esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Numerical Simulation Result
### Analysis of TBM Chamber Model
#### 3.2.3 Pressure Acting on the Surface of the Chamber  
Figure 12 shows the pressure acting on the chamber surface during simulation. As explained in Figure 11, the expected sudden inflow of particles from the cutterhead to the opening area would affect the chamber system regardless of the conditions of screw rotation speed in the first 90 s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Case**} & **Operating Condition** & **Standard Deviation of Number of** \\  & **(Cutterhead RPM/Screw RPM)** & **Particles in Chamber** \\ \hline
1 & 1.0/12.0 & 446 \\
2 & 1.0/9.0 & 109 \\
3 & 1.0/6.0 & 74 \\
4 & 1.5/12.0 & 49 \\
5 & 1.5/9.0 & 45 \\
6 & 1.5/6.0 & 55 \\
7 & 2.0/12.0 & 37 \\
8 & 2.0/9.0 & 18 \\
9 & 2.0/6.0 & 70 \\
10 & 3.0/12.0 & 43 \\
11 & 3.0/9.0 & 66 \\
12 & 3.0/6.0 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Standard deviation of the number of particles in the chamber depending on operating conditions.  
Figure 12: Variation of the chamber pressure during the simulation.  
Table 6 summarizes the average chamber pressures and the SD of chamber pressures, with respect to operating conditions. As the cutterhead speed increased, the chamber pressure increased at the same screw-conveyor speed. The chamber pressure increased as the screw-conveyor speed decreased to 6.0 rev/min at the same cutterhead speed. Furthermore, the chamber pressure increased by 6.0% to 16.0%, as the screw-conveyor speed decreased to 6.0 rev/min and the cutterhead speed increased to 3.0 rev/min. For Case 4 to Case 6, the pressure increased by \(\sim\)6.0%; for Case 10 to Case 12, it increased by \(\sim\)16.0%. Comparing cases 3 and 4, the average chamber pressures were almost constant with the cutterhead speed increasing from 1.0 to 1.5 rev/min and screw-conveyor speed from 6.0 to 12.0 rev/min. In cases 9 and 10, the average chamber pressure increased by \(\sim\)3.8%, implying that the screw-conveyor rotation speed should be \(>\)12.0 rev/min to balance chamber pressure.  
Case 4 shows the lowest value of the SD of chamber pressure (0.72) under the conditions of 1.5 rev/min cutterhead rotation and 12.0 rev/min screw conveyor rotation, followed by Cases 3 and 7. The pressure acting on the chamber surface could be estimated differently, depending on the total mass of particles in the chamber and each particle's mechanical energy contacting with the chamber surface. Therefore, it was expected that the trends of the two plots in Figures 11 and 12 would be slightly different.
###### Abstract
The improved TOPSIS sorting and optimization technology was adopted, and the optimal process parameters were obtained. The remanufacturing experiment verifies that the optimized parameters meet the experimental requirements and verify the model and algorithm can effectively reduce the energy consumption of the laser remanufacturing process [17]. Kf311 ferrous metal powder was chosen to repair steel EA4T by LC. The study has shown that smaller grain size and the precipitation of Fe\({}_{2}\)B phases caused by remelting and recrystallization of alloying powder lead to the highest microhardness value of cladding layers [18]. Laser cladding technology was used on the wind power gearbox spline shaft. The Fe-based coatings with different WC addition were manufactured by laser cladding to improve the wear resistance of the wind turbine gears, increasing microhardness and wear resistance with increasing WC content in iron-based coatings [19]. Although some scholars have conducted some research on deposition strategy and parameter optimization, their research studies are incomplete. The whole repair process has not formed a system. Because of the complex morphology and the high height of the deposition layer to be repaired, the requirements for the deposition strategy are higher. In this study, the systematic research combination of process parameters' optimization, deposition strategy optimization, microstructure analysis, and mechanical properties are studied in depth. The above scholars have studied the repair of the wheel, gear, and even damaged substrates, but the repair scope is relatively small, and there is no study on the large-scale deposition layer with complex morphology with high hardness. Some studies are not targeted, such as considering the substrate as a broken part. This paper makes up for the deficiency in this respect. In this paper, to ensure mechanical performance, Ni60 power was chosen as the LC powder. The process parameters' optimization, deposition strategy determination, microstructure analysis, mechanical properties detection, and analysis of high hardness 20 CrMnTi gear tooth surface restoration were systematically studied. The optimal process parameters include powder feeding rate (\(V_{p}\)), laser power (\(P\)), scanning speed (\(V_{s}\)), carrier gas flow rate (\(Q\)), overlap rate, and Z-axis increment. The deposition strategy includes scanning mode, the lap mode of the track, and the way of the lap between layers. Phase and microstructure analysis, microhardness test, and wear performance were systematically investigated to improve the surface quality. Finally, the gear tooth was repaired successfully, and the properties of the repair part were similar to those of the original gear.
