esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 4 Discussion and Conclusions  
This paper considers the acquisition problem of the GPS L1C signal. One difficulty of GPS L1C signal acquisition is the ambiguity problem, which comes from the multiple peaks in the correlation function between the received signal and the generated signal. We use the DM method to solve the ambiguity problem and propose two unambiguous DM acquisition schemes for GPS L1C signal. We have chosen the optimal delay \(\delta_{0}\) of the DM signal as 33-chip delay of the ranging code with the condition of \(\cos(2\pi f_{TF}\delta_{0})=\pm 1\). We have proven that by using our proposed DM methods the correlation function between the received signal and the generated signal has a triangular shape with respect to the code phase as in the correlation function of GPS L1 C/A code, which completely removes the ambiguity problem.  
However, this 33-chip delay is so long that the Doppler frequency has an effect on the magnitude of the DM signal and thus the proposed DM method can be used for moving receivers up to approximately 200 km/h, for which the magnitude of the DM signal may decrease to the half due to the Doppler frequency for the worst case. One remedy for the magnitude decrease of DM signal is to increase the sampling speed. Faster sampling can recover the SNR and increases the probability of detection as proved in Lemma 7. In the case of the use of L1C signal only the proposed DM method can be used only for low speed application. Second remedy is to use the acquisition result of the legacy GPS L1 signal, which will provide the Doppler frequency. GPS receivers can receive both L1 and L1C signal together and thus the limitation of the DM method can be solved by using the combined L1/L1C acquisition method.  
We also analyze the performance of the proposed DM method. The SNR of the DM acquisition method and that of the conventional method are derived analytically and the detection performance of the two methods is compared. The probability of detection is used as the performance measure in the acquisition process, with fixed false alarm probability. The performance result of the proposed DM acquisition methods is shown via simulations.  
[PERSON] conceived this topic, and derived the main results, such as the Lemmas and proofs, while [PERSON] programmed the Matlab coding, and performed simulations.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1D1A1B03035207).  
The authors declare no conflicts of interest.
# Numerical Study on Scale Effect of Repetitive Plate-Loading Test
## 4 Analysis of the Results
### Loading-Plate Scale Effect of the Repetitive Plate-Loading Test under Subgrade Condition  
In a previous study mentioned earlier, [PERSON] and [PERSON] [10] suggested that the scale effect applied to the plate-loading test be differently applied depending on the subgrade condition. In other words, the scale effect of the repetitive plate-loading test expressed by Equation (7) represents the value under the field-test subgrade condition, and it can be changed if the subgrade condition changes. Thus, in this section, the subgrade was classified into four types by referring to the embankment roadbeds classified in the Road Design Manual [16], and numerical analysis was conducted under each subgrade condition using the loading-plate diameters applied earlier. In the same manner, areas with various strain moduli were simulated by varying the value of the elastic modulus among the input properties. As a result, the scale effect of the loading plate varied depending on the subgrade condition. Therefore, the scale effect apparently must be considered for strain-modulus measurement results of a loading plate that is smaller than 762 mm according to the subgrade condition of the repetitive plate-loading test, as listed in Table 6. Figure 6 shows the trend curves of the scale-effect results of the six loading plate diameters used in this study, and the trend equations (Equations (8)-(11)) were derived from the curves. These results can be used to roughly estimate the scale effect to be considered when a loading plate with a diameter that is not considered in this study is used.  
\[(\textit{Gravel and fine gravel})\quad\frac{E_{v2,762 mm}}{E_{v2,d}}=d^{-0.30}.7.24 \tag{8}\]  
\[(\textit{Sand})\quad\frac{E_{v2,762 mm}}{E_{v2,d}}=d^{-0.20}.3.85 \tag{9}\]  
\[(\textit{Sandy soil})\quad\frac{E_{v2,762 mm}}{E_{v2,d}}=d^{-0.12}.2.21 \tag{10}\]  
\[(\textit{Cohesive soil})\quad\frac{E_{v2,762 mm}}{E_{v2,d}}=d^{-0.11}.2.16 \tag{11}\]  
where \(E_{v2,d}\) is the secondary strain modulus when the loading plate diameter is d.  
Figure 5: Stress–settlement curve according to the loading-plate size: (**a**) 762-mm loading plate; (**b**) 300-mm loading plate; (**c**) 150-mm loading plate; (**d**) 50-mm loading plate.
# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 3 Method
### ROI Assignment Algorithm
#### 3.3.2 MAB Update  
IVUS images can be viewed as 360-degree cross-sections of the vessel wall. Starting from the vessel center, the first adventitia pixel in the radial direction at each angle is sought. If no  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Definition**} & \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Value**} \\ \cline{3-4}  & & & **Dataset I** & **Dataset II** \\ \hline \(D_{c}\) & The radius of the catheter zone \(R_{c}\) & Equation (4) & 43 pixels \\ \(L_{out}\) & The thickness of the shadow left by a calcification & Figure 4 & 23 pixels \\ \(T_{d}\) & The distance threshold for guidewire artifact processing & Section 3.3.1 & 3 pixels \\ \(T_{\theta}\) & The angle threshold & Section 3.3.2 & 281\({}^{\circ}\) \\ \(T_{arctan}\) & The area threshold & Section 3.3.2 & 100 pixels\({}^{2}\) \\ \hline \(T_{d1}\) & & & 12.4 & 18.8 \\ \(T_{d2}\) & Thresholds for the anatomical & & 9.4 & 12.4 \\ \(T_{d3}\) & indicator \(D_{a}\) & & 11.5 & 17.8 \\ \(T_{a4}\) & & & 38 & \\ \hline \(T_{s}\) & The threshold for the spatial indicator \(D_{s}\) & Section 3.3.3 & & \(-\)20 & \\ \hline \(T_{g1}\) & & & 0.73 & 0.81 \\ \(T_{g2}\) & Thresholds for the grayscale & & 0.61 & 0.78 \\ \(T_{g3}\) & indicator \(D_{g}\) & & 0.55 & \\ \(T_{g4}\) & & & 0.16 & \\ \hline \(\mu\) & Parameters controlling level set & & 0.2 & \\ \(\beta\) & \multirow{2}{*}{Equation (17)} & \multirow{2}{*}{\(0.03\) (MAB)/0.03 (LIB)} \\ \(\
u\) & & & \(-\)0.03 (MAB)/0.03 (LIB) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter selection in the proposed method.  
Figure 5: An example of the assignment flow on the regions of interest (ROIs), (**a**) media-adventitia border (MAB) initialization, (**b**–**g**) assignment of ROIs in B_1 and update of MAB, (**h**) B_2, (i) assignment of ROIs in B_2, (j) lumen-intima border (LIB) extraction.  
adventitia pixel is found, the \(L_{out}\) pixel outside the last calcification pixel is selected. The MAB is then obtained according to the radial linear interpolation of the selected pixels, as shown in Figure 5g. Finally, topological constraints are imposed, i.e., the MAB is outside the soft plaque, calcification, and lumen.  
The update of the MAB runs once an ROI is assigned, and is terminated when the angle \(\theta_{adven}\) of the arc composed of adventitia pixels exceeds a threshold \(T_{\theta}\) or all ROIs larger than the threshold \(T_{area}\) have been assigned.
## 3 Result and Discussion  
### Simulation Results of Temperature Distribution  
The output results of the inner grid d02 are analyzed to obtain the monthly average temperature within the simulated area, as shown in Figure 9a-f. The results show that a minimum temperature of \(-14\)\({}^{\circ}\)C in the study area is calculated by the WRF model, which ensures that the snow particles do not degrade due to excessively low temperatures. The temperature distribution around the railway is relatively regular. Region 1 bounded by Kangding is divided into two parts. The eastern side of Ya'an City has warmer temperatures than the western side, and the average monthly temperature stays above 0 \({}^{\circ}\)C, which matches the sub-tropical humid monsoon climate of Ya'an City that does not have harsh winters. On the other hand, most areas on the western side of Kangding are colder, with an average monthly temperature of around \(-2\)\({}^{\circ}\)C. These temperatures change with time, and the average temperature in January is lower than that in December and February. In region 2, along the railway from Litang to Qamdo, the temperatures are frigid, always below 0 \({}^{\circ}\)C. Notably, the temperature in January is significantly lower than that in December and February.
