esa-sceva/satcom-chunk-collection huggingface dataset:
# Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding
## 1 Introduction
The basic core concept of this study, which is based on the DFT operation and zero-padding, is an extended smoothing method for two-dimensional discrete data. The DFT operation has the characteristics of fast computation and transformation between the spatial domain and the wavenumber domain. The zero-padding and partial angular spectral adjustment operation has the characteristics of data expansion. Combining the properties of these two methods allows for extended smoothing of discrete data in two dimensions. The smoothing method proposed in this paper is mainly suitable for sparse sampling data measured in engineering tests. It is possible to improve the density of sampling data to obtain a smoother distribution of measured spatial data.
## 5 Experimental Validation
### Experimental Result for Uniform Weather Condition  
Figure 17a-c shows the tracking outcomes of the conventional P&O, incremental conductance, and proposed MPPT schemes under no partially shaded condition. Figure 17a represents the power, voltage, and current waveforms of the conventional incremental conductance algorithm. This algorithm tracks MPP within 0.3 s. However, it contains numerous power oscillations (210.5-212.66 W) under STC. Instead, of a duty-cycle perturbation at SSC, this algorithm moves the operating point based on the range of the duty cycle (0.273-0.278).  
Hence, the average tracking efficiency is 99.59% by considering the highest peak of the power curve. Similarly, MPPT efficiency is found at 99.88% using the P&O algorithm (Figure 17b). The P&O MPT took 0.28 s to reach MPP under this condition while experiencing power fluctuations (211-213.29 W). Figure 17c shows the line graphs of the tracking GMPP and P-V characteristic curves of the proposed algorithm. The operating point of this algorithm starts propagating from a higher voltage location to a lower one to find GMPP on the power curve. After completing some iterations of the duty cycle, the proposed algorithm achieves a faster GMP location, which is 0.17 s. This algorithm terminates the searching process while confirming GMPP at the right place during the SSC. Therefore,  
Figure 16: Characteristic curves of the four irradiance profiles for experimental test are showed as: (**a**) uniform weather; (**b**) shading pattern 1; (**c**) shading pattern 2; and (**d**) shading pattern 3.  
oscillations in power, voltage, and current graphs are negligible because no perturbation is required, and a constant duty cycle (0.273) of the boost converter was maintained at GMPP. The proposed algorithm is found more efficient (99.99%) than the conventional MPPT techniques. Because more changes occurred in the conventional algorithms at the GMPP location, the duty cycle of the converter was inconsistent under the SSC. However, these constraints were overcome by the proposed technique with rapid tracking and convergence speeds.
## 2 System
### Position Estimation  
Position estimation is performed based on the results of step detection, step length estimation, and step direction estimation. Processing is performed when the number of steps is counted, and the position is computed by adding up the results based on the step length obtained from the step length estimation and the direction of travel obtained from the step direction estimation. We define the position at the count one step ago as \(\mathbf{P_{t-1}}=(P_{t,x},P_{t,y})^{T}\), the current position as \(\mathbf{P_{t}}=(P_{t-1,x},P_{t-1,y})^{T}\), the estimated step length as \(l\), and the step direction as \(\theta\). Then, \(\mathbf{P_{t}}\) can be obtained by the following Equation (44).  
\[\begin{bmatrix}P_{t,x}\\ P_{t,y}\end{bmatrix}=\begin{bmatrix}P_{t-1,x}\\ P_{t-1,y}\end{bmatrix}+\begin{bmatrix}l&\cos\theta\\ l&\sin\theta\end{bmatrix} \tag{44}\]  
Note that \(\mathbf{P_{t}}\) is the position where \(y\)-axis positive is north and \(x\)-axis positive is east. This can be changed in any direction by adding offsets.
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 4 Data Collection Scheme Based on Opportunistic Node Connections
### The Optimal Path Calculation Based on the Opportunistic Connection Random Graph
For neighbors \(v_{4}\) and \(v_{6}\) of \(v_{2}\) that are on the spanning tree, \(v_{2}\) calculates link connectivity \(P_{v_{2}v_{4}}\) and \(P_{v_{2}v_{6}}\), respectively, by obtaining the value of PC stored in \(v_{4}\), \(v_{6}\) locally it then can calculate (\(P_{v_{2}v_{4}}\times PC_{path(v_{4}v_{\text{sink},*})}\)) and (\(P_{v_{2}v_{6}}\times PC_{path(v_{4}v_{\text{sink},*})}\)), respectively, to get the PC of the path \(v_{2}\)-\(v_{4}\)-\(v_{5}\)-\(v_{\text{sink}}\) and path \(v_{2}\)-\(v_{6}\)-\(v_{7}\)-\(v_{8}\)-\(v_{\text{sink}}\). Since the PC of the path \(v_{2}\)-\(v_{4}\)-\(v_{5}\)-\(v_{\text{sink}}\) is greater than that of the path \(v_{2}\)-\(v_{6}\)-\(v_{7}\)-\(v_{8}\)-\(v_{\text{sink}}\), node \(v_{4}\) is selected as the forwarder, then the sensing data is forwarded to \(v_{\text{sink}}\) along the path \(v_{4}\)-\(v_{5}\)-\(v_{\text{sink}}\).
