esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Band Single-Layered Frequency Selective Surface Filter for LTE Band with Angular Stability
## 3 Optimization and Analysis of Proposed FSS  
### Parametric Study  
To achieve the desired transmission characteristics, careful selection of unit cell parameters is crucial for controlling the distribution of induced currents. These parameters also influence the operating bandwidth, polarization insensitivity, and angular stability. Therefore, it is essential to examine the impact of variations of parameters like length and width on the frequency response characteristics of the proposed C-FSS.  
It focuses on analyzing the impact of the length of the cross structure, represented as L1, on the reflection and transmission coefficients of the C-FSS. Figure 5a demonstrates the dependency of the frequency and L1. It has been noted that when L1 increases, there is a significant shift in the transmission band toward lower frequencies. The shift can mainly be attributed to the increased inductance caused by the extended length of the cross structure. After initial optimization, it shows that the optimal length for the cross structure is 45 mm.  
Figure 5b presents a detailed analysis of how the width, represented as W1 of the cross structure, impacts the reflection and transmission coefficients of C-FSS. It shows that the increase in W1 leads to the shift of the transmission band toward a higher frequency. From the given analysis, the optimized W1 of the cross structure is 1.9 mm.  
Consequently, parametric analysis of rectangular monopoles is also conducted. The variation of resonant frequencies with varying widths and lengths is shown in Figure 5c,d, respectively. The optimized length (L2) of the rectangular monopole is 15 mm, and the width (W2) is 1 mm.
# Study on the Causes and Control Measures of Mg-Al Spinel Inclusions in U75V Heavy Rail Steel
## 3 Results and Discussion  
### Analysis of the Tensile Fracturing of the Rail  
The chemical composition of the U75V heavy rail is shown in Table 1. The tensile fracture morphology of the rail was observed under an electron microscope, and the existence of large size inclusions was found, as shown in Figure 1. The main composition was MgO-Al2O3-SiO2-CaO, and the inclusion contained large particles of MgO-Al2O3 spinel. As a brittle inclusion with a high melting point, the MgO-Al2O3 spinel had poor deformation ability, which seriously reduced the mechanical properties of the steel matrix--which was the main reason for the tensile fracture. However, U75V heavy rail steel implements a strict aluminum-free deoxidation process, so it is necessary to conduct further research on how these MgO--Al2O3 spinel inclusions are formed.
## 4 Simulation Results and Discussion
### Simulation Results of Corona Characteristics under Different Factors
This shows that as the background pressure rises, the corona inception voltage increases and the amplitude of current pulse decreases [36]. This is because when the air pressure increases, the number density of gas molecules increases, and electrons are more likely to collide with gas molecules, so the average free path of electrons becomes shorter and the average energy of electrons decreases. Furthermore, the microscopic chemical reaction rate of electrons changes with a decrease in the average energy of electrons. In detail, the probability of obtaining effective electrons by means of collision and ionization is reduced and the free electrons are more likely to attach to gas molecules to form more negative ions, which eventually leads to the inhibition of the initiation and development of corona discharge, which is manifested as an increase in corona inception voltage and a decrease in the average amplitude of current pulse. Figure 16: Simulation results under different background air pressures (The value of \(r\) and \(h\) as well as \(R\) are 0.1 mm, 6 mm, and 5 mm).
###### Abstract  
Based on the theory of uniform finite-length incoherent line source radiation and real vehicle online test data of Shanghai Maglev trains, a prediction model for environmental noise is established using an equivalent segmented line sound source approach. The noise produced by Shanghai high-speed Maglev trains running at speeds of 235, 300, and 430 km/h is tested and analyzed using microphones. The test data are combined with computational fluid dynamics simulations to divide the train's sound sources equally into five sections. Theoretical calculations are carried out on the noise test data collected as the train passes by, and the source strength of each individual sub-sound source during the train operation is determined using the least-squares method. As a result, a prediction model for the environmental noise of high-speed Maglev trains, represented as a combination of multiple sources, is developed. The predicted results are compared with the measured values to validate the accuracy of the model. The proposed model can be used for environmental assessments before new train lines are launched, allowing for appropriate mitigation measures to be taken in advance to reduce the impact of Maglev noise on the surrounding residential and ecological environments.  
