esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network
###### Abstract  
The neural network denoising technique has achieved impressive results by being able to automatically learn the effective signal from the data without any assumptions. However, it has been found experimentally that the performance of the method using neural networks gradually decreases with increasing pollution levels when processing contaminated seismic data, and how to improve the performance will become the direction of further development of the method. As a traditional method widely used for tainted seismic data, the wavelet transform can effectively separate the signal from the noise. Thus, we propose a method combining wavelet transform and a residual neural network that achieves good results in suppressing random noise data.  
wavelet transform; noise suppression; residual neural network; random noise; wavelet decomposition level +
Footnote †: journal: 3
## 2 Circuit Topology of RF-Energy-Harvesting Integrated Circuit  
Figure 2 shows a functional diagram of the RF-energy-harvesting IC, which receives RF energy from the receiving antenna, and this energy charges energy storage devices. Thus, the proposed IC is used as a backup stable power source. An off-chip matching network from MuRata Manufacturing Co. Ltd. was used to guarantee maximum power transfer. The RF-DC rectifier received the transferred RF energy from a matching network and converted this energy into DC voltage. An over-voltage protection circuit was then designed to protect the RF-DC rectifier when the output voltage of the designed rectifier was higher than the breakdown voltage [19]. A stable voltage of 1.5 V is passed through the low-power LDO circuit and sent to the charging control circuit for charging storage devices such as a battery.
# Conceptual Framework for Future WSN-MAC Protocol to Achieve Energy Consumption Enhancement
## 2 WSN MAC Protocol
The load should spread evenly depending on the residual energy load. * Error detection and correction: It is another duty of the MAC layer in conventional computer networks to test the correctness of the information received; however, in WSN, MAC's predominant focus is achieving the energy utilization only [26]. * Packet loss: This occurs when one or multiple transmitted packets fail to reach their intended destination, where the packet delivery ratio is reduced to the bare minimum. * Energy harvesting: This refers to converting energy into electrical energy to power electronic devices or circuits of the devices. If the surrounding sources are insufficiently harvesting energy to balance the energy consumption, those nodes will run out of energy, resulting in node death.
## 5 Results and Discussion
Table 6 provides both LBs and UBs values and the best-found stochastic travel times obtained by our Sim\({}_{\text{BRLH}}\). The solutions reported in the Sim\({}_{\text{BRLH}}\) column are the best-found stochastic travel times. Figure 6: Comparison between MS\({}_{\text{BRLH}}\) and MAC on solving large-sized instances. As we can see in Table 6, all the \(\text{Sim}_{\text{BRLH}}\) solution costs are between the LB and the UB, as expected. For 24 problem instances, the solution returned by our simheuristic is better than the best deterministic solution when it is tested in the stochastic scenario (the \(UB\) column). From this, we can assert that our \(\text{Sim}_{\text{BRLH}}\) is able to generate competitive results for the stochastic scenario. The reliability value is calculated by simulation for each solution and represents the probability that all routes are completed within maximum tour duration. For visualizing this trade-off between the deterministic cost of the solutions and their reliability rate, which are conflicting objectives, a Pareto frontier of non-dominated is presented. Accordingly, Figure 7a-c present the non-dominated solutions for three different instances (\(b17\), \(b37\), and \(b57\)), each one belonging to a different inventory scenario. A solution is non-dominated if, no other solution has a greater reliability and a lower or equal travel cost, or if no other solution has a lower travel cost and a greater or equal reliability level. The \(b17\) solution was randomly chosen, while the \(b37\) and \(b57\) solutions are for the same problem but set in the two other inventory scenarios. The square orange dot represents the best deterministic solution found by our \(\text{MS}_{\text{BRLH}}\), while the remaining ones, round and blue, represent different solutions with a higher reliability rate, but with higher operating costs. As we can see in Figure 7, despite being the solutions with the lowest cost, the best deterministic solutions (square dots) are the least reliable ones for stochastic scenarios. Particularly in Figure 7a, the best deterministic solution is approximately only 21% reliable under stochasticity. By selecting higher-cost solutions, the reliability rate reaches more than 70%. The same behavior is noticed in Figure 7b,c, however, the deterministic \(b57\) solution is reasonably reliable. Usually, low-cost solutions are made up of a small number of large vehicle routes, in terms of travel distance and time. Therefore, when increasing the travel time variability in those scenarios, the risk of exceeding the time constraint is higher. On the other hand, higher cost solutions are built from a larger number of smaller routes, and smaller routes exhibit a lower risk of violating the maximum tour duration constraint in stochastic scenarios. In this way, decision-makers should consider that low-cost solutions under deterministic scenarios might not necessarily be the best option when stochasticity is taken into account.