# A Miniaturized and Fast System for Thin Film Thickness Measurement
## 6 Conclusions  
Based on the reflection spectrum interference theory, this paper built a low-cost and miniaturized thin-film thickness measurement system with the measurement range of 1-75 \(\upmu\)m, an angle of \(\pm 5^{\circ}\), and an uncertainty of 0.1 \(\upmu\)m. The measurement speed of 10 ms and no need of focusing within millimeter working distance met the efficient and convenient requirements of industrial-grade measurement. The validity of the system was verified by experiments of the standard PVC films, the micron-grade film layer on PCB chip, and Germanium-based SiO\({}_{2}\) film. In the proposed phase power spectrum analysis method, the high-frequency spectral interference term was extracted adaptively from the full spectrum data through loop iterations, avoiding the use of the reference sample. For single-layer film thickness measurement, power spectrum analysis resolved the film thickness from the spectral interference term and corrected the gross error caused by local spectral noise, thereby ensuring the stability of the algorithm.  
It is also clear that the industrial film thickness measurement results in this article need to be further optimized due to the lack of hardware performance. In the following work, the measurement range can be increased by selecting a high-resolution spectrometer. We can continue to optimize the algorithm model and the probe design to further improve the uncertainty of the measurement system.  
Funding acquisition, F.F. and X.Z.; methodology, R.H.; project administration, X.Z.; supervision, L.Z.; writing--original draft, R.H.; writing--review & editing, L.Z. and Z.L. All authors discussed the results and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2017 YFA0701200), the Science Challenge Program (Grant No. TZ2018006-0203-01), the Tianjin Natural Science Foundation of China (Grant No. 19 JCZDJC39100), and the Postdoctoral Innovative Talent Support Program of China (BX20190230).  
The authors express their sincere thanks to [PERSON], [PERSON], and [PERSON] for the preparation of the experiment and valuable discussions.  
The authors declare no conflict of interest.  
Figure 12: Measurement experiment of film thickness in different areas on PCB.
## 4 Design and Implementation
### Design Expectations  
Based on the pain points derived from the research, this study sought to design a portable product for improving visual comfort, thus constructing a sun-responsive shading system. The main functional expectations are listed below.  
First, the device should be of a portable size. The appearance of the product is a cuboid. The sunshade curtain is hidden inside the device when it is not activated. The cuboid shape of the device when closed makes it easy to carry, and it can be placed anywhere the user wants.  
Secondly, the process of controlling this device should be simple. The main target users of this device are students and office workers, so we designed a mobile phone application to control the sunshade.  
Thirdly, with the sensors we put inside the device, it can automatically adjust the height of the sunshade curtain to ensure shading effectiveness. The design can automatically change the lifted height of the sunshade curtain according to environment parameters collected by sensors inside the device, helping it to keep a specific area covered. According to the users' personal needs, the device can be switched on or off on the mobile phone application.  
Fourthly, the product and the mobile phone application work together to implement information collection and communication. When a relatively high number of sunshades are used in a specific area, such as a library, sensors in the sunshades can collect data relating to environmental parameters, such as temperature and humidity, and publish them on the application, keeping the user informed of the latest information related to the building's environment.
