esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 2 Materials and Methods
### Model Evaluation Criteria  
In this study, mean absolute error (MAE), the root mean squared error (RMSE), and R-squared (R\({}^{2}\)) were chosen as the evaluation metrics for prediction performance. The MAE represents the mean absolute difference between predicted and true values, reflecting the actual magnitude of prediction errors. RMSE, which reflects the spread of errors, is the square root of the mean squared difference between predicted and true values. In the definitions, the sum of squares of the differences between the predicted values and the true values is called the residual sum of squares, while the sum of squares of the differences between the true values and their mean is called the total sum of squares. The R\({}^{2}\) value denotes the quality of the model's fit, calculated as 1 minus the ratio of residual sum of squares to total sum of squares. The closer R\({}^{2}\) is to 1, the better the fit of the model. The specific expressions for these three metrics are as follows:  
\[RMSE=\sqrt{\frac{1}{n}\frac{\sum\limits_{i=1}^{n}{{{{\left({y_{i}-\hat{y}_{i}} \right)}^{2}}}}}} \tag{4}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}{{{\left|{{\left({y_{i}-\hat{y}_{i}} \right)}}\right|}}} \tag{5}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{{{\left({y_{i}-\hat{y}_{i}}\right)}^{2}}}} {\sum\limits_{i=1}^{n}{{{\left({y_{i}-\overline{y}}\right)}^{2}}}} \tag{6}\]  
where \(y_{i}\) represents the true values, \(\hat{y}_{i}\) represents the predicted values, \(\overline{y}\) represents the mean value, and \(n\) represents the number of samples. A higher \(R^{2}\) and lower \(RMSE\) and \(MAE\) indicate better model performance, with smaller discrepancies between the predicted and true values.
# Hardware Platform-Aware Binarized Neural Network Model Optimization
## 5 Experimental Results and Discussion
### Optimal BNN Search  
Based on the basic design strategies defined in Section 3 and the **Deepbit** method, in this subsection, the searching process performed on our experiments with a specific configuration is described. In addition, the corresponding results are also shown to prove the efficiency of the proposed method. In particular, for FPGA, the target for the optimal BNN architecture is 98.4% accuracy with minimal hardware resources. Meanwhile, the target for GPU and RRAM is simply developing a BNN architecture with maximum accuracy. As mentioned in the previous section, the **Deepbit** method takes three inputs:  
1. L = {3, 4, 5, 6, 7, 8}
2. maximum number of channels = 50
3. minimum accuracy threshold = 98.4%  
In terms of the training process, a combination of the Adam and SGD optimizer is applied. More specifically, the SGD optimizer is used for the 100 epochs. The learning rate and momentum are set at 0.03 and 0.5, respectively. Each training is conducted 10 times for phase 1 and phase 2 of the architectural search method. In the final phase, at the time searching for the final model, the training has repeated a total of 10 times. Six required optimal BNN models correspond to a depth from 3 to 8. The width configuration for each model is defined in Table 5. On the other hand, as mentioned in Section 3, a layer module for BNN models is depicted in Figure 2. Each module comprises a convolution operation followed by a batch norm and a Tanh activation function. Moreover, the last two convolution layers comprise the Max pooling layer, and the last layer of all BNN models is always the fully connected layer. Finally, the optimal width for each layer of our BNN models is found by the **Deepbit** architecture search method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **md1** & **md2** & **md3** & **md4** & **md5** & **md6** \\ \hline  & **3 Layers** & **4 Layers** & **5 Layers** & **6 Layers** & **7 Layers** & **8 Layers** \\ \hline
**Layers** & **Channels** & **Channels** & **Channels** & **Channels** & **Channels** & **Channels** \\ \hline
1 & 26 & 23 & 20 & 19 & 11 & 8 \\
2 & 24 & 21 & 21 & 19 & 9 & 15 \\
3 & 31 & 22 & 21 & 14 & 20 & 17 \\
4 & & 21 & 21 & 16 & 15 & 17 \\
5 & & & & 20 & 18 & 20 & 17 \\
6 & & & & & 19 & 20 & 10 \\
7 & & & & & & 20 & 17 \\
8 & & & & & & 17 \\ MAC OPs & 5,989,368 & 7,756,896 & 10,206,896 & 8,916,432 & 9,964,640 & 9,854,684 \\ \hline \end{tabular}
\end{table}
Table 5: The width configuration of optimal BNNs found by Deepbit method.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 4 Constellation Navigation and Orbit Determination Technology
### LiAISON Autonomous Navigation Method
As the Pathfinder mission of NASA's Gateway program, the Cislunar Autonomous Positioning System Technology Operations and Navigation Experiment (CAPSTONE) was launched on 28 June 2022 [102], heading towards a near-linear halo orbit around the Moon to demonstrate the core technology components of the lunar autonomous positioning system, and to test the ranging ability between the CAPSTONE satellite operating in NRHO orbit and the LRO probe.
