esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[24] solved the problems of linear parameter varying (LPV) switching attitude control for a near-space hypersonic vehicle with parametric uncertainties. [PERSON] et al. [25] proposed the control-oriented switched model for air-breathing hypersonic vehicles and designed two adaptive tracking controllers for the purposes of velocity and altitude tracking. [PERSON] et al. [26] proposed a multimodel switching control strategy for air-breathing hypersonic vehicles with variable geometry inlet. [PERSON] et al. [27] proposed an adaptive switching control scheme for hypersonic vehicle attitude systems with unknown control direction and actuator faults and solved the problem of system instability. [PERSON] et al. [28] proposed an adaptive switching control method for NMVs based on the type-2 fuzzy logic system. In [29], authors given the nonlinear longitudinal dynamical model of an air-breathing hypersonic vehicle. [PERSON] et al. [30] designed switching adaptive backstepping controllers for the altitude and velocity subsystems and alleviated the chattering problem caused by the switching. In this paper, a novel linear/nonlinear active disturbance rejection switching control method for the longitudinal dynamical model of NMVs based on the type-2 fuzzy logic system is proposed. The contribution of this paper can be concluded as follows:  
1. The mismatched uncertainty model for NMVs was established considering the large range of parameter uncertainty and external disturbance. A cascade ADRC method was developed for a class of multi-input multioutput high-order coupled system. 2. According to the model characteristics of NMV, both linear active disturbance rejection control (LADRC) and nonlinear active disturbance rejection control (NLADRC) were designed for velocity and altitude subsystems of NMVs. Furthermore, the stabilization of the cascade closed-loop and switching control systems were analyzed. 3. A novel switching control strategy based on the interval type-2 fuzzy logic system was developed. Furthermore, both fuzzy rules and the corresponding results of the velocity and altitude subsystems were examined, and the defuzzification algorithm of each step was established. 4. The change rules of aerodynamic parameters with Mach number and angle of attack under the condition of winglet stretch out and draw back were analyzed. The efficiency and robustness of the proposed control method were demonstrated by numerical simulations. The remainder of this paper is organized as follows. In Section 2, the mismatched uncertainty model for NMVs is established considering the large range of parameter uncertainty and external disturbance. Section 3 presents the linear and nonlinear cascade ADRC design methods for NMVs and proposes a novel switching control strategy based on the interval type-2 fuzzy logic system. The NMV case simulations and discussions are provided in Section 4, followed by the conclusion in Section 5.
# Fractional-Order SIR Epidemic Model for Transmission Prediction of COVID-19 Disease
## 4 Numerical Simulations
### Dynamical Properties of Fractional-Order SIR Model  
Consider three sets of simulations with following parameters: \(S_{0}=950\), \(I_{0}=45\), \(R_{0}=5\), \(N=1000\), \(\beta=0.4\), \(\gamma=0.1\), \(t=60\) and various \(\alpha_{1,2,3}=\{0.2,0.4,0.6,0.8\}\). In each set, only one \(\alpha_{i}\) changes while two remain are equal to \(1\). That way, we can see how the particular fractional order \(\alpha_{i}\), \(i\in\{1,2,3\}\) affects the model. Figures 1-3 present the simulation results.  
As we can see in Figures 1-3, fractional orders \(\alpha_{1}\) and \(\alpha_{2}\) affect the whole model, while \(\alpha_{3}\) only affects the removed compartment. It can be expected if we look at the model's Equation (9). With more parameters, the presented model is more complicated but is also more versatile. The results presented above show that fractional-order SIR can be more adjustable in the modeling of real-life processes.  
Figure 2: Simulation results for various \(\alpha_{2}\) and \(\alpha_{1}=\alpha_{3}=1\).
## 3 Results
### Discharge Method with an Isothermal Chamber  
Figure 5 shows the experimental setup for the discharge method. An isothermal chamber that can provide almost isothermal condition is achieved by filling it with copper wire, which has a diameter of approximately 50 \(\upmu\)m. The volume of the filled wire is about 5% compared with that of the chamber. The used isothermal chamber has a volume of 0.5 L, 0.65 L, and 0.8 L for the test samples of groups 1, 2 and 3, respectively. The procedure is simple in comparison with the steady-state method because it is not required to record data point by point. First, the solenoid valve is closed and the hand valve is opened to charge the chamber until its pressure reaches the regulated level. Then, close the hand valve and send a control signal to the solenoid valve to start the discharge process until the pressure in the chamber reaches the atmospheric pressure. A calibrated pressure sensor (GE Druck, UK, PTX5012, range: 0-1 MPa, accuracy: 0.1%) is used to measure the chamber pressure. The pressure data were transferred into a personal computer through a 14-bit A/D board (Advantech 4704), and the sampling  
Figure 4: Schematic of the apparatus for steady-state method.  
time was 5 ms. The flow rate discharged from the isothermal chamber can be calculated according to the differentiated pressure, as below:  
\[G=\frac{V_{\mathrm{c}}}{R\theta}\frac{\mathrm{d}P_{\mathrm{c}}}{\mathrm{d}t}, \tag{9}\]  
where \(P_{\mathrm{c}}\) is the pressure in the chamber, \(t\) is the time, \(V_{\mathrm{c}}\) is the volume of the chamber, \(R\) is gas constant, and \(\theta\) is the room temperature.  
