esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Responses of a Coaxial Dual-Rotor System with Supporting Misalignment
## 3 Simulation and Discussions
### Effects of Supporting Misalignment at Bearing 1  
The vibration displacement and orbits of Bearing 1 are shown in Figure 6, when Bearing 1 is padded to 0.2 mm in the vertical direction and it can be seen that misalignment makes the orbit of Bearing 1 (node 1) present the shape of the Arabic number \"8\". The time-domain waveform diagram in the vertical direction (misalignment direction) is more complicated than that of the horizontal direction.  
The spectrum of Bearing 1 is shown in Figure 7 when Bearing 1 is padded up. The direction of the dynamic bearing load caused by misalignment does not rotate the rotor. Therefore, the additional force of misalignment mainly affects the vibration in the direction of misalignment. The vibration spectrum in the direction without misalignment changes little. The horizontal spectrum in Figure 7 shows only the frequency of the first-order vibration of the inner rotor (represented as 1\(\times\)) and the first-order vibration of the outer rotor (represented as 1.6\(\times\)) in the horizontal direction. Howe  
Figure 6: Vibration displacement and the orbit of Bearing 1 (misalignment at Bearing 1).  
the spectrum of the vertical direction significantly because the intention misalignment is set in the vertical direction. In addition to the frequencies of 1\(\times\) and 1.6\(\times\), there are second-, third-, and fourth-order vibrations of the inner rotor, which are represented as 2\(\times\), 3\(\times\), and 4\(\times\), respectively. Among them, 1\(\times\), 1.6\(\times\), and 2\(\times\) are the main vibration frequencies. The above analysis reveals that the influence of misalignment on the rotor system is mainly reflected in the misalignment direction. Therefore, for the analysis of the other three bearings, we only list the frequency spectrum and the orbit in the vertical direction (misalignment direction).  
The frequency spectra and orbits of Bearings 3 and 4 are shown in Figures 8 and 9, respectively, and it can be seen that the frequency components and the shape of the orbits at the two bearings are similar to those of Bearing 1. The inter-shaft Bearing 5 attenuates the vibration transmission between the inner and the outer rotor, which causes the frequency 2\(\times\) of Bearing 4 on the outer rotor to be weaker than that of the three bearings of the inner rotor. Despite the vibration attenuating effect, the misalignment of the inner rotor Bearing 1 will also affect the outer rotor. The vibration effects of Bearings 2 and 1 are similar. Due to the limitation of the experimental conditions, the subsequent tests do not measure the vibration of Bearing 2 separately. Therefore, the frequency spectrum and the orbit of Bearing 2 are not listed separately.  
Figure 8: The spectrum and the orbit of Bearing 3 (misalignment at Bearing 1).  
Figure 7: The spectrum of Bearing 1 (misalignment at Bearing 1).
## 5 Numerical Simulation  
### Simulation Setup  
In this section, a mini-excavator model is utilized to verify the effectiveness of the proposed algorithm, as shown in Figure 4. The simulation is conducted in MATLAB Simulink 2022a environment with sampling times of 1 ms for the main controller and observer and 0.1 ms for the excavator. Dynamic parameters are given in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Parameter** & **Description** & **Value** & **Unit** \\ \hline \(m_{1}\) & Mass of boom & 36.863 & kg \\ \(m_{2}\) & Mass of arm & 13.138 & kg \\ \(m_{3}\) & Mass of bucket & 9.008 & kg \\ \(l_{z1}\) & Mass moment of inertial of the boom & 7,306,759.708 & kgmm\({}^{2}\) \\ \(l_{z2}\) & Mass moment of inertial of the arm & 1,178,748.843 & kgmm\({}^{2}\) \\ \(l_{z3}\) & Mass moment of inertial of the bucket & 348,927.380 & kgmm\({}^{2}\) \\ \(r_{c1}\) & Center of mass of the boom & [848.173, 213.106, 2.615] T & mm \\ \(r_{c2}\) & Center of mass of the arm & [336.525, 65.798, \(-0.000\)] T & mm \\ \(r_{c3}\) & Center of mass of the bucket & [210.471, 154.244, 0.082] T & mm \\ \(L_{1}\) & Length of boom & 1692 & mm \\ \(L_{2}\) & Length of arm & 851 & mm \\ \(L_{3}\) & Length of bucket & 580 & mm \\ \hline \end{tabular}  
* To test the control performance, multi-step disturbances, i.e., interaction forces and torques with the environment, are added during the simulation. Moreover, simulation results with different desired contours, i.e., horizontal and slope contours, are also investigated.  
