esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimum Slot and Pole Design for Vibration Reduction in Permanent Magnet Synchronous Motors
## 2 Materials and Methods
### Analytical Calculation of the Magnetic Pressure  
The electromagnetic pressure is calculated from the magnetic flux density by means of the Maxwell stress tensor. The vibration response is induced by the radial pressure, hence, the tangential component of the magnetic flux density can be disregarded. The radial pressure is thus calculated as [19]  
\[q_{r}(\theta,t)\approx\frac{B_{r}(\theta,t)^{2}}{2\mu_{0}}, \tag{1}\]  
where \(B_{r}\) is the radial magnetic flux with respect to the tangential position \(\theta\) and time \(t\), \(q_{r}\) the radial pressure, and \(\mu_{0}\) the magnetic permeability of the air. The magnetic flux is then obtained multiplying the magnetomotive force by the air-gap permeance:  
\[B_{r}(\theta,t)=\Lambda(\theta,t)\mathcal{F}(\theta,t), \tag{2}\]  
where \(\Lambda\) is the air-gap permeance and \(\mathcal{F}\) is the magnetomotive force. The magnetomotive force is expressed as  
\[\mathcal{F}(\theta,t)=\sum_{k=1}^{\infty}\mathcal{F}_{k}\cos(\omega_{k}t-kp \theta), \tag{3}\]  
where \(\mathcal{F}_{k}\) is the amplitude of the \(k\)th magnetomotive force (MMF) wave and \(\omega_{k}\) is its rotational frequency in rad/s, which is \(\omega_{k}=k\,p\,\Omega\) for the \(k\)th harmonic, where \(\Omega\) is the rotational frequency of the rotor in rad/s, and \(p\) is the number of pole pairs.  
If the slots of the machine are taken into account, the air-gap permeance variation due to the slots must be considered, which is usually represented by a Fourier series [24; 25; 26; 27]:  
\[\Lambda_{\text{slot}}(\theta)=\Lambda_{0}+\sum_{v=1}^{\infty}\Lambda_{v}\cos( vQ_{s}\theta), \tag{4}\]  
where \(v\) is the harmonic, \(Q_{s}\) the number of slots, \(\Lambda_{0}\) the air-gap permeance without slots, and \(\Lambda_{v}\) the coefficients of the Fourier series.  
The magnetic flux density is obtained by substituting Equation (4) into Equation (2):  
\[B_{r}(\theta,t)|_{exc=e_{s}}=\mathcal{F}(\theta,t)\Bigg{[}\Lambda_{0}+\sum_{v =1}^{\infty}\Lambda_{v}\cos(vQ_{s}\theta)\Bigg{]}. \tag{5}\]  
When the effect of the slots is included, new spatial harmonics are obtained due to the combination of the harmonics of the MMF and the slots. Therefore, the harmonics of spatial orders \(kp\pm vQ_{s}\) are obtained, in addition to the harmonics of the magnetomotive force of spatial order \(kp\)[13; 27].  
The radial pressure is obtained by Equation (1), which means that the harmonics of the magnetic flux are added to and subtracted from each other to determine the harmonics of the pressure. Thus, the harmonics of spatial distributions \(kp\pm ip\) and \(kp\pm ip\pm vQ_{s}\) are obtained for the harmonics of the magnetic flux density \(k\) and \(i\), with the frequencies\((k\pm i)\,p\Omega\). Simplifying, this can be reduced to spatial harmonics \(2 kp\) and \(2 kp\pm vQ_{s}\) with frequencies \(2 kp\Omega\).
## 5 Demonstration Using TerraSAR-X
### Homogeneous Scene: Amazon Rainforest  
A scene of homogeneous distributed scatterers in the Amazon rainforest has been acquired as the reference scenario to compare with the conventional method. The difference dataset in amplitude and phase is shown in Figure 5 without masking perturbations like rivers which is usually done for the conventional method [17]. From this difference dataset, the profile shown in Figure 6 has been generated by the complex mean operator and can be compared to the expected gain and phase profiles  
Figure 3: Theoretical antenna gain pattern for the boresight, notch, and difference pattern derived from the TerraSAR-X antenna model.  
from Figures 3 and 4. The steep zero in the top panel equals the expected antenna gain pattern and the 180\({}^{\circ}\) phase jump is also clearly visible in the bottom plot. The expected notch position and the gain drop in the profile are both at about 0.3\({}^{\circ}\) elevation angle with no visible discrepancy as expected for a well-calibrated system like TerraSAR-X [8].  