It should be noted that, in theory, both the charge method and the discharge method are capable of obtaining the flow rate characteristics of the test element. The discharge method is to discharge air through the test resistance into the atmosphere from a pressurized chamber, while the charge method uses a constant air supply to charge air into the chamber. However, in this work, the discharge method is preferred since the charge method exhibits a sudden drop in the upstream pressure at the start of the charging process.
# Echelle Grating Spectroscopic Technology for High-Resolution and Broadband Spectral Measurement
## 5 Applications
### Astronomical Instruments
Thus, the relationship between them needs to be balanced. Some spectrographs, such as CRIRES+ and IRD [118], were adjusted by an adaptive optics system to improve the performance of spectrometers.
# Continuous Variable Quantum Secret Sharing with Fairness
###### Abstract  
The dishonest participants have many advantages to gain others' shares by cheating in quantum secret sharing (QSS) protocols. However, the traditional methods such as identity authentication and message authentication can not resolve this problem due to the reason that the share has already been released to dishonest participants before realizing the deception. In this paper, a continuous variable QSS (CVQSS) scheme is proposed with fairness which ensures all participants can acquire or can not acquire the secret simultaneously. The quantum channel based on two-mode squeezing states provides secure communications through which it can send shares successfully, as long as setting the squeezing and modulation parameters according to the quantum channel transmission efficiency and the Shannon information of shares. In addition, the Chinese Remainder Theorem (CRT) can provides tunable threshold structures according to demands of the complex quantum network and the strategy for fairness can be incorporated with other sharing schemes, resulting in perfect compatibility for practical implementations.  
Keywords:quantum secret sharing; fairness; two-mode squeezed vacuum state +
Footnote †: journal: Applied
## 2 Previous Research
### Fuzzy Theory  
As previously mentioned, fuzzy theory is often used to solve complex engineering problems that involve multiple and inter-dependent influencing factors with large uncertainties. In particular, it is possible to numerically quantify uncertain variables through fuzzification [14]. Fuzzification is the process that assigns a fuzzy value to a crisp value through a membership function, where the fuzzy value (\(Y_{i}\)) can be expressed as follow:  
\[Y_{i}=(y_{i},\mu_{A}(y_{i})), \tag{1}\]  
where\(y_{i}\) is the crisp value, \(\mu_{A}(y_{i})\) is the degree of membership on the variable \(y_{i}\) and the subscript \(A\) stands for a fuzzy set that is composed of the fuzzy values (\(Y_{i}\)). The fuzzy set plays an important role in fuzzy theory, such as the fuzzy inference, the fuzzy measure and the fuzzy integral. In addition, the fuzzy sets are also used to compose fuzzy rules that define the correlation between the input and output variables [14]. In this study, as shown in Figure 1, the fuzzification was performed on the input variables of concrete discoloration, crack width, spalling, etc., and the fire damage grades were estimated through the fuzzy inference approach.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Investigation** & **Method** & \\ \hline \multirow{3}{*}{Primary investigation} & \multirow{3}{*}{Visual inspection} & (1) Concrete discoloration \\  & & (2) Crack width \\  & & (3) Spalling \\ \hline \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } & Simple & (1) Schmidt hammer test \\  & & Simple method & (2) Concrete carbonation test \\ \cline{2-3}  & & & (1) Concrete core sampling \\  & & Mechanical test & (2) Steel bar sampling \\  & & & (3) Vibration test \\  & & & (4) Loading test \\ \cline{2-3}  & & & (1) UV spectrum method \\ \cline{2-3}  & Detailed inspection & Material analysis & (2) Microwave measurement \\  & & & (3) Thermoluminescence measurement \\  & & & (4) Ultrasonic test \\  & & & (5) X-ray diffraction method \\ \cline{2-3}  & & Numerical analysis & (1) Estimation of Heating temperature \\ \cline{2-3}  & & & (2)Evaluation of residual strength \\ \cline{2-3}  & & & (3) Structural analysis of fire damaged RC member \\ \hline \hline \end{tabular}
\end{table}
Table 2: Checklist for fire inspection [1,2].
