esa-sceva/satcom-chunk-collection huggingface dataset:
# Tunable Beam Splitter Based on Acoustic Binary Metagrating
## 5 Conclusions  
In conclusion, we demonstrated the beam splitter based on the ABM, which is constructed by etching a straight-walled groove per period on the planar hard surface. The structure of the ABM in our proposal gives rise to the reduced intrinsic loss and the easy fabrication. The diffraction orders in the proposed ABM take a value of \(n=-1\) (split beam 1) and \(n=1\) (split beam 2), and hence, the beam splitting occurs. In our design, the directions of the split beams can be readily controlled by setting a proper incident angle. Theoretical analysis and numerical simulations were undertaken to provide the proof of concept for the proposed acoustic beam splitter. In the proposed ABM, the beam splitting occurs when the period length \(a\) is in the range of \(\lambda_{0}<~{}a~{}<~{}2\lambda_{0}\) and the incident angle \(\theta_{i}\) is in the range of \(0~{}<~{}\sin\theta_{i}<~{}1~{}-~{}\lambda_{0}/a\). For example, in the case of \(a~{}=~{}1.4\lambda_{0}\), the beam splitting occurs when \(0~{}<~{}\theta_{i}<~{}16.6^{\circ}\). In the case of \(a~{}=~{}1.8\lambda_{0}\), the beam splitting occurs when \(0~{}<~{}\theta_{i}<~{}26.4^{\circ}\). The proposed beam splitter based on the ABM may have potential applications in information processing and biomedical diagnostics.  
Conceptualization, Z.L. and F.J.; methodology, S.Q.; software, S.Q.; validation, Z.L., S.Q. and F.J.; writing--original draft preparation, Z.L.; writing--review and editing, F.J. and X.L.; visualization, F.J.; funding acquisition, F.J. 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 (NSFC), grant number 12104149 and the Doctoral Science Foundation of Hunan Normal University, grant number 0531120-4222. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 3 Results and Discussion
### Fire Behavior  
#### 3.3.1 Fire Reaction Test Results  
Figure 11 shows the flame extinction time, for a total of 23 applications, reaching a total of 90.34 s., which nearly doubles the number of tests reported in previous research on wood dust [29]. The average of all the extinguishing times is 3.93, with the first ignition time being 3.93 s. A significant observation was made at the 9 s mark, when a carbonized protective layer (Figure 12) began to form on the surface of the sample. This layer acted as a thermal barrier, preventing further combustion and allowing the material to endure 23 flame applications without complete degradation. This behavior aligned with previous TGA findings, where the initial moisture release was followed by the decomposition of cellulose and hemicellulose, leading to the formation of a char layer that enhanced fire resistance.  
Figure 11: Extinction times and accumulation times.  
Figure 10: SEM. (**A**) and (**B**) show the internal longitudinal external; (**C**) shows the internal longitudinal internal.  
Furthermore, a previous study [11] indicates that this self-protective carbonization effect in rice husk is primarily due to its high silica content. Silica contributes to the material's resistance to combustion by forming a stable insulating layer that limits heat penetration and delays thermal degradation.  
These results demonstrate the effectiveness of the material in delaying flame propagation compared to traditional passive fire protection strategies due to its self-protective charring mechanism. This natural flame-retardant behavior suggests its potential application in fire-resistant materials. The ability to withstand nearly twice as many flame applications as wood dust [29] indicates its suitability for use in thermal insulation and fire-resistant construction material.
## 4 Model-Assisted Reduced-Order Extended State Observer (ESO) Based Cascade Controller Design
### Cascade Feedback Control Law Design  
#### 4.2.1 Feedback Control Law for the Position Loop  
In this section, the aim is to design a feedback control law for the position loop to track the reference command. As shown in Figure 4, the desired reference trajectory for position control is generated by a second-order transfer function. The mathematical expression is as follows:  
\[\left\{\begin{array}{l}\dot{S}_{d}=v_{d}\\ \dot{v}_{d}=a_{d}\\ a_{d}=-{\omega_{n}}^{2}S_{d}-2\xi\omega_{n}v_{d}+{\omega_{n}}^{2}r\end{array}\right. \tag{33}\]  
where \(\xi\) and \(\omega_{n}\) is the desired damping ratio and system bandwidth, \(r\) is the target position, which is a step function.  
Desired damping ratio \(\xi\) and system bandwidth \(\omega_{n}\) should be reasonably chosen with respect to both the system dynamics and the constraints of the input \(u\). For a given input signal \(r\), the reference signal is generated on line, and \(S_{d}\), \(v_{d}\) and \(a_{d}\) are the desired position, velocity and acceleration, respectively.  
