esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Beamformer Combined with Phase Coherence Weighting Applied to Ultrafast Ultrasound
## 4 Discussion
In the proposed method, the penetration depths in the obtained B-mode images were about 30 mm because the center frequency of the linear array probe used in the present study was 7.5 MHz and it was fabricated for imaging of superficial tissues. To increase the penetration depth up to 10-20 cm, we need to use an ultrasonic probe at a lower center frequency. The performance of the proposed method on such probes at lower center frequencies will be investigated in our future work. In the present study, the B-mode images obtained without the sub-aperture beamforming were also evaluated as the previously proposed method. With respect to the lateral resolution, the appearance of the fine wire depicted in Figure 6a does not seem to change as compared to Figure 2c. On the other hand, the speckle visibility in the obtained B-mode image was degraded. In ultrafast ultrasound imaging, the adaptive beamformer incorporating with the coherence factor degrades the speckle visibility in the obtained image [39]. In the present study, the output signals from the sub-aperture beamformers were used in estimation of both the beamformer weights and the gPCF, and experimental results obtained with sub-aperture beamforming show that such an implementation is effective to avoid degradation of the speckle visibility while maintaining the improvement of the spatial resolution. With respect to image contrast, better contrast was obtained by the proposed method without sub-aperture beamforming. However, when the proposed method is performed without sub-aperture beamforming, the dimension of the covariance matrix becomes large and high computational cost is required to calculate the inverse matrix of the covariance matrix. The sub-aperture beamforming can reduce the dimension of the covariance matrix, and accordingly, the computation time becomes shorter [27]. As described above, although the proposed method needs to carry out an additional processing for weighting the coherence factor on the conventional modified adaptive beamforming, the gPCF estimated with sub-aperture beamforming is a computationally effective method. In the present study, better lateral resolution and contrast were realized with slightly increased the processing time compared to the mAPES beamformer only. The computation time of the proposed method was 339% of the conventional DAS beamformer, when an Intel(R) Core(TM) i5-6500 CPU was used with 8 GB of RAM. The proposed method has potential to be used in practical applications by incorporating parallel processing techniques with the proposed method.
## 4 Inverse Problems
### Fluid Mechanics and FSI
#### 4.2.2 Optimization Algorithms
Statistics-Based Methods  
Some statistical methods applied in the field of biomechanics rely on the use of Bayesian inferences, also known as inverse probability. Unlike the methods described previously, Bayesian inference-based methods provide not only an estimation of the parameters to be fitted but also a confidence interval for such values. The method requires a set of measured data along with its probability distribution (which can be often assumed normal due to random experimental error), a predictive model, and a prior probability distribution for the model parameters. The latter can be estimated from previous experiments and published data or can be simply assumed as uniform within a given range. Then, a selection of parameter combinations is used to run the prediction model and compare it to the experimental data. Finally, the Bayes theorem is used to produce a map for the probability of the model that reproduces the experimental data in a parameter hyperspace. This map is used to update the prior parameter probability distribution to iteratively repeat the process [129]. There are many different computational implementations, some of the methods used on patient-specific inverse problems are the Gaussian process regression, Kalman filters in its many variations, and linear-quadratic-Gaussian estimations. These methods differ mostly on how the sampling is carried out, how the parameter probability distribution is assumed or calculated on each step, and how the model predictions and experimental measurements are weighted to determine the converged parameter solution [130; 131; 132].
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 4 Robust Estimation via the Genetic Algorithm  
In this paper, a modified version of the integer-coded genetic algorithm, originally proposed by [50] is applied to stochastically search for a sample set of correspondences that minimizes Equation (5). In GA terminology, each candidate sample set of correspondences is called an individual. The characteristics of an individual are represented by a chromosome. Each chromosome is made of a number of elements, called genes. In the context of this study, each gene accounts for a putative correspondence. Considering the minimal number of correspondences needed to find a solution for \(F\), the length of a chromosome remains constant (e.g., eight points). A group of individuals, e.g., 30 ones, forms a population. The first step in applying the GA is to generate an initial population. The size of the population and the way its individuals are drawn (sampling strategy) are the most important factors to decide. Afterwards, in any iteration of the evolution, the model estimated from each individual is evaluated based on the objective function (Equation (5)). Then, the genetic reproduction operators are performed to reproduce a new population. This evolution continues the same way until reaching an optimal solution. The overall pseudo-code of the proposed technique is given in Algorithm 1.  
