esa-sceva/satcom-chunk-collection huggingface dataset:
# A Nonlinear Method for Characterizing Discrete Defects in Thick Multilayer Composites
## 1 Introduction
However, thick multilayer composites have large number of layers and diverse layering methods, and they are difficult to be described by a generic model. A data processing method that is capable of dealing with nonlinearity in ultrasound signals would be more useful and suitable for thick multilayer composites for now. Recurrence analysis has been applied in a wide range of fields, including weather analysis, biological medicine, economic analysis, signal processing, and so on [12; 13]. Recently, recurrence analysis has proven to be useful in the ultrasonic testing of porous materials. Using recurrence quantification analysis (RQA), [PERSON] et al. [14] propose the ultrasonic signal modality as a new  
Figure 1: Schematic diagram of the pulse-echo method and an A-scan signal of Carbon Fiber Reinforced Polymer (CFRP) with interior defects and layers with different direction. approach for damage evaluation in concrete, and the results show that one of the RQA variables is more sensitive to damage in spoiled series than other NDT techniques. They also adopt recurrence analysis for the characterization of scattering material with different porosity and propose the measurement of predictability as an indicator of percentages of porosity [15]. Besides, [PERSON] has used RQA in ultrasonic testing of CFRP [16; 17] for the assessment of porosity. The works focus on structures, where the evaluation of the back wall echo from the opposite side of the ultrasonic probe is not able to made, and try to find the relationship between RQA variables and porosity to get an equivalent back wall echo. For porosity that is distributed throughout the volume, recurrence analysis of the entire time series yields good results, while discrete defects are locally distributed in the composite. The nonlinearity of ultrasonic pulses has been proven to be sensitive to distributed voids, while the recurrence analysis method may also be useful in characterizing the nonlinearity of discrete defects and needs to be experimentally studied. Therefore, in this paper, a 10 mm thick CFRP specimen with 80 layers and zero porosity was tested while using the ultrasonic pulse echo method. Blind holes with different diameters that are smaller than or equal to 1 mm at depth of 6 mm were artificially conducted in the specimen and the signals were analyzed by the recurrence analysis method. The statistics of RQA variables were used to characterize the stability of backscattered signals for inspection of discrete defects with small sizes.
# A Unified Approach for the Control of Power Electronics Converters. Part II: Tracking
## 5 Tracking with Perturbations
### Rejection of Non-Measurable Perturbations
To show the last part, suppose that (16) satisfied. Then, for any admissible reference signal, there exists \(\lambda_{ref}(t)\) such that \(0\leq\lambda_{ref}(t)\leq 1\) and  
\[\Pi_{2}(z)(t)=B_{0}(x_{ref}(t),\lambda_{ref}(t))\]  
We have (time is omitted for convenience)  
\[e^{T}W[B_{0}(x)\lambda+E_{0}(w)\mu-\Pi_{2}z] = e^{T}W[B_{0}(x)+E_{0}(w)\mu+B_{0}(x_{ref})\lambda_{ref}]\] \[= e^{T}W[B_{0}(x)(\lambda-\lambda_{ref})+E_{0}(w)\mu+\left(B_{0}( x)-B_{0}(x_{ref})\right)\lambda_{ref}]\] \[= e^{T}W[E_{0}(w)\mu+B_{0}(x)(\lambda-\lambda_{ref})+\left[ \begin{array} []{cc}A_{1}e&\cdots&A_{M}e^{T}\end{array} \right]\lambda_{ref}\right]\] \[= e^{T}WE_{0}(w)\mu+e^{T}WB_{0}(x)(\lambda-\lambda_{ref})+e^{T}W \underset{i=1}{\overset{M}{\sum}}\lambda_{ref_{i}}(A_{0}+A_{i})e-e^{T}WA_{0}e\]  
Using the fact that \(\lambda_{ref}\in\Lambda_{R}\) and \(\mu\in\Lambda_{R}\), we have  
\[e^{T}W[B_{0}(x)\lambda+E_{0}(w)\mu-\Pi_{2}(z)] =\frac{1}{2}\underset{i=1}{\overset{M}{\sum}}\underset{i=1}{ \overset{M}{\sum}}\lambda_{ref_{i}}\mu_{i}\Big{[}e^{T}\ w^{T}\Big{]}\left[ \begin{array}{cc}(A_{0}+A_{i})^{T}W+W(A_{0}+A_{i})+2I&WE_{j}\\ E_{j}^{T}W&-2\delta^{2}I\end{array} \right]\left[ \begin{array}{c}e\\ w\end{array} \right]\] \[+e^{T}WB_{0}(x)(\lambda-\lambda_{ref})-e^{T}WA_{0}e+\delta^{2}w^{ T}w-e^{T}e\] \[<-e^{T}e+\delta^{2}w^{T}w+e^{T}WB_{0}(x)(\lambda-\lambda_{ref})-e^ {T}WA_{0}e\]  
Introducing the change of variable \(\beta=\delta^{2}\), it is possible to minimize \(\gamma\) by solving the following LMI optimization problem: \(\underset{W>0}{\min}\beta\) under (16). Its optimal solution \(\beta^{*}\) giving the optimal value of \(\gamma\), \(\gamma^{*}=\sqrt{\beta^{*}}\).