## 2 Materials and Methods
### Experimental Conditions  
Before the combustion test, manual inputs and corresponding outputs of operating conditions, which included the fuel-feeding rates and air flow rates, were correlated. Fuel-feeding rates of poultry litter (4.6-774.0 lb/h) were first characterized across different manual inputs on the speed motor controller of feeder (0-99.9%) within an error margin of 0-5%. Then, the air flow rates of PA and SA were calculated with an estimated uncertainty of about 3% by multiplying the air velocity in the pipe center and pipe diameter. The air velocity was measured under various voltage regulator settings, using the airflow meter (922, Fluke, Everett, WA, USA). Ratios of SA and TA (SA/TA) were computed from the calculated air flow rates. After that, the EA was computed by dividing the total weight of fuel (poultry litter plus natural gas) by air (PA plus SA) before subtracting one. In order to calculate the weights of natural gas, PA, and SA from volumetric flow rate readings, the densities of natural gas and combustion air were assumed to be 0.800 kg/m\({}^{3}\) and 1.225 kg/m\({}^{3}\), respectively. The MR was then also computed from poultry litter weight and natural gas weight.  
At the beginning of each test run, volumetric flow rate of PA (about 10 ft3/min) and volumetric flow rate natural gas (5 ft3/h) were used to ignite the combustion process. Next, natural gas was added to the system until a maximum flow rate of 38 ft3/h reached and the chamber temperature reached values between 595 \({}^{\circ}\)C and 620 \({}^{\circ}\)C. Feeding rate of poultry litter (4.6 lb/h) was then slowly fed into the chamber. By adjusting the feeding rates of poultry litter and natural gas, the total heat input of fuels was kept at 110,000 Btu/h throughout the test. Working conditions for experiments were as follows: EA in a range of 0.04 and 1.31; SA/TA ratio at 0.22, 0.33, 0.44, 0.56, 0.67, and 0.78; MR at 1.89, 2.29, 2.61, 4.51, and 7.42. When studying the effect of one particular operating condition on system performance, all other parameters were kept constant.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fuel Components** & **As Received (wt \%)** & **Dry Basis (wt \%)** \\ \hline Proximate analysis (analysis method) & & \\ Moisture (D3302/D3173) & 21.20 & - \\ Volatile matter (D3175) & 50.40 & 63.96 \\ Fixed carbon (difference, calculated) & 9.44 & 11.98 \\ Ash (D3174) & 18.96 & 24.06 \\ \hline Ultimate analysis (analysis method) & & \\ Carbon (C) & 26.88 & 34.11 \\ Hydrogen (H) & 4.44 & 5.64 \\ Oxygen (O) (difference, calculated) & 24.20 & 30.71 \\ Nitrogen (N) & 3.28 & 4.16 \\ Sulfur (S) & 1.04 & 1.32 \\ \hline Heating value (Btu/lb) (D5865/5864) & 4856 (11.30 MJ/kg) & 6163 (14.34 MJ/kg) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proximate and ultimate analysis of poultry litter samples.
## 2 Study Area and Data
### Polar-Orbiting VIIIRS-IM Fire Radiative Power  
The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the polar-orbiting Suomi NPP (since 2011) and NOAA-20 (since 2017) satellites has transitioned much of the capability of the AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS) instrument for environmental monitoring [40] (Table 1). VIIRS provides a total of 21 bands (plus a day and night band, DNB [41]), spanning from visible light to long-wave infrared, providing better spatial resolution. The nadir pixel resolution is between 375 m and 750 m [42]. The width is 3000 km. Compared with MODIS, VIIRS has a wider bandwidth and provides gapless global coverage [43]. In addition, VIIRS pixels are generated using a pixel aggregation scheme. The samples include: the sample at the lowest point along the scanning direction (scan angle \(<\)31.72\({}^{\circ}\)), the sample in the middle area (scan angle: 31.7244.86\({}^{\circ}\)) and the sample at the edge of the scan (scan angle \(>\)44.86\({}^{\circ}\)). Therefore, the pixel size of VIIRS decreases significantly as the viewing angle increases, and the pixel size is limited to less than four times as the scanning angle increases. When the scanning angle of MODIS changes from the lowest point to the scanning edge, the pixel size along the scanning direction increased by three to nine times [44]. In addition, the VIIRS airborne bow-tie removal algorithm removes two and four over-sampled scan lines in the central gathering area and the scan edge, respectively [45], thereby reducing repeated fire detection between adjacent scans near the lowest point. MODIS does not handle it. VIIRS 14 band (3.55 to 3.93 \(\upmu\)m) spans the wavelengths of peak spectral radiance for blackbodies emitted at temperatures between 737 and 817 K, in combination with the improved spatial resolution, the 14 band is therefore well suited for distinguishing pixels of sub-resolution combustion components [46]. [PERSON] et al. [46] showed that compared with MODIS AF products, VIIRS AF products have more sensitive \"small fire\" active detection performance. Due to the relatively low saturation temperature of VIIRS I band, combined with improved spatial resolution, it is easy to cause frequent fire pixel saturation, so it needs to be used in combination with the M band.  
