esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fault-Tolerant Aircraft Controller Design
### B Extended State Observer (ESO)  
The classical extended state observer treats the problem of system identification as a problem of disturbance suppression and treats the quantity of total disturbance as the state variable of the system. However, when there are faults or strong external disturbances in the system, full disturbance compensation can over-excite the system. Therefore, the application of the previous method is no longer able to solve practical problems and second-order ADRC is too costly in its application to accurately measure the attitude angle of the vehicle using instruments. Estimating angular signal values through sensor fusion introduces additional delays and uncertainties. Although using higher-order ESO provides more accurate data, it also results in a greater phase lag. This problem occurs due to the delay caused in the system due to the dynamic nature of the digital filters and actuators and the inability of conventional ADRCs to handle the situation effectively. Therefore, considering the above reasons, a reduced-order LESO was designed to reduce the phase lag problem of the ESO while providing reasonable disturbance compensation  
\[\begin{cases}e=z_{1}-y\\ z_{1}=z_{2}+g_{0}(x)u-\beta_{1}fal(e,v_{1},\tau)-\varsigma(x)\\ z_{2}=-\beta_{2}fal(e,v_{2},\tau)\end{cases}, \tag{18}\]  
where \(z_{1}\) is used to estimate the attitude angular velocity, \(z_{2}\) is used for an overall estimate of uncertainty and gust disturbance, and \(\xi(x)\) is used for estimating the additive fault of the actuator. \(\beta_{1}\) and \(\beta_{2}\) are the positive scalars. With the given observer bandwidth \(w_{0}\), \(\big{(}\beta_{1}=2w_{0},\beta_{2}=w_{0}^{2}\big{)}\). \(y\) is the system output. \(g_{0}(x)\) is the input gain parameter of the system that can be derived. The saturation function \(fal(\cdot)\), which was used to suppress dither, has the following form  
\[fal(e,v,\tau)=-\bigg{\{}\begin{array}{l}|e|^{v}\text{sgn}(e),0<\tau<|e|\\ e/\tau^{1-v},0<|e|\leq\tau\end{array}, \tag{19}\]  
where \(fal(\cdot)\) is a nonlinear function. When \(v<1\), the error and gain cannot be small or large simultaneously. Thus, when the error is small, the gain is large; when the error is large, the gain is small.
## 5 TSN Scheduling Problem Formulation
### TSN Scheduling Problem Formulation Assumptions
#### 5.3.7 Assumptions on the Hardware  
Assumptions on the hardware include assumptions on switches, links, and end-hosts. As for the switches, [119] required all nodes to be TSN-capable, [48, 86, 123] assumed identical switches in the network, and [48, 78] were more precise and assumed identical memory and fixed buffer size. As for the links, [14, 72, 126] required all links in the network to be identical, [49, 117] assumed all links to be full-duplex, and [34] assumed that there is no overprovisioning of the links. More specific assumptions were discussed in [75], where 25% of the link bandwidth was assumed to be used by BE traffic, and [41], which established an equivalence between egress ports and links connected to them. This assumption is realistic for wired communication since every port can be connected only to one link. However, for wireless communication, this assumption does not hold. Ref. [48] instead assumed a given number of replicas on wireless links. Finally, [61] made multiple assumptions on the fog end-host such as limiting the number of CPU cores to one.
