esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Shearography for NDT: Phase Measurement Technique and Recent Developments
## 3 Phase Measurement Technique  
In Section 2.1, the relative phase change is calculated by counting fringes. Therefore, the method's measurement sensitivity is only one fringe, or \(2\pi\), and smaller phase changes cannot be determined. This means the direct subtraction method has a relatively low measurement sensitivity, limiting its use in practical applications. The phase-shift technique was introduced to obtain higher phase measurement sensitivity. Theoretically, the sensitivity can reach \(2\pi/256\) when the hardware bit resolution is 8 bits. Considering speckle noise, hardware, and other factors, the measurement sensitivity of digital shearography based on the phase-shift technique can be at least \(2\pi/10\), which is 10 times higher than the real-time subtraction version. This has led to tremendous development in digital shearography. The comparison between real-time subtraction and phase-shift shearography results is shown in Figure 4.  
Phase-shift digital shearography can be divided into two categories: temporal phase-shift digital shearography (TPS-DS) and spatial phase-shift digital shearography (SPS-DS). The temporal phase shift has high accuracy and is normally suitable for static or quasi-static measurements, while the spatial phase shift is suitable for dynamic measurements. Both categories will be described in detail in the following sections.
## 2 Background
### Patent Retrieval Tasks  
Patent prior art searches are specialized tasks tailored according to the characteristics of the input, such as ideas, invention disclosures, patent applications, claims, or granted patents, and the desired outputs, which may include scientific publications, a collection of patent documents, or a single document [4]. These search tasks aim to identify relevant documents to meet specific information needs and are known by various names depending on their purpose: State of the Art (SOA) search, patentability search, infringement search, freedom-to-operate search, invalidity search, and patent portfolio search. The objectives, relevance assessments, and effectiveness vary significantly based on the type of search activity. The state of the art (SOA) search serves as a critical pre-R&D stage process, enabling inventors and researchers to gain insights into technological developments. Prior to filing a patent application, a preliminary pre-filing patentability search is normally conducted to assess novelty and non-obviousness. Subsequent to this initial decision, a comprehensive patentability search is conducted by examiners to confirm the novelty, inventive step, and industrial applicability of the invention. Before moving towards commercialization, a Freedom To Operate (FTO) search is performed to ensure that the innovation does not infringe on other active patents. Post-commercialization, competitors may engage in infringement searches to identify potential IP violations. For an in-depth exploration of these patent retrieval tasks and their contextual application, ardent readers are urged to refer to Section S2.1 Patent Retrieval Tasks in Section S2 of the Supplementary Materials. Table 1 summarizes different aspects of various patent retrieval search tasks based on who conducts the search, the stage of the innovation lifecycle the search is taking place, the status of the patent at the time of the search, expected output from the search operation, and the literature used to perform the search on [8, 18, 19]. Figure 2: Innovation lifecycle.
[59] identified the antibacterial activity of _Cladophora socialis_-derived phenolic compounds, such as 2,3,8,9-tetrahydroxybenzo[c]chromen-6-one, 3,4,30,40-tetrahydroxy-1,10-biphenyl, and  
Figure 1: (**––u**) Examples of marine macroalgae. (**a**) _Chaetomorpha antenna_ (green); (**b**) _Conallina_ sp. (red); (**c**) _Sargassum linearifolium_ (brown); (**d**) _Palisada perforata_ (red); (**e**) _Sargassum cineureum;_ (**f**) _Palisada perforata_ (as _Laurencia papillosa_); (**g**) _Laurencia glandulfifera_ (red); (**h**) _Sargassum tenterrimun_ (brown); (**i**) _Sargassum tenterrimun_ (brown); (**j**) _Ulva lactuca_ (green); (**k**) _Laurencia_ sp. (red); (**l**) _Gracilaria_ sp. (red); (**m**) _Gracilaria debilis_ (red); (**n**) _Llodea indica_ (green); (**o**) _Champia compressa_ (red); (**p**) _Triclecarpa fragilis_ (red); (**q**) _Cauleria taxifolia_ (green); (**r**) _Botrycoladia leptopada_ (red); (**s**) _Centroceras clavulatum_ (red); (**l**) _Scinia moamiliformis_ (red); (**u**) _Padina tetrastromatica_ (brown). cladophorol against methicillin-resistant Staphylococcus aureus. Based on a study carried out by [PERSON] et al. [60], bromophenols, such as 5\({}^{\prime}\)-Hydroxysioavrainvilleol found in green macrocalga _Avarainvillea nigraus_, demonstrate promising antimicrobial activity. The green macrocalga, _Caulerpa_ sp., has been reported for various flavonoids, such as kaempfer and quercetin. These compounds have been studied and identified with antioxidant benefits [61]. In addition, _Vidalia colensoi_ (as _Osmundaria colensoi_) derived from lanosal methyl ether, lanosal butenome, and Hudomelol has revealed antibacterial and antifungal activity against various bacterial and fungal pathogens. These effects proved to be bactericidal and bacteriostatic or fungicidal, fungistatic, and antiacene, and demonstrated a dose-dependent curve effect against the pathogenic organisms [62]. Mycosporine amino acids such as palshine, shinorine, asterina-330, _Porphyna_-334, palythinol, and usijniene have already been isolated and have antioxidant, photoprotective, and antiproliferative activity in the HeLa and HaCat cancer cell lines. In other studies, [PERSON] et al. [63], [PERSON] et al. [64], and [PERSON] et al. [65] reported the anti-inflammatory and immune-modulatory properties of mycosporine-like amino acids. These compounds can act as UV filters against photodamage. Moreover, _Ecklonia cava_-derived phlorotannic acts as an anti-UVB protective and reduces the photodamage effect provoked by UVB radiation [66]. Some other brown macrocalgal species-derived phlorotannins such as dieckol, dioxinodehydroeckol, eckolol, eckolstonol, phlorototovtoexclol A, and 7-phloroeckol are being researched for skin-whitening and antivrinkle properties, as well as tyrosinase and hyaluronidase inhibition [67; 68; 69; 70; 71; 72; 73]. [PERSON] et al. [74] reported the hair growth-promoting activity of 7-phloroeckol isolated from _E. cava_.
