esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Satellite Product Intercomparison
#### 5.1.4 Intra-Annual Precision  
The Probability Density Functions (PDFs) of the intra-annual precision (the so-called smoothness) are analyzed (Figure 10). The computation was performed over LANDVAL sites during the overlap period between SPOT/VGT, PROBA-V and MODIS products (i.e., December 2013-May 2014 period).  
The three products present similar distributions of the smoothness (\(\delta\)). Most of the delta values are below 0.01, which demonstrates the high stability over a short time scale for the albedo products. In all satellite products, worse intra-annual precision (i.e., higher \(\delta\) values) was found for white-sky albedos compared with equivalent black-sky albedos. The median \(\delta\) values (Table 12), which are indicative of the intra-annual precision, show improved results (i.e., lower \(\delta\) values) of PROBA-V SA v1.0 compared to SPOT/VGT SA v1.0 for visible and total shortwave and worse performance for NIR. Both C3S products provide much worse intra-annual precision than MODIS C6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**PROBA-V SA v1.0 vs. SPOT/VGT SA v1.0 (December 2013–May 2014)**} \\  & **AL-DH-VI** & **AL-DH-NI** & **AL-DH-BB** & **AL-BH-VI** & **AL-BH-NI** & **AL-BH-BB** \\ \hline \hline
**\% optimal (GCOS)** & 28.9 & 48.6 & 50.2 & 23.9 & 39.9 & 41.8 \\ \hline
**\% target (C3S KPI)** & 62.9 & 76.8 & 78.5 & 51.3 & 68.5 & 69.9 \\ \hline
**\% threshold** & 83.1 & 92.6 & 88.1 & 72.8 & 90.2 & 82.6 \\ \hline  & \multicolumn{3}{c}{**PROBA-V SA v1.0 vs. MCD43A3 C6 (2014 year)**} \\  & **AL-DH-VI** & **AL-DH-NI** & **AL-DH-BB** & **AL-BH-VI** & **AL-BH-NI** & **AL-BH-BB** \\ \hline
**\% optimal (GCOS)** & 20.4 & 24.5 & 6.5 & 20.9 & 22.9 & 6.7 \\ \hline
**\% target (C3S KPI)** & 55.5 & 50.4 & 24.8 & 52.7 & 46.4 & 23 \\ \hline
**\% threshold** & 83.5 & 78.7 & 50.7 & 80.5 & 77.4 & 49.8 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Compliance matrix (percentage of pixels filing the predefined uncertainty levels) of C3S PROBA-V SA v1.0 versus SPOT/VGT SA v1.0 (December 2103 to May 2014 period) and MCD43A3 C6 (2014 year) broadband albedo products. Computation over LANDVAL sites for good quality pixels according to QFLAGS (Table 3).  
Figure 10: Histograms of the \(\delta\) function (smoothness) for black-sky C3S PROBA-V SA v1.0, C3S SPOT/VGT SA v1.0 and MCD43A3 C6 for visible (**a**), NIR (**b**) and total shortwave (**c**). Computation over LANDVAL sites during the overlap period (December 2013–May 2014 period). The median \(\delta\) values are presented for each product.
# Supporting Immersive Video Streaming via V2X Communication
## 2 Bandwidth Allocation for Diverse Streaming Services in V2X Communication
### Bandwidth Allocation for Non-Real-Time Applications
\[\delta_{\tau_{\varsigma}^{\sigma,v}}\cdot\Delta\cdot\sum_{\emptyset\sigma^{ \prime}}db_{\tau_{\varsigma}^{\sigma,v}}^{\sigma,v}\geq sl_{\varsigma}^{\sigma,v}\cdot\left(dvr_{\overline{t}_{\varsigma}^{\sigma,v}}^{\beta}+dvr_{\overline{ t}_{\varsigma}^{\sigma,v}}^{\varepsilon}\right)\cdot\text{f}^{\sigma,v},\quad 1 \leq\delta_{\tau_{\varsigma}^{\sigma,v}},\;is^{\sigma,v}\leq s\leq\overline{s}^{ \sigma,v},\]4 (49)The above equation depends on:  
\[\delta_{\tau_{\varsigma}^{\sigma,v}}\cdot\Delta\cdot\sum_{\emptyset\sigma^{ \prime}}db_{\tau_{\varsigma}^{\sigma,v}}^{\sigma,v}\geq sl_{\varsigma}^{\sigma,v}\cdot\left(dvr_{\overline{t}_{\varsigma}^{\sigma,v}}^{\beta}+dvr_{\overline{ t}_{\varsigma}^{\sigma,v}}^{\varepsilon}\right)\cdot\text{f}^{\sigma,v},\quad 1 \leq\delta_{\tau_{\varsigma}^{\sigma,v}},\;is^{\sigma,v}\leq s\leq\overline{s}^{ \sigma,v},\]5  
Equations (37)-(45)  
where \(\mu\) is the index value representing the non-real-time application for regular users, and \(db_{\tau^{\sigma,p}}^{\varphi^{\mu}}\) is the bandwidth provided by CF XL-MIMO access point \(\varphi^{\mu}\) for pre-downloading \(\mu\) at time \(\tau^{\sigma,p}\) over duration \(\Delta\). * If the bandwidth obtained in the previous step meets the bandwidth requirements, return the result to the vehicle passenger, and conclude the execution of this module. Otherwise, continue to the next step. * Activate the \"Vehicle Fleet Bandwidth Coordination\" module to locate CF XL-MIMO access points on less congested road segments for non-real-time applications experiencing bandwidth deficits. Transfer bandwidth to these applications via inter-vehicle communication and notify the vehicle passenger of the outcome.