## 6 Design of Experimental Scheme and Result Analysis
### Result and Error Analysis  
The strain components in three directions are collected for each point. To eliminate vibrating disturbance beyond the unit, the dynamic strains are filtered and de-noised by filter function, and the new dynamic strains are acquired. After the computational analysis of the new dynamic strains, the dynamical stress states for each point are determined. Table 2 lists the principal stresses on three monitoring points when their maximum strains appear within an average period of rotation.  
Point 1 is located at the region where stress is concentrated and the maximum stress appears on the suction surface of the blade. The maximum dynamic stress at Point 1 is about 18% less than that in the simulation. The test results of both Points 2 and 3 are also less than their simulated values. The discrepancies between these two methods may be caused by the following reasons: the test heads are lower than the design heads, indicating that the water pressure load on the blade surface in the test is lower than that in the simulation; the specific location of the maximum dynamic stress changes within the stress concentration region and is not fixed, although Point 1 is designed to be located at the place with maximum dynamic stress, so it may be located on a point nearby during actual testing; and the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Numerical Simulation**} & \multicolumn{2}{c}{**Experimental Value**} \\ \cline{2-5}  & **Maximum/MPa** & **Minimum/MPa** & **Maximum/MPa** & **Minimum/MPa** \\ \hline Point 1 & 151.0 & 119.4 & 123.9 & 94.6 \\ Point 2 & 31.9 & 25.5 & 27.5 & 21.6 \\ Point 3 & 14.4 & 10.4 & 11.8 & 7.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between numerical results and experimental results for dynamic stress.  
Figure 15: Monitoring the point arrangement and the on-site test instrument, (**a**) the arrangement of the on-site monitoring point, (**b**) the installation and fixation of a test instrument.  
installation of the offline data acquisition instrument on the hub and strain gauges and wires for stress tests may have certain influences on flow fields.
## 4 Sub-Problem 2
### Feasibility Analysis  
The first question to be answered is whether the gravity-field recovery is possible with the inter-satellite range data. To answer this question, we remove errors from the simulated observation data, i.e, the observations are accurate. We compute the residuals as functions of the gravity coefficients \(C_{20}\) and \(C_{22}\) to be determined. We also assume that the orbits are already determined without errors, and the initial conditions for the two orbits are the same as test 1 in Section 3.2: \(\sigma_{1}=[3,0,90^{\circ},60^{\circ},60^{\circ},0^{\circ}]\) and \(\sigma_{2}=[3,0,0^{\circ},0^{\circ},0^{\circ},0^{\circ}]\). For the example orbits above, the \(RMS\) as a function of \(C_{20}\) and \(C_{22}\) is displayed in the contour  
Figure 3: \(RMS\) of different arc lengthsmap, Figure 4a. There is no doubt that there is only a single minimum in the contour map, which indicates that the gravity-field recovery is possible, in theory. The integration time is 100(T) and the measurement interval is 0.05(T).  
