esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study proposes a data processing method based on BP imaging coupled with RPCA and deal with the GPR detection data of tunnel lining defects. BP imaging can restore the responses of lining defects and make they closer to the real defects in shape and position. However, in case of poor data quality or complex detection environment, there will be multiple clutter interferences, which will affect the identification of abnormal bodies. On this basis, RPCA can be employed to decompose the migration results, thus separating the imaging part of the minor anomalous body with sparse characteristics and improving the convenience and accuracy for recognizing the minor anomalous body. Numerical simulation cases and measured cases verified the effectiveness of the proposed method, which can identify minor anomalies efficiently and accurately. The experimental results show that this method is suitable for point, linear and planar anomaly bodies and could deal with defects with different reflectance. It could make the radar detection results in complex situations easy to identify and interpret and has the best effect on the imaging and extraction of point anomalies. This algorithm has application potential in detecting and identifying tunnel lining defects.  
In addition to lining detection, the algorithm also has certain application potential in other small abnormal body detection scenarios, such as unexploded ordnance detection and pipeline detection. However, it must be pointed out that RPCA is only a matrix factorization method, the extracted imaging results still contain some interference, and the discarded low rank matrix contains some layered or large anomaly information. Therefore, the method of this paper is only to provide an analysis tool for relevant practitioners, rather than to replace the professional interpretation.  
Conceptualization, D.L. and E.Y.; methodology, D.L.; writing--original draft preparation, D.L.; writing--review and editing, E.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
# Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach
###### Abstract  
Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse head related transfer function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions into high frequency representations of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of convolutional auto-encoder (CAE) and denoising auto-encoder (DAE) models is proposed to restore the high frequency distortion in SH-interpolated HRTFs. Results were evaluated using both perceptual spectral difference (PSD) and localisation prediction models, both of which demonstrated significant improvement after the restoration process.  
deep learning; head related transfer function (HRTF); restoration; ambisonics; spatial audio; spherical harmonic; audio signal processing; denoising; auto-encoder; neural network Article
## 3 Results and Discussion
### Scanning Electron Microscopy and Energy Dispersive X-Ray Spectroscopy (SEM and EDX)  
Figure 7 shows the results of the SEM morphology analysis for La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) and La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\)-Pt nanoparticles. The SEM micrographs highlight the influence of the impregnation of the noble metal on the morphology of the obtained pure La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) nanoparticles. The LTA sample shows irregular particles with a size ranging between 50 and 100 nm. The particles are between 50 and 100 nm in size and appear as clusters of nanoparticles (Figure 7a).  
Figure 6: XPS spectra and the deconvolutions for the LTA-Pt sample for (**a**) Ti 2p, (**b**) O 1s, (**c**) La 3d, (**d**) Pt 4f, and (**e**) C 1s.  
In the case of the LTA-Pt sample (Figure 7b), a better individualization of the particles can be observed, as they are less aggregated, with uniformly distributed Pt.  
To confirm the elemental composition, EDX measurements were performed. The elemental composition obtained by EDX is presented in Table 4. Qualitative EDX analysis confirms the presence of elements La, Ti, O, and Pt.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Element Type** & **Element** & **Relative Intensity (\%)** & **Binding Energy (\%)** & **Attribution** & **Atomic Percentage** \\  & & & **(\%)** & **(eV)** & & **(\%)** \\ \hline \multirow{8}{*}{LTA} & C 1s & 1 & 62.2 & 284.6 & C–C & \\  & & 2 & 29.5 & 286.27 & C–O & \\  & & 3 & 8.3 & 289.07 & O–C=O, La carbonates & \\ \cline{2-6}  & O 1s & 1 & 45.8 & 529.34 & TiO\({}_{2}\) (surf.)