esa-sceva/satcom-chunk-collection huggingface dataset:
# Four-Channel Polarimetric-Spectral Intensity Modulation Imager
## 6 Conclusions  
In this paper, the four-channel polarimetric-spectral intensity modulation imager (FCPS) is proposed to realize the real-time and error-free acquisition of the incident Stokes vector spectra. Four Stokes parameters are modulated by four phase retarders, and polarization spectra of the incident light can be reconstructed from four modulation curves. The principle and the detection mode of the scheme have been demonstrated. Comparing the simulation results of the FTTS and FCPS data demodulation, the results show that FCPS Stokes demodulation is better. The influence of phase retarders angle error and the maximum calibration light allowable angle error for Stokes demodulation has been discussed. The phase retarders have little influence and the maximum allowable angle error of the linear polarizer cannot exceed 0.4\({}^{\circ}\). FCPS does not have the problem of edge jitter and the FCPS-reconstructed Stokes is better; therefore, the FCPS can promote polarimetric-spectral intensity modulation engineering development.  
Conceptualization, [PERSON] and X.J.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and C.Y.; formal analysis, [PERSON]; investigation, [PERSON]; resources, C.Y.; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) under Grant 61627819, Grant 61727818, Grant 6187030909, Grant 61875192; in part by the HY-multiangle polarizing spectral camera; in part by the K.C.Wong Education Foundation; in part by High-altitude large UAV airborne visible infrared spectral imager procurement project under Grant 21540; and in part by the National Science Fund for Distinguished Young Scholars under Grant 62105331.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
We declare no conflicts of interest.
# Fatigue Performance and Model of Polyacrylonitrile Fiber Reinforced Asphalt Mixture
## 2 Materials  
The aggregate used in this study was taken from a basalt quarry. It was completely crushed, with the larger particles having an angular shape and rough surface texture. Its basic properties are listed in Table 1. The design gradation of aggregate met the gradations requirements of the AC-16 dense-graded asphalt mixture according to the Chinese Technical Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004) [24]. As shown in Figure 1, a 70# penetration \"A\" grade asphalt binder was selected. The main physical properties of the asphalt binder were tested following the Chinese Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering (JTG E20-2011) [25]. The results are listed in Table 2. All properties of the aggregate and asphalt comply with the requirements of the Chinese Technical Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004) [24]. A commercially available PAN fiber was used to reinforce the asphalt mixture. The properties of the PAN fiber are presented in Table 3. Moreover, the PAN fibers are shown in Figure 2. In this study, four kinds of PAN fiber contents (0, 0.15%, 0.30%, and 0.45% by weight of the mixture) are used.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test** & **Deatenation** & **Detility** & **Softening** & **Penetration** & **Viscosity at** & **Flash Point** \\
**Items** & **(25 \({}^{\circ}\)C, 0.1 mm)** & **(15 \({}^{\circ}\)C, cm)** & **Point (\({}^{\circ}\)C)** & **Index** & **60 \({}^{\circ}\)C (Pa.s)** & **(\({}^{\circ}\)C)** \\ \hline results & 65 & \(>\)100 & 48 & \(-\)0.24 & 271 & 269 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic physical properties of the asphalt.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & **Deatenation** & **Detility** & **Softening** & **Penetration** & **Viscosity at** & **Flash Point** \\
**Items** & **(25 \({}^{\circ}\)C, 0.1 mm)** & **(15 \({}^{\circ}\)C, cm)** & **Point (\({}^{\circ}\)C)** & **Index** & **60 \({}^{\circ}\)C (Pa.s)** & **(\({}^{\circ}\)C)** \\ \hline results & 65 & \(>\)100 & 48 & \(-\)0.24 & 271 & 269 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The properties of PAN fiber.  
Figure 1: AC-16 aggregate gradation curve.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & **Lo Angeles** & **Soundness** & **Elongated and Flat** & **Water** & **Apparent Specific** \\
**Items** & **Abrasion (\%)** & **(\%)** & **Particles (\%)** & **Absorption (\%)** & **Gravity** \\ \hline results & 17 & 1.4 & 8.2 & 1.27 & 2.741 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical properties of the aggregate.  