# Shape and Weighting Optimization of a Subarray for an mm-Wave Phased Array Antenna
## 2 Weighting Optimization for Individual Subarrays by Quadratic Programming
### Array Factor Definition of the Subarray
Therefore, prior to performing subarray weighting optimization, optimization for subarray shapes should be performed to prevent the grating lobe from occurring when the beam is steered. \[f_{k}(\theta,\phi)=\sum_{n=1}^{N_{k}}e^{j\frac{2\pi}{\lambda}(x_{k,n}\mu+y_{k }x^{n})} \tag{2}\]0  
The optimization of subarray shape was conducted so that the numbers of radiating elements in each subarray were identical, using a form with origin symmetry. The procedures of optimization for subarray shape and weighting are as below:  
1. Design of a 24 \(\times\) 24 phased array antenna with uniform weighting. 2. Arbitrarily allocate the phase centers to any points on the array antenna in the first quadrant. 3. Shape the subarray by clustering the radiating elements based on the assigned phase centers. 4. Place an origin symmetry on the optimized subarray formed in the first quadrant of the array antenna. 5. Check the grating lobe by calculating \(AF(\phi,\theta)\) (Equation (1)) on the condition of uniform weighting (\(w_{k}=1\)). 6. If grating lobe takes place, repeat Step 2-5. 7. If completing optimization of subarray shape, such as in Figure 2b, weighting optimization of each subarray is performed by using Equations (22) and (23), which are the objective function of QP and derived by Equations (8)-(21). 8. Calculate the SLL from \(AF(\phi,\theta)\) (Equation (1)), applying optimized weighting derived by QP. 9. If the SLL is not satisfied, repeat Step 2-8 by reshaping the subarray or updating phase centers until the last iteration of GSO. In the next section, the weighting optimization method by QP on the optimized subarray shape, which is an aperiodic environment, is introduced.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 3 Kinematics Modeling and Control Methodology
### Forward Kinematics Analysis  
the cable-joint kinematics can be derived from the established geometric model presented in Figure 4. The Frame \(F_{i}\) and \(F_{i}^{\prime}\) are fixed on the center of where rear and proximal endplate concatenates the \(i\)-th tubular structure whose axial direction is parallel to the Y-axis. The universal joints in the geometric model can be mounted in an odd or even manner but do not influence the formulation. The cross-section for cable mounting is illustrated in Figure 5.  
The main purpose of forward kinematics is to inference the end-effector's position based on the given joint angles \([\alpha_{i},\beta_{i}](i=1,2,\dots,N)\) but for CHSM the mapping transformation from unique and defined cable length \(L_{i,j}(j=1,2,3)\) to the joint angles is critical and principal.  
Figure 4: A geometric model between two tubular structures.  
Figure 3: Overall structure design of cable-driven hyper-redundant snake-like manipulator (CHSM).
## 5 Design of Shutters Based on Side Wing Efficiency  
The original benchmarking data of shutter geometry optimization are obtained in the grid sensitivity analysis of Section 4.4 under the basic size of 0.5 m. Specifically, the original negative lift of side wing is \(-\)88.6270 N, the original drag is 28.9974 N, and the lift of whole vehicle is \(-\)462.26 N. In the simulation, the evaluation is mainly based on the optimization objectives of side wing efficiency indexes, including side wing lift, side wing drag, overall lift, and overall drag.  
Since we are focusing on the aerodynamic efficiency enhancement as well as negative lift reinforcement, we take the parameters of \(L/D\) (lift of side wing divided by drag of side wing) and side wing lift as two objectives to optimize. Therefore, a performance index \(I_{perfm,i}\) is defined in Equation (5) in directing the process of optimization.  
\[I_{perfm,~{}i}=\left(\frac{1}{2}\left(\frac{(\frac{L_{i}}{D_{i}})}{(\frac{L_{ 0}}{D_{0}})}+\frac{L_{i}}{L_{0}}\right)-1\right)\times 100\%, \tag{5}\]  
where \(I_{perfm,i}\) is the performance index for each part. Subscript 0 indicates the value from prototype car; thus, \(L/D\) and \(L_{0}/D_{0}\) are the side wing lift divided by side wing drag of each simulation case and prototype car simulation, respectively (see data in Table 1, line \"base size = 0.5 m\"). Besides, taking the practically tantamount importance of lift value and \(L/D\), weights of both parameters are set as \(1/2\).  
Figure 11: Convergence of lift results on a side wing only compared with a side wing and shutters.  
Moreover, to considerate both enhancement on side wing alone and effect on side wing + shutters as a whole, a performance index \(I_{perfm}\) used in optimization is defined in Equation (6).  
\[I_{perfm}=\frac{I_{perfm,s}+I_{perfm,a}}{2}, \tag{6}\]  
where \(I_{perfm,s}\) means performance index of side wing, while \(I_{perfm,a}\) means that of side wing and shutters as a whole. We simply choose an equal importance \(1/2\) for both indexes.
