esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyclostationary Crosstalk Cancelation in High-Speed Transmission Lines
###### Abstract
The principal components can be expressed as single bit responses [7,8] of cyclostationary sources for the unit interval. The estimation of measured waveforms is performed by using the cyclic Wiener filter. The principle of orthogonality provides an optimum estimator for random cyclostationary signals [15]. The cyclic Wiener filter is realized as a linear periodic time-varying filter. The statistical signal processing of measured data can be performed using several techniques. The sliding window filtering of discrete-time signals is equivalent form of the frequency shift filtering (FRESH) [10,16]. It can be shown that simple matrix form of sliding window filtering corresponds to the multi-input to multi-output linear time-invariant transforms of the frequency shifted components of the signal. The serial to parallel transformation of the cyclostationary signals for sliding window filtering can be viewed as forming of a stationary vector. The modern description of synthesis and analysis of signal estimators is presented using a matrix transformation of extended correlation matrices. The paper is organized as follows. After a short introduction, the fundamentals of linear periodic time-varying filtering theory (LPTV) are provided in Section 2. The description includes frequency shifting with subsequent linear time invariant filtering (FRESH) and sliding window (SW) filtering implemented by serial to parallel transformation of the measured time series. The equivalence of both approaches is shown, and compact matrix form of the SW filtering is provided. The implementation of the cyclic Wiener filtering algorithm using the LPTV SW approach for the estimation of the input random cyclostationary data sequence corrupted by random cyclostationary crosstalk with the same cyclic period and stationary interference signals are discussed Section 3. The Wiener solution of the estimation problem is based on the principle of orthogonality, assuming the independence between the random processes constituting the measured data sequence. The independence of crosstalk and the informative cyclostationary processes can be justified by the independence of the information bit sequences in communication lines. The experimental research of the influence of the crosstalk from adjacent transmission line on the quality of the data communication link with and without the proposed Wiener filtering algorithm is investigated in Section 4. The quality of the data transferring is characterized by the BER related to the eye-opening width for bit rates from 0.4 Gbit/s to 2.5 Gbit/s. The paper is concluded by a discussion of obtained results. Figure 2: Bit error rate versus voltage for time 0.5 ns. Bit rate is 1 Cbit/s.
## 3 Results and Discussion
### Influence of Phthalic Anhydride Solution Concentration on the Surface Properties of PPTA Fiber
#### 3.1.3 Study on Crystalline Structures of Aramid Fibers before and after Modification  
X-ray diffraction (XRD) was used to analyze the changes in the crystal structure of the PPTA fiber before and after modification. The molecular structure segments of the cortex of PPTA fiber are arranged in a highly regular manner, exhibiting high crystallinity and orientation. Figure 6 shows the XRD diagram of the PPTA fiber after modification with different concentrations of phthalic anhydride solution. From the figure, the PPTA fiber has three obvious characteristic diffraction peaks at 20 = 20.7\({}^{\circ}\), 22.9\({}^{\circ}\), and 29.0\({}^{\circ}\). These are the characteristic peaks of PPTA fiber, corresponding to the (110), (200), and (004) crystal planes of PPTA fiber, respectively; after modification with 0.15 mol/L and 0.3 mol/L concentrations of phthalic anhydride solution, the three characteristic diffraction peaks of the fiber show no significant changes, indicating that the fiber structure remains unaffected. However, when the concentration of phthalic anhydride solution increases to 0.45 mol/L, the diffraction peaks of the fibers broaden, and the two main peaks at 20 = 20.7\({}^{\circ}\) and 22.9\({}^{\circ}\) are weakened, indicating that at this point, the surface etching effect on the fiber is too strong, leading to delamination of the fiber cortex and a decrease in crystallinity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fiber Name**} & \multicolumn{3}{c}{**Surface Element Content**} & \multicolumn{1}{c}{**Element Ratio**} \\ \cline{2-5}  & **C** & **N** & **O** & **O/C** \\ \hline unmodified PPTA & 79.02 & 9.21 & 11.77 & 0.149 \\ PPTA-0.15 & 78.32 & 9.45 & 12.23 & 0.156 \\ PPTA-0.30 & 76.58 & 9.93 & 13.49 & 0.176 \\ PPTA-0.15 & 78.58 & 9.78 & 11.64 & 0.148 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Surface element content of PPTA fibers before and after modification.  
Figure 5: C1s spectra of PPTA fibers before and after modification: (**a**) PPTA; (**b**) PPTA-0.15; (**c**) PPTA-0.3; (**d**) PPTA-0.45.  