Figure 2: View of PAN fiber.  
The Marshall method of mix design was used to determine the optimum asphalt content (OAC) for all kinds of asphalt mixture. The Marshall test results of all mixtures at the OAC are shown in Table 4.
###### Abstract  
This paper discusses the role of the discrete wavelet transform algorithm in processing error signals present in the input quantities of the algorithm. In considering the error model of the measurement chain, the parameters of the error signals in the input quantities of the wavelet transform algorithm are estimated. Subsequently, in accounting for the algorithm's properties, the parameters of its output values are determined, and the resulting uncertainty values of the output quantities of the measurement chain are estimated. The interval reduction arithmetic method is employed in the calculations for estimating the expanded uncertainty. All findings were validated through measurements conducted using the implemented measurement chain.  
an analytical example of determining this quantity for selected algorithm parameters. Section 3 is devoted to the algorithm's own errors, explaining their origins and describing the algorithm for identifying their parameters. Section 4 contains a description of the method used in this work to determine the resultant expanded uncertainty value. Section 5 presents an example of the application of the discussed analysis method and summarizes the results of a measurement experiment aimed at verifying its correctness. Section 6 contains the most important conclusions from the work.  
All considerations presented in the work were verified through measurements using a previously constructed measurement chain. This article does not include a metrological analysis of the discussed measurement chain regarding the error model and metrological parameters of the input values of the WT algorithm. This aspect is addressed separately and has been detailed in another work [19]. The uncertainty budget of the signal processed by the WT algorithm has already been established and will serve as a reference for the metrological properties of this signal. Therefore, this work, in conjunction with the previous one [16; 19], can offer guidance to the designers of measurement chains on how to implement the presented considerations in their own applications of measurement chains.  
According to the division introduced in some works [16; 19; 20], the error signals can be divided according to the nature of the implementation, and we distinguish the following:  
* **Static** signals, where subsequent values are constant within a single measurement window;
* **Dynamic** signals, where subsequent values change within a single measurement window, and it is possible to deterministically describe the course of these signals;
* **Random** signals, where subsequent values change within a single measurement window, and it is not possible to deterministically describe the course of these signals;  
due to the origin of the signals, we distinguish the following as well:  
* **Own** signals, introduced by the analyzed object, resulting from its imperfect properties;
* **Propagated** signals transferred from the input to the output of an object, present in the input quantities.
# State of the Art in Metal Matrix Composites Research: A Bibliometric Analysis
## 2 Publication Distribution along Various Verticals
### Year on Year Vertical  
The year on year progression of WoS research publications in the field of metal matrix composites is depicted in Figure 2. The \(x\)-axis of this figure shows the publication years from 2001 to 2020, whereas the \(y\)-axis depicts the WoS publication numbers. In addition, the exact publication counts for each year have been marked on top of the yearly publication bars. The figure shows a stagnant number of MMC publications from 2001 to 2006. These numbers started rising gradually from 2007, indicating an increasing interest in MMC research. The years 2016 and 2017 witnessed a significant jump in publication counts as compared to the previous years. In fact, the number of MMC articles published in WoS in the year 2017 tripled the number of manuscripts published in 2002. The MMC research outputs crossed 1000 WoS articles in 2018, with 2019 and 2020 registering almost similar publication levels. The year on year publications count analysis highlights the rising interest in MMC research sustained over a period of two decades.  
Figure 2: Annual publications from 2001 to 2020.  
The primary reason behind this phenomenon is the sheer variety of metal matrix compositions that can be prepared using various combinations of base alloys, particle inclusions, reinforcement fractions and processing techniques. There is also a rising interest in the synthesis and analysis of metal matrix composites infused with various nano particles. In addition, there is an almost limitless scope for MMC modeling, application specific deployments, conventional and non conventional MMC machining and much more. Hence, the interest of researchers in this ever green topic is likely to be sustained in the future as well. The following subsection discusses the diversity of the publication types in the area of metal matrix composites.
