esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Predictive RBF Compensation Fuzzy PID Controller for 3D Laser Scanning System
## 1 Introduction
Neural networks are also an important branch of intelligent control algorithms. They can be used to solve the control problems of nonlinear and uncertain systems. [PERSON] studied the tracking control problem of an uncertain n-link robot with all-state constraints, and designed an adaptive neural network control to deal with the uncertainty and interference of the system [33]. [PERSON] used a radial basis function (RBF) to identify unknown dynamic errors in nonlinear switching systems, but there was always a certain steady-state error in the identified output value [34]. In addition to the intelligent control algorithms mentioned above, model predictive control, as a new type of computer control algorithm, has good prediction ability, which can optimize and compensate the controller output in real time. In [35], [PERSON] used a feedforward neural network to fit the unknown nonlinear autoregressive moving average (NARMA) model; combined with predictive control theory, the control output signal is obtained by using the gradient descent method to minimize the error. [PERSON] proposed a new PID control method that inherits the advantages of fuzzy PID control and predictive function control [36]. It used the current process input signal to predict the future process to improve the traditional fuzzy PID control. Therefore, it is reasonable to apply a neural network and the prediction ability of predictive control to improve PID control. This paper proposes a predictive RBF compensation fuzzy PID controller (PRFPID) to control the pitching motion of 2D Lidar quickly and accurately. The structure of the article is as follows: In Section 2, a mathematical model of 2D Lidar pitching motion is established. Section 3 introduces the design of a feedback compensation fuzzy PID controller (FPID), which uses feedback compensation to eliminate the nonlinear part of the system and a fuzzy algorithm to adaptively adjust PID parameters. Section 4 introduces the design of the RBF compensation fuzzy PID controller (RFPID), which uses an RBF neural network to fit the unknown nonlinear part of the system. Then, the PRFPID is proposed by using predictive control to optimize the output of the controller based on the RPPID. In Section 5, the proposed PRFPID is simulated and compared with FPID and RFPID on a Lidar pitching motion system. In Section 6, the results reveal that the PRFPID can achieve favorable tracking performance and disturbance rejection capability.
## 2 Materials and Methods
### Definition and Calculation of \(S_{\text{GPP}}\) and \(S_{\text{ER}}\)  
Researchers typically represent the sensitivity of carbon dioxide flux to precipitation using the slope of an ordinary least squares (\(OLS\)) linear regression [11; 23]. Nonetheless, considering that the slope of \(OLS\) linear regression is fixed and symmetric, while the actual response of vegetation productivity to changes in environmental factors is often asymmetric [25; 39; 40], we calculated the sensitivity based on previous studies as the ratio of carbon dioxide flux (GPP and ER) to the net change in precipitation, i.e.,  
\[S_{\text{GPP}}=\left[\left(\frac{\text{GPP}-\text{GPP}_{\text{ mean}}}{\text{GPP}}\right)/(\text{P}-\text{P}_{\text{mean}})\right]\times 100 \tag{1}\]  
\[S_{\text{ER}}=\left[\left(\frac{\text{ER}-\text{ER}_{\text{mean}}}{\text{ ER}}\right)/(\text{P}-\text{P}_{\text{mean}})\right]\times 100 \tag{2}\]  
where \(S_{\text{GPP}}\) and \(S_{\text{ER}}\) represent the sensitivities of GPP and ER to precipitation, respectively. GPP denotes the gross primary productivity at the site-year, while \(\text{GPP}_{\text{mean}}\) refers to the multi-year average annual total gross primary productivity at the site. \(P\) indicates the total annual precipitation at the site-year, and \(\text{P}_{\text{mean}}\) represents the multi-year average annual precipitation at the site. Similarly, ER stands for the annual total ecosystem respiration at the site-year, and \(\text{ER}_{\text{mean}}\) denotes the multi-year average annual total ecosystem respiration at the site. Positive values of \(S_{\text{GPP}}\) and \(S_{\text{ER}}\) indicate a direct relationship, where precipitation and GPP or ER increase or decrease together. Negative values reflect an inverse relationship, where increases in precipitation lead to decreases in GPP or ER, and vice versa.