Figure 1: The spatial extent of the study area (144–154\({}^{\circ}\)E, 28–38\({}^{\circ}\)S). The land cover taken from Global 30 m Fine Surface Coverage Product in 2020 [39].
## 2 HSR-NMR System Equation and Issues of NMR Inversion
\begin{array}{ccccccccc}r_{1}&0&0&0&0&0&0&0&0\\ r_{2}&r_{1}&0&0&0&0&0&0&0\\ r_{3}&r_{2}&r_{1}&0&0&0&0&0&0\\ r_{4}&r_{3}&r_{2}&r_{1}&0&0&0&0&0\\ r_{5}&r_{4}&r_{3}&r_{2}&r_{1}&0&0&0\\ 0&r_{5}&r_{4}&r_{3}&r_{2}&r_{1}&0&0\\ 0&0&r_{5}&r_{4}&r_{3}&r_{2}&r_{1}&0\\ 0&0&0&r_{5}&r_{4}&r_{3}&r_{2}&r_{1}\\ 0&0&0&0&r_{5}&r_{4}&r_{3}&r_{2}&r_{1}\\ 0&0&0&0&r_{5}&r_{4}&r_{3}&r_{2}&r_{1}\\ 0&0&0&0&r_{5}&r_{4}&r_{3}&r_{2}\\ 0&0&0&0&0&r_{5}&r_{4}&r_{3}\\ 0&0&0&0&0&0&0&r_{5}&r_{4}\\ 0&0&0&0&0&0&0&r_{5}\\ \end{array} \right)\left( \begin{array}{c}a_{1}\\ a_{2}\\ a_{3}\\ a_{4}\\ a_{5}\\ a_{6}\\ a_{7}\\ a_{8}\\ a_{9}\\ \end{array} \right)=\mathbf{s}. \tag{6}\]  
Figure 2: Illustration of the NMR measurements. The sample is moving to the right by one grid each time, and the coil is at a fixed position. The relationship between the measured NMR signal and the fluid content is given by Equation (5). It should also be pointed out that Equation (5) can also be written as \(\mathbf{Ar}=\mathbf{s}\), where \(\mathbf{A}\) is composed by the sequence \(a_{j}\) and \(\mathbf{r}\) is the response map. This duality can be used for calibration of the system response function using known fluid content. In order to design an optimal numerical scheme for the solution of the NMR equation, we need to have an in-depth understanding of the features of Equation (5). First, the reliability of the information contained in the NMR measurements varies with the changing positions of the core. When the core is at or close to the ends of the coil, due to the inhomogeneous distribution of the magnetic field, the measurements cannot fully reflect the real fluid contents, and therefore, the NMR signals are noisy, and their impacts on the fluid content inversion shall be suppressed. Secondly, the NMR measurements may contain noise and errors, both of which cannot be completely avoided in field core measurements. If the noises follow Gaussian distribution with an average of zero, ordinary least square inversion is adequate. When the NMR signal contains data samples that deviate from most of the other measurements, then inverted results will be greatly impacted by these large deviations if we fail to identify them. For data processing of a large number of cores in oil and gas exploration, it is challenging and, most of the time, impossible to have full quality control of all the measurements. It is therefore highly desirable for the inversion algorithm to have automatic detection of the high-quality data and to mitigate the impacts of low-quality data or corrupted measurements. In the next section, we will review some of the inversion algorithms developed for typical geophysical problems such as velocity, impedance, and absorption inversion. The inversion of these geophysical parameters shares similar features to the fluid inversion from NMR data.
