esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sliding Mode Control Law of the DFIG System  
Figure 1 shows the schematic diagram of the DFIG-based wind power system, in which the grid-side converter (GSC) is to provide the stable DC link voltage and limited reactive power, and the rotor-side converter (RSC) is connected to the DFIG's rotor winding to regulate the active and reactive power output of DFIG. Under unbalanced and distorted grid voltage conditions, the fundamental, the unbalanced, and the \(5-\) and \(7+\) harmonics are taken into consideration in this paper. The control strategy is designed in the \(\alpha\beta\) frame to avoid the need of PLL.
## 3 Filtering Procedure for Synthesis of a New Reference Signal
Observing Equation (10), one can see that, when \(\phi=0^{\circ}\), the original signal remains unaltered, i.e., it is not filtered and Equation (13) is equivalent to Equation (3), whereas, when \(\phi=180^{\circ}\), all of the original signal is filtered out, thus \(H_{\phi,\tilde{\varepsilon}}(\chi)\) is nullified. That is, the higher is the value of \(\phi\) in \(M_{\phi,\tilde{\varepsilon}}(\chi)\) in Equation (10), the more sensitive to strain is the focused signal, \(y_{\phi}(\varepsilon,t)\) in Equation (13). The choice for an adequate value of \(\phi\) depends on the system under study. Another parameter that affects the sensitivity of the technique is the choice of the foreknown strain value, \(\tilde{\varepsilon}\). If \(\tilde{\varepsilon}\) is low, then the effect of strain on the ultrasonic wave is less intense and the signal \(h(\varepsilon,t)\) is not too different from \(h(0,t)\). Thus, if one takes two values of \(\tilde{\varepsilon}\), namely low and high values, for the same threshold angle, \(\phi\), the former would, ideally, provide a filtered signal, \(h_{\phi,\tilde{\varepsilon}}(t)\), that is more sensitive to strain, since the selected components present a phase shift of at least \(\phi\) with low strain. In the next section, results obtained with \(FT\) and \(STFT\) techniques for several threshold angles and different foreknown strain are shown.
## 5 Case Study
### Experiment Results
#### 5.3.3 Velocity Estimation  
Positioning and velocity are two important parameters for the navigation application. Positioning accuracy has been discussed, and velocity estimation will be investigated in this part. We take the calculated velocity and analyze the angle error and magnitude error. To better illustrate the results, reduce the noise and smooth out abrupt changes in the signal, we applied a smoothing filter (Savitzky-Golay), and the results are shown in Figure 16.  
From the figures, we can clearly see that the magnitude error is less than 1 m/s, which is sufficiently accurate. Meanwhile, the maximum value of angle error is not more than 30 degrees. It is reasonable since no additional angle measurement method has been implemented. However, the results show the navigation capability of our system.
# Forming Difficulty Evaluation for Curved Hull Plates Based on Grey Relational Analysis
## 2 Analyze the Influence Law of Forming Difficulty of Complex Curved Hull Plates
### The Influence of Plate Length on the Forming Difficulty of Complex Curved Hull Plates  
Except for the plate length, other influencing factors keep constant. The plate thickness is 0.014 m, the plate width is 2 m, the radius of curvature is 3 m, the heating line length is 0.3 m, the heating line position \(l_{p}\) is 0.5, (As shown in Figure 6, \(l_{p}\) is the ratio of the distance  
Figure 4: Formed double curved plate.  
Figure 5: Data measurement of line heating: (**a**) sketch map; (**b**) forming experiment.  
between the measuring point and the edge of the ship aft (D) and plate length (L), which is between 0 and 1, such as 0.3 represents that the measuring point is located at 0.3 L from the aft edge). Set 5 groups of different plate length (L), which are 2.5 m, 3 m, 3.5 m, 4 m, 5 m separately, get the plate length on the deflection of the law as shown in Figure 7a. The longer the plate, the greater the change in deflection. Therefore, with other condition kept constant, the smaller the curved plate length, the greater the energy required to deform the curved plate, the smaller the resulting deflection deformation, the greater the forming difficulty. The plate length is negatively correlated with the forming difficulty.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 4 Hybrid Engineered Water-Polymer Flooding (EWFF)
### Recovery Mechanisms
#### 4.2.3 Effect of Brine pH  
The pH of the solution also has a strong influence as it controls mineral dissolution reactions involved in wettability alteration using EW in carbonates. An analysis of effluent pH values by [PERSON] et al. [135] showed a higher pH increase resulting from acidic solution injection due to calcite dissolution in acid-base reactions. However, the pH increase must be sufficient (\(\geq\)11) for wettability modification and incremental oil recovery. The polymer adsorption was also significantly increased in the acidic medium, indicating that acidic conditions are not favorable for PF.
