esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this article, a method based on composite surface modeling theory is put forward to establish the typical canyon/valley environment containing two slopes and a plain bottom which forms the semi-enclosed cavity structure. Then modified SBR algorithm improved by introducing a high-order reflection model is proposed. Facet-based SSA method is investigated and integrated with the modified SBR algorithm to solve the composite scattering from a target above the canyon/valley environment efficiently and accurately. The composite EM scattering characteristics influenced by surface types and roughness, slope angles, incident pitch, and azimuth angles are thoroughly analyzed. It can be concluded that: the incident angle and the slope angle determine the peak numbers and positions of the RCS curves; the roughness and Brewster Effect affect the peak values; and the target above the composite surface, and the particular structure of the canyon/valley, make the multi-bounce scattering mechanism more complicated. The work presented in this article has significance for detecting targets in the canyon/valley environment.  
However, there are some limitations in the articles and future work will focus on two aspects. Firstly, in our canyon/valley model, the Gauss and PM surfaces are still approximations. If the real geometry data for the canyon/valley environment surface can be obtained, it will make the modeling more practical. Secondly, the EM scattering simulation results for a target above the canyon/valley environment are compared with the accurate numerical algorithm results, instead of the measured data. Due to the absence of the measured data from the public literature or experimental results, we would like to carry out some experiments to obtain the real composite EM scattering characteristics, which can be used to justify and correct our model.  
Conceptualization, Y.W. and T.W.; methodology, Y.W. and T.W.; software, Y.W.; validation, T.W.; writing--original draft preparation, Y.W. and T.W.; writing--review and editing, X.L., Q.W. and Z.W.; supervision, C.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Founding of China, grant number 62201611, and the Natural Science Foundation of Shaanxi Province, grant number 2021 JQ-365.  
Not applicable.  
**Data Availability Statement:** All data generated or analyzed during this study are included in this published article.  
The authors declare no conflict of interest.
## 2 Material and Methods
### Biodegradable Synthetic Vascular Grafts
#### 2.1.1 Pga  
Poly (glycolic acid) (PGA) is among the most commonly used polymers in TEVGs. It was one of the first biodegradable polymers to be utilized to fabricate a scaffold. PGA degrades into glycolic acid, which is metabolized by cells and broken down into water and carbon dioxide.  
Previous reports have shown that a mouse PGA inferior vena cava (IVC) interposition graft takes up to six weeks to lose its mechanical properties [13].  
In an arterial model, a scaffold made of PGA alone does not have sufficient structural characteristics to withstand the arterial blood pressure. Furthermore, the rate of PGA degradation does not allow for adequate neotissue to form, often resulting in aneurysmal dilatation as the PGA begins to lose its mechanical integrity. However, the mechanical properties of PGA grafts can be improved through smooth muscle cell seeding or by being combined with other polymers [14].  
PGA can be synthesized singly or as a copolymer such as PLA (PLGA). The strength, hydrophilicity, and degradation rate can be adjusted by changing the lactic: glycolide ratio in the PLGA copolymer or the D:L ratio in the lactic monomer [15]. PGA/PLGA degrade more quickly than PLA alone, and the ease with which the degradation time can be adjusted makes them clinically attractive. For this reason they are used as absorbable sutures and orthopedic implants [15; 16].
## 2 Model Introduction
### LSTM Neural Network  
LSTM is a particular type of RNN that was initially designed to address the long-term dependency problem of RNNs by improving the architecture to avoid this issue. LSTM was proposed by [PERSON] and [PERSON] in 1997 [31]. The structure of LSTM is illustrated in Figure 5. LSTM is able to maintain the flow of information in long sequences by introducing memory units. The proposed network draws inspiration from the memory mechanism of the human brain, which selects whether to retain or forget information based on its importance when processing it. This mechanism allows the human brain to handle complex dependencies. Following this brain mechanism, LSTM is designed with gates and cell states, where the gate structure controls the inflow and outflow of information, and the cell states are used to store the states. The introduction of gates and cell states enables LSTM to better capture dependencies in long sequences.  
Figure 4: RNN long-term dependence limitations.  
Figure 3: RNN unfolding.  