## 4 Simulation of Communication Performance
The BER threshold is considered to be \(1\times 10^{-6}\). At the altitude \(h=10\) km, the required SNR increases by approximately 3.5 dB as the moving speed increases from 0.4 Ma to 0.8 Ma. However, the required SNR increase is only approximately 2 dB at the altitude \(h=5\) km. Thus, it can be concluded that, during high-altitude flights, the effect of the aircraft speed on the airborne communication performance is more noticeable. The communication performances are compared for several different communication light wavelengths under identical motion conditions, as shown in Figure 9. Formula (2) shows that \(K_{\text{GD}}\) increases as the communication beam's wavelength decreases. A higher \(K_{\text{GD}}\) will also intensify the optical path change brought on by density fluctuations, increasing the degree of wavefront distortion and ultimately resulting in a further reduction in the beam's communication quality under the same motion conditions. Figure 7: Trend of the communication performance with the motion speed.
## 3 Principles of Operation
### Energy Flow in the System
#### 3.4.1 Equivalent State of the Charge of the Energy Storage System  
A water electrolyzer, the hydrogen/oxygen tanks, and the fuel cells constitute the energy storage system in this study. Hydrogen and oxygen are generated by consuming electricity in the similar way as charging batteries. Then the fuel cell regenerates electricity by consuming hydrogen and oxygen while the amounts of hydrogen and oxygen decrease gradually. In order to evaluate the remaining hydrogen and oxygen levels in the storage tanks, an equivalent state of charge is introduced as shown in Equations (27)-(29).  
\[ESOC_{H}=\frac{P_{h}}{P_{h}^{0}} \tag{27}\]  
\[ESOC_{O}=\frac{P_{o}}{P_{o}^{0}} \tag{28}\]  
\[ESOC_{S}=\frac{ESOC_{H}V_{H}+ESOC_{O}V_{O}}{V_{H}+V_{O}} \tag{29}\]  
where \(ESOC_{H}\) and \(ESOC_{O}\) are the levels of the hydrogen and oxygen tanks, respectively; \(P_{h}\), \(P_{h}^{0}\), \(P_{o}\) and \(P_{o}^{0}\) are the current pressure of the hydrogen tank, the full pressure of the hydrogen tank, the current pressure of the oxygen tank and the full pressure of the oxygen tank, respectively. \(ESOC_{S}\), \(V_{H}\), and \(V_{O}\) represent the equivalent state of the charge of the storage system, the volumes of the hydrogen tank and the oxygen tank, respectively. For the safe operation of the storage systems, the level of the \(ESOC_{S}\) is controlled between 0.2 and 0.9.
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 5 Results & Discussions
### Extra Tress Regressor Usage as an Estimator for Iterative Imputation  
Five popular machine learning regressor methods were applied to the datasets of all locations. The train and test sets were split in the ratio of 75% trained and 25% tested. While doing so, if any of the missing values were present, the complete row was eliminated. For the Aotizhonhxin location, the RMSE values for the extra tree regressor, random forest regressor, light gradient boosting machine, gradient boosting regressor, and decision tree regressor are 16.8418, 18.7612, 18.1819, 22.0409, and 27.2191, respectively, with R\({}^{2}\) values of 0.9560, 0.9455, 0.9488, 0.9250, and 0.8853, respectively as shown in Table 3. The RMSE and R\({}^{2}\) values show that the extra tree regressor outperforms the random forest (RF) regressor, light gradient boosting machine (LGBM), gradient boosting (GB) regressor, and decision tree (DT) regressor models. The same is observed for all datasets of all locations. This is the reason we chose the extra tree regressor as the estimator during the imputation of the missing values in the raw data in preprocessing stage.  
The dataset used for this experimentation contained missing values for around 7 to 15% of the available data, which was imputed using iterative imputation that used extra tree regression as an estimator as mentioned above.
# A Novel Disturbance Rejection Control of Roll Channel for Small Air-to-Surface Missiles
## 4 Numerical Simulation
### Simulation Conditions  
The initial conditions of the simulation experiment in this paper are as follows: the missile is 3000 m above the ground, the initial pitch angle is 0\({}^{\circ}\), the yaw angle is 0\({}^{\circ}\), the roll angle is 0\({}^{\circ}\), the roll angle speed is 3 rad/s, the yaw angle speed is 0 rad/s, and the pitch angle speed is 0 rad/s. The moment of inertia \(J_{x}\), \(J_{y}\) and \(J_{z}\) of the three axes of the missile are 0.3 kg\(\cdot\)m\({}^{2}\), 8.58 kg\(\cdot\)m\({}^{2}\) and 8.58 kg\(\cdot\)m\({}^{2}\) respectively, and the sampling time is 0.05 s.  
The parameter design of the controller in this paper is: \(b=m_{x}^{b_{x}}\cdot Q\cdot S_{ref}\cdot L_{ref}/J_{x}\), \(b_{1}=210\), \(b_{2}=38\).  
