esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
## 4 Joint Simulation of the Calibration Process
### Verification and Planning  
The above studies in this paper are pure FEA simulations. It is more convincing if the same phenomenon can be found experimentally. To the best of our knowledge, the only study that can partially prove our study is by Franck Larsonner et al., who calibrated the phase frequency response of MB2005 and MB3 microbarometers. The amplitude and phase responses of the MB2005 microbarometer and the MB3 microbarometer shows the same tendency [15]. Apparently, the low-frequency phase characteristics of both microbarometers show a typical phase leading phenomenon. This is in good agreement with the results of the vent in field calibration, as shown in Figure 16. However, the results of the vent out field calibration are not given, which may be because the microbarometers are only used in the air and will be wholly placed in the sound field. Thus, the vent out field calibrations are not conducted.  
Figure 16: (**a**) Phase sensitivity of the microphone in two venting states, in which the properties of the microphone change. (**b**) Phase sensitivity of the microphone in two venting states, in which the properties of the pistonphone change.  
To verify the phase sensitivity of the microphone at low frequencies, an experimental plan is briefly given as follows. A low frequency microphone with matched preamplifiers (PCD 378A07 and 426E01) will be selected as the research object. A special mounting seat, which can realize both the vent in field and vent out field calibrations, should be manufactured as shown in Figure 17. The vibration displacement waveform of the piston will be measured by the laser vibrometer, as shown in Figure 1. A phase comparison between the voltage output of the microphone and the vibration displacement of the piston will be made to verify the correctness of the simulation. The above experimental study will be the next major work for our team.
# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 4 Optimization of ST-GCN Neural Networks
### Constructing a New Partition Strategy  
A good partition strategy can significantly improve model feature extraction capability. In order to make up for the lack of global information, a new partition strategy is used in this paper. We set the sampling function \(p\) to use the adjacent region with distance D = 2. It is divided into four subsets: (1) the root node itself (green dot); (2) the centripetal group (blue dot)--skeletal points that are closer to the center of gravity of the skeleton than the root node and that are adjacent to the root node; (3) the centrifugal group (yellow dots)--skeletal points further away from the center of gravity of the skeleton than the root node and adjacent to the root node; (4) the telecentric group (pink dots)--nodes at D = 2 from the root node. The partitioning strategy NEW-STGCN is shown in Figure 5. The purple dotted line part is the newly proposed extension region.  
Figure 4: Network structure diagram of STGCN-CA.  
Figure 5: New partitioning strategy. The root node is represented by a green dot, the centripetal point is represented by a blue dot, the yellow dot represents a centrifugal dot, and the pink dot represents a point 2 from the root node.  
This partitioning strategy expands the sampling region to consider the movement form of local joints as well as the mutual interconnection between joints. It can strengthen the link between the root node and the farther node by connecting information from all parts of the body, allowing the model to better perceive local body information and improve recognition accuracy.
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
## 4 Results and Discussion
### FTIR Test Result
The peak area at 1700 cm\({}^{-1}\) in the spectrum of short-term aged and long-term aged SBSBF is 4.28% and 44.22% higher than that of the unaged SBSBF. The corresponding increasing percentage of the SBSLF indexes is 28.24% and 323.00%. Therefore, the aging process has a higher impact on the chemical composition of SBSLF than the SBSBF. This is mainly because that the lignin fiber is a kind of plant fiber and it could degrade in the aging process, because lignin fiber is a kind of plant fiber and asphalt is a weak acid material [45]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wave Number** & **Mixture** & **1700 cm\({}^{-1}\)** & **1376 cm\({}^{-1}\)** & **1030 cm\({}^{-1}\)** & **966 cm\({}^{-1}\)** & **698 cm\({}^{-1}\)** \\ \hline \multirow{2}{*}{Peak area (unaged)} & BFSMA-13 & 10.994 & 146.239 & 195.657 & 33.376 & 29.939 \\  & LFSMA-13 & 6.236 & 145.334 & 129.157 & 34.395 & 24.767 \\ \hline \multirow{2}{*}{Peak area (short term aged)} & BFSMA-13 & 11.464 & 148.064 & 226.468 & 29.242 & 29.640 \\  & LFSMA-13 & 7.997 & 134.164 & 167.747 & 28.416 & 23.093 \\ \hline \multirow{2}{*}{ Peak area (long term aged)} & BFSMA-13 & 15.855 & 148.208 & 242.961 & 26.392 & 26.723 \\  & LFSMA-13 & 26.378 & 119.484 & 184.640 & 27.330 & 22.754 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Characteristic peak areas of SBS modified extracted from mixtures. In the manufacturing process (chemical treatment) of lignin fiber, the surface of lignin fiber has many polar groups like carboxyl groups and phenolic hydroxyl groups [46], these polar groups might make the lignin fiber and the asphalt interact with each other more in the aging process and make the lignin fiber degrade.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 2 Dielectric Permittivity Measurements
