esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Buckling and Vibration Analysis of SMA Hybrid Composite Sandwich Beams
## 3 Governing Equations Derivation
### Stability Equations
In addition, \(\gamma_{ij}^{L}\) and \(\varepsilon_{ij}^{L}\) correspond to the linear terms of the shear and normal strain tensor, respectively, whereas \(\gamma_{ij}^{NL}\), \(\gamma_{ij}^{NL}\), and \(\varepsilon_{ij}^{NL}\) are nonlinear terms of the shear and normal strain tensor, respectively. By considering Equations (13) and (33), the variational strain energy, \(\delta PE_{p}\), is defined as:  
\[ \begin{array}{l}\delta PE_{p}=\int\limits_{0}^{L}\left(N_{x}\frac{d\omega} {dx}+M_{x}\Big{(}\frac{5}{4}\frac{d\delta\omega_{x}}{dx}+\frac{1}{4}\frac{d \delta\omega_{0}}{dx^{2}}\Big{)}+\right.\\ P_{x}\Big{(}\frac{5}{3\mu^{2}}\frac{d\delta\rho_{x}}{dx}+\frac{5}{3\mu^{2}} \frac{d^{2}\omega_{0}}{dx^{2}}\Big{)}+\\ N_{x}\Big{(}\frac{5}{4}\delta\phi_{x}+\frac{5}{4}\frac{d\omega_{0}}{dx}\Big{)} +R_{xx}\Big{(}-\Big{(}\frac{5}{\mu^{2}}\delta\phi_{x}+\frac{5}{\mu^{2}}\frac{ d\omega_{0}}{dx}\Big{)}\Big{)}\Big{)}dx\end{array}  \tag{35}\]  
In addition, the variational energy related to primary stresses (34) \(\delta PE_{T}\) is defined as:  
\[\delta PE_{T}=\int_{0}^{l}N_{0x}\frac{dw_{0}}{dx}\frac{d\delta w_{0}}{dx}\;dx \tag{36}\]  
where:  
\[ \begin{array}{l}N_{0x}=N_{0x}^{T}+N_{0x}^{T}\\ N_{0x}^{T}=-\int_{-(h_{c}/2+l_{h})}^{l_{c}/2+l_{h}}\left(C_{11}(z)\alpha_{x} \Delta T\right)dz\\ N_{0x}^{r}=\int_{-(h_{c}/2+h_{h})}^{h_{c}/2+h_{a}}V_{s}\sigma_{x}^{r}dz\end{array}  \tag{37}\]  
By substituting Equations (30), (35), and (36) into Equation (28), we obtain the following equations of motion:  
\[\begin{array}{l}\delta u_{0}=0\Rightarrow\partial\frac{2N_{x}}{dx}=I_{0} \frac{\partial^{2}u_{0}}{\partial t^{2}}+\frac{1}{4}I_{1}\frac{\partial^{3}u_ {0}}{\partial x\partial t^{2}}+\frac{5}{4}I_{1}\frac{\partial^{2}\phi_{x}}{ \partial t^{2}}-\frac{10}{6\mu^{2}}I_{3}\Big{(}\frac{\partial^{2}\phi_{x}}{ \partial t^{2}}+\frac{\partial^{3}u_{0}}{\partial x\partial t^{2}}\Big{)}\\ \delta\phi_{x}=0\Rightarrow\frac{5}{4}\frac{dM_{x}}{dx}-\frac{5}{3\mu^{2}} \frac{\partial P_{x}}{dx}-\frac{5}{4}N_{xx}+\frac{5}{4}I_{xx}=\frac{5}{4}I_{1} \frac{\partial^{2}u_{0}}{\partial t^{2}}+\frac{25}{16}I_{2}\frac{\partial^{2} \phi_{x}}{\partial t^{2}}+\\ \frac{5}{16}I_{2}\frac{\partial^{3}u_{0}}{\partial x\partial t^{2}}-\frac{10}{6 \mu^{2}}I_{3}\frac{\partial^{2}u_{0}}{\partial t^{2}}-\frac{5}{2\mu}I_{4} \frac{\partial^{2}u_{0}}{\partial x\partial t^{2}}-\frac{25}{6\mu^{2}}I_{4} \frac{\partial^{2}\phi_{x}}{\partial t^{2}}+\frac{50}{
# A Novel Impact Rotary-Linear Motor Based on Decomposed Screw-Type Motion of Piezoelectric Actuator
## 2 Working Principle  
### Piezoelectric Actuator  
The PA is composed of a piezoelectric tube which has an even number of helical interdigitated electrodes on its outside surface. The structure of the PA and the expanded surface of the PA cutting along the longitudinal direction are shown in Figure 1. The piezoelectric tube has a total length of \(l_{1}\). The outer radius and inner radius of the piezoelectric tube are \(r_{1}\) and \(r_{2}\), respectively. The effective length of the electrodes is \(l_{2}\), the angle of helical electrodes is \(\alpha\), and the driving voltage applied to each two adjacent electrodes is \(2U\). When the direction of a stable driving electric field is the same as that of poling direction and they are perpendicular to the electrode lines, the contraction strain and expansion strain will be produced simultaneously. Thus the PA will have screw-type motion ability. The contraction strain is caused by \(d_{31}\) piezoelectric coefficient which is parallel to the direction of helical electrodes and the expansion strain is caused by \(d_{33}\) piezoelectric coefficient which is perpendicular to the direction of the electrode lines.  
