esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.4 Experiment Scenario
The effect of the Daubechies basis function and the decomposition level is illustrated in Figure 9. As shown in the figure, the sensitivity deteriorates when the decomposition level changes from DL\({}_{1}\) to DL\({}_{10}\) (Sensitivity@Level). However, the decrease in the sensitivity is not as great as the decrease in the accuracy in Figure 8. The lowest  
Figure 10: Specificity due to DWBF and DL. Figure 9: Sensitivity due to DWBF and DL. sensitivity is in DL\({}_{10}\). The effect of DWBF can be seen in Sensitivity@DB in Figure 9. If it is compared to the accuracy due to DWBF in Figure 8, the sensitivity due to DWBF in Figure 9 fluctuates constantly in a wider range (between 80% to 91%). Table 7 is the 5-CV average specificity due to DWBF and DL parameters. As shown in Table 7, the highest specificity is achieved when DBWF is DB\({}_{2}\) and DL is on Level 4, i.e., 96.75%. Then, it is followed by DB\({}_{2}\)DL\({}_{5}\) and DB\({}_{4}\)DL\({}_{2}\), which have specificity at 96.57% and 94.89%. The effect of DBWF and DL can be seen in Figure 10. Specificity@level in Figure 10 shows that the specificity decreases from 92% to 75%. However, the fluctuation of the decline looks unstable. Additionally, Specificity@DB in Figure 10 provides information that the DBWF fluctuates constantly in the range of 76% to 90%, which is wider than the fluctuation of sensitivity in Figure 9.
## 2 Proposed FP-AFPMG and Optimization Process
### Proposed FP-AFPMG

Table 1: Dimensions of the proposed generator. Figure 4: Winding type (**a**) core wound, (**b**) tooth wound. Figure 5: Modification of permanent magnet pole. (**a**,**c**) original shape of the permanent magnet pole, (**b**,**d**) modified shape of the permanent magnet pole. In this study, the stator teeth of the generator were wound with a double-layer non-overlap stator winding type due to the several advantages possessed by this winding type as stated in [12]. The number of stator coils per phase required to implement the double-layer non-overlap winding type is counted by Equation (1) [13]. The machine periodicity and the number of spokes can be calculated by Equations (2) and (3). Equation (4) formulates the electric angle between two phasors in Figure 7a, and Equation (5) computes the angle of the sector in Figure 7a [14]. \[n_{c}=\frac{N_{s}}{m} \tag{1}\]  
\[\delta=GCD\;(N_{s},\;p) \tag{2}\]  
\[Spoke=\frac{N_{s}}{\delta} \tag{3}\]  
\[\alpha_{e}=p\;\frac{2\pi}{N_{s}} \tag{4}\]  
\[\alpha_{s}=\frac{\pi}{m} \tag{5}\]  
In Equations (1)-(5), \(N_{s}\) is the stator slot number, \(m\) is the phase number, \(GCD\) is the greatest common divisor, and \(p\) is the pole pairs. With the combination of twenty stator slots and eight PMs, the calculated \(\delta\) obtained from Equation (2) is 4 and the calculated \(\alpha_{e}\) obtained from Equation (4) is \(2\pi/5\). Thus, the star of slots includes five spokes in which each spoke geometrically shifts by \(\alpha=2\pi/5\) and each spoke consists of four phasors, as shown in Figure 7a. In the case of \(\alpha_{e}=\alpha\), the positive phasors of each phase (phase a+, b+, c+, d+, and e+) are located in one sector, so the opposite sectors (negative phasor of phase a-, b-, c-, d-, and e-) are vacant. The winding arrangement for each phase converted from the phasor diagram in Figure 7a is shown in Figure 7b. As the opposite sector of the positive phasor is vacant, the winding polarity of each phase will not be differently wound in each slot, as shown in Figure 7b. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Part** & **Dimension (mm)** \\ \hline
11 & Arc height of the permanent magnet & 2 \\
12 & Pole width to pole pitch ratio & 0.95 \\
13 & Total axial length of the FP-AFPMG & 175 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 6: Modified shape of the axial flux permanent magnet generator in Figure 1.