# Seismic Performance of Modal Transfer Stations on Soft Clays
## 4 Numerical Model
The Sig3 model is defined according to Equation (2):  
\[\frac{G}{G_{max}}=\frac{a}{1+exp\left(-\frac{L-x_{0}}{b}\right)} \tag{2}\]  
\(G/Gmax=\) normalized secant modulus. \(L=\) logarithmic strain defined as \(\log_{10}(\gamma)\). \(a\), \(b\), and \(x_{0}=\) fitting curve parameters used by the sig3 model, to be matched with the modulus degradation curves. Table 5 summarizes the Mohr-Coulomb parameters of each material, as well as the parameters of the hysteretic model Sig3. These parameters were defined by a fitting process using the least square method to reproduce the stiffness degradation and damping curves of Figure 5. The fitting is driven by the modulus shear stiffness degradation, \(G/Gmax\), curves, considering that in a nonlinear response history analysis, the soil stiffness is a function of the evolving shear strains developed during seismic loading, which diverges from the steady-state response used to obtain the experimental damping curves. Figure 9: (**a**) Global domain of the 3D numerical model and boundary condition used in the dynamic interaction analysis, and (**b**) structural elements used to simulate the underground and on-ground structures. The coupling springs of the pile elements were computed with the following equations, which simulate the shear and normal interface behavior at the pile-soil contact, according to [PERSON] et al. (2015). Table 6 shows the calculated pile-soil spring values. \[cs_{c}=\alpha\ C_{soil}\ p \tag{3}\]  
\[cs_{f}=\alpha\ \varphi_{soil} \tag{4}\]  
\[cs_{k}=\left[10\ Max\ \frac{\left(K+\frac{4}{3}G\right)}{\Delta Z_{min}}\right]p \tag{5}\]  
\[cn_{c}=9C_{soil}D_{pile} \tag{6}\]  
\[cn_{f}\approx\varphi_{soil} \tag{7}\]  
\[cn_{t}=\left(\frac{C_{soil}}{\tan(\varphi_{soil})}\right)D_{pile} \tag{8}\]  
\[cn_{k}=\left[10\ Max\ \frac{\left(K+\frac{4}{3}G\right)}{\Delta Z_{min}} \right]D_{pile} \tag{9}\]  
\(\alpha\) = the value of this parameter depends on the condition of the pile surface. For the rough surface (like the cast-in situ pile), it is suggested to be equal to 1 ([PERSON], 2015). \(c_{soil}\) and \(\varphi_{soil}\) = cohesion and internal friction angle of the soil adjacent to the pile. \(P\) = exposed perimeter of the pile. \(K\) and \(G\) = bulk and shear moduli of the soil adjacent to the pile. \(\Delta Z_{min}\) = smallest width of an adjoining zone in the normal direction. \(D_{pile}\) = pile diameter. \(cs_{c}\) and \(cs_{f}\) = cohesion and frictional strength components of the shear springs. \(cn_{c}\), \(cn_{f}\) and \(cn_{f}\) = cohesion, frictional, and tensional strength components of the normal springs. \(cs_{k}\) and \(cn_{k}\) = stiffness of the shear and normal springs. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil**} & \(\epsilon s_{c}\) & \(cs_{f}\) & \(\epsilon s_{k}\) & \(\epsilon n_{c}\) (kN/m) & \(cn_{f}\) & \(cn_{k}\) (kN/m) & (\({}^{\circ}\)) & \(cn_{k}\) (MPa) \\ \hline Very soft clay & 60 & 0.1 & 1077 & 180 & 0.1 & 270 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Coupling spring properties of the pile elements.