## 5 Experimental Results
### XAI Results  
The SHAP summary plot presents a comprehensive visualization of feature importance and impact magnitude for the random forest model classifying network traffic into DDoS and benign categories. Features are ranked vertically, with their horizontal position and color indicating the directional impact on the model's prediction. The color-coded representation delineates the directional impact of features across the two class labels, providing a nuanced understanding of how individual network characteristics modulate the model's decision boundaries. Figure 9a shows the relative significance of various network characteristics in predicting malicious activities. This figure displays the mean absolute SHAP values, which quantify the average magnitude of each feature's contribution to the model's output. Prominent features such as \"Number\", \"Weight\", \"urg count\", and \"IAT\" emerge as critical determinants in distinguishing between DDoS and benign network traffic, providing insights into the most influential predictors in the classification process.  
After the utilization of the SMOTE approach, the SHAP summary plot of the random forest classifier is shown in Figure 9b. Figure 9b represents the mean absolute SHAP values, measuring the average magnitude of each feature's contribution to the model's predictive performance. Important features such as \"rst count\", \"Magnitute\", \"Weight\", and \"urg count\" emerge as critical variables in the classification process, demonstrating substantial variability in their predictive significance.  
Based on the explainable artificial intelligence (XAI) results, a comparative analysis of feature importance was conducted before and after applying the SMOTE. The range of SHAP values significantly increased after SMOTE, indicating more robust feature contributions. Some features, such as Header_Length, remained important in both before and after scenarios. The set of most significant features changed, with new predictors becoming more prominent after applying the oversampling technique. The expanded range of SHAP values shows that SMOTE helped in balancing the dataset and improving the model's ability to identify hidden feature impacts. These results show that SMOTE effectively addressed potential class imbalance, leading to a more comprehensive and accurate random forest model for classifying DDoS and benign categories. This enhancement improves the effectiveness of the model and provides more information about the key factors influencing network behavior.  
Figure 9: The summary plot for the RF model with feature importance and impact magnitude before and after SMOTE. (**a**) The summary plot before SMOTE. (**b**) The summary plot after SMOTE.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Development of Training and Testing Data  
To identify AF and normal rhythms, a support vector machine (SVM)-based classifier must be trained using training data. The training dataset is based on normal and AF features derived from the statistic of wavelet coefficients. Two steps must be taken in developing the training dataset, i.e., 1. Remove the raw data that is labeled other than AF. Perform the same process to delete the data that is not labeled as normal in the dataset from NSRDB. 2. Implement a DWT feature extraction algorithm to obtain the statistic of wavelet coefficients (maximum, mean, minimum, and standard deviation). The wavelet coefficients signal can be defined as in Equation (8).  
\[S=A_{n}+D_{n}+D_{n-1}+\cdots+D_{1} \tag{8}\]  
where \(A_{n}\) and \(D_{n}\) are the approximation and detail at a decomposition level \(n\), respectively. Figure 4 shows an example of raw data labeled as normal and AF from both NSRDB and AFDB. As shown in Figure 4, the peak-to-peak distance of the adjacent waves in a normal ECG signal is the same. On the other hand, in an ECG AF signal, the distance tends to fluctuate.