The output of the correlation process as described in Section 3 is shown in Figure 7. When compared to Figure 5, the the angular offset of \(-27.8\) mdeg retrieved from the correlation function was compensated for which equals the extant mispointing in the system. Now, the modeled antenna profile and the measured profile match in gain and phase. To verify the accuracy of the antenna pointing estimation, the sample Pearson correlation coefficient is calculated. Using the complex data, a correlation value of 0.90 is achieved. Recognizing the spread of the phase information at the position of the notch with it's log signal levels, a higher correlation value of 0.98 is achieved for the real-valued  
Figure 4: Theoretical antenna phase pattern for boresight, notch, and difference pattern derived from the TerraSAR-X antenna model.  
Figure 5: Difference scene generated from a nominal and a notch beam acquired over the homogeneous distributed target ‘Amazon rainforest’. The data is shown in amplitude on the left and in phase on the right.  
gain only in reverse of losing valuable information. Both values indicate a very good agreement between the antenna model and measurement after mispointing correction.  
Figure 6: Range profile of the difference scene generated from a nominal and a notch beam acquired over the Amazon rainforest, cf. Figure 5. The gain pattern measured over the scene and the modeled data are shown in the top panel. The corresponding phase pattern are shown on the bottom.  
Figure 7: Output of the correlation process for pointing determination for the rainforest case: logarithmic gain of the \(\gamma\)-profiles from model (green) and measurement (blue); aligned \(\gamma\)-profiles in gain (green and blue) and phase (yellow and red).
# Simultaneous Quantitation of Isoprenoid Pyrophosphates in Plasma and Cancer Cells Using LC-MS/MS
## 1 Introduction
The main challenge in developing a routine quantification method for IsoP analysis in biological samples is that the concentration of these biomolecules in mammalian cells, plasma and tissue are very low and there are several co-eluting interfering endogenous isobaric compounds. Measuring all the intermediates of the IBP pathway in one procedure is a major challenge because the metabolites differ markedly in their structure and physio-chemical properties. Furthermore, chromatographic resolution is required from interfering peaks for their separation and accurate quantification. Here, we report the development of a sensitive (0.04-20 ng/mL) and accurate bioanalytical method using LC-MS/MS, which allows the direct detection and quantification of GPP, FPP and GGPP in bio-matrices without the use of derivatization or fluorescent labels. We also demonstrated the applicability of our method by quantification of individual IsoP in six different human cancer cell lines and human plasma.
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 5 Discussion
### Experimental Results and Comparative Analysis
The \(p\) value and \(F_{1}\) average are 99.73% and 99.72%, respectively, reflecting the role of the improved VMD in removing part of the interference in the fault signal, retaining the impact part of the fault signal and improving the accuracy of model recognition. To further reflect the advantages of the proposed method, an experimental comparison and analysis will be conducted between the proposed method and existing fault diagnosis methods. These include the improved residual dense network (IRDN) proposed by [PERSON] et al. [35], the AO-LSTM (ACM-GIF-AOLSTM) network proposed by [PERSON] et al. [36], the LSTM-Cascade Catboost network proposed by [PERSON] et al. [37], and the hybrid multimodal fusion network (HMF-DL) based on deep learning proposed by [PERSON] et al. [38]. IRDN uses the XJTU data set and uses accuracy, standard deviation, and loss as evaluation indexes; ACM-GIF-AOLSTM uses the XJTU data set and uses accuracy and standard deviation as evaluation indexes; LSTM Cascade Catboost uses the CWRU data set and XJTU data set, with accuracy as the evaluation index; HMF-DL uses the XJTU data set and draws the confusion matrix to obtain the accuracy as the evaluation index. Considering comprehension, this paper uses the diagnostic accuracy of different models as the comparison index, as shown in Table 7.  
