esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Research on the Positioning of Armored Composite Materials
### Experimental Research on Single Damage Imaging and Localization
Figure 8 depicts the time-domain plots of the narrowband signals with a center frequency of \(\omega_{c}=50\) kHz, obtained through the Shannon continuous complex wavelet transform, for the response signals in the 1-2, 1-3, and 1-4 excitation sensor channels under both healthy and damaged conditions of the structure. From the figure, it can be observed that the amplitude of the damaged signal is slightly smaller than that of the healthy signal during propagation, and there is a phase shift, indicating propagation attenuation. Furthermore, in the 1-3 channel, the direct wave arrival time is noticeably delayed compared to the other channels due to the longer propagation distance. Figure 9 presents the damage-scattered signals obtained by subtracting the narrowband response signals from the aforementioned channels. The signals within the range from 0 to 0.3 ms from the damage scattering signals were selected as direct wave signals. These selected signals underwent processing to calculate the distance from each unit point \((x,y)\) within the monitoring area to the sensor. Subsequently, the time delay of the response signal was calculated. The delayed damage scattering signals, obtained by reversing the order of time, were utilized to construct an image matrix, with the pixel values representing the corresponding amplitude at each moment. The image was scanned with a step size of 0.1 \(\times\) 0.1 mm, capturing pixel values for all unit points. After normalizing the pixel  
Figure 7: Experimental procedure for damage imaging and localization. Figure 8: The response signals in the excitation sensor channels when the structure is in both a healthy and damaged state. (**a**) Time-domain plot of the response signal in the 1–2 excitation sensor channel; (**b**) time-domain plot of the response signal in the 1–3 excitation sensor channel; (**c**) time-domain plot of the response signal in the 1–4 excitation sensor channel. Figure 9: The damage scattering signals. (**a**) Time-domain plot of the damage scattering signal in the 1–2 excitation sensor channel; (**b**) time-domain plot of the damage scattering signal in the 1–3 excitation sensor channel; (**c**) time-domain plot of the damage scattering signal in the 1–4 excitation sensor channel. Figure 10: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline
**Serial Number** & **Actual** & **Image** & **Radial Error/mm** \\  & **Coordinates/mm** & **Coordinates/mm** & **Radial Error/mm** \\ \hline Damage 1 & (70, 120) & (70, 122) & 2 \\ Damage 2 & (120, 80) & (124, 79) & 4.12 \\ Damage 3 & (130, 130) & (131, 129) & 1.41 \\ Damage 4 & (50, 40) & (47, 39) & 3.16 \\ Damage 5 & (80, 80) & (82, 83) & 3.61 \\ \hline \end{tabular}
\end{table} 
Table 7: Coordinates and error statistics for the imaging localization of each damage location.
###### Abstract  
Due to sharp changes in local brightness in high dynamic range scenes, fused images obtained by the traditional multi-exposure fusion methods usually have an unnatural appearance resulting from halo artifacts. In this paper, we propose a halo-free multi-exposure fusion method based on sparse representation of gradient features for high dynamic range imaging. First, we analyze the cause of halo artifacts. Since the range of local brightness changes in high dynamic scenes may be far wider than the dynamic range of an ordinary camera, there are some invalid, large-amplitude gradients in the multi-exposure source images, so halo artifacts are produced in the fused image. Subsequently, by analyzing the significance of the local sparse coefficient in a luminance gradient map, we construct a local gradient sparse descriptor to extract local details of source images. Then, as an activity level measurement in the fusion method, the local gradient sparse descriptor is used to extract image features and remove halo artifacts when the source images have sharp local changes in brightness. Experimental results show that the proposed method obtains state-of-the-art performance in subjective and objective evaluation, particularly in terms of effectively eliminating halo artifacts.  
multi-exposure fusion; halo artifact; sparse representation; gradient feature; high dynamic range imaging +
Footnote †: journal: Applied Sciences
# Fault Classification and Localization Scheme for Power Distribution Network
## 4 Proposed Fault Classification and Location  
In this section, the proposed fault classification and detection scheme is discussed. The fault classification scheme is depicted in Figure 4.  
The process of fault classification begins with the post-fault measurement at the source terminal. Thereafter, the measured current is passed through numerous filters using the WPD tool. Subsequently, statistical features are extracted from the decomposed signal at level 4. The total features are 80 (16 WPD coefficients \(\times\)5 statistical features). The statistical features are used to formulate a future matrix. The feature matrix is used to train the SVM for classification. The SVM optimal parameters are obtained using the PSO. To reduce the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Type** & \(\mathbf{SVC_{A}}\) & \(\mathbf{SVC_{B}}\) & \(\mathbf{SVC_{C}}\) & \(\mathbf{SVC_{G}}\) &  
computational complexity, four (4) SVCs are used to classify the faults corresponding to each phase and the ground. In most cases, the line-to-line (LL) fault is misclassified as the line-to-line-to-ground (LLG) fault. In the present work, the misclassification problem is solved by installing the ground SVC at the generating source, as depicted in Figure 5.  