## 3 Recent Advances in Different Configurations of STSCs
### Multilayered-Based STSCs
The optimized material, applied to stainless steel, achieved an absorptance of 0.96 and emittance of 0.103 at 500 \({}^{\circ}\)C. The multilayered stacks demonstrated exceptional thermal stability, enduring 3072 h at 500 \({}^{\circ}\)C.  
For solar tower systems, [PERSON] et al. [94] numerically developed a high-efficiency STSC stack composed of three-layer TiN-Si3N4 nano-discs embedded in a tungsten substrate. Their calculations indicated that the optimal operating temperature for the structure as a thermal emitter is 2000 K, with a thermal radiation efficiency of 94.8%. This novel STSC demonstrates polarization independence, insensitivity to variations in incident angle, and identical absorption spectra for both transverse magnetic and transverse electric modes. The reviewed investigations clearly indicate that solar selectivity can be enhanced by maximizing solar absorptance with minimal material usage in multilayered STSCs. Increasing the thickness of absorbing layers that are partially transparent in the infrared region will elevate the thermal emittance of the surface. However, achieving low thermal emittance is possible by using minimal film thickness. Consequently, multilayered STSCs have proven to be the most effective design for attaining excellent solar selectivity. Materials possessing appropriate intrinsic spectral properties, such as compounds with relatively high metallicity, are typically used as selective absorbing layers. These materials demonstrate a distinctly sharp absorptance edge. Moreover, it also concluded that utilizing a multilayer design can broaden the absorption range within the solar spectrum. Solar absorptance can be greatly enhanced by precisely designing the anti-reflection layer. On  
Figure 7: Diagram of the six-layer selective absorber design [37]. the other hand, while multilayered absorbing designs can achieve high solar absorption, maintaining compositional consistency and thermal stability during cyclic heating remains a significant challenge in developing practical solar absorbers using this architecture. In addition, abrasion and corrosion can lead to the degradation of the multilayered absorbing STSCs. A further challenge encountered in large-scale manufacturing is ensuring a uniform thickness throughout the deposition processes. STSCs are suitable for both high- and mid-temperature solar power applications. Table 2 outlines the absorptance, emittance, structure, and photoconversion efficiency of various deposited STSCs.
# On-Orbit Polarization Calibration for Multichannel Polarimetric Camera
## 2 The Multichannel Polarimetric Camera and Main Polarization Elements  
### The Multichannel Polarimetric Camera  
In the field of remote sensing, it has great significance in measuring the polarization information of the target, especially the information of degree of linear polarization (DoLP). In this paper, the DoLP describes the proportion of the linear polarized light among all the polarization states of light (unpolarized, linear polarized, circularly polarized and elliptically polarized light), and the angle of linear polarization (AoLP) describes the oscillation direction of the linear polarized light [13]. As we know, the polarization information (DoLP, AoLP) of the light can be measured by using a rotating linear analyzer [14]. The polarization information can be calculated by using Equation (1) with the radiance at three specific angles (\(I_{0^{\circ}}\), \(I_{60^{\circ}}\), and \(I_{120^{\circ}}\)) of the analyzer.  
\[\left\{\begin{array}{l}I=\frac{2}{3}(I_{0^{\circ}}+I_{60^{\circ}}+I_{120^{ \circ}})\\ Q=\frac{2}{3}(2I_{0^{\circ}}-I_{60^{\circ}}-I_{120^{\circ}})\\ U=\frac{2}{\sqrt{3}}(I_{60^{\circ}}-I_{120^{\circ}})\\ \end{array}\right., \tag{1}\]  
\[DoLP=\sqrt{\frac{Q^{2}+U^{2}}{I^{2}}},AoLP=\frac{1}{2}a\tan(\frac{U}{Q}),\]  
where \(I\), \(Q\), \(U\) denote the Stokes parameters. The measurement method for the polarization information on partially linear polarized light is shown in Figure 1. A linearly polarized parallel light of known polarization direction may be employed, and the non-ideal linear polarizer is incident. By rotating the non-ideal linear polarizer, the light intensity at different analyzer angles is obtained, and the parameters of the non-ideal linear polarizer are obtained by fitting.  
