}\)C-NMR (100 MHz, CDCl\({}_{3}\)) 8 167.4, 135.1, 125.0, 125.0, 120.5, 105.4, 100.9, 98.7, 80.1, 71.5, 60.2, 33.6, 14.0; HR-MS (Q-ToF): Calcd for C\({}_{14}\)H\({}_{15}\)N\({}_{2}\)O\({}_{2}\)\({}^{+}\) (M + H\({}^{+}\)): 243.1128. Found: 243.1131.  
1.5 Representative Procedure of Transition Metal-Catalyzed Cycloisomerization for Ethyl 7H-pyrrolo [2,3-h]quinoline-5-carboxylate (Aaptoline B; **1**)  
To a solution of **2** (21.8 mg, 0.09 mmol) in DMSO (2.3 mL) was added AgSbF\({}_{6}\) (3.1 mg, 9 \(\mathrm{\SIUnitSymbolMicro mol}\)) at ambient temperature. The reaction mixture was stirred at 110 \({}^{\circ}\)C for 4.5 h, cooled to ambient temperature, diluted with EtOAc and quenched with saturated aq. NaHCO\({}_{3}\) solution. The organic layer was washed with water and brine, dried over Na\({}_{2}\)SO\({}_{4}\), and concentrated in vacuo. Purification of residue via column chromatography on silica gel (EtOAc:\(n\)-hexane = 1:3-1:1) afforded aaptoline B (**1**; 14.1 mg, 73%) as a yellowish solid. Melting point: 187\(\sim\)190 \({}^{\circ}\)C; 1\({}^{\mathrm{1}}\)H-NMR (500 MHz, CDCl\({}_{3}\)) 8 9.56 (d, 1H, \(J\) = 8.4 Hz), 9.43 (s, 1H), 8.96 (d, 1H, \(J\) = 3.2 Hz), 8.52 (s, 1H), 7.52 (m, 3H), 4.47 (q, 2H, \(J\) = 7.1 Hz), 1.46 (t, 3H, \(J\) = 7.1 Hz); 1\({}^{\mathrm{3}}\)C-NMR (125 MHz, CDCl\({}_{3}\)) 8 167.3, 148.2, 134.1, 136.3, 133.1, 127.1, 126.8, 122.9, 120.4, 120.1, 119.5, 104.0, 61.2, 14.6; 1\({}^{\mathrm{1}}\)H-NMR (700 MHz, CD\({}_{3}\)OD) 8 9.52 (dd, 1H, \(J\) = 1.4, 8.4 Hz), 8.79 (dd, 1H, \(J\) = 1.8, 4.6 Hz), 8.51 (s, 1H), 7.59 (d, 1H, \(J\) = 2.8 Hz), 7.51 (dd, 1H, \(J\) = 4.6, 8.8 Hz), 7.34 (t, 1H, \(J\) = 1.4 Hz), 4.45 (q, 2H, \(J\) = 7.2 Hz), 1.46 (t, 3H, \(J\) = 7.0 Hz); 1\({}^{\mathrm{3}}\)C-NMR (175 MHz, CD\({}_{3}\)OD) 8 168.7, 149.2, 144.7, 137.2, 134.7, 128.6, 128.2, 124.0, 121.1, 120.9, 120.8, 103.9, 62.2, 14.9; HR-MS (Q-ToF): Calcd for C\({}_{14}\)H\({}_{13}\)N\({}_{2}\)O\({}_{2}\)\({}^{+}\) (M + H\({}^{+}\)): 241.0972.
# EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
## 4 Experimentation and Results  
In this section, we present experimental details regarding the proposed system. The reported algorithms were implemented in MATLAB. Specifically for the \({}^{+}\)FEGP algorithm, the GPLAB ([[http://gplab.sourceforge.net/](http://gplab.sourceforge.net/)]([http://gplab.sourceforge.net/](http://gplab.sourceforge.net/))) [62] toolbox was used as a starting point for the implementation. The experiments were executed in a setup with an Intel Xeon at 2.4 GHz with 32 GB of RAM. Given the stochastic nature of GP, a series of multiple runs were executed in order to statistically determine the algorithm performance. Following the LOOCV scheme for data partitioning and validation, the experiments involved 30 independent runs for each fold. The results presented in this work contain 390 runs in total (\(13\times 30\)). Details of the values used for filtering, CSP, and LDA were mainly discussed in Section 2. Furthermore, the \({}^{+}\)FEGP parameters are summarized in Table 1. Some comments in the third column are derived from experimental tuning of the algorithm. The training classification performance of the proposed enhanced system is shown in Figure 14 with the overlapped performance of the reference system for comparison. In addition, the predictive performance calculated over the testing partitions is presented in Figure 14. Basic statistical results for the \({}^{+}\)FEGP-based system per LOOCV fold are given in Figure 15. Supporting these visual representations, the average classification accuracies and [PERSON]'s kappa values are presented in Table 2. To validate our results, non-parametric two-sample Kolmogorov-Smirnov and Wilcoxon rank sum tests were used to calculate pairwise statistical differences at a LOOCV fold level. The results are presented in Tables 3 and 4, corresponding to the training and testing phases. \({}^{+}\)FEGP fitness performance and LDA classification accuracy during the training phase are shown in Figure 16a. Although the \({}^{+}\)FEGP algorithm only uses the fitness measure (bottom plot), the top plot shows the convergence of the actual classification accuracy achieved by LDA across the generations. Please note that the bottom convergence plot condenses all 390 runs by first computing the median of 30 runs per fold and finally calculating the mean of all fold results. Similarly, in Figure 16b, prediction performance in terms of classification accuracy is presented as well. Figure 14: Training and testing performance for both the reference system and the proposed enhanced system for each Leave-One-Out Cross-Validation (LOOCV) fold. Mean values for all folds are shown.
