esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 3 Evaluation Method
### High-Performance Computing Benchmark
#### 3.3.2 Finite Impulse Response Benchmark (FIR)  
In the field of space signal processing, there is a growing demand for real-time and fast signal processing. Finite impulse response (FIR) filters are a type of filter structure that can be used to implement almost any type of frequency response digitally. FIR filters achieve filtering by using a series of delays, multipliers, and adders to create the filter's output. The relationship between the output of an FIR filter with length \(N\) and the input time sequence \(x[n]\) is given by a finite convolution form, as shown below:  
\[y[n]=b_{0}x[n]+b_{1}x[n-1]+\cdots+b_{N}x[n-N] \tag{1}\]  
\[y[n]=\sum_{i=0}^{N}b_{i}x[n-i] \tag{2}\]  
where \(x[n]\) is the input signal; \(y[n]\) is the output signal; \(b_{i}\) represents the filter coefficients that constitute the impulse response, also known as tap weights.  
\(N\) is the filter order; an \(N\)th order filter has \(N+1\) terms on the right side. \(x[n-i]\) is commonly referred to as a tap, and based on the structure of tap delay lines, it provides delayed input to the multiplication operation in many implementation methods or block diagrams.
# The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection
## 1 Introduction
### Review and Further Discussion of BEMD
#### 2.1.2 Extraction Criterion of True Intrinsic Mode Functions (IMFs)  
The majority of vibration signals will be decomposed into excessive IMFs, which are described as spurious vibratory components that directly affect researchers' capacity to analyze signal features. These spurious IMFs also result in enormous interferences, reflecting the peculiarities of the chatter and allowing diagnosis of the mechanical faults of the grinder. One of the major deficiencies is that the estimation of the authenticity of IMF heavily depends on the user's experience. Thus, a reliable method is required to identify and remove spurious components, which is of great importance in extracting the actual vibration mode and the corresponding features of the time-frequency domain.  
Spurious IMFs may be caused by several factors: (1) IMFs defined by BEMD algorithms are based on numerical analysis only and do not consider the vibration characteristics of system in the physical sense; (2) the stopping criterion of sifting results in a phenomenon of excessive decomposition and end effects are not well processed; (3) vibration signals mixed with noise or pulse interference will produce a series of spurious components with high frequency in the decomposition process. Since IMFs are recognized as the orthogonal expression of the signals, true IMFs have a higher correlation with original signals than spurious IMFs. Mathematically, a correlation coefficient is used as a statistical indicator to reflect the degree of correlation between variables. It is based on the deviation of two variables and their respective average mean, computed by the method of product comments, as shown in Equation (5).  
\[\rho=\frac{Cov(X,Y)}{\sqrt{D(X)}\sqrt{D(Y)}}=\frac{E[(X-E(X))(Y-E(Y))]}{\sqrt{ D(X)}\sqrt{D(Y)}} \tag{5}\]  
where \(Cov(X,Y)\) denotes the covariance between the original signal and the IMF, \(E(X)\) and \(D(X)\) denote the expectation and variance of the original signal, respectively, and \(E(Y)\) and \(D(Y)\) denote the expectation and variance of the \(k\)th IMF, respectively. Therefore, it is reasonable to employ the correlation coefficient as an index to exactly distinguish the spurious components from true IMFs and then classify them as a part of residual [16; 29]. In view of this, if the correlation coefficient of each IMF, \(\rho_{k}\) (\(k=1,2,...\), \(n\)), is computed, the true IMFs can be extracted by the following method (Table 1).  
The variable \(\lambda\) in Table 1 is a fixed threshold that is generally adopted as a ratio of the maximum correlation coefficient, wherein \(\eta\) is a ratio coefficient larger than 1.  
\[\lambda=\max(|\rho_{k}|/\eta),\ k=1,2...n, \tag{6}\]  
\begin{table}
\begin{tabular}{c} \hline \hline If \(|\rho_{k}|\geq\lambda\), \\ Reserve the \(k\)th IMF \(c_{k}\), \\ Else, \\ Estimate \(k\)th IMF, and \(r_{n}=r_{n}+c_{k}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: The extraction criterion of true intrinsic mode functions (IMFs) based on the correlation coefficient.
## 3 Blast Hole Pressure Measurement Experiment
### Blast Hole Pressure Measurement System  
The purpose of the experiment was to measure the explosion pressure in a blast hole for each string material and to evaluate the pressure resistance of the stemming material. Conventional sand and STF-based stemming materials were applied to evaluate their resistance capability under explosive pressure. Figure 6 shows the shape of each stemming material.  