Figure 1: Schematic diagram of PA.  
Theoretically, the PA will produce a load-free torsional angle and longitudinal displacement under the quasi-static driving as following [28; 29]:  
\[\theta=\frac{k_{1}(d_{33}-d_{31})El_{2}\sin 2\alpha}{r_{o}}, \tag{1}\]  
\[\zeta=k_{2}\Big{(}d_{33}\cos^{2}\alpha+d_{31}\sin^{2}\alpha\Big{)}El_{2}, \tag{2}\]  
\[E=\frac{2U\cdot n}{\pi r_{o}\sin\alpha}, \tag{3}\]  
where \(k_{1}\) is compensation factor in torsional direction, \(k_{2}\) is compensation factor in longitudinal direction, \(E\) is the average driving electric field intensity, \(2U\) is the driving voltage between two adjacent electrodes, \(n\) is the pairs of electrodes.  
Thus, the PA will produce a screw-type motion under the control of driving voltage in theory.
## 2 Methodology
### Psychoacoustic Metrics  
The data were processed to calculate several metrics for each configuration. These are outlined below:  
1. Sound pressure level  
The sound pressure level is a measure of the effective pressure of a sound. This is predicted by taking root mean square (RMS) of a recorded wave and using a reference pressure converted into decibels (dB). This metric does not consider the different frequencies present within a wave, and therefore the human ears perception of them.
2. A-weighted sound pressure level  
The frequency of a sound significantly affects the ears perception of it. Therefore, an 'A weighting' (ANSI(r) S1.42 Standard) filter was used to acquire dB(a), as per customer specification. This involves passing the waveform through a filter (Figure 2) to better align it with the human ears' ability to hear the sound.
3. Loudness  
Although an A weighted filter gives a better idea of how a human ear interoperates sound pressure, there are alternative options that are more effective. Figure 3 shows equal loudness curves, which show the sound pressure levels at each frequency which are perceived as having equal 'loudness', loudness being a perceptual measure of the sounds effect of the ear [33].  
Within this report loudness will be represented in two ways. Firstly, specific loudness (N'). N' is the loudness unit of Sone, which a linear measure of loudness, at each Bark level. The Bark scale ranges from 1 to 24, representing the 24 critical bands of hearing which broadly speaking is the range in which two tones will interfere with each other through auditory masking [33].  
The relationship between the Bark scale and frequency is relatively linear between 0 Hz and 500 Hz and logarithmic above that. The total loudness (N), which gives an overall loudness metric of a sound can be calculated by the integration of the critical bands N'. This gives a more representative measure than sound pressure level, or A-weighted pressure, of how load the human ear hears a sound, however, does not identify if a sound is unpleasant. Although neither of these metrics identify 'anroying' sounds, they are more representative of what a human hears than sound pressure level or using a fast Fourier transform (FFT) of the signal for frequency analysis. By comparing specific loudness plots it is possible to identify why a certain sound is different than others.  