# A Random Subcarrier-Selection Method Based on Index Modulation for Secure Transmission
## 4 Simulation Results
### The Simulation Results of Random Degree  
To measure the random degree of the selected subcarriers of the proposed RSCS-IM and the PSS Plus RP [18], we calculated \(\delta_{l}\) in (7) of the two random subcarrier-selection methods many times. The results are presented in Figure 6. As shown in Figure 6, the random degree of the proposed scheme RSCS-IM was always larger than that of PSS Plus RP in ten thousand simulations, meaning that the selected subcarriers employing RSCS-IM were more random. Additionally, comparing Figure 6a with Figure 6b, we could see that when \(N_{s}=1024\), the random degree was improved using both methods because the number of available subcarriers increased. Moreover, the increase in random degree was greater when employing RSCS-IM rather than employing PSS Plus RP since the number of increased subcarriers employing RSCS-IM was more than the number of increased primes employing PSS Plus RP.
# Roasting Extraction of Lithium from Fly Ash: A Study of Influential Parameters and Mechanisms
## 3 Results and Discussion
### Roasting Effects
#### 3.2.2 Roasting Time  
This study investigated the roasting effect on the leaching rate of lithium. The experimental conditions were fixed at a mass ratio of fly ash to sodium carbonate of 1:2, a roasting temperature of 850 \({}^{\circ}\)C, an NaOH concentration of 4 mol/L, a solid-liquid ratio of 1 g:30 mL, an extraction temperature of 80 \({}^{\circ}\)C, an extraction time of 120 min, and a roasting time in the range of 0.5 h to 3 h. The results are shown in Figure 4.  
Figure 4: Effect of the roasting time on the leaching rate of lithium.  
Figure 3: Effect of mass R\({}_{\rm fs}\) on the leaching rate of lithium.  
As shown in the Figure 4, the leaching rate is proportional to the roasting time, with a rapid increase between 0.5 h and 2 h, followed by a slow rise from 2 h to 3 h. The leaching rate reached its peak at 87.44% after 3 h. After a reaction time of 3 h, a further increase of the roasting time would not improve the roasting effect or the leaching rate significantly.
## 3 Methods
### VS-GRU: Variable Sensitive GRU  
#### 3.4.1 Missing Rate Impact  
In real life, different variables usually have different monitoring frequencies based on their characteristics. Specifically, in the health care field, the doctor decides which variable needs to be monitored according to the patient's physical condition. Therefore, it is very common to have variable \(d_{i}\) completely missing while variable \(d_{j}\) fully recorded in a clinical time series. It is a convincing  
Figure 2: GRU structure (symbol in red refers to input).  
conclusion that the missing rate of the variable is related to its characteristics. Therefore, we should consider the impact of the missing rate of different variables in multivariate time series.  
The time interval \(\delta\) between current time step and last observation used in the dynamic imputation mechanism implies the missing rate difference to some extent. Usually, a variable with a high missing rate has a larger time interval \(\delta\) than others in all time steps. However, it only contains the missing situation before the current time step and it is not enough for the model to fully understand the missing situation in all time steps. We should add the missing rate \(\mu\) into the GRU update functions so that every time step in GRU can use the information from the whole time series to solve the classification problem. The missing rate directly tells the differences between variables in multivariate time series. On the other hand, there are experiments to prove that the prediction performance using records within 30 h after admission is close to using records within 48 h [23]. Thus, when we use VS-GRU in practice, if we have new observations to come, we can keep the missing rate unchanged or calculate a new missing rate within a new time interval (like 24 h) and update it. By this way, we can execute VS-GRU efficiently on the fly.  