Influence of Anhydrous AlCl\({}_{3}\) Catalysis Modification Time on Surface Properties of PPTA Fiber
## 6 Conclusions  
The design of an ultra-tight coupling structure realized by a single regulating loop has a certain \"inverse\" effect on tracking satellite signals under special circumstances. In this paper, a cascade super-tight coupling structure based on double loop adjustment was designed, which reduces the \"inverse\" effect of the common vector tracking method and improves the reliability of the integrated navigation system.  
At the same time, in order to improve tracking accuracy, a new nonlinear filtering algorithm called CKF was studied. The robust M estimation in robust estimation theory is used to improve the CKF, which solves the influence of the abnormal error of the observed quantity. On the other hand, an adaptive adjustment factor was used to modify the algorithm, which can effectively deal with the influence of the dynamic disturbance error. The simulation results of highly dynamical scenes based on the satellite signal simulator show that the proposed structure and the proposed robust adaptive CKF algorithm effectively improve the tracking accuracy and tracking stability of the GPS satellite signal so that the navigation system has better dynamic adaptability.  
Data curation, X.Z. and X.Y.; Investigation, X.Z.; Methodology, X.Z.; Project administration, J.L.; Software, S.J.; Validation, X.Y.; Writing--original draft, X.Z.; Writing--review & editing, X.Z. and J.L.; Funding Acquisition, X.Z. and X.Y.  
The work was partially supported by National Natural Science Foundation of China (No. 61503393 and No. 11602296.).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 Results
### Main Impact Factors of the Field GPP Footprint  
3.5.1. Influence of Measurement Height on Footprint of Field GPP  
Measurement height was one of the main factors affecting the footprint distance of field GPP [44; 54]. In general, the GPP footprint distance increased with the increase in the height of the eddy covariance systems. In the study area, the footprint distances were the furthest (more than 800 m) at the forest sites (Huyanglin and Hunhelin, where the measurement heights were also the highest (22.00 m). At Huazhaizi and Arou, the footprint distances (less than 200 m) were the nearest, and the measurement height were also the lowest (2.85 m at Huazhaizi, 3.50 m at Arou). In order to illustrate this, we simulated the footprint distance as the measurement height increased by 25% and 50% or decreased by 25% and 50%. As shown in Figure 8, the footprint distance decreased by 23.87%-30.2% when the measurement height decreased by 25%, and decreased by 43.90%-65.20% when the measurement height decreased 50%. The footprint distance increased by 27.72%-36.21% with a 25% increase in the height of the eddy covariance systems, and increased 56.51%-74.90% with a 50% increase in the measurement height.
## 2 Experimental Setup and Details
### Test Model with the Trailing Edge Jets  
To realize the trailing edge jet flow, four isolated circular jet holes were uniformly spaced in the trailing edge of the SBG model. The space between two neighboring jet-holes was set as 100 mm (\(\sim\)2.88 H), and the diameter of each jet-hole was 6 mm (\(\sim\)0.17H), as shown in Figure 1 a,b. The center of the section of all the jet holes was situated at the cusp of the trailing edge of the SBG model. Polyvinyl chloride (PVC) tubes were used to connect the jet holes with the air accelerators. To allow for real-time control, the velocity of the jet flow of the jet-holes, the mass, and the volumetric flow controllers (FMA-2621A, measuring range: 0-1500 \(SLM\)) were utilized to set and monitor the airflow rate in the PVC tubes. A total of nine volumetric flow rates (\(Q_{i}\)) were conducted in the experimental investigation, as shown in Table 1. Based on the flow rates and the area of the jet-hole, the average velocity jet out from the jet-hole was obtained. In the present study, the non-dimensional jet momentum coefficient (\(J_{ij}\)) was applied to evaluate the strength of the jet flow blowing out from the jet-hole of different controlled cases [18; 20; 21; 22], which could be defined as Equation (1).  
