esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Structural Monitoring System for Cable Bridges by Using Seismic Accelerometers
## 3 Measured Data and Condition Assessment
The angle of torsion of the deck is calculated by the relative vertical displacement between the two accelerometers at the center of the deck divided by the width of the deck. Figure 8: Measured time histories of the acceleration at the ground and top of the pylon. (**a**) Ground motion. (**b**) Top of the pylon. The result of the overall condition assessment should be derived based on the careful observation of the multiple response indices in Table 1. In general, for the safety reason, the worst case scenario is often assumed in the final decision, i.e., if one of the response indices were out of the safe boundary, the overall system would not be in the safe condition and need inspections. Despite the theoretical discussions given in this paper, unexpected errors in the field are the most difficult issue. Most of the latter problems may be dealt with by experienced field experts and these issues are beyond the boundary of this research. Determining a value of each of the limit states for the response indices requires is very difficult and requires huge amount of research effort. Since presenting the limit state is out of the boundary of this research, some examples of the limit states are given in Table 1.  
Figure 7.0 Fourier amplitude spectra of measured accelerations. (**a**) Top of the pylon (longitudinal direction). (**b**) Center of the span (vertical direction). (**c**) Cable (horizontal direction). Figure 7.1 Displacement time histories. (**a**) Relative displacement of the pylon (longitudinal direction). (**b**) Vertical displacement of the center of the span.
## 3 Numerical Simulations and Experiment Validation
### Discussion  
The results obtained from both the simulation and experimental investigations demonstrate the effectiveness of the proposed grating laser ultrasonic method for non-contact porosity evaluation in metal AM. The simulation results indicate that the SAW velocity decreases with increasing porosity, showing good agreement with the theoretical velocity-porosity model. These findings are further supported by experimental measurements on WAAM-fabricated specimens with differing porosity levels. Although the experimental analysis is limited to qualitative trends due to the absence of accurate material property data, their observed consistency with the simulation results confirms the method's sensitivity to internal voids. Furthermore, the fully non-contact configuration--based on laser excitation and interferometric detection--highlights the method's strong potential for online, in situ monitoring during the AM process.
## 6 Conclusions  
The results presented demonstrate that the use of CNN can significantly enhance the efficiency of SSVEP-based BCIs. Compared to traditional machine learning methods, CNN can provide up to 20% better results. This improvement leads to a substantially higher ITR and more effective BCI system operations. A CNN classifier trained for this purpose is more resistant to artifacts in the EEG signal than other SSVEP detection methods. The data augmentation method proposed for calibration sessions enables effective CNN training. Unfortunately, the use of CNN is not without practical limitations. One drawback is the extensive training time required, which may span several hours. Additionally, high classification accuracy is typically achieved only when the data from a specific individual's calibration session are used for training. Furthermore, the same network structure cannot be directly applied to different databases. The CNN structure must be modified for signals recorded with varying equipment and different sampling frequencies.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Subject** & **CNN**[26] & **Our CNN** & **CCA** \\ \hline S01 & 0.85 & 0.81 & 0.75 \\ \hline S02 & 0.55 & 0.88 & 0.54 \\ \hline S03 & 0.30 & 0.42 & 0.40 \\ \hline S04 & 0.52 & 0.75 & 0.54 \\ \hline S05 & 0.82 & 0.75 & 0.63 \\ \hline Mean & 0.61 & 0.72 & 0.57 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of classification accuracy for CNN [26].  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://github.com/kolodzima/CNN_limited_SSVEP_dataset](https://github.com/kolodzima/CNN_limited_SSVEP_dataset)]([https://github.com/kolodzima/CNN_limited_SSVEP_dataset](https://github.com/kolodzima/CNN_limited_SSVEP_dataset)) (accessed on 16 Decmeber 2023). The source code, which presents the proposed augmentation method, along with the structure of the CNN and methods for training and testing, has been placed at the link.  
**Author Contributions:** Conceptualization, M.K., A.M., R.J.R. and P.W.; methodology, M.K., R.J.R. and A.M.; software, M.K. and P.W.; validation, M.K. and A.M.; formal analysis, A.M. and R.J.R.; investigation, A.M. and M.K.; resources, M.K., P.W., A.M. and R.J.R.; data curation, A.M., R.J.R. and M.K.; writing--original draft preparation, A.M., R.J.R., P.W. and M.K.; writing--review and editing, A.M., M.K., R.J.R. and P.W.; visualization, M.K.; supervision, A.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** [PERSON], [PERSON]. (2022). SSVEP classification with a limited training dataset. IEEE Dataport. [[https://dx.doi.org/10.21227/kc6z-8447](https://dx.doi.org/10.21227/kc6z-8447)]([https://dx.doi.org/10.21227/kc6z-8447](https://dx.doi.org/10.21227/kc6z-8447)) (accessed on 16 December 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Evaluation and Analysis
### Performance Evaluation on the Average Number of Relaying Hops  
In Figure 6a-d, the results of the average number of relaying hops (_AH_) versus the number of requests per minute on different topologies are depicted, where the \(\varpi\) is set as 100 kbits. The proposed algorithms achieve a lower average number of relaying hops compared to the benchmark algorithms, which can save the precious key resources by decreasing the key wastage for relaying. This is because our proposed algorithms implement routing on the abstracted topology, which has far more links than the physical  
Figure 5: (**a**) Max average key volume supported and (**b**) max number of requests supported per minute for different algorithms on the four satellite network topologies.  