## 4 Propagation Loss Calculation Using the Improved TWPE Model
### Effects of Different Land Covers
The LC of this section can be roughly assumed using the simplified dielectric permittivity to simulate the PL distribution and could be performed to save time and costs. From the PL distribution of 70-93 km, it can be inferred that when the terrain fluctuation exceeds 80 m, the PL might exceed the minimum resolution value of the GPS receiver, so that the possibility of remote interference on the link can be ignored. Moreover, when long-distance communication is required, the corresponding 5G relay base station deployment can be reasonably arranged with reference to this result. Figure 22: The propagation loss using the improved TWPE at a height of 40 m using different land covers, on Shanghai–Wuxi. (**a**) PL on S–W at different LCs at an antenna height of 40 m. (**b**) PL on S–W at a range of 20–40 km at different LCs. (**c**) PL on S–W at a range of 40–70 km at different LCs. (**d**) PL on S–W at a range of 70–115 km at different LCs. Figure 20.0 The MSE of PL using the improved TWPE between IGBP LC and different LCs on Shanghai–Wuxi. (**a**) PL on J–W with BSV LC. (**b**) PL on J–W with URB LC. (**c**) PL on J–W with SAV LC. Figure 20.1 The MSE of PL using the improved TWPE between IGBP LC and different LCs on Shanghai–Wuxi. (**a–c**) BSV-IGBP MSE of PL on J–W. Figure 20.2 The Propagation loss using the improved TWPE at a height of 60 m using different land covers, on Jiaxing–Wuxi. (**a**) PL on J–W at different LCs at at antenna height of 60 m. (**b**) PL on J–W at range of 0–40 km at different LCs. (**c**) PL on J–W at a range of 40–70 km at different LCs. (**d**) PL on J–W at a range of 70–93 km at different LCs. Figure 26 shows the range-dependent PL distribution at the height of the receiving antenna when the receiving antenna is 40 m on the Jiaxing-Wuxi link. At 40-70 km in the middle section of the link, the PLs simulated by different types of LC begin to show obvious differences. Compared with Figure 25 when the receiving antenna height is 60 m, the overall PL is reduced; that is, when the receiving antenna height is lower, the propagation distance of the signal increases. Within the range of signal-noise-ratio (SNO) that the antenna can distinguish, when the receiving antenna is 40 m, the signal can be transmitted to about 45 km. In addition, if the LC on the link develops in a unified manner, such as highway development, greening, desertification, etc. ; that is, if the LC on the link does not change much, as shown in Figure 23b, the overall PL is reduced, and the propagation distance may increase by 10-20 km.
## 4 Numerical Implementation
### Implicit Integration Algorithm  
In each iteration, after obtaining the displacement increment \(\Delta u\), suction increment \(\Delta s\) and degree of saturation increment \(\Delta S_{\text{r}}\), the steps to determine the new stress state are as follows.  
(1) Elastic trial calculation  
The geometric equation is used to calculate the increment of the solid deformation, and the trial value of the stress is calculated according to the pure elastic stress-strain relationship.  
\[\left\{\begin{array}{c}[\Delta\epsilon]=[B][\Delta u]\\ \left[\Delta\vec{\sigma}_{\epsilon}^{*}\right]=[D_{\epsilon}^{*}][\Delta\vec{ \epsilon}]\\ t^{+\Delta t}[\vec{\sigma}^{*}]=t[\vec{\sigma}^{*}]+[\Delta\vec{\sigma}_{ \epsilon}^{*}]\end{array}\right. \tag{26}\]  
where \({}^{t+\Delta t}[\Delta\vec{\sigma}^{*}]\) is the stress value at the end of the last iteration step.  