### Materials  
#### 2.2.1 Polymers for Additive Manufacturing  
Traditional materials for AM, as PLA or ABS polymers used with FDM technologies, have been characterized at microwave frequencies, and their dielectric constant and tangent loss are well known [28; 29]. Nevertheless, the advent of new technologies and materials raises the need for analysing their dielectric properties. In this work, six different polymers were characterized: Thought 200 and Rigid 10K resins from Formlabs, MLSA Modelling Dental resin from ApplyLabWork, Composite X (CX) resin from Liqcreate, Ultrasint PP (UPP) powder from BASF, and Polyamide 11 (PA11) Bio powder from Stratasys. Dental and Thought 200 are acrylic resins used by VPP, similar to Rigid 10K and Composite X. However, these last two resins have a load of silica, making them more resistant to temperature, harder, and less prone to deformations. UPP is a polypropylene powder used for Multijet PBF technology, while PA11 is a polyamide powder used in PBF technology.  
Three different types of samples were printed in order to obtain the measurements at the three frequencies: a cylinder of 6.50 mm of radius and 40 mm of height for the Resonant Coaxial Bireentrant Microwave Cavity at 2.45 GHz, a flat square size 95 \(\times\) 95 \(\times\) 2 mm for the Split-Cylinder Resonant Cavity at 6.25 GHz and a cuboid size 10.668 \(\times\) 4.318 mm and 3 mm of length for the WR42 Rectangular Waveguide at 24.5 GHz, as can be seen in Figure 4. It should be emphasized that it is not required for the cuboid sample to have precise micron-level dimensions to be measured using the WR42 Rectangular Measurement Kit; it only needs to fit within the waveguide. The small deviations can be corrected with the measurement method used. To design the three geometries, Solidworks software was used.  
Figure 4: Solid samples of: (**a**) PA11; (**b**) Dental; (**c**) Rigid 10K.  
Figure 3: WR42 Rectangular Waveguide Measurement Kit at 24.5 GHz.
## 3 Results and Discussion
### Natural Frequencies and Modal Shapes of the Stator with and without Windings
More accurate results can be obtained with a more precise definition of all geometrical and material details of the slot and windings, which in turn may require an additional computational time, additional experimental work and/or additional theoretical analysis (e.g., statistical energy analysis) [5]. The output results \(f_{m=2}(k_{\rm cu})\) shown in Figure 11 also indicate that the natural frequency strongly depends on the fill factor \(k_{\rm cu}\), and consequently, the equivalent density \(\rho_{\rm eq}\) and the stiffness of the stator windings \(E\) (Equation 7). The numerical results in Figure 11 also show that the natural frequency of the whole stator-winding assembly increases if the \(k_{\rm cu}\) of the end-winding is higher compared to the \(k_{\rm cu}\) of the slot windings; on the other hand, the natural frequency of the stator assembly decreases if the \(k_{\rm cu}\) of the end-winding is lower compared to the \(k_{\rm cu}\) slot windings. These results can also be useful for mechanical vibration analysis of other electrical machine stators that have different stiffnesses of the slot windings and end-windings. Figure 8: The calculated modal shapes of the IPM motor with the presence of the whole motor frame at the natural frequencies: (**a**) \(f=616\) Hz and (**b**) \(f=1382\) Hz. The stator frame in the lower figure is hidden in order to visualize the inner structure (i.e., the stator with windings). Figure 11: The numerically calculated dependency of the second modal natural frequency on the fill factor \(f_{m}=2(k_{\rm cu})\) for 1) the slot windings with \(k_{\rm cu}\) and end-windings with 2 \(\cdot\)\(k_{\rm cu}\); 2) equal \(k_{\rm cu}\) is applied to both slot winding and end-windings; and 3) slot windings with \(k_{\rm cu}\) and end windings with 0.5 \(k_{\rm cu}\). Figure 10: Numerical modal analysis of the IPM rotor with magnets and the shaft assembly: (**a**) the first bending mode \(f=5212\) Hz; (**b**) the second bending mode \(f=5933\) Hz; (**c**) the deformation of rotor and shaft \(f=18,\)449 Hz. The deflections are indicated from minimum (blue color) to maximum (red color). Figure 4:0 Results of the numerical modal analysis simulations performed for: (**a**) rotor without magnets and (**b**) rotor with magnets - IPM rotor. The deflections are indicated from minimum (blue color) to maximum (red color).