According to the diagnosis results, the bearing fault diagnosis method proposed in this paper is the best in the diagnosis effect compared with other models, reaching 99.72%. The above experiments reflect that the fault diagnosis method based on improved VMD and parallel hybrid neural network has certain advantages.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 3 System Model of Optical Wireless Fronthaul-Enhanced Hybrid Space Network  
The new hybrid space network (HSN) is an integration of wired multi-level FC-AE-1553 communication and self-organizing wireless visible light communication (Figure 3). It builds upon the existing multi-level FC-AE-1553 network by incorporating optical communication modules at the terminal nodes.  
In the HSN, the wired multi-level FC-AE-1553 communication remains the backbone of the network. This wired communication infrastructure provides a reliable and established means of data transmission. However, to enhance network capabilities and address certain limitations, the HSN extends the functionality of the terminal nodes by including optical communication modules.  
The optical communication modules enable self-organizing wireless visible light communication within the network. This means that in addition to wired communication, the terminal nodes can communicate with each other using visible light as the medium. This wireless communication adds flexibility and scalability to the network, allowing direct communication between nodes without relying solely on the wired infrastructure.  
By combining the wired multi-level FC-AE-1553 communication with the self-organizing wireless visible light communication, the HSN achieves a hybrid network architecture. This hybrid approach leverages the strengths of both wired and wireless communications, providing a more versatile and robust network for various applications.  
Overall, the HSN offers a heterogeneous network that integrates wired multi-level communication with self-organizing wireless visible light communication, expanding the capabilities and improving the performance of the original multi-level FC-AE-1553 network.  
Figure 3: Optical wireless fronthaul-enhanced hybrid space network.
## 3 Results  
An example of the control performance of the developed PI controller with fuzzy-based parameter adaptation is presented in Figure 6. The system remains stable in a wide setpoint range. Additive white Gaussian noise was used to corrupt the OUR signal during the simulations.  
Figure 5: Selected specific growth rate setpoint profile for the biotechnological process simulation.  
Figure 6: Specific growth rate (SGR) (system output variable) (**a**), substrate feeding rate (control variable) (**b**), oxygen uptake rate (OUR) (**c**) and biomass concentration (**d**) trajectories during the simulated process.  
The change over time of the corresponding PI controller tuning parameter is presented in Figure 7. The controller gain parameter \(K_{c}\) (see Figure 7a) changes over time by approx. 10% only due to the fact that the parameter correlates with the culture broth weight \(w\) that increased only slightly during the simulated process. The integration time constant \(T_{i}\) (see Figure 7b) follows the changes of the _OUR_ (see Figure 6c) profile and, therefore, reflects the significantly varying dynamics of the process.  
The performance evaluation summary of all the methods can be found in Table 2. Here one can see that the PI controller with fuzzy-based adaptation was able to perform similar to the gain scheduling PI and MFA control algorithms in the investigated process with a given setpoint profile and acting disturbances.  
All methods performed very similarly during setpoint tracking, while the fuzzy-based adaptation model performed best during disturbance rejection when comparing with the other two models. The MFA model tends to perform better in phase II of the cultivation process as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Control Type**} & \multicolumn{3}{c}{**ITAE**} \\ \cline{2-4}  & **MFA Adaptation** & **GS Adaptation** & **Fuzzy Adaptation** \\ \hline Setpoint-tracking & \(0.6865\times 10^{-3}\) & \(0.6592\times 10^{-3}\) & \(0.6723\times 10^{-3}\) \\ Disturbance rejection & \(0.3783\times 10^{-3}\) & \(0.3962\times 10^{-3}\) & \(0.3431\times 10^{-3}\) \\ Setpoint-tracking and disturbance rejection & \(0.7598\times 10^{-3}\) & \(0.7357\times 10^{-3}\) & \(0.7349\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance results of the investigated control algorithms.  
Figure 7: Development of the PI controller tuning parameters during the simulated process, (**a**) the controller gain parameter \(K_{c}\), (**b**) the integration time constant \(T_{i}\).  