Based on the given initial simulation conditions, the proposed rolling attitude control method based on ESO is verified by simulation.
# Optimized Isolation and Characterization of the Major Polysaccharide from Grape Pomace
## 3 Results and Discussion
### Monosaccharide Composition of Purified GPP  
The purified GPP was acid-hydrolyzed and PMP-derivatized. Its monosaccharide composition was analyzed by comparing the retention time of hydrolysate with monosaccharide standards. The content of monosaccharides was calculated by each their peak area. As indicated in Figure 3, the monosaccharide composition of GPP was mainly composed of mannose and glucose with small quantities of galactose and arabinose. Monosaccharide standards of mannose, glucose, galactose, and arabinose were analyzed and establish the curve for calculating each mole fraction simultaneously [17]. The peak area of the chromatogram for each of the above monosaccharides was linear to their concentration, which allowed us to calculate the mole ratios of the APP monosaccharide composition in detail. The molar ratios of monosaccharides were: 3.0:14.1:10.8:18.4 of mannose: glucose: galactose: arabinose in the purified GPP. The most crucial step in identifying the polysaccharide's physicochemical characteristics, structure, and structure-bioactivity relationship is to measure its monosaccharide composition [25]. This finding provides a significant metric for determining the quality standard of GPP. Additionally, we found that GPP had a simple and uniform monosaccharide composition, which might contribute to its high water solubility and benefit subsequent processing and usage [26].
## 2 Optical Analysis of Imaging through Refractive Media
### Optical Properties of Sea Water  
The energy attenuation of light in water is mainly caused by the absorption attenuation of the water body and the scattering effect of suspended particles in the water. Studies have shown [28; 29; 30] that the transmission of light in water is caused by two independent physical processes, namely absorption and scattering, and the energy decays exponentially. According to the Beer-Lambert law, the radiance attenuation of monochromatic light can be expressed as  
\[I_{d}=I_{0}\;e^{-c(\lambda_{i})\cdot d}, \tag{1}\]  
where \(I_{d}\) is the intensity of a radiation of wavelength \(\lambda_{i}\) with the starting intensity \(I_{0}\) after traveling the distance \(d\) through a material with the attenuation coefficient \(c(\lambda_{i})\). The attenuation coefficient \(c(\lambda_{i})\) consists of the energy losses due to the absorption coefficient \(a(\lambda_{i})\) and the scattering coefficient \(b(\lambda_{i})\):  
\[c\;(\lambda_{i})=a(\lambda_{i})+b(\lambda_{i}). \tag{2}\]  
[PERSON] et al. [31] performed a detailed measurement of the attenuation coefficient of light in clear seawater and gave the measurement data of the absorption attenuation coefficient and scattering coefficient of seawater as a function of wavelength. The absorption and scattering attenuation coefficients of seawater as a function of wavelength are shown in Figure 3. The results show that seawater displays the selective absorption of light of different wavelengths, in addition to the existence of scattering properties. As shown in Figure 3, it is not difficult to find that the transmittance of seawater in the blue-green band of the spectrum is relatively large, and the light energy attenuation is the smallest, which is called the \"blue-green window\". Therefore, in structured light projection, a light source in this band can be selected for wavefront sampling to reduce the influence of light absorption and scattering on the WAI reconstruction.
## 5 Results  
### Model Execution Speed  
Execution speed testing are carried out using the parameters in Table 1 and varying the number of sub-interval integrations. Of course the CNT and SIMULINK-DSCR models do not have this variable and their execution time remains constant, apart for a slight improvement between the first and second run, the reason of which is to be found in the Simulink solver engine.  
Results are given in Figure 10 normalized with respect to the CNT execution time and the times are found taking the average total running time over 10 simulations. For single sub-interval integration every proposed algorithm is faster than the normal discretization of the SIMULINK-DSCR. Of course, as it will be shown later, the accuracy is not as good. For all the proposed algorithms, then, more than a couple of subdivisions are required in practice to obtain a reasonable accuracy and the calculation manipulation can improve significantly the performance.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Description** & **Value** & **Unit** \\ \hline Solver & ode45 & - \\ Max-Min Step Size & auto & - \\ Simulation Time & 5 & s \\ Discrete Sample Time & 0.125 & ms \\ Discrete Sample Frequency & 8000 & Hz \\ Number of Sub-intervals & variable & - \\ Nominal Voltage & 360 & V \\ Nominal Current & 230 & A \\ Max Speed & 15,000 & rpm \\ Pole Pairs & 4 & - \\ Stator Resistance & 3.4 & m\(\Omega\) \\ Rotor Resistance & 1.3 & m\(\Omega\) \\ Stator Inductance & 0.16 & mH \\ Rotor Inductance & 0.16 & mH \\ Mutual Inductance & 0.143 & mH \\ Magnet Flux & none & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulink general simulation and machine parameters for every implementation used.  