## 4 Discussion
### The Discrimination of Nitrogen-Saturated and -Unsaturated Plots Using Worldview-3 Imagery  
The RF model identified nine important variables for characterizing grassland plots within the initial stages of nitrogen saturation (Figure 6). However, the most important wavebands identified were the Red-Edge (705-745 nm), Coastal (400-450 nm), and NIR3 (838-950 nm). The Red-Edge and NIR bands, which are sensitive to vegetation health, have been identified by several studies as crucial segments of the electromagnetic spectrum used for assessing foliar biochemicals [47; 49; 55]. In particular, the Red-Edge is sensitive to changes in chlorophyll concentration and is less affected by background effects [47; 48]. Saturated grassland plots demonstrated elevated foliar nitrogen concentrations, which are consistent with higher chlorophyll levels. Subsequently, the Red-Edge facilitated a greater discernment between nitrogen-saturated and -unsaturated grassland plots. Likewise, [PERSON] et al. [49] identified both the NIR and Red-Edge parts of the spectrum as crucial variables in mapping key grassland nutrients in KZN, South Africa. The Coastal band, however, is not usually associated with vegetation condition and is more typically applied for the imaging of shallow water and aerosol-based investigations [84; 85]. Nevertheless, [PERSON] et al. [86], in their study of mapping foliar nutrition in Queensland, Australia, similarly found both the Coastal and NIR bands as important variables for mapping foliar nitrogen.  
The most important vegetation indices identified were the NDVI3 and the SAVI. The performance of these indices could be attributed to the fact that both indices contain the NIR region which responded positively to increases in foliar nitrogen content, that was associated with nitrogen-saturated plots. The normalized difference index is widely used as an indicator of vegetation condition and to assess the nutritional status of vegetation [48; 54; 87]. [PERSON] et al. [47] attained related results, noting that Red-Edge-based Simple ratio and NDVI were crucial in detecting both biomass variation and mapping leaf nitrogen concentrations. Likewise, [PERSON] et al. [88] showed the importance of NDVI as a potential tool for predicting foliar nitrogen content (R\({}^{2}\) = 0.86) and other physiological variables in wheat subjected to water and nitrogen stress. However, although SAVI was identified as an important predictor--likely due to the percentage of vegetation cover and various soil background influences prevalent within the site--this outcome contradicts studies such as [PERSON] et al. [52], [PERSON] and [PERSON] [89] and [PERSON] et al. [90], which did not find SAVI to be among the most important predictors for assessing vegetation condition.
## 2 Study Area and Data Source
### Data and Processing
#### 2.2.2 Land Use/Land Cover from Landsat TM  
A series of Landsat Thematic Mapper (TM) multispectral images acquired on November 2001, January 2003 and November 2006 in Guangzhou served for the actual Land use/Land covers in Guangzhou. Landsat TM image had a spatial resolution of 30 m with seven bands from visible to thermal infrared. High resolution images were used to provide land cover details to reference samples of classification. There are a SPOT image with four multispectral bands of 10 m and a 2.5-m panchromatic band acquired in December 2006, and digital orthorectified aerial photographs acquired in 2001 and 2003. TM and SPOT multispectral images were calibrated into the geographic reference system of WGS_1984 and initially calibrated to at-sensor radiance and atmospherically corrected to estimate the surface reflectance using a radiometric calibration technique based on the image header file, solar elevation angle, gain offset and calibration parameters of the image. Next, an atmospheric correction was performed on both images using one tool of ENVI software, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH) atmospheric correction method [49]. FLAASH is a method used to correct sensor radiance for atmospheric effects by mathematically modelling the physical behaviour of radiation as it passes through the atmosphere.