###### Abstract  
Deep neural networks (DNNs) require large amounts of labeled data for model training. However, label noise is a common problem in datasets due to the difficulty of classification and high cost of labeling processes. Introducing the concepts of curriculum learning and progressive learning, this paper presents a novel solution that is able to handle massive noisy labels and improve model generalization ability. It proposes a new network model training strategy that considers mislabeled samples directly in the network training process. The new learning curriculum is designed to measures the complexity of the data with their distribution density in a feature space. The sample data in each category are then divided into easy-to-classify (clean samples), relatively easy-to-classify, and hard-to-classify (noisy samples) subsets according to the smallest intra-class local density with each cluster. On this basis, DNNs are trained progressively in three stages, from easy to hard, i.e., from clean to noisy samples. The experimental results demonstrate that the accuracy of image classification can be improved through data augmentation, and the classification accuracy of the proposed method is clearly higher than that of standard Inception_v2 for the NEU dataset after data augmentation, when the proportion of noisy labels in the training set does not exceed 60%. With 50% noisy labels in the training set, the classification accuracy of the proposed method outperformed recent state-of-the-art label noise learning methods, CleanNet and MentorNet. The proposed method also performed well in practical applications, where the number of noisy labels was uncertain and unevenly distributed. In this case, the proposed method not only can alleviate the adverse effects of noisy labels, but it can also improve the generalization ability of standard deep networks and their overall capability.  
DNNs; curriculum learning; progressive learning; noisy labels; image classification Article
## 5 Experimental Verification and Results  
After the effectiveness of the proposed control method is tested through simulation experiments, the VPDPI control method is further loaded into the hardware-in-the-loop experimental platform to better verify the reliability of the proposed algorithm. The structure of the experimental platform is shown in Figure 30. In addition, the experimental platform is clearly presented in Figure 31. Hence, the performance differences of various control algorithms can be accurately compared. Without losing generality, the simulation control parameters are completely consistent with the experimental control parameters.
## 3 Results and Discussion
### Electrooxidation of Glucose
#### 3.2.1 Reproducibility, Repeatability, and Stability  
Copper oxide electrodes were also characterized in terms of reproducibility among different electrodes, repeatability, and stability with time for different storage conditions: NaOH 0.1 M, distilled water, and ambient conditions.  
Measurements from nine different electrodes with different glucose concentrations are represented in Figure 8. The variation among the electrodes is attributed to the variability of the active area. Error bars corresponding to error less than 18% in the worst case. All measurements were performed under basic media with cyclic voltammetry electrochemical technique. Data were collected at 0.55 V vs. Ag/AgCl.  
Regarding repeatability, there is no significant variability among measurements within the same copper oxide electrode. For this experiment, a glucose concentration of 4 mM was measured up to 25 times with the same electrode. As it can be seen in Figure 9, all profiles coincide pointing to good repeatability of the copper oxide electrode. Not all measurements are represented in the chart below.  
Figure 8: Reproducibility of copper oxide electrode. Data from 9 copper oxide electrodes synthesized with the same protocol and conditions.  
Figure 9: Repeatability of copper oxide electrode. 25 measurements of 4 mM of glucose in 0.1 M NaOH. (x) refers to electrode measurement repetition number.  