# Fractional-Order LQR and State Observer for a Fractional-Order Vibratory System
## 6 Numerical Calculation Method for Fractional-Order Dynamical System
### Comparison between Numerical and Exact Solutions  
Next, the solution obtained by the above-mentioned numerical calculation method is compared with the exact solution. The exact solution of Equation (15) can be given as follows using the Mittag-Leffler function \(\mathbf{E}_{a_{1},\ a_{2}}\)[29; 37]:  
\[\mathbf{x}(t)=\mathbf{E}_{\frac{1}{2},\ 1}\Big{(}\mathbf{A}t^{\frac{1}{2}}\Big{)}\mathbf{x}(0)+ \int_{0}^{t}(t-\tau)^{\frac{1}{2}-1}\mathbf{E}_{\frac{1}{2},\ \frac{1}{2}}\Big{(}\mathbf{A}(t-\tau)^{\frac{1}{2}}\Big{)}\mathbf{B}u(\tau)d\tau, \tag{54}\]  
\[\mathbf{E}_{a_{1},\ a_{2}}(z)=\sum_{k=0}^{\infty}\frac{z^{k}}{\Gamma(a_{1}k+a_{2}) },\ (a_{1}>0,\ a_{2}>0). \tag{55}\]  
In addition, the parameters for the viscoelastic damper are set to \(\zeta=0.1\) and \(\omega_{n}=3.0\) rad/s. The initial conditions for the states are set to \(\mathbf{x}(0)=\big{[}\ 0\ \ 0\ \ \ 1\ \ \ 0\ \ \big{]}^{T}\). For each state, the numerical calculation results and the exact solutions obtained under the free vibration condition are compared in the following figures.  
First, Figure 3 shows calculation results for simulation conditions with a larger \(\Delta t\) than those in Figure 4. The numerical calculations in Figure 4 are more accurate than those in Figure 3, because the gap between the numerical calculation results and the exact solution in Figure 4 is smaller than that in Figure 3. Based on this result, it is understood that a smaller \(\Delta t\) improves the accuracy of numerical calculations. A numerical calculation method that enables a highly accurate simulation of a state equation that includes fractional-order derivative terms has thus been clarified.  
Figure 3: _Cont._  
Figure 4: Numerical calculation results (blue lines) and exact solutions (red broken lines) (\(\Delta t=0.0005\)).  
Figure 3: Numerical calculation results (blue lines) and exact solutions (red broken lines) (\(\Delta t=0.001\)).
# Geometric Design of a Face Gear Drive with Low Sliding Ratio
## 2 Mathematical Models of the Face Gear Pair with Low Sliding Ratio
### Construction of Shaper
\[\delta_{1}=\frac{v_{t}^{(1)}-v_{t}^{(2)}}{v_{t}^{(1)}}=1-\frac{1}{\frac{v_{t}^ {(1)}}{v_{t}^{(1)}}}=1-\frac{1}{\frac{H+r_{s}}{r_{s}}}=\frac{H}{H+r_{s}} \tag{11}\]  
\[\delta_{2}=\frac{v_{t}^{(2)}-v_{t}^{(1)}}{v_{t}^{(2)}}=1-\frac{v_{t}^{(1)}}{v_ {t}^{(2)}}=1-\frac{H+r_{s}}{r_{s}}=-\frac{H}{r_{s}} \tag{12}\]  
where \(v_{t}^{(1)}\) and \(v_{t}^{(2)}\) are the tangential velocities of each tooth profile at the meshing point; \(H\) is the ordinate value of the intersection of the common tangent at the meshing point and the axis \(y\). From Equations (11) and (12), it can be seen that when the absolute values of \(H\) at the entry and exit points of engagement are as small as possible, the absolute value of the sliding ratio will also be as small as possible. Thus, the equation for the engagement path can be described by a cubic function as in Equation (13). \[y=a_{1}x+a_{2}x^{3},\ \ (a_{1}<0,\ a_{2}<0) \tag{13}\]  
where the coefficients \(a_{1}\) and \(a_{2}\) should meet the following conditions: (1) the shaper tooth tip thickness should be greater than 0.3 times the modulus and (2) the contact ratio must be greater than 1. To illustrate the low sliding ratio that can result from the provided meshing trajectory profile, a gear with module \(m=4\) mm, gear tooth number \(N_{s}=32\), coefficients \(a_{1}=-\tan 20^{\circ}\) and \(a_{2}=-0.001\) is used as an example. The tooth profile and sliding ratio of the gear pair are shown in Figure 3.  