high-speed Maglev train; segmented incoherent line source modeling; source strength characterization; least-squares method; prediction model; environmental impact assessment Article
## 2 Materials and Methods  
### Study Site and Dataset  
The study site is located in Changnyeong-gun (\(\sim\)128\({}^{\circ}\)21\({}^{\prime}\)-128\({}^{\circ}\)39\({}^{\prime}\) E, \(\sim\)35\({}^{\circ}\)22\({}^{\prime}\)-35\({}^{\circ}\)44\({}^{\prime}\) N), which is located in the southeastern part of South Korea and is covered by forests, crops, barren lands, water, and built-up areas. This area is mainly composed of vegetation, forests, grasses, and crops, where the spectral characteristics of the forests vary with the season and the spectral characteristics of grasses and crops are heterogeneous, as the state continuously changes. In other words, since it is sensitive to phenological characteristics, it is considered to be suitable for identifying radiometric and phenological effects.  
For the datasets used in this experiment, images that were obtained from the Korea Multi-Purpose Satellite (KOMPSAT)-3A, which is a high-resolution satellite sensor, are used. The product level is L1G, which is corrected for radiometric, sensor, and geometric distortions and projected to Universal Transverse Mercator (UTM). The KOMPSAT-3A multispectral images, consisting of four spectral bands with 2.2-m spatial resolution, were acquired on 30 October 2015 and 18 June 2016, which have significant differences in phenological characteristics. The images were co-registered from twenty-five ground control points that were identified in each image, and the warping method used was a first-order polynomial combined with nearest-neighbor resampling. The root mean squared error (RMSE) is less than 0.5 pixels. Furthermore, experiments are performed with subsets of 1200 \(\times\) 1200 pixels, and a total of three sites
## 2 Materials and Methods
### Time Series Construction  
The minimum blue compositing method [11] was employed to create EVI products with uniform intervals. Within each compositing period, the EVI value with the lowest reflectance in the blue band was extracted. This approach is particularly effective in identifying the clearest conditions owing to the blue band's sensitivity to aerosol contamination [33]. Considering the good-quality satellite observations and typical crop phenology in Shaanxi, a 10-day compositing period was chosen to generate 10-day composite EVI time series. Still, the composite time series might contain gaps due to a continuous absence of good-quality observations during the compositing period. Linear interpolation was utilized to fill these gaps by referencing EVI values before and after the respective time of the data gap.  
We utilized the Savitzky-Golay filter [34] to smooth the composite EVI time series. Disturbances in the observations caused by snow and other factors often result in dramatic fluctuations within EVI curves. These fluctuations, theoretically, should not normally manifest themselves due to the limited duration of crop growth. Therefore, it was imperative to apply smoothing techniques to the composite time series before further analysis. The Savitzky-Golay filter is a widely used approach for reconstructing vegetation index time series [35; 36]. This method performs polynomial least squares fitting over successive sets of adjacent values within a temporal window. The window size dictates the number of observations considered, while the polynomial degree regulates the order of the smoothing function. We implemented a moving window comprising nine composite dates, considering that the crop growth period typically exceeds 90 days in Shaanxi. A second-order polynomial was chosen to preserve subtle information related to crop phenology. Consequently, we generated a gap-filled and smoothly refined 10-day composite EVI time series for subsequent analysis (Figure 6).  
Figure 6: Typical enhanced vegetation index (EVI) patterns of different cropping intensities throughout a year in Shaanxi Province. (**a**) Spatial locations of three sites in Shaanxi. (**b**) EVI time series derived from Sentinel-2 and Landsat-8 data. Symbol and line colors correspond to the label colors in panel (**a**). (**c**) True color Sentinel-2 images at three sites.