(2) Yield determination  
According to the stress test value, the yield function value is calculated: \(f({}^{t+\Delta t}[\vec{\sigma}^{*}])\). If \(f({}^{t+\Delta t}[\vec{\sigma}^{*}])\leq 0\), the Gaussian point will maintain the elastic state under the obtained strain increment; if \(f({}^{t+\Delta t}[\vec{\sigma}^{*}])>0\) and \(f({}^{t}[\vec{\sigma}^{*}])<0\), the Gaussian point will change from the elastic state to the plastic state under the obtained strain increment.  
(3) Plasticity correction  
If \(f^{\{t+\Lambda t|}\widehat{\phi}^{*}\}\leq 0\), then \([\Delta\widehat{\sigma}^{*}]=[\Delta\widehat{\sigma}^{*}_{e}]\), and no plastic correction is needed; if \(f^{\{t+\Lambda t|}\widehat{\phi}^{*}\}>0\) and \(f^{\{t|}\widehat{\phi}^{*}\}<0\), the test stress value is outside the yield surface, and this state is not allowed. A stress value outside the yield surface should be corrected to coincide with the yield surface. According to the actual yield situation, the plasticity correction should be carried out using Equation (18).
## 1 Introduction  
Plants are one of the most important sources of natural compounds used in the discovery of new pharmaceutical agents, cosmetics, and pesticides. Their essential oils (EOs) consist of thousands of components that are potential agents in the treatment of many diseases, such as 1,8-cineole for respiratory diseases, cancer, digestive disorders, dysphoria, AD, and cardiovascular illness, as well as antimicrobial applications [1], or that can be used to produce toxic effects and repellence in insects, such as eugenol [2]. Moreover, geraniol, linalool, citronello1, citronellal and citral represent five of the most important compounds in the perfume industry [3]. Of all these compounds, terpen are the most prominent,followed by phenylpropanoids. Monoterpenes are the most emblematic in the terpen group, being widely distributed and sometimes representing 90% of the total oil content. Monoterpenes are known for possessing different properties, such as antioxidant, enzyme inhibitory and antifungal activities, being hepatoprotective, having sedative functions and stimulating glucose uptake and lipolysis [4; 5]. Monoterpenes can be divided into regular and irregular monoterpenes and iridoids. Regular monoterpenes are the most common and of these, monoterpene hydrocarbons comprise several important compounds, such aspinene, limonene and phellandrene [6].  
Alpha-phellandrene, isolated for the first time from _Eucalyptus phellandra_ (now called _E. radiata_) and from which it was named, is one of a pair of phellandrene cyclic monoterpene and double-bond isomers. It can be extracted from the EOs of the turmeric leaf (54%), _Boswellia scra_ (42%), _Eucalyptus elata_ (35%), dill weed (30%) and _Eucalyptus dives_ (17%) [7; 8], but is also found in several different plant species, such as _Cryptomeria japonica_, _Heraeleum antasiaticum_, _E. camalalalensis_, _Gossypium hisutum_, _Cistus ladanifer_, and _Cannabis sativa_, among others [9].  
Alpha-phellandrene has been in public use since the 1940s and has been considered a Generally Recognized as Save (GRAS) product from the Federal Emergency Management Agency (FEMA) since 1965. It has recently been reassessed as not being hazardous for public health [10]. It is approved by the FDA for food use (21 CFR 172.515). It is used to produce fragrances, soaps, detergents, creams and lotions, and its biological activity is of interest in agriculture, food and feed applications and its pharmacological properties are of interest to the pharmaceutical and cosmetic industries.  
This review collected all the information related to alpha-phellandrene and alpha-phellandrene-rich EOs up until the present, starting from their principal sources, and reviewing their potential pharmaceutical and food applications, focusing on their bioactivities.
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## Appendix B ChangeFinder  
ChangeFinder is an algorithm that computes the likelihood of change-points in time-series data. A change-point is a time when the behavior of time-series data changes significantly. In this study, we use the SDAR model, which has a forgetting function and can deal with non-stationary data to some extent. The SDAR model is effective in detecting essential change-points because it suppresses the increase in change-point scores caused by noise through two-stage learning [14]. The calculation procedure for each time-series data is as follows.  
