esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Performance Evaluation Using Bi-Objective Benchmarks  
To evaluate the performance, two well-known metrics are calculated. Since the optimal PF (Pareto-Front) is unknown for the benchmarks, it is not possible to compute all the metrics. The spacing metric is computed as follows to evaluate the diversity of the PF [14]. Likewise, the hypervolume metric evaluates the convergence and spread of PF [31]. Meanwhile, in the evaluations, one of the recently proposed algorithms, NSGA-II-WA, and the ensemble method proposed in [8] are taken into account as competitors. The Gaussian Elimination Graph (GE) is the first benchmark, which is shown in Figure 5.  
A graph with 10 and 54 nodes was taken into consideration in [13]. Also, maintenance and reliability were considered objectives.  
The results obtained by the proposed hybrid method and five competitors (Bi-objective GA (BGA) [42], Modified GA (MGA) [41], Firefly-based algorithm (FA) [13], NSGA-II-WA, and Ensemble System [8]) are represented in Table 3. The results are calculated according to the different values of CCR (1, 5, and 10). The following equation indicates the way to compute the value of CCR:  
\[CCR=\frac{Average\,Communication\,Cost}{Average\,Computation\,Cost} \tag{9}\]  
It should be pointed out that BGA, MGA, and FA methods adapt the multi-objective problem to a single-objective problem using a weighted-sum approach, but NSGA-WA, the ensemble method, and the proposed hybrid method extract the Pareto front. Figures 6-8 show the Pareto front extracted by the proposed hybrid method for different CCR values.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \multicolumn{5}{c}{**Parameter Values**} \\ \hline \multirow{2}{*}{MODE} & \(\mid\)Pop\(\mid\) & Scaling\_Factor & \#of Generations & PC & PM \\ \cline{2-6}  & 200 & 0.5 & 300 & 0.8 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters values.  
Figure 5: Gaussian Elimination Graph for size = 5.  
Figure 6: Extracted PF for CCR = 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Objective** & **Method** & **CCR = 1** & **CCR = 2** & **CCR = 3** \\ \hline \multirow{6}{*}{Makespan} & FA & 458 & 687 & 1144 \\ \cline{2-5}  & MGA & 591 & 1070 & 1426 \\ \cline{2-5}  & BGA & 616 & 1103 & 1490 \\ \cline{2-5}  & NSGA-II-WA & 433 & 841 & 1065 \\ \cline{2-5}  & Ensemble System & 420 & 657 & 1069 \\ \cline{2-5}  & Hybrid Method & 418 & 642 & 1055 \\ \hline \multirow{6}{*}{Reliability index} & FA & 9.45 & 7.4 & 15.48 \\ \cline{2-5}  & MGA & 13.17 & 15.93 & 23.54 \\ \cline{2-5}  & BGA & 9.48 & 12.20 & 22.47 \\ \cline{2-5}  & NSGA-II-WA & 10.56 & 8.30 & 16.66 \\ \cline{2-5}  & Ensemble System & 8.29 & 6.76 & 14.83 \\ \cline{2-5}  & Hybrid Method & 8.05 & 6.61 & 13.40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Obtained Objective Values.  
Figure 7: Extracted PF for CCR = 2.  
Table 3 indicates that the proposed method produces better results than its competitors in terms of reliability and time span. The spacing and hypervolume values are represented in Table 4.
## 5 Results and Discussion  
To evaluate the functionality of our proposed solution, we adopted a black-box testing approach. Both functional and non-functional software testing was conducted, considering the key security aspects of our solution. Overall, software functional testing is a type of testing in software development that focuses on verifying whether the application or system functions correctly and performs its intended functions according to the specified requirements. The primary goal of functional testing is to ensure that the software meets the functional requirements and behaves as expected by the end users. While functional testing focuses on validating whether the software functions correctly and meets its specified requirements, non-functional testing evaluates how well the software performs and behaves under various conditions. Table 2 highlights the functional testing evaluation results.  
Upon the completion of functional testing, the results obtained indicated that the system was able to pass all the functional test cases. This demonstrated that the system was functioning as intended, with no flaws or errors. The results of the transactions performed to test the overall functionality of the implemented system and the time taken for authentication are showcased in Table 3.  
Figure 7: Status of the ATM inquiry.  
These results indicated that, on average, it took approximately 3.715 s for the user to complete the geolocation-based authentication process after submitting their PIN. According to the average time taken, it was evident that the geolocation authentication time was negligible compared to traditional PIN-based authentication, as the proposed approach provided higher security as opposed to traditional PIN-based authentication.  