also reported that the HER performance of metallic TMDs can be enhanced by the creation of Te vacancies [27]. Even though many metallic TMDs show the HER performance, the detailed mechanism of their basal plane-catalytic activity for HER is still unclear. Here, we report the intrinsic catalytic activity of the basal plane of metallic transition metal (_TM_) ditellurides (_TMTe\({}_{2}\)_), where the _TM_s are V, Mo, and W. We synthesized _TMTe\({}_{2}\)_ single crystals using previously reported methods [27; 28; 29] and characterized it by optical microscopy, X-ray diffraction, and electrochemical measurements. X-ray diffraction results confirmed that _TMTe\({}_{2}\)_ single crystals have the orthorhombic, monoclinic, and monoclinic structure for WTe\({}_{2}\), MoTe\({}_{2}\), and VTe\({}_{2}\), respectively [27; 28; 29]. The optical microscopy and the SEM images (Figure S1) show that their surfaces were well prepared with the clean surface minimizing the cracks, the edges, or the defects, which were usually regarded as extrinsic active sites in many TMDs. We found that clean surfaces of _TMTe\({}_{2}\)_ behaved as HER-active surfaces due to the inherent catalytic activity of their basal planes. Among the three metallic _TMTe\({}_{2}\)_, VTe\({}_{2}\) achieved the best HER performance with the lowest overpotential and Tafel slope, which demonstrated that it was promising for active hydrogen production.
## 2 Transmission Setup and Principles
### Principle of Equalization Enhanced Phase Noise  
As illustrated in Figure 1, the EDC is used to compensate for dispersion, and the ideal CPE is applied for the CPE in the coherent optical transmission system. The dispersion compensation module  
Figure 1: Schematic of the 9-channel 32-Gbaud Nyquist-spaced superchannel transmission system. (NPS: Nyquist pulse shaping, PBS: polarization beam splitter, PBC: polarization beam combiner, LO: local oscillator, CPE: carrier phase estimation, MI: mutual information).  
(either EDC or MC-DBP) is applied prior to the CPE module [13]. It can be clearly seen that the phase noise from the transmitter passes through the transmission fiber and the dispersion compensation module, so the net dispersion experienced by the phase noise from the transmitter is close to zero. However, the phase noise from the LO laser only goes through the dispersion compensation module. Due to the non-commutability of the phase noise and fiber dispersion, the phase noise of the LO laser will interact with the DSP-based dispersion compensation module. Consequently, the electronic equalizers (EDC or MC-DBP) will enhance the distortions from the LO laser phase noise, and the resulting EEPN will seriously reduce the performance of the large-capacity transmission system.  
It has been reported in [11; 14; 27] that the variance of EEPN has a linear relationship with the accumulated chromatic dispersion, the linewidth of LO laser, and the symbol rate in the system. The noise variance generated by EEPN can be expressed as [14]  
\[\sigma_{\text{EEPN}}^{2}=\frac{\pi\lambda^{2}D\cdot L\cdot\Delta f_{LO}}{2 cT_{S}} \tag{1}\]  
where \(\lambda\) is the center wavelength of the carrier laser, \(D\) is the dispersion coefficient of SSMF, \(L\) is the total length of fiber link, \(\Delta f_{LO}\) is the 3-dB linewidth of the LO laser, \(c\) is the speed of light in vacuum, and \(T_{S}\) is the symbol period of the transmission system.  
Therefore, the total noise variance of coherent transmission system considering the impairment of EEPN is expressed as follows:  
\[\sigma_{\text{Total}}^{2}=\sigma_{Tx}^{2}+\sigma_{LO}^{2}+\sigma_{EEPN}^{2}+2 \rho\sigma_{LO}\sigma_{EEPN} \tag{2}\]  
\[\sigma_{Tx}^{2}=2\pi\Delta f_{Tx}T_{S} \tag{3}\]  
\[\sigma_{LO}^{2}=2\pi\Delta f_{LO}T_{S} \tag{4}\]  
where \(\sigma_{Tx}^{2}\) and \(\sigma_{LO}^{2}\) are the variances of the phase noise from the transmitter and receiver, respectively, \(\rho\) is the correlation coefficient of the LO phase noise and the EEPN. It has been proved that \(\rho\approx 0\) when the length of the optical fiber link exceeds 80 km [11]. Therefore, in a high-speed and long-distance superchannel optical communication system, the total noise variance can be approximately expressed as  
\[\sigma_{\text{Total}}^{2}=\sigma_{Tx}^{2}+\sigma_{LO}^{2}+\sigma_{EEPN}^{2} \tag{5}\]  
It is noted that when the ideal CPE is applied, the intrinsic phase noise \(\sigma_{Tx}^{2}\) and \(\sigma_{LO}^{2}\) will be fully removed.