To measure the explosive pressure data from the blast hole, a pressure meter with cerabar (PMC) model of manufacturer Sensys capable of instantaneous dynamic shock pressure measurement was applied, and the MREL's MicroTrap was used to collect the data and set the detonation time trigger. The hydraulic shock pressure sensor had a pressure measurement range of 0-50 MPa. To measure the explosive pressure in the blast hole, the sensor was connected to a nylon tube filled with water using a coupling connector, placed in a water tube. To measure the explosive pressure in the blast hole, the sensor was connected to a nylon tube filled with water using a coupling connector, placed in a water tube. An explosive pressure-measuring device through hydraulic pressure was manufactured. Additionally, the manufactured water tube was protected with an industrial hose made of piezoelectric material to prevent damage when inserted into the blasting hole. Figure 7 shows the measuring tool applied to the blast pressure measuring system in the blast hole through hydraulic pressure. To collect the corresponding explosive pressure data as a time history at the same time as detonation using MicroTrap, a trigger line was attached to the explosion in the blast hole, and the break circuit trigger method was applied in which the connection signal was cut by the detonation of the explosive, and the measurement was finally started.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Pressure Range (MPa)** & **Analog Output (V)** \\ \hline
1 & 0 & 0.985 \\
2 & 25 & 2.98 \\
3 & 50 & 4.984 \\
4 & 0 & 0.988 \\
5 & 25 & 2.97 \\
6 & 50 & 4.955 \\
7 & 0 & 0.98 \\
8 & 25 & 2.99 \\
9 & 50 & 4.987 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the Explosion Pressure sensor calibration.  
Figure 6: Stemming materials used in blast hole pressure measurement experiment.  
The length of the blast holes drilled for measuring the impact pressure of the stemming material was 3.2 m, and the explosives charge length was 1 m. The emulsion series Newmite Plus 1 (\(\Phi\)50 mm, 2.5 kg) manufactured by Hanhwa with an explosion speed of 5700 m/s were applied to the experiment. On top of the explosive, a test stemming material (sand or STF) of 0.6 m was applied; a 0.5 m water tube was inserted to measure the pressure caused by the explosion as the water pressure; and in the 1.1 m remaining at the top of the blast hole, general sand stemming was inserted. Figure 8 shows a schematic of the blast hole measurement system.  
Figure 7: Measuring tool applied to blast pressure measuring system.
# Roll Control of Morphing Aircraft with Synthetic Jet Actuators at a High Angle of Attack
## 2 Dynamic Modeling for Morphing Aircraft with SJA
### Modeling of Roll Moment Due to the Morphing Motion  
When the left wing is deployed, the roll moment of the aircraft increases, whereas the roll moment decreases if the right wing is deployed. The change of the roll moment coefficient \(\Delta C_{y,\text{morphing}l}\) due to the morphing of the left wing takes the form  
\[\Delta C_{y,\text{morphing}l}=k_{2}\Delta L_{l}, \tag{7}\]  
where \(k_{2}=0.06466\) is a constant calculated by the Tornado Vortex Lattice Method (VLM) [29]. The corresponding change of the roll moment is  
\[\Delta M_{x}^{\text{morphing}l}=q\Delta S\Delta L_{l}\Delta C_{\text{morphing }l}=qck_{2}(\Delta L_{l})^{3}. \tag{8}\]  
Therefore, the total change of the roll moment caused by the morphing motion can be expressed in the following form:  
\[\begin{array}{ll}\Delta M_{x}^{\text{morphing}}&=\Delta M_{x}^{\text{morphing }l}+\Delta M_{x}^{\text{morphing},r}\\ &=qck_{2}(\Delta L_{l})^{3}-qck_{2}(\Delta L_{r})^{3}\\ &=qck_{2}\delta_{\text{morphing}l}-qck_{2}\delta_{\text{morphing}r}\end{array}, \tag{9}\]  
where \(\delta_{\text{morphing}}\) is the intermediate input variable of the morphing span.