/La–O–Ti & \\  & & 2 & 26.7 & 530.72 & TiO\({}_{\chi}\),TiO\({}_{2}\) bulk/TiO\({}_{2}\) & \\  & & 3 & 23.3 & 531.94 & C=O, OH & \\  & & 4 & 4.2 & 533.48 & C-O & \\ \cline{2-6}  & La 3d & 1 & 2.4 & 833.82 & La\({}^{3+}\) multiplet peak 1 & \\  & & 2 & 49.9 & 836.46 & antibonding & \\  & & 3 & 25.2 & 840.44 & La\({}^{3+}\) multiplet peak 2 & \\  & & 4 & 22.5 & 847.5 & satellite & \\ \cline{2-6}  & Ti 2p & 1 & 66.3 & 458.06 & TiO\({}_{2}\) (surf.)/La–O–Ti & \\  & & 2 & 33.7 & 459.99 & TiO\({}_{\chi}\), TiO\({}_{2}\)-bulk/TiO\({}_{2}\) & \\ \hline \multirow{8}{*}{LTA-Pt} & C 1s & 1 & 76.2 & 284.6 & C–C & \\  & & 2 & 17.4 & 285.84 & C–O & \\  & & 3 & 6.4 & 288.34 & O–C=O, La carbonates & \\ \cline{2-6}  & O 1s & 1 & 67.6 & 529.95 & TiO\({}_{2}\)/La–O–Ti & \\  & & 2 & 27.3 & 531.73 & C=O, OH & \\  & & 3 & 5.1 & 532.8 & C–O, Pt oxide & \\ \cline{2-6}  & La 3d & 1 & 31.1 & 833.78 & La\({}^{3+}\) multiplet peak 1 & \\  & & 2 & 20.2 & 835.48 & antibonding & \\  & & 3 & 34.8 & 838.31 & La\({}^{3+}\) multiplet peak 1 & \\  & & 4 & 13.9 & 847.90 & satellite & \\ \cline{2-6}  & Ti 2p & 1 & & 458.32 & TiO\({}_{2}\)/La–O–Ti & 9.8 \\ \cline{2-6}  & Pt 4f & 1 & 77.4 & 70.36 & Pt & \\  & & 2 & 22.6 & 71.55 & PtO & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compositional table—Binding energies with the attributions of the different components with each component’s relative intensity and percentage of total integral intensity obtained from XPS data and deconvolutions.  
Figure 7: SEM images of the nanopowders: **(a)** LTA and **(b)** LTA-Pt.
## 3 State-of-the-Art MIMO-SDR Systems
### Mimo-Sdr Waveforms
#### 3.2.4 MIMO-OFDM Variants Enhancement Studies
In addition, a time-reversal (TR) technique was used to resolve the saturation problems with the conventional frequency-domain-combining techniques: combining zero forcing and minimum mean square error detectors. The designated architecture achieves high spectral efficiency in realistic conditions. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [106] introduced the Time-Interleaved Block Windowed Burst-OFDM (TIBWB-OFDM) waveform technique, which is easily applied to MIMO, as presented in Figure 16. The idea was to cluster the data into windowed (with a time-square root-raised cosine filter) short OFDM blocks, zero padded (ZP). The small size FFTs and ZP integrated blocks widely improve the power efficiency and reduce the PAPR. Figure 14: The resource mapping scheme. Figure 15: FB-OFDM transceiver.
# A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring
## 4 Experimental Results and Discussion
### Discussion
* Other potential applications and future work Idea of global-local blur disentangling analysis and its implementation, dual-branch network with phased training scheme, can be directly extended to video deblurring applications. For every single blurry frame in a video, the restoration task usually can be formatted as a deblurring issue for dynamic scene images. Note that the sharp images in the GoPro dataset are taken by a high-speed camera and selected from video frames. In addition, in video deblurring applications, there are more temporal priors that can be further explored to enhance the temple consistency of the restored frames. Towards real-world low-quality image restoration tasks, there are several other degradation factors that should be considered, such as noise, very high or low illumination, compressed artifacts, and so on. It is an interesting topic to solve the image restoration issues with the coexisting of different degradation factors via the idea of disentangling analysis. However, the degradation models for different factors are quilt different. For example, noise in real-world can be extra complex. To further explore this topic, we should pay more attention to prior knowledge of the degradation models and large-scale realistic training data.
# 2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation
## 2 Materials and Methods
### Methods
#### 2.2.3 Multiresolution Strategy  
We use the multiresolution strategy to achieve image registration and reduce registration time consumption [42]. A multiresolution registration strategy is a kind of registration technology from coarse to fine, increasing the probability of searching global optimum in parameter space and accelerating the algorithm's convergence. The basic idea is as follows: first, rough registration is carried out in the case of a low-resolution image, and then the result of low-resolution image registration is taken as the initial value of the next higher resolution image registration, and this step is repeated until the registration reaches the finest scale (original image). This coarse-to-sufficient registration strategy can further improve the image's robustness by smoothing the low-resolution image.  