topology. This can be beneficial to find the shortest path with a lower number of hops for the request. The \(AH\) values for the Benchmark-LFN and Benchmark-LFU are relatively lower than those for the Benchmark-GFN and Benchmark-GFU, respectively. This is because the number of relaying hops for GEO-based QKD is normally two or three, which can pull down the value of \(AH\). The Benchmark-GFU and Benchmark-LFU have relatively higher \(AH\) values than the Benchmark-GFN and Benchmark-LFN, respectively, since the topology-updating process can increase the success probability at the cost of increasing the number of relaying hops. The GF-TAR algorithm and LF-TAR algorithm show similar performance in terms of the \(AH\), since the number of relaying hops for GEO-based QKD is normally two or three, which can hardly influence TAR algorithms, where the \(AH\) approaches two. Among the proposed algorithms, the MS-TAR algorithm shows the best \(AH\) performance. This is because the abstracted topology has more links for the MS-TAR algorithm compared to that for the GF-TAR and LF-TAR algorithms thanks to the adding of GEO nodes and related links to the abstracted topology for the MS-TAR algorithm.
## 1 Introduction
### Related Works  
Codebook-based MIMO precoding techniques have been proposed in LTE (Long Term Evolution) through ten transmission modes to provide high diversity, array gain, and spatial multiplexing [22; 23]. However, the increasing demands of data rate and the necessity to provide communication services to a much denser network with heterogeneous technologies make advanced MIMO precoding even more crucial in 5G New Radio (NR). Therefore, the 3 rd Generation Partnership Project (3 GPP) has developed and improved the existing LTE advanced multi-antenna techniques since Release 15 to enable codebook-based precoded transmission mode in 5G [24; 25]. The structure of the vectors that integrate the codebooks used in 5G-NR resembles the discrete Fourier transform (DFT); thus, they are typically denoted as DFT-codebooks. They support multi-layer transmissions with low signal overhead and complexity [2].  
The 5G-NR precoding matrix implementation for both the downlink and uplink is described in the 3 GPP technical specifications (TSs) 38-211 [24] and 38-214 [25]. The downlink precoding design is much more complex than the uplink counterpart because the NR base station (gNB) is equipped with a higher number of antennas than the user equipment (UE). Therefore, more sophisticated processing is required to coherently combine the signals into multiple paths. Two types of downlink precoding, named Type I and Type II, have been proposed since Release 15 with different spatial resolution properties. Type I codebook is mainly applied in SU-MIMO and hence it is focused on reporting information of the strongest channel path to focus the energy on the target UE. On the contrary, Type II is addressed to support communication to multiple UEs in an MU-MIMO approach. Therefore, more accurate channel estimation is required to avoid interference between different UEs at the cost of large-signal feedback overhead. This is the main issue in Type II that is improved in Release 16 with the incorporation of an enhanced precoding strategy that reduces feedback overhead and provides finer frequency granularity [4; 25]. Severalworks have been proposed to reduce feedback overhead in Type II codebook based on a multi-stage quantization approach [26] and a DFT-compression [27; 28]. In Release 17, the CSI feedback overhead is further reduced by exploiting reciprocity in Frequency Division Duplex (FDD) operation [29]. NR standardization process is continuously evolving and it is foreseen that more advanced precoding techniques will be part of the forthcoming releases as a key feature for 5G/B5G networks.