It can be seen that the proposed rack-and-gear transmission has a lower sliding ratio, and the maximum sliding ratio is about 47% lower than that of the involute gear with the same basic parameters.
## 3 Results and Discussions
### Oil Spill Transport Behavior under Various Influencing Factors
#### 3.2.3 Leakage Mode  
To explore the oil spill transport behavior under different leakage modes, three leakage volumes of 180 t (2 h), 720 t (2 h), and 1630 (2 h) were assumed under perforated leakage mode, and 2600 t (0.5 h) under fracture leakage. The river flows are 1000 m\({}^{3}\)/s, 3000 m\({}^{3}\)/s, and 5000 m\({}^{3}\)/s, and the wind speed is 0 m/s. Figure 11 shows **v**, **a**, and **l** for different leakage parameters, and it can be seen that the variation of **v** is not significant in both  
Figure 10: Transportation state of oil spill under NE and SW winds reaching the 2\(\#\) interception point.  
Figure 9: **v, a, and l for different wind directions.**  
leakage modes. Under perforated leakage, **a** and **l** increase with the increase of leakage volume. This indicates that the spill mode has less effect on the oil spill migration rate; under perforated leakage, the increase in oil spill volume causes the oil spill to diffuse and extend longitudinally in the river channel more quickly. In (b), the value of an under the scenario of perforated leakage-1630 t (2 h) is larger than that of fractured leakage-2600 (0.5 h) for Q = 5000 m\({}^{3}\)/s and 3000 m\({}^{3}\)/s because, under perforated leakage, the initial dispersion of the oil spills is large. There is more space for the diffusion of the oil film, so the rate of diffusion of the oil spills is faster within a short period.  
The difference in the timing of the oil spill in different spill modes results in different lengths of the oil spill contamination zone. For the oil spill accident under two kinds of leakage modes, under static wind conditions, with Q = 3000 m\({}^{3}\)/s, fracture leakage-2600 t (0.5 h) and perforation leakage-1630 t (2 h), for example, the head of the oil spillage migrates to 2\(\#\) oil spill interception point after 3.3 h, and the distribution of oil spills in the river is shown in Figure 12. At this time, the length of the oil spill pollution zone is 1.97 km and 6.92 km, respectively. In the case of perforated leaks, emergency personnel can go to both 1\(\#\) and 2\(\#\) interception points to conduct interception operations. Therefore, emergency managers can develop emergency response plans for simultaneous interception and recovery operations at multiple interception points according to different spill modes.
## 3 Results
### Energy-Dispersive X-ray Analysis  
The prepared samples containing tooth-restoration structures were assessed using EDX analysis. For the purpose of this study, 10 sections for each restorative material were evaluated. Three specific regions of interest (ROIs) were defined for each of the selected samples (resin composite, enamel, and dentine), and the element content values were provided as percentage weight (wt %). Mean values (\(\pm\) SD) for the results are included in Table S1. As predicted, it could be observed that the composition of tested dental materials was not uniform. One example included silicon (Si). This type of content was higher in conventional composites than in correspondent flow composites (B vs. BF = 9.2 \(\pm\) 1.09 vs. 4.9 \(\pm\) 0.1 and D vs. DF = 15.68 \(\pm\) 1.10 vs. 11.66 \(\pm\) 2.04) due to the higher glass-based filler load in conventional composites. However, it could be found that all resin samples displayed a predominance of oxygen (O), carbon (C), and silicon elements. Contrastingly, no trace of strontium (Sr) could be identified in D or DF, whereas similarly high levels could be detected in B and BF (8.92 \(\pm\) 0.58 and 8.56 \(\pm\) 0.61). On the contrary, barium (Ba) was detected in high levels in D and DF materials (11.14 \(\pm\) 1.27 and 11.14 \(\pm\) 3.15), but it was almost non-detected in the other two composites. Low levels of fluoride (F) were detected only in B and BF materials. The concentration of oxygen, carbon, calcium, and phosphorus (P) were relatively similar for enamel and dentin specimens.