## 4 Conclusions  
Based on the GNSS-R reflection reference surface elevation correction principle, this study uses the main parameters that determine the real-time variation of sea surface height, the ocean tide, to construct ocean tidal correction positioning method. The OTCP method is applied to correct the specular reflection point from geoid to the ocean tidal surface. The positioning error caused by the time-varying elevation difference of the reflection reference surface is reduced, the positioning based on the static-state elevation correction is improved. In addition, the different effects of the tides on the gradient of the tidal positioning accuracy improvement in the offshore and deep sea are discussed. According to the research results, the OTCP method improves the positioning by about 0.31 m relative to the geoid positioning. The improvement of positioning accuracy has a good correlation with the tidal height of different amplitudes and phases combinations. In the offshore, the tidal height gradient is larger than the deep sea, and the tidal positioning correction gradient modulo has a good response to the tidal height gradient modulo. In the deep sea, the sensitivity of the response decreases compared to the offshore.  
[PERSON] and [PERSON] conceived the experiments and wrote the paper, [PERSON] designed and performed the experiments; [PERSON] provided theory support for the index requirement of GNSS-R sea surface altimetry; [PERSON] provided suggestion to the experiments and the analysis of the results.  
This work was supported by the National Nature Science Foundation of China (41574014, 41774014), the Frontier Science and Technology Innovation Project (085015) and the Innovation Workstation Project of the Science and Technology Commission of the Central Military Commission, and the Outstanding Youth Foundation of the China Academy of Space Technology.  
The TDS-1 SGR-RESI data used in this study is supported by the MERRByS team of Surrey Satellite Technology Limited.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Offshore**} & \multicolumn{2}{c}{**Deep Sea**} \\ \cline{2-5}  & **mean** & **std** & **mean** & **std** \\ \hline Tidal Height Gradient & & & & \\ Modulo (m) & & & & \\ \hline Correction Gradient & & & & \\ Modulo (m) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tidal height gradient modulo and the corresponding tidal positioning correction modulo of offshore and deep sea segments.  
Figure 8: Tidal height gradient modulos, the corresponding tidal positioning correction gradient modulos and their fittings of the offshore and the deep sea segments.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
###### Abstract  
This work presents an algorithm to perform autonomous navigation in spacecraft using onboard magnetometer data during GPS outages. An Extended Kalman Filter (EKF) exploiting magnetic field measurements is combined with a Single-Hidden-Layer Feed-forward Neural Network (SLFN) trained via the Extreme Learning Machine to improve the accuracy of the state estimate. The SLFN is trained using GPS data when available and predicts the state correction to be applied to the EKF estimates. The CHAOS-7 magnetic field model is used to generate the magnetometer measurements, while a 13 th-order IGRF model is exploited by the EKF. Tests on simulated data showed that the algorithm improved the state estimate provided by the EKF by a factor of 2.4 for a total of 51 days when trained on 5 days of GPS data.  
space systems; orbit determination; autonomous navigation; nanosatellites; Extended Kalman Filter; Artificial Intelligence; Neural Networks; Extreme Learning Machine
## 2 Materials and Methods
### Dynamic Vapor Sorption (DVS)  
DVS is a gravimetric technique used to measure changes in sample mass as a function of relative humidity (% rh). This analysis is focused on the adsorption and desorption capacity of the sample. The protocol involves varying the humidity from 0% to 85% at a constant temperature of 23 \({}^{\circ}\)C. For adsorption experiments, equilibrium points were determined at 0, 5, 10, 15, 20, 30, 35, 50, 75, and 85% rh. For desorption experiments, equilibrium points were determined at 0, 15, 30, 35, 50, 75, and 85% rh. Each measurement was performed in duplicate using the Sorption Test System from Prolumid [32].  
The adsorption and desorption isotherms typically exhibit three distinct regions, corresponding to monolayer adsorption, multilayer adsorption, and liquid water. To model  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{6}{c}{**Conditions of Preparation of the HS1 and HS2/Water Mixture (Ratio = 0.01) for “Agro-Based**} \\  & & & & \\  & & & & & \\ \hline Simulation parameters for & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table}
Table 1: Parameters studied to simulate the preparation conditions of “agro-based concrete”.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Condition of Preparation of the HS1 and HS2/Water Mixture (Ratio = 0.01) for “Binderless Particle Board” Simulation**} \\  & & & \\  & & & \\ \end{tabular}
\end{table}
Table 2: Parameters studied to simulate the preparation condition of a “binderless particleboard”.  
physical water adsorption, the Brunauer-Emmett-Teller (BET) model [37] was employed, particularly to describe monolayer adsorption at lower relative humidities (0% to 35% rh), which is characteristic of Van der Waals interactions between water molecules and hydrophilic groups (Equation (1)).  
\[X_{eq}=\frac{X_{m}C\varphi}{(1-\varphi)(1+(C-1)\varphi)} \tag{1}\]  
The BET model was used to fit the experimental data (equilibrium moisture content, \(X_{eq}\), and relative humidity, \(\varphi\)) to calculate the specific surface area of the sample, \(a_{s}\) (m\({}^{2}\)/g) (Equation (2)).  
\[a_{s}=\frac{X_{m}La_{m}}{M_{w}} \tag{2}\]  
where \(X_{m}\) is the monolayer moisture content (kg water/kg dried solid, d.b.) and \(C\) is dimensionless. This model assumes that \(X_{m}\) represents the quantity of water molecules required to form a monolayer to cover the entire surface and without diffusion of water through the material.  