The fault location scheme begins with the fault current measurements at the terminal source. The measured current is passed through several filters and decomposed to level 4 using WPD. Thereafter, the extracted features are used to test and train the SVR locator to estimate the position of the fault. The performance of the fault estimation scheme is determined by calculating the mean square error (MSE) and the absolute error (AE). The absolute error is expressed mathematically as:  
\[AE=|A_{FD}-E_{FP}| \tag{13}\]  
where \(A_{FP}\) is the actual fault position, and \(E_{FP}\) is the estimated fault position. A high \(AE\) means that the scheme is out of range and produces inaccurate results.
## 1 Introduction  
Cardiovascular disease is one of most common diseases threatening human health and life. In China, as a result of improvements in the standard of living and ageing of the population, morbidity due to cardiovascular disease has increased, and the age of onset has decreased. Additionally, the World Health Organization estimated that 17.5 million people died worldwide from cardiovascular-related diseases in the year 2013, and this number is expected to grow to 23.6 million by 2030 [1]. Atherosclerosis is the main cause of cardiovascular disease, leading to a reduction or block of the blood flow, which results in myocardial infarction, apoplexy, as well as other severe complications. Currently, the main treatment methods for cardiovascular disease in the clinic include drug thrombolysis, percutaneous transluminal coronary angioplasty (PTCA), intravascular stents, and blood vessel bypass surgery [2].  
In blood vessel bypass surgery, autologous blood vessels, such as the internal thoracic or saphenous vein, are commonly used. Repairing diseased blood vessels using autologous vessels has many advantages such as good tissue compatibility, no risk of immune reaction, and good long-term patency. However, on the other hand, autologous vessels can cause secondary trauma, and their source is limited. Therefore, artificial blood vessels are needed to take the place of autologous vessels. In addition, other clinical diseases such as congenital cardiac anomalies [3] and lower extremity arterial occlusion also create a great demand for vascular grafts.  
In clinical settings, vascular grafts using synthetic materials like expanded polytetrafluoroethylene (ePTFE) and Dacron have shown good results in large-diameter blood vessel surgeries. However, for small-diameter (<6 mm) blood vessels, vascular grafts have not shown ideal results [4]. With the development of tissue engineering, the tissue-engineered vascular graft (TEVG) is a new method for repairing blood vessels. Currently, there are various types of materials and fabrication methods used in the production of TEVGs. However, there are also some challenges related to TEVGs, such as the long in vitro preparation time and the risk of immune reactions. The use of acellular TEVGs that regenerate into autologous tissue in situ is a promising method in the future [5].  
This review focuses on the current state of acellular TEVGs based on scaffolds; it provides a summary of methodologies and in vitro/in vivo test results from recent years related to this field.
# A Current-Mode Optoelectronic Receiver IC for Short-Range LiDAR Sensors in 180 nm CMOS
## 3 Voltage-Mode Optoelectronic Receiver (V-OER)  
### Experimental Methods  
An optoelectronic optical receiver IC with the suggested on-chip APDs was realized in a TSMC 180 nm CMOS process to verify the feasibilities for the applications of short-range LiDAR sensors. As a front-end transimpedance amplifier (TIA), a conventional voltage-mode feedforward input circuit (VFIC) is exploited in this work since it can provide low-noise high-gain performance [5]. Figure 8 shows the block diagram of the voltage-mode optoelectronic receiver (V-OER) which consists of an APD as an optical detector, a VFIC as a TIA to convert the generated photocurrents to electrical voltages, a single-to-differential (S2D) converter for differential signaling, a differential gain stage for gain boosting, and a cross-coupled inverter post-amplifier (CI-PA) for offset cancellation. An output buffer (OB) is inserted not only for 50 \(\Omega\) impedance matching, but also for the effective isolation of the V-OER circuit from the following time-to-digital converter (TDC) that is typically  
Figure 8: Block diagram of the voltage-mode optoelectronic receiver (V-OER) with an on-chip APD.  
Figure 7: (a) Measured I-V curves and (b) Measured responsivities of the three on-chip APDs.  
employed to estimate the time interval between the transmitted pulses and the received echo pulses [9; 10].  
In the VFIC, a voltage-mode inverter (INV) input stage with a feedback resistor (\(R_{F}\)) is merged with a feedforward common-source amplifier of which the gate node is connected to the gates of the INV input stage [4; 5]. The CI-PA consists of four inverters and two diode-connected output buffers, where the output voltages (\(V_{ON}\) and \(V_{OP}\)) can be enhanced by merging the input signals (\(OUT_{N}\) and \(OUT_{P}\)) with other small portions of another path. However, circuit designs should be carefully conducted to match \(\partial g_{mn}/\partial v_{gs}\) with \(\partial g_{mp}/\partial v_{gs}\) because the amplitude mismatches between two outputs might occur in the cases of short-distance detection, where \(g_{mn}\) and \(g_{mp}\) represent the transconductance of NMOS and PMOS transistors, respectively.