Figure 3: Equal loudness contours red (from ISO 226:2003 revision [34]). Original ISO standard shown (blue) for 40-phons.  
Figure 2: ‘A weighting’ filter.  
Sharpness  
Sharpness gives a measure of the high-frequency content of a sound in proportion to the rest of the frequency content. There is not yet a standardised unit of sharpness; within this work, the unit 'acum' will be used, which is calculated through the Zwicker and Fastl method [35].
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 2 Intra-Vehicle Security
### Cryptography in Intra-Vehicle Networks  
Cryptography plays a key role in intra-vehicle networks and in the security solution adopted. The role of cryptography is to provide important security features to intra-vehicle networks, e.g., _confidentiality_, _integrity_, _authentication_, and _availability_. Among those, _authentication_ in intra-vehicle is a hot research topic [42], since not all the intra-vehicle communication protocols incorporate embedded (and secure) authentication mechanisms. Among the possible authentication mechanisms, there are context-specific solutions [43], bus-specific solutions [44], watermark-based solutions [45], and Elliptic Curve Cryptography digital signatures (ECDSA) [46]. In this regard, our solution does provide authentication services but it is designed to be bus-agnostic. While this decision precludes us from accessing bus-specific metadata useful for authentication, it allows us to abstract the communication bus and offer better flexibility. On the cryptography side, we adopt an ECDSA version that is customized to work with the hybrid cryptography scheme while maintaining its security properties (as demonstrated in [47] and summarized in Section 3.3).  
Regarding _confidentiality_, state-of-the-art symmetric ciphers (e.g., the Advanced Encryption Standard--AES) are commonly adopted with one critical aspect: the cryptographic key distribution/exchange problem. In intra-vehicle networks this problem can also be addressed in context-specific ways, e.g., using dynamic keys and a semi-centralized solution [48]. While the security level and performances offered by AES are undisputed, there are many possible pitfalls on how AES is used (e.g., mode of operations). In this regard, our solution makes full use of its hybrid cryptographic scheme to generate AES keys which do not require full distribution and that are bound to the network topology. Additionally, AES is used in CCM mode with an Encrypt-Then-MAC approach to provide _Authenticated Encryption_.  
A useful cryptography-related feature that has been recently addressed is the _data immutability_ via the use of blockchains. Examples of the use of blockchain-based mechanisms can be found in [49; 50]. In those works, the authors list the increased computational cost among the big challenges. This is a critical concern for low-latency security solutions and may limit the performance while requiring additional resources and services. Due to this, our solution does not include blockchain technologies support as core feature but remains open to supporting them in scenarios with relaxed performance requirements.
# A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
## 2 Basic Principle  
VMD is an adaptive signal processing technique that relies on the formulation and resolution of variational problems [38]. Essentially, the VMD algorithm partitions the input signal into \(K\) IMFs while simultaneously minimizing the estimated broadband sum. To achieve this, the algorithm operates under the assumption that the sum of the IMF components is equivalent to that of the original signal. The corresponding variational problem is subject to certain constraints and can be expressed as:  
\[\left\{\begin{array}{l}\min_{\{u_{k}\},\{\omega_{k}\}}\bigg{\{}\sum_{k}\ \Big{|}\partial_{t}[(\delta(t)+\frac{j}{\pi t})*u_{k}(t)]e^{-j\omega_{k}t} \Big{|}\Big{|}_{2}^{2}\bigg{\}}\\ s.t.\underline{\sum_{k}u_{k}}=f\end{array}\right. \tag{1}\]  
where; \(u_{k}\) is the decomposed IMF components, and \(\omega_{k}\) is the central frequency of each IMF component.  
