esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Second-Order Approximation Procedure for Damping Factor Estimation
Create the power spectrum plot of recorded data, define the value of resonant frequency \(\omega n\) of the mode under examination, as well as the set of spectrum points [\(\omega_{i}\), M\(i\)], \(i=1\)... \(q\) on either side of \(\omega n\), which can be distinguished over the noise;
2. Choose a set of parameters, namely the static gain A and damping factor \(\zeta\), for the trivial second order system with transfer function {A\(\cdot\omega n^{2}\)/(s\({}^{2}+2\cdot\zeta\cdot\omega n\cdot\)s + \(\omega n^{2}\))}, obtain its discrete time counterpart and estimate the power spectrum (using the same settings as in step 1) of the output to a given periodic input (preferably white noise);
3. Focusing on frequencies \(\omega_{i}\), \(i=1\)... \(q\) (see step 1), compute the quantity: \[r(k)=\sum_{i=1}^{q}[\text{M}_{i}-M_{i}]^{2}\] (1) at the \(k\)-th iteration, with \(M_{i}\) the magnitude of the power spectrum estimate of the second order system at \(\omega_{i}\);
4. Go back to step 2, pick up a new set of A and \(\zeta\), and proceed to the next iteration by repeating steps 3 and 4, until a value of \(k\) for which \(r(k)\) becomes minimal is reached. Then, store the value for damping factor \(\zeta\), which corresponds to that of the considered (and monitored) mode. This minimization procedure may be carried out by means of relevant nonlinear least squares routines, readily available in most software packages. For instance, in MATLAB(r) one may use the command _lsqnonlin.m_ for this purpose. During testing of the second-order approximation, simulations were carried out with higher order linear systems, which were driven with periodic input usually of triangular or sawtooth forms. Use of the second-order approximation procedure led to quite accurate results when dealing with modes with damping factor values lower than \(10^{-3}\). Finally, note that, as its name suggests, this procedure is intended as a means of approximating the true dynamics associated to a mode. In other words, it may not be used as a substitute for stochastic system identification procedures, which, as also demonstrated in [19], are extremely effective in capturing the _global_ system dynamics. The only problem with system identification procedures is that identifying an effective and accurate system model requires significant user expertise and time for choosing the correct model structure. Hence, for a given set of signals, when rapid evaluation of a specific mode is necessary, then one may proceed with [20] or the currently presented second-order approximation procedure.
## 2 Progress in Various Nanotechnology Disciplines
### Biomedical Devices
He provided the example of a hip implant comprised of carbon nanotubes. These carbon nanotubes can measure the conductivity of the cell that attaches to the implant, and thus can determine whether it is a bone cell or a bacterial or an inflammatory cell. The hip implant also incorporates a wireless technology so that the sensor can then communicate the current status to a computer to inform the patient and the surgeon. Finally, a responding unit is incorporated made up of a polymer that can degrade under an applied electromagnetic field to release nanoparticles that could kill the bacteria, release bone growth factors that could increase bone growth, or release an anti-inflammatory agent if inflammation is the problem (Figure 1). [PERSON] (University Grenoble Alpes, France) described the concept of \"symbiotic nanostructured medical devices,\" taking into account the fact that the implanted device is intended to have some communication of energy or materials with the body. If this is one-way communication from the device to the body, such as for a drug delivery, the challenge is to avoid the degradation or encapsulation of the device. However, if the implanted device is intended to restore body or organ function, then it needs to mimic the two-way (duplex) communication that is required for transplanted living organs or cells [34]. Examples of these implantable duplex communicating systems include biofuel cells or open-loop feedback devices where a molecule from the body is utilized by the duplex communicating system to produce a different material (e.g., molecule or energy) for use by the body. The challenges for such symbiotic systems are to be biocompatible and to maintain two-way transport communication of materials. Symbiotic devices extend the classical biocompatibility concept to include this functional requirement for continuous two-way (duplex) communication of materials with the body. It is likely that by utilizing such a biomimetic approach, scientists can be more efficient in mimicking what is happening at a molecular and cellular level to create a porous membrane that allows long-term exchanges of molecules between an implanted device and the body (Figure 2). Having this  
Figure 1: The SMARTHIPTM can detect cell presence and function next to a hip implant in real time and possesses on-demand release of nanoparticles, antibiotics, anti-inflammatory agents, and more to ensure implant success. Further, all information can be sent to a hand-held device to provide personalized medicine. novel concept in mind will guide the research in a new field between medical implant and regenerative medicine to create actual symbiotic devices.