```
Input A set of \(n\) putative correspondences Output Estimated fundamental matrix and the entire set of inlier correspondences (a) Genetic Algorithm -Input: The lookup table of matches (Section 4.1) -Output: The fundamental matrix (\(F\)) and an inlier set of minimum cardinality 1.1 Decide the minimum ratio of inliers, \(n^{*}/n\) 2.1 Initialize the first population by guided sampling (Section 4.2) While the best solution gets improveddo 3.1 Compute \(F\) from each individual (Section 3.1) 4.1 Evaluate each individual by computing the sum of the \(n^{*}\) smallest residuals (Section 3.2) 5.1 Perform genetic operators (selection, crossover, mutation, random exploration) on the individuals of the current population, and reproduce the next population through the replacement process (Section 4.3) 6.1 Save the best overall solution achieved so far and the inlier-set-of-minimum cardinality associated with it (\(\hat{\boldsymbol{l}}\) with cardinality of \(n^{*}\)) End while 7.1 Re-estimate the fundamental matrix (\(\hat{F}\)) using the inlier set of minimum cardinality from the best solution (\(\hat{\boldsymbol{l}}\)) (Section 4.4) (b) Estimate the uncertainty of the model (\(\hat{\sum}_{F}\)) (Section 4.4) (c) Estimate the average and uncertainty of the Sampson residuals (\(\bar{\eta}_{d},\bar{\sigma}_{d}\)) for matches belonging to \(\hat{\boldsymbol{l}}\) to determine the outlying threshold (Section 4.4) (d) Compute and threshold the Sampson residuals on other matches to identify the entire set of inliers (Section 4.4)
```  
**Algorithm 1**The proposed robust-estimation technique via the genetic algorithm.
## 6 Discussion  
During the interviews, we noticed that while all interviewees find protocol compliance very important, the meaning of the term \"protocol compliance\" is not well defined, and every interviewee had a different interpretation of what compliance with the protocol means to them. One of the doctors mainly reasoned at the department level, and the other at the patient level (how many of the treatment actions completed for one patient are according to the protocol), while one of the nurses mainly wanted to know whether she personally complied with the protocol (i.e., whether the actions she took were according to the protocol), and the other nurse as to whether the activities performed with the patients were compliant with the protocol. While the term protocol compliance in theoretical definitions refers to whether the process described in the protocol is followed for a patient, we noticed different interpretations on different aggregation levels. _\"[...] We approach the issue of the evaluation of the protocol or guideline always from a patient perspective assuming that the protocol is properly constructed.\"_ (Doctor W) _\"[...] Obviously managers are interested in glucosedeterminations we do, because it cost money for us. And these people do not understand medical terminology. So linguistic summaries for these groups are more easy to understand as well [...] If you approach it from value-based healthcare perspective, so if you want to contribute to the increased or same level of care with decreased level of cost, then for these managers linguistic summaries are easier to understand. Depends on which level of management you consider.\"_ (Doctor W).  
From this research, we can conclude that the topic of compliance is interesting yet remains an open topic to be explored in more depth. We noticed that there are three independent dimensions to analyze the protocols, as shown in Figure 5, namely:  
* Regulation perspective (action vs. protocol perspective).
* Object perspective (single patient vs. cohort of patients).
* Resource perspective (individual nurse vs. department).  
Those dimensions can have different granularity levels, and they can be combined together, e.g., analyzing all young nurses' activities if a certain protocol is used for a certain group of patients.  
One more interesting observation is the role of the experience of adhering to the protocol. It seems that more experienced nurses (are allowed to) deviate more from a protocol than novice nurses. This could result in flexible (or dynamic) membership functions for compliance for individual healthcare providers.  
An issue not considered here is the interaction of the protocols. For instance, in the case of the glucose management protocols, medications administered to the patient can influence glucose levels. Therefore, administering those medications can influence the optimal insulin dosage for the patients. Currently, such cases are not included in the protocols. Including all cases where there may be a reason for deviation is unfeasible; therefore, allowing and capturing deviations from the protocol is a simpler option.