The core idea of LSTM is the cell state, where relevant information can be added or irrelevant information can be removed from the flow of the cell state, allowing it to retain relevant information and forget irrelevant information. To realize this mechanism, LSTM utilizes three gate structures: forget gates, input gates, and output gates. The formulas are as follows:  
\[f_{t}=\sigma(W_{f}[h_{t-1},x_{t}]+b_{f}) \tag{3}\]  
\[i_{t}=\sigma(W_{t}[h_{t-1},x_{t}]+b_{t}) \tag{4}\]  
Among them, the forget gate \(f_{t}\) is responsible for forgetting how much information in \(h_{t-1}\) is saved in the memory unit, \(w_{f}\) is the parameter of the current neuron, and \(b_{f}\) is the bias parameter. The input \(i_{t}\) gate then adds new information to the state information. \(i_{t}\) controls the updating of the state information. \(C_{t}\) controls the updating of the data, and the formula for \(C_{t}\) is as follows:  
\[\widetilde{C}_{t}=tanh(W_{c}[h_{t-1},x_{t}]+b_{c}) \tag{5}\]  
\[C_{t}=f_{t}C_{t-1}+i_{t}\tilde{C}_{t} \tag{6}\]  
The output gate \(o_{t}\) outputs the accumulation of previous information. The output gate decides whether to output certain information into the hidden state of the current time step based on the input information of the current time step and the hidden state of the previous time step. The formula for the output gate is as follows:  
\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{7}\]  
\[h_{t}=o_{t}\tanh(C_{t}) \tag{8}\]  
By combining and regulating the input gates, forget gates, and output gates, LSTM is able to selectively store, forget, and output information, enabling it to handle long-term dependencies better and memorize critical information in long sequences. This gating mechanism allows LSTM to perform well in many sequence modeling tasks.
# Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems
## 2 Channel Estimation Model
### LS-Based Channel Estimation Model  
In practical applications, channel estimation is typically accomplished through pilot symbols. Let the pilot signal at the transmitter be denoted as \(x_{p}\), the pilot signal at the receiver as \(y_{p}\), and the noise as \(n_{p}\). The received pilot signal at the receiver can then be expressed as  
\[y_{p}=\text{diag}(x_{p})\cdot h_{p}+n_{p} \tag{2}\]  
The objective of the LS method is to minimize the error between the received signal and the model signal. Thus, in matrix form, the received signal can be expressed as  
\[y_{p}=X_{p}\cdot h_{p}+n_{p} \tag{3}\]  
where \(X_{p}\) is the equivalent representation of the diagonal matrix \(\text{diag}(x_{p})\). The error can be represented as  
\[\epsilon=y_{p}-X_{p}\cdot h_{p} \tag{4}\]  
The goal of the LS method is to minimize the sum of the squared errors, which is  
\[\min_{h_{p}}\lVert\epsilon\rVert^{2}=\min_{h_{p}}\lVert y_{p}-X_{p}\cdot h_{p} \rVert^{2} \tag{5}\]  
To find the channel estimate \(h_{p}\) that minimizes the error, we can solve the following equation:  
\[\frac{\partial\lVert y_{p}-X_{p}\cdot h_{p}\rVert^{2}}{\partial h_{p}}=0 \tag{6}\]  
Expanding the sum of squared errors gives the following equation:  
\[\lVert y_{p}-X_{p}\cdot h_{p}\rVert^{2}= (y_{p}-X_{p}\cdot h_{p})^{H}. \tag{7}\] \[(y_{p}-X_{p}\cdot h_{p})\]  
Figure 1: Comb Pilot Pattern with 1/4 Pilot Density.  
where \({}^{H}\) denotes the conjugate transpose. Taking the derivative of the above expression with respect to \(h_{p}\) and setting the derivative to zero can be expressed as  
\[\frac{\partial}{\partial h_{p}}\Big{[}(y_{p}-X_{p}\cdot h_{p})^{H}(y_{p}-X_{p} \cdot h_{p})\Big{]}=0 \tag{8}\]  
Expanding the above equation, we obtain  
\[\frac{\partial}{\partial h_{p}}[y_{p}^{H}y_{p}-y_{p}^{H}X_{p}h_{p}-(X_{p}h_{p}) ^{H}y_{p}\ +(X_{p}h_{p})^{H}X_{p}h_{p}]=0 \tag{9}\]  
Noting that \(y_{p}^{H}y_{p}\) is a constant term and its derivative with respect to \(h_{p}\) is zero. The derivatives of the remaining terms are  
\[\frac{\partial}{\partial h_{p}}[-y_{p}^{H}X_{p}h_{p}-(X_{p}h_{p})^{H}y_{p}+(X_ {p}h_{p})^{H}X_{p}h_{p}]=0 \tag{10}\]  
Since \(y_{p}^{H}X_{p}h_{p}\) and \((X_{p}h_{p})^{H}y_{p}\) are conjugate pairs with the same derivative, setting the derivative to zero gives  
\[-X_{p}^{H}y_{p}+X_{p}^{H}X_{p}h_{p}=0 \tag{11}\]  
Rearranging the equation, we obtain  
\[X_{p}^{H}X_{p}h_{p}=X_{p}^{H}y_{p} \tag{12}\]  
Thus, the final LS estimation formula is  
\[h_{p}={(X_{p}^{H}X_{p})}^{-1}X_{p}^{H}y_{p} \tag{13}\]  
To distinguish the above formula from subsequent formulas, the LS method can be used in the following way to determine the frequency channel response at the pilot points:  
\[h_{LS}={(X_{p}^{H}X)}^{-1}X_{p}^{H}y_{p} \tag{14}\]
# Injection Molding Process Control of Servo-Hydraulic System
## 5 Conclusions  
This study has constructed a servo-hydraulic system to simulate the main processes of an injection molding machine. The experimental results support the following main conclusions.  