# Water Body Mapping Using Long Time Series Sentinel-1 SAR Data in Poyang Lake
## 3 Results
### Annual and Interannual Variation of Water Area
Since the data for 2014, 2015, and 2021 are not available throughout the year, the IF distribution maps for these years are not so meaningful. Thus, here we focus on the analysis of the IF from 2016 to 2020 (Figure 13). Figure 12: The inundation frequency distribution map of Poyang Lake generated from all the 324 SAR observations during 2014-2021. From Figure 13, we can see that the IF values in 2020 are generally the highest, followed by those in 2016, 2017, 2019, and 2018, which is consistent with the order of the duration of a high water level. Due to the longer duration of the water level higher than 15 m, most of the IF values in 2020 are higher than 0.2. The monthly statistics from the IF were obtained to study the interannual variation in the IF. Figure 14 shows the IF distribution maps for our study area in different months. The missing SAR data in January, February, and March 2015 did not influence the IF in these months. From Figure 14, we can see that, as Poyang Lake entered the wet season in April, the monthly IF of the study area increased until October. As Poyang Lake entered the dry season, the monthly IF began to decrease, which lasted until March of the next  
Figure 13: The IF distribution maps for different years from 2014 to 2021. The numbers in the brackets are for the observation times in that year. year. The central area of the lake (orange area in Figure 14 for September) suffered the most pronounced cyclical change, cycling back and forth between inundation and un-inundation.
# Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification
## 4 Experimental Result and Discussion
### Computational Complexity  
As shown in Table 11, we use three metrics--training time, testing time, and model parameters--to reflect the computational efficiency of different methods under the experimental condition of five labeled samples per category in the target domain. Compared to other methods, DIFSCL has a shorter training time than GIA-CFSL, although its testing time and parameter count are slightly higher than the others.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **DCFSL** & **GIA-CFSL** & **CDFSCL** & **ADAFSL** & **DIFSCL** \\ \hline \multirow{3}{*}{UP} & training time & 1118.7 s & 2647.0 s & 837.0 s & 1626.6 s & 2006.3 s \\  & testing time & 4.89 s & 4.64 s & 4.96 s & 5.7 s & 6.52 s \\  & \#params & 59,869 & 208,692 & 59,869 & 49,237 & 6,875,989 \\ \hline \multirow{3}{*}{IP} & training time & 1670.0 s & 3906.7 s & 1371.2 s & 2417.2 s & 2593.1 s \\  & testing time & 1.32 s & 1.16 s & 1.31 s & 1.51 s & 1.73 s \\  & \#params & 70,696 & 218,392 & 70,696 & 60,064 & 6,886,592 \\ \hline \multirow{3}{*}{Salinas} & training time & 1720.5 s & 3984.1 s & 1417.1 s & 2474.9 s & 3153.5 s \\  & testing time & 7.15 s & 6.35 s & 7.12 s & 7.9 s & 8.3 s \\ \cline{1-1}  & \#params & 71,096 & 218,792 & 71,096 & 60,464 & 6,886,992 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Computational time (s) and number of model parameters for different methods.  
Figure 14: Performance (OA) changes with varying labeled sample per class on (**a**) UP, (**b**) IP, (**c**) Salinas datasets.  
Figure 13: Performance (OA) changes with varying temperature coefficients on (**a**) UP, (**b**) IP, (**c**) Salinas datasets.
## 3 Results and Discussion  
### Physico-Mechanical Properties of Road-Base Binder  
The density and CS of road-base binders decrease when different wastes are added in any dosage, as shown in Table 3. The density was reduced because 1) wastes present a higher particle size distribution than cement, which increases porosity between particles, and 2) wastes have lower specific gravity than cement and, consequently, a higher porosity within the particles (see Table 1). In this study, the specific gravity and particle size distribution of the egg and scallop shells are similar, producing densities very similar. When OR is used, the particle size is lower than both shells, and the specific gravity is like the other two wastes, producing a denser material.  
Also, the CS decreased when all wastes were added and when the proportion was increased. From a physical point of view, egg and scallop shells present a similar density, and, therefore, they have similar porosities, but they are higher than only Portland cement binders (CP-100). From a chemical point of view, scallop and eggshells are principally calcium carbonate and, therefore, they are an inactive addition because they do not have pozzolanic properties [52]. Both effects diminish the compressive strength when both wastes are added at a similar level. When OR is added, the compressive strength is reduced more than the shells' addition due to the low reactivity of this kind of ash and because when OR is added, the compressive strength is reduced more than shells' addition, due to the low reactivity of OR and because the number of principal hydration outputs of cement such as C-S-H and Ca(OH)\({}_{2}\) are diminished [53].  