With regard to the non-functional testing of our solution, we mainly focused on security testing to prove that our solution can withstand most cyberattacks. The next subsection briefly discusses the security testing of our proposed solution.
# Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
## 6 Characteristics and Physical Properties of Phobos Regolith
### Physical Properties of Regolith
#### 6.2.4 Water Ice  
Although no evidence of water has been observed on the surface of Phobos, it cannot be ruled out that its interior contains a certain amount of water ice. The temperature conditions inside Phobos do not exclude its presence, and the regolith covering the surface may protect it from sublimation and transport to the surface in large quantities [1].  
Depending on the density of the analog rocky material, water ice will occupy a different volume. The density of the analog rocky material \(\rho_{rock}\), depending on the amount of water ice and porosity, can be calculated using the relationship [8]  
\[\rho_{rock}=\frac{\rho_{b}-\rho_{ice}\cdot wi}{1-n-wi} \tag{2}\]  
where \(\rho_{b}\) is the average volumetric density of Phobos (1857 kg/m\({}^{3}\), Table 3), \(\rho_{ice}\) is the density of water ice (0.97 g/cm\({}^{3}\)), \(wi\) is the water ice content, and \(n\) is the volumetric porosity.  
On Figure 24, the density of the analog rocky material is presented as a function of porosity and water ice content, calculated from Equation (2). Depending on the density of  
Figure 23: The total porosity of Phobos (dark green curve) estimated based on the grain density of analog materials, which might hypothetically constitute the moon (Table 4), with an error range (dark-green dashed curve).  
the analog rocky material and considering the porosity given in [PERSON] et al. (2018), ranging from 25-35% (30 \(\pm\) 5%), water ice may constitute from about 0% to about 20% of Phobos' mass (Figure 24). Assuming a porosity of 25-35% and a 0% water ice content, the required density of the rocky material should fall within the range of 2.48-2.86 g/cm\({}^{3}\). These values are consistent with the densities of materials such as hydrated carbonaceous chondrites. On the other hand, for a 20% water ice content, the density of the rocky material should range from 3.02 g/cm\({}^{3}\) to 3.70 g/cm\({}^{3}\), corresponding to the densities of silicate material or black chondrites. Assuming a porosity of 25%-30%, the water ice content could even reach 30%.
## 2 Materials and Methods
### Data Analysis  
The PLFA detection range was between ECL 9.000 (FAME 9:0) and ECL 20.000 (FAME 20:0). For data processing, we refer to the methods provided by other research institutes. PLFAs were organized by biomarker group. Peaks with multiple possible FAME identifications were grouped as summed features, and unknown peaks were grouped and named by their respective ECL [14]. The biomarker panel is described in Table 1.  
All treatments were replicated three times in the experiments. Considering that the data obeyed normal distribution, statistical analysis was performed using the SPSS 20.0 with independent-sample \(t\)-test. Principal component analysis (PCA) was performed to analyze the correlation between soil samples and different microbial marker levels.  
The Shannon-Wiener diversity index (\(H\)) was used to calculate the PLFA diversity of soil microorganisms in the analysis samples at different periods. The specific formula is as follows:  
\[H=\sum_{i=1}^{n}P_{i}lnP_{i}(i=1,2\ldots,n) \tag{1}\]  
where \(P_{i}=n_{i}/n\), \(n_{i}\) represents the \(i\)-th fatty acid content, and \(n\) represents the fatty acid content.