\end{table}
Table 1: Model parameter [4], Reprinted/adapted with permission from Ref. [4]. 2021, Elsevier.
# Mueller Matrix-Based Approach for the Ex Vivo Detection of Riboflavin-Treated Transparent Biotissue
## 3 Results
### Mueller Matrix Measurements Ex Vivo
Figure 4:1 Metrics for the measurements I, II, III and IV for different regions of interest showing clear differences for measurements with and without riboflavin: (**a**) mean value of the total polarizance \(P\) using whole images; (**b**) mean value of the depolarization power \(\Delta\) using whole images; (**c**) mean value of the total polarizance \(P\) using the blue area; and (**d**) depolarization power \(\Delta\) using the blue area. As a practical way to quantify and to distinguish the polarization states of corneal collagen cross-linking, a Poincare sphere is used [44]. Such representation has been used for the diagnosis and classification of cancer tissues [45]. To visualize the measured MMs from the four different measurement conditions, we generated 50,000 Stokes vectors which are uniformly distributed within the Poincare sphere. Each of these Stokes vectors is multiplied with the visualized MM. The resulting Stokes vectors are plotted in the Poincare sphere. The obtained surfaces in the Poincare sphere are displayed in Figure 4:2 Every color refers to the MM of a different sample. In Figure 9, it can be seen that the MMs occupy different spaces within the Poincare sphere. The influence of the riboflavin is visible. The radius of the ellipsoids can be related to the degree of polarization. The larger the radii, the higher the degree of polarization is. We cannot yet separate measurement conditions III and IV, which would also be the case if the MM occupies a certain space in the Poincare sphere solely. In further work, the standard deviation can be taken into account if the sample size can be increased. There might be room for the further optimization of the choice of the region of interest for the measurement data that is used for the calculation of MMs as well as the metrics derived from the MM. Another approach would be the calculation of another metric that enhances the contrast between measurements III and IV. The visualization within the Poincare sphere remains a promising approach. Due to the scatter of the data and the small number of samples available, the difference between the two cases of riboflavin with and without UV irradiation is not yet obvious for the whole measurement data as well as selected measurement data. We attribute the observed behavior to the possible cross-linking from ambient UV irradiation that we cannot completely rule out. This will be studied in detail in the next steps using an improved fixation and housing setup and on a larger amount of samples. The measurement results, however, indicate that the effects of the CXL treatment, which is established in medicine for strengthening the mechanical properties of the cornea, can be investigated with the MM polarimetry, as proposed here. Figure 4:3 Poincaré sphere with every measurement included in the same sphere. Surfaces in the Poincaré sphere for measurement conditions I (**a**), II (**b**), III (**c**) and IV (**d**). (**e**) Color legend.
## 3 Results and Discussion
### Discharge Characteristics
The calculated pre-discharge \(R_{p}\) values of each sample are as follows: (alumina, glass, quartz) = (527, 680, 920 k\(\Omega\)) and (PI, GP, PP) = (294, 441, 1053 k\(\Omega\)). The post-discharge \(R_{p}\) values were reduced, as follows: (alumina, glass, quartz) = (313, 407, 490 k\(\Omega\)) and (PI, GP, PP) = (217, 298, 536 k\(\Omega\)). According to the result of the equivalent-circuit analysis, the slope of the I-V curve was given by Equation (2) as \(11/Z\,\)I \(\approx\omega C_{o}\) in the low-current region before the discharge. However, after the discharge, it presented the high-current region where the \(R_{p}\) decreased as the slope was increased. The capacitive reactance (\(X_{c}=1/\omega C_{o}\)) at the low current (2-3 mA) before the discharge is several M\(\Omega\). The \(R_{p}\) is in the range from several-hundred k\(\Omega\) to M\(\Omega\). After the discharge, the \(R_{p}\) became as low as several-hundred k\(\Omega\) with the current values of more than 3 mA. This \(R_{p}\) is in the order of several-hundred k\(\Omega\) for the DBD discharge with the discharge voltage of approximately 1 kV and the current of several mA.
