esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The analysis shows that this code improves communication efficiency and ensures data transmission accuracy, along with strengthening device security. However, some challenges still exist to implementing them in the real IoT system. According to the authors, extended Hamming code has a lot of potential for optimization and development regarding IoT technologies. In the context of LoRaWAN and IoT networks [18], Hamming codes are integrated to improve signal decoding and increase system accuracy at low signal-to-noise ratios. In addition, they find application in serially concatenated schemes, where they significantly reduce the bit error rate (BER) in noisy communication channels while maintaining high speed. Modern software packages such as the MATLAB R2024b Communications Toolbox [19], GNU Octave 10.1.0 [20], Python Libraries v0.8 [21], and Simulink v0.8 [22] offer powerful tools for analyzing and simulating error-correcting codes. Although MATLAB and Simulink provide very rich analysis and visualization capabilities, their high prices often make them inaccessible to individual users or educational institutions with limited budgets. GNUOctave is a free alternative that provides quite similar functionality to MATLAB, though it may be limited in the number of libraries and documentation available. Otherwise, Python libraries such as CommPy and PyFinite are popular owing to their open ecosystem and flexibility, though they do require a high level of programming knowledge, which could be a problem for inexperienced users. While these tools give powerful simulation capabilities, the lack of an interactive interface may make them less effective in teaching students entering the field.
## 5 Results of the Proposed Model
### Evolution of Pore Pressure  
The simulated time history of pore pressure is shown in Figure 9, whereas the corresponding experimental data are not available. Three obvious peak amplitudes can be seen for PPT1, PPT2, and PPT3 when the pile toe passes by the observation point. This phenomenon has also been reported in other studies [16, 17, 19, 24]. Field test data of [PERSON] et al. [22] showed that if the pore pressure was measured near the pile shaft, the peak value would be reached before the pile toe passes. The reported peak response may need further investigation because only one pore pressure transducer was used, and there were five different pile installations. As for PPT4 and PPT5, the amplitudes increase monotonously as the pile toe does not pass them. Finally, a slight increase in the overall trend of the pore pressure development can be observed for PPT1, PPT2, and PPT3, and this increase is more evident for PPT4 and PPT5.  
Figure 7: Comparison between simulated and tested results of the excess pore pressure in different PPTs: (**a**) PPT1, (**b**) PPT2, (**c**) PPT3, and (**d**) PPT4.  
In soil dynamic analyses, with vibration time increasing, the excess pore pressure accumulates before the soil liquefaction. As shown in Figure 9, however, the pore pressure does not keep accumulating along with the pile driving time after the pile toe reaches the depth of the observation point. The possibility of the soil liquefaction can be excluded as the pore pressure falls off rather than remains at a stable value. As reported in [PERSON] et al. [16] and [PERSON] et al. [22], liquefaction-like phenomena also do not appear in vibratory pile driving. The pore pressure evolution can be paid more attention to better understand the penetrating mechanism of vibratory driven piles.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 5 Description of the Considered Signaling Schemes
### Aco-Ofdm  
In ACO-OFDM, data are sent only on odd sub-carriers. After applying the Hermitian symmetry, the resulting transmitted frame of symbols (before IFFT) has the following form:  
\[\big{[}\,0,\,X_{0},\,0,\,X_{1},\,0,...,X_{N/4-1},\,0,\,X_{N/4-1}^{*},\,0,...,X _{1}^{*}\,\big{]}.\]  
This way, after IFFT, the negative part of \(x_{n}\) can be clipped without loss of information [40]. The other steps are similar to those described for DCO-OFDM in the previous subsection. Note that in practice, as we will explain later in Section 5.5, a DC bias should still be added to \(\tilde{x}_{n}\) after adding the CP and  
Figure 1: Block diagram of the DCO-OFDM signaling scheme.  
scaling, in order to account for the LED I-V characteristics. Considering an \(M\)-QAM signal constellation for \(X_{k}\), the spectral efficiency of ACO-OFDM is  
\[\gamma_{\text{ACO}}=\frac{\log_{2}M\ N}{4\left(N+N_{\text{CP}}\right)}\quad\text {(pps/Hz)}. \tag{9}\]
# Visualizing Small Objects Using Amplitude-Modulated Laser Light at Microwave Frequencies
## 3 Experimental Set-Up
### SAIL System  
The second type of experiment was demonstration of synthetic aperture processing (SAIL). The target on the stage was rotated instead of rotating the beam splitter. The data were collected by rotating the rotation stage from \(-45\) degree to \(+45\) degree by \(0.1\) degree step. Here, \(100\) times measurements at each angle were performed for noise reduction. Total measurement time was about \(3\) min. The beam diameter at the target was enlarged to be larger than the rotating stage. Corner cube(s), which were placed on the rotating stage, were retroreflective point target(s). The setting is depicted in Figure 4.