## 5 Conclusions  
Based on the monitoring zone index system constructed by climate, vegetation type, and typical ecological zones, the implementation area of GGP is divided into 77 monitoring zones with spatial heterogeneity, among which 88.31%, 70.13%, 67.53%, and 89.61% are located or partially located in NMEPREA, NEFA, NEBA, and NKEFA, respectively, which can serve the network layout of ecological monitoring stations of GGP.  
The ecological function monitoring network of GGP is laid out with 99 ecological monitoring stations (20 compatible level-1 stations, 31 compatible level-2 stations, 18 professional level-1 stations, and 30 professional level-2 stations). Among them, 83 are in NMESPRE, 79 are in NEFA, 41 are in NEBA, and 58 are in NKEFA. The proportion of types of NMEPREA, NEFA, NEBA, and NKEFA covered by monitoring is 66.7%, 100%, 100%, and 76%, respectively.  
The ecological monitoring system of GGP can not only accurately quantify the neutral carbon capacity of GGP and the heterogeneity of its ecological product value but also effectively monitor the effectiveness of protection and restoration of typical ecological zones, promote the high-quality development of GGP, and facilitate the construction of national ecological civilization. In addition, this study can provide a methodological basis for other countries or ecological projects to build a more scientific and reasonable ecological monitoring system.  
Conceptualization, K.G., B.W. and X.N.; methodology, K.G.; software, K.G.; validation B.W. and X.N.; formal analysis, K.G.; investigation, K.G.; resources, K.G.; data curation, K.G.; writing--original draft preparation, K.G.; writing--review and editing, K.G., B.W. and X.N.; visualization, K.G.; supervision, X.N. and B.W.; project administration, X.N. and B.W.; funding acquisition, X.N. and B.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Plan (No: 2021 YFF0703905), the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFYBB2020 ZD002, CAFYBB20202 JE003), the Ecological benefit monitoring of The Grain for Green Project, Forest resources accounting and Forestry Green Development in China.  
**Acknowledgments:** The resource data of the GGP is provided by the State Forestry and grassland administration. We gratefully acknowledge their invaluable cooperation in this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Soil moisture is one of the critical variables in maintaining the global water cycle balance. Moreover, it plays a vital role in climate change, crop growth, and environmental disaster event monitoring, and it is important to monitor soil moisture continuously. Recently, Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technology has become essential for monitoring soil moisture. However, the sparse distribution of GNSS-IR soil moisture sites has hindered the application of soil moisture products. In this paper, we propose a multi-data fusion soil moisture inversion algorithm based on machine learning. The method uses the Genetic Algorithm Back-Propagation (GA-BP) neural network model, by combining GNSS-IR site data with other surface environmental parameters around the site. In turn, soil moisture is obtained by inversion, and we finally obtain a soil moisture product with a high spatial and temporal resolution of 500 m per day. The multi-surface environmental data include latitude and longitude information, rainfall, air temperature, land cover type, normalized difference vegetation index (NDVI), and four topographic factors (elevation, slope, slope direction, and shading). To maximize the spatial and temporal resolution of the GNSS-IR technique within a machine learning framework, we obtained satisfactory results with a cross-validated R-value of 0.8660 and an ubRMSE of 0.0354. This indicates that the machine learning approach learns the complex nonlinear relationships between soil moisture and the input multi-surface environmental data. The soil moisture products were analyzed compared to the contemporaneous rainfall and National Aeronautics and Space Administration (NASA)'s soil moisture products. The results show that the spatial distribution of the GA-BP inversion soil moisture products is more consistent with rainfall and NASA products, which verifies the feasibility of using this experimental model to generate 500 m per day the GA-BP inversion soil moisture products.  