Instead of directly adding the missing rate \(\mu\) into the GRU update functions, we choose to use the exponential negative rectifier to map the missing rate \(\mu\) into missing factor \(\beta\) between 0 and 1. Given that the missing rate of clinical time series is above 80%, the missing rate \(\mu\) of different variables in one time series may be close to 1 in many circumstances. The decay equation automatically detects the slight differences between the missing rate of different variables and the model can learn better from missing factor \(\beta\):  
\[\beta=\exp\left\{-\max\left(0,W_{\beta}\cdot\mu+b_{\beta}\right)\right\}. \tag{11}\]  
It should be noted that the difference between Equation (11) and Equation (10) is \(W_{\beta}\), which is a vector instead of a matrix. \(W_{\beta}\) and \(b_{\beta}\) share the same dimension as \(\mu\). We calculate the dot product of \(W_{\beta}\) and \(\mu\) and then add the bias \(b_{\beta}\). Thus, the missing factor \(\beta\) of one variable is only related to itself.
# Trends in Quantification of HbA1c Using Electrochemical and Point-of-Care Analyzers
###### Abstract  
Glycated hemoglobin (HbA1c), one of the many variants of hemoglobin (Hb), serves as a standard biomarker of diabetes, as it assesses the long-term glycemic status of the individual for the previous 90-120 days. HbA1c levels in blood are stable and do not fluctuate when compared to the random blood glucose levels. The normal level of HbA1c is 4-6.0%, while concentrations \(>\) 6.5% denote diabetes. Conventionally, HbA1c is measured using techniques such as chromatography, spectroscopy, immunassays, capillary electrophoresis, fluorometry, etc., that are time-consuming, expensive, and involve complex procedures and skilled personnel. These limitations have spurred development of sensors incorporating nanostructured materials that can aid in specific and accurate quantification of HbA1c. Various chemical and biological sensing elements with and without nanoparticle interfaces have been explored for HbA1c detection. Attempts are underway to improve the detection speed, increase accuracy, and reduce sample volumes and detection costs through different combinations of nanomaterials, interfaces, capture elements, and measurement techniques. This review elaborates on the recent advances in the realm of electrochemical detection for HbA1c detection. It also discusses the emerging trends and challenges in the fabrication of effective, accurate, and cost-effective point-of-care (PoC) devices for HbA1c and the potential way forward.  
Keywords:glycated hemoglobin; HbA1c; electrochemical sensors; biosensors; PoC devices +
Footnote †: journal: Sensors
# FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders
## 3 FFT-Based Scan-Matching
### Translation Parameters  
After calculating the rotation parameter, the translation parameters can be solved by projection of the image in horizontal and vertical directions. Let \(C(x,y)\) be the grid map after \(B(x,y)\) is rotated by rotation angle \(\alpha\) and its projection can be represented by:  
\[C_{x}(y)=\int\limits_{-\infty}^{\infty}C(x,y)dx \tag{22}\]  
\[C_{y}(x)=\int\limits_{-\infty}^{\infty}C(x,y)dy \tag{23}\]  
Similarly, projection of \(A(x,y)\) can be represented by  
\[A_{x}(y)=\int\limits_{-\infty}^{\infty}A(x,y)dx \tag{24}\]  
\[A_{y}(x)=\int\limits_{-\infty}^{\infty}A(x,y)dy \tag{25}\]  
By taking a projection of the image and converting 2D to 1D, 1D FFT can be used to calculate the translation parameters.  
From Equation (21) to (25), the rotation parameter and translation parameters are eventually solved by 1D FFT. The FFT scan-matching process is shown in Figure 4.
## 3 Hybrid Test  
### Experimental Setup  
The test facilities were located at the Structural and Seismic Testing Center, Harbin Institute of Technology. The hardware of the test system mainly consisted of a PC, dSpace-DS1104 RD controller board, servo-hydraulic controller and three MTS actuators (1000 kN, \(\pm\)500 mm), as shown in Figure 2. The displacement command calculated from the FE program described in Section 2.3 was transmitted to dSpace through OpenFresco software [24]. To make the actuators run smoothly, the displacement command was interpolated in dSpace, and then passed to Servo-Hydraulic Controller to drive the actuators. The forces of actuators were measured and sent back to the FE program for the iteration of the next step.  