# GPU-Accelerated Eclipse-Aware Routing for SpaceWire-Based OBC in Low-Earth-Orbit Satellite Networks
###### Abstract  
Low-Earth-Orbit (LEO) satellite networks offer a promising avenue for achieving global connectivity, despite certain technical and economic challenges such as high implementation costs and the complexity of network management. Nonetheless, real-time routing remains challenging because of rapid topology changes and strict energy constraints. This paper proposes a GPU-accelerated Eclipse-Aware Routing (EAR) method that simultaneously minimizes hop count and balances energy consumption for real-time routing on an onboard computer (OBC). The approach first employs a Breadth-First Search (BFS)-based K-Shortest Paths (KSP) algorithm to generate candidate routes and then evaluates battery usage to select the most efficient path. In large-scale networks, the computational load of the KSP search increases substantially. Therefore, CUDA-based parallel processing was integrated to enhance performance, resulting in a speedup of approximately 3.081 times over the conventional CPU-based method. The practical applicability of the proposed method is further validated by successfully updating routing tables in a SpaceWire network.  
LEO-SN; routing; K-Shortest Path; energy; GPU; OnBoard computer; SpaceWire 2025 IEEEexample:BSTcontrol
# Complex and Hypercomplex-Valued Support Vector Machines: A Survey
## 2 Complex and Hypercomplex Support Vector Machines Extensions
### Division Algebras
#### 2.2.3 A Division Algebraic Framework for Multidimensional Support Vector Regression  
In 2010 [PERSON] et al. tackled the multidimensional SVR design using the division algebras framework, to present an \(\epsilon\)-insensitive loss function independent of the coordinate system or basis used [23]. Although the octonions are included in the division algebras, this paper only deals with real, complex and quaternions extensions of SVR. Indeed, the authors only present the design of a quaternionic \(\epsilon_{H}\)-SVR because the real and complex extensions are cases included as restrictions of the quaternionic SVR. The approach proposes the use of a strictly L2-norm-based loss function to avoid that the trained machine could be influenced by the axe choices, due to the fact that this loss function is coordinate independent and less sensitive to outliers than a quadratic cost function. Additionally, the L2 norm includes an \(\epsilon\)-insensitive region that ensures sparseness. The authors present the derivation of a primal quaternionic SVR using this loss function, and then the Lagrange multipliers optimization method is used to compute the quadradic-valued dual training problem, which at the end, is analogous to the standard real SVR dual, but considering the use of a quaternionic-valued kernel function. Their proposal is applied to solve problems of complex-valued function approximation, chaotic time-series forecasting and channel equalization. Experimental results show that the proposal outperforms Clifford SVR (C-SVR) presented in 2000 and 2001 [7; 8], least-square SVR (LS-SVR) [24] and multidimensional SVR (M-SVR) [11].  
Furthermore, a useful comparative analysis against C-SVR [7; 8], LS-SVR [24], M-SVR [11], extreme learning machine (ELM) [25; 26], and a kernel method [27] is performed in terms of the definition of primal loss functions, dual optimization problem, independence of coordinates of the loss functions, sparsity of solution, and outlier sensitivity. It is important to note that the methodologies of the comparative analysis were proposed in 2000, 2002, 2004, 2006, and 2002 respectively.
# Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization
## 4 Results and Discussion
### Validation of the Proposed Method  
To study the denoised signal performance of the proposed signal cancellation method, the signal-to-noise ratio (\(SNR\)), mean squared error (\(MSE\)), relative squared error (\(RSE\)), and normalized root mean squared error (NRMSE) were used to evaluate the quality of the reconstructed signals. The \(SNR\) is the ratio of the original signal power to noise signal power. The \(MSE\) calculates the error by squaring the difference between the reconstructed and original signals and averaging all values. The \(RSE\) is the ratio of the \(MSE\) to the square of the difference between the original signal and the mean of the original signal. The smaller the values of \(MSE\), \(RSE\), and \(NRMSE\), the better the noise reduction effect. In contrast, a smaller \(SNR\) implies a poor noise reduction effect. The following equations were used for the comparisons (Equations (10)-(13)).  