## 3 Compression Standards
### Image Compression Standards
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Algorithm** & **MicroZip** & **Yeast** & **ApoA1** & **ISREC** & **Stanford** & **Arizona** \\ \hline CALIC & 9.582 & 8.502 & **10.515** & **10.615** & 7.592 & – \\ JBIG & 9.747 & 6.888 & 10.852 & 10.925 & 7.776 & 8.858 \\ JPEG-LS & **9.441** & 8.580 & 10.608 & 11.145 & **7.571** & **8.646** \\ JJ2000 (0 levels) & **10.064** & **6.862** & **11.569** & 10.929 & 8.581 & **9.584** \\ JJ2000 (1 level) & 9.583 & 9.123 & 11.142 & **11.521** & 8.160 & 9.258 \\ JJ2000 (2 levels) & 9.530 & 9.092 & 11.068 & 11.376 & 8.039 & 9.142 \\ JJ2000 (3 levels) & 9.519 & 9.081 & 11.062 & 11.363 & 8.004 & 9.110 \\ JJ2000 (4 levels) & 9.517 & 9.079 & 11.063 & 11.365 & 7.992 & 9.108 \\ JJ2000 (5 levels) & 9.515 & 9.079 & 11.063 & 11.366 & 7.990 & 9.106 \\ Kakadu (0 levels) & 10.063 & 6.863 & 11.566 & 10.930 & **8.601** & 9.582 \\ Kakadu (1 level) & 9.577 & **9.128** & 11.134 & 11.517 & 8.179 & 9.253 \\ Kakadu (2 levels) & 9.523 & 9.094 & 11.057 & 11.370 & 8.056 & 9.135 \\ Kakadu (3 levels) & 9.511 & 9.084 & 11.050 & 11.357 & 8.020 & 9.102 \\ Kakadu (4 levels) & 9.509 & 9.081 & 11.051 & 11.359 & 8.008 & 9.100 \\ Kakadu (5 levels) & 9.508 & 9.082 & 11.052 & 11.360 & 8.007 & 9.099 \\ \hline Battiato [19] & 8.369 & – & 9.52 & 9.49 & – & – \\ Bzip2 & 9.394 & 6.075 & 11.067 & 10.921 & 7.867 & 8.944 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Lossless compression results in bpp for generic (not microarray-specific) image compressors and individual image compression, obtained by the authors. Standard compression parameters have been used for all algorithms except for JPEG2000, where different DWT decomposition levels have been applied. All images are 16 bpp. Best results are highlighted in **green** and worst results in **red**. Results for the best microarray-specific technique and the best general compressor are repeated at the bottom for ease of reference.
## 4 Fault-Tolerance Control Strategy
### Differential Drive Control  
When the failure of a single steering motor occurs during trajectory tracking of an unmanned fully line-controlled distributed drive electric vehicle, the vehicle is controlled in a fault-tolerant manner by using differential drive. The main working principle of differential drive is shown in Figure 14.  
As demonstrated in Figure 14, the trajectory tracking-control strategy resolves the expected wheel angle and allows the desired transverse swing angular velocity to be solved. The yaw torque controller selects the PID control technique, which receives the yaw angular velocity difference as input and outputs excess transverse swing torque, which is distributed to the separate wheel drive motors via the drive force distributor.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 2 Modelling the Network
### Packet Error Rate and Packet Drop Probability  
Random bit errors in the link-layer frame will lead to errors in the Ethernet frame check sequence (FCS, also known as cyclic redundancy check) or in the IP header checksum or the TCP checksum. If a destination node receives an Ethernet frame with a wrong checksum, the destination node drops the frame immediately. In rare cases, the destination node does not detect the error in the Ethernet frame or the IP header, or in the TCP header in the very improbable case where several bit errors lead to the same cyclic redundancy check (CRC) code. The first router receiving the faulty packet will discard the packet after verifying its checksum [46]. The router will not inform the sender about discarding the packet. If the bit error occurs in the TCP segment, the receiver will detect it while verifying the TCP checksum. If the checksum does not match, the receiver will discard the segment and will not acknowledge receiving it [47]. Because a bit error in a packet will always lead to a silent packet drop (the sender is not informed about the packet drop), a noisy channel can be simulated by a network that drops packets with a certain probability \(p_{drop}\). In the simulation, \(PER\) is simulated by the packet dropping probability \(p_{drop}\), i.e.,  
\[p_{drop}=PER \tag{4}\]
## 2 Materials and Methods
### UV/Vis Measurements  
All solutions containing macrocycles **6** and **7** were prepared before their absorbance measurements. UV-Vis absorption spectra were recorded using a JASCO 770 spectrophotometer in the wavelength range from 190 to 800 nm. Solvatochromic studies were collected in 12 solvents: dichloromethane (DCM), chloroform, acetone, diethyl ether, ethyl acetate, acetonitrile, 1,4-dioxane, THF, 1-butanol, toluene, dimethyl sulfoxide (DMSO), and DMF using the same quantity of either **6** or **7** in each solvent.