## 1 Introduction
(2021) [30] recently found COMISA participants to have the highest average levels of total sleep time underestimation and sleep onset latency overestimation. These markers of sleep misperception were also significantly greater compared to the OSA alone group, and significant relationships between SOSD and reduced quality of life were also observed in those with insomnia and COMISA [30]. Overall, [PERSON] et al. (2021) [30] concluded that insomnia (whether insomnia alone or COMISA) plays a significant role in SOSD of sleep parameters. No study to date has investigated the effect of CBT-I on SOSD in people with COMISA to help clarify the extent to which insomnia and/or OSA may contribute to perceptual sleep time discrepancies. Such a study could help to resolve the relative contributions of insomnia and OSA mechanisms to this COMISA cohort. This study aimed to use RCT data to investigate patterns of SOSD at baseline in people with COMISA, exploratory associations between SOSD and other sleep, daytime function, and sociodemographic information at baseline, and changes in SOSD following a CBT-I program, versus no-treatment control.
# Charge-Line Dual-FET High-Repetition-Rate Pulsed Laser Driver
## 4 Test Results
### Pulsed Laser Driver Integrated with Infra-Red Laser
Figure 11: Pulsed optical signal peak amplitude stability measured for \(\rm{t_{FWHM}}\) = 900 ps, \(\rm{f_{rep}}\) = 1 MHz and peak power of: (**a**) 250 mW, \(\sigma_{\rm A}\) = 6.5 mW; (**b**) 520 mW, \(\sigma_{\rm A}\) = 17.3 mW; (**c**) 750 mW, \(\sigma_{\rm A}\) = 28.6 mW. \begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Optical Pulse Measurements**} \\ \hline
**Amplitude [mW]** & \(\sigma_{\rm A}\) **[mW]** & \(\rm{S_{A}}\) **= Amp./\(\sigma_{\rm A}\)** \\ \hline
250 (\(\rm{U_{+HV}}\) = 45 V) & 6.5 & 38 \\
520 (\(\rm{U_{+HV}}\) = 60 V) & 17.3 & 30 \\
750 (\(\rm{U_{+HV}}\) = 80 V) & 28.6 & 26 \\ \hline \multicolumn{3}{c}{**Electrical Pulse Measurements (\(\rm{R_{L}}\) = 33 \(\Omega\))**} \\ \hline
**Amplitude [mA]** & \(\sigma_{\rm C}\) **[mA]** & **Sc = Amp./\(\sigma_{\rm C}\)** \\ \hline
510 (\(\rm{U_{+HV}}\) = 45 V) & 1.3 & 392 \\
705 (\(\rm{U_{+HV}}\) = 60 V) & 1.8 & 391 \\
960 (\(\rm{U_{+HV}}\) = 80 V) & 2.5 & 384 \\ \hline \end{tabular}
\end{table} 
Table 3: Pulse-to-pulse amplitude stability (\(\rm{t_{FWHM}}\) = 900 ps, \(\rm{f_{rep}}\) = 1 MHz).