1. The proposed PID controller successfully detects the designed V-P switchover point and switches from the constant-velocity mode to a constant-pressure mode accordingly with the servo proportional valve. The opening size of the valve should be carefully designed to accomplish the pull-back motion.
2. The response time and the pressure overshoot at the cylinder rise as the target pressure increases. In the study of [PERSON] et al. [14], performances of the PID controller and self-tuning fuzzy PID controller have been compared. By introducing the self-tuning fuzzy PID controller in the system can improve the response time, pressure RMSD and pressure overshoot.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Velocity (mm/s)**} & \multirow{2}{*}{**Response Time (s)**} & \multicolumn{2}{c}{**Position (mm)**} & \multicolumn{2}{c}{**Pressure 1 (bar)**} & \multicolumn{2}{c}{**Pressure 2 (bar)**} & \multirow{2}{*}{**Motor Speed (rpm)**} \\ \cline{3-3} \cline{5-8}  & & Overshoot & RMSD & Overshoot & RMSD & Overshoot & RMSD \\ \hline
50 & 2.15 & 0.26 & 0.14 & 7.87 & 0.83 & 4.2 & 0.85 & 46.58 \\
60 & 1.78 & 0.3 & 0.33 & 11.36 & 0.92 & 4.2 & 0.78 & 46.38 \\
70 & 1.63 & 0.09 & 0.31 & 15.94 & 0.88 & 4.8 & 0.79 & 47.05 \\
80 & 1.56 & 0.33 & 0.21 & 20.01 & 0.85 & 4.93 & 0.65 & 47.86 \\ \hline \hline \end{tabular}
\end{table}
Table 10: 65 mm \(\rightarrow\) 64 mm–120 bar, 1000 HzWhen the sampling frequency is set as 1000 Hz, the system achieves all of the displacement, velocity, and pressure performance targets with the exception of the maximum overshoot of the pump outlet pressure in performing V-P switchover.
* In practical applications, the optimal pull-back distance of the cylinder prior to the packing stage depends not only on machine performance but also on polymer resin properties, and quality requirements of the product, etc. In general, the position control accuracy of servo-hydraulic systems depends on the sampling rate. For the system developed in the present study, a pull-back distance of 1 mm can be achieved using a sampling frequency of 1000 Hz.  
conceptualization, S-J.H.; data curation, C.-Y.L.; formal analysis, C.-Y.L. and F.-C.S.; funding acquisition, S-J.H.; investigation, C.-Y.L. and F.-C.S.; methodology, K.-T.W.; project administration, K.-T.W., H.-H.L. and S.-J.H.; Software, K.-T.W.; supervision, S-J.H.; validation, H.-H.L.; writing--original draft, F.-C.S.; writing--review and editing, H.-H.L. These authors contributed equally to this work. Funding Funding of this project is from the Metal Industries Research & Development Centre (MIRDC) and SYNTEC company.  
The authors would like to thank the Metal Industries Research & Development Centre (MIRDC) and SYNTEC for funding this project.  
To the best of our knowledge, the named authors have no conflict of interest, financial or otherwise.