# Method for Estimating the Optimal Coefficient of L1C/B1C Signal Correlator Joint Receiving
## 2 The Mathematical Model and Characteristics of the B1C/L1C Signal
### The Characteristic of Autocorrelation  
In the navigation receiver, acquisition and tracking are based on the cross-correlation function of the satellite signal and local reproduction signal. Therefore, the signal autocorrelation function is very important in signal reception and processing. The autocorrelation function of the BOC signal is written in the form of a trigonometric function, which is shown in Equation (5) as follows [35]:  
\[R_{BOC_{s}(m,t)}(\tau)=\wedge_{T_{s}}(\tau)+\sum_{k=1}^{M-1}{(-1)}^{k}(1-\frac{ k}{M})\wedge_{T_{s}}{(|\tau|-kT_{s})} \tag{5}\]  
where \(T_{s}\) stands for Subcarrier period. \(M=\frac{2m}{n}\) stands for modulation order of the BOC signal. \(\wedge_{L}(\tau)\) stands for the trigonometric function, and its mathematical expression is as follows:  
\[\wedge_{L}{(\tau)}\triangleq\begin{cases}1-\frac{|t|}{L},&|t|\leq L\\ 0&other\end{cases} \tag{6}\]  
According to the design scheme of B1C and L1C, the autocorrelation functions can be obtained as follows:  
\[R_{L1 Cd}(\tau)=R_{B1 Cd}(\tau)=R_{L1 Cp(\text{QZSI})}(\tau)=\wedge_{T_{C}/2}( \tau)-\frac{1}{2}\wedge_{T_{s}}{(|\tau|-T_{C}/2)} \tag{7}\]  
\[R_{L1 Cp(\text{GPS},\text{QZSI}\sim_{4})}(\tau)=R_{B1 Cp}(\tau)=\frac{29}{33} \cdot[\wedge_{T_{C}/2}(\tau)-\frac{1}{2}\wedge_{T_{s}}{(|\tau|-T_{C}/2)}]+\]  
\[\begin{array}{c}\frac{4}{33}\cdot[\wedge_{T_{C}/12}(\tau)+\sum_{k=1}^{11}{(- 1)}^{k}(1-\frac{k}{\Gamma_{k}})\wedge_{T_{s}}{(|\tau|-kT_{C}/12)}]\end{array} \tag{8}\]  
The data/pilot joint autocorrelation function for B1C and L1C is deduced as follows:  
\[R_{\text{QZSI}}(\tau)=\wedge_{T_{C}/2}(\tau)-\frac{1}{2}\wedge_{T_{s}}{(|\tau |-T_{C}/2)} \tag{9}\]  
\[R_{B1C/L1C(\text{GPS},\text{QZSI}\sim_{4})}(\tau)=\frac{10}{\Gamma}\cdot[ \wedge_{T_{C}/2}(\tau)-\frac{1}{2}\wedge_{T_{s}}{(|\tau|-T_{C}/2)}]+\]  
\[\begin{array}{c}\frac{1}{\Gamma}\cdot[\wedge_{T_{C}/12}(\tau)+\sum_{k=1}^{11 }{(-1)}^{k}(1-\frac{k}{\Gamma_{k}})\wedge_{T_{s}}{(|\tau|-kT_{C}/12)}]\end{array} \tag{10}\]
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 2 An Overview of Rail Defects and of the Techniques for Their Detection
### Rail Diagnostics Techniques
#### 2.2.3 Visual Inspection  
Visual inspection can be exploited by a human expert walking on tracks and searching for defects, or by automated techniques. Automated visual inspection is based on video cameras, optical sensors, and custom-designed algorithms to analyze the rail surface as the diagnostic train rides over [28; 29; 30; 31]. Test speeds can be as high as the velocity of high-speed trains [4], but only the visible surface of the rail is inspected and internal defects cannot be detected [5]. Furthermore, since detection performance is influenced by lighting conditions, proper countermeasures need to be adopted [32]. An example of a high-speed diagnostic train also making use of visual inspection techniques is shown in Figure 8[33].