## 2 Basic Theory
### Initial Displacement Excitation Method
The zero-mean normalized least square distance correlation function can effectively avoid the mismatch phenomenon caused by the change of light intensity, and ensure the stability of the experiment. Its expression is:  
\[C_{ZNSSD}=\sum_{\xi}\left\{\frac{\overline{f}[\psi+w(\xi,\Delta p)]}{\sqrt{ \frac{\sum\{\overline{f}[\psi+w(\xi,\Delta p)]}}{\xi}\left(\frac{\overline{f} [\psi+w(\xi,\Delta p)]}{\xi}\right)^{2}}-\frac{\overline{g}[\psi+w(\xi,\Delta p )]}{\sqrt{\frac{\sum\{\overline{g}[\psi+w(\xi,\Delta p)]}}{\xi}\left(\frac{ \overline{g}[\psi+w(\xi,\Delta p)]}{\xi}\right)^{2}}}\right\}^{2} \tag{19}\]  
Figure 2: Calculation steps of 3D displacement. where \(C_{ZNSSD}\) is the optimization coefficient, \(p=[u\), \(u_{x}\), \(u_{y}\), \(v\), \(v_{x}\), \(v_{y}]\) is the deformation parameter vector, and \(\Delta p=[\Delta u\), \(\Delta u_{x}\), \(\Delta u_{y}\), \(\Delta v\), \(\Delta v_{x}\), \(\Delta v_{y}]\) is its incremental vector, \(\psi=[x,y\), 1]\) represents the whole pixel coordinates of the selected test point in the subregion of the image, \(\xi=[\Delta x\), \(\Delta y\), 1]\({}^{T}\) represents the sub-pixel coordinates corresponding to the whole pixel coordinate point. Perform a firstorder \(\Delta p\) expansion on \(C_{ZNSSD}(\Delta p)\), and get:  
\[C_{ZNSSD}(\Delta p)=\sum_{\xi}\left\{\frac{\overline{f}(\psi+\xi)+\
abla f( \psi+\xi)\frac{\partial w}{\partial p}}{\sqrt{\sum_{\xi}\left\{\overline{f}[ \psi+w(\xi,\Delta p)]\right\}^{2}}}-\frac{\overline{g}[\psi+w(\xi,p)]}{\sqrt{ \sum_{\xi}\left\{\overline{g}[\psi+w(\xi,\Delta p)]\right\}^{2}}}\right\}^{2} \tag{20}\]  
where \(\
abla f(\psi+\xi)=[\partial f(\psi+\xi)/\partial x\), \(\partial f(\psi+\xi)/\partial y]\) represents the gray gradient of the reference image subregion, and the Jacobian matrix of the shape function is expressed as \(\frac{\partial w}{\partial p}=\left[ \begin{array}{cccc}1&\Delta x&\Delta y &0&0&0\\ 0&0&0&1&\Delta x&\Delta y\end{array} \right]\). When the reference image subarea is most similar to the target subarea, \(C_{ZNSSD}(\Delta p)\) gets the minimum value, which can be obtained by \(\partial C_{ZNSSD}(\Delta p)/\partial\Delta p=0\), where \(H^{-1}\) is the inverse of the Hessain matrix. \[\Delta p=H^{-1}\Bigg{\{}\bigg{[}\
abla f(\psi+\xi)\frac{\partial w}{ \partial p}\bigg{]}^{T}\Bigg{[}\frac{\overline{f}_{n}}{\overline{g}_{n}} \overline{g}\Big{[}\psi+w(\xi,p)-\overline{f}(\psi+\xi)\Big{]}\Bigg{]}\Bigg{\}} \tag{21}\] \[H=\sum_{\xi}\Bigg{\{}\bigg{[}\
abla f(\psi+\xi)\frac{\partial w} {\partial p}\bigg{]}^{T}\bigg{[}\
abla f(\psi+\xi)\frac{\partial w}{\partial p }\bigg{]}\Bigg{\}}\]
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 4 PoENF: A Proof-of-ENF Consensus Protocol
### PoENF-Based Block Proposal Mechanism
#### 4.3.1 Transactions Pooling  
Given a certain period of sliding window for ENF collection, each validator collects transactions from valid nodes. If a transaction stores reference that point to ENF proof, it is an ENF proof transaction. In the current block generation round, each node is required to send only one ENF proof transaction. After receiving the broadcasted transactions, each validator verifies buffered transactions according to predefined conditions:  
Figure 3: The PoENF consensus protocol overview.  
1. Transaction sender \(u_{i}\in U\) and \(RSA.verify(tx\_hash,pk_{i},T_{stamp},data)=\sigma_{i}\) by using the sender's public key \(pk_{i}\);
2. ENF proof \(tx\) sent by \(u_{i}\) should not exist in the transactions pool;
3. Time stamp \(T_{stamp}\) must fall into current time slot.  
The condition (i) prevents transactions from invalid nodes or any malicious modification, while conditions (ii) and (iii) are mainly for preventing duplicated ENF proof \(tx\) in current period time slot. After the verification process, only valid transactions are cached as local transaction pools denoted as \(TX=\{tx_{1},tx_{2},...,tx_{N}\}\), where \(N\) is the transactions pool size. The validator also uses condition (iii) to regularly check the local transaction pool and removes outdated transactions that have not been recorded in the latest confirmed block.