## 3 Case Study
### Optimization Results
As the \(\theta\) increased from \(-\)0.05 to 0.1, the CEI of the DES-HOS decreased by 1.8% to 2.6%, respectively. Figure 10: _Cont._  
Figure 9: The energy flow of the DES with HOS, FEL and ETL. The reasons for the above differences are related to the capacity configuration of the DES-HOS and the input of energy. Tables 5 and 6 show the equipment capacity and the input and waste energy of the DES-HOS optimization results with a \(\theta\) of \(-\)0.05, 0, 0.05, and 0.1. Table 5 shows that with the increase in \(\theta\), the rated capacity of the gas turbine decreased from 723 kW to 550 kW, which means that the compatibility between the DES-HOS and the building load became worse. Table 6 shows that with the increase in \(\theta\), the input of gas decreased from 6.94 \(\times\) 10\({}^{5}\) m\({}^{3}\) to 6.22 \(\times\) 10\({}^{5}\) m\({}^{3}\) and the input of power decreased from 3021 MWh to 4156 MWh. With the increase in \(\theta\), the waste energy of the DES-HOS increased from 1811 MWh to 2141 MWh. Moreover, the change in the equipment capacity and energy consumption structure of the DES-HOS led to an increase in carbon dioxide emissions. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\theta\) & **Gas (\(\times\)10\({}^{5}\) m\({}^{3}\))** & **Power * (MWh)** & **Waste (MWh)** \\ \hline \(-\)0.05 & 6.94 & 3021 & 1811 \\
0 & 6.74 & 2993 & 1924 \\
0.05 & 6.51 & 3710 & 2045 \\
0.1 & 6.22 & 4156 & 2141 \\ \hline \hline \end{tabular}  
* Power supply from the power grid.  
\end{table} 
Table 6: The input and waste energy of the DES-HOS with a \(\theta\) from \(-\)0.05 to 0.1. Figure 10: Variations of the DES-HOS annual total cost (**a**), carbon emission (**b**), energy efficiency (**c**) and CEI (**d**) with the rise in load fluctuation.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 4 Discussion
### The Transient Response Performances  
Tables 10 and 11 show the parameters of the transient response with the implementation of different controllers ([PERSON]--D, modified Dahlin--MD, Ziegler-Nichols--ZN, Takahashi--T, and Auto-tuning--AT) in the analyzed systems.  
In comparison to the application of the Dahlin controller, standard procedures based on the Ziegler-Nichols, Takahashi, and Auto-tuning methods, provided responses with an overshoot and longer setting time. Only the rise time was slightly shorter with the use of standard PID controllers as shown in Tables 10 and 11.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameters** & **D** & **MD** & **D PI** & **PI ZN** & **PI T** & **PID AT** \\ \hline Rise time & 10.8 s & 10.8 s & 11 s & 5.65 s & 9.2 s & 4.87 s \\ \hline Overshoot & 0 & 0 & 3.8\% & 23.2\% & 23\% & 14.3\% \\ \hline Setting time & 20 s & 20 s & 29 & 30.2 s & 38 s & 26.7 s \\ \hline Steady-state error & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 10: The parameters of the transient response with different controllers in the ST system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameters** & **D** & **MD** & **D PID** & **PID ZN** & **PID T** & **PID AT** \\ \hline Rise time & 21 s & 21 s & 21 s & 7.27 s & 14.6 s & 5.78 s \\ \hline Overshoot & 0 & 0 & 0 & 66.5\% & 16\% & 9.69\% \\ \hline Setting time & 41 s & 41 s & 41 s & 233 s & 82.4 s & 59.1 s \\ \hline Steady-state error & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 11: The parameters of the transient response with different controllers in the CT system.  
Moreover, the modified Dahlin controller always provides stability, with no error in the aperiodic response and a \"non-ringing\" control signal, which is not the case when applying standard PID controllers.
## 4 Experiment and Results
### Experimental Setup  
BMFA-Net was implemented using the PyTorch framework. A multi-scale strategy was used in the training phase, considering the differences in the size of each polyp image. The details of the hyperparameters are shown next. To update the network parameters, we used the Adam optimizer. The learning rate was set to \(1\times 10^{-4}\) and the weight decay was also adjusted to \(1\times 10^{-4}\). In addition, the input images were re-sized to \(352\times 352\), \(150\) epochs, and \(16\) mini-batches during training. For testing, only the images were resized to \(352\times 352\) without any post-processing optimization strategy.
# Influence of TTS Systems Performance on Reaction Times in People with Aphasia
## 3 Results
### System-Level Analysis  
The plots in Figure 1 show the DHV and TTS systems' performance in terms of WER, TER, and MOS. We assessed all sentences with 25% and 50% reductions in speech rate. As expected, the DHV achieved the best scores in terms of naturalness and intelligibility compared to the TTS systems. However, the WER increased when the speech velocity was reduced to 50%. This may happen because the ASR system is not robust enough for such a reduction. Bluemix obtained very similar MOS scores to the human ones, while WER and TER were higher than Festival, the TTS system that achieved the lowest MOS score. Ogmios obtained the highest WER and TER values, meaning it had lower intelligibility  
than the other voices, although it presented a better score compared to Festival TTS in terms of naturalness. It must be considered that, differently from the unit selection (Ogmios), the statistical parametric synthesis technology (Festival TTS) tends to be better in terms of intelligibility. However, at the same time, the naturalness of the speech easily degrades, especially in long sentences [27]. In the context of this experiment, the sentences were short enough not to allow a relevant worsening of the naturalness and were still better in terms of intelligibility compared to Ogmios. This might explain the difference in intelligibility and naturalness between Ogmios and Festival TTS.  