There is only one global minimum value of the RMS, so it is possible for us to achieve the gravity-field recovery. However, if the measurements are too sparse, the number of local optimum will be more than one. In Figure 4b, the measurement interval is set as 1(T). Obviously, there is more than one local minimum value in Figure 4b. In this case, it is difficult to recover the accurate gravity field. Therefore, dense measurement data is recommended for the gravity-field recovery. For this reason, in the calculations below, the measurement interval is set as 0.05(T).  
Here is an example. The initial conditions are the same as the example in Section 3.2. The initial estimate of gravity-field coefficients is \(C_{20}=-5\times 10^{-2}\) and \(C_{22}=9\times 10^{-2}\) and the measurement interval is set as 0.05(T). The recovered gravity-field coefficients are  
\[C_{20}=-5.247792\times 10^{-2}\ ;\ C_{22}=8.253777\times 10^{-2} \tag{17}\]  
Compared with Table 1, the result is very close to the true value \(C_{20}=-5.2478\times 10^{-2}\) and \(C_{22}=8.2538\times 10^{-2}\).
## 5 Genetic Counselling
### Genetic Discrimination  
Fear of genetic discrimination and protection of personal data are major factors present among individuals considering genetic testing [138]. Furthermore, a person's culture, race, and ethnicity may influence their willingness to pursue genetic testing, so it may be important to address genetic discrimination fears and cultural influences during genetic counselling [139].  
Genetic discrimination and the laws to protect patients vary widely depending on a patient's country, and many countries have started to adopt laws to protect patients [140]. Canada's Genetic Non-Discrimination Act (GNDA) prohibits companies and employers from requiring genetic testing or the results of genetic tests. In Europe, a collection of guidelines have been adopted concerning the use of genetic information for insurance purposes [141]. In the United States, all individuals pursuing genetic testing are protected against genetic discrimination by the Genetic Information Nondiscrimination Act of 2008 (GINA) [142]. This act protects individuals from discrimination based on their genetic status with employment and health insurance. Employers are prevented from using employee genetic information to make employment decisions and are prevented from requesting genetic information from employees. Health insurers are prevented from denying coverage, making decisions on premiums or coverage based on genetic information, or requesting genetic information from individuals or family members. GINA does not include protections from life, disability, or long-term care insurance discrimination. There are exceptions to the employment and health insurance protections explained above, and relevant details should be discussed with patients depending on their specific circumstances and jurisdiction.
## 2 Literature Review and Hypotheses Development
### The Anti-Oxidant Effect  
Previous studies have highlighted that the anti-oxidant effect refers to the ability of certain compounds to neutralize or reduce the damage caused by oxidative stress, which is linked to conditions such as heart disease and cancer [40; 41]. Propolis products have emerged as functional foods emphasizing their anti-oxidant properties. Functional foods are designed to offer health benefits beyond basic nutrition, promoting overall well-being [3; 11]. With the growing interest in healthier lifestyles, consumers have become increasingly attracted to the anti-oxidant claims associated with propolis products [2; 42]. Consequently, marketers of propolis products have focused on promoting the health benefits, particularly emphasizing the anti-oxidant effect [2; 4]. It can be inferred that consumers are likely to associate the anti-oxidant effect with improved health. Existing literature suggests that the motivation for consuming functional foods is often driven by the belief and desire for better health, which is closely tied to healthiness perception [10; 11]. However, despite the marketing strategies employed, limited research has investigated how the anti-oxidant effect influences healthiness perception focusing on consumer behavior. While previous studies have scientifically confirmed the anti-oxidant effects of propolisis [43; 44; 45], this research focuses on the underexplored area of consumer behavior related to these effects. To minimize such a research gap in the literature, this study proposes the following research hypotheses:  
**Hypothesis 2a.**_The anti-oxidant effect positively affects healthiness of propolis products._  
**Hypothesis 2b.**_The anti-oxidant effect positively affects intention to purchase propolis products._
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
## 4 Proof-of-Concept Implementation
### The mIBC Infrastructure  
The mIBC infrastructure was described in detail in [13, 14]. A similar identity-based cryptographic (IBC) model is presented in RFC6507 and RFC6508. In particular, in \"Appendix A: Test Data\", these RFCs provide an IBC model with specific cryptographic parameters for testing purposes. This IBC model, along with its cryptographic values, is used herein to implement the mIBC-AIS infrastructure. By doing so:  
* We are able to check the validity of all cryptographic computations in this work by simply comparing our results to those in \"Appendix A: Test Data\" of RFC6507 and RFC6508;
* We implement the mIBC in an over-demanding cryptographic environment. This is because both RFC6507 and RFC6508 adopt a security level higher than what is necessary for the mIBC. Therefore, by using the cryptographic values of RFC6507 and RFC6508, we demonstrate the workings of our solution in the worst-case scenario, with sizes of the transmitted cryptographic data larger than the ones required in the real world. For example, in the proposed mIBC, the ID of each vessel would be its nine-digit MMSI; in the proof-of-concept implementation, we use the ID in \"Appendix A: Test Data\" of RFC6507 and RFC6508, namely, the (much longer) ID \"2011-02\(\backslash\)0 tel: +447700900123\(\backslash\)0\".  