# Microscope-Based Optical Frequency Comb Generator for 200 GHz Spaced WDM Data Transmission System
## 3 Results
### OFC Generation in DKS Regime  
Next, we simulate OFC generation in the DKS regime in the framework of Equation (7). We consider TE modes (for TM modes, the results will be almost the same). It is known that stable DKS can exist in an anomalous dispersion region only for specific conditions for a pump power and normalized detuning \(\Delta\) (\(\Delta=\delta_{\text{p}}/\alpha\)) [33; 41; 42; 43]. If the solution in the form of DKS exists in this case, one particular value of DKS peak power corresponds to each admissible detuning [41; 43]. For a pump power less than a threshold for this detuning, DKS cannot exist, but CW is a solution. We set pump power slightly higher than this threshold and study properties of generated DKS for different values of \(\Delta\). We set pump frequency at 193.1 THz assuming that the nearest resonant WGM can be shifted to 193.1 THz due to the thermal effects caused by partially dissipated pump power and/or external heating of a microsphere. Figure 8a shows the DKS spectrum calculated for \(\Delta=50\). This spectrum is asymmetric with respect to the pump frequency, which is explained by the influence of the Raman nonlinearity and agrees with the results presented in [43; 44]. Figure 8b demonstrates the spectral envelopes of stable DKSes simulated for different \(\Delta\). The larger the normalized detuning, the broader the spectrum is. For example, for \(\Delta=10\), the spectral width at the level of -30 dB is 3.8 THz, but for \(\Delta=70\), the spectral width is 8.8 THz. Next, we count a quantity of spectral lines (harmonics) in OFC spectra with intensity higher than -30 dB (see Figure 8c). The quantity of lines satisfying this condition increases from 19 for \(\Delta=10\) up to 44 for \(\Delta=70\). For \(\Delta>70\), DKS is unstable and we do not consider intracavity nonlinear dynamics for this case. Note that due to higher-order dispersion, the range of DKS stability is slightly wider than with allowance for only the second-order dispersion presented in [43]. We also find DKS duration (full width at half maximum, FWHM) in the time domain as a function of \(\Delta\) (see Figure 8d). For larger \(\Delta\) (when the spectrum is wider), the duration is shorter according to the Fourier-transform limitation (379 fs for \(\Delta=10\) and 169 fs for \(\Delta=70\)).  
Figure 8: (**a**) Normalized intracavity spectral intensity of DKS for \(\Delta=\)50. (**b**) Envelopes of normalized DKS spectra. (**c**) Number of spectral lines (harmonics) with spectral intensity higher than -30 dB relative to the maximum as a function of normalized detuning. (**d**) FWHM DKS duration as a function of normalized detuning.
## 4 Conclusions  
Recently, ESSs for electricity systems have been utilized in numerous ways. (For example, they are connected to renewable resources and used to discharge large quantities of electricity at peak usage times.) A plan to implement ESSs for F/R has recently been introduced in South Korea; other countries have already implemented them, because they allow for the stabilization of electricity systems in a way that compensates for its higher costs and encourages more efficient fuel use. We carried out a benefit estimation in anticipation of the introduction in South Korea of an F/R ESS.  
We present a novel methodology for assessing the anticipated UBs. First, we extrapolated the future SMP by using a weighted average of marginal plant probability and fuel cost for each resource. We then calculated the UB as the sum of the energy and A/S market benefits, as determined by the electricity market and industrial structure. In the case study, we found the scale of benefits to range from $91 million to $540 million for 500 MW, and noted that among the three cases, case C--in which ESS for F/R is operated all day--offers the largest benefit. Although the results of the simulation models present different benefit levels, all cases show large and positive benefits; none show a negative result. Thus, we conclude that the introduction of ESSs for F/R in South Korea would enhance power system stability and bring about substantial UBs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**EP Benefits**} & \multicolumn{2}{c}{**ASP Benefits**} & \multicolumn{2}{c}{**UBs**} \\ \cline{2-7}  & **52 MW** & **500 MW** & **52 MW** & **500 MW** & **52 MW** & **500 MW** \\ \hline A & 33,031 & 317,606 & 3192 & 30,689 & 36,223 & 348,295 \\ B & 7900 & 75,966 & 1596 & 15,345 & 9496 & 91,311 \\ C & 49,454 & 475,515 & 6703 & 64,448 & 56,157 & 539,963 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Projected Energy Market Price (EP) Benefits, Ancillary Service Price (ASP) Benefits, and Utility Benefits (UBs), 2019–2028 (Unit: Thousands of $).  
Funding: This work was funded by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (grant number 20171210200810). Conflicts of Interest: The authors declare no conflict of interest.