## 3 Results
### Grade Performance of Test Samples  
The grading performance with the most sensitive band combination RIRSI (R732, R878, R712) was further tested along with RPSI (R519, R438, R706), RIDSI (R1051, R1049, R747), RNDSI (R643, R656), RRSI (R656, R643), and RDSI (R702, R715); the other five indices used the thresholds calculated according to Table 2. It is evident from Table 3 that the classification accuracy of cotton aphid severity with RIRSI (R732, R878, R712), chosen from six types of band indices, showed promising results, which were superior to all of the other five indices. The classification performance of RRSI (R656, R643) was essentially the same as RPSI (R519, R438, R706), while the performance for RIDSI (R1051, R1049, R747) was lower.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Name** &
\begin{tabular}{c} **Band** \\ **Combination** \\ \end{tabular} & \(\theta_{1}\) & \(\theta_{2}\) & \(\theta_{3}\) & \(\theta_{4}\) \\ \hline \multirow{3}{*}{Two Dimensions} & RDSI & (R\({}_{702}\), R\({}_{715}\)) & 0.006 & 0.019 & 0.039 & 0.061 \\  & RNDSI & (R\({}_{643}\), R\({}_{656}\)) & 0.003 & 0.017 & 0.027 & 0.033 \\  & RRSI & (R\({}_{656}\), R\({}_{643}\)) & 0.006 & 0.031 & 0.048 & 0.062 \\ \hline \multirow{3}{*}{Three Dimensions} & RIDSI & (R\({}_{1051}\), R\({}_{1049}\), R\({}_{747}\)) & 0.018 & 0.052 & 0.122 & 0.189 \\  & RPSI & (R\({}_{519}\), R\({}_{438}\), R\({}_{706}\)) & 0.006 & 0.022 & 0.025 & 0.034 \\ \cline{1-1}  & RIRSI & (R\({}_{732}\), R\({}_{878}\), R\({}_{712}\)) & 0.006 & 0.018 & 0.029 & 0.041 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The thresholds of the sensitive RISIs and band combinations.
## 2 Materials and Methods
### Methods  
The main CO\({}_{2}\) flux footprint region is approximately 500 m around the flux tower [72; 73], which is spatially representative of GPP\({}_{\text{EC}}\). The spatial resolution of the MOD17A2H product is 463 m. To compare the remote-sensing data, including Sentinel-2-based VI and MOD17A2H products, with tower-based CO\({}_{2}\) flux data at a fine scale, we selected the standard spatial window as \(450\times 450\) m around the flux tower, which was approximately \(15\times 15\) pixels of the Sentinel-2 data aggregated at 30-m spatial resolution.
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 3 Satellite Scheduling by a Ground Station
The robustness to such variations in the problem along with the ability to solve large instances suggest that genetic or other bio-inspired algorithms are appropriate for handling the satellite scheduling problem at hand.
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## 2 Scientific Rationale of the UVSQ-SAT Mission
### Solar Spectral Irradiance in the Herzberg Continuum
During the UVSQ-SAT lifetime mission, the DEVINS sensor will have to measure the UV irradiance variability in the Herzberg continuum with an uncertainty at 1\(\sigma\) better than \(\pm\)0.5% and to increase in the future the reliability in the long term data record. To conclude, there is a need for a better understanding of how the Sun affects the climate, particularly for the UV radiation affecting ozone (the Herzberg continuum) since it links the stratospheric ozone with regional effects. The Herzberg continuum corresponds to a spectral region (200-242 nm) where atmospheric absorption is relatively low and, hence, solar UV radiation penetrates deeply in the atmosphere, down to the lower stratosphere, where it converts molecular oxygen (O2) by photolysis to produce ozone (O3). Absolute solar spectral irradiance and variability in the Herzberg continuum are necessary to better understand the stratospheric ozone response to solar UV irradiance changes [35]. This is important because the Sun has long term and short term variations, and we need to know how these interact with anthropogenic effects. It is also important to understand natural factors in climate variability to give a basis for a future where it might be predicted. The accurate measurements of the solar spectrum at the top of the atmosphere and its variability are fundamental inputs for Earth's climate (climate modeling) and terrestrial atmospheric photochemistry. This is also important for long term variations of solar cycle minima, which are of fundamental importance for solar physics modeling (dynamo, energy transfer, magnetic and 11 y cycles, etc.). Thus, it is necessary to monitor continuously the Herzberg continuum region over years [36]. One of the objectives of the UVSQ-SAT mission is to validate a new technology for future continuous UV observations using small satellites.
## 1 Introduction
### Molecular characteristics of HCV  
HCV has been classified as a Hepacivirus within the Flaviviridae family [7,8] and separated into six genotypes or clades (numbered 1-6) [7-9]. Some genotypes have a restricted geographical distribution (genotypes 4-6) while others (genotypes 1-3) are more broadly disseminated. Although all of the genotypes can establish chronic infection, there are specific properties associated with particular genotypes. For example, steatosis is more prevalent in genotype 3 infections and there is evidence for a direct role for a viral factor in the development of this pathology [10,11]. Moreover, strains from genotypes 1 and 4 respond less well to pegylated alpha-interferon (IFN-\(\alpha\)) and ribavirin combination therapy as compared to genotype 2 and 3 strains [12].  