## 1 Introduction
In the current analysis, the mobility efficiency was measured in terms of average travel speed (km/h) and average delay (seconds/vehicle), while traffic safety was assessed using simulated conflicts. The time-to-collision (TTC) values \(\leq\) 2.50 s for low-severity conflicts, TTC \(\leq\) 1.50 s for moderate conflicts, and TTC \(\leq\) 0.50 s for severe conflicts were generated using the SUMO model.
# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
###### Abstract  
Contemporary studies emphasize theoretical and analytical aspects of monitoring water quality within lacustrine settings. The X-ray fluorescence (XRF) core scanner provides the most rapid, non-destructive high-resolution elemental measurements for unprocessed sediments. However, the analytical precision of measured elemental composition may be offset due to water content and inhomogeneities in the physical properties of the sediment. A range of calibration approaches developed specifically for converting XRF scanning intensities to element fractions has been made available. Here, two lake sediment-cores retrieved from southwest China were used to evaluate the performance of various calibration methods. In particular, the influence of sediment properties on XRF scanning intensities was assessed by redundancy analyses (RDA) and the generalized additive model (GAM). The results demonstrate that for fine-grained sediments, the impact of grain size results in only minor deviations in the XRF scanning intensities. Water content of the lake sediment was shown to be the most important factor influencing the XRF scanning intensities, especially for light elements (e.g., Al to Fe). Significant decreases in XRF scanning intensities may occur when sediment water content is greater than 47%. We recommend testing the element fractions obtained via conventional techniques throughout the core and applying the multivariate log-ratio calibration for high-resolution XRF scanning elements within lake sediments.  
core scanner; elements; log-ratio; water content; grain size; lake sediments +
Footnote †: journal: Applied Sciences
# Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
## 2 Design of Cascaded Adaptive Vector Tracking Loop
### Extended Kalman Navigation Filter Design
According to the corresponding relationship in Figure 2, the correction value of the code phase change can be derived:  
\[\Delta\tau^{i}=\left(\Delta X^{i}_{sat,k,k+1}-v^{i}_{k+1}T\right)\cdot\alpha+ \delta t_{b,k+1} \tag{36}\]  
where \(\Delta X^{i}_{sat,k,k+1}\) represents the satellite movement distance in the update period, and then the C/A code phase estimation value can be solved:  
\[\varphi^{i}_{code,k+1}=\varphi^{i}_{code,k+1}+\Delta\tau^{i} \tag{37}\]  
Using the above-mentioned navigation filter output to correct the code phase, code frequency, and carrier frequency in the tracking loop, the receiver vector loop calculation can be completed. Before the vector tracking loop, it is necessary to process the IF signal through the traditional loop, decode the ephemeris, and initialize the PVT information.
###### Abstract  
The aim of the study was to analyze the relationship between the angle of spinal curvature and surface electromyography (SEMG) amplitude of the erector spinae in young school-children. A total of 251 children aged 7-8 participated in the study. The analysis involved 103 (41%) children with scoliosis, 141 (56.17%) with scoliotic posture, and seven (3.0%) with normal posture. Body posture was evaluated using the [PERSON]'s formetric III 4D optoelectronic method. Analysis of SEMG amplitude of the erector spinae was performed with the Noraxon TeleMyo DTS apparatus. A significant correlation was found between the angle of spinal curvature and the SEMG amplitude of the erector spinae. The most important and statistically significant predictor of the SEMG amplitude and scoliosis angle in the scoliosis group was the standing position, chest segment, right side. The largest generalized SEMG amplitude of the erector spinae occurred in both boys and girls with scoliosis. Impaired balance of muscle tension in the erector spinae can trigger a set of changes that create a clinical and anatomopathological image of spinal curvature.  