Figure 9: Model overview used for the tests of the Unified Machine Model.  
The NAT-DSCR implementation of Figure 5 uses full transformation matrices, standard inversions and two matrix power elevations all inside the for loop, thus the calculation time increases steeply at the increase of steps. Already at 2 subdivisions the execution time is more than the discrete model SIMULINK-DSCR. The INV-DSCR implementation of Figure 6 that uses a fast inversion technique permits to calculate only one power elevation, therefore the operations inside the loop are reduced significantly and this model is already much faster. Moreover, since the optimization is focused inside the loop, the execution time is much less dependent on the number of sub-intervals.  
By following the description of the solvers, the tendency is to move calculations outside of the for loop, thus it can be seen how all the models are quite close at lower steps, while the difference gets bigger increasing the number of sub-intervals. This is what happens in ROTOR-DSCR of Figure 7 where the _for loop_ calculations are substantially simplified.  
The best solution described, FAST-DSCR of Figure 8, is still more time efficient than the SIMULINK-DSCR integration process with 15 integration intervals and it is done by having only one matrix multiplication, two sums, one delay and 3 custom simplified matrix transformations.
## 4 Experiment and Results
### Experiment
Figure 14 shows that the real-time node reliability evaluation for node weight measurement is performed stably as the epoch progresses. The accuracy of each node model is directly related to node reliability, correcting the node model's contribution to the integrated vote. Each feedback of the node reliability evaluation has 1000 frames of examples and a 1%p change range. Figure 12: (**a**) ROC curve of ICP node + vote and (**b**) confusion matrix of integrated voting of the proposed system. Figure 13: Graph of AUC change according to feedback epoch in integrated voting.
## 3 Results  
In this section, we shall investigate and analyze the relationship between the radiation characteristics of NTS arising from the cross-collision of a linearly polarized Gaussian intense laser with an electron and the beam waist radius \(b_{0}\) of the laser. The initial position of the electron is on the negative half-axis of the x-axis and it moves in the +x-axis direction. The Gaussian linearly polarized intense laser is incident along the direction of the +z-axis and collides with the high-energy electron at the origin of the Cartesian coordinate system, giving rise to radiation. In this paper, the electric field vector of the laser oscillates along the x-axis, and thus the polarization plane of the linearly polarized laser is the XOZ plane.  
Gaussian pulse lasers present distinct characteristics under varying beam waist radii \(b_{0}\). To investigate the impact of the beam waist radius on NTS, it is requisite to rationally set other parameters of the laser pulse and the electron. This paper is grounded in classical electrodynamics, and the parameters configured are required to fall within a rational range [24]. When necessary, the recoil correction in quantum mechanics needs to be taken into account; namely, Thomson scattering transforms into Compton scattering [25]. In this paper, the parameters set hereinafter only comply with classical dynamics. We configure the parameters of the incident linearly polarized intense laser pulse as follows: the normalized laser intensity \(a_{0}=8\), the pulse width \(L=3\lambda\), the initial phase \(\varphi_{0}=0\), and the wavelength \(\lambda=1\)\(\upmu\)m. For the convenience of studying and depicting the phenomenon, it is necessary to set the interaction point where the linearly polarized Gaussian intense laser pulse and the electron cross and collide at the origin. The electron's normalized initial energy is set to \(\upgamma=10\), indicating that its initial velocity is nearly equivalent to the speed of light. Under the condition that the initial position of the electron remains invariant, the emission time of the linearly polarized Gaussian pulse is adjusted. A delay time \(t_{0}\) is introduced. Either an excessively long or overly short delay time will result in the electron being unable to encounter and collide with the laser pulse. When \(t_{0}=3.26\) fs, the electron precisely undergoes collision at the trailing edge of the Gaussian pulse. The electron precisely undergoes collision at the front edge of the Gaussian pulse when \(t_{0}=6.74\) fs.