## 2 Results and Discussion  
### Physicochemical Parameters  
In order to monitor the effect of the different terrain conditions of the Loess Plateau Region on the Cabernet Franc wines, the physicochemical parameters of the Cabernet Franc wines from the flat land (F-Land) vineyard, low slope land (LS-Land) and high slope land (HS-Land) vineyards were determined (Table 1).  
Total sugar content of must was higher in F-Land wine than in the other two slope land wines, and total acidity content of the must showed no differences between the three terrain vineyards, but the sugar-acidity ratio from the three terrain berries were more than 20, so they all had a good ripeness. Total phenolics and pH of must displayed no obvious differences in the three terrain conditions. After fermentation, the wines from the three terrain conditions basically had similar physicochemical properties within an acceptable range, except for total phenolics contents [19]. Total phenolics contents of wines from F-Land vineyard was significant higher than from the slope land vineyards (\(p<0.05\)), a discrepancy that perhaps affects the aroma characteristics of the corresponding wine.
## 5 Analysis and Discussion
However, as exploration progresses and due to the characteristics of the overall environment, the number of position cells increases, and the burden of adjusting connection weights between position cells increases, leading to overlap in their response patterns and consequently affecting the accuracy of localization. The proposed method in this paper activates boundary cells when encountering boundaries, and the correction function of boundary cells on position cells enables real-time updating and constraint of position cell discharge responses, reducing position estimation uncertainty, and thereby improving the localization accuracy of the proposed method. At the end of the operation, the absolute trajectory error of the proposed method is approximately 0.031m, which is about 47.2% lower than that of the competitive Hebbian learning network, and about 56.9% lower than that of the RatSLAM algorithm.
# Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field
## 2 Doppler Center Frequency Retrieval Method  
### Components of Doppler Frequency  
For the ATI-SAR, the interference phase of the images corresponding to two antennas after complex multiplications is as follows:  
\[\phi=-\frac{2\pi}{\lambda}\cdot 2\cdot\Delta R=-\frac{4\pi Bv_{r}}{\lambda v_{p} }, \tag{1}\]  
where \(\Delta R\) is the range difference of the same position, \(B\) is the baseline length of the satellite, \(v_{r}\) is the radial velocity, \(\lambda\) is the wavelength of the radar relative to the operating frequency, and \(v_{p}\) is the station velocity.  
The radial velocity can be expressed as:  
\[v_{r}=-\frac{\lambda v_{p}}{4\pi B}\phi, \tag{2}\]  
Then, the Doppler frequency can be retrieved following the principles of ATI-SAR:  
\[f_{dc}=\frac{2v_{r}}{\lambda}, \tag{3}\]  
The Doppler frequency retrieved using ATI-SAR from the single-look complex data consists of several items [17]:  
\[f_{dc}=f_{dc}^{phys}+f_{dc}^{geo}+f_{dc}^{ele}+\Delta f_{dc}, \tag{4}\]  
where \(f_{dc}^{phys}\) is the geophysical term, \(f_{dc}^{geo}\) is the geometric attitude term, \(f_{dc}^{ele}\) is the antenna electronic miss-pointing, and \(\Delta f_{dc}\) is the residual error from an imperfect prediction of the non-geophysical terms or other unknown biases. For the ATI-SAR, the effect of \(f_{dc}^{ele}\) on the overall result is negligible. For a stable satellite orbit and attitude, the measured Doppler shift \(\Delta f_{dc}\) can be estimated from the SLC data or processed data. Generally, the residual variation after recalibration is about 3.8 Hz. The attitude is pre-corrected by \(f_{dc}^{geo}\) when imaging the satellite SAR RAW data. \(f_{dc}^{phys}\) includes the contributions of various wind-generated currents in the current field, the Bragg wave phase velocity, and the orbital velocity.