1. Reading time-series data \(x_{t}\).
2. Learning SDAR model and computing probability density function \(p_{t}\).
3. Calculation of outlier scores during one-step learning \[\text{Score}_{1}(x_{t})=-\log_{p_{t-1}}(x_{t})\] (A7)
4. Smoothing of scores and derivation of moving average \(y_{t}\) using smoothing width \(T_{1}\). \[y_{t}=\frac{1}{T_{1}}\sum_{i=t-T_{1}+1}^{t}\text{Score}_{1}(x_{i})\] (11)
5. Apply SDAR to \(y_{t}\) and calculate the probability density function \(q_{t}\) for two-step learning.
6. Smoothing again yields the moving average at time \(t\) as the change-point score. \[\text{Score}(t)=\frac{1}{T_{2}}\sum_{i=t-T_{2}+1}^{t}(-\log_{q_{i-1}}\left(y_{ i}\right))\] (12)  
The higher the score, the greater the degree to which it is a change point. The following is a description of the SDAR algorithm.  
1. Read the time-series data \(x_{t}\).
2. The computed estimator \(\mu\) is used to update the estimate of the statistic to be used. Where \(r\in(0,1)\) is the forgetting coefficient. The higher the forgetting coefficient, the greater the influence of the past state. \[\hat{\mu}_{t}=(1-r)\hat{\mu}_{t-1}+r\hat{\mu}\] (13) \[C_{j_{t}}=(1-r)C_{j_{t-1}}+r(x_{t}-\hat{\mu}_{t})(x_{t-j}\hat{ \mu}_{t})^{T}\] (14)
3. Solve the Yule-Walker equation for \(\omega_{i}\). \[\left[\begin{array}{cccc}C_{0_{t}}&C_{1_{t}}&\cdots&C_{k-1_{t}}\\ C_{0_{t}}&C_{0_{t}}&\cdots&C_{k-2_{t}}\\ \vdots&\vdots&\ddots&\vdots\\ C_{k-1_{t}}&C_{k-2_{t}}&\cdots&C_{0_{t}}\end{array}\right]\left[\begin{array}[] {c}\omega_{1}\\ \omega_{2}\\ \vdots\\ \omega_{k}\end{array}\right]=\left[\begin{array}{c}C_{1_{t}}\\ C_{2_{t}}\\ \vdots\\ C_{k_{t}}\end{array}\right]\] (15)
4. Compute the variables used in the score calculation. \[\hat{\Sigma}_{t}=(1-r)\hat{\Sigma}_{t-1}+r(x_{t}-\hat{x}_{t})(x_{t}-\hat{x}_ {t})^{T}\] (16) \[\hat{x}_{t}=\sum_{i=1}^{k}\omega_{i}(x_{i-j}-\hat{\mu}_{t})+\hat{ \mu}_{t}\] (17)
# Independent System Calibration of Sentinel-1B
## 8 Polarimetric Characterization  
While Sentinel-1 is designed to transmit only one polarization signal (H or V), both polarization signals reflected by the earth surface are received simultaneously by the two receiving channel systems of the SAR instrument, to provide dual polarization products for the SM, IW and EW modes. The DLR transponders were operated with a polarization orientation of 45 degrees, that means the received signal from the satellite (in H or V polarization) is retransmitted in both polarizations (H and V) simultaneously by the transponder. Thus, the channel imbalance can be derived from the processed co- and cross-polarized SAR images.
## 5 Conclusions  
In this paper, a nonlinear dynamic inversion control algorithm based on a finite-time disturbance observer and an anti-saturation auxiliary system is designed for the actuator saturation problem and the unknown disturbance in the attitude tracking process of a UAV. Firstly, for the actuator saturation problem, a finite-time anti-saturation strategy is proposed, and the anti-saturation control of the actuator is achieved by constructing a second-order anti-saturation auxiliary controller and introducing an anti-saturation auxiliary state in the system error term. Secondly, for the problem of unknown disturbance, an FDO is  
Figure 9: Comparison of observations with literature [46]. (**a–c**) denote the disturbance estimation results of the two algorithms for the roll, pitch, and yaw channels, respectively.  
designed, and a hyperbolic tangent function is introduced into the disturbance observer, which effectively improves the jitter phenomenon of the existing finite-time disturbance observer. Finally, simulation tests show that the algorithm proposed in this paper can achieve accurate attitude tracking control, which not only reduces the time that the actuator is in the saturation state but also quickly estimates the unknown disturbance to which the system is subjected.  