\[J_{ij}=\frac{{U_{j}}^{2}A_{h}}{{U_{0}}^{2}HL} \tag{1}\]  
where \(U_{j}\) and \(U_{0}\) are the velocity of the jet flow and incoming flow, respectively; \(A_{h}\) is the total area of all the jet holes. The \(J_{ij}\) of the SBG model with various jet speeds was calculated and displayed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Volumetric Flow Rate \(Q_{j}\) (m\({}^{3}\)/h)**} & **Jet Velocity \(U_{j}\)** & **Non-Dimensional Jet** \\  & **(m * s\({}^{-1}\))** & **Momentum Coefficients \(J_{ij}\)** \\ \hline
0 (Original main girder model) & 0 & 0 \\
1.80 & 4.42 & 0.0016 \\
2.88 & 7.07 & 0.0042 \\
3.60 & 8.84 & 0.0066 \\
4.32 & 10.61 & 0.0094 \\
5.40 & 13.26 & 0.0147 \\
7.20 & 17.68 & 0.0262 \\
9.00 & 22.10 & 0.0409 \\
10.80 & 26.53 & 0.0590 \\
12.60 & 30.95 & 0.0802 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(J_{ij}\) for the SBG model with different jet rates.  
Figure 1: The geometric scale of the present test model (unit: mm). (**a**) The three-dimension view, (**b**) cross-section in plane I, (**c**) cross-section in plane II.
## 4 Results
### Characteristics of Sea Surface Temperature Errors
#### 4.4.4 Coastal Effects on SST Errors  
In order to analyze the characteristics of SST errors in the coastal region, the mean biases and standard deviations of the SST residuals were investigated with respect to distance from the coast (Figure 10). The errors of COMS/MI SSTs do not show any characteristic trend at offshore regions (>10 km) for all algorithms, regardless of daytime and nighttime data (Figure 10c). However, it is noteworthy that negative biases appear in coastal regions with a distance of less than 10 km. Moving closer to the coastline, the errors enlarge with significant negative biases (<0.3 \({}^{\circ}\)C). In coastal regions within 10 km from the coast, the radiance measured by COMS/MI with 4 km resolution at nadir is likely to include land contaminations. In addition, the characteristics of SSTs with small scale and high variability of small-scale features in the coastal region could induce the differences between the spatially-averaged SSTs of the satellite and the temperature measured by in situ temperature  
Figure 9: Distributions of MCSST errors during the (**a**) daytime and (**b**) nighttime and NLSST errors during the (**c**) daytime and (**d**) nighttime as a function of the latitude (\({}^{\circ}\)N).  
measurements [67; 68]. Thus, the analysis of the coastal effect on SST estimation suggests that the SST derived from present algorithms should be carefully used to understand spatial and temporal variability of coastal phenomena requiring accurate SST fields.
## 4 Discussion  
It was observed in this study that there were various changes in forest growth at different preset points of the planted forest. On the basis of the vegetation index data, the phenological change in _P. tometosa_ B301 varied under the different irrigation methods, which supports our initial hypothesis. To explain this phenomenon, six vegetation indices were analyzed in three groups of trees to select the most appropriate one. Next, the same vegetation indices were applied to study the phenological growth of all three groups. The impact of the rainy season on the trees was analyzed. _P. tometosa_ B301 grows under different micro-ecological environments, so the chlorophyll produced varies. Additionally, it cannot be excluded that the external differences were affected by the selected preset points and the orientation of the camera obtaining the images. This variation may have been caused by the various irrigation methods, meaning that the _P. tometosa_ B301 was not able to absorb the nutrients evenly. However, all six selected vegetation indices were able to reflect the phenological growth phenomenon of the forest. It was found on the basis of comparison that GEI and GCC conformed to the law of the forest growth. In view of the amplitude, GCC is more appropriate for phenological studies of tree species. For example, it was found that, with respect to the sensitivity of the determination of leaf color index on the basis of digital photos, the hue was the most appropriate color index [34]. In a rubber plantation, measured results indicated that Sgreen and GEI showed a better relationship than hue and GPP [35]. In addition, some researchers have indicated that hue is not associated with GPP, and