The exact values of each cryptographic parameter that are used in this paper can be found in \"Appendix A: Test Data\" of RFC6507 and RFC6508. To maintain consistency with the nomenclature used in [13, 14], Table 1 depicts the cryptographic components of the mIBC of [13, 14] and the corresponding cryptographic parameters of RFC6507 and RFC6508.
# Lateral Stability of a Mobile Robot Utilizing an Active Adjustable Suspension
## 5 Case Studies
### Case Study for Lateral Stability
#### 5.2.4 Discussion  
The above results show that a mobile robot with an active suspension is able to increase its lateral stability by properly adjusting its configuration. More specifically, the configuration is changed by adjusting the linear actuator lengths on both the downhill and uphill sides.  
When the linear actuator lengths on both sides are equal, the ground clearance and the lateral stability can be adjusted. The lowest ground clearance is the most stable with respect to others. In other words, the mobile robot is most stable when the linear actuator lengths on both sides are all at their minimum values. Then, for the initial state, the mobile robot is the most stable at its lowest ground clearance.  
Thus, expending the linear actuator on the downhill side when the initial state is at its lowest ground clearance is chosen as the method. With the method, the robot increases its lateral stability, because the initial state at the lowest ground clearance is the most stable with respect to the other states. Thus, the robot's configuration can enhance its lateral stability with an active suspension amid different ground clearances.  
In addition, the leveled posture can be considered as an appropriate configuration, because the equipment on the robot can run smoothly with the level configuration. The mobile robot can level its configuration when the angle of the side slope is less than \(12.33^{\circ}\). The leveled configuration is achieved during the process of the method (expending the linear actuator on the downhill side when the initial state is at its lowest ground clearance). During this process, the leveled attitude is not the configuration with maximum stability. Overall, the leveled configuration is a kind of posture that can both enable the robot work smoothly and increase the robot's lateral stability, to some extent.