## 3 Literature Review
### Cost of Census  
Conducting a census is an extensive and resource-intensive task that involves significant costs, including human capital, financial expenses, and materials required for data collection and enumeration. The scale of census activities contributes to their steep costs, which tend to increase over time. The type of census also influences the costs, with a full-field research census being more expensive compared to administrative censuses or small-scale enumerations in specific areas [20].  
The cost of conducting a decennial census in the United States serves as an illustrative example. The 2010 census cost approximately thirteen billion dollars, which was twice as much as the cost of the census in the year 2000. Similarly, the cost of the 2000 census was double that of the 1990 census. These escalating costs are due to the growing complexity of census processes as the population increases [8]. In fact, the estimated cost for the latest decennial census in 2010 increased from an earlier projection of 11 billion dollars to 14 billion dollars. This increase was a result of abandoning the handheld devices called NRFUs and reverting to the traditional paper approach, which required more trained field staff to conduct in-person data collection [21].
## 4 Survey Findings
### Drivers and Benefits
The organizations participating in the survey are not completely satisfied with regards to the strategic drivers (that can be considered as the main ones), with a gap between expectations and perceived benefits achieved of \(-0.49\). The situation is slightly better about the environmental aspects, with a \(-0.22\) gap, and the economic aspects, with a \(-0.2\) gap (Table 6). In Table 6, the differences between public administrations and private companies are also described in relation with strategic, environmental, and economics drivers and benefits. The major differences are related to environmental drivers. Public administrations are more satisfied for the environmental benefits achieved, while private organizations are more satisfied concerning the economic benefits. Specifically, regarding the strategic drivers, public administrations consider relations with the staff and the incentive for innovation more important than private enterprises. Private companies, instead, gives more importance to the improvement of access to public funds and call for tenders. Concerning the strategic benefits section, the public administrations are more satisfied than enterprises for improvements in relations with staff (Table 4). Environmental aspects are generally more motivating for public administrations which are more focused on the effects on biodiversity, reduction of waste generation, and the use and contamination of soil. From the economic viewpoint, private companies consider savings on insurance premiums, the increase in turnover, and competitiveness more important if confronted with public organizations, both for drivers and benefits (Table 6). In Tables 4-6 a constant is that the average score for drivers is always higher than the average score for perceived benefits. This indicates that expectations of organizations are in most cases not enough satisfied. The difference between strategic drivers and benefits (Table 4) is the highest (0.49), especially for the \"Improvement of the relationship with customers/citizens\". This finding is \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Economic Drivers**} & \multicolumn{4}{c}{**Economic Benefits**} \\ \cline{2-9}  & **Tot.** & **P.A.** & **P.** & **Gap** & **Tot.** & **P.A.** & **P.** & **Gap** \\ \hline Average score for drivers and benefits & 3.02 & 2.97 & 3.03 & \(-0.06\) & 2.82 & 2.91 & 2.86 & 0.05 \\ Energy savings & 3.66 & 4.72 & 3.46 & 1.26 & 3.40 & 4.09 & 3.26 & 0.83 \\ Savings in the use of raw materials & 3.36 & 3.85 & 3.26 & 0.59 & 3.24 & 3.48 & 3.20 & 0.28 \\ Savings on insurance premiums & 2.54 & 1.78 & 2.68 & \(-1.08\) & 2.49 & 1.57 & 2.66 & \(-1.09\) \\ Lower expenses for environmental protection & 2.86 & 2.97 & 2.85 & 0.12 & 2.72 & 2.54 & 2.76 & \(-0.22\) \\ Increase in competitiveness & 3.39 & 3.17 & 3.44 & \(-0.27\) & 3.00 & 2.87 & 3.02 & \(-0.15\) \\ Increase in turnover & 2.31 & 1.35 & 2.49 & \(-1.14\) & 2.08 & 1.33 & 2.22 & \(-0.89\) \\ \hline \hline \end{tabular} Source: Author’s elaboration on survey database.  
\end{table} 
Table 6: Economic drivers and benefits.