In the above equation, \(a_{s}\) is the specific surface area of the sample (m\({}^{2}\)/g), \(L\) is Avogadro's constant (6.023 \(\times\) 10\({}^{-23}\) mol\({}^{-1}\)), \(a_{m}\) is the cross-sectional area of a water molecule (1.08 \(\times\) 10\({}^{-19}\) m\({}^{2}\)) and \(M_{w}\) is the molecular weight of a water molecule (18 g\(\cdot\)mol\({}^{-1}\)).
# 6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap
## 4 The Technologies for Bridging the Service-Fruition Divide  
Over the past couple of years, the presence of a pandemic has accentuated the problem of the service-fruition divide, affecting those who have inabilities to access digital services, not because of infrastructural issues, but rather of their physical conditions, handicaps, age, or digital illiteracy. Applications revolving around 6G are proposing to support a set of digital interfaces at a large scale, not only in localized and specialized environments. This is possible for the first time in history, as these interfaces, while promoting ease of use and human centrality, have requirements in terms of latency and reliability that are toostringent to overcome with legacy technologies. The most debated solutions are presented in this section.
# Multi-Phase Stator Current Tracking with Gradual Penalization of Commutations
## 2 Multi-Phase Predictive Stator Current Control
### Control Action Computation in Single-Vector and Multi-Vector Cases  
A unified approach is now delivered to cope with control action computation both in (a) the case of multi-vector approximation to the deadbeat solution and (b) the case of single-vector minimization of a cost function.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Stator resistance \(R_{s}\) & 12.85 & \(\Omega\) \\ Rotor resistance \(R_{r}\) & 4.80 & \(\Omega\) \\ Stator leakage inductance \(L_{ls}\) & 79.93 & mH \\ Rotor leakage inductance \(L_{tr}\) & 79.93 & mH \\ Mutual inductance \(L_{M}\) & 681.7 & mH \\ Rotational inertia \(J_{m}\) & 0.02 & kg m\({}^{2}\) \\ Number of pairs of poles \(P\) & 3 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the utilized 5 pIM.  
In case (a) and using Equation (10), one can derive that the voltage applied during the \(k+1\) interval would drive the error to zero at \(k+2\). Denoting it by \(V^{*}\), it turns out to be  
\[V^{*}=B^{-1}\big{(}i^{*}(k+2)-A(\omega)\hat{i}(k+1)\big{)}. \tag{11}\]  
Now, voltage \(V^{*}\) may not be one of the 32 VVs that the VSI can produce. Then, it must be synthesized using various basic VVs applied at different times and for different sub-periods during the whole sampling period. These algorithms suppose open-loop \(x-y\) current elimination, so they can work with just the \(\alpha-\beta\) plane. Simple trigonometric rules and look-up tables can be used to identify the region with very low computing requirements.  
In case (b), just a single basic VV is applied. The deadbeat concept cannot be applied, so the commanded voltage is found to minimize a cost function, such as  
\[J(k+2)=\|\hat{e}_{\alpha\beta}(k+2)\|^{2}+\lambda_{xy}\|\hat{e}_{xy}(k+2)\|^{2 }+\lambda_{sc}SC \tag{12}\]  
The VSI configuration minimizing \(J\) is applied and held for the \(k+1\) interval. Parameter \(\lambda_{xy}\) is a weighting factor that allows balancing \(\alpha-\beta\) tracking and \(x-y\) regulation. The parameter \(\lambda_{sc}\) is another weighting factor used to penalize changes at the VSI, and \(SC\) stands for switch changes and is computed considering the state \(K_{j}\) of each VSI phase when \(u(k)\) is changed to \(u(k+1)\). In mathematical form,  
\[SC=\sum_{j}|K_{j}(k+1)-K_{j}(k)|. \tag{13}\]  
Weighting factors have been proposed for elimination in several works, including [32; 33]; however, it was shown in [34; 35] that this parameter allows balancing the solution depending on the application.
# ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
## 3 Methodology
### Residual Group Dual-Attention Network  
This study proposes ResGDANet (Residual Group Dual-Attention Network) based on ResGANet, improving the original model through integrated attention modules.  
In Figure 6, the network first groups the input image matrix (\(C\times H\times W\)) according to channel number \(N\). For broader feature extraction, basic feature transformation is performed following channel reorganization. For each coordinate \((i,j)\) in the matrix, the transformation is represented as:  
\[g(r,i,j)=\begin{pmatrix}\cos\left(\frac{\pi T}{2}\right)&-\sin\left(\frac{\pi T }{2}\right)\\ \sin\left(\frac{\pi T}{2}\right)&\cos\left(\frac{\pi T}{2}\right)\end{pmatrix} \begin{pmatrix}i\\ j\end{pmatrix} \tag{15}\]  
where \((i,j)\) represents the matrix coordinate points, and \(r\in[0,4]\)[35].  
Figure 6: ResGDANet block structure diagram. \(\times\) represents the multiplication of spatial dimensions of the tensor, while * indicates the expansion of the channel dimension.  
The transformed feature map passes through ResNet bottleneck blocks, operating with \(3\times 3\) convolution kernels to enlarge the model's receptive field, providing a foundation for subsequent attention weight allocation. Using \(K_{1}()\) to denote the \(3\times 3\) convolution operation, with output represented as \(y_{s}\) in Equation (16):  
\[y_{s}=\left\{\begin{aligned} K_{1}(g_{r}(x_{s}))r,s=0\\ K_{1}(g_{r}(x_{s}))\odot y_{0}0<r=s<4\end{aligned}\right. \tag{16}\]  
In Equation (16), \(x_{s}\) represents the s-th group of the input feature map, and \(y_{0}\) denotes the initial convolution output.  
Following basic feature extraction, the Dual-Channel Attention Fusion (DCAF) module is incorporated to enhance feature representation. The module utilizes a dual-branch architecture combining max pooling and global average pooling, fusing channel attention and coordinate attention via adaptive weighting as in Equation (12).  
The spatial attention module generates spatial feature descriptors through global average pooling (GAP) and global max pooling (GMP), enhancing spatial dimension feature relationships.  
\[M_{sa}(X)=\sigma(W_{sa}(X)) \tag{17}\]  
In Equation (17), \(W_{sa}\) represents the spatial attention weight, and \(\sigma\) denotes the Sigmoid activation function.  
Following this, the attention scores for the RMT module are computed as per Equation (14). The final output feature is:  
\[MaSA(X)=Atten_{H}\Big{(}Atten_{W}X)^{T} \tag{18}\]  
This design enables the RMT module to extract features at stable resolution while maintaining low computational complexity, enhancing feature rotational invariance and thereby improving medical image classification performance.
## 3 Results and Discussion  
The exemplary results of the simulations are presented in Figure 5. As was expected, the electric field was distributed only in the vicinity of the structural elements, where the copper layer was removed during the etching process. Based on the presented frequency responses of the scattering parameters, it is clearly visible that similar operating conditions were obtained in the case of all of the evaluated structures. The achieved resonant response was more selective in the case of the SRR.  
Figure 5: Exemplary results of simulations—normalized electric field distribution (in case of resonant frequency) and frequency characteristics of scattering parameters \(s_{11}\) and \(s_{21}\) in the case of base structures (dimensions shown in Table 1): (**a**) cross cell geometry, (**b**) Jerusalem cross cell geometry, and (**c**) split ring resonator cell geometry.  
The comparison of the resulting reflection coefficient \(|s_{11}|\)obtained using numerical simulations and measurements is shown in Figure 6 for the selected MS structures. One can observe the good agreement. The resonant frequencies in both cases were very similar (the difference is less than 75 MHz), as well as the amplitudes in resonance (the difference of about 3 dB). The frequency responses obtained as a result of the calculations have standard, smooth shape characteristics for the resonance curves. In the case of the frequency responses obtained through the measurements, one can observe periodic oscillations caused by calibration errors during the de-embedding procedure. The agreement between the results of the simulations and measurements proves the correctness of the metasurface design procedure and numerical models. This means, the assumptions made during the modeling process, like the application of plane wavefront and Floquet-periodic boundary conditions, are valid and appropriate. Thus, the developed numerical models can be used for further analyses.