## 3 Theoretical Foundations
### Generalization Guarantees
If the constraints are not accurately modeled or do not fully reflect the actual physical system, a mismatch arises. Addressing this error typically involves refining the physical model or more accurately incorporating the constraints into the network's loss function. * Data Noise (10%) represents the uncertainty or noise inherent in real-world data. Experimental data often contain noise due to measurement errors, which can lead to discrepancies between the network's predictions and the observed values. Reducing this error may involve improving data quality or applying noise filtering techniques. This error decomposition provides valuable insights into the sources of error in PINN-based models and emphasizes the importance of addressing each component to improve the model's generalization performance. Researchers and practitioners can focus on refining the approximation model, optimization techniques, physical constraints, and data quality to achieve more accurate and reliable predictions.
## 4 Discussion
### Prediction of Plant Biomass and Biological Yield Based on PLSR  
The performance of SRIs for estimating the three plant traits depends on the growth stage and type of SRIs. Additionally, almost all vegetation-related SRIs were grouped in one dimension as shown in PCA (Figure 5). One reason for these findings may be that the different SRIs are always formulated based on two to three sensitive wavelengths, making it difficult to build a unified index that includes enough wavelengths that are sensitive to all biophysical and biochemical changes that logically occur in plants in diverse phenological stages. These limited wavelengths in each index make it difficult to overcome the impacts of growth conditions, soil background, and saturation of the leaf area index, chlorophyll content, and biomass on the performance of the index of interest. The same type of SRIs exhibits high multicollinearity among themselves, which reduces the ability of such indices to detect a variation among genotypes that exhibit similar reflectance and absorption behavior. Therefore, previous studies have reported that using a combination of SRIs and multivariate regression models such as PLSR can overcome these limitations and significantly improve the predication accuracy of relevant plant traits [43; 52; 54; 80; 82; 84; 85; 86; 87; 88; 89]. Most of these studies reported that using several SRIs in tandem with the PLSR model resulted in better performance in the estimation of plant traits, such as plant biomass, grain yield, and plant water content, as compared with a single index. In this study, by combining SRIs with PLSR, assessing plant biomass at the early growth stage was possible. Interestingly, by coupling both methods, a good prediction of BDW-BT (57%), BDW-AN (82%), and BY (55%) was obtained at the BT stage (Figure 6). However, the highest predictions of BDW-BT, BDDW-AN, and BY were obtained at the BT (57%), AN (86%), and EMG (61%) stages, respectively (Figure 6). The results in Figure 4 generally suggest that the combination of different SRIs and PLSR models is a promising approach to predict the plant biomass of a large number of wheat genotypes under high-salinity conditions at the BT stage because this combination can simultaneously cover several response variables and can account for multicollinear variables.
# Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization
## 4 Simulations and Performance Verification
### Performance Comparison of the LC-PSO-ELM Based on Different Fuzzy Membership Functions  
The choice of activation (basis) functions of the ELM learning algorithm is problem dependent [33], which means that different fuzzy membership function in the LC-ELM and the LC-PSO-ELM algorithms will affect the generalization performance. Meanwhile, [PERSON] pointed out that the window function that is used in the LC-ELM does not satisfy the necessary conditions of window function that are required by LCFNN. As a result, it is possible that the improper window function can cause the LC-ELM to have the same discriminant with the basic ELM [34]. For this reason, three different fuzzy membership functions of Gaussian function, reversed sigmoid function and reversed tanh function were used to verify the results. The simulation results of 10 trials with the three different fuzzy membership functions in the LC-PSO-ELM algorithms on regression and classification problems are listed in Table 10.  
As can be seen from Table 10, the simulation results demonstrate that the LC-PSO-ELM learning algorithm has different generalization performance with different fuzzy membership functions, and the better test accuracy can be obtained in the LC-PSO-ELM algorithm using the reversed sigmoid function.
## 3 Simulation Process of Transformer Inrush Current Using EMTP
### Method 1
#### 3.1.2 Step 2: Input of Current-Flux Values  
(1) Input of current-flux values using Type 98 device  
Type 98 device in the Branch/Nonlinear library of EMPT can be used to enter the current-flux value. The results obtained by running SATURA in Step 1 are inputted in this device. The current value in steady state is inputted in the CURR section of the Type 98 device, while the flux value in steady state is inputted in FLUX section. However, for this value, the first value among the current-flux values extracted in Step 1 is inputted. Meanwhile, the current-flux values are inputted in the Characteristic tab. The current-magnetic flux curve is then generated by clicking the View button at the bottom of the screen.  
Meanwhile, this Type 98 device corresponds only to the magnetizing current in the actual transformer equivalent circuit. The leakage resistance and leakage reactance in the primary side can be represented by introducing resistor and inductor model in the Branch/Linear library of EMPT. In addition, the transformer in the equivalent circuit can be represented by the transformer model. It should be noted that a single Type 98 device supports only a single-phase transformer model.  