A pulsed amperometric detection technique was employed to determine the stability through time. This electrochemical technique uses a fixed potential to measure glucose concentration in a bulk solution. The fixed potential was set to +0.55 V according to previous experiments. Figure 10 shows 3 days measurements for the same copper oxide electrode. A total number of 120 measurements were performed every day.  
For the first day, the signal for 4 mM is slightly smaller than the one obtained with cyclic voltammetry. However, its value remains constant at 360 \(\pm\) 7 \(\mu\)A for 1000 s. For the second and third day, the signal increased up to 426 and 416 \(\mu\)A, respectively. However, the error percentage also increased to 6% in both cases. This signal increase could represent a higher copper oxide/copper ratio due to the surface contact with the atmospheric oxygen.  
Considering the electrode sensitivity, 904 \(\mu\)A mmol\({}^{-1}\) L\({}^{-1}\) cm\({}^{-2}\), this increase represents a 12.5% over the concentration of the study (4 mM of glucose).
## 5 MIMO Diversity Analysis
### Channel Capacity  
The channel capacity is an essential metric to evaluate MIMO antenna performance in a practical environment and is shown in Figure 13. The channel capacity for the proposed four-port flexible MIMO antenna is calculated using Equation (6), as mentioned in [27; 28]. From Figure 13, it is observed that the measured efficiency is around 21.50 bps/Hz under the condition of averaging 10,000 Rayleigh fading with a signal-to-noise ratio (SNR) of 20 dB. It is also noted that the channel capacity of the proposed four-port MIMO antenna is much closer to the ideal channel capacity and is almost 1.89 times larger than the maximum limit for a \(2\times 2\) MIMO antenna.  
\[C=k\Big{\{}log_{2}\Big{[}det([I]+\eta\frac{SNR}{k}[H][H^{*}])\ \Big{]}\Big{\}} \tag{6}\]  
In Equation (6), I is the identity matrix, \(SNR\) denotes the mean \(SNR\), \(k\) is the rank of the matrix \(H^{H}\), \(H\) is the channel matrix, and \(H^{H}\) is the Hermitian Transpose matrix.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Frequency** \\ **(GHz)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MEGAnt-** \\ **1/2** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MEG** \\ **Ant-1/3** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MEG** \\ **Ant-1/4** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MEG** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ant-2/3** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Ant-2/4** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Ant-3/4** \\ **(dB)** \\ \end{tabular} } \\ \hline
3.5 & \(-3.01\) & \(-3.01\) & \(-3.01\) & \(-3.01\) & \(-3.01\) & \(-3.01\) \\ \hline
5.1 & \(-3.01\) & \(-3.01\) & \(-3.01\) & \(-3.01\) & \(-3.01\) & \(-3.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: MEG of the proposed antenna.
## 3 Results and Discussion
### Optical Properties of GST  
PCMs were characterized by their amorphous and crystalline states related to covalent and resonant bonding, respectfully. The phase transition between two metastable states was nonvolatile [34].  
Figure 1: (**a**) Illustration of the reconfigurable and programmable metamaterial perfect absorber (RPMPA) and resistive switching memory (ReRAM) structures, including the incident light polarization conformation. Glass (silica) was selected as a substrate with a thickness of 250 \(\upmu\)m. (**b**) Schematic of a unit cell of the RPMPA and ReRAM with an Ag/Ge\({}_{2}\)Sb\({}_{2}\)Te\({}_{5}\) (GST)/Al\({}_{2}\)O\({}_{3}\)/Pt/SiO\({}_{2}\) structure. (**c**) Top view of the RPMPA and ReRAM, and the Ag nanosquares with d\(x\) and d\(y\) dimensions and chips of \(L\) (the middle to middle distance between the patterned squares).  
The refractive index \(n\) and extinction coefficient \(k\) of amorphous and crystalline GST were obtained from ellipsometry data in the ultraviolet (UV) spectral range up to a wavelength of \(\lambda\) = 1000 nm (Figure 3a,b). In the amorphous state, the predominant peaks of \(n\) and \(k\) were recorded in the visible range (\(\sim\)400 nm); however, a smaller second peak of \(k\) was observed at 300 nm.  