# Research on Pore Pressure Detection While Drilling Based on Mechanical Specific Energy
## 3 Results
It can be observed that the overall low pore pressure reflected in the case of using wellhead torque, the detected values range from 1.27 to 2.35 sg with an average of 1.85 sg. The MSE computation based on the wellhead torque does not take into account the effect of downhole friction, resulting in a high reflected effective stress and low pore pressure. In contrast to the former, when calculating MSE based on downhole torque, downhole friction is taken into account, and pore pressure detection is fairly close to the reference line of pore pressure estimated from MW, ranging from 1.34 to 2.38 sg with an average of 1.92 sg. Interestingly, in the case of torque estimated from WOB, since the MSE values are overall significantly lower than the previous two cases, the final indirectly reflected pore pressure detection trend is instead closer to the reference line, especially in the interval from 5395 m to 5944 m. Similarly, acceptable downhole torque appears to be achieved with a more reasonable bit sliding friction coefficient. Figure 3: Drilling parameters under compound drilling for well M. (**a**) shows the weight on bit data; (**b**) plots the theoretical total torque on the bit, considering downhole friction and PDM; (**c**) presents the theoretical rotary speed of the bit under compound drilling.
# Antinociceptive Effect of an Aqueous Extract and Essential Oil from _Baccharis heterophylla_
###### Abstract  
Infusions and poultices prepared from the aerial parts of _Baccharis heterophylla_ Kunth (Asteraceae) are widely used in Ouxaca (Mexico) for relieving painful and inflammatory complaints. Therefore, the antinociceptive potential of an aqueous extract (31.6-316 mg/kg, p.o.) and essential oil (30-177 mg/paw, i.pl.) of the plant was assessed using the formalin test. Both preparations inhibited the formalin-induced nociception response (100-316 mg/kg and 100-177 mg/paw, respectively) during the test's second phase. Chemical analysis of the aqueous extract revealed that the major active components were chlorogenic acid (**1**), 3,4-di-O-(_E_)-caffeoylquinic acid (**2**), 3,5-di-O-(_E_)-caffeoylquinic acid (**3**), 4,5-di-O-(_E_)-caffeoylquinic acid (**4**), 3,5-di-O-(_E_)-caffeoylquinic acid methyl ester (**5**), apigenin (**6**), genkwanin (**7**), acacetin (**8**). Compounds **1-5** and **8** are new for _B. heterophylla_. A high-pressure liquid chromatographic method for quantifying chlorogenic acid (**1**) and di-caffeoylquinic acids **2-4** in the plant was developed and validated. Analyses of the essential oil and the headspace solid-phase microextraction products, via gas-chromotography-mass spectrometry, revealed that the major volatiles were \(\beta\)-pinene, myrcene, D-limonene, \(\beta\)-caryophyllene, and \(\alpha\)-caryophyllene, which have demonstrated antinociceptive properties.  
B. Heterophylla; Asteraceae; antinociception; infusion; essential oil; quantification; di-caffeoylquinic acids; formalin test 2021  
15 December 2020  
5 January 2021  
Published: 8 January 2021  
**[PERSON]'s Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 3 Evaluation Criteria Analysis for De-Speckling Performance
### Significance of the Visual Measurement  
The non-availability of the original image of BUS image leads to the difficulty in channel estimation. That is to say, the degradation function cannot be accurately estimated. Therefore, the restoration for medical ultrasound image seems to be very difficult. However, in practical applications, such as medical diagnosis, we tend not to require image restoration. On the contrary, we are concerned more about the visual measurement, which consists primarily of the smoothness of the image in the homogeneous region and the clarity of edge characteristic of edge region. For the same instance, mean and standard deviation were applied to evaluate filtering algorithms [3], and Laplace response was used to evaluate the sharpness degree of filtered images [32]. In that way, a contaminated medical ultrasound image \(I_{g}=I_{f}\cdot n_{m}+n_{a}\)[4] can be seen as the homogeneous area which is polluted by additive noise, and the edge of the area becomes vague caused by the degraded function. Therefore, we regard the de-noising and inverse degradation process as: (1) improvement of the smoothness in the homogeneous region; and (2) sharpening the details of the edge region.
# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## 2 Proposed Methodology
### Stationarity Analysis  
The CNN structure is based on image as an input and for this reason the first step of our methodology is to transform the time series-sequential data into an appropriate, image-like form in order to be processed by the CNN. Image data can be more efficiently processed by CNNs because of their ability to handle the local stationarity of the respective pixels. Image data, namely pixels are characterized as highly stationary and this is the feature that our investigation will leverage. By detecting such behavior in our data we will accordingly transform our time series into \"image\" like structures. Electrical load data, as mentioned before, appear to have time stationarity.  