The problem is solved by introducing a penalty factor \(\alpha\) and a Lagrange multiplier operator \(\lambda\) into the model, thereby transforming it into an unconstrained variational problem.  
\[L(u_{k},\omega_{k},\lambda)=\alpha\sum_{k}\left\|\partial_{t}[\left(\delta(t) +\frac{j}{\pi t}\right)*u_{k}(t)]e^{-j\omega_{k}t}\right\|_{2}^{2}+\left\|f- \sum_{k}u_{k}\right\|_{2}^{2}+\left\langle\lambda_{s}f-\sum_{k}u_{k}\right\rangle \tag{2}\]  
where; \(f\) is the original signal.  
The steps for solving Equation (2) are as follows:  
Initialize parameters \(\{\hat{a}_{k}^{1}\}\), \(\{\hat{\alpha}_{k}^{1}\}\), \(\{\hat{\lambda}^{1}\}\), and set \(n=0\);  
Let \(n\to n+1\), an update \(u_{k},\omega_{k}\), \(\lambda\) iteratively according to Equations (3)-(5):  
\[\hat{a}_{k}^{n+1}(\omega)=\frac{\hat{f}(\omega)-\sum_{i\
eq k}\hat{u}_{i}( \omega)+\frac{\hat{\lambda}(\omega)}{2}}{1+2\alpha(\omega-\omega_{k})^{2}} \tag{3}\]  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega|\hat{u}_{k}(\omega)|^{2}d \omega}{\int_{0}^{\infty}|\hat{u}_{k}(\omega)|^{2}d\omega} \tag{4}\]  
\[\hat{\lambda}^{n+1}(\omega)=\hat{\lambda}^{n}(\omega)+\tau(\hat{f}(\omega)- \sum_{k=1}^{k}\hat{n}_{k}^{n+1}(\omega)) \tag{5}\]  
where; \(n\) is the number of iterations, \(\wedge\) is the Fourier transform, and \(\tau\) is the noise tolerance.  
Repeat Step 2 until the cycle ends, when the components satisfy Equation (6), and \(K\) IMFs are obtained.  
\[\sum_{k=1}^{k}\frac{\left\|u_{k}^{n+1}-u_{k}^{n}\right\|_{2}^{2}}{\left\|u_{k }^{n}\right\|_{2}^{2}}<\varepsilon \tag{6}\]  
where; \(\varepsilon\) is the discriminant accuracy.  
Proper parameter selection is a prerequisite for VMD signal decomposition, where the parameters \(K\) and \(\alpha\) exert major influence on the decomposition effect, whereas the parameters \(\varepsilon\) and \(\tau\) play a less prominent role [39]. Hence, achieving the optimal VMD decomposition effect warrants the identification of suitable \(K\) and \(\alpha\) values.
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 4 Recent Developments of Shearographic NDT
### Recent Applications of Shearographic NDT  
Digital shearographic NDT has some limitations at present, but it is still a very practical NDT tool for material evaluation and quality inspection. In the field of nondestructive testing of traditional aerospace materials, it can be used for detecting flaws such as delamination, sticking, cracking, voids, and damage in aircraft components, composite materials, and honeycomb structures. In the automotive industry, it can also be used for applications such as tire testing and table inspection. In addition, digital shearography is a good prospect for applications such as the detection and measurement of composite materials in machinery, construction, transportation, and other industries [58; 59; 60; 61].  
In 2015, [PERSON] studied the inner defect detection of pressure vessels via the theoretical mode, numerical simulation (finite element method), and experiment in which digital shearography and the phase-shifting method was used [62]. This work proved that it is feasible and advantageous to use digital shearography for the detection of inner defects of pressure vessels. In 2017, [PERSON] et al. used an endoscopic shearography system with radial sensitivity for inner inspection of adhesion faults in composite material pipes [63]. The system used two conical mirrors to achieve radial sensitivity, and a special Michelson-like interferometer was formed, replacing one of the plane mirrors with a conical mirror. In 2017, [PERSON] used the digital shearography technique for defect detection on artwork while using the lock-in-technique to control the penetration depth of the thermal wave, enabling the determination of depth information [64]. Due to the high rigidity and/or high thermal conductivity of metals, it is difficult for shearography with conventional loading methods to detect defects. In response to this problem, in 2017, [PERSON] et al. proposed shearography using wave-defect interactions for crack detection in metallic structures [54]. In 2017, [PERSON] et al. presented a new application of shearography using the technique to measure viscoelasticity in terms of creep compliance of elastomeric materials [65; 66]. The results were compared to that obtained with DIC, and this work demonstrated that shearography can be successfully applied to the creep analysis of viscoelastic materials. The applications and scope of the digital shearography technique is still expanding. At the same time, research on the specific effects of loading methods and parameters on digital shearography is also ongoing.