# A Size-Dependent Cost Function to Solve the Inverse Elasticity Problem
## 3 Results
### Modulus Reconstruction by a One-Dimensional Coupled Model  
To represent enormous cases with varying sizes and shear moduli of these two inclusions, we employ an 1-D coupled model for analysis, shown in Figure 4. In Figure 4, the black regions in the two elastic bars represent two inclusions in the two-dimensional (2-D) cases presented in Section 3.1, and the rest denotes the background. The target shear modulus value of the background is fixed to be 1. The target shear modulus of the left and right inclusions is \(\overline{\mu}\). In this case, the cost function in the standard approach can be simplified to  
\[F=\sum_{i=1}^{2}\left(\frac{1}{2}\int_{0}^{L}\left(u_{i}^{c}-u_{i}^{meas} \right)^{2}\mathrm{d}y+\alpha\big{|}\mu_{in}^{i}-1\big{|}\right) \tag{5}\]  
where \(i=1\) denotes the variables for the left and right bars, and \(\mu_{in}^{i}\) denotes the recovered shear modulus value in the inclusion of the \(i\)-th bar. The recovered shear modulus value of the background is assumed to be reconstructed perfectly, thus being equal to 1. Figure 3: In the first row, the target and reconstructed shear modulus distributions, where the target right inclusion is 1.5 times as large as the left one, are shown. In the second row, the target and reconstructed shear modulus distributions, where the target right inclusion is twice as large as the left one, are presented. (**a**) Target shear modulus distribution where the right inclusion is 1.5 times as large as the left one. (**b**) Reconstructed shear modulus distribution with the regularization factor of \(2.0\times 10^{-10}\), utilizing the standard method. (**c**) Reconstructed shear modulus distribution with the regularization factor of \(2.0\times 10^{-10}\), utilizing the proposed method. (**d**) Target shear modulus distribution where the right inclusion is twice as large as the left one. (**e**) Reconstructed shear modulus distribution with the regularization factor of \(2.0\times 10^{-10}\) utilizing the standard method. (**f**) Reconstructed shear modulus distribution with the regularization factor of \(4.0\times 10^{-10}\) utilizing the proposed method. To establish the relationship between \(\mu_{in}^{1}\) and \(\mu_{in}^{2}\), we take the gradient of the cost function \(F\) with respect to \(\mu_{in}^{i}\) and set it to zero. Accordingly, we reach the following relation:  
\[\frac{\left(\mu^{1}-\overline{\mu}\right)\!\!\left(L-a^{1}\right)^{2}\!\!a^{1}}{ \left(L\mu^{1}+a^{1}-\mu^{1}a^{1}\right)^{3}\!\!\left(L\overline{\mu}+a^{1}-a^ {1}\overline{\mu}\right)} = \frac{\left(\mu^{2}-\overline{\mu}\right)\!\!\left(L-a^{2}\right) ^{2}\!\!a^{2}}{\left(L\mu^{2}+a^{2}-\mu^{2}a^{2}\right)^{3}\!\left(L \overline{\mu}+a^{2}-a^{2}\overline{\mu}\right)} \tag{6}\]  
where \(L\) and \(a^{i}\) are the total length of each elastic bar and the size of the black region of the i-th bar. Since we have elaborated on a similar derivation in [15, 16], we will not discuss herein.
## 5 EEG Signal Processing and Classification Techniques in Rehabilitation Research
### EEG-Based Machine Learning and Deep Learning Algorithms
Pseudocode for RNN:  
Initialize parameters and load EEG dataset  
Preprocess the data (filtering, normalization)  
Define RNN architecture (number of layers, units)  
Split data into training and testing sets  
Train RNN model on training data (sequence data)  
Evaluate model on testing data  
Tune hyperparameters for optimal performance  
{Output sequence prediction accuracy}  
Long Short-Term Memory Networks (LSTMs):  
LSTMs are a type of recurrent neural network capable of learning long-term dependencies, depicted in Figure 8. They excel in tasks that involve sequential data, such as learning motor sequences from EEG signals. * Sample Size: Requires a large dataset. * Accuracy: High for temporal pattern recognition. * Training Cost: High due to complex architecture. Figure 8: Architecture of a Long Short-Term Memory Network. Figure 7: Structure of a Recurrent Neural Network. Pseudocode for LSTM:  
Initialize parameters and load EEG dataset  
Preprocess the data (filtering, normalization)  
Define LSTM architecture (number of layers, units)  
Split data into training and testing sets  
Train LSTM model on training data (sequence data)  
Evaluate model on testing data  
Tune hyperparameters for optimal performance  
Output sequence prediction accuracy  
Multi-Layer Perceptrons (MLPs):  
MLPs are a type of feedforward neural network that can be used for classification tasks, explained in Figure 9. They are simpler compared to CNNs and RNNs but still effective for basic EEG data classification. Sample Size: Effective with medium to large datasets. Accuracy: Moderate to high, depending on the complexity of the task. Training Cost: Moderate. Pseudocode for MLP:  
Initialize parameters and load EEG dataset  
Preprocess the data (filtering, normalization)  
Define MLP architecture (number of layers, neurons)  
Split data into training and testing sets  
Train MLP model on training data  
Evaluate model on testing data  
Tune hyperparameters for optimal performance  
Output classification accuracy  
Both machine learning and deep learning techniques offer unique advantages in EEG-based rehabilitation. Machine learning algorithms like SVM and k-NN are advantageous for their simplicity and effectiveness with smaller datasets, providing reliable classification and pattern recognition. On the other hand, deep learning algorithms such as CNNs, RNNs, LSTMs, and MLPs excel in automatically extracting complex features from large datasets, albeit at a higher computational cost and requiring more extensive training data. Choosing the appropriate algorithm depends on the specific requirements of the rehabilitation task, including the available dataset size, the need for accuracy, and the computational resources at hand.