scolisis; electromyographic examination; Diers's formetric III 4D +
Footnote †: journal: Applied Sciences
## 3 Results
Regarding the ice condition presented in Figure 7d, all three RDPs are efficient in de-icing mode. KFO, KAC and HYB give BPNs of 48 \(\pm\) 2, 47 \(\pm\) 1 and 42 \(\pm\) 1, respectively, all in the range of the wet conditions. By comparing their results to the untreated counterparts, the BPNs are increased to more than 200%. When used in de-icing mode, all three RDPs sufficiently melted the ice at the interface, rendering it rougher and therefore less slippery. However, in anti-icing mode, even if the time of failure is not reached, i.e., the time taken for ice to start forming on the concrete, all three RDPs only increased the BPN to values ranging from 30 to 37. The best RDP in anti-icing mode was KFO, with a BPN of 37 \(\pm\) 1. The phenomena were not exactly the same in the snow on ice condition presented in Figure 7e. The BPN increased from 11 \(\pm\) 2 to 42 \(\pm\) 1, 48 \(\pm\) 1 and 42 \(\pm\) 1, in de-icing mode, and to 49 \(\pm\) 1, 39 \(\pm\) 1 and 44 \(\pm\) 1 in anti-icing mode for KFO, KAC and HYB, respectively. In this particular condition, the addition of snow in de-icing mode promotes better melting of the ice, with this impact even  
Figure 6: BPN obtained in wet condition as compared to the dry condition. more pronounced in anti-icing mode. HYB showed a better efficiency in icy conditions than in snowy conditions, while the glycol complex is more efficient on snow than on ice. Literature is scarce in this area of study, however, a few researchers have observed similar trends of improvements in the BPN, but with using other RDP molecules such as chlorine-based products [26] and corn-derived polyols (sorbitol, mannitol, and maltitol) added to traditional salt brine de-icer [28]. Overall, it is obvious that the use of RDPs in both de-icing and anti-icing modes improves the BPN and consequently, the surface conditions. KFO was the best RDP, especially in anti-icing mode for snow and ice conditions, followed closely by KAC. Once applied to the concrete, KFO and KAC helped the melting of the contaminants and kept the surface wet only. This study could be extended to solid RDPs to observe their efficiency in both de-icing and anti-icing modes. Further study is in-progress with other non-contact methods to  
Figure 7: Skid resistance of different RDPs used in de-icing mode (D) and anti-icing mode (A) under various icing conditions (**a**) Removed snow (**b**) snow (**c**) wet snow (**d**) ice and (**e**) snow on ice. determine the surface macrotexture to ensure the validity of the current apparatus. These results will be published as a follow-up article. The results presented focus on only one type of substrate, namely concrete cement. The results are comparative, however, and allow some conclusions to be drawn about RDPs. Further studies are planned with the use of asphalt concrete as well as other alternative materials.
## 2 Experimental Descriptions
### Superconducting Cavities Treatment  
#### 2.2.1 Nb\({}_{3}\)Sn Inner Coating  
Two 1.3 GHz TESLA-type single-cell Nb cavities (2.6 mm thickness, RRR \(>\) 300), numbered NS04 and NS03, were used for experiments in this paper. Nb\({}_{3}\)Sn thin films were successfully prepared on the inner surface of the two cavities using tin vapor diffusion. Prior to the deposition, the Nb cavities were treated with buffered chemical polishing (BCP) and high-pressure water rinsing (HPR). Three evaporation boats made of tungsten were placed at different heights along the center axis inside the superconducting cavity with different weights of tin, supported by fine Nb wires. The nucleating agent SnCl\({}_{2}\) was placed in a tungsten crucible with an independent heater. The cavities were placed in a high-temperature vacuum furnace and were heated to different temperatures to undergo nucleation, coating, and annealing processes. The specific process parameters are shown in Table 2. The results of the vertical test in LHe are shown in Figure 4. The maximum \(E_{acc}\) reached 17.3 MV/m for the NS04 cavity and \(Q_{0}\) exceeded 1.1 \(\times\) 10\({}^{10}\) at low \(E_{acc}\) for the NS03 cavity.
###### Abstract  
Recently, in order to satisfy the requirements of commercial communication systems and military communication systems, automatic modulation classification (AMC) schemes have been considered. As a result, various artificial intelligence algorithms such as a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) have been studied to improve the AMC performance. However, since the AMC process should be operated in real time, the computational complexity must be considered low enough. Furthermore, there is a lack of research to consider the complexity of the AMC process using the data-mining method. In this paper, we propose a correlation coefficient-based effective feature selection method that can maintain the classification performance while reducing the computational complexity of the AMC process. The proposed method calculates the correlation coefficients of second, fourth, and sixth-order cumulants with the proposed formula and selects an effective feature according to the calculated values. In the proposed method, the deep learning-based AMC method is used to measure and compare the classification performance. From the simulation results, it is indicated that the AMC performance of the proposed method is superior to the conventional methods even though it uses a small number of features.  
automatic modulation classification; cumulant; correlation; effective feature; deep neural network +
Footnote †: journal: Applied Sciences  
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