In order to determine the stationarity of each dataset we calculate their sample means (Mean in Table 1) and standard deviations (STD in Table 1). In a stationary process the unconditional joint probability distribution (and consequently its mean and standard deviations) does not change when shifted in time, so we split each dataset into two sets of equal size and we check how close their corresponding mean and standard deviations values are. Table 1 contains the results of this analysis.  
As it can be seen, only the office dataset display a clearly stationary behavior, while the Trento and Household data need further investigation. For these two datasets the second step of the stationary analysis, the Unit Root ADF test is performed. Those results are presented in Table 2.  
Apparently, the results of the ADF test indicate that both datasets can be assumed to be stationary, based on their corresponding \(p\)-values. Therefore the CNN model can now be further exploited.
## 3 Flexible Merging Decision Methods
### Incomplete Information Static Game Models
#### 3.1.1 Quantification of Strategies  
Because the strategy is semantic information, this needs to be quantified as the desired speed. But the quantized set of strategies has an infinite number of elements, resulting in an extremely large computation of the returns. The optimal strategy can be found from the set for the game, but the selection of the optimal strategy is also difficult. However, this problem is greatly simplified if it does not consider the merging states and focuses only on the final steady state. If MDVs choose \"Allow\", the final steady state is that MDVs follow CAVs. Its desired speed is the CAV's speed, so strategy is \(s_{M1}=v_{CAV}\). If MDVs choose \"Not Allow\", the final steady state is that MDVs follow the mainline front loader. So, its desired speed is the front loader's speed \(v_{Mf}\), and the strategy is \(s_{M2}=v_{Mf}\). If CAVs choose \"Merge\", steady state is following MDV's front loader, strategy \(s_{C1}=v_{Mf}\); if CAV choose \"Not Merge\", the final steady state is that CAVs follow their front loader and for their desired speed to equal the front loader's speed \(v_{Cf}\), and the strategy is \(s_{C1}=v_{Cf}\). Therefore, steady state is used in this study to quantify the strategy. Then, the intelligent driver model is used to calculate the desired acceleration as shown in Equation (1).  
\[\overline{a}=a_{max}\left[1-\left(\frac{v}{\overline{v}}\right)^{\delta}-\left( \frac{d*}{d}\right)^{2}\right] \tag{1}\]  
where \(\overline{a}\) is the desired acceleration, \(a_{max}\) is the maximum acceleration, \(v\) is the current speed, \(\overline{v}\) is the desired speed, \(\delta\) is the acceleration index, \(d\) is the current distance between the main vehicle and the vehicle in front of it, and \(d*\) is the desired following distance.
# Calibrated Phase-Shifting Digital Holographic Microscope Using a Sampling Moire Technique
## 5 Experiment
### Experimental Results and Discussion  
One example of the recorded hologram including the object information and one interferogram used for calculating the phase-shifting errors are presented in Figure 7a,b, respectively. We continuously recorded 100 groups of four phase-shifted holograms and found that large phase-shifting errors occurred several times. One example of the reconstructed images is presented. The reconstructed distance was 0.5 mm. The amplitude images reconstructed by the conventional phase-shifting method [11] and the phase-shifting error compensation algorithm [15] are revealed in Figure 8a,c, and the magnified images of the areas indicated in Figure 8a,c are represented in Figure 8b,d, respectively. Furthermore, the phase images were also reconstructed by the conventional method and the proposed method. The results are shown in Figure 9. Figure 9a,c are the original reconstructed phase images, and Figure 9b,d are the magnified images of the areas indicated in Figure 9a,c, respectively. Here, the detected phase-shifting errors were 0.0763, 0.01945 and \(-\)0.9727 radian, respectively. As found in the numerical simulation, there are many more residual interference fringes in the images reconstructed by the conventional method than there are in the images reconstructed by the phase-shifting error compensation algorithm because of the large phase-shifting errors. Thus, the effectiveness of the proposed system is experimentally demonstrated.  
\begin{table}
\begin{tabular}{c c c} \hline
**Image** & **Conventional** & **Proposed** \\ \hline Amplitude & 6.433 & 0.042 \\ Phase & 0.044 & 0.000 \\ \hline \end{tabular}
\end{table}
Table 1: Normalized root-mean-square error results.  
Figure 7: (**a**) One hologram recorded by Camera 1; (**b**) One interferogram recorded by Camera 2.  