The structure to be emulated was a steel frame with single floor and single bay as shown in Figure 3. The mass was lumped at the top of the columns. The Rayleigh damping matrix was determined based on the damping ratio of 5% for the first two modes. Before seismic analysis, the structure was subjected to static gravity load in the form of concentrated force. The numerical substructure in the FE program contains two beam-column elements with six DOFs, and the physical substructure was the left column as shown in Figure 3. There are three DOFs, _i.e._, two translational and one rotational, on the top of the tested column. Three actuators were used to achieve the three DOFs of the column as shown in Figure 4: two vertically placed actuators for the vertical displacement and rotation, and the horizontal actuator for the horizontal displacement. The actuators were connected to the column through a rigid loading girder. The bottom of the column was fixed on the ground. The displacements of the actuators were related to those at the DOFs of the specimen through  
\[\mathbf{u}_{\mathrm{a}}=\mathbf{T}_{\mathrm{ca}}\mathbf{u}_{\mathrm{c}} \tag{24}\]  
where \(\mathbf{u}_{\mathrm{a}}\), \(\mathbf{u}_{\mathrm{c}}\) were displacement vectors of the actuators and specimen, respectively; \(\mathbf{T}_{\mathrm{ca}}\) was the transformation matrix which was expressed as  
Figure 2: Block diagram of data communication and hardware for hybrid simulation.  
\[\mathbf{T_{ca}}=\left[\begin{array}{ccc}0&-1&0\\ 1&0&-L\\ 1&0&L\end{array}\right] \tag{25}\]  
in which \(L\) = 2 m is half length of the loading girder. The transpose of \(\mathbf{T_{ca}}\) was used for force transformation backwards.
## 5 Conclusions  
This article is dedicated to the analysis of the thermoviscoelastic response of the sandwich plate with a viscoelastic core and two surrounding piezoelectric face-layers simultaneously exposed to electro-thermal loading. The formulation is based on the 3D elasticity theory. The obtained equations were solved analytically using the state-space Fourier series method. It is noted that the governing equations before solving were converted to the Laplace domain and after solving the results were converted to the time domain by using an inverse Laplace transform. Our numerical results are summarized as the following list of conclusions:  
* Increasing the length-to-thickness ratio \(a/h\) leads to decrease in deflections and increase in stresses.
* In the absence of an applied voltage when \(h/h_{p}\geq 150\), the effect of the piezoelectric layer thickness on the thermo-elastic behavior becomes negligible.
* Stiffness of the plate decreases by increasing \(T_{o}\) and, accordingly, stresses and the deflection decrease.
* The effect of a temperature difference in the lower region is more significant than in the upper region due to the thermal barrier behavior of the FGM core at the upper surface.
* Increasing the relaxation time constant causes the stiffness of the viscoelastic plate and, accordingly, stress components to increase and the displacement to decrease.
* Increasing the relaxation time constant causes the rate of convergence to the elastic behaviour to decrease.
* The effect of the applied voltage near the outer region of the FGM layer is more significant due to the actuator layer's effect.
* Deflection \(\overline{\mathrm{w}}\) of the plate increases by increasing the applied voltage.  
Figure 8: Influence of applied voltage variation of longitudinal normal stress, transverse shear stress, and transverse displacement along the thickness direction.  
* Through-thickness distribution of deflection is linear in piezoelectric and FGM layers with different slope.
* Increasing the time constant \(\tau\) causes delay in the steady state condition for stresses and displacement.
* Increasing the time constant \(\tau\) causes a decrease in the transverse displacement \(\overline{\mathbf{w}}\).
* The maximum values of transverse normal stress \(\overline{\sigma}_{z}\) are not at the mid-thickness of the plate, which is due to the FGM property.  
Conceptualization, M.F., M.K. and A.A.; formal analysis and software, M.F.; writing--original, M.F., M.K. and A.A.; writing--review, M.F., M.K. and A.A.; visualization, M.F., M.K. and A.A. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme.  
The authors declare no conflict of interest.