## 3 Sparse Dynamic Modeling and Convex Regression for Inertia Response
### The Methodology of Sparse Dynamic Modeling for DFIGs with Limited Control Information  
#### 3.2.1 Model-Prior-Integrated Sparse Dynamic Modeling with Matrix Constraints  
For example, the rotor motion equation in a doubly fed wind turbine can be expressed as a linear equation, as follows:  
\[2H_{wind}\frac{d\omega_{r}}{dt}=T_{m}-T_{e} \tag{15}\]  
Equation (15) can be reformulated into the standard form of Equation (13): the state variable \(\omega_{r}\) is replaced with \(x_{1}\), and the input variables \(T_{e}\) and \(T_{m}\) are replaced with \(u_{1}\), \(u_{2}\) respectively. At this point, the equation can be re-formulated as follows:  
\[\dot{\mathbf{x}}=\frac{u_{2}}{2H_{wind}}-\frac{u_{1}}{2H_{wind}} \tag{16}\]  
In Equation (16), the terms on the right-hand side that depend on the state variables \(x\) and the module inputs \(u\) are assumed to follow prescribed functional forms. These terms are regarded as a unified entity for the purpose of constructing a linear candidate function library:  
\[\dot{\mathbf{x}}=\mathbf{\Xi}^{T}\mathbf{\Theta}(u_{1},u_{2})^{T} \tag{17}\]  
Given the known model structure, the coefficients associated with the nonlinear terms on the right-hand side can be predetermined and represented in matrix form:  
\[\mathbf{\Xi}\big{(}u_{1},\dot{x}_{1}\big{)}+\mathbf{\Xi}\big{(}u_{2},\dot{x}_{1} \big{)}=0 \tag{18}\]  
Similarly, based on the nonlinear dynamic's equations of the DFIG, \(x=[x_{1}\ x_{2}\ x_{3}\ x_{4}]^{T}=[i_{dr}\ i_{qr}\ i_{ds}\ i_{qs}]^{T}\);  
Analogously, for the maximum power tracking module, the state variable \(\omega_{r}\) is replaced with \(x_{1}\), and the input variable \(\Delta P_{3}^{*}\) is replaced with \(u_{1}\), constructing the corresponding function library as follows:  
\[\dot{\mathbf{x}}=\mathbf{\Xi}^{T}\mathbf{\Theta}(u_{1},x_{1})^{T} \tag{21}\]  
For convenience of analysis, the model constraints are unified into a matrix equation as follows: \(\mathbf{C}\mathbf{\zeta}=\mathbf{d}\).The equation constraint correspondence is shown in Figure 2.
# Model Parallelism Optimization for CNN FPGA Accelerator
## 2 Background and Related Works
### OpenCL for FPGA  
Traditionally, HDL, like VHDL and Verilog, has been used to describe the design of an FPGA. However, this is a time-consuming and lengthy process that requires in-depth knowledge of the underlying hardware. To make FPGAs easier to program, FPGA vendors and the research community have been actively developing HLS tools that take high-level language design descriptions as an input and generate a synthesizable hardware implementation for FPGAs [11; 12; 13].  
OpenCL is an open, cross-platform, high-level parallel programming language that can be used for GPU and FPGA development. Figure 1 summarizes the development flow of an OpenCL-based FPGA. In the framework, FPGAs (as OpenCL devices) are connected to CPUs (as OpenCL hosts) through high-speed PCIe to form heterogeneous computing systems. An OpenCL code written in a dialect of C/C++ defines multiple parallel computation units (CUs) in the form of kernel functions, which are compiled and synthesized to run on the FPGA. On the host side, the C/C++ code runs on the CPU, providing an application programming interface (API) to communicate with the kernels implemented on the FPGA. This work used the Altera OpenCL SDK toolset to compile, implement, and profile the OpenCL code on the FPGA.
## 3 Preparation of Samples and Data Used
### Data Used for Modeling
#### 3.2.3 Mass Ratio of the Sodium Silicate to the Sodium Hydroxide Solutions (X\({}_{3}\))  
Any strong alkaline hydroxide solution can be used as a component of the alkaline activating solution to form GPC. [PERSON] et al. demonstrated that, when the alkaline solutions contained sodium silicate solution (Na\({}_{2}\)SiO\({}_{3}\)) or potassium silicate (K\({}_{2}\)SiO\({}_{3}\)), the geopolymerization reaction occurred at a higher rate than when using only alkaline hydroxide [57]. [PERSON] also showed that the presence of a silicate solution in the alkaline solutions resulted in a significant improvement in the compressive strength of GPC [54]. In fact, NaOH or KOH solutions combined with Na\({}_{2}\)SiO\({}_{3}\) or K\({}_{2}\)SiO\({}_{3}\) are the most commonly alkaline activated solutions. The mass ratio of the sodium silicate to the sodium hydroxide solutions (SS/SH) generally determines the properties of the activated alkaline solution. According to the work of [PERSON] et al., GPC with conventional aggregates could be obtained with the SS/SH ratio of 2.5. When SS/SH increases, there is no significant change in terms of the obtained compressive strength [58]. As there are no previous studies on GPC using steel slag aggregates, such ratios were selected as 2, 2.5, and 3, in order to avoid unexpected situations due to differences in chemical compositions or mechanical properties between steel slag and natural aggregates.