Since the method above needs to rotate the analyzer three times, the measurement cannot be updated in real time. Therefore, we propose a multichannel polarization camera that enables real-time shooting. Three fixed analyzers with three polarization axis angles, which are \(0^{\circ}\), \(60^{\circ}\), and \(120^{\circ}\), are proposed to measure the polarization information simultaneously. The schematic of the multichannel polarimetric camera is shown in Figure 2a. The optical design model of the multichannel polarimetric camera is illustrated in Figure 2b. The image of the target is caught by the pre-optical system. The collimating lens transforms the imaging light into parallel light. The parallel light is separated into three channels by two beam splitters located in the rear of the collimating lens. The polarization state of the imaging light can be detected by three polarization analyzer channels, which are composed of three separate linear analyzers. In each analyzer channel, the parallel light is focused by a converging lens and received by a detector. The three polarizer axis angles of the linear analyzers are \(0^{\circ}\), \(60^{\circ}\), and \(120^{\circ}\), respectively.  
Figure 1: Measurement method for the polarization information on partially linear polarized light.
## 3 Methodology
### Label Adjustment  
Table 2 shows a summary of the labeling results by two experts. After comparing the details, 89 clashes were found with different labels by the two experts and excluded, which means only the remaining 326 cases were used for the processing. Moreover, there was no negligible clash among the clash types labeled by both experts. An investigation reveals that most negligible clashes may occur when pipes penetrate a beam. According to the specification of reinforced concrete structures [26] published by the Chinese Society of Structural Engineers in Taiwan for the positions of legal pipes penetrating through beams, if the position of clash falls on the grid area in Figure 6, the structural behavior of the beam will not be affected. In other words, this clash can be classified as a negligible clash, or a pseudo clash. Applying this specification requires the precise dimensions of the clashing objects and the clashing position. The experts were not able to determine whether those clashes were negligible or not by merely viewing the snapshots with their naked eye. Therefore, to be on the conservative side, the experts mostly labeled those clashes with pipes penetrating through beams as \"serious clashes\"; a few cases were labeled as unknown.  
In order to increase the granularity of the training data and improve the filtering rate of irrelevant clashes later on, researchers decided to further apply the specification as mentioned above toward the original labeling results. The research team used the Model Builder embedded by Environmental Systems Research Institute (ESRI) ArcScene to implement the specification, as shown in Figure 7, and obtain the adjusted labeling result, as shown in the rightmost column of Table 2. The original 58 serious clashes and five unknowns were adjusted as negligible clashes. After adjustment, the numbers of labeling for four clash types turn to be 100 errors, 63 pseudo clashes, 127 deliberate clashes, and 36 unknowns.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Clash Type**} & \multicolumn{2}{c}{**Expert \#1**} & \multicolumn{2}{c}{**Expert \#2**} & \multicolumn{2}{c}{**Common**} & \multicolumn{1}{c}{**Adjusted**} \\ \hline Serious clashes & Errors & 159 & 208 & 158 & 100 \\ Negligible clashes & Pseudo clashes & 0 & 0 & 0 & 63 \\ Legal intervenes & Deliberate clashes & 174 & 148 & 127 & 127 \\ Unknown & Unknown & 82 & 59 & 41 & 36 \\ \hline Total & 415 & 415 & 326 & 326 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of training and testing data.  
Figure 5: Spreadsheet-based clash detection report for labeling of clash type by experts.
## Materials and Methods
### Experimental Setup  
The performance of the wireless carotid wearable ultrasound Doppler device prototype was evaluated with phantom and in vivo studies. In the phantom study, as shown in Figure 3, a Model 043 Doppler string phantom (CIRS Inc., Norfolk, VA, USA) was used. In the string phantom, as shown in Figure 3, a filament was attached to a series of pulley wheels and a drive wheel was controlled by an electronic controller producing steady or pulsatile movement of the filament. In the phantom study, the pre-programmed pulsatile flow (i.e., adult common carotid mode) was selected and the filament is accordingly moved. The movement of the filament produced a flow around the filament. A 2.5-MHz bipolar electrical pulse with 20 cycles was generated, and the peak-to-peak voltage applied to the ultrasound transducer was 40 Vpp. The PRF was set to 4 kHz. The \(PSV\) and end diastolic velocity (\(EDV\)) were first measured, and then, the \(RI\) value was computed by  
\[RI=\frac{PSV-EDV}{PSV}. \tag{1}\]  
The \(RI\) values measured by the developed prototype were compared with those from a commercial rack-based ultrasound machine (Accuvix V10, Samsung Medison Co., Seongnam, South Korea).  
Figure 3: Experimental setup for the Doppler string phantom.  
In addition, to demonstrate the feasibility of the wireless carotid wearable ultrasound Doppler device for clinical use, an in vivo study was conducted on a healthy volunteer to measure the _PSV_ and _RI_ values under the institutional review board (IRB) approval. The same parameters used in the phantom study were applied to the in vivo study.