Regardless of the type of waste, the compositions made with 7.5% waste and CP meet the minimum mechanical requirements to be used as normal hardening road-base binders according to EN 13282-2 2. CP-92.5-OR-7.5 and CP-92.5-E-7.5 could be classified as N2 (compressive strength between 12.5 and 32.5 MPa), while CP-92.5-SC-7.5 and CP could be classified as N4 (compressive strength between 32.5 and 52.5 MPa). If the compressive strength is compared with road binder containing waste-paper ash in a similar dosage [54], the compressive strength is higher in the three wastes analyzed in this work, but lower when steel slag is used [55].
_Algorithms_**2009**, \(2\), 158-182; doi:10.3390/a2010158  
_algorithms_  
ISSN 1999-4893  
www.mdpi.org/algorithms  
_Review_  
A Survey on Position-Based Routing Algorithms in Wireless  
Sensor Networks  
[PERSON] 1,2*, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 2  
1 School of Software, Hunan University, Changsha 410082, China  
2 National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China  
E-mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-mail: [EMAIL_ADDRESS]  
_Received: 29 October 2008; in revised form: 17 December 2008 / Accepted: 29 January 2009 / Published: 9 February 2009_  
**Abstract:** Wireless sensor networks (WSN) have attracted much attention in recent years for its unique characteristics and wide use in many different applications. Routing protocol is one of key technologies in WSN. In this paper, the position-based routing protocols are surveyed and classified into four categories: flooding-based, curve-based, grid-based and ant algorithm-based intelligent. To each category, the main contribution of related routing protocols is shown including the relationship among the routing protocols. The different routing algorithms in the same category and the different categories are compared based on popular metrics. Moreover, some open research directions in WSN are also discussed.  
**Keywords:** wireless sensor network, routing, position, flooding, curve, grid, intelligence
# Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development
## 3 Methods
### Illustrating an Example of a CCSD Project  
To demonstrate the effectiveness of the proposed approach in predicting success, we consider a sample of TopCoder's software development project (30049261) as an example to explain its working. The project commenced on 13 March 2015 and concluded on 9 December 2017.  
* Project Name: \"Google Drive Legal Hold Time Capsule Management Tool Prototype Challenge\".
* Project Type: \"UI Prototype Competition\".
* Detailed Requirements: (a snippet from the requirements) \"This challenge is to create a working HTML Prototype for the Legal Hold Time Capsule based on the provided requirements document. This is currently envisioned as working within a browser environment. It is not expected that users will access this application beyond a secure environment, and not typically on a mobile device. The clickable prototype should be accessible from traditional browsers, allowing movement among views and basic navigation, dynamically rendered on laptop; desktop screens of various sizes. (There are no device resolution constraints at this time, however, extra points will be awarded for adaptive design for device screen size flexibility). Possible future challenges related to this concept include application development using APIs.\"
* Project Id: \"30049261\"  
Figure 1: Overview of the proposed approach.  
* Required Technologies: \"CSS, HTML, and JavaScript\" are the technologies specified for this project's development.
* Required Platform: \"GoogleEC2\" is the platform on which the completed project will run (could be more than one).
* Prize: \"[1300, 650]\" in the detailed information section of a TopCoder project refers to the prize money that will be awarded to the competition winners. In this case, there will be two winners, and the first-place winner will receive a prize of USD 1300, while the second-place winner will receive a prize of USD 650.
* Current Status: \"Completed\" is a project status that indicates whether the project was successfully completed.
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
## 4 Proposed Low-Complexity Demapping Algorithm
### Create the Subsets
Only the subsets when the I component of the received symbol is positive need to be created, and the subsets when the I  
Figure 4: The subsets of ED comparison to calculate LLR(\(b_{3}\)) when the received symbol is in the first quadrant. (**a**) Subsets of Max-Log-MAP algorithm; (**b**) Subsets obtained after some constellation points are removed. Figure 5: The subset of ED calculation when the received symbol is in the first quadrant. (**a**) Subset before using condensation characteristic; (**b**) Subset after using condensation characteristic. component is negative can be derived based on symmetry. Since the Q component of 1D-NUC is also obtained according to symmetry, the same subsets can be used for the Q component and the I component.