However,with the rapid development of computer simulation, space electromagnetic field modeling, and analytical theory, as well as the continuous in-depth study of artificial intelligence, new solutions to the problem of suppression rigging detection are constantly emerging [23]. The latest method is to use a convolutional neural network to analyze and extract the defect size of magnetic flux leakage detection signals to improve the detection and recognition rate [24]. The neural network algorithm can achieve a good quantification of defects with multiple parameters, but it needs enough training samples [25] to reduce the detection error of the neural network. It is very difficult to produce a large number of standard defects in actual steel wire rope, and retraining of different steel wire ropes and different detection equipment cannot meet the actual detection accuracy and is difficult to popularize and apply. In this paper, given the problems existing in the defect analysis and detection of the pressing rigging, the defect signal of the pressing rigging is collected by using CM-801 equipment for marine rigging detection. A Kalman filter is used to reduce the noise of the data, and the processed data are presented in the form of a defect cloud image. The defect cloud map can directly reflect the existence of defects in the pressed rigging, and the stress defect and the broken wire defect in the defect cloud map are marked respectively. YOLOv5 (You Only Look Once, version 5) conducted deep learning on the tagged images, and established and verified the convolutional neural network model to suppress the rigging defect damage signal. The model uses the YOLOv5 deep learning object detection method [26; 27; 28] and verifies it. This paper provides a new detection method for the defect detection of pressed rigging. With the progress of convolutional neural networks and hardware performance, the real-time efficiency of target detection technology is continuously improving, and this technology will make it possible to detect the feasibility of pressed rigging defects in real time.
# Impact of Shifts in Vegetation Phenology on the Carbon Balance of a Semiarid Sagebrush Ecosystem
###### Abstract  
Dryland ecosystems are critical in regulating the interannual variability of the global terrestrial carbon cycle. The responses of such ecosystems to weather and environmental conditions remain important factors that limit the accurate projections of carbon balance under future climate change. Here, we investigated how shifts in vegetation phenology resulting from changes in weather and environmental conditions influenced ecosystem carbon cycling in one semiarid ecosystem in the Hanford area of central Washington, United States. We examined two years of measurements of the phenology camera, eddy covariance, and soil chamber from an upland semiarid sagebrush ecosystem. Both years had contrasting diet and seasonal patterns of CO2 fluxes, primarily driven by differences in vegetation phenology. The net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) in 2019 were enlarged by shifted vegetation phenology, as a cold and snow-covered winter and warm and dry winter in 2020 resulted in constrained magnitudes of NEE and ET during the summer months. The annual gross primary productivity (GPP) was much higher in 2019 than in 2020 (\(-211\) vs. \(-112\) gC m\({}^{-2}\)), whereas ecosystem respiration was comparable in these two years (164 vs. 144 gC m\({}^{-2}\)). Thus, the annual NEE in 2019 was negative (\(-47\) gC m\({}^{-2}\)) with the sagebrush ecosystem functioning as a carbon sink, while the positive annual NEE in 2020 indicated that the sagebrush ecosystem functioned as a carbon source. Our results demonstrate that winter snowpack can be a critical driver of annual carbon uptake in semiarid sagebrush ecosystems.  