## 5 Summary and Conclusions
It should be mentioned that due to the lack of sufficient triples of COSMIC-2 profiles that meet the spatiotemporal constraints, it is hard to derive further the propagation direction of gravity waves in the present work, which will be the goal of our next research work. Moreover, the terrain is not considered an independent influencing factor, which is because the correlation analysis and the dominance analysis are based on the time series of the multiple factors and GW Ep, and the temporal variations of terrain heights are not significant. Furthermore, it is reasonable to assume that the impact of terrain on GWs is partially implied in the convective activity generated by the interaction between terrain in the Indian region and the Indian summer monsoon. In future work, we will further explore in detail the impact of terrain on GWs in the Indian region. In addition, we will also apply the DA method to the GW source studies of the other monsoon regions in the future, such as the East Asian monsoon region and the monsoon region in northern Australia, by using the datasets from multi-RO missions. Conceptualization, X.X. ; Investigation, J.L. and J.H. ; Methodology, J.H. ; Project administration, J.L. and X.X. ; Supervision, J.L. and X.X. ; Writing--original draft, J.L. and J.H. ; Writing--review and editing, X.X. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (Grant Nos. 42174017, 42074027, 41774033 and 41774032). The data used in the manuscript are all public data. The atm-Prf data of the COSMIC-2 RO missions were downloaded from the CDAAC Data Center: [[https://cdaac-www.cosmic.ucar.edu/cdaac/products.html](https://cdaac-www.cosmic.ucar.edu/cdaac/products.html)]([https://cdaac-www.cosmic.ucar.edu/cdaac/products.html](https://cdaac-www.cosmic.ucar.edu/cdaac/products.html)) (last accessed on 11 January 2024). The ERA5 reanalysis dataset was downloaded from the ECMWF: [[https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era55](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era55)]([https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era55](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era55)) (last accessed on 11 January 2024). The monthly gridded outgoing long-wave radiation (OLR) data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Climate Diagnostics Center (CDC): [[https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html](https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html)]([https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html](https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html)) (last accessed on 11 January 2024). The authors would like to express their gratitude to the University Corporation for Atmospheric Research (UCAR) for providing the COSMIC-2 data. We are also very grateful to the NOAA Climate Diagnostics Center and the ECMWF for providing the data of OLR and the ERA5 reanalysis dataset, respectively. The authors declare no conflicts of interest.
# Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation
## 2 Elastic Wave Monitoring for Sand-Filling of Immersed Tube Tunnel
### Elastic Wave Monitoring Method
The feature parameters, including primary frequency \(F_{1}\), primary spectrum \(A_{1}\), secondary frequency \(F_{2}\), and secondary spectrum \(A_{2}\), are selected to represent the characteristics of the elastic wave in the frequency domain. As the sand-filling progresses, the inside of the medium changes, causing the energy of the elastic wave to change. When the sand-filling process has not started, the material properties inside the medium vary greatly, the wave impedance varies greatly, and elastic waves are reflected multiple times inside. After the sand-filling is completed, the internal wave impedance difference becomes smaller, causing the elastic wave energy to attenuate faster. For time-frequency analysis of the response waveform through the continuous wavelet transform, the calculation formula is  
\[WT_{x}(\alpha,\tau)=\frac{1}{\sqrt{\alpha}}\int x(t)\varphi^{*}(\frac{t-\tau} {\alpha})dt \tag{2}\]  
Figure 1: Elastic wave monitoring method. where \(\frac{1}{\sqrt{a}}\varphi^{*}\left(\frac{t-\tau}{\alpha}\right)\) is the function obtained by the basic wavelet \(\varphi(t)\) through displacement \(\tau\) and scaling \(\alpha\), \(x(t)\) is a square-integrable function \((x(t)\in L^{2}(R))\), and \(\tau\) and \(\alpha\) are continuous variables. This is the continuous wavelet transform. By analyzing the attenuation information of the waveform in the time-frequency domain, the state information of the sand deposit can be inferred. Response time \(T\), the decay time of the predominant frequency was selected as one of the feature parameters.