The tracking error between the actual and the reference signal is defined as \(e_{p}=S-S_{d}\), then the following expression can be obtained:  
\[v=\dot{S}=\dot{e}_{p}+\dot{S}_{d}=\dot{e}_{p}+v_{d} \tag{34}\]  
\[a=\ddot{S}=\ddot{e}_{p}+\dot{S}_{d}=\ddot{e}_{p}+a_{d} \tag{35}\]  
Considering the model of mechanical subsystems in the following form:  
\[\ddot{S}=-\frac{c}{m}v+\frac{k_{e}}{m}I+d_{1} \tag{36}\]  
Inserting Equations (34) and (35) into (36) yields  
\[\ddot{e}_{p}+a_{d}=-\frac{c}{m}(\dot{e}_{p}+v_{d})+\frac{k_{e}}{m}I+d_{1} \tag{37}\]  
Due to the effect of the model-assisted reduced-order ESO, nonlinear uncertainties and disturbances that exist in the mechanical subsystem can be compensated, so the new system dynamic can be approximated as a linear system. Therefore, two proportion structures are adopted as the feedback control law. One of them uses the difference between the desired and actual position, and the  
Figure 4: Second-order dynamic system for reference trajectory generation.  
other one uses the difference between the desired and actual velocity. The overall control law for the mechanical subsystem is as follows:  
\[I_{v}=\frac{m}{k_{e}}(a_{d}+\frac{c}{m}v_{d}-h_{1}e_{p}-h_{2}\dot{e}_{p}-\hat{d} _{1}) \tag{38}\]  
where \(h_{1}\) and \(h_{2}\) are proportion parameters.  
Insert Equation (38) into (37), the error dynamics of the position loop system is given by:  
\[\ddot{e}_{p}+(\frac{c}{m}+h_{2})\dot{e}_{p}+h_{1}e_{p}=e_{01} \tag{39}\]  
Considering (32), then  
\[\ddot{e}_{p}+(\frac{c}{m}+h_{2})\dot{e}_{p}+h_{1}e_{p}=0 \tag{40}\]  
In order to guarantee the stability of the system, the formula \(s^{2}+(\frac{c}{m}+h_{2})s+h_{1}\) should be a Hurwitz polynomial. To simplify the tuning process, it can be set as  
\[(s+\omega_{c})^{2}=0,\omega_{c}>0 \tag{41}\]  
Thus,  
\[h_{1}=\omega_{c}{}^{2} \tag{42}\]  
\[h_{2}=2\omega_{c}-\frac{c}{m} \tag{43}\]  
where \(k\) is the only parameter to be tuned.
# Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area
## 3 Case Study
### Coefficients Determination of Regression Equation  
According to the selected five boundary constraint conditions and the distribution of 13 on-site measured points, the established three-dimensional geological model is numerically simulated, and the theoretical values of stress components at 13 corresponding measuring points of model are obtained. To verify the reliability of the inversion results of the ground stress by the multiple linear regression, the calculated data of measuring points 1 and 2 are retained not to participate in the regression fitting calculation. Based on the calculation results of the three-dimensional geological model under five boundary conditions, each component of the ground stress at the corresponding point of the model is taken as the regression target value. The application of three linear regression coefficient estimation methods is realized by using Python code, and the multiple linear regression coefficients of each group are obtained as shown in Table 5. The multiple linear regression equation of the initial ground stress field in the tunnel area is obtained as Equations (8)-(10).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Boundary Condition** & **Ux** & **Uy** & **U’x** & **U’y** & **Uz** \\ \hline Displacement/m & 0.1 & 0.1 & 0.1 & 0.01 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Boundary condition of the numerical model.  
Figure 3: 3D geological model.  
The equations with different coefficient estimation methods are as follows.  
\[\text{OLS}:\sigma_{m} =376.510+0.994\sigma_{g}\ +449.840\sigma_{t1}\ +139.220\sigma_{t2}\ -37.940\sigma_{t3}\ -17.010\sigma_{t4} \tag{8}\] \[\text{RR}:\sigma_{m} =303.703+0.978\sigma_{g}\ +442.477\sigma_{t1}\ +144.120\sigma_{t2}\ -49.772\sigma_{t3}\ -32.500\sigma_{t4}\] (9) \[\text{LR}:\sigma_{m} =394.779+1.003\sigma_{g}\ +433.987\sigma_{t1}\ +140.273\sigma_{t2}\ -37.052\sigma_{t3}\ -22.863\sigma_{t4} \tag{10}\]  
In the formula:  
\(\sigma_{m}\)--the stress component of a point in the equivalent stress field calculated by the model;  
\(\sigma_{g}\)--the stress component of a point under the independent action of the gravity stress field calculated by the model;  
\(\sigma_{t1}\)--the stress component of a point under the independent action of horizontal squeezing stress in the X direction calculated by the model;  
\(\sigma_{t2}\)--the stress component of a point under the independent action of horizontal squeezing stress in the Y direction calculated by the model;  
\(\sigma_{t3}\)--the stress component of a point under the independent action of shear stress in the XOY coordinate plane calculated by the model;  
\(\sigma_{t4}\)--the stress component of a point under the independent action of shear stress in the XOZ coordinate plane calculated by the model.  