carbon fluxes; PhenoCam; semi-arid regions; snowpack; vegetation phenology Article  
Impact of Shifts in Vegetation phenology on the Carbon Balance of a Semiarid Sagebrush Ecosystem. _Remote Sens._**2022**, _14_, 5924. [[https://doi.org/10.3390/rs14235924](https://doi.org/10.3390/rs14235924)]([https://doi.org/10.3390/rs14235924](https://doi.org/10.3390/rs14235924))
## 5 Results of the WDM and OAM Channel Allocation Methods  
For any selection of the operation technology, the proposed P2 MP OAM&WDM-PON architecture is considered. The maximum bandwidth of one WDM channel that can be assigned to an ONU bandwidth request is 1 Gbit/s. This corresponds to the maximum data rate of HSI customer service. From realized simulation trials and efforts with regard to different conditions and input parameters of WDM and OAM channel allocation methods, we selected a presentation of output parameters for the number of ONUs equal to 16 in this section. For WDM channels, the 50 GHz channel spacing is assumed. From the total number of WDM channels, the separate wavelength channel is assigned to ONUs according to the specification in the concrete algorithms. The requested bandwidth for particular ONUs is determined by the dedicated customer service (more details can be found in Section 4.3). These options present a basic and initial version of the simulation model. In the future, a comparison to other allocation methods can be faster and simpler. With an increasing number of ONUs, an increased difference between resource efficiencies for considered allocation methods of WDM and OAM channels, along with associated possible channel savings, can also be expected.
## 6 Experimental Results  
The datasets we used as shown in Figure 2 were:  
1. The original dataset, which contains only the Greek tokens.
2. The mixed dataset, which contains Greek tokens and their translation.
3. The translated dataset (no Greek tokens).
4. The negative imbalanced dataset, which was derived from the mixed dataset removing 95% of the positives.
5. The positive imbalanced dataset, which was derived from the mixed dataset removing 95% of the negatives.  
The underlying idea in these datasets is to approximate realistic conditions in opinion mining and sentiment analysis systems. The imbalanced datasets have been dealt with using the under-sampling and over-sampling technique mentioned above. Each of the aforementioned datasets also underwent the PCA data dimensionality technique and the feature weighting step using information gain. PCA and FS were applied to all used classifiers (decision trees, naive Bayes and support vector machines), except GBM.  
The experiments were carried out on a rack server, with 2 Intel Xeon E5-2650/2 GHz 8-core processors, with 64 GB of RAM, running under Linux (CentOS). The RapidMiner(r) framework was  
Figure 2: Methodology flowchart. FS, Feature Selection.  
used throughout the experimental evaluations. In the cases of GBM and deep learning, the H\({}_{2}\)O Open Source Fast Scalable Machine Learning API was called within RapidMiner. Since these algorithms are highly dependent on their parameters, optimization of GBM, deep learning, SVM, as well as the optimal number of principal components and, finally, the top-k features in the FS step was carried out using a grid search over numerous different parameter settings, upon evaluation on a held-out set and having the harmonic mean of precision and recall, named the F-measure, as the performance criterion during the optimization search. In order to reduce the training time, for the cases of PCA and FS, we experimented with five different parameter settings. We asked the PCA algorithm to retain five thresholds of variance, starting from 20% and climaxing at 60% using 10% incremental steps. Furthermore, if the FS weighting process, we set five different top-k values of features to be retained, i.e., 100, 250, 500, 750 and 1000. For the technical details of the optimization operators, please refer to the RapidMiner documentation ([[http://docs.rapidminer.com/studio/operators/modeling/optimization/parameters/optimize_parameters_grid.html](http://docs.rapidminer.com/studio/operators/modeling/optimization/parameters/optimize_parameters_grid.html)]([http://docs.rapidminer.com/studio/operators/modeling/optimization/parameters/optimize_parameters_grid.html](http://docs.rapidminer.com/studio/operators/modeling/optimization/parameters/optimize_parameters_grid.html))), namely the \"Optimize Parameters/Grid\" operator. Since the following paragraphs describe experiments on a different dataset each time, the optimal parameters found at each set will be tabulated.