# A Novel Multi-Task Performance Prediction Model for Spark
## 5 Conclusions  
In this paper, we present an approach that combines multi-head attention networks and CNN models for spark performance prediction. We first introduce the basic principle of a multi-head attention network. It can effectively capture complex dependencies in data and extract deep features to help improve the accuracy of prediction models. Then, we combine the multi-head attention network with the CNN to utilize the advantage of the CNN in processing local features in grid-structured data to further improve the performance of the prediction model.  
We conducted comprehensive evaluations of the proposed model using datasets of various sizes and multiple common Spark applications. Experiments were also performed on mixed datasets. The experimental results indicate that the model can effectively predict Spark performance. Compared to traditional methods, it offers higher accuracy and better generalizability.  
However, there is still some potential room for improvement in our model. For example, the structure of multi-head attention networks can be further optimized to try different attention mechanisms to adapt to different data characteristics. Meanwhile, other types of neural network structures, such as recurrent neural networks, can be explored for processing data with sequential characteristics. In addition, more feature engineering and data enhancement techniques can be considered to be introduced to further improve the performance of the model.  
In summary, our work provides a new solution in the field of Spark performance prediction and also provides a valuable reference for other research on similar tasks. Future work can further improve and extend our model, and validate and apply it in a wider range of application scenarios. We believe this research will contribute to improving the performance and efficiency of distributed computing systems.  
[PERSON] and [PERSON] proposed the idea of the paper. [PERSON] and [PERSON] helped manage the annotation group and helped clean the raw annotations. [PERSON] conducted all experiments and wrote the manuscript. [PERSON], [PERSON] and [PERSON] revised and improved the text. [PERSON] is the person in charge of this project. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy issues.  
The authors declare no conflict of interest.
# Parameter Calibration and System Design of Material Lifting System for Inclined Shaft Construction
## 4 Case Study
### System Design  
#### 4.3.1 Control System Design  
In terms of the control system design, the conventional electronic control system is not selected because of high energy consumption and poor speed regulation. Although the PCL electronic control system is small and versatile, it is costly, technically complex and requires professional training; thus, it is also not considered. Therefore, due to the comprehensive cost, equipment energy consumption, speed control performance and other factors, the choice is the frequency conversion electronic control method as the control system design.  
The selected inverter electric control system mainly includes two major parts: PCL cabinet and integrated operation desk. The main equipment includes an EMC filter reactor, main contactor, dual Siemens PLC, main command controller, isolation control transformer 380 V/220 V, etc. The control system adopts vector the control mode and the input power supply voltage is 15 KV, which can guarantee 220% output torque when low frequency operation is used, and has advantages in energy saving and stable motor performance.  
The monitoring system is designed to be divided into two parts according to the construction needs of the inclined shaft, which are the data center and the terminal monitoring equipment. In summary, the main equipment selected for the main control and monitoring system is shown in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Name Model** & **Main Device Composition** \\ \hline \multirow{6}{*}{PCL cabinet} & \multicolumn{2}{c}{EMC filter reactor} \\  & & Dual power supply molded case circuit breaker \\  & PCL cabinet & Main contactor \\  & & Motor protection quick fuse \\  & & Isolation control transformer 380 V/220 V \\ \cline{2-3}  & & Control Contactors \\ \multirow{2}{*}{System} & \multicolumn{2}{c}{Dual Siemens PLC} \\  & All-in-one & Proportional amplifier \\ \cline{1-1}  & operation desk & Switching power supply \\ \cline{1-1}  & & Relays \\ \cline{1-1}  & & Master command controller \\ \hline \hline \end{tabular}
\end{table}
Table 6: Main control system main equipment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parts** & **Selection** \\ \hline Wire Rope & \(6\times 7\) + NF Type \\ Rollers & Diameter 2000 mm, width 1500 mm \\ Hoist & Single cylinder JK-2 \(\times\) 1.5 P type \\ Motor & YTS355L2-8 type \\ Overhead Wheel & Diameter 1.2 m, Wheel groove depth 50 mm \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calibration scheme for lifting system parameters.