## 3 Results
### Important Features  
Correlation-based feature selection was applied to the 30 training sets to find the optimal feature subset. Table 3 lists the features and the times that each feature was selected from the training sets. Among them, age, prior ED visits within one year, pre-stroke functional status as assessed by the mRS, initial stroke severity as assessed by the NIHSS, BMI, consciousness level as assessed by the Glasgow Coma Scale, and use of nasogastric tube were the most important features that predict readmission  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Clinical Feature**} & \multirow{2}{*}{**Total (_n_ = 3422)**} & **Readmission or Mortality** & \multicolumn{2}{c}{**No Readmission or**} \\  & & **within 90 Days** & **Mortality within 90 Days** & \(p\) \\  & & **(_n_ = 602)** & **(_n_ = 2820)** & \\ \hline Age, years, mean (SD) & 68.3 (12.6) & 71.3 (12.3) & 67.7 (12.6) & \textless{}0.001 \\ Female, n (\%) & 1366 (39.9) & 264 (43.9) & 1102 (39.1) & 0.030 \\ Stroke type, n (\%) & & & 0.924 \\ Ischemic stroke & 2622 (76.6) & 465 (77.2) & 2157 (76.5) & \\ TIA & 467 (13.7) & 80 (13.3) & 387 (13.7) & \\ ICH & 333 (9.7) & 57 (9.5) & 276 (9.8) & \\ NIHSS, median (IQR) & 4 (2–9) & 6 (3–14) & 4 (2–8) & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Demographics and characteristics of stroke of the study population. TIA = transient ischemic attack. ICH = intracerebral hemorrhage. NIHSS = National Institute of Health Stroke Scale. SD = standard deviation. IQR = Interquartile range.  
or mortality at 90 days after stroke. These features were constantly selected by the feature selection algorithm from the 30 different training sets. In addition, failed dysphagia screening test, coronary artery disease, cancer, heart failure, atrial fibrillation, recent infection, prior hospitalization within one year, the stage of renal dysfunction as assessed by the estimated creatinine clearance rate, and use of Foley catheter, which were selected in more than 20 out of the 30 feature selection processes, were also key features.
# A 2-V 1.4-dB NF GaAs MMIC LNA for K-Band Applications
## 2 Circuit Design
### Design Approach
Finally, a 2 \(\times\) 40 \(\upmu\)m transistor is studied in Figure 2e,f. Further geometries are discarded directly since their Re[\(S_{opt}\)] are significantly lower than 50 \(\Omega\), making them unsuitable for the design. In addition, a higher total width yields a higher power consumption for the same current density. From this analysis the designer can determine the optimal device and current density for minimum NF, maximum gain, or a reasonable trade-off between NF, gain and power consumption. Note that, as previously discussed, a 4 \(\times\) 20 \(\upmu\)m transistor with a current density of 0.12 mA/\(\upmu\)m (120 A/m) is required for minimum NF, which results in a \(G_{max}\) of about 12.8 dB and \(NF_{min}\) of \(\sim\)0.8 dB. However, after applying source degeneration, the value of \(G_{max}\) is greatly reduced. A drain current (\(I_{D}\)) of 4 (fingers) \(\times\) 20 (\(\upmu\)m) \(\times\) 0.12 (mA/\(\upmu\)m) = 9.6 (mA) is needed to bias the active device. In this step, it is critical to fix a device size and current density which facilitate the input matching considering the discussion of Figure 1b[1]. Once the components of the first stage are selected, the same procedure is followed to determine the device size and biasing of the upcoming stages to define their optimum source and load impedances to meet (3). When they are known, the interstage matching network can be designed. The last step is the design of the DC bias lines to feed the devices of each stage. In our circuit, quarter-wavelength lines with bypass capacitors were used to bias the gate and drain of each transistor. The transmission lines (TLs) used in the bias paths show high impedance in the LNA's operating frequency band, and therefore, they do not contribute to impedance matching significantly.
# A Novel Design of Permanent Magnets for the Air Gap Magnetic Field of Hollow-Cup Motor
###### Abstract  
Spacecraft motors are often driven with trapezoidal phase currents to achieve higher output torque. For hollow cup motors (HCM) driven by trapezoidal wave currents, parallel magnetised permanent magnet (PM) motors produce an air gap magnetic field (AMF) waveform which is significantly different from the trapezoidal wave, causing the motor to generate noise or vibration. The existing control optimisation method or structure improvement design method is difficult to directly apply to HCM due to its large gas gap. In this paper, according to the fundamental theory of a constant magnetic field, the AMF of HCM is analysed using the equivalent surface current method (ESCM) and its mathematical model is established. The analytical expression of the AMF is solved, and the influencing parameters of the AMF are clarified. The structural design of the HCM with eccentric PMs sintered with high-performance NdFeB is further improved. On this basis, a prototype motor is designed. Simulation results show that the structure can effectively increase the width of the flat section of the AMF and make the AMF close to an ideal trapezoidal wave (ITW). Experiments verify the correctness of the method.  