The multiresolution registration strategy's key is a multiresolution sampling of the reference image and floating image multiresolution technology. Wavelet transform [43], Laplacian pyramid [44], Gauss pyramid [45], average pyramid, and sampling pyramid are widely studied. This paper needs to obtain low-resolution images from the original high-resolution image, so a Gaussian pyramid in downsampling is used. Compared with the wavelet transform, downsampling can improve the signal-to-noise ratio. In addition, it samples and stores the sampling information of each point signal to improve the registration speed. At the same level, the Gaussian low-pass filter is used to smooth the image to increase the smoothing factor.  
Moreover, smoothing in the low-resolution subsampled image can avoid the significant fluctuation of the registration objective function, thus preventing the optimizer from falling into the optimal local value. As a result, the registration algorithm can skip the optimal local value in low-resolution registration. As a result, the success rate of registration is improved. Multiresolution technology has strong robustness to image noise, speeding up the optimization speed and improving the capture range [46; 47].
## 4 Experimental Research
### Optimal Selection of Time Interval for MI-EEG  
The MI-EEG signals on channels C3 and C4 from 280 trials of samples were selected as the experimental data. Based on the event-related desynchronization (ERD)/event-related synchronization (ERS) phenomenon associated with hand movement or imaging movement, the optimal sampling interval of MI-EEG may be determined to ensure there is a significant difference between fuzzy entropies corresponding to two motor imaginary tasks. First, for 140 trials of imaginary left hand movement EEG signals on channel C3, the FE time series of each MI-EEG could be obtained by using a sliding time window, where the window length was 1 s, the interval was one sampling point, and parameters \(m\), \(n\), and \(r\) were set to 2, 2, and 0.1 \(SD\), respectively. Next, the 140 FE time series of imaginary left hand movement EEGs on channel C3 were superimposed, and averaged to obtain their mean FE time series. In a similar way, the mean FE time series of 140 trials of imaginary left hand movement MI-EEGs on channel C4 could be calculated. Furthermore, the mean FE time series of 140 trials of imaginary right hand movement MI-EEGs on channels C3 and C4 were obtained as well. The experimental results are shown in Figure 4. The solid magenta line represents the mean FE time series of 140 trials of MI-EEG on channel C3 for each imaginary task, and the green dotted line expresses the mean FE time series of 140 trials of MI-EEG on channel C4 for each imaginary task.  
As seen in Figure 4, the means of FE on channels C3 and C4 also change with the variation of sampling point for any one of two imaginary tasks. When the sampling interval is [451,900], the difference of mean FE values between C3 and C4 channels is remarkable. In this paper, the sampling interval will be chosen in the following feature extraction of MI-EEG.  
Figure 3: Electrode placement.  
Figure 2: The timing diagram of the collection experiment.
# Fault Diagnosis via Neural Ordinary Differential Equations
## 4 Fault Diagnosis via Neural ODE Applied to Nonlinear Benchmark System
### Actuator Faults  
#### 4.3.1 Training  
Training is performed in two stages, in the first stage the Neural ODE [33] is trained using the block representation presented in Figure 5, the measurements (\(y(t)\)) pass through 4 linear layers, where the first three layers are followed by activation functions of Tanh type, meanwhile, the inputs (\(u(t)\)) are considered using a linear layer. For this stage, the training is done in 2000 epochs, with a learning rate that starts at \(10^{-3}\) and is varied at epoch 1000 to \(10^{-4}\). The ODE tolerance is kept at \(10^{-7}\) during the whole process. Inputs (\(u(t)\)) are added sequentially using a linear layer that is kept constant.  
For the second stage of training, one additional linear layer with a Tanh activation function and a softmax classifier for four classes (two actuator faults) are added. The model considered is presented in Figure 6. The input for this stage is the propagated derivatives of the internal states (\(\dot{y}\)) and the derivatives for the measurements (\(\dot{y}\)). This training is performed in 20,000 epochs starting with a learning rate of \(10^{-3}\) that is reduced at epoch 15,000 to \(10^{-4}\). For simplicity, we kept the model of the first stage, the neural ODE, constant during this second stage training. However, is possible to continue adapting the neural ODE in this stage or even during evaluation for possible model changes.  
Figure 4: Actuator faults in an extract of the data-set, shaded areas indicate that a fault has occurred, and signals are normalized.  
Figure 5: Neural network model for ODE training.
# Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks +
## 2 Personal Picocell Scheme Using Adaptive Control CRE
### Proposal of Personal Picocell Scheme  
In conventional CRE technology, a fixed CSO is applied to all UE within the macrocell or macro-sector. However, as mentioned in Section 2.1, CRE actually induces a decreased downlink RSRP for the UE in the CRE zone. Because the received downlink SINR of the UE from the pico-eNB decreases, the throughput of the UE must be lower unless the radio resource assignment is unchanged. However, CRE can be expected to increase the throughput because the pico-eNB can assign more radio resources to the UE in the CRE zone.  
Therefore, to solve this problem, we propose a personal picocell scheme in which the picocell coverage should be independent of each UE, i.e., the CRE zone should be independent of each UE when the CRE is activated on the condition that the downlink transmission power of the pico-eNB is fixed [18]. The basic concepts are to provide a positive high CSO for UE with lower downlink SINR from macro-eNB to encourage the connection to pico-eNB, and conversely to provide a positive low CSO for UE with higher downlink SINR to maintain the connection to macro-eNB. Figure 4 shows an example of a personal picocell scheme in which CRE can form an independent extended picocell: \"a picocell for UE\({}_{1}\) with a CSO\({}_{1}\) around pico-eNB\({}_{1}\)\" and \"a picocell for UE\({}_{2}\) with a CSO\({}_{2}\) around pico-eNB\({}_{2}\).\" Similarly, CRE for pico-eNB\({}_{3}\) can also form an independent extended picocell, \"a picocell  
Figure 3: Principle of cell range expansion (CRE) with a positive cell selection offset (CSO) in HetNet.  
for UE\({}_{3}\) with a CSO\({}_{3}\)\" and \"a picocell for UE\({}_{4}\) with a CSO\({}_{4}\),\" even though UE\({}_{3}\) and UE\({}_{4}\) are connected to the same pico-eNB\({}_{3}\).
# The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
## 6 Experimental Assessment
### MBoSDM Transmission Assessment
So, despite the MCF is designed to reduce the XT levels, when the signal power of the neighbouring/adjacent cores is very high, it can lead to XT issues and interfere with the signal of interest by inducing optical interference and unwanted coupling between the cores, degrading the overall performance and limiting the capacity, as seen in the results of Figure 12. This highlights the importance of managing signal power levels of the different cores, to minimize the in-band XT for reliable communication.
## 4 The Asymmetric Case
To validate the simulation results, measurements were also conducted on the asymmetric model. As shown in Figure 11c, the measurement results for the asymmetric shape confirm that this shape can reduce sensitivity to changes in the polarization angle, which is clearly visible through a less sharp voltage drop compared to the symmetric shape shown in Figure 10. These measurements align well with the simulation results. The voltage drop observed in the measurements is slightly deeper than in the simulations, which can be attributed to the fact that the asymmetric model was physically created by cutting the fabricated symmetric structure. Such manual modifications may introduce non-ideal edge geometry, surface roughness, or slight discontinuities, locally altering the current distribution and increasing polarization sensitivity. A shift in the maximum voltage frequency is also observed between the simulation and measurement; the simulated maximum occurs at 4.998 GHz, while the measured maximum is at 4.75 GHz. This shift may result from fabrication imperfections, such as minor deviations in via placement, inaccuracies during manual cutting, soldering of the connected resistors, and connector issues. Material properties, like the dielectric constant and loss tangent of the FR-4 substrate, may also differ from the ideal values used in the simulation, contributing to the shift in the maximum voltage frequency.
## 2 Data and Methodology
### Data Analysis from Sampling and Response Designs
#### 2.4.3 Seasonal Patterns of Savanna Clearing and Fire Events  
The relationships between savanna clearing and fire are not completely understood in the _Cerrado_ environment. In order to look for temporal patterns of occurrence of these events in the study area, we first inspected our reference dataset by plotting the type of disturbance on a per-month basis in 2019. We compared these results with the CCDC-derived disturbance maps.  
Because of the difficulties of CCDC to separate both types of disturbance in the time series, we complemented our analysis using ancillary data from savanna clearing in 2019 provided by the INPE's program entitled _\"Sistema de Deteccao de Desmatamento em Tempo Real\"_ (DETER _Cerrado_; [[http://terrabrasilis.dpi.inpe.br/](http://terrabrasilis.dpi.inpe.br/)]([http://terrabrasilis.dpi.inpe.br/](http://terrabrasilis.dpi.inpe.br/)); accessed on 21 July 2021). In addition, we used the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) FireCCI v5.1 product (European Space Agency--ESA) to compare the CCDC results with the burned areas detected by MODIS in 2019. Such comparisons used the validation points of disturbance that were also recorded on each product.