\[SNR(\mathrm{db})=10\log\left(\frac{P_{s}}{P_{n}}\right) \tag{10}\]  
Figure 9: Relationship between IMF indices and the bandwidth threshold.  
\[MSE =\frac{1}{N}\sum_{n=1}^{N}\left(x(n)-\hat{x}(n)\right)^{2} \tag{11}\] \[RSE =\frac{\sum_{n=1}^{N}(\hat{x}(n)-x(n))^{2}}{\sum_{n=1}^{N}(\overline {x}(n)-x(n))^{2}}\] (12) \[NRMSE =\frac{\sqrt{\frac{1}{N}\sum_{n=1}^{N}(x(n)-\hat{x}(n))^{2}}}{x_{ \max}-x_{\min}} \tag{13}\]  
where \(P_{s}\) and \(P_{n}\) represent the effective powers representing the signal and noise, respectively. Here, \(x(n)\), \(\hat{x}(n)\), and \(\overline{x}(n)\) denote the original signal, reconstructed signal, and the average of the original signal, respectively.  
A detailed study was conducted, and signal cancellation performance was evaluated for different denoising methods in terms of _SNR_, _MSE_, _RSE_, and _NRMSE_. The CO\({}_{2}\) signals studied from the university classroom had a capacity of 100 students. As shown in Table 2, the proposed noise cancellation model yielded the largest absolute _SNR_ value. The _MSE_, _RES_, and _NRMSE_ of the proposed method were all smaller than those obtained by the TVF-EMD, EMD, moving average, and exponential smoothing methods, indicating that the proposed noise cancellation model had superior noise reduction quality for the CO\({}_{2}\) concentration signal.
## 4 Numerical Method
### Predictive Capability Evaluation of the Number of Layers and Neurons of Hidden Layers  
Initially, different numbers of layers and neurons in the hidden layers of the trained ANN model are consisted to choose the optimal number of layers and neurons for the PSDM. Under a constant number of 10 neurons and the ReLu activation function, one hidden layer, two hidden layers, and three hidden layers are utilized to reveal the effect of the number of layers on the predicted database. Meanwhile, the PGV of the earthquake IMs, namely the correlation, efficiency, practicality, and proficiency scalar-valued IMs used for deep underground structures, is used for the input layer. Figure 10 displays the MSE, RMSE, MAE, and R\({}^{2}\) of the ANN based on incorporating a different number of layers. As shown in Figure 10a, the most significant correlation coefficient of 0.81 belongs to the case with two layers, which means that the predicted value strongly correlates with the numerical simulation results. In addition, the MSE, RMSE, and MAE for the case of two layers are lower than or equal to the cases with one layer and three layers, while similar trends can be found in the testing database (see Figure 10b). For the case with three layers, it can be found from Figure 10b that the prediction of the ANN model compared to the testing database appears to be overfitted, which means that the number of hidden layers needs to be reasonably determined.  
Figure 11 presents the values of MSE, RMSE, MAE, and R\({}^{2}\) for the training database and testing database with different numbers of neurons for the ANN models. It is observed from Figure 11a that the coefficient of RMSE exhibits the lowest value for 10 neurons among all the examined ANN models. As the number of neurons increases, the coefficient of RMSE gradually stabilizes at 0.36. Compared to the examined ANN models, the result for 5 neurons, 10 neurons, and 15 neurons of RMSE, MSE, and MAE is the same, while their corresponding coefficients are 0.35, 0.13, and 0.25, respectively. Furthermore, the use of 5 neurons and 10 neurons in the ANN models may be regarded as more efficient than the use of other numbers of neurons according to the coefficients of R\({}^{2}\), RMSE, MSE, and MAE. From a comprehensive perspective, it is suggested that 10 neurons can be utilized to predict the structural EDP, which results in the highest prediction accuracy.  
Figure 10: Comparing the coefficients obtained from RMSE, MSE, MAE, and R\({}^{2}\) for the different numbers of hidden layers: (**a**) target database and (**b**) predicted database.