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 3 Experimental Results  
We conducted extensive experiments to demonstrate the performance of our proposed network. For performance evaluation, the prediction accuracy of the mIoU value of the image in the testing set was calculated, and the accuracy of the failure detection was calculated through the predicted mIoU value. The mean absolute error (MAE) was used as the evaluation metric to calculate the error because, when calculating positive and negative numbers, the meaning may be canceled out.  
The MAE is calculated as follows:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|(y_{i}-\hat{y_{i}})|, \tag{5}\]  
where \(y_{i}\) denotes the predicted mIoU value and \(\hat{y_{i}}\) indicates the GT mIoU value.  
The mIoU prediction accuracy is calculated as follows:  
\[\text{mIoU prediction accuracy}=(1-\text{MAE})\times 100. \tag{6}\]  
Failure detection accuracy is calculated as follows. The precision, recall, and F1-score values are considered simultaneously to determine whether the detection result is reasonable. The method of calculating the performance indicators can be easily understood in Figure 9.  
The results of the experiment are classified into each situation according to the following criteria: True positive (TP) and true negative (TN) are the cases when the performance of ESPNet is well predicted by mIoUNet. In more detail, TP is defined when the mIoU prediction value and the GT mIoU value are greater than a threshold value of 0.5 where TN is smaller than 0.5. In both cases, the mIoUNet successfully detects not only the failure case but also success case for ESPNet. On the other hand, false positive (FP) and false negative (FN) are when mIoUNet fails to predict the failure or success case of ESPNet. FP is defined as the mIoU prediction value is larger than 0.5, but the GT mIoU prediction value is less than 0.5. On the contrary, FN is defined as the mIoU prediction value is smaller than 0.5, but the GT mIoU prediction value is greater than 0.5. In both cases, we define that the mIoUNet fails to detect properly the failure cases and success cases.  
The evaluation metrics for failure case detection are calculated as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FN}+\text{FP}+ \text{TN}}, \tag{7}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{8}\]  
Figure 9: Confusion matrix.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{9}\] \[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}. \tag{10}\]
# Rain Removal of Single Image Based on Directional Gradient Priors
## 1 Introduction
* Considering that the rain streaks have a variety of sizes, multi-scale dictionaries are designed to encode and refine the rain layer. * To solve the rain removal model, the ADMM method is derived to alternately solve the background layer and rain layer of the rainy image. The remainder of this article is structured as follows. In Section 2, we summarize the related work on rain removal theory and the sparse representation of a single image. In Section 3, the proposed model is introduced in detail. In Section 4, experiments comparing the proposed model with several main-stream methods are carried out on both synthetic and real images. Finally, we provide some concluding remarks in Section 5.
## 1 Introduction
When an emergency happens, we assume that a few cloud access points can be rapidly deployed between the emergency locations and the cloud and that the evacuees themselves are only equipped with smart phones that constitute an energy-efficient _ad hoc_ network between each other, so as to reach the access points for two-way communication. Since mathematical models [10] cannot handle the full complexity that is encountered during an emergency and evacuation drills are high-priced and time-consuming, agent-based simulation methods [11; 12; 13] are indeed the tools of choice to study such systems. Hence, in this work, the proposed algorithms are evaluated in a simulated built environment where a fast-spreading fire breaks out. The remainder of the paper is organized as follows: Section 2 presents the related studies of cloud-enabled emergency navigation systems and coordinated behaviour-involved algorithms in emergency evacuation. In Section 3, we recall the concept of the cognitive packet network and then describe the system architecture and related algorithms in Section 4. The simulation model and assumptions are introduced in Section 5, and results are presented in Section 6. Finally, we draw conclusions and present future work in Section 7.
## 2 Correlation Theory  
### Atmospheric Scattering Model  
In the field of image dehazing, common approaches include the Retinex-based dehazing enhancement model inspired by the human visual system (HVS) [29], as well as diffusion models integrated with deep learning. Among these, the atmospheric scattering model proposed by [PERSON] remains one of the most widely adopted frameworks for describing and processing haze-induced image degradation [30]. As illustrated in Figure 1, under this model, the light received by the detector is decomposed into two components:  
* Attenuated target radiance: Light reflected from the target that is weakened through scattering and absorption by suspended particles in the atmosphere.
* Atmospheric light: Ambient light scattered by fog droplets, which dominates under dense haze conditions.  