It should be noted that the components represented by \(\sigma_{g}\), \(\sigma_{t1}\), \(\sigma_{t2}\), \(\sigma_{t3}\), and \(\sigma_{t4}\) correspond to \(\sigma_{m}\), and the equation should be substituted into the calculation in kPa.
## 2 Necessity and Principles of ESC  
### Performance of Different ES Technologies  
The technical development level of ESs is different, including the following parameters: power grade, continuous discharge time, time of stored energy, cost, lifetime and conversion efficiency. The main technical parameters of ESs are shown in Table 1.
## 2 Materials and Methods
### Generation of Stratification and Performed EIS  
All EIS measurements were performed at 25 \({}^{\circ}\)C and 80% State-of-Charge (SoC). The test cell was charged before for 24 h with a constant-current-constant-voltage charging strategy (5I\({}_{20}\), 2.6 V). After 24 h pause 2 Ah were discharged with \(-\)2I\({}_{20}\) to set the SoC.  
The impedance spectra were executed with superimposed discharging or charging currents in the same way as presented in a previous work [17]. DC-currents of \(\pm\)4.5 I\({}_{20}\), \(\pm\)1.5 I\({}_{20}\), \(\pm\)8 I\({}_{20}\), \(\pm\)3 I\({}_{20}\), \(\pm\)1 I\({}_{20}\), \(\pm\)6 I\({}_{20}\) and again \(\pm\)4.5 I\({}_{20}\) were used. This set of EIS measurements (EIS set) was performed at different levels of stratification. The total duration of this set was 14.5 h including a initial pause of 2 h. The corresponding voltage and current profiles are presented in Figure 1.  
In Figure 2 the sequence of EIS sets at different levels of stratification is presented. The sets performed while acid was homogeneous are named _Ref._\(X\) and the sets performed at stratification are named _Strat._\(X\). The density values specify the acid density difference between top and bottom in the test cell, which were measured before the EIS sets of _Strat._\(X\). At the end an additional reference measurement was executed after the test cell was charged again with constant-current-constant-voltage charging (5 I\({}_{20}\), 2.6 V) for 24 h and the SoC was set to 80%. This makes _Ref._4 comparable with _Ref._1. Between _Strat._2 and _Ref._3 the acid was mixed with the mixing system, because _Strat._2 ended at the end of working week. Before, the acid was replaced to either generate stratification or to remove it.  
To obtain stratifcation of a defined level the homogenous acid was completly removed. Therefore the test cell was unplugged from the measurement equipment and the cover of the test cell housing was demounted. Afterwards higher concentrated acid was poured in the lower half of the cell box. Then the lower concentrated acid was poured slowly in the upper half to have only slight dilution of the stratification. For measurements the cover of the housing was placed and the measurement equipment was plugged to the test cell. A defined amount of the differently concentrated acid was prepared previously to get the same amount of instance of H\({}_{2}\)SO\({}_{4}\) in the stratified acid as in the homogeneous acid. The prepared densities, volumes and corresponding amounts of H\({}_{2}\)SO\({}_{4}\) are given in Table 1 for _Strat._1 and _Strat._2. The finally generated density differences are presented in Table 2.  
Figure 1: The voltage and current profiles of the EIS set containing different superimposed DC-currents in charging and discharging direction.  
Figure 2: The sequence of EIS sets performed with homogeneous and stratified acid. The density values specify the measured acid concentration difference between top and bottom.
# Research on Joint Resource Allocation for Multibeam Satellite Based on Metaheuristic Algorithms
## 4 Results
We selected the average allocation algorithm (abbreviated as ABAP), the scheme GA(UBA) from [20], the comparison algorithm GA(NDBC), and the QPSO algorithm applying NDBC, with the best results, in order to investigate the service capability of the four aforementioned algorithms in the continuous demand scenario. Figure 12b depicts the following phenomena: (1) the metaheuristic algorithm improved the serviceability of the fixed-average algorithm, (2) NDBC can improve the service capability of UBA when the algorithm is GA, and (3) when using NDBC, QPSO can achieve a better solution than GA. Owing to the limited resources, the system service capacity reaches the upper session limit of its service capacity during the peak demand period of the day. In general, the NDBC method proposed in this paper can adapt to continuous-time scenarios. Figure 11: Evolution of total demand over time.