HCV has a positive-sense, single-stranded RNA genome of some 9.6 kilobases that encodes a polyprotein of about 3000 amino acids [13,14]. The open reading frame for the polyprotein is flanked by 5' and 3' untranslated regions (UTRs), which contain elements that regulate translation and replication. The polyprotein is generated by the host cell translation machinery and cleaved co- and post-translationally by viral and cellular proteases to yield the mature viral proteins. The N-terminal segment of the polyprotein encodes the structural components of the virus, which consist of core protein and two glycoproteins termed E1 and E2. Core forms the capsid shell into which the virus genome is packaged while the glycoproteins are considered to locate to the lipid envelope surrounding the capsid. However, it should be emphasized that the structure and composition of HCV virions is poorly defined since virus isolated from infectious serum is associated with lipoprotein [15,16]. A small protein called p7, which resides immediately downstream from E2, is required for virus assembly [17,18] although it is not known whether it is also a component of virions.  
The C-terminal component of the polyprotein contains non-structural proteins (NS2, NS3, NS4A, NS4B, NS5A and NS5B) that provide functions necessary for synthesis of viral RNA [19,20]; several of these proteins also participate in assembly of infectious particles [17,21-25]. Expression of a polyprotein encoding NS3-NS5B, along with the 5' and 3' UTR elements (referred to as sub-genomic replicons), is sufficient to initiate and sustain constitutive HCV RNA replication. Viral RNA replication occurs at rearranged internal membrane structures known as the membranous web [26]. Viewed under the electron microscope, the membranous web is observed as networks of membrane-bound vesicles, derived from the ER, and similar in appearance to those observed in poliovirus-infected cells [27,28].
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## Appendix B CMC-Based Error Correction as Carrier-Smoothing of Code Pseudoranges  
The main benefit of the CMC metric can be used for pseudoranges. This is another presentation of carrier-smoothing of pseudoranges. To this end, recall the equation of CMC-based error correction given by Equation (10) and repeated here for convenience. For the \(i\)th satellite at the \(k\)th monitoring epoch, one has  
\[\hat{P}_{i}(k)=P_{i}(k)-\big{(}CMC_{i}(k)-\left\langle CMC_{i}\right\rangle_{ k}\big{)}, \tag{3}\]  
where \(\hat{P}_{i}(k)\) and \(P_{i}(k)\) are respectively corrected (or estimated) and measured pseudoranges for the \(i\)th PRN at the \(k\)th monitoring epoch. \(CMC_{i}(k)\) is the corresponding CMC value and \(\left\langle CMC_{i}\right\rangle_{k}\) denotes the mean value of the CMC metric at the \(k\)th epoch computed using a moving average. By replacing the definition of the CMC metric in Equation (3), one obtains  
\[\begin{array}{ll}\hat{P}_{i}(k)&=P_{i}(k)-\big{(}P_{i}(k)-\phi_{i}(k)-\left \langle P_{i}-\phi_{i}\right\rangle_{k}\big{)}\\ &=\phi_{i}(k)+\left\langle P_{i}-\phi_{i}\right\rangle_{k},\end{array} \tag{4}\]  
where \(\phi_{i}(k)\) is the corresponding carrier phase measurement (in metres) for the \(i\)th PRN at the \(k\)th monitoring epoch. Assuming a cumulative moving average (see Appendix A), Equation (4) can be rewritten as  
\[\begin{array}{ll}\hat{P}_{i}(k)&=\phi_{i}(k)+\frac{n-1}{n}\big{\langle}P_{i }-\phi_{i}\big{\rangle}_{k-1}+\frac{1}{n}\big{(}P_{i}(k)-\phi_{i}(k)\big{)}\\ &=\phi_{i}(k)+\frac{n-1}{n}\big{(}\hat{P}_{i}(k-1)-\phi_{i}(k-1)\big{)}+\frac{ 1}{n}\big{(}P_{i}(k)-\phi_{i}(k)\big{)}.\end{array} \tag{5}\]  
Exploiting some basic mathematical calculations, Equation (5) can be rewritten as  
\[\hat{P}_{i}(k)=\frac{1}{n}P_{i}(k)+\frac{n-1}{n}\big{(}\hat{P}_{i}(k-1)+\phi_ {i}(k)-\phi_{i}(k-1)\big{)}, \tag{6}\]  
which is a definition of carrier-smoothing of code pseudorange [1].