# Radiofrequency Performance Analysis of Metal-Insulator-Graphene Diodes
## 2 MIG Device and Modeling
The phenomena occurring at the interface between the graphene layer and the cathode contact are represented by the contact resistance \(R_{\text{contact}}\). Nickel-graphene interfaces have been reported as Ohmic-like, with low values of contact resistances [19] (as expected for cathodes), and were used for the development of the selected technology, as well as other MIG-diode applications [13; 14; 15; 20]. The value of \(150\,\Omega\) for this device was achieved by considering \(R_{\text{contact}}\) as a fitting parameter [16]. The corresponding contact resistivity of \(12\,\mathrm{k}\Omega\times\,\upmu\mathrm{m}\) of the graphene-Ni contact was larger than the one reported for these interfaces obtained with methods based on test structures [21]. This difference might be explained by a different fabrication process, yielding both a higher value of residual charge in the graphene used in the diode and a different contact geometry (top contact in [21], semi-embedded contact here). Figure 1: (**a**) Device cross-section of the MIG diode under study and (**b**) current density versus applied voltage in a semilogarithmic scale. Markers are experimental data [13] and lines represent the modeling results. Inset shows the same plot in a linear scale. An analysis of the simulated S-parameters (scattering parameters) of the diode including extrinsic elements (cf. Figure 2) was conducted over a frequency range of 100 MHz to 4 GHz. Figure 3a shows the magnitude of the reflection S-parameters \(S_{11}\) and \(S_{22}\) for bias voltages of \(-0.5\) V, 0 V, and 0.5 V. As depicted, \(|S_{11}|\) exhibited an identical response for the three different applied voltages; the impedance matching was poor, and thus most of the energy incident on the diode was reflected. The transmission parameters in Figure 3b consequently indicated a low transmission across the entire frequency range. In addition, reciprocal network conditions were found, i.e., \(S_{12}=S_{21}\). Due to the reactance of the diode, the phase change of the \(S_{12}\) and \(S_{21}\) parameters was \(<\)120\({}^{\circ}\) in the frequency range under study, while the phase change of \(S_{11}\) and \(S_{22}\) was \(<\)90\({}^{\circ}\). Further device engineering would be required to obtain specific phase shifts by adjusting the reactive elements in the diode (cf. Figure 2).
## 4 Methods
### \(E_{0}\) Calculation for FASat-C  
The exo-atmospheric spectral solar irradiance coefficients (\(E_{0}\)) were employed to calculate the compensation factors and to derive FASat-C TOA reflectances. In order to avoid introducing additional systematic errors, hence to ensure a consistent calculation of compensation factors and TOA reflectances for all involved sensors, the same solar irradiance model was employed. The reason is that depending  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & **5 October** & **4 August** & **12 January** & **15 June** & **25 February** & **22 July** & **7 February** \\  & **2007** & **2008** & **2009** & **2009** & **2010** & **2010** & **2013** \\ \hline UTC Time & 18:14:29 & 18:11:15 & 18:03:01 & 18:08:19 & 18:07:44 & 18:06:15 & 17:56:38 \\ Sun Azimuth Angle & 152.137 & 125.500 & 151.502 & 116.224 & 145.151 & 119.035 & 145.445 \\ Sun Elevation Angle & 44.722 & 60.636 & 26.370 & 65.377 & 37.620 & 61.920 & 30.766 \\ Inclination Angle & 98.10 & 98.09 & 98.20 & 98.10 & 98.19 & 98.08 & 98.12 \\ Satellite View Angle & 12.425 & 5.981 & \(-\)9.094 & 0.432 & \(-\)0.595 & \(-\)3.364 & 6.603 \\ \hline \hline \end{tabular}
\end{table}
Table 3: EO-1 Hyperion acquisitions employed for SBAF estimation.  
on the solar model, relative differences of up to 2% will arise [9; 51; 86], affecting the _Ai_ factor and cross-calibration results. \(E_{0}\) values were estimated with Equation (1), using the solar irradiance model of [PERSON] [87], as recommended by CEOS [31]. Sampling intervals of the solar model were linearly interpolated to the RSRs.  
\[E_{0}=\int S_{\lambda}\ RSR_{\lambda}\ d\lambda/\int RSR_{\lambda}\ d\lambda \tag{1}\]  
where S\({}_{\lambda}\) is the solar irradiance model of [PERSON] [87] expressed in W m\({}^{-2}\cdot\)m\({}^{-1}\) and RSR\({}_{\lambda}\) is the relative spectral response of the band.