eccentric PMs; hollow-cup motor; high-performance NdFeB +
Footnote †: journal: Applied Physics Letters
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
## 3 Results and Discussion
### Characterization of Surface Modification of PAN Fibers  
The surface morphologies of the surface-modified PAN fibers were observed by FE-SEM (Figure 2). The surface of pure PAN fiber was smooth and uniform, while the surface roughness increased as it reacted with DETA. The thickness of the fibers also increased due to the reaction between DETA and the PAN fibers [8; 19]. The reaction was also confirmed by the FTIR spectra (Figure 3). The pure PAN fiber showed adsorption peaks of stretching vibrations at 1070 cm\({}^{-1}\) (C-O), 1450 cm\({}^{-1}\) (CH\({}_{2}\)), and 2936 cm\({}^{-1}\) (CH stretching in CH, CH\({}_{2}\), and CH\({}_{3}\) groups) [1; 20]. The FTIR spectra of the PAN fibers showed the most distinctive bonds of the nitrile (C\(\equiv\)N) vibration at 2242 cm\({}^{-1}\), which decreased with increasing DETA concentration and almost disappeared with 85% EDTA [19; 21; 22]. The peaks of the amine modified at 1561-1571 cm\({}^{-1}\) (Figure 3c-e) correspond to the stretching bending vibration of NH groups, demonstrating the presence of primary amine groups [20]. The broad band around 3300 cm\({}^{-1}\) was attributed to the O-H spectrum [23].  
Figure 2: Field emission-scanning electron microscope (FE-SEM) images of (**a**) unmodified polyacrylamide(PAN) fiber and PAN fiber with different concentrations of DETA: (**b**) 40% DETA, (**c**), 55% DETA, (**d**) 70% DETA, and (**e**) 85% DETA.
## 2 Materials and Methods
### Simulation Tests with the Use of the Finite Element Method  
Simulation tests involved determination of the coordinates of the collision point, trajectory of particles, the distance after collision, and the identification of the wrong indications of the piezoelectric sensor counting grains on the basis of the number of collisions with the sensor surface. The simulation model was consistent with a real grain transport tube represented in the test station (Figure 4). The image of a model for the two considered exit hole configurations (straight and diagonal) is shown in Figure 7 (consistent with real configurations presented in Figure 6). The simulation was carried out using RockyDEM software for rape grains with parameters presented in Table 1. Rape grains were assumed to be identical, in the form of a spere with 2.2 mm diameter.  
Figure 7: Geometry of simulation using the discrete element method: (**a**) straight system, (**b**) diagonal system.  
Based on similar tests carried out by other authors [33; 36; 37], the Hertzian Spring Dashpot model was accepted for normal force, the Mindlin-Deresiewicz model for tangential force, and the linear model for adhesion forces. Due to the grain single contact with the sensor during the collision, rolling resistance was neglected. Table 2 shows the values of the particle-particle and particle-surface contact parameters used in the simulation tests using parameters provided by the literature [38; 39]. The simulation lasted 5 s, and the output frequency was 0.1 s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Particle-Particle** & **Particle-Surface** \\ \hline Restitution coefficient & 0.5 & 0.6 \\ Tangential friction coefficient & 0.6 & 0.8 \\ Dynamic friction coefficient & 0.2 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rape grain contact parameters used in the simulation.  
Figure 8: Axes of the coordinate system during determination of the grain point of collision with the sensor and the distance after the impact for the vertical tube (tilt 0\({}^{\circ}\)).
## Appendix A  
Three measurement series, consisting of 200, 1000, and 603 spectra, were conducted for three different temperature settings: 20, 50, and 80 \({}^{\circ}\)C. Before each measurement series, the temperature inside the chamber was stabilised for at least 60 min to ensure measurement accuracy and repeatability. The stabilisation process minimised the thermal fluctuations that could impact the performance of the FBG sensor and the accuracy of the measured spectra. Additionally, the spectra were measured during a temperature change from 20 to 50 \({}^{\circ}\)C (1000 measurements) and from 50 to 80 \({}^{\circ}\)C (1926 measurements). The course of the wavelength change calculated using the centroid algorithm is presented in Figure 11. The centroid algorithm was chosen to present the wavelength change because the cross-correlation and fast phase correlation algorithms are algorithms that determine the spectrum shift relative to the reference spectrum. When analysing Figure 11, attention is drawn to the apparent greater content of the random component during the temperature change and setting process. This was caused by vibrations during the operation of the temperature chamber.  
Figure 12 shows a fragment of the wavelength change for a set temperature of 50 \({}^{\circ}\)C. The chamber maintains the temperature within 1 \({}^{\circ}\)C, which can be observed during measurements in the form of a visible drift in the determined wavelength. During a thousand measurements of the spectrum, the slowly changing component of the \(\Delta\lambda\) value increased by 2.8 pm, which meant a temperature change of 0.285 \({}^{\circ}\)C. The wavelength shift was determined using the fast phase correlation method. However, before determining the  
Fourier transform, the spectrum was squared. The course of the wavelength shift changes after subtracting the slowly changing component (trend) is shown in Figure A3.  
The standard deviation of the trendless wavelength shift value is 0.56 \(\times\) 10\({}^{-3}\) nm. At a sensitivity of 9.87 pm/\({}^{\circ}\)C, this corresponds to a standard deviation of the determined temperature of 0.0567 \({}^{\circ}\)C. The histograms of the error distributions are shown in Figure A4. The error distributions are close to a normal distribution.