###### Abstract  
Faster-than-Nyquist (FTN) signaling is regarded as a potential candidate for improving data rate and spectral efficiency of 5G new radio (NR). However, complex detectors have to be utilized to eliminate the inter symbol interference (ISI) introduced by time-domain packing and the inter carrier interference (ICI) introduced by frequency-domain packing. Thus, the exploration of low complexity transceiver schemes and detectors is of great importance. In this paper, we consider a discrete Fourier transform (DFT) block transmission for multi-carrier FTN signaling, i.e., DBT-MC-FTN. With the aid of DFTs/IDFTs and frequency domain windowing, time- and frequency domain packing can be implemented flexibly and efficiently. At the receiver, the inherent ISI and ICI can be canceled via a soft successive interference cancellation (SIC) detector. The effectiveness of the detector is verified by the simulation over the additive white Gaussian noise channel and the fading channel. Furthermore, based on the characteristics of the efficient architecture of DFT-MC-FTN, two pilot-aided channel estimation schemes, i.e., time-division-multiplexing DBT-MC-FTN symbol-level pilot, and frequency-division-multiplexing subcarrier-level pilot within the DBT-MC-FTN symbol, respectively, are also derived. Numerical results show that the proposed channel estimation schemes can achieve high channel estimation accuracy.  
faster-than-Nyquist; multicarrier transmission; non-orthogonal waveform; channel estimation; equalization; pilot-aided; interference cancellation +
Footnote †: journal: Applied  
_Article_
# BGPcoin: Blockchain-Based Internet Number Resource Authority and BGP Security Solution
### Security and Function Requirements  
A simple and secure solution to record and audit the Internet number resource allocation and validate BGP origin route attestation is needed, and it should meet the following demands:  
* The infrastructure gives the global consistency of all allocation and updating of the resource to defend against the mirror world attack by presenting different misleading versions of the ownership directory.
* The attestation for auditing is tamper-resistant to avoid authority derecognition after resource delegation.
* An involved mechanism like the consent of revocation must avoid the delegator unilaterally withdrawing its delegations and ensure a consonant resource revocation.
* The solution must be cost-effective and easy to deploy for all involved parties. It requires no expensive introduction of new infrastructure or replacement of existing infrastructure.
## 4 Design and Simulation of Gradient and Code Metasurface
The overall coding layout is shown in Figure 9.  
Figure 6: The relationship between the rotation angle of the top structure of the polarizer and (**a**) the reflection magnitude and (**b**) the reflection phase. Figure 8: (**a**) Coded metasurface elementary particles. (**b**) Phase. Figure 7: (**a**) One dimensional periodic gradient metasurface. (**b**) Normalized electric field power distribution. (**c**,**d**) E-field distribution at different frequencies. (f = 15 GHz, f = 17 GHz). To verify the RCS reduction characteristics of the coded metasurface, a full-wave simulation was performed in CST using the time-domain solution method. As shown in Figure 10, compared with the same size PEC, the monostatic RCS is reduced by more than 10 dB in the range of 15.3-28.0 GHz at normal incidence of both TE and TM EM waves with a relative bandwidth of 65%, which indicates that the encoded metasurface is insensitive to polarization. It is worth noting that the simulated results coincide with the bandwidth in Figure 8b, where the phase difference satisfies 180\({}^{\circ}\)\(\pm\) 37\({}^{\circ}\). In addition, we note that the RCS reduction of the coded metasurface is less than the PEC reduction below 13 GHz because the phase difference of the elementary unit cell does not strictly satisfy 180\({}^{\circ}\), so it cannot be eliminated entirely. This phenomenon can be further improved by adjusting the size. More importantly, the structure proposed in this paper is compared with the conventional checkerboard design, and simulated results show that the encoded metasurface has a more significant RCS reduction. To more clearly describe the RCS reduction characteristics between the encoded metasurface and PEC, Figure 11 shows the radiation patterns of the E-plane and H-plane at 16.0 GHz, 18.5 GHz and 27.0 GHz. The phase difference at these three frequencies is 180\({}^{\circ}\) so they are selected. We can notice that the encoded metasurface at normal incidence is reduced by more than 10 dB compared with the far-field energy of the PEC surface in the range of \(\pm\)15\({}^{\circ}\), so the designed structure has a good RCS reduction capability. Figure 10: Comparison of monostatic RCS of coded metasurface and checkerboard under vertical incidence of TE and TM waves. Figure 9: Coded metasurface layout schematic. \[\theta_{r}=\frac{\mathbf{k}_{0}\sin\theta_{i}+\
abla\alpha}{\mathbf{k}_{0}}, \tag{5}\]0 Two-dimensional scattering patterns at **(a)** 16.0 GHz, **(b)** 18.5 GHz and **(c)** 27.0 GHz. Coded metamaterials are simple in design structure, easily available and conformal, and can be well integrated with equipment to reduce its RCS while keeping their own performance parameters basically unchanged. Therefore, it has broad application prospects in stealth.