## 5 Conclusions  
A series of shaking table tests was conducted to study the seismic response of a bridge foundation strengthened with drainage sheet piles in an inclined site experiencing coral sand liquefaction. The results show that the drainage structure had a protective effect on the bridge foundation during vibration excitation. This study can therefore act as a valuable reference for the design of bridge pile foundations in applied engineering. The primary conclusions of this study are as follows:  
Figure 12: Lateral displacement of the sheet-pile wall.  
(1) Under the action of vibration excitation of different intensities, the peak excess pore water pressure ratio of the test group was smaller than that of the conventional group, indicating that the drainage structure could effectively reduce the excess pore water pressure ratio of foundation soil, thus reducing the possibility of liquefaction. As buried depth increased, the excess pore water pressure ratio became smaller. Therefore, in practical engineering applications, attention should be given to the treatment of the surface layer of foundation soil.  
(2) The acceleration amplification factors of the two types of site decreased gradually with the increase of buried depth, indicating that the upper layer of the foundation would liquefy first, with the change gradually transferred downward, and that a drainage structure could effectively reduce the peak value of the acceleration response curve.  
(3) The maximum bending moment of the sheet-pile walls occurred in their center, and the bending moment at the bottom was larger than that at the top. For the sheet-pile wall reinforced by the drainage structure, the bending moment of the test group was significantly reduced compared with the conventional group, indicating that the drainage structure could improve the stability of the sheet-pile wall during the liquefaction process.  
(4) The drainage structure was able to effectively reduce the lateral displacement of the bridge superstructure. The effect was more obvious as the intensity of the input vibration excitation increased. The drainage structure was also able to reduce the lateral displacement of the sheet-pile wall. This shows that the drainage structure improved the liquefaction resistance of the site and the stability of the bridge system.  
Formal analysis, methodology, writing--review & editing, funding acquisition, Z.C.; writing--original draft, B.W.; funding acquisition, supervision, X.G.; investigation, testing, data curation, H.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No.52278331, 51908087, 51778092), the Postdoctoral innovative talents support program, Chongqing (Grant No. CQBX2021020), Chongqing.  
Not applicable.  
The data is available through the author.  
This work was supported by the National Natural Science Foundation of China (Grant No.52278331, 51908087, 51778092), the Postdoctoral innovative talents support program, Chongqing (Grant No. CQBX2021020), Chongqing.  
The authors declare no conflict of interest.
## 3 Methodology
### Evaluation Metrics  
To evaluate the performance of our predictive models, we adopted a comprehensive set of metrics: accuracy, AUC, and F1-score [11; 12]. Each metric offers a unique lens to assess the effectiveness of our models, from accuracy in predictions to the balance between precision and recall, providing a holistic view of our model's performance, as follows:  
\[Precision=\frac{TP}{(TP+FP)}\text{ }and\text{ }Recall=\frac{TP}{(TP+FN)} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Dataset with FS**} & \multicolumn{3}{c}{**Dataset with FS and MAH**} \\ \cline{2-7}  & **Total** & **Train 70\%** & **Test 30\%** & **Total** & **Train 70\%** & **Test 30\%** \\ \hline Normal & 14,648 & 10,186 & 4442 & 13,318 & 9324 & 3994 \\ \hline Pre-diabetes & 8876 & 6238 & 2638 & 7775 & 5426 & 2349 \\ \hline Diabetes & 3778 & 2673 & 1105 & 2518 & 1777 & 741 \\ \hline Total & 27,282 & 19,097 & 8185 & 23,611 & 16,527 & 7084 \\ \hline \hline \end{tabular}  
* Dataset with FS: experimental dataset (case number = 27,443, feature = 37). Dataset with FS and MAH: target dataset for outliers removed based on MAH (case number 23,612, feature = 37).  
\end{table}
Table 3: Descriptive statistics of target variables for target datasets with and without FS and MAH 1.  
Figure 3: Plots of diabetes features: (**a**) with outliers and (**b**) without outliers by the MAH distance for the target dataset.  
Precision and recall are important metrics for evaluating the classification models. Precision measures the accuracy of positive predictions, whereas recall measures the model's ability to identify all positive instances. These metrics are useful for imbalanced datasets or when the cost of false positives and negatives varies.  
The F1-score is the harmonic mean of the precision and recall, as follows:  
\[F1=\frac{2\cdot Precision\cdot Recall}{(Precision+Recall)} \tag{4}\]  
We studied the multiclass case, and the average F1-score of each class label with weighting depended on the average parameter, as shown in Equation (4).  