## Materials and Methods
### Discriminant Analysis Algorithms
#### 2.5.4 Support Vector Machine Discriminant Analysis (SVM-DA)  
As a supervised machine learning algorithm, the SVM function can efficiently handle linear and nonlinear data for regression or classification purposes [40]. The principle of the SVM algorithm is to map the original data into higher dimensional spaces, and it optimizes a hyperplane with an appropriate margin to classify different classes. The SVM used to be designed as a linear algorithm for two-class classification cases by calculating the widest margin between classes [33]. While using some complex mapping functions, the SVM algorithm could deal with data which were not linear in the initial space to achieve regression and classification goals. To conduct SVM-DA, the radial basis function (RBF) was chosen as the mapping function in this study. The grid-search method was utilized to determine the parameter of gamma (g) and the value of the penalty coefficient (c). The parameter g was a parameter belonging to the RBF, which was used to control the width of the Gaussian kernel in the RBF and then control the shape of the separating hyperplane. The value of c represented a penalty associated with samples which were not correctly separated by the classifying hyperplane, and the width of the margin between different categories could finally be determined. Both of these two parameters could affect the performance of the SVM-DA model, where the grid-search method was used to obtain optimal c and g values from \(10^{-8}\) to \(10^{8}\) spaced uniformly at one in the log. Eventually, the optimal values were used in the final SVM-DA model to classify samples in the testing set.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 5 Analysis of the Simulation and Test
### Simulation in NS2
#### 5.1.3 Influence of \(T_{ref}\) on the Synchronization Precision  
In the simulation, the network contained 300 nodes and the communication delay was a random variable with a mean of 1 ms and variance of 2 ms\({}^{2}\). \(T_{ref}\) was set as 0.5, 1, and 3 ms, respectively. The result is shown in Figure 7. In the simulation when \(T_{ref}\) was 0.5 ms, the number of hops required to complete the time synchronization of the network was two times greater than when \(T_{ref}\) was 1 or 3 ms. Meanwhile, the accumulation error of synchronization in the simulation where \(T_{ref}\) was 0.5 ms was relatively larger than in the others and the growth was more obvious. Therefore, a larger \(T_{ref}\) was beneficial for selecting a reference node with a better performance. However, the cumulative errors when \(T_{ref}\) was 1 or 3 ms were nearly the same; that is, \(T_{ref}\) was a factor of synchronization precision, but its influence became lower when \(T_{ref}\) became bigger. In applications, an appropriate \(T_{ref}\) should be selected to satisfy the need of the convergence time and synchronization precision.  
Figure 5: Performance of the accumulation error.  
Figure 6: Convergence time of different numbers of nodes.
## Article
###### Abstract  
Ultrasonic guided wave testing is an essential technique in non-destructive testing for structural integrity of oil and gas pipelines. This technique, based on the pulse-echo method, is often used for the long-range detection of pipelines at any location. However, guided waves suffer from high attenuation when they propagate in attenuating material structures and multiple wave modes due to the excitation, which reduces the power of echo signals and induces corruption caused by coherent noise. In this paper, a developed hybrid coded excitation method that uses the convolution of a Barker code and Golay code pair is proposed and applied for an ultrasonic guided wave testing system to excite the torsional guided wave mode T(0,1) in a steel pipe. The proposed method combines the advantages of these two coding methods and increases the flexibility of code lengths. The performance is evaluated by signal to noise ratio and peak sidelobe level of the processed signal. Both theoretical simulations and experiments have investigated using the proposed codes composed of Barker codes and Golay code pairs of different lengths and combinations. The experimental results show the significant improvement of the signal to noise ratio and the peak sidelobe level due to the proposed hybrid code usage for the excitation of guided waves. The values are further improved to around 32 dB and around \(-\)24 dB, respectively. Overall, the proposed hybrid coded method for improving the echo SNR can benefit from guided wave testing to reduce coherent and random noise levels and many other potential applications.  
guided wave testing; coded excitation; SNR; Golay code; Barker code; pipeline
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 2 Background
### Hyperspectral Data Processing  
The amount of data per pixel in hyperspectral data can be an order of magnitude higher than multispectral data. In addition, with their higher dimensionality, hyperspectraldata are more difficult to visualize and extract desired information from than multispectral data and advanced data processing is required.  
Advanced data-processing algorithms investigated by the HYPSO research team include the following:  
* Band-ratio-based estimation [38],
* Spectral target detection [39],
* Dimensionality reduction [40; 41],
* Deconvolution (deblurring) [42],
* Super-resolution by pan-sharpening [43; 44],
* Super-resolution by slewing [32],
* and Compressive sensing [45].  
On-board Hyperspectral (HS) data processing is constrained by large datasets, limited processing time and communication links. The Consultative Committee for Space Data Systems (CCSDS) has developed data and image compression algorithms CCSDS121 [46], CCSDS122 [47], CCSDS123 [25; 48] specifically designed for space data systems, notably to reduce data transmission time [49]. The first issue of the CCSDS123 compression standard from 2012 [48] is an efficient lossless prediction-based algorithm characterized by low complexity. In fact, in recent years several FPGA implementations of the CCSDS123 standard have been presented in the literature [50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. The second issue of the CCSDS123 document [25] presents a lossy counterpart.