## 3 Results and Analysis
### The Relationship between b-Value, \(\gamma\)-Value, and \(\beta_{t}\)-Value  
From the above analysis, it is evident that the values of \(b\), \(\gamma\), and \(\beta_{t}\) can all be used to analyze the material's damage process and assess whether the specimen is on the verge of catastrophic collapse. This section explores the relationships among these three indicators.  
The relationship between AE amplitude and \(PSD\) can be explained using Fourier transformation. Fourier transformation converts a time-domain signal into a frequency-domain signal, during which the temporal information of the signal is transformed into frequency-domain information. \(PSD\) represents the energy magnitude at different signal frequencies, with larger amplitudes typically corresponding to higher \(PSD\). Additionally, higher AE amplitudes are associated with a higher number of AE events. Assuming the following relationship:  
\[A\propto PSD\propto\frac{H}{H_{m}} \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & **Stage** & **tf/s** & \(\beta_{t}\) \\ \hline \multirow{3}{*}{0–1} & \multirow{3}{*}{2} & 1 & 800–1000 & 1.72 \\  & & 1000–1400 & 1.14 \\  & & 800–1400 & 1.95 \\ \hline \multirow{3}{*}{0–2} & \multirow{3}{*}{2} & 1 & 0–50 & 0.98 \\  & & 2 & 50–61 & 1.76 \\  & & 3 & 0–61 & 1.07 \\ \hline \multirow{3}{*}{1–2} & \multirow{3}{*}{2} & 1 & 0–380 & 1.32 \\  & & 2 & 380–840 & 0.97 \\  & & 3 & 0–840 & 1.59 \\ \hline \multirow{3}{*}{1–3} & \multirow{3}{*}{2} & 1 & 0–105 & 2.27 \\  & & 2 & 105–500 & 1.16 \\ \cline{1-1}  & & 3 & 0–540 & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(\beta_{t}\)-value at different stages.  
Therefore, by performing equivalent substitutions based on Equations (4), (7) and (11), we can derive the relationship between \(b\) and \(\gamma\):  
\[log_{10}N(N\geq M)=a-bk_{1}(c+\gamma\cdot log(f)), \tag{13}\]  
\[b=\frac{a-log_{10}N(N\geq M)}{k_{1}(c+\gamma\cdot log(f))}. \tag{14}\]  
where \(k_{1}\) is the proportionality coefficient between \(A\) and \(PSD\), and \(k_{1}\) is greater than 0. Similarly, we can obtain the relationship between \(b\) and \(\beta_{t}\):  
\[log_{10}N(N\geq M)=a-bk_{2}\bigg{(}\beta_{t}log_{10}\bigg{(}\frac{t}{t_{max}} \bigg{)}\bigg{)}, \tag{15}\]  
\[b=\frac{a-log_{10}N(N\geq M)}{k_{2}\Big{(}\beta_{t}log_{10}\Big{(}\frac{t}{t_{ max}}\Big{)}\Big{)}} \tag{16}\]  
where \(k_{2}\) is the proportionality coefficient between \(A\) and \(log_{10}(H/H_{max})\), \(k_{2}\) is less than 0. From Equations (13) to (16), it is evident that the \(b\)-value is inversely proportional to both \(\gamma\)-value and \(\beta_{t}\)-value. This is also consistent with the experimental results presented in this paper.
## 5 Conclusions  
A comparison of the state of art TF distributions has been performed for the estimation of instantaneous respiratory frequency (IRF) from the heart rate variability signal. The ability of time-frequency distributions (TFD) to concentrate signal energy in the time-frequency domain and accurately estimate the peak heart rate variability frequency was used as performance criteria. For estimating energy concentration of a TFD, we used the measure developed in [45]. For estimating the accuracy of the IRF estimate, the instantaneous frequency estimate from the respiratory signal was used as a reference signal. It was noted that the adaptive directional time-frequency distribution (ADTFD), due to its local time-frequency kernel, was able to achieve high energy concentration for both weak and strong components. The ADTFD also has superior performance to avoid outlier estimates as well as to accurately follow frequency changes. As a result, the ADTFD based method gave the most reliable estimate of IRF in 25 out of total of 35 test participants. The next best performance was achieved by the S-method that correctly estimated IRFs for only 15 test participants.  