## 3 Model Architecture
### Environmental Threat Entropy Variation (\(\sigma\))  
The denominator term \(1+\sigma\cdot\Delta T\) introduces a dynamic constraint on resilience capacity, accounting for environmental threat entropy variation rate (\(\sigma\)) over a specified evaluation time window (\(\Delta T\)). The threat entropy variation rate is computed as follows:  
\[\sigma=\frac{\Delta H}{\Delta t}\cdot\frac{1}{k_{B}}\ln\Omega \tag{2}\]  
where \(\Delta H\) represents the incremental entropy of system vulnerability information acquired by the attacker, \(\Omega\) denotes the number of states in the system's attack surface (e.g., open ports, services, permission combinations), and \(k_{B}\) is a normalization factor (Boltzmann constant). This formulation leverages Shannon entropy (\(H=-\sum p_{i}\ln p_{i}\)) to assess the uncertainty of attack paths and models the attacker's behavior using a Boltzmann distribution.  
The computation of \(\sigma\) is closely tied to indicators in the P dimension of the P-R-A-L framework. For instance, \(\Delta H\) is influenced by the update frequency and quality of \"threat intelligence\" and changes in \"vulnerability knowledge base coverage\". Similarly, \(\Omega\) correlates with indicators such as \"asset vulnerability scanning coverage rate\" and \"dynamic attack surface assessment capability\". A spike in \(\Delta H\) (e.g., due to a new vulnerability disclosure) increases \(\sigma\), reducing \(RC(t)\) and signaling the need for immediate defensive action. The time window \(\Delta T\) balances short-term responsiveness (e.g., hourly assessments for real-time threats) with long-term trends (e.g., monthly evaluations for Evolutionary Learning), ensuring that the model adapts to both acute and chronic threat dynamics.
# Objective Prediction of Human Visual Acuity Using Image Quality Metrics
## 2 Materials and Methods
### Subjects  
We studied 52 randomly selected eyes of 52 subjects of both sexes (50% women, 50% men) who had not suffered any eye disease or trauma. Their age range was wide (18 to 62 years old). The study did not present any invasive action. The tests to perform, their nature, and their purpose were explained to all the subjects. They agreed to undergo them and provided their consent. Experiments were conducted with the approval of the Ethics Committee of the University of Alicante and in accordance with the Declaration of Helsinki. Non-cycloplegic subjective test refractions and subjective monocular logMAR VA without correction for distant vision under photopic lighting conditions (85 cd/m\({}^{2}\)) were conducted by optometrists. Visionix VX-120 was used to capture corneal topographies by [PERSON]'s ring-based technology, aberration maps of the eye, and tonometry, pachymetry, and anterior chamber data. All these measurements were taken three times per subject during sessions separated by a 24 h time interval. LogMAR VA values were converted to decimal VA for convenience reasons following the relation \(VA_{decimal}=10^{(-logMAR)}\). Pupillary diameters were not measured during the VA assessment. Photopic conditions establish natural pupil diameters ranging between 2 and 4 mm [23] and it is assumed that VA does not vary within that pupil diameter range.  
In Figure 2, we represent the characteristics of the studied eyes. Figure 2A shows the refractive errors associated with each examined age group. The prevalence of nearsightedness over farsightedness, with a maximum spherical equivalent refraction of 0.75 D, is evident in most subjects. Figure 2B illustrates the average spherical equivalent of the eyes associated with each age range. Finally, Figure 2C depicts a histogram of the number of eyes with different subjective VA ranges.
## 5 Discussion
Despite its higher complexity and computational requirements, the GA-LSTM hybrid model shows a promising ability to improve the accuracy of unemployment rate prediction. Regarding the limitations of this study, it is essential to highlight some assumptions and restrictions that could affect the generalization of the findings to other contexts. First, the research focused exclusively on predicting the unemployment rate in Ecuador, which could limit the applicability of the results to other countries or regions with different economic and social conditions. Furthermore, the data set's small size could affect the trained models' robustness, especially when considering additional variability factors that could influence the unemployment rate. Selected input characteristics, such as minimum wage, gross domestic product (GDP), and gross fixed capital formation (GFCF), may not fully capture the complexity of the factors influencing unemployment, which could limit the ability of the model to generalize to other contexts where different variables might be more relevant. Finally, although the GA-LSTM approach showed good results in this study, training on larger data sets could require considerable time due to its computational complexity. Consequently, it is recognized that generalization of the findings to other contexts should be done with caution, considering these limitations and the need to validate the model in different contexts before its practical implementation. The selection of input data used in prediction systems is a critical issue. Inflation, minimum wage, GDP, and GFCF characteristics were selected from official sources to ensure consistency and reliability of the analyses. However, some possible limitations or biases in the data set are the variability in the availability of certain data (periods of missing data) and the fact that the selected economic indicators may not fully capture all the factors that influence unemployment trends.