# Cathode Shape Design for Steady-State Electrochemical Machining
## Appendix A Experimental Tests of the Potential Model
### Anode Shapes in Steady State  
ECM using curved tools was studied using a quasi 2D cathode consisting of two plane regions at the borders and a sine-like shape composed of arcs in the center (see Figure 11(a)). The experiments were carried out at two different values of the machining voltage while the feed rate was kept fixed. The initially plane anode was machined electrochemically until the process reached the steady-state indicated by a constant process current. The anode shape after machining was determined by scanning the machined surface using a digital indicator. The \"theoretical\" shapes of the anodes were computed using the simulation tool, whose implementation is described in more details in Section 2.2. The initial gap geometry shown in Figure 11(a) was used as the initial computational domain; values of voltage and feed rate used in the experiments were used as input parameters for the simulation.  
Figure 11: Evolution of the gap width (panel (**a**)) and the current density (panel (**b**)) in an ECM experiment at constant feed rate compared with theoretical computations based on the potential model. (The scales are omitted due to reasons of non-disclosure).  
Figure 14 displays experimental and simulated anode shapes obtained for two values of gap voltages. The position of the cathode obtained in the simulation is indicated as well to visualize the shape of the steady-state interelectrode gap. As evident from Figure 14, the results of the experiments agree quite well with those obtained in the simulations. They show that the underlying potential model is sufficient for describing the (continuous-mode) ECM process in our cases, where the widths of the interelectrode gaps are in the magnitude of several hundreds micrometers, ensuring the effective removal of reaction products in the interelectrode gap. However, more complex models would be needed if the interelectrode gap widths were smaller or when working with pulsed current, such as, e.g., in precise electrochemical machining (PECM).
## 2 Materials and Methods
### Experimental Setup  
Starting twice from the left and twice from the right side, in randomized order, each skier performed four runs on a corridor-shaped slalom course with 20 gates placed symmetrically at 12-m intervals and with a displacement of 4 m (Figure 1). To ensure that this course was set precisely, the gates were positioned using the Leica Geodetic Global Satellite Navigation System (GNSS) 1200 with its built-in Stake-Out application (Leica Geosystems AG, Heerbrugg, Switzerland). The terrain selected had an average incline of 16\({}^{\circ}\), with a maximal tilt to either side of \(<\)1\({}^{\circ}\), and was groomed on each day of testing. In light of the hard, icy snow and temperatures between \(-\)2 and 0 \({}^{\circ}\)C, the coaches and experimental team smoothed the course prior to each and every run in an attempt to standardize conditions for side-skidding.  
As described previously [24; 25], three-dimensional whole-body kinematics were monitored utilizing the MVN Biomech V2018 inertial system (Xsens Technologies B.V., Enschede, The Netherlands) and Leica Zeno GG04 plus Real-Time Kinematics RTK GNSS (Leica Geosystems AG, Heerbrugg, Switzerland). The inertial system (calibrated twice prior to each run) was worn under the skier's racing suit and the smart antenna (RTK GNSS) was integrated into the back protector and positioned at shoulder height to allow unobstructed satellite reception (Figure 2). Data collected by the inertial system were recorded on a memory card, while data from the GNSS RTK system were transmitted wirelessly to a handheld device (Conker NS6, Conker, Takeley, England). In connection with each measurement, the precise position of the smart antenna relative to the thoracic (T12) and cervical vertebrae (C7) was determined to allow reliable integration of these two sets of data.  
Figure 1: Schematic illustration of the corridor-shaped slalom course.  
In addition, the skier's boots were equipped with pressure insoles (Loadsol, Novel GmbH, Munich, Germany) that assessed the total ground reaction force acting perpendicular to the sole of the ski boot, the individual forces acting on the entire inside and outside foot and the distribution of force between the fore and rear foot. To assist analysis, all runs were also filmed at 50 Hz with a high-resolution camera (GC-PX100, The Japan Victor Company Ltd., Yokohama, Japan). To allow synchronization of all measurements, each skier performed three active squats and three hits with one of his skis on the ground before each start.