## 3 Signal Conditioning Circuit
### Sensor Biasing and Heat Pulse Circuit
Switch \(S_{W1}\) is controlled by the I/O pins of the microcontroller, but since it is biased with \(GND\) and \(-5\) V, a level shift circuit is required to actuate in the digital inputs of \(S_{W1}\). In the built prototype, we decided to store the measured values of \(V_{BE}\) in a secure digital card (SD card), so we could analyze the data and plot the curves of \(V_{BE}\) as a function of the time, to verify if the sensor was working properly. The A/D converter was programmed to make, for every second,  
Figure 3: Basic diagram of the sensor’s bias and heat pulse circuit. ten measurements (one at each 10 ms). Next, for these ten measured points, the microcontroller calculated the mean of the first five points (\(M_{i}\)) and the mean of the last five points (\(M_{f}\)). Finally, the mean \((M_{i}+M_{f})/2\) was calculated and written to the SD card. Except for the period when the heat pulse was being applied, the current consumption of the conditioning circuit was very low (approximately \(2.5\,\mathrm{\SIUnitSymbolMicro A}\)) because, except for the voltage regulator \(IC_{1}\) and the microcontroller, the rest of the circuit was in shut-down mode. Since the current during the heat pulse was 10 mA during 10 s, the average current consumption of the complete circuit was only \(3.7\,\mathrm{\SIUnitSymbolMicro A}\), and laboratory tests showed that even if the harvesting system is not operating, a 2-F supercapacitor charged with 5 V can keep the circuits working for more than eleven days.
## 2 Materials and Methods
### Monte Carlo Simulations  
All MC simulations of radiation transport were performed using the GATE/Geant4 code, version 8.1 [32], on a desktop computer equipped with an Intel Core i7 Extreme Edition 8-Core Processor and 16 GB of Random Access Memory (RAM). The simulations were set up as described in the previously published work [9]. A brief summary of the simulation parameters is reported in Table 3.  
The simulation input files included activity and density map distributions in Metaimage format, a file with tissue composition information, and \"macro\" files with all the simulation parameters. The density description of the lungs was obtained from the calibration curve of the CT image, whereas the ICRU lung tissue composition [24; 25] was assigned to all the lung voxels based on user-defined HU ranges to identify the tissue class.  
Dosimetric calculations using MC simulations were performed for all the activity maps defined for the reference phantom (see Section 2.1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Tag** & **General Formulation** &
\begin{tabular}{c} **Phantom-Specific** \\ **Feature** \\ \end{tabular} \\ \hline Mono-compartmental & MIRD & \(15.87\times\frac{A_{0}-LS}{M_{\text{tungs}}}\) & Organ Mass \\ Convolution kernel ST & kST & \(\tilde{A}\otimes VSV_{\text{ST}}\) (\({}^{\text{t}}\)) & Activity Distribution \\ Convolution kernel ST + & \(\text{kST}_{L}\) & \(\frac{\tilde{A}\otimes VSV_{\text{ST}}}{\rho(X,y,z)^{2}}\times\rho_{ST}\) (\({}^{\text{t}}\)) & Activity and Density \\ Convolution kernel LT & kLT & \(\tilde{A}\otimes VSV_{\text{LT}}\) (\({}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text{\text{\text{\text{\text{\text  
Additionally, a dedicated simulation was conducted to compute the VSV kernels for ST and LT (available as Supporting Material of this paper in \".CSV\" format), assuming ICRU soft tissue (density of 1.04 g/cm\({}^{3}\)) or ICRU lung tissue (density of 0.296 g/cm\({}^{3}\)) as medium [24; 25; 33], each uniformly distributed over a cube with a 600 mm side and cubic voxels of 2.21 mm. The \({}^{166}\)Ho VSV kernels were validated by comparing the \(\overline{AD}\) to the liver and lungs obtained via convolution with the values from a direct MC simulation. In this validation, the reference phantom had activity only in the lungs (LS = 10%), or in the liver (without lesions), with uniform liver and lung densities of 1.04 g/cm\({}^{3}\) and 0.296 g/cm\({}^{3}\), respectively.