In heavy fog, the transmittance (fraction of light reaching the detector) decreases significantly. This causes the attenuated target radiance to become far weaker than the atmospheric light, ultimately submerging the target's optical information within the over-powering airlight component.  
\[I=D+A=Lt+A_{\infty}(1-t) \tag{1}\]  
In Equation (1), \(I\) represents the foggy image captured by the detector; \(D\) represents the directly transmitted light; \(A\) represents the atmospheric light; \(A_{\infty}\) is the atmospheric light intensity at an infinite distance. \(t\) represents transmittance. By rearranging Equation (1), the radiance of the haze-free image can be derived as:  
\[L=\frac{I-A_{\infty}(1-t)}{t} \tag{2}\]
# A Review on Microfluidic Platforms Applied to Nerve Regeneration
## 4 Microfluidic Platforms Applied to Axonal Guidance  
Axonal guidance is an important research field on nerve regeneration and also has significant implications for the treatment of complete SCI. There are many studies focusing on guiding axon growth in the specified orientation. Microfluidic platforms provide conducive experimental conditions for these studies. There are currently three mainstream approaches to neural guidance based on microfluidic platforms including: topographical guidance, physical guidance and chemical guidance.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods  
In general, the fault diagnosis flowchart of PMSM is as shown in Figure 1. All the fault diagnosis methods, in order to determine the fault type of an actual motor, need to acquire its signal data first. The acquired signals include current, vibration, and the like mentioned above, which are used for analysis after pretreatment such as denoising and amplification. Some researchers have studied the information fusion method for the data of multiple signal types or multiple sensors [29; 30]. However, there are also some methods that use raw data directly without feature reaction and information fusion. At last, different diagnosis methods used to deal with these data were applied.  
In summary, commonly used motor fault diagnosis methods can be divided into the following three categories: model-based fault diagnosis methods [31], signal-based fault diagnosis methods, and knowledge-based fault diagnosis methods [32]. Among them, the model-based methods can penetrate the essence of fault generation. It can predict the signal performance after different faults occur in the motor and by comparing the actual output data with the output of the motor model it can determine the fault type. Signal-based methods are most commonly used to process and extract different features from the motor signals, and these features are then manually diagnosed using prior experience or using knowledge-based methods. Knowledge-based methods can automatically determine the fault state based on expert knowledge, but due to the research in artificial intelligence and machine learning, data-driven intelligent diagnosis systems [33], which do not rely on the prior  
Figure 1: Fault diagnosis flowchart of permanent magnet synchronous motor (PMSM).  
knowledge, have demonstrated broad application prospects recently. The motor data can be used for the training of a machine learning model.
## 2 Background: CNN-Based Classification  
Among the state-of-the-art approaches for anomaly detection described in the Introduction, CNN-based classification using converted images [26] was selected for comparison. This approach involves converting both time- and frequency-domain data into images that are, in turn, used as the input to a CNN model for classifying multi-class data anomalies. A total of seven classes are considered: _Missing_ (most or all of the data are missing), _Minor_ (the vibration response oscillates with a small amplitude), _Outlier_ (one or more outliers appear in data), _Square_ (the vibration response oscillates abnormally over a range of the accelerometer), _Trend_ (the response is non-stationary with a monotonous trend), _Drift_ (the response is non-stationary with random drift), and _Normal_. This method, validated using acceleration data measured from a cable-stayed bridge in China, exhibited 94% accuracy.  
In the data preprocessing step, the measured time-history data and their Fourier transforms are converted into images to prepare the input to the CNN. Image conversion includes plotting the time and frequency responses without axes and storing them as images. The single-channel images of the time- and frequency-domain data were stackedtogether to form a dual-channel image in which the time and frequency responses were stored in the red and green channels, respectively. The size of each dual-channel image was 100 \(\times\) 100 pixels. Only the acceleration response of the bridge was considered for anomaly detection [26].  
The neural network used for data anomaly detection includes typical convolutional layers. The architecture of the CNN is shown in Figure 1. The first hidden layer is a feature map with a size of 60 \(\times\) 60, generated after implementing the convolutional layer with 20 filters of a size of 41 \(\times\) 41 and a stride of one. The third layer is of size 30 \(\times\) 30, generated after implementing a max pooling layer with a pooling size of 2 \(\times\) 2 and a stride of two.  
Although the CNN-based approach has shown potential for data anomaly detection, several important issues have been identified, as discussed previously.  
* Conversion of measured data into images: low-resolution input images have a similar effect to quantization errors;
* Intrinsic class ambiguity in measured data: the presence of data that cannot be classified into a specific class;
* False detections: classes with similar shapes are falsely detected.  
To address these issues, this study introduces a rule-based classification method that does not require data-to-image conversion, allows the investigation of class ambiguity, and is less sensitive to false detections. The rule-based classification is described in detail in the following section by defining the classes of data anomalies [26].