# Shape Design Optimization of a Robot Arm Using a Surrogate-Based Evolutionary Approach
## 3 Shape Design Optimization of the 2 nd Robot Arm
### Construction of the BBD Matrix  
Box-Behnken design is a rotatable second-order design based on three-level incomplete factorial designs, which can decrease the number of experiments to reduce laboratory work. A detailed description of the method can be found in [28]. Herein, a three-factor and three-level Box-Behnken design matrix with 46 runs was chosen as the basic form of the RSM. The Model-3 case is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{5}{c}{**Coded Variables**} & \multicolumn{5}{c}{**Uncoded (Real) Variables**} & \multicolumn{5}{c}{**Response (FEM)**} \\ \cline{2-13}  & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & \(\mathbf{X_{4}}\) & \(\mathbf{X_{5}}\) & \(\mathbf{H_{1}(x_{1})}\) & \(\mathbf{H_{m}(x_{2})}\) & \(\mathbf{H_{y}(x_{3})}\) & \(\mathbf{R_{h}(x_{4})}\) & \(\mathbf{T_{a}(x_{5})}\) & \(\mathbf{W(y_{1})}\) & \(\mathbf{J(y_{2})}\) & \(\mathbf{D(y_{3})}\) \\ \hline
1 & \(-1\) & \(-1\) & 0 & 0 & 0 & 150 & 450 & 110 & 55 & 95 & 37.8640 & 7.4954 & 0.03921 \\
2 & \(-1\) & 1 & 0 & 0 & 0 & 150 & 550 & 110 & 55 & 95 & 38.3861 & 7.6226 & 0.03809 \\
3 & 1 & \(-1\) & 0 & 0 & 0 & 200 & 450 & 110 & 55 & 95 & 38.4216 & 7.4789 & 0.03700 \\
4 & 1 & 1 & 0 & 0 & 0 & 200 & 550 & 110 & 55 & 95 & 39.2435 & 7.6958 & 0.03555 \\
5 & 0 & 0 & \(-1\) & \(-1\) & 0 & 175 & 500 & 100 & 50 & 95 & 37.0219 & 7.1141 & 0.04079 \\
6 & 0 & 0 & \(-1\) & 1 & 0 & 175 & 500 & 100 & 60 & 95 & 36.0889 & 7.0655 & 0.04287 \\
7 & 0 & 0 & 1 & \(-1\) & 0 & 175 & 500 & 120 & 50 & 95 & 41.1573 & 8.1635 & 0.03313 \\
8 & 0 & 0 & 1 & 1 & 0 & 175 & 500 & 120 & 60 & 95 & 40.2243 & 8.1149 & 0.03449 \\
9 & 0 & \(-1\) & 0 & 0 & \(-1\) & 175 & 450 & 110 & 55 & 90 & 35.3401 & 8.6825 & 0.04671 \\
10 & 0 & \(-1\) & 0 & 0 & 1 & 175 & 450 & 110 & 55 & 100 & 41.6777 & 8.2743 & 0.03387 \\
11 & 0 & 1 & 0 & 0 & \(-1\) & 175 & 550 & 110 & 55 & 90 & 35.97670 & 7.0237 & 0.04520 \\
12 & 0 & 1 & 0 & 0 & 1 & 175 & 550 & 110 & 55 & 100 & 42.3765 & 8.4515 & 0.03272 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Box-Behnken design matrix with data for Model-3.
# Application of USCCD on Girth Weld Defect Detection of Oil Pipelines
## 1 Introduction  
Girth Welds (GWs) of oil and gas pipelines are more and more concerning because of frequent failures and the accompanying high consequences [1]. They are always the weak points of the pipelines due to the field joining and worsened laying locations because of limited right-of-way. GW defects of newly built pipelines are much narrower, which challenges the traditional ILI technologies and analyzing methods when detecting, and are more dangerous because of higher diameters and pressures [2; 3].  
As conventional MFL (Magnetic Flux Leakage) is universally used in the industry and qualitatively known to be sensitive to volumetric metal loss [4; 5; 6], ultrasonic crack detection is a superior method for cracks and crack-like defects, because it is more sensitive to defect edges that are close to each other [7; 8; 9; 10]. The objective of this test was to evaluate and quantify the performance for UltraScanCircumferential Crack-Like Detection (USCCD) technology, based upon the pull test and in field data by PetroChina Pipeline Company (PPC) using GE PII 32\" UltraScan(tm) CCD Tool.