Figure 1: The simulation methods of transformer inrush current.  
Moreover, a three-phase transformer model can be developed using three Type 98 devices. The method will be described in detail in Step 3.  
(2) Input of current-flux values in the transformer model  
The next step is to input the current-flux value in the transformer model. The saturable three-phase transformer model in the Transformers library is selected among the models provided by EMTP. On the Attributes dialog box that appears, the primary and secondary voltages, leakage resistance, and leakage reactance can be inputted. Meanwhile, the Characteristic tab allows the users to input current-flux values.
###### Abstract  
The focus of this article is to present a novel crypto-accelerator architecture for a resource-constrained embedded system that utilizes elliptic curve cryptography (ECC). The architecture is built around Binary Edwards curves (BEC) to provide resistance against simple power analysis (SPA) attacks. Furthermore, the proposed architecture incorporates several optimizations to achieve efficient hardware resource utilization for the point multiplication process over \(GF(2^{\text{m}})\). This includes the use of a Montgomery radix-2 multiplier and the projective coordinate hybrid algorithm (combination of Montgomery ladder and double and add algorithm) for scalar multiplication. A two-stage pipelined architecture is employed to enhance throughput. The design is modeled in Verilog HDL and verified using Vivado and ISE design suites from Xilinx. The obtained results demonstrate that the proposed BEC accelerator offers significant performance improvements compared to existing solutions. The obtained throughput over area ratio for \(GF(2^{233})\) on Virtex-4, Virtex-5, Virtex-6, and Virtex-7 Xilinx FPGAs are 9.43, 14.39, 26.14, and 28.79, respectively. The computation time required for a single point multiplication operation on the Virtex-7 device is 19.61 us. These findings indicate that the proposed architecture has the potential to address the challenges posed by resource-constrained embedded systems that require high throughput and efficient use of available resources.  
elliptic curve cryptography; binary Edwards curve; scalar multiplication; Montgomery radix-4 multiplier; FPGA 2023
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 3 Proposed Underwater Acoustic Modem
### Multi-Carrier Spread Spectrum
Then, to perform the despreading process, each subcarrier is multiplied by the conjugate of the correct spreading code. Before summing in frequency and time to despread the signal, subcarriers are reordered and equalized. Like in OFDM, equalization is performed by multiplying each subcarrier by conjugate of channel frequency response. A least-square estimation based on comb type pilot arrangement [18] is used. QPSK pilots are allocated in both time and frequency according to the half time and bandwidth coherence. Finally, despreading is performed in the frequency domain by summing \(Q\) subcarriers and in the time domain by summing \(M\) consecutive OFDM symbols. Moreover, a method to detect the signal, synchronize it and estimate the Doppler factor is used. This method is described in Appendix A.
# An Approach to the State Explosion Problem: SOPC Case Study
## 4 Preliminaries
### State Space Explosion Problem  
In the term state space, state refers to an ordered set of variables that can determine the minimum number of system states in a system. State space refers to the collection of all possible states of the system. The state space can simply be regarded as a space with a state variable as the coordinate axis, so the state of the system can be represented as a vector in this space. State space representation is a mathematical model that represents a physical system as a set of inputs, outputs, and states, and the relationship between inputs, outputs, and states can be described by many first-order differential equations.  
The state variable of a system refers to the smallest subset of system variables that can represent the complete state of the system at any time. To represent the minimum value n of the required state variable for a system, it is usually also the order of the system's differential equation. If the system is represented by a transfer function, the minimum number of state variables is equal to the order of the denominator polynomial of the transfer function. The number of state variables in a circuit is often the number of states in the circuit.  
The problem of state space explosion is that the number of states generated during model detection increases exponentially, far exceeding the memory storage and search capabilities of computers, which is one of the challenges faced by model detection as shown in Figure 4. The upper half of Figure 4 represents a typical SOPC structure, where the SOPC system connects multiple IPs through the AXI bus, and data exchange between IPs is achieved through bus interfaces. The lower half of Figure 4 represents the state transformation diagram of the SOPC model. The S represents a set of finite states, \(S_{0}\in S\) is the initial state set, and \(R\subseteq S\times S\) represents all state transition relationships.  
Figure 4: _Cont._  
From the analysis in the previous section, it can be seen that the scale of the SOPC model is getting larger and larger. Currently, model verification tools require more than 2 h which has exceeded the capacity of ordinary computing. At the same time, the SOPC model faces multiform IPs that cannot be recognized by formal tools. Therefore, formal verification technology cannot be well applied to the SOPC model at present.  
Aiming at the problem of SOPC state space reduction, the method of this paper is to establish a system-level formal design SOPC model and propose a variable reduction method based on RTL code and a branch relationship reduction method based on verification properties. Through the above methods, the problem of SOPC state space explosion can be effectively tackled.