## 2 Generation of Converter Transformer Inrush Current and Its Time-Frequency Characteristics
### Time-Frequency Characteristic Analysis of Magnetizing Inrush Current
#### 2.2.4. Frequency Domain Characteristics  
The equivalent circuit of the inrush current based on the conventional DC converter station of Dongfang Converter Station based on PSCAD is shown in Figure 2. The specific simulation parameters are shown in Table 1.  
The working conditions are as follows: (1) the closing angle is 0\({}^{\circ}\), and the remanence is 0.8 p.u.; (2) the closing angle is 0\({}^{\circ}\), and the remanence is 0.2 p.u.; (3) the closing angle is 30\({}^{\circ}\), and the remanence is 0.8 p.u.; (4) the closing angle is 30\({}^{\circ}\), and the remanence is 0.2 p.u.; (5) the closing angle is 60\({}^{\circ}\), and the remanence is 0.8 p.u. and (6) the closing angle is 60\({}^{\circ}\) and the remanence is 0.2 p.u. The main frequency of the inrush current of the converter transformer is concentrated in the range of 0-500 Hz, and the inrush current is mainly concentrated at 50 Hz, as is its frequency doubling component, except the DC component, for which the main frequency is 50 Hz. The current components and their contents under different working conditions are shown in the following Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Converter transformer capacity & 237.4 MVA \\ Frequency & 50 Hz \\ Grid side/valve side voltage & 303.12 kV/93.07 kV \\ Hollow reactance & 0.2 p.u. \\ Magnetizing current & 0.084\% \\ Leakage reactance & 0.16 p.u. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electromagnetic simulation parameter settings.  
Figure 2: Electromagnetic simulation circuit diagram of the actual converter station.  
From the results, it can be seen that for the spectrum under different working conditions, as the amplitude of the inrush current increases, the current component at the fundamental frequency (50 Hz) will also increase. The DC component accounts for about 50-70% of the fundamental component, and in addition to the main frequency, other frequency doublings are affected by factors such as current amplitude and remanence, and it is difficult to judge their size by single-factor characteristics. When the harmonic is more than eight times (400 Hz), the content is less than 5%.
To date, no paper is available in the literature on the determination of lipophilic compounds such as tocopherols and carotenoids in \"Ragusano\" cheese. Currently, the only available source refers to the yellow color index, originating from fresh plant materials, that could be associated with \(\beta\)-carotene and other related carotenoid compounds [9]. Tocopherols (also known as vitamin E) and carotenoids are fat-soluble microconstituents of numerous dietary sources. Some potential benefits that have been attributed to these compounds in humans include protection against cardiovascular disease, cancer, and age-related eye diseases [22; 23]. Vitamin E and carotenoids are involved in sensory and nutritional cheeses' distinctiveness, and their concentrations and relative compositions are often related to genetic and physiological factors, the animal's forage diet, metabolism by the animal tissues, original milk composition, and cheese-making processes, which are also dependent on several environmental factors [24; 25; 26]. Studies, characterizing different cheeses, examine the quality of dairy products and their consumer's acceptability, which often depends on lipid autoxidation. In this context, tocopherols and carotenoids act as fat-soluble antioxidants and contribute to the oxidative stability of the product [25]. Particularly, it was shown that among ruminants, only bovine accumulate high concentrations of carotenoids (mainly \(\beta\)-carotene and lutein) [25], while \(\alpha\)tocopherol is the most representative among vitamin E group [26]. Additionally, in recent years, the importance of the type and quality of fodder in the diet of cows for lactation has received much emphasis, and several studies have shown that the concentration of vitamins in milk and dairy products vary according to the nature of the animals' foraging [26]. The objective of this research was to study the \"Ragusano\" composition and determine the carotenoids (\(\alpha\)-carotene, \(\beta\)-carotene, and lutein), and vitamin E (\(\alpha\)-tocopherol, \(\delta\)-tocopherol, and \(\gamma\)-tocopherol) contents, to protect the quality of this Italian cheese. Particularly, this investigation is aimed to track possible changes in the liposoluble antioxidant profile by following the cow-feeding and cheese-production seasons. The second objective of this study was inspired by specific strategies that Italy (among the top ten cheese producers in the world) is pursuing to progressively implement innovative guidelines to enhance dairy quality toward both the consumers' health and the supply chain's sustainability. Furthermore, since dairy management can affect the final cheese composition, the second objective was to investigate the seasonal variation of micro-constituents in \"Ragusano\" using a mineral-vitamin dietary supplementation.