Figure 1: System-level analysis of the WER, TER, and MOS prediction test of the three synthetic voices and the DHV at 25% and 50% slower speech rates.
# Comprehensive Analysis of PPP-B2b Service and Its Impact on BDS-3/GPS Real-Time PPP Time Transfer
## 3 Results
### Quality Evaluation of PPP-B2b Corrections  
The quality of the PPP-B2b orbit, clock offset, and DCB products was evaluated in this section. Then, the performance of B2b-RTPPP time transfer was analyzed. With the GBM final product as the reference, the PPP-B2b orbit and clock offset products' quality were assessed. In addition, the quality of PPP-B2b DCB was compared with that of the CAS DCB products. At present, both PPP-B2b and CNES RTS can provide real-time correction messages for BDS-3 (except C31) and GPS. The B2b-RTPPP time transfer performance is investigated employing the G, C, and GC observations. Note that due to space limitations, we only evaluated the PPP-B2b corrections for a specific time period. However, this will not have a limiting significance for the assessment of PPP-B2b product quality. Many works in the literature have evaluated the quality of PPP-B2b products [25; 26; 35; 36].
# A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection
## 5 Experiment Evaluation
### Detection Accuracy  
Next, we compared our method with the Online PCA by applying OPCA to our data sets. Here we set the selected features as 15. As shown in Table 2 is the false positive rate of our anomaly detection results. It is noted that the false positive rate of the two methods is basically at the same level. For the original data set, our method performs better than OPCA. However, for the two bigger datasets with noise data, OPCA express its advantage in filtering noise data, and performs better than our method.  
As shown in Table 3 is the false negative of our anomaly detection results. Different from false positive rate, for all 3 data sets, our method outperforms the OPCA method. The false negative rate of our method is at the level of 20-30%. However, the false negative rate of the OPCA method is at about 50-60%. As a matter of fact, our method works better because the data set corresponds to our assumption that the anomalies come along with the correlation changes. Among all the equipment failures, 28.9% of such failures come up with a correlation change, and our method manages to capture all such equipment failures. Our experiment results also indicate that correlation changes cannot be accurately captured by the PCA method. A PCA method aims at getting the most informative features which can almost represent the original data, which has no deal with correlation change.
## 4 Statistical Prediction Model
### RF Classification Model Training
Other studies [45; 46] have already employed the sample-balance technique to randomly cut back samples of the majority class to equate the numbers of minority and majority class samples in the training dataset. The use of original majority class samples likely leads to a poor performance for predicting minority or majority classes. Thus, as mentioned above, we use this sample-balance technique to improve the probability of detection of medium and severe convective storm samples (minority class). \begin{table}
\begin{tabular}{l l c} \hline \hline
**Classification** & **Variable** & **Unit** \\ \hline Satellite & T6,2-10,4, T6,9-10,4, T7,3-10,4, T8,6-10,4, T9,6-10,4, T10,4, T11,2-10,4, T12,3-10,4, \\ measurements & \(\Delta\)T13,3-10,4, \(\Delta\)T8,6-11,2, \(\Delta\)T11,2-12,3, \(\Delta\)T3,9-11,2, \(\Delta\)T3,9-7,3 & K \\ \cline{2-3}  & Area (pixel number of convective storm system) & \\ \hline  & K-Index & \({}^{\circ}\)C \\  & CAPE (Convection Available Potential Energy) & J\(\cdot\)kg\({}^{-1}\) \\  & CIN (Convective Inhibition) & J\(\cdot\)kg\({}^{-1}\) \\  & LI (Lifted Index) & \\  & EBS (Effective Bulk Shear) & m\(\cdot\)s\({}^{-1}\) \\  & TPW (Total Precipitable Water) & mm \\  & \(\theta_{\text{se850/925}}\) (Pseudo-equivalent potential temperature at 850/925 hPa) & K \\ \cline{2-3}  & PV (Potential Vorticity) & \\ \cline{2-3}  & Div\({}_{925/850/10}\) (Convergence at 925 and 850 hPa/10m) & s\({}^{-1}\) \\ \cline{2-3}  & MR\({}_{850/925}\) (Mixing Ratio at 850/925 hPa) & g\(\cdot\)kg\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Predictor variables for the RF classification model training and predicting.