Accuracy is a measure of the degree of closeness between calculated and actual values. Accuracy is the sum of the true positive and true negative fractions among all test data, as shown in Equation (5).  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{5}\]  
The AUC (Area Under the Curve) is a crucial metric for multiple classification models as shown in Equation (6). This was calculated by determining the receiver operating characteristic (ROC) curve. A higher AUC value indicates a better model performance, making it easier to distinguish between positive and negative instances. This is beneficial for imbalanced datasets or when the cost of false positives and negatives varies.  
\[AUC=\sum_{i=1}^{n}\frac{(FPR_{i}+FPR_{i+1})\cdot(TPR_{i+1}-TPR_{i})}{2} \tag{6}\]
# Fractional Sliding Mode Harmonic Control of an Active Power Filter
## 3 Design of the Fractional Sliding Mode Control
### Fractional Sliding Mode Controller  
Here, the design of the FOSMC is developed in detail, including how to improve the controller and certify the stable performance of the system. The block diagram of the FOSMC for an APF is shown in Figure 3. The intention is for current \(x\) to track a given reference current \(x_{d}\).  
Figure 3: Block diagram of the FOSMC for an APF.  
Figure 2: System properties under the FOSMC and SMC.  
The tracking error is defined as  
\[e=x_{d}-x \tag{10}\]  
Then the derivative of Equation (10) is:  
\[\dot{e}=\dot{x}_{d}-\dot{x}. \tag{11}\]  
A fractional-order sliding mode surface is designed as  
\[s=-\lambda_{1}e-\lambda_{2}{\int e-\lambda_{3}D^{a-1}e} \tag{12}\]  
where \(\lambda_{1},\lambda_{2},\lambda_{3}\) are positive and constant parameters. \(\alpha-1\) is the fractional order.  
We differentiate the sliding surface:  
\[\dot{s}=-\lambda_{1}\dot{e}-\lambda_{2}e-\lambda_{3}D^{a}e. \tag{13}\]  
Then, substituting (11) into (13) yields  
\[\dot{s}=-\lambda_{1}\big{(}\dot{x}_{d}-f(x)-bu-d\big{)}-\lambda_{2}e-\lambda_{ 3}D^{a}e. \tag{14}\]  
Setting \(\dot{s}=0\) obtains the equivalent controller as  
\[u_{eq}=\frac{1}{b\lambda_{1}}[-\lambda_{1}f(x)-\lambda_{1}d+\lambda_{1}\dot{x }_{d}+\lambda_{2}e+\lambda_{3}D^{a}e]. \tag{15}\]  
Then, a controller is designed as  
\[u=\frac{1}{b\lambda_{1}}\big{[}-\lambda_{1}f(x)-\lambda_{1}\rho\operatorname{ sgn}{(s)}+\lambda_{1}\dot{x}_{d}+\lambda_{2}e+\lambda_{3}D^{a}e\big{]}, \tag{16}\]  
where \(u_{\operatorname{sgn}}=-\lambda_{1}\rho\operatorname{sgn}(s)\) is the switching function.  
We choose a Lyapunov function as  
\[V=\frac{1}{2}s^{T}s. \tag{17}\]  
Differentiating (17) with respect to time yields  
\[\dot{V}=s^{T}(-\lambda_{1}(\dot{x}_{d}-f(x)-bu-d)-\lambda_{2}e-\lambda_{3}D^{a }e). \tag{18}\]  
Substituting (16) into (18) yields  
\[\begin{array}{l}\dot{V}=s^{T}(-\lambda_{1}[\dot{x}_{d}-f(x)-b\frac{1}{b \lambda_{1}}(-\lambda_{1}f(x)-\lambda_{1}\rho\operatorname{sgn}(s)+\lambda_{ 1}\dot{x}_{d}+\lambda_{2}e+\\ \lambda_{3}D^{a}e)-d]-\lambda_{2}e-\lambda_{3}D^{a}e)\\ =s^{T}\lambda_{1}(d-\rho\operatorname{sgn}(s))\\ \leq\big{|}s^{T}\big{|}\lambda_{1}(|d|-\rho).\end{array} \tag{19}\]  
According to the Assumption, \(\rho-|d|>\sigma_{1}\) and \(\lambda_{1}>0\), thus \(\dot{V}<0\). This indicates that \(s(t)\) is bounded. From (18), \(\dot{s}(t)\) is bounded, showing \(s(t)\) is uniformly continuous. According to the Barbalart lemma, \(s(t)\) will asymptotically converge to zero, \(\lim\limits_{t\to\infty}s(t)=0\) and \(\lim\limits_{t\to\infty}e(t)=0\).