## 3 Results and Discussion
### Raman Spectroscopy Analysis of Composite Photocatalyst  
Figure 3 shows the Raman spectra of TiO\({}_{2}\) and TiO\({}_{2}\)-GO composite photocatalysts. There were three peaks of 396 cm\({}^{-1}\) (B1g), 513 cm\({}^{-1}\) (A2g), and 639 cm\({}^{-1}\) (Eg) in the Raman spectrum of TiO\({}_{2}\), as the vibration mode of anatase phase TiO\({}_{2}\)[21], two peaks appeared at 1330 and 1600 cm\({}^{-1}\) in the Raman spectrum of TiO\({}_{2}\)-GO composite photocatalysts, with I\({}_{\rm D}\)/I\({}_{\rm G}\) = 1.03, which was slightly lower than that of GO (I\({}_{\rm D}\)/I\({}_{\rm G}\) = 1.6) [22]. This is probably due to the GO reduction during reaction. In addition, three peaks appearing in the low frequency region were identical to those of pure TiO\({}_{2}\) vibration peak, indicating that TiO\({}_{2}\) in the composite photocatalyst is still at anatase phase.
# Personal Exposure to Radio Frequency Electromagnetic Fields among Australian Adults
## 3 Results  
### Participant Profile  
Personal RF-EMF exposure measurements were conducted of 63 adults, aged between 18 and 72 years (mean \(\pm\) SD age of 36.9 \(\pm\) 12.5 years and 66.7% women). Almost all (98.2%) lived in metropolitan areas. A Wi-Fi router at home was reported by 61 participants (96.8%), with 38 (61.2%) having a Wi-Fi enabled smart TV. A mobile phone base station in the neighborhood was noticed by 26 (41.3%) participants. The average estimated distance of the closest mobile phone tower of which the participant was aware of was 500 m.  
Overall, 17 (27%) were working in the healthcare sector, 16 (25.4%) in office support and administration, 10 (15.9%) were researchers or university teachers, 10 (15.9%) were working in the education sector or studying (school teacher, primary/childrene educator or student), while the rest (\(n\) = 10; 15.9%) were from other sectors such as engineering, food and hospitality services (Table 1).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Characteristics** & \(n\) **(\%)** \\ \hline Age, mean \(\pm\) SD (years) & 36.9 \(\pm\) 12.5 \\
18–24 years & 8 (12.7) \\
25–34 years & 26 (41.3) \\
35–44 years & 14 (22.2) \\
45+ years & 15 (23.8) \\ \hline Sex & \\ Female & 42 (66.7) \\ \hline Race/Ethnicity & \\ Caucasian & 33 (52.4) \\ Asian & 23 (36.5) \\ Other & 7 (11.1) \\ \hline Residential location (\(n\) = 56) & \\ Metropolitan & 55 (98.2) \\ \hline Education & \\ High school or less & 25 (39.7) \\ Beyond high school & 38 (60.3) \\ \hline Occupation & \\ Office support/admin/finance & 16(25.5) \\ Healthcare & 17(27.1) \\ Research & 10(15.8) \\ Education sector & 10(15.8) \\ Other & 10(15.8) \\ \hline Noticed a base station in the vicinity & \\ Yes & 26 (41.3) \\ No & 22 (34.9) \\ Not sure & 15 (23.8) \\ \hline Base station distance from usual residence (median; meters) & 600 \\ \hline Have a Wi-Fi router at home & \\ Yes & 61(96.8) \\ \hline Smart TV connected to Wi-Fi in house & \\ Yes & 38(61.3) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Socio-demographic characteristics of participants.
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 2 Materials and Methods
### Equipment  
#### 2.2.1 Powder Mixing  
The raw material powder mixing process was performed using a 3D high-energy planetary ball mill. To address the issue of uneven distribution of copper-tungsten-graphene mixed powder resulting from density and volume deviations, a combination of multi-dimensional high-energy mixing and multiphase addition technology was employed. This approach ensured the uniform dispersion and distribution of graphene within the copper-tungsten mixed powder.