soil moisture; GNSS-IR technology; high spatial and temporal resolution; GA-BP neural network +
Footnote †: journal: Geo-Information  
0021
## 1 Introduction
Robotic Total Stations (RTSs) have gained significant attention for their versatile applications in various fields, especially in environments where the GNSS signal is unreliable [27]. In outdoor environments, RTSs are the preferred choice to obtain high-accuracy measurements in the order of millimeters. In indoor robot navigation, their contribution is marked by high precision and stability [28]. However, few studies use RTS to correct UWB locations. Therefore, this paper presents the following main contributions:  
1. A method for improving the UWB positioning accuracy is proposed. Synchronous RTS coordinate observations at the mm level are used as the benchmark value for the UWB positioning error, which are then inputted into the neural network for training. 2. An experimental system for data acquisition was designed and developed, including control software running on a PC, one RTS, four UWB base stations, and a moving target consisting of a prism and a UWB tag. 3. Two kinds of neural networks (a combined neural network that fits all three coordinate components concurrently and three separate networks dedicated to optimizing a single coordinate component) and two traditional methods (a polynomial model and a locally weighted linear regression model) were compared. An evaluation was implemented using external coincidence accuracy based on the result from the verification dataset. The remainder of this paper is organized as follows: Section 2 introduces the basic idea and steps of our method, including a description of the UWB and RTS experimental sets in Sections 2.1 and 2.2, respectively; an introduction of the control software is in Section 2.3, and Section 2.4 describes the matching method for the RTS and UWB observations. Experimental steps are listed in Section 3.1, the neural network structure is given in Section 3.2, two traditional techniques are provided in Section 3.3, and Section 3.4 shows the accuracy indicators for the network evaluation. Section 4 presents the results and discussion of the validation and testing experiments. Finally, our conclusions are drawn in Section 5.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 2 Cognitive Radio Networks
### Cognitive Radio Functions
#### 2.1.3 Spectrum Sharing  
Since there might be different CR nodes attempting to get to the spectrum, the spectrum sharing functionality coordinates CR users' transmissions to keep different users from crashing in covering parts of the spectrum [53]. Channel allocation does not only depend on spectrum availability, but in order to reduce interference with PU users, it is also determined based on existing spectrum access policies. It also requires at the link layer spectrum awareness together with sensing to achieve an intelligent packet scheduling scheme [54]. As is shown in Figure 2, spectrum sharing can be classified based on different parameters. The spectrum sharing architecture can be categorized into centralized and distributed, where in centralized, the spectrum allocations are controlled by one entity, whereas in the distributed architecture (applied in CRAHN), each individual node can access a spectrum based on a local policy. Spectrum can be allocated cooperatively or non-cooperatively. In cooperative allocation, interference measurement of each node is considered, whereas in the non-cooperative method, only a single node measurement is considered. Based on the access technology, spectrum sharing can be categorized into overlay and underlay spectrum sharing. In the overlay scenario, the unused part of the spectrum is considered for SUs' transmission, whereas in underlay, SUs transmission is considered as noise for the PUs' signal.
## 3 NOAA Microwave Integrated Retrieval System (MiRS)
Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background.
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
## 5 Simulation Results
### BER Performance Evaluation of DWT-OFDM and FFT-OFDM  
An experiment was conducted in order to provide the wavelet with which the best performance in wireless communications can be obtained. In this experiment, the BER performances of (FFT/DWT)-OFDM systems have been evaluated with several mother wavelets such as Haar, Daubechies (db3), coiflets (coif1), symlets (sym3), biorthogonal  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of sub-carriers & 512 \\ Number of symbols with each & 16 \\ sub-carrier & 1/8 symbol duration \\ CP length & 1/8 symbol duration \\ Modulation type & QPSK \\ Equalization & MMSE \\ Channel model & Rayleigh, SUI, and vehicular A outdoor channels \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 6: Block diagram of the proposed (FFT/DWT)-OFDM system model.  
(bior1.3), and reverse biorthogonal (rbi01.3) [34] over a multipath Rayleigh fading channel model in the absence of AWGN.  
Figure 7 presents the BER vs. \(E_{b}\)/\(N_{0}\). The figure shows that the DWT-OFDM outperforms the conventional OFDM for all chosen wavelet families. For instance, at BER = \(2.4\times 10^{-4}\), the DWT (Haar)-OFDM system provides \(E_{b}\)/\(N_{0}\) gains of about 11 dB over the FFT-OFDM system. In addition, it can be seen that there is no significant difference between the kinds of wavelets except Haar, which performs slightly better than the other wavelets at high \(E_{b}\)/\(N_{0}\) values. Therefore, the Haar mother wavelet will be the best choice for DWT-OFDM implementation. For instance, at BER = \(1\times 10^{-7}\), the Haar wavelet provides \(E_{b}\)/\(N_{0}\) gains of about 1 dB over coif1 and rbi01.3 and about 0.5 dB over bior1.3, db3, and sym3. After performing this experiment, we can recommend that the Haar wavelet gives better performance parameters for implementing DWT-OFDM.