# Boosting the Performance of CDCL-Based SAT Solvers by Exploiting Backbones and Backdoors
## 1 Introduction
In particular, upon restart, conflict, and/or backtrack, variables that are likely to be backbones and/or backdoors are bumped. Accordingly, competitive CDCL SAT solver(s) are developed by improving two state-of-the-art CDCL SAT solvers, MapleLCMDistChronoBT-DL-v3 and LSTech, to exploit backbones and backdoors. The main contributions of this work are threefold:  
1. To compute backbones and backdoors based on a low-overhead computational heuristic, in order to guide the CDCL SAT solver to branch on backbone/backdoor variables (Section 4.1). 2. To extend the VSIDS variable decision heuristic to exploit backbones and backdoors (Section 4.2). 3. To develop a competitive CDCL based SAT solver by improving two state-of-the-art solvers, MapleLCMDistChronoBT-DL-v3 and LSTech, to exploit backbones and backdoors (Sections 4 and 5). The rest of the paper is organized as follows: In Section 2, the definitions and notations used throughout the paper are introduced. In Section 3, a review of state-of-the-art CDCL SAT solvers is provided. In particular, the base solvers that are the focus of this study, MapleLCMDistChronoBT-DL-v3 and LSTech, are described. In addition, algorithms for computing backbone and backdoor variables are reviewed. In Section 4, a description of the proposed CDCL SAT solvers that exploit backbones and backdoors is presented. In Section 5, results of the experimental evaluation are reported. This is followed, in Section 6, by a discussion of the main findings and issues. Finally, Section 7 contains concluding remarks and suggests future directions.
## 5 Results
### Emission Reduction
#### 5.2.1 Isolation Forest  
Starting with the hours-set (Figure 5(a) for Flexible Start and Figure 7(a) for Pause & Resume), it is evident that both strategies tend to increase the percentage of reductions as the value of the hours-set increases. This increment happens because extending the time window by one day compared with the minimum duration of the workload allows for choosing the optimal starting time (as observed by the authors in [9], carbon intensity tends to vary throughout the day).  
The checking time with a value of 15 min leads to better results with Flexible Start (Figure 5(a)). This can be explained by the fact that a shorter check period makes the workload much more sensitive to the variability of the marginal emissions in different regions. With Pause & Resume, however, the best checking time is 60 min (Figure 7(a)--note the small peak on the blue bar's upper border), which represents a compromise between the bounds of the set (15 and 120). While checking with a lower frequency may lead to missing the MOER variability, checking too often may result in choosing resume moments that are local optima, thus missing better subsequent moments. In general, the maximum  
Figure 9: Emission reduction percentage for the four AI workloads using Pause & Resume strategy and the percentage set in Northern Carolina. The \(x\) axis represents the time extension (+25%, 50%, 75%, 100% of the original training time) assigned to the workload to complete the job. The \(y\) axis represents the checking time (15, 30, 60, 120 min) for carbon intensity. The \(z\) axis represents the emission reduction percentages for each specific combination of time extension and checking time: (**a**) Isolation Forest workload; (**b**) SVM workload; (**c**) HF-SCA workload; (**d**) autoencoder workload.  
average percentage reduction is 6.52% with a value of 24 h for the hours-set. This value is achieved by the Pause & Resume strategy.  
In the case of the percentage set (Figure 7a for Flexible Start and Figure 9a for Pause & Resume), regardless of the value taken from it, both strategies show very low percentage reductions. This can be attributed to the duration of the workload. For example, the fact that the Isolation Forest training lasts 4 h implies a temporal window of 8 h if the percentage-set value is 100%. In fact, the reductions are very similar to those calculated based on an hours set value of 6 h (Figure 8a).