Future research will be carried out for the case where the saturation boundary is unknown and time-varying. Currently, we have only performed Software-in-the-Loop (SIL) simulation, and we will follow up with Hardware-in-the-Loop (HIL) simulation work to further validate the control effect of the algorithm. In addition, our current study focuses on attitude control, on which we plan to design position controllers in the future with an aim to automoize navigation tasks for UAVs. Due to the importance of the field of intelligent control, we will also introduce intelligent control into our research in our subsequent work.  
Conceptualization, Z.Z. and X.L.; Methodology, Z.Z.; Software, Z.Z.; Validation, Z.Z., X.L. and L.Z.; Formal Analysis, Z.Z.; Resources, Z.Z.; Data Curation, Z.Z.; Writing--Original Draft Preparation, Z.Z.; Writing--Review and Editing, Z.Z.; Visualization, Z.Z.; Supervision, X.L. and L.Z.; Project Administration, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
###### Abstract  
Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km\({}^{2}\) in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture.  
vegetation dynamics; ESTARFM; MODIS; Landsat; phenology; West Africa; cloud gap filling; time series analysis +
Footnote †: journal: Remote Sensing  
123 March 2016; 425 doi:10.3390/rs8050425
# Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline
## 2 Experimental Setup and Data Acquisition  
To simulate leakage in a piping system, a specific testbed was elaborately designed, as illustrated in Figure 1. Two ductile iron pipe testing sections of 2 m length, 34 mm outside diameter, and 3.38 mm wall thickness were employed. At each end of the segment, a blind flange was fitted to connect the section to the remaining part of the system. A water circulatory system was constructed to mimic the operational state of the pipeline and included a water tank and pump. The water from the tank was driven by the pump and flowed through the pipeline and test section and then returned to the tank. Two regulators and pressure meters were located on either end of the test section to adjust and record the pressure of the flow inlet and outlet, respectively. Two flow transmitters were also installed at each end of the test segment to measure the flow rate. The leakage was simulated by a drill hole. In this study, the four levels of leak were 2, 1, 0.5, and 0.3 mm, as shown in Figure 1c. The solenoid valve was used to control the leak state, as illustrated in Figure 1b.  
The AE sensor system utilized in the test was composed of a data acquisition card along with a peripheral component interconnect bus (PCI-2) [25], which could interface with the piezoelectric WDI-AST wideband differential sensors [26] from MISTRAS corporation to measure the AE signal generated by the turbulent flow once the leak occurred. A detailed view of the data acquisition devices is shown in Table 1. On top of the pipe, two AE sensors were installed by adhesive types. The internal pressure at the inlet was first stabilized at 1.77 bar by adjusting the regulators at both ends of the test section. The experiments were conducted at a stable temperature of approximately 30 \({}^{\circ}\)C. The flow rate of water in the pipeline was maintained as a constant of 7 m\({}^{3}\)/h during the measurement process by a pump. The signal was recorded with a 1 MHz sampling rate. The captured signals were then amplified and filtered using a signal conditioning unit to provide the appropriate signal-to-noise ratio for further processing.  
Vibration sensors were also used to measure leak signals in the test section. For vibration recording, accelerometer-type sensors were used. For each level of leakage, the normal and the leak state refer to the valve at the position of closed or opened, respectively. First, the pump is operated and the leak is deactivated--valve closed. The normal AE signal is recorded over time as the baseline for comparison. Later, the valve is opened, activating a leak. The acquisition device waits until the flow is stable and then begins to record the abnormal signal.  
Figure 1: Pipeline testbed for leak simulation (**a**) Overall system installation (**b**) Solenoid valve to control the leak state (**c**) Four type of drill hole to simulate the leakage.