## 6 Various Criteria for Assessing the Power Quality and Efficiency of the Studied System
### Power Quality
#### 6.1.2 Common-Mode Voltage (CMV)
Additional modulation-based strategies have been developed to eliminate common-mode voltages by using only a specific subset of the output space vectors [146; 147]. However, these methods face a limitation regarding the maximum phase voltage, which restricts the optimal use of the DC bus voltage.
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 6 Fiber Optic SPR Sensor Based on MITs  
### Sensing Mechanism  
As discussed in the earlier section, SPR is sensitive to changes in the dielectric function of the sensing medium. If there exists any change in the dielectric function, then the refractive index of the sensing/recognition layer changes, and a shift in the resonance wavelength would be found using the fiber optic configuration and spectral interrogation method. The shift in resonance wavelength occurs because of the satisfaction of the resonance condition [32; 36; 148]. In the case of FO-SPR-based MIP sensing, the MIP layer is used as the recognition layer which has the memory imprinted sites of the template molecule to be detected. When the template molecules come near these imprinted sites, they bind non-covalently with the imprinted sites which results in the change in the dielectric function or the effective refractive index of the sensing medium. This has been recognized by the shift in resonance wavelength while recording the transmittance spectra with variation in analyte concentration. The sensitivity of the FO-SPR is further enhanced by the several methods such as including a thin film of material having a high refractive index with nm thickness between the metal layer and the sensing layer [8; 57; 149] or tapering the fiber core [46; 47]. When metal nanostructuresare introduced instead of the metal thin film, the LSPR phenomenon occurs and this is more sensitive as compared to SPR studies in the case of biomolecular interactions due to the increased surface area to volume ratio [150]. Although SPR is more sensitive towards the refractive index measurements as compared to LSPR [151], the sensitivity of LSPR in the case of bio-molecular interaction is higher because of confinement of the electric field in a small sensing volume. This causes the LSPR to be more sensitive towards molecular binding, etc. [150]. In the case of LSPR-based sensors, the MIP layer is coated around the metal nanoparticles which work as the sensing medium. Another case of highly selective detection of analytes using the plasmonic phenomenon is the combination approach of localized and propagating SPR phenomena [65; 66]. This method shows the highest sensitivity as compared to SPR/LSPR methods.
## 5 Discussion
### Complexity Analysis
#### 5.6.1 Memory Consumption  
We compared the memory usage of our Att-ResBiGRU model with several baseline architectures (CNN, LSTM, BiLSTM, GRU, and BiGRU) during prediction on the test datasets. We employed the compute unified device architecture (CUDA) profiling tools to measure memory consumption. Figure 10 shows the results. Our model required an average of 3 MB of RAM, which was used to store the encoder features, temporal hidden states, classifier weights, and outputs. This was lower than the baseline CNN, which consumed 4.27 MB, on average, for shorter sequences. However, the standalone BiGRU model did not capture spatial information through convolutions and used slightly less memory (3.60 MB, on average). Overall, Att-ResBiGRU offered a good balance between accuracy and memory efficiency.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Performance**} \\ \cline{2-7}
**Model** & \multicolumn{2}{c}{**Opportunity**} & \multicolumn{2}{c}{**PAMAP2**} & \multicolumn{2}{c}{**REALWORLD16**} \\ \cline{2-7}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline Att-ResBiGRU without the attention block & 89.95\% & 86.31\% & 95.25\% & 95.07\% & 95.14\% & 95.23\% \\  & (\(\pm\)0.26\%) & (\(\pm\)0.28\%) & (\(\pm\)0.15\%) & (\(\pm\)0.16\%) & (\(\pm\)0.25\%) & (\(\pm\)0.25\%) \\ Att-ResBiGRU with the attention block & 90.27\% & 86.69\% & 96.61\% & 96.44\% & 96.11\% & 96.23\% \\  & (\(\pm\)0.23\%) & (\(\pm\)0.30\%) & (\(\pm\)0.26\%) & (\(\pm\)0.25\%) & (\(\pm\)0.17\%) & (\(\pm\)0.19\%) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Impact of the attention block.