## 2 Features of Hydrogen Combustion Processes
### Formation of Pollutant Emissions: CO and CO\({}_{2}\)  
One of the main benefits of using hydrogen is to reduce the carbon footprint of power plants. A decrease in carbon concentration in the initial fuel leads to an unambiguous decrease in CO\({}_{2}\) in the combustion products per unit of the heat received (or per unit mass of the fuel). For example, [PERSON] [39] reports a considerable CO and HC emission reduction that is achieved already at the H2 10% content, which is related to an increased combustion efficiency. With the further H2 increase, the CO and HC reduction is not so considerable, which is related to a decreased CO content in the fuel. However, when burning the methane-hydrogen mixtures, changes in CO concentration in the combustion products do not behave so unambiguously. Thus, [PERSON] [35] and [PERSON] [40] report on the increased CO concentration in the combustion products (Figures 16 and 17).  
Figure 15: Karlovitz and Damköhler numbers for hydrogen volume fraction [31].  
An increase in CO emissions with an increased hydrogen concentration may be partly due to a shorter residence time of gases in the combustion chamber at higher hydrogen concentrations. Another reason for an increase in CO emissions with hydrogen enrichment is that, due to a higher reactivity and adiabatic flame temperature of hydrogen compared to hydrocarbon fuels, the hydrogen enriched fuel mixtures will burn more efficiently. [PERSON] [41] also reports that high temperatures (above the CO2 dissociation threshold), during the combustion of hydrocarbons, stimulate CO conversion and therefore lead to a more complete combustion and lower CO emissions. However, despite all this, competition for oxygen between highly reactive hydrogen and hydrocarbons in the mixture can contribute to an increased CO emissions [42]. In other words, hydrogen inhibits the oxidation of hydrocarbons and their intermediates in an environment with a limited oxygen  
Figure 16: Dependence of CO emissions on the molar concentration of hydrogen in methane-hydrogen mixture [35,40].  
Figure 17: Dependence of CO and CO2 emissions on the molar concentration of hydrogen in a hydrogen–butane mixture [41].  
content (stoichiometric or rich). The competition described above disappears when burning lean mixtures, as shown in Figure 18.
## 3 Results and Discussion
### Near-Surface Wind Speed Inter-Comparisons  
To examine the distribution of near-surface wind speed (W) in the mesocyclonic system (PL2 and PL3) considered in this study, we compared the estimates derived from the ERA5, MERRA-2 and CFSv2 data sets with satellite observations. Using measurements of passive (AMSR2 and WindSat microwave radiometers) and active (ASCAT-A/B scatterometers) sensors enabled achieving a combined temporal resolution of fewer than 3 h for satellite data.  
The time series of the median and extreme (95 th percentile) wind speeds are plotted for 19 October within a 300-km radius of the PL3 center tracked from the ERA5, MERRA-2 and CFSv2 relative vorticity at 850 hPa (Figure 7). The wind speed increase corresponds to the rapid deepening of the mesocyclonic system when the SLP minimum by ERA5 representing of it core decreased to the value of 989 hPa. Figure 7a demonstrates a good agreement for the median wind speed across all reanalysis data sets. The discrepancies between the median wind speed by reanalyzes and satellites lie in the range of 2-3 m/s, which is close to satellite measurement error. Note that MERRA-2 detected PL3 only from 10:00 UTC.  
Figure 7: The surface wind speed (m/s) in the mature PL3 derived from the satellite measurements and ERA5 (red), MERRA-2 (green) and the CFSv2 (blue) data sets for the median (solid lines) and extreme (dotted lines) winds (**a**); probability density function and box plots of the ERA5 (red), MERRA-2 (green), CFSv2 (blue) and satellite-based (black) wind speed within a 300-km radius of the PL center (**b**). Black circles and triangles on (**a**)—the median and extreme values of the satellite wind, respectively. The whiskers indicate the 5 th–95 th percentile range; the median and average are marked in the boxes with vertical bars and plus signs, respectively.  
The extreme wind speed is in reasonable agreement with satellite measurements in the initial and decay phases of the mesocyclonic system development. However, during the PLs mature stage from 12:00 UTC to 22:00 UTC (Figure 7a)--when the storm winds were observed--the ERA5 and MERRA-2 extreme wind speeds were significantly underestimated (up to 8 m/s) compared to satellite measurements. Simultaneously, the CFSv2 analysis reproduced the PL3 intensification in extreme wind values well. The exception is the PL's peak intensity, for which the CFSv2 and satellite-based extreme winds discrepancy reached 5 m/s.  
The ERA5, MERRA-2 and CFSv2 extreme wind speed underestimations are also identified in the probability density functions (PDFs) presenting wind speed distribution over the area within a 300-km radius of the mesocyclonic system center (Figure 7b). The wind speed corresponding to the PDF peak varies for all datasets within the range of 15-18 m/s and the highest for the satellite-based (SAT) and ERA5 winds. In the wind speed range over 22 m/s, the SAT winds have much higher probabilities than all the other data sets. The ERA5 and CFSv2 PDFs have similar distributions for values above 20 m/s with a slight shift, while the MERRA-2 has the lowest speeds in the tail of its distribution.