# The Influence of Co-Firing Coal with Biomass Syngas on the Thermodynamic Parameters of a Boiler
## 5 Conclusions  
The thermal calculations are conducted to investigate the effect of co-firing coal with biomass syngas on the thermodynamic parameters in a 300 MW tangentially fired boiler in this study. The biomass syngas is applied as a supplementary fuel entering the furnace with various consumption rates under BMCR. In addition, the comparisons of thermodynamic parameters between two typical conditions are performed under different boiler loads. The conclusions are listed as follows:  
1. The introduction of biomass syngas weakens the average temperature in the furnace and the radiative characteristics of flame, which leads to a reduction of furnace exit flue-gas temperature. The alterations of these thermodynamic parameters are monotonically related to the biomass syngas consumption rate;
2. The heat absorption of flue gas would be enhanced after the introduction of biomass syngas. Coupling palm syngas and wood syngas with coal would increase the heat transfer on the convective heating surfaces, while that for the straw syngas would be weakened. Thus, a combination of these two parameters leads to an increment in the exhaust gas temperature of the boiler and a decrement in the thermal efficiency of the boiler;
3. The variations of acidic dew point, total air, and flue-gas flow rates are not monotonous with increasing biomass syngas consumption rates. The variations are within \(\pm\)1 \({}^{\circ}\)C, \(\pm\)4.0%, and \(\pm\)7.6%, respectively. The difference between the acidic dew point and exhaust gas temperature ascends after the introduction of biomass syngas. Therefore, the retrofitting of the boiler is not necessary and the corrosion of a low-temperature heating surface would not appear. The coal consumption rate descends dramatically after coupling;
4. The CO\({}_{2}\) annual emission reduction increases dramatically with increasing biomass syngas consumption rates under BMCR, which equals 0.001 to 0.095 million tons for palm syngas, 0.005 to 0.069 million tons for straw syngas, and 0.013 to 0.107 million tons for wood syngas. Although the variations are not monotonous, the introduction of wood syngas enlarges the difference of these thermodynamic parameters between two different consumption rates of wood syngas as the boiler load decreases.  
Conceptualization, J.W., Q.Y., X.J. and L.D.; Data Curation, J.W., Q.Y. and L.D.; Investigation, J.W., Q.Y., X.J. and L.D.; Resources, L.D.; Supervision, L.D.; Writing--Original Draft, J.W., Q.Y. and X.J.; Writing--Review and Editing, L.D. 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, grant number [51406147].  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 4 Results and Discussion  
### Machine Learning Model for PL Estimation  
In Sections 3.2 and 3.3, the resulting datasets for training/validating/testing the models were presented. Every one of them contains 11 parameters, 10 inputs, and 1 output resulting from the indoor environment and the physical locations of the AP and the receiver point. We analyzed two models based on the diffraction loss factor.  
For model 1, we tested the constructed ML model using the iGent building and for model 2 using the Vooruit and Koutitas buildings. Figure 3 shows a scatter plot of the ML-estimated values vs. the assumed true values (WHIPP IDP estimation) for the two models. A perfect estimation would lie on the black line. It can be noticed that both models perform well, particularly for PL values from 50 dB up to around 80 dB. In network planning, the maximum allowable path loss determines if a receiver point is covered. So, the model will perform well, especially for low-power APs and high-throughput requirements, both of which impact the link budget and thus also the maximum allowable path loss.  
To illustrate the accuracy of the path loss estimations, we compare the received power over the building area, as estimated by the proposed ML models and by the WHIPP IDP calculation (AP EIRP = 20 dBm). Figure 4 compares the simulated Rx due to a randomly placed AP, according to the WHIPP tool (1 st row) and according to the ML PL model (2 nd row) for the three buildings used in the testing process (samples not included in the training/validating dataset), i.e., iGent building for model 1, Vooruit and Koutitas buildings for model 2. The figure shows a good correspondence between both Rx calculations.  
The absolute average difference between the WHIPP IDP model and the ML model is 2.5 dB for the iGent office building (Figure 4a), 2.3 dB for the Vooruit arts centre building (Figure 4(b.1)), and 1.3 dB for the Koutitas office building (Figure 4(b.2)).  
Figure 4: Received power according to WHIPP IDP model (1 st row) versus the estimation using the ML models (2 nd row) with an R\({}^{2}\) = 0.94. Received power (**a**) using PL Model 1 and testing on the iGent building. (**b**) using PL Model 2 and testing on Vooruit (**b.1**), and Koutitas (**b.2**) buildings.  
Figure 3: Comparison between the IDP path loss values and the ML-estimated ones for (**a**) Model 1 and (**b**) Model 2.