## 2 Materials and Methods
### Test Methods  
After sintering, synthesized clinkers were finely ground to pass a sieve of 45 \(\upmu\)m for X-ray diffraction (XRD) testing. A Bruker diffractometer with CuK\(\alpha_{1,2}\) radiation (without a monochromator, \(\lambda_{1}\) = 0.15406 nm, \(\lambda_{2}\) = 0.15444 nm) was employed at 40 kV and 40 mA. For quantitative phase analysis, the range of data collection was 5-80\({}^{\circ}\) (in this paper, all angles refer to a 20 value) with a step size of 0.02\({}^{\circ}\), and the total measurement time for each sample was approximately 30 min. When the lattice parameters needed to be refined, the range of data collection was expanded to be 5-120\({}^{\circ}\), and the total measurement time for each sample was increased to approximately 120 min.  
XRD patterns obtained by direct tests were firstly analyzed qualitatively in Bruker Evolution software to determine the phases in the clinkers. Then, Topas 4.2 software was employed to perform RQPA for dealing with the obtained XRD patterns. Table 1 exhibits the COD codes for all the phases involved in the RQPA.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Phases** & **Formula (Abbreviation)** & **COD Code** & **Reference** \\ \hline Typical solid-solution ye’elimite & Ca\({}_{3.8}\)Na\({}_{0.2}\)Al\({}_{5.6}\)Fe\({}_{0.2}\)Si\({}_{0.2}\)SO\({}_{16}\) (ss-C\({}_{4}\)A\({}_{3}\)S) & 45,119,60 & [21] \\ Monocalcium aluminate & Ca\({}_{3}\)La\({}_{3}\)O\({}_{4}\) (CA) & 43,080,75 & [23] \\ Anhydrite & CaSO\({}_{4}\) (CS) & 50,000,40 & [24] \\ Mayenite & Ca\({}_{12}\)Al\({}_{4}\)O\({}_{33}\) (C\({}_{12}\)A\({}_{7}\)) & 21,029,55 & [25] \\ Tricalcium aluminate & Ca\({}_{3}\)Al\({}_{2}\)O\({}_{6}\) (C\({}_{3}\)A) & 90,159,66 & [26] \\ Calcium oxide & CaO (C) & 72,006,86 & [27] \\ \hline \end{tabular}
\end{table}
Table 1: COD codes and references of phases involved in the RQPA.
## 3 Results  
A total of 73 images from five patients (three patients with d1 pressure injuries, one patient with d2, and one patient with D5) were analyzed as test data. Of all 73 images with an unclear layer structure, 7 showed a cobblestone-like pattern, 14 showed a cloud-like pattern, and 15 showed an anechoic area. Table 1 presents the results of the test for each US finding. All four US findings showed a detection performance of 71.4-100%, with a mean value of 0.38-0.80 for IoU and 0.51-0.89 for the DICE score. Figure 3 shows examples of successful results of automatic classification based on deep learning. Figure 4 shows examples of failed results of automatic classification based on deep learning.  
Figure 3: Examples of successful results of automatic classification based on deep learning. (**a**) Unclear layer structure. (**b**) Cobblestone-like pattern. (**c**) Cloud-like pattern. (**d**) Anechoic pattern. The “ground-truth” images show the regions that were manually annotated by the expert. The “deep-learning” images show the regions extracted as a result of automatic classification. White indicates an unclear layer structure, yellow indicates a cobblestone-like pattern image, red indicates a cloud-like pattern image, and purple indicates an anechoic pattern image. The values of the intersection over union (IoU) are, from left to right, 0.88, 0.57, 0.68, and 0.80.  
Figure 2: Area of overlap and union between the ground truth and AI result. Area of overlap indicates the common part of the area of the ground truth and AI result; area of union indicates the union set of the area of the ground truth and AI result.