## 5 Conclusions  
The model of incident elastic P/S wave scattering by the two-ring borehole has been presented to investigate the influence of the expansive grout on the theoretical bandwidth of borehole tensor strainmeters. The existence of grout decreases the zero-frequency gains and peak values of \(FV_{2}\), \(FV_{4}\), \(TV\), \(FS\), and \(TS\), and increases the corresponding \(f\) for peak value relative to the empty borehole. Except for the zero-frequency gains of gauge combinations, the theoretical bandwidths of gauge combinations are not influenced by the mechanical properties of the expansive grout compared with the empty borehole, which suggests the quantitative measurability of high-frequency seismic strain waves by borehole tensor strainmeters with a theoretical bandwidth of more than 200 Hz. Furthermore, it provides a theoretical basis for the in situ calibration of borehole tensor strainmeters and the further study of seismology.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Wave Types**} & **Bandwidth of Gauge** & \(E_{2}\) & \(\
u_{2}\) & \(\rho_{2}\) \\  & **Combinations** & **(20 GPa\_40 GPa)** & **(0.15\_0.35)** & **(1.6 g/cm\_3-2.2 g/cm\_3)** \\ \hline \multirow{5}{*}{P wave} & 1\% bandwidth of \(FV_{2}\)(Hz) & 923–1029 & 1019–1068 & 796–912 \\  & 1\% bandwidth of \(FS\)(Hz) & 619–649 & 708–715 & 643–705 \\  & 1\% bandwidth of \(FV_{4}\)(Hz) & 916–1018 & 1008–1056 & 797–907 \\  & 1\% bandwidth of \(TV\)(Hz) & 916–1018 & 1008–1056 & 979–907 \\  & 1\% bandwidth of \(TS\)(Hz) & 689–732 & 808–713 & 641–705 \\ \hline \multirow{2}{*}{S wave} & 1\% bandwidth of \(FS\)(Hz) & 599–605 & 691–701 & 515–586 \\  & 1\% bandwidth of \(TS\)(Hz) & 599–606 & 641–703 & 517–586 \\ \hline \hline \end{tabular}
\end{table}
Table 9: 1% bandwidth of gauge combinations for different mechanical parameters of the expansive grout by incident P/S wave.  
Figure 10: The influence of the Poisson ratio \(\
u_{2}\) of the expansive grout on 1% bandwidth of \(FV_{2}\), \(FV_{4}\), \(TV\) and \(FS\) for borehole tensor strainmeters for the incident P wave.  
Here we only discuss the influence of the expansive grout on the theoretical bandwidth of borehole tensor strainmeters, and prove the possibility of quantitative measuring of high-frequency seismic strain waves in theory. However, the actual situation is more complicated. It is necessary to do the job using the in situ observed data in the future.  
Conceptualization, J.T.; Methodology, J.T.; Software, K.Z.; Validation, J.T., K.Z. and Z.H.; Formal analysis, J.T.; Investigation, K.Z. and Z.H.; writing--original draft preparation, K.Z.; writing--review and editing, J.T.; Supervision, J.T.; Project administration, J.T.; Funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
The authors kindly acknowledge the support of research grants from Institute of Crustal Dynamics, China Earthquake Administration (ZDJ2019-15-1 and ZDJ2018-22).  
The authors declare no conflict of interest.