## 4 Measured Results and Discussion
### Radiation Patterns and Gains  
Figure 12 shows the radiation patterns in the phi = 0\({}^{\circ}\) and 90\({}^{\circ}\) planes at two resonant points of 27 GHz and 30 GHz.  
At the frequency of 27 GHz, the maximum simulation gain in the main radiation direction is 19 dB, and the measured peak gain is 19.6 dB. At the frequency of 30 GHz, the simulation result is 16 dB, and the measured peak gain is 18.8 dB. It can be observed that the results of the simulated and measured peak gains agree well in the main beam direction. However, there are strong minor lobes because the distance between the parasitic subarrays (as shown in Figure 4) is up to 1.45\(\lambda\), which is greater than one \(\lambda\), where \(\lambda\) is the wavelength of 30 GHz. The strong minor lobes can be suppressed by a more compact feeding network to reduce the antenna unit space. Generally speaking, there is a stable radiation pattern of the antenna array.  
Table 4 shows the gain and radiation efficiency of each frequency point of 25-32 GHz. It can be seen that the overall gain of the antenna is greater than 18 dB, the radiation efficiency is greater than 30%, and the maximum radiation efficiency within the frequency bandwidth is 58%. Compared to the efficiency of the same type of antenna in the literature, the efficiency of the array antenna designed in this paper is slightly insufficient. This is because, although the design of the laminated element subarray can effectively expand the frequency bandwidth of the antenna, it also aggravates the energy loss of the multilayer plate feed.
## 3 Impedance Model of Converters with Improved Virtual Impedance Control Strategy
### Verifying Impedance Modeling Through Perturbation Signal Testing  
Perturbation signal testing is used to verify the accuracy of the proposed small-signal model. An AC current source should be placed parallel to the DC side of the system as an input signal, as Figure 6a shows. It is necessary to inject AC current at different frequencies to measure the DC side impedance at different frequencies. At each injection frequency, a simulation experiment is conducted. The DC voltage and DC current data of each experiment are analyzed by fast Fourier transformation. The components under the disturbance frequency are taken out, and the ratio of DC voltage and DC current is calculated as the calculated impedance [13]. Figure 6b,c shows the DC-side impedance verification of the DC system rectifier and inverter sides, respectively. The solid line in Figure 6b represents the analytical impedance of the rectifier converter as in (42), and the points represent the simulation results. Figure 6b represents the analytical impedance of the inverter station and the DC cable as (51), and the points represent the simulation results.  
Figure 6: Frequency response of the impedance and verification. The solid line represents the model prediction, and the black points denote the simulation. (**a**) Disturbance signal testing. (**b**) Impedance of the DC voltage-controlled converter (**c**). Impedance of the power-controlled converter.
# Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG
## 4 Methodology
### EEG Headset-Based Command  
#### 4.2.1 Data Acquisition  
To obtain EEG data with the EMOTIV Insight headset, subjects were exposed to 4 different emotions (Smile, Blink, Wink Left, and Wink Right) for 5 min, so that EEG data were available for each subject for a total of 20 min. A twenty-minute data collection protocol was created, with the different emotions first recorded in a separate file. Subsequently, all emotions of each person were consolidated into one file to avoid interference between different expressions and persons. This consolidation also ensured a balanced distribution of each emotion and facilitated a more accurate analysis without compromising the integrity of the dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Type** & **Filter Shape** & **Input Size** \\ \hline Convolution (ReLu) & \(3\times 3\times 3\times 128\) & \(227\times 227\times 128\) \\ Max-Pooling & \(2\times 2\) & \(225\times 225\times 128\) \\ Convolution (ReLu) & \(3\times 3\times 3\times 64\) & \(112\times 112\times 64\) \\ Max-Pooling & \(2\times 2\) & \(110\times 110\times 64\) \\ Convolution (ReLu) & \(3\times 3\times 3\times 32\) & \(55\times 55\times 32\) \\ Max-Pooling & \(2\times 2\) & \(27\times 27\times 32\) \\ Fully Connected & \(32\times 4\) & \(27\times 27\times 32\) \\ Dense (Sigmoid) & 4 & \(4\times 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the 3D-CNN structure [30].  
Figure 6: The architecture of algorithm 3D-CNN [30].  