The present study indicates that the IRF can be reliably estimated from ECG recordings without requiring additional acquisition of the respiratory frequency. An accurate estimation of the IRF is crucial for a more reliable estimate of HF-HRV based on the more narrow frequency band around the IRF. The narrow band HF-HRV can be shown to give more reliable measures of clinical importance such as diagnosis of coronary artery diseases and other cardiac disorders. In future research, we will apply time-frequency filtering algorithms with the aim to improve estimates of the narrow band HF-HRV power using the IRF estimated from the respiratory related frequency of the HRV signal.  
Funding for this study was provided by the Swedish Research Council for Health, Working Life and Welfare (FORTE, grant 2012-0711).  
[PERSON] conceived and designed the experiments; [PERSON] and [PERSON] acquired the data; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] contributed with analysis of the results and writing of the paper.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Tilted Layer Stratum Model  
The results of Tilted Layer Stratum as shown in Figure 16. In the tilted layer stratum, when the upper layer had relatively low resistance, the tilted layer was unlikely to be identified. When the upper layer had relatively high resistance, the larger the difference between R1 and R2, the more obvious was the tilted layer, as shown in Figure 16a. When the tilt angle decreased, the tilted layer was more identifiable. However, when the upper layer had relatively low resistance, the deeper part was less likely to be identified, as shown in Figure 16b. When there was noise interference in the measurement process, the profile had jumping phenomena. When the upper layer had relatively low resistance, the tilted layer almost turned into a vertical layer as the noise increased, as shown in Figure 16c.  
Figure 16: Results of Tilted Layer Stratum: (**a**) different resistivities, (**b**) different tilt angles, and (**c**) different noises.
## 3 Results and Discussion
### Part Classification Results
#### 3.1.5 Results of Local Feature Performance Experiment Using the Deep Neural Network  
When the deep neural network was trained repeatedly for five generations using the focal loss method, the accuracy reached 99.9%. As a result of learning through the learning system described in this study, it took an average of 2000 s to repeat the first generation. Of a total of 400 images, 75% were used for training.  
As a result of classifying through a deep neural network using 400 images (100 images for each class of fruit, stem, leaf, and sucker), the accuracy was 92.93%. Table 8 shows results of classification using a deep neural network for fruits, stems, leaves, and suckers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parts** & **Training Feature** & **Test Feature** \\ \hline Fruit & 694 & 6250 \\ Node & 10,720 & 96,478 \\ Leaf & 6144 & 55,298 \\ Sucker & 317 & 2849 \\ Total feature & 17,875 & 160,875 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of features used for learning.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Parts**} & \multicolumn{4}{c}{**True Condition**} \\  & & **Fruit** & **Node** & **Leaf** & **Sucker** \\ \hline \multirow{4}{*}{Predicted} & Fruit & 6616 & 1 & 325 & 2 \\  & Node & 102 & 103,196 & 3758 & 142 \\ \cline{1-1} condition & Leaf & 154 & 2416 & 58,718 & 154 \\ \cline{1-1}  & Sucker & 7 & 115 & 53 & 2991 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the BP algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Parts**} & \multicolumn{4}{c}{**True Condition**} \\  & & **Fruit** & **Node** & **Leaf** & **Sucker** \\ \hline \multirow{3}{*}{Predicted} & Fruit & 75 & 0 & 25 & 0 \\  & Node & 0 & 68 & 11 & 21 \\ \cline{1-1} condition & Leaf & 27 & 3 & 60 & 10 \\ \cline{1-1}  & Sucker & 0 & 40 & 8 & 52 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of SVM classifier using RBF kernel.  
The accuracy for fruits was 99.5% and the recall rate was 97.79%. The accuracy for stems was 87.75% and the recall rate was 87.31%. The accuracy for leaves was 90.50% and the recall rate was 99.45%. The accuracy for suckers was 87.25% and the recall rate was 87.69%. F-measure results for fruits, stems, leaves, and suckers were 98.63, 87.53, 94.76, and 87.47%, respectively. In the process of acquiring the 3D shape as a two-dimensional image, the shape of the sucker and the node was not clearly distinguished, so 49 out of 400 nodes were classified as suckers, and 51 out of 400 suckers were classified as nodes. In the case of leaves, 29 leaves in images were not recognized as leaves. The more samples used for training with the CNN algorithm, the better the performance. Therefore, in order to solve the problem of accuracy deterioration due to confusion between nodes and sucker and the problem of not recognizing leaves that occurred in this paper, it is necessary to acquire images of nodes, suckers, and leaves from various directions and angles and increase the number of samples used for training.