## 3 _HoloSec_: Proposed Conceptual Holochain Security Framework for IoT Distributed Networks
### Agent Initialization and Trust Establishment  
The framework begins with the initialization of an agent (or IoT device), where a pair of public/private keys are generated using elliptic curve cryptography (ECC) for efficient and secure key management by using lightweight cryptographic libraries such as TinyCrypt or MicroECC. Lightweight cryptographic libraries such as TinyCrypt or MicroECC can be used to generate ECC key pairs directly on the IoT device. The agent's identity (AID) is created by combining the device's unique identifier (e.g., MAC address) with its public key, ensuring cryptographic uniqueness. This AID is then signed using the agent's private key, creating a cryptographically verifiable identity. The signed AID is stored on the global shared Holochain Distributed Hash Table (DHT), ensuring decentralization and immutability. A local monitoring module can also be deployed on the edge device as a lightweight agent process written in C, Rust, or Python, depending on the IoT platform, and can even publish its metrics to the Holochain DHT or local ML model for Trust Score (TS) updates and anomaly detection. It will continuously track its behavior--monitoring metrics like uptime, response times, and participation in the network.  
Once initialized, the agent's trust is established through a dynamic TS mechanism. The TS is initialized to a default value (e.g., 0.5 on a scale of 0 to 1) and updated in real-time based on the agent's behavior using the local monitoring module, with the following examples.  
* Positive behaviors (e.g., timely data sharing, valid transactions) increase the TS.
* Negative behaviors (e.g., delayed responses, invalid data) decrease the TS.  
If the TS falls below a predefined threshold (e.g., 0.3), the agent is flagged as untrusted, and adaptive security protocols are applied, such as isolating the agent or requiring additional authentication.
## 3 Examples and Discussion
### Acoustic Radiation Property of the Finite LR Plate
Conversely, though the vibration over the whole surface of the FFFF LR plate is not strong, as shown in Figure 13b, the acoustic power radiated from the vibrating area near the excitation can be very high when no cancellation exists. Figure 12: The acoustic power radiated from the mass-loaded (ML) plate, the locally resonant (LR) plate with undamped resonators (\(\eta_{\text{R1,2}}=0\)), and the LR plate with damped resonators (\(\eta_{\text{R1,2}}=0.05\)), with (**a**) fully free boundary conditions, (**b**) fully simply supported boundary conditions, and (**c**) fully clamped boundary conditions. However, when considering the damping loss factors of the resonators, the high acoustic power radiated from the FFFF LR plate can be decreased. The vibration profile of the FFFF LR plate with damped resonators is shown in Figure 14. It can be seen that the LR plate with undamped resonators in Figure 13b vibrates vigorously near the excitation and hardly over the other surface. When replaced by damped resonators, the response of the FFFF LR plate is suppressed in the region where it is originally vibrating strongly, as shown in Figure 14, and thus the radiated power is reduced. This is why the acoustic power radiated from the LR plate with undamped resonators is peculiarly high and decreased greatly after considering the damping characteristic of the resonators at 304 Hz, as shown in Figure 12a. Overall, the acoustic power radiated from the plates is related to their boundary conditions. For the FFFF ML plate, the radiation cancellation could appear more easily in a wide frequency range, and the plate radiates sound weakly. However, for the FFFF LR plate without good radiation cancellation, despite a lower RMSV in the band-gap frequency ranges, the higher radiation efficiency will translate to a higher acoustic power. In other cases where the base plate has greater constraints, such as the SSSS and CCCC LR plates in the manuscript, even though the radiation efficiency is higher in the band-gap frequency ranges, a lower RMSV will lead to a lower acoustic power.
## 1 Introduction
* We propose a heuristic-based detection scheme, called HED, to efficiently mitigate the suppression attack in MPL-based LLNs. In the HED, each node maintains an increment rate of the minimum sequence number in the Seed Set, and compares the recent increment of sequence numbers within a time period with the heuristically calculated increment threshold of sequence numbers to detect potential malicious node. * We propose a simple analytical model of the HED and show its numerical result in terms of miss detection rate. We also revisit and implement the original MPL with and without adversary for performance comparison. In addition, the original MPL without adversary will be used as the upper bound of packet reception rate. We develop a customized discrete event-driven simulation framework by using OMNeT++ [14] and evaluate its performance through extensive simulation experiments in terms of detection rate, packet reception rate, false detection rate, and changes of increment rate. The simulation results indicate that the proposed countermeasure is a viable detection approach to suppression attack in MPL-based LLNs. The remainder of the paper is organized as follows. Prior approaches are presented and analyzed in Section 2. The basic MPL operations, analysis of suppression attack with a preliminary result, and the proposed countermeasure are presented in Section 3. An analytical model of the proposed countermeasure is presented in Section 4. Performance evaluation, including extensive simulation experiments and analysis, is provided in Section 5. In Section 6, we analyze and compare the suppression attack with well-known jamming attack in terms of attack method, stealthiness, attack energy efficiency, and level of denial of service. Finally, Section 7 concludes the paper with possible future research directions.
# Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
## 3 Materials and Methods
### Offset Value Caching and Coarse Registration  
To improve the degree of overlap between the two images and reduce the error when the method fails, the offset values of some control points, such as the national core provincial capitals of China, are cached before the method is registered. Before fine registration, the cached value closest to the center of the current map is used to carry out registration, and then the current images are obtained. In addition, the offset values calculated each time are cached in the database to achieve progressive registration during the map zoom-in process.