The data collected in the different situations mentioned above need to be preprocessed for later use in the machine learning component. To better understand the preprocessing, the data source as well as the format and features are discussed below.  
The 5-channel EEG device provided 128 samples for each channel in a second. After transforming the collected data for the frequency domain, each collected datum is represented by the weighted and arithmetic mean for each of the 5 device channels and the 4 wave classifications.
# An Intelligent Diagnosis Method for Machine Fault Based on Federated Learning
## 1 Introduction
However, none of these recognition methods of fault diagnosis consider the problem of the data privacy and sharing, resulting in the established fault diagnosis model not being able to recognize the newly added fault types. Therefore, a shared fault diagnosis model is expected to be proposed to recognize the newly added fault types without exchanging fault data. Federated Learning (FL) [19; 20], as a distributed machine learning algorithm, is mainly used to train data distributed on a large number of client nodes to obtain a high-quality model. That is, the local model can be trained by FL on the client node without exchanging data samples, and these local model parameters are updated at a certain frequency, so that the local model is updated to the global model. [PERSON] et al. [21] applied FL into electronic health medical prediction. For a large number of medical data distributed in individuals or other hospitals, they fused the patient information by FL to predict the probability of a patient being hospitalized due to heart disease. [PERSON] et al. [22] applied FL into the input prediction of the mobile phone keyboard and predicted the next input word by establishing a shared model based on the data of multiple mobile phones. [PERSON] et al. [23] introduced the reputation metric into FL to improve the reliability of federated learning models in mobile networks. [PERSON] et al. [24] applied FL into health care so that medical institutions could diagnose new types of diseases without exchanging data. Therefore, the idea of FL provides the possibility for a fault diagnosis model with a shared mechanism. Based on the distinguished advantages of FL, FL was introduced into mechanical fault diagnosis. An intelligent diagnosis method based on FL was proposed for mechanical faults. The main works of this paper are as follows. 1. The proposed method can effectively recognize new fault types and improve the generalization ability of fault diagnosis model. 2. A local fault diagnosis model is established for different types of fault data and is fused into a federated fault diagnosis model by the proposed method, which solves the hardly shared problem in different types of fault data. 3. The process of federated fault diagnosis model is explained by visualizing the weight distribution. The rest of this paper is organized as follows. In Section 2. the theoretical basis and implementation steps of a fault diagnosis model based on federated learning are proposed. In Section 3. the effectiveness of the proposed method is verified by two bearing cases, and the proposed method is compared with the traditional fault diagnosis method. The value conclusions are drawn in Section 4.
# Correction Strategy of Online Midcourse Guidance for High-Speed Gliding Target Interceptor
## 2 Model Description
### Motion Model of Interceptor in Geocentric Coordinate System  
Considering the fact that the interceptor is slightly affected by aerodynamic force in the midcourse guidance stage, the maneuvering process of the interceptor in the midcourse guidance can be regarded as the orbit transfer process in the geocentric coordinate system in this paper, and the motion model is \[\left\{\begin{array}{l}\dot{\mathbf{r}}=\mathbf{v}\\ \dot{\mathbf{v}}=\frac{T}{m(t)}T^{0}+\mathbf{g}(r)\\ \dot{m}=-m_{0}\delta_{T}\end{array}\right. \tag{4}\]  
where \(\mathbf{r}\) is the Earth center vector, \(\mathbf{v}\) is the velocity vector of the interceptor, and \(\mathbf{T}^{0}\) is the thrust direction of the engine. The gravity acceleration of the interceptor is  
\[\mathbf{g}(\mathbf{r})=-\frac{\mu}{r^{3}}\mathbf{r} \tag{5}\]  
it can be seen from Equation (4) that the simplified interceptor motion model is equivalent to the two-body motion model. Therefore, in the case of weak aerodynamic force, the moving process can be regarded as two-body motion, and the trajectory characteristics of the interceptor with direct force adjustment are consistent with the two-body trajectory characteristics.  