is susceptible to white balance. Therefore, RCC has been reported to be more suitable for estimating the abscission period of evergreen forest [36]. [PERSON] et al. [37] applied RCC to trace the phenology of the canopy's photosynthesis in the ENF ecosystem, and found it to be more efficient than GCC. [PERSON] et al. [38] surmised that GEI was more suitable for monitoring the seasonal variation of _Robinia pseudoacacia_, but GCC was not sensitive to seasonal changes in birch trees [39]. Consistent with our findings, previous studies have shown that the six color indices are able to reflect the phenological growth period with slight differences, and appropriate vegetation indices should be selected according to the ecosystem of the tree species studied. This study found that, due to there being insufficient precipitation in March, the canopy's vegetation index of trees at CK (preset point 1) was significantly lower than DIFI (preset point 3) and BIFI (preset point 5). Due to the large irrigation volume, the tree species with full furrow irrigation had the fastest growth rate in the leaf budding stage, followed by DIFI and CK. As the water contents gradually satisfied the growth demand, the irrigation volume increased, and the trees grew while absorbing nutrients. Therefore, the further
## 2 Material and Methods
### Multi-Channel and Multi-Spatial Attention Module  
This subsection covers the technical details of the MCMS attention module. Given an intermediate CNN feature map \(\mathbf{F}\in\mathbf{R}^{\mathrm{C}\times H\times W}\), a MCMS attention module sequentially infers a 1D channel attention map \(\mathbf{F}_{\mathrm{C}}\in\mathbf{R}^{\mathrm{C}\times 1\times 1}\) and a 2D spatial attention map \(\mathbf{F}_{S}\in\mathbf{R}^{1\times H\times W}\) as shown in Figure 3a,b. The overall process of MCMS attention module can be summarized as:  
\[\mathbf{F}^{\prime}=\mathbf{F}_{\mathrm{C}}(\mathbf{F})\otimes\mathbf{F} \tag{1}\]  
\[\mathbf{F}^{\prime\prime}=\mathbf{F}_{S}(\mathbf{F}^{\prime})\otimes\mathbf{F} ^{\prime} \tag{2}\]  
where \(\otimes\) denotes element-wise multiplication. With different reduction ratios in the multi-channel attention layer and different convolutional kernel sizes in the multi-spatial attention layer, the MCMS attention module enables the backbone network to learn more discriminative features for classifying PCa.
# A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
## 3 The Proposed GGS-MAF Algorithms
### The GGS-MAF Algorithms
The GGS-MAF-3 algorithm is achieved when \(\mathbf{G}_{1}\) is achieved from (14) and \(\mathbf{G}_{2}\) is from (13). If \(\mathbf{G}_{1}=\mathbf{G}_{2}\) and they are both implemented by using (14), we can obtain the GGS-MAF-4. We can see that the proposed GGS-MAF algorithms are realized by the combination of \(\mathbf{G}_{1}\) and \(\mathbf{G}_{2}\) that control the gain assignment matrix. Herein, the \(\mathbf{G}_{1}\) and \(\mathbf{G}_{2}\) are given by  
\[\mathbf{G}_{i}\left(n\right)=\text{diag}\left\{g_{1}\left(n\right)\mathbf{1}_{ B},g_{2}\left(n\right)\mathbf{1}_{B},\cdots,g_{N}\left(n\right)\mathbf{1}_{B}\right\} \tag{16}\]where \(\mathbf{1}_{B}\) in (16) denotes \(B\)-length row-vector, and \(g_{k}(n)\) are calculated by  
\[g_{k}(n)=\frac{\varphi_{k}(n)}{\sum\limits_{i=1}^{N}\varphi_{i}(n)},1\leq k\leq N \tag{17}\]  
in which  
\[\varphi_{k}(n)=\max\left\{b\max\left\{a,q_{1},q_{2},\cdots,q_{N}\right\},q_{k}\right\} \tag{18}\]  
for utilizing \(\left\|\mathbf{\hat{h}}(n)\right\|_{2,0}\) and \(q_{k}=1-e^{-\beta\left\|\mathbf{\hat{h}}_{[k]}\right\|_{2}}\). Similarly,  
\[ \begin{array}{l}\varphi_{k}(n)=\\ \max\left\{b\max\left\{a,\left\|\mathbf{\hat{h}}_{[1]}\right\|_{2},\left\| \mathbf{\hat{h}}_{[2]}\right\|_{2},\cdots,\left\|\mathbf{\hat{h}}_{[N]} \right\|_{2}\right\},\left\|\mathbf{\hat{h}}_{[k]}\right\|_{2}\right\}\end{array}  \tag{19}\]  
for using \(\left\|\mathbf{\hat{h}}(n)\right\|_{2,1}\), where time index \(n\) is ignored. From the derivation of the GGS-MAF algorithms, we can see that the proposed GGS-MAF algorithms are realized by using mixture errors and the mixed \(l_{2,\epsilon}\)- norm to give a resistant to the impulsive noise and to use the prior sparse-structure-information. In the GGS-MAF algorithms, the \(l_{2}\)-norm penalty aims to divide the groups, while the \(l_{\epsilon}\)-norm exploits the sparseness of the systems.