Although the maximum \(n\) of crystalline state occurred at 400 nm, different peaks of \(k\) (at 0.6 and 0.55) happened in UV ranges (about 200, 300, and 380 nm) following a peak of 0.45 in NIR (>800 nm) regions. Upon crystallization, the temperature \(n\) increased from 1.4 to 1.7 at approximately 400 nm (Figure 3a); however, \(k\) was enhanced by about 45 times at a wavelength of 820 nm (Figure 3b).  
A significant change in bonding was responsible for the pronounced increase in the optical properties associated with attenuation in a bandgap [42]. We concluded that the dielectric constant was dispersive [37] and that the large, imaginary part of the dielectric constant of GST indicated a high \(A\)[43].  
Figure 2: (**a**) Scanning electron microscope (SEM) images of the fabricated resistive switching memory (ReRAM) and metamaterial perfect absorber (MPA) devices with 5 \(\times\) 5 arrays of nanostructures; (**b**) A magnified image of one square patch with dimensions of 100 \(\upmu\)m; (**c**) 120 \(\times\) 120 nm patterned square-shaped nanostructures; and (**d**) 20\({}^{\circ}\) tilted square patterns; inset: a perspective view of the ReRAM. (**e**) AFM image of the patterned Ag top electrode and (**f**) the magnified AFM image.
# De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction
## 4 Results Analysis and Discussion
### Performance of the GBSS Algorithm Factors
A change in the over-subtraction factor with an \(\alpha_{0}\) value larger than 3 led to an improvement of approximately 8 dB in the dynamic range, and the estimation decay range was extended to \(-12\) dB. However, when the noise levels were higher than \(-60\) dB, the estimation decay range failed to extend to \(-10\) dB using a \(\alpha_{0}\) lower than 3. In this case, the over-subtraction factor, \(\alpha_{0}\), lower than 3 will not be applied to explore the de-noising effects on the RIRs regarding the dynamic range improvement. The application of \(\alpha_{0}=4\) yielded the best dynamic range improvement of the EDCs. In this case, the decay curve was almost identical to the reference EDC above \(-23\) dB. At the same time, the de-noised RIR showed similar energy levels to the reference RIR above 0.4 s, which can be seen in Figure 3c. With a larger \(\alpha_{0}\), the deviation on the reverberation decay was enhanced, which can be noticed from the energy-time curves given in Figure 3d. Using \(\alpha_{0}=6\) could remove more noise compared with \(\alpha_{0}=4\). On the contrary, a larger loss of the original signal occurred close to the end of the reverberation decay part, around 0.4 s, contributing to a severe deviation in the decay curve. With \(\alpha_{0}=6\), the RT estimated in the range from 0 to \(-19\) dB showed the same result as the noiseless decay curve. However, the reverberation decay curve goes below the reference decay curve in the decay range from \(-19\) dB to \(-29\) dB. In this case, it resulted in the degradation of the reverberation decay and caused a smaller estimated dynamic range than the value achieved by \(\alpha_{0}=4\). Figure 3: Comparison of the noisy RIRs and de-noised RIR at 1 kHz by varying the noise over-subtraction \(\alpha_{0}\) for a fixed value of \(\beta\). (**a**,**b**) Generated EDCs of the RIRs. (**c**,**d**) De-noised RIRs with \(\alpha_{0}\) 4 and 6 in the time domain. It follows that the over-subtraction factor had a significant effect on the EDC dynamic range improvement when factor \(\alpha_{0}\) increased to 3. Nevertheless, the increased decay range was smaller than the best-achieved results using the value of \(\alpha_{0}\). The case presented here used a value of 4 for de-nosing the RIR. The value in this regard was called the optimal factor regarding the achieved best dynamic range improvement. However, further subtracting of noise from noisy RIRs, up to a certain point, with values higher than the optimal \(\alpha_{0}\) contributed to the signals distortion starting from the point, causing the decreasing of the dynamic range and a deviation in the reverberation decay rates. The estimated time is equal to the knee of the original noisy RIR [13]. Therefore, it is crucial to find the point or the decay level corresponding to the optimal level of \(\alpha_{0}\) to avoid the distortion of the EDCs generated using a higher \(\alpha_{0}\).