When handling disturbances in the system, the PI controller with fuzzy-based parameter adaptation is able to outperform the GS and MFA algorithms in the first phase of the process; however, the MFA algorithm is again better suited for the second phase of the process as seen in Figure 9.
## 2 Methods  
Landsat TM/ETM+ and MODIS reflectance data were used in the study to simulate synthetic images with high spatial and temporal resolution. We selected Landsat and MODIS, since they have similar bandwidths, though MODIS bandwidths are narrower than TM/ETM+ [5]. For our simulation, the default size and the increased step of the moving window are set to be 1,500 m \(\times\) 1,500 m [5] (Landsat TM/ETM+: 50 pixels \(\times\) 50 pixels; MODIS: 3 pixels \(\times\) 3 pixels) and 60 m, respectively. The effect of pixels with long a distance to the central pixel is assumed to be negligible, and the maximum size of the moving window size is set to be 3,000 m to save computing time. The number of classes within the moving window is a function of the amount of land cover types found within this window. The acceptable levels for uncertainties of Landsat and MODIS surface reflectance for the visible band and the NIR band were set to 0.002 and 0.005, respectively [5].  
mESTARFM data processing includes three major processing steps: (i) two pairs of fine-resolution reflectance and land cover data (optional) are used for the selection of spectrally similar neighbor pixels for the central pixel within the local moving window; (ii) within the area of similar pixels, the weighting and conversion coefficients for the central pixel are calculated; and (iii) the weighting and conversion coefficients are applied to the available coarse-resolution reflectance to produce the fine-resolution reflectance for the date of simulation. Three steps would repeat with increasing local moving window size until the most similar simulated fine-resolution reflectance is obtained compared to the observed fine-resolution reflectance.
## 3 Proposed Method
### Feature Extraction and Fusion Domain  
The second stage of the proposed method is focused on performing the information fusion and data compression by means of the LDA technique. In this sense, it must be highlighted that the evaluated condition is represented by the available signals that are characterized by a high-dimensional set of hybrid features, where this meaningful set of features comprises the characteristic patterns of the available signals that are processed in different domains. Even though the characterization of signals through a hybrid set of features leads to a high-performance characterization in condition monitoring strategies, the estimation of correlated and non-useful information is inevitable even if complex signal processing is implemented.  
In this regard, the LDA technique is applied over all the resulting feature matrices (\(M^{T_{V}}\), \(M^{F_{V}}\), \(M^{T_{C}}\), and \(M^{F_{C}}\)) estimated from analyzing the vibration and stator current signals in two different domains, TD and FD. The main objective of the LDA is reducing the original high-dimensional space of the hybrid set of features into a low-dimensional space, where the features of the hybrid set are fusioned during the dimensionality reduction procedure. The information fusion and/or feature fusion is symbolized by the linear combination, in different weights, of the original set of features (high-dimensional set of hybrid features) that is intended to be projected into a new and reduced dimensional space (i.e., 2D or 3D spaces). The new resulting features projected in the new reduced space are also known as the extracted features.  
Consequently, the fusion of vibrations and the stator current is represented by the new extracted features, and the resulting projection contains the fusion of information provided by statistical features estimated from different signal processings in both domains, TD and FD. On the other hand, the LDA is a suitable machine learning technique to be applied in condition monitoring methodologies due to the projection of the extracted features into a 2D space allows the visualization of all considered conditions. Additionally, it may deal with multi-class problems, but this peculiarity is an advantage since the LDA may maximize the linear separation as much as possible between clusters of different classes.