## 3 System Model
### Energy Model  
We use the first order radio energy model [10] here in Equation (2). To transmit an \(l\)-bit length message through a distance \(d\), the energy consumption by the radio is given by:  
\[\begin{split} E_{T_{x}}(l,d)&=E_{T_{x-eloc}}(l)+E_{T _{x-amp}}(l,d)\\ &=\begin{cases}IE_{elec}+lE_{\beta_{d}}d^{2},d<d_{o}\\ IE_{elec}+lE_{mp}d^{4},d\geq d_{o}\end{cases}\end{split} \tag{2}\]  
where \(E_{T_{x-eloc}}\) represents transmitter electronics, \(E_{T_{x-amp}}\) represents receiver electronics, \(E_{elec}\) is the energy expended to transmit or receive one bit data, \(\varepsilon_{\beta_{n}}\) and \(\varepsilon_{mp}\) illustrate the amplifier model we use, and \(l\) is the length for data waiting to be transmitted. The electronics energy \(E_{elec}\) depends on factors like the  
Figure 3: A non-uniform distributed WSN with a mobile sink moving on the centre line.  
digital coding, modulation, filtering, and spreading of the signal, whereas the amplifier energy varies according to the distance \(d\) between a receiver and a sender. When \(d<d_{0}\), a free space channel model is accepted, while multi-path channel model is used when \(d\geq d_{0}\).  
We have \(d_{0}=\sqrt{\mathcal{E}_{{}_{H}}/\mathcal{E}_{{}_{mp}}}\) by equaling \(\mathcal{E}_{{}_{H}}d^{2}\) and \(\mathcal{E}_{{}_{mp}}d^{4}\), where \(\mathcal{E}_{{}_{H}}\) represents free space fading and \(\mathcal{E}_{{}_{mp}}\) represents multipath fading. To receive the message, the radio consumes:  
\[E_{{}_{Rx}}(l)=E_{{}_{Rx-ice}}(l)=lE_{{}_{elx}} \tag{3}\]  
The total energy consumption in the network is calculated in Equation (4), where \(E_{{}_{DA}}\) represents the energy for data aggregation and \(N\) is the number of nodes distributed uniformly in the network:  
\[E_{{}_{tot}}=L\cdot\begin{pmatrix}2 NE_{{}_{elx}}+NE_{{}_{DA}}\\ +\mathcal{E}_{{}_{mp}}k\cdot d_{{}_{n0}\text{S}}^{4}+N\mathcal{E}_{{}_{H}} \dfrac{M^{2}}{2\cdot\pi\cdot k}\end{pmatrix} \tag{4}\]  
The optimum number of clusters can be found by setting the derivative of \(E_{{}_{tot}}\) with respect to k to zero, which is shown in Equation (5) as follows:  
\[k_{{}_{opt}}= \sqrt{\dfrac{N}{2\pi}}\sqrt{\dfrac{\mathcal{E}_{{}_{H}}}{ \mathcal{E}_{{}_{mp}}}}\dfrac{M}{d_{{}_{n0}\text{S}}^{2}} \tag{5}\] \[P_{{}_{opt}}= \dfrac{k_{{}_{opt}}}{N} \tag{6}\]
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 3 Materials and Methods
### Prediction Interval Estimation
#### 3.2.2 Prediction Error Extraction  
For prediction error extraction, the obtained ANN models are used to predict the \(V_{t}\) values contained in \(D_{TrainPI}\). The ANN models takes the values of \(W_{i}=\{V_{t-s},\ldots,V_{t-2},V_{t-1},V_{t}\}\) as inputs and produces \(\hat{V}_{t}(p,h)\) as output. Every measured error is stored in a separate database (\(D_{e}\)). The obtained ANN models are purposely trained to predict only 1 horizon ahead (this to get 96 specialists ANN models in its correspondent period). Therefore, to predict the 8 horizons \(h\) needed for our problem, we use the predictions of the previous models of the last horizon as inputs, thus, \(D_{e}\) contains a set of \(\epsilon(p,h)\) prediction errors for every period \(p\) for each horizon \(h\). For every \(\epsilon(p,h)\), the \(\sqrt{Var[\epsilon(p,h)]}\) value is estimated to obtain the final prediction interval per horizon and per period. In Figure 9 a series of graphs of the obtained prediction intervals are shown.  
We call these prediction intervals the _normal prediction intervals_. Normal prediction intervals are used in conjunction with the support values of the association rules method to construct the Adjusted prediction intervals. In the next section, the extraction of association rules from the dataset is explained.