Figure 8: Experimental results of the amplitude images. (**a**,**c**) Amplitude images reconstructed by the conventional method and the proposed method, respectively; (**b**,**d**) Magnified images of the areas indicated in (**a**,**c**), respectively.  
Figure 9: Experimental results of the phase images. (**a**,**c**) Phase images reconstructed by the conventional method and the proposed method, respectively; (**b**,**d**) Magnified images of the areas indicated in (**a**,**c**), respectively.  
Figure 10 plots the phase values of the dotted red lines indicated in Figure 9b,d. The test target is made of glass whose surface is extremely flat. Obviously, the fluctuation of phase values obtained by the conventional method is much greater than that obtained by the proposed method. In other words, the proposed system is capable of improving the quality of the reconstructed images to achieve a high precision phase measurement.
# Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
## 3 The Radial Gradient Seismic Metamaterial Structure
### The Effect of Filling Angle on the Band Gap  
For further research and analysis, the low-frequency broadband characteristics of Lamb waves generated by RGSM. Under the condition that other parameters remain unchanged, we analyze the influence of the filling angle on the band gap. The angle in Figure 4a is 0 degrees, and the band gap characteristics are analyzed from the special angles of 0 degrees, 45 degrees, 90 degrees, and 270 degrees. From Figure 4, it can be observed that the first band gap will change due to different filling angles. As shown in Figure 4a, the starting frequency and ending frequency of the first band gap are 5.3484 Hz and 13.882 Hz, respectively; the starting and ending frequencies of the first band gap shown in Figure 4b are 5.28561 Hz and 14.025 Hz, respectively. The starting frequency and ending frequency of the first band gap shown in Figure 4c are 5.4536 Hz and 14.19 Hz, respectively. The starting frequency and cutoff frequency of the first band gap shown in Figure 4d are 5.2493 Hz and 14.285 Hz, respectively. The vibration modes of the band gap terminal are consistent, which are the vibration of the steel ring and the vibration of the soil ring respectively.  
Figure 4: Effects of different filling angles on the band gap; (**a**) the rotation angle of the internal triangle filler is 0\({}^{\circ}\); (**b**) the rotation angle of the internal triangle filler is 45\({}^{\circ}\); (**c**) the rotation angle of the internal triangular filler is 90\({}^{\circ}\); (**d**) the rotation angle of the internal triangle filler is 270.
## 2 Formation Mechanisms of Effective Reservoir in Ultra-Deep
### Sedimentation Lays the Foundation for Mineral Fabric and Primary Porosity of Reservoirs
Recently, Sinopec's Zheng-10 well successfully tested high-yield oil from the delta-front sandbodies of the Triassic Kelamayi Formation, confirming the presence of high-quality reservoirs with 13% porosity at a depth of 6700 m. In contrast, while delta-front channel sandbodies also developed in the contemporaneous Triassic Xujiahe Formation of the Sichuan Basin, stronger compaction and cementation (Figure 2e) have significantly reduced their quality. Currently, porosity in these reservoirs is generally less than 5% (Table 1). The influence of sedimentary hydrodynamic conditions on reservoir properties is evident not only across different sedimentary microfacies but also within various sections of the same sandbody. Detailed core descriptions and sampling observations from the Silurian (-444-419 Ma) in Shun-9 (Tarim Basin) reveal significant variations in material composition and reservoir quality within a single sandbody (Figure 3a). Generally, at the top of a sandbar, the grain size is coarsest and the plastic grain content is lowest. This zone primarily consists of massive fine-grained sandstones with moderate compaction, and well-developed pores (Figure 3b). In the middle section, fine-grained sandstones with parallel cross-bedding dominate. Compaction is slightly stronger, and primary intergranular pores are only locally preserved (Figure 3c). Towards the bottom, interbedded siltstone and mudstone with wavy bedding appear. Plastic grain content increases, reducing compaction resistance and resulting in strongly compacted lithic sandstones with poor reservoir properties (Figure 3d). Multiple Geological Processes Favor the Formation and Preservation of High-Quality Clastic Reservoirs  
After deep burial, constructive diagenetic processes play a crucial role in identifying and preserving the reservoir properties of clastic rocks. Scholars have examined the formation mechanisms of deep effective sandstone reservoirs from multiple perspectives [6; 14; 18; 38; 39; 40; 41]. Based on previous research, this study critically analyzes the roles of grain coatings, overpressure, geothermal gradients, hydrocarbon charging, dissolution, and fracturing in development and preservation.