# Fast and Robust Asynchronous Rendezvous Scheme for Cognitive Radio Networks
## 4 Proposed Channel-Hopping Scheme
### FRARS for Asymmetric System
However, the number of random replaced channels can be either \(2(M-|m_{1}|)\) or \(2(M-|m_{1}|)-1\) according to whether the last channel of the permutation part is included in the replaced channels. The probability of choosing \(M-|m_{1}|\) channels including the last channel of the permutation part out of \(M\) channels is \(\frac{M-1}{M^{\text{C}M-|m_{1}|}}\), hence the probability \(P_{R}\) can be computed as:  
\[P_{R}=\Big{(}1-\frac{M-1}{M^{\text{C}M-|m_{1}|}}\Big{)}\cdot\frac{2(M-|m_{1}|) }{2M-1}+\frac{M-1}{M^{\text{C}M-|m_{1}|}}\cdot\frac{2(M-|m_{1}|)-1}{2M-1}. \tag{7}\]  
Since the \(ETTR_{case2}\) is the same as \(ETTR_{sym}\), I need to figure out \(ETTR_{case1}\) to derive the final ETTR value. If the random variable \(X\) is defined as a random independent sample extracted from \(m_{1}\), then the probability of choosing one channel \(\mathbb{P}\{X=x\}\) is \(\frac{1}{|m_{1}|}\) for any \(x\in m_{1}\). Thus, the probability of selecting the rendezvous channel is expressed as:  
\[\mathbb{P}\{X=x\}=\frac{G}{|m_{1}|},\forall x\in G. \tag{8}\]  
Now, I denote the number of attempts at \(R\) before the first rendezvous occurs. Then the rendezvous probability \(\mathbb{P}\{R=n\}\) can be computed by:  
\[\mathbb{P}\{R=n\}=(1-p)^{n-1}p, \tag{9}\]  
where \(n\) is the \(n\)-th independent trial and \(p\) is the success probability, which is equal to \(\mathbb{P}\{X=x\}=\frac{G}{|m_{1}|}\). Therefore, the expected TTR value for case 1 can be expressed as:  
\[ETTR_{case1}=\sum_{k=1}^{\infty}k(1-p)^{k-1}p=\frac{1}{p}=\frac{|m_{1}|}{G}. \tag{10}\]  
Consequently, using (7) and (10) the modified \(ETTR_{sym}\) can be determined by:  
\[ETTR_{new\;sym}=P_{R}\cdot ETTR_{case1}+(1-P_{R})\cdot ETTR_{sym}. \tag{11}\]  
If I take the lower bound of \(P_{R}\) (i.e., \(P_{R}=\frac{2(M-|m_{1}|)-1}{2M-1}\)) in order to simplify the expression of the final ETTR, Equation (6) can be rewritten as \((\frac{3}{4}M+\frac{1}{4M})\)). Numerical results show that the enhanced FRARS system outperforms the asymmetric EJS system. In particular, when the \(G\) is small, the performance is more excellent. Moreover, the robustness of my proposed scheme against jamming attack is significantly higher than that of the EJS scheme.
## 3 Research Method
### Positioning Model of the PPP Method for GPS and GLONASS Solutions
\right] \tag{4}\]  
\((X_{i-1}^{r},Y_{i-1}^{r},Z_{i-1}^{r})\)--geocentric \(XYZ\) coordinates of the aircraft per observation time epoch \((i-1)\),  
\((i-1)\)--previous observation time epoch,  
\(d_{i-1}\)--approximately the geometric satellite-receiver distance in GPS or GLONAS system;  
\(P\)--matrix of weights,  
\(C_{x}\)--variance-covariance matrix,  
\(C_{x}=\left(A^{T}\cdot P\cdot A+C_{x}^{-1}\right)^{-1}+C_{n}\),  
\(C_{n}\)--the process disturbance noise matrix,  
\(l\)--vector of difference between observations and modelled parameters. The final aircraft coordinates from the PPP method from the GPS or GLONAS solutions are determined as recorded below [40]:  
\[\left\{ \begin{array}{l}X_{i}^{r}=X_{i-1}^{r}+\delta X\\ Y_{i}^{r}=Y_{i-1}^{r}+\delta Y\\ Z_{i}^{r}=Z_{i-1}^{r}+\delta Z\\ \end{array} \right. \tag{5}\]  
where:  
\((X_{i}^{r},Y_{i}^{r},Z_{i}^{r})\)--geocentric \(XYZ\) coordinates of the aircraft per observation time epoch \((i)\), and \((i)\)--the current observation time epoch.