# Highway Tunnel Defect Detection Based on Mobile GPR Scanning
## 3 Methodology
The \(RA\) of each pixel at A-scank can be described as follows:  
\[RA_{k}=N+M+D+T \tag{8}\]  
Since the signals of different kinds have little correlation, Equation (7) can be expressed as:  
\[IV_{k}=\sum\limits_{i=1}^{n}\Bigl{(}T_{k,i}^{400\text{M}}\cdot T_{k,i}^{900 \text{M}}+N_{k,i}^{400\text{M}}\cdot N_{k,i}^{900\text{M}}+D_{k,i}^{400\text{M} }\cdot D_{k,i}^{900\text{M}}+M_{k,i}^{400\text{M}}\cdot M_{k,i}^{900\text{M}} \Bigr{)} \tag{9}\]  
Considering that:  
* For a target pixel (A), the responses would be found at each A-scan according to TWT. Furthermore, the two series of signals concerning different frequencies will maintain excellent synchronization at each A-scan. As a result, the signals from the target are strongly reinforced by the process \(IV_{A}=\sum\limits_{i=1}^{n}T_{A,i}^{400\text{M}}\cdot T_{A,i}^{900\text{M}}\). * The \(N\) subjects to the Gaussian distribution and the synchronization between \(N^{400\text{M}}\) and \(N^{900\text{M}}\) is quite weak because of their randomness. Therefore, the IV of background represented by \(IV_{k}=\sum\limits_{i=1}^{n}N_{k,i}^{400\text{M}}\cdot N_{k,i}^{900\text{M}}\) could be ignored. * The blue dashes in the picture represent the TWT of pixel B (the pixel with diffraction) at each A-scan. According to the TWT, only one response could be observed at all A-scans. It means \(D_{B}\) = [0,0,..,D,0,..,0]. Consequently, the IV of the pixels diffraction \(IV_{B}=\sum\limits_{i=1}^{n}D_{B,i}^{400\text{M}}\cdot D_{B,i}^{900\text{M}}\) is a small value compared to \(IV_{A}\). * The synchronization between \(M^{400\text{M}}\) and \(M^{900\text{M}}\) sharply decreases because of the time delay inequality (TDI) between multiple reflections concerning different frequencies. Figure 8: There is only one target at pixel A. However, the response of radar data can be found at pixel B and pixel C because of the diffraction and multiple reflections. The dashes beyond the radar signal are the TWT of pixels at each A-scan [15]. Consequently, the IV of pixels with multiple reflections \(IV_{\text{C}}=\sum\limits_{i=1}^{n}M_{\text{C},i}^{400M}\)-\(M_{\text{C},i}^{900M}\) should be less than \(IV_{A}\). As a result, the interference due to noise, multiple reflections, and diffraction could be eliminated with this innovative methodology.
## 3 Communication Results by Experiments
### Results from Experiment I
The obtained bit error rates under the four data rates are summarized in Table 2. Each bit error rate is averaged over \(40\) independent trials. For each trial, the information stream consists of \(1,000\) randomly generated binary bits. Both the mean and the standard deviation of BER is shown in the table and is plotted in Figure 6(a). We study the effect of noise level in the data acquisition system on the communication BER. We average the received responses \(10\) times to reduce the white noise and compare the results from data acquisition without averaging. Figure 6(b) shows that the mean and the standard deviation of BER over multiple trials reduces to \(4.33\times 10^{-2}\) and \(5.55\times 10^{-3}\), respectively, as compared to \(5.01\times 10^{-2}\) and \(1.17\times 10^{-2}\), when the white noise is not suppressed by averaging. However, the improvement is not \begin{table}
\begin{tabular}{l l c c c c} \hline  & **Data Rate (kbps)** & **10** & **20** & **50** & **100** \\ \hline BER & mean & \(0\) & \(5.75\times 10^{-4}\) & \(1.09\times 10^{-2}\) & \(5.01\times 10^{-2}\) \\  & std & \(0\) & \(8.33\times 10^{-4}\) & \(5.15\times 10^{-3}\) & \(1.17\times 10^{-2}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Bit error rate (BER) obtained from experiments. Figure 5: Snapshots of the received information carrying waveforms in the time window of \(4.0\) ms–\(4.2\) ms. The data rates are: (**a**) \(10\) kbps; (**b**) \(20\) kbps; (**c**) \(50\) kbps; and (**d**) \(100\) kbps. significant. This implies that the improvement of the signal-to-noise ratio by averaging is insignificant compared to that by applying the time reversal focusing technique.