In geocentric coordinate systems, the position vector \(\mathbf{r}(t_{0})\) and velocity vector \(\mathbf{v}(t_{0})\) of the interceptor at the instant \(t_{0}\) are known, and it can be obtained that  
\[(a,e,i,\Omega,\omega,M_{0})=f_{1}(\mathbf{r}(t_{0}),\mathbf{v}(t_{0})) \tag{6}\]  
the position and velocity vector in (6) determine the characteristic root number \((a,e,i,\Omega,\omega,M_{0})\) of the interceptor, where \(a\) is the semi-major axis of the hyperbolic trajectory, \(e\) is the eccentricity, \(i\) is the inclination angle of the trajectory, \(\Omega\) is the longitude of the ascending point, \(\omega\) is the amplitude angle of the near-day point, and \(M_{0}\) is the angle of the epoch approaching point. The specific solving process is shown in reference [32] Conversely, according to the law of orbital transfer, if the number of orbital elements of the interceptor orbit is known in the orbital maneuver, the orbital characteristics at all positions in the orbit can be obtained by adding time. Namely  
\[(\mathbf{r}(t),\mathbf{v}(t))=f_{2}(a,e,i,\Omega,\omega,M_{0},t) \tag{7}\]  
after establishing the motion model of the above two coordinate systems, each variable can be expressed through the conversion between the two coordinate systems.
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 4 Results and Discussion
### Analysis of the Effect of Monthly Feature  
In order to verify the effect of month features on the inversion of Chl-a concentration in Lake Wufu, this paper establishes the XGBoost, support vector regression (SVR), and linear regression models with and without month features for comparison, and the experimental results are summarized in Table 7. The experimental results demonstrate that the R\({}^{2}\) of XGBoost and SVR on both training and testing sets is higher than 0.7, the RMSE is less than 5 \(\upmu\)g/L, and the MAE is less than 4 \(\upmu\)g/L with the introduction of month features. XGBoost, SVR, and linear regression increased the R\({}^{2}\) by roughly 18-80%, decreased the RMSE by 13-34%, and decreased the MAE by roughly 3-37% when compared to the models without month features. The results show that the introduction of month features significantly improves the inversion accuracies of the three models, especially the XGBoost model, which outperforms SVR and linear regression on both the training and test sets, with R\({}^{2}\)s of 0.8183 and 0.7711, RMSEs of 4.0982 and 4.1845 \(\upmu\)g/L, and MAEs of 2.5775 and 3.3462 \(\upmu\)g/L, respectively. Figure 6 demonstrates the fitting degree of the three models for the inversion of Chl-a concentration in Wuliangsu Lake with and without monthly features, where the straight line is a 1:1 straight line, which represents the ideal state (the inversion value is exactly equal to the measured value). It can be seen that the Chl-a concentration inversion value of the M-XGBoost model has the smallest difference with the measured value and is closest to the 1:1 straight line, indicating that its inversion is the best. Based on this finding, the monthly feature was chosen to be introduced as a feature in the subsequent inversion model experiments to improve the accuracy of the model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Monthly** \\ **Feature** \\ \end{tabular} } & \multicolumn{3}{c}{**Training Set**} & \multicolumn{3}{c}{**Test Set**} \\ \cline{3-8}  & & **R\({}^{2}\)** & **RMSE (\(\upmu\)g/L)** & **MAE (\(\upmu\)g/L)** & **R\({}^{2}\)** & **RMSE (\(\upmu\)g/L)** & **MAE (\(\upmu\)g/L)** \\ \hline \multirow{3}{*}{XGBoost} & Yes & 0.8183 & 4.0982 & 2.5775 & 0.7711 & 4.1845 & 3.3462 \\  & No & 0.5799 & 6.2315 & 4.1351 & 0.4783 & 6.3177 & 4.5693 \\  & Yes & 0.7465 & 4.8409 & 2.6262 & 0.7434 & 4.4309 & 3.6722 \\  & No & 0.6331 & 5.8235 & 3.1678 & 0.4116 & 6.7095 & 4.9062 \\ Linear & Yes & 0.6903 & 5.3507 & 3.7161 & 0.6370 & 5.2700 & 4.5299 \\ regression & No & 0.4416 & 7.1845 & 4.9852 & 0.51368 & 6.0999 & 4.7047 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Chl-a concentration inversion results from the XGBoost model.  
Figure 6: Prediction results of the model for (**a**) M-XGBoost, (**b**) XGBoost, (**c**) M-SVR, (**d**) SVR, (**e**)M-Linear regression, and (**f**) Linear regression.