# Storage and Processing of Big Data for Geomagnetic Support of Directional Drilling
## 4 Results
In this case, the azimuth angle correction error of the drill direction in the MAGNUS system based on the averaged \(D\) values for 15 min exceeds the 1\(\sigma\) threshold in 7.4% of cases, and the 3\(\sigma\) threshold in 1.3% of cases (Figure 8b). The observed deviations are associated with a data processing delay, which can exceed 10 min. The use of real-time processing in the proposed MAGNUS BD system significantly reduces the correction error (Figure 8c). In this case, only 0.4% of \(D\) errors go beyond the \(\pm\)1\(\sigma\) range. It should be noted that the requirements for correction accuracy only grow over time, as production rates and drilling speeds are continuously increasing [38; 39].
## 4 Discussion
### Temperature Sensor  
The temperature of the polymer gear reaches an almost static bound temperature after about 1:20 h into a two-hour sequence. This temperature rise is related to the heat flow that is generated by friction in the contact of the gears, which causes the rise of temperature to the static bound temperature on the flanks of the POM gear of approximately 41 \({}^{\circ}\)C. Due to the low thermal conductivity of the gear, a temperature rise also takes place at the measurement point of the ISM, but with a delay and a smaller amplitude. The temperature measured using the ISM on the gear body correlates with the temperature measured using the infrared camera on the tooth flanks. This correlation means that although the temperature values measured using the ISM are lower than the flank temperature, the ISM provides reliable temperature data from which conclusions can be drawn about the tooth flank temperature.  
The maximum temperature measured using the ISM was always well below the 100 \({}^{\circ}\)C specified by the gear manufacturer. This temperature means that the operating point of the gears was never temperature-critical. The relationship between the static difference temperature curve with its two phases as shown in Figure 8 and the linear wear curve of the POM gear in Figure 6 is not linear. Comparing the evolution of the static difference temperature over time (Figure 8) with the curves of the \(GMF_{1}\) signal characteristics (Figures 9-11), correlations with the temperature curve can be seen. In the first two identified phases of increase in the GMFs and sidebands, the temperature also increases. With the transition to the third phase, the increase in the amplitude of the remaining frequency band, the static differential temperature begins to slowly decrease. This similarity confirms that, similar to [PERSON] et al., temperature also has an influence on abrasive wear. [PERSON] et al. mapped the different phases of the signal properties to different wear mechanisms [23]. This could mean that the different temperature phases also indicate different wear mechanisms, which causes different friction and therefore different temperatures.  
Regarding research question 2 on the integrated temperature sensor, it can be said that the sensor can provide reliable data that correlate with the wear of the POM gear. This correlation is complex and requires further investigation to extract the actual wear condition from the temperature data.
## 4 Numerical Results and Discussion
From a mathematical perspective, the change in the rise span ratio causes a change in the expression of the curvature radius, thereby resulting in a change in the response. From an engineering viewpoint, for this type of curved beam structure, such as an arch, with the same span, the increase in the rise implies an increase in the arch height, which can offset part of the mid-span response generated by vertical external excitations. Finally, it is discovered that regardless of how the rise span ratio changes, among the arches of the same rise span ratio and different types, the absolute value of the deterministic response and the absolute value of the upper and lower bounds of the uncertain response of the parabolic arch are the smallest, which indicates that under the same conditions, whether uncertain parameters are considered or not, the parabolic arch is the best choice from the perspective of safety and comfort. For intuitive understanding, the maximum absolute values of both the positive and negative responses, and the corresponding upper and lower bounds of the uncertain responses presented in Figures 6-9, are enumerated in the following Tables 4 and 5. \[x_{\eta}=x^{L}+\eta\frac{x^{U}-x^{L}}{\hat{\eta}}\in\left[x^{L},x^{U}\right], \eta=1,2,\ldots\hat{\eta} \tag{28}\]3 The uncertain transient dynamic response of the general shaped arch with a higher rise span ratio, 5% deviation degree in the inner radius of cross section and 5% deviation degree in the Young’s modulus: (**a**) circumference, (**b**) cycloid, (**c**) ellipse, and (**d**) parabola.
## 3 Equivalent Circuit Parameters Calculation (Rotary Part)
### Equivalent Circuit Establishment  
The establishment process of ECM is listed as follows, and Figure 8 shows the computational program diagram.  
(1) Calculate the resistance and reactance of the stator and rotor.  
(2) Assume the initial relative permeability of the rotor core \(\mu_{Fe}=\underset{H\to 0}{\lim}\frac{B}{H\mu_{0}}\), and calculate the excitation.  
(3) On the basis of the above calculation result, the initial ECM is established.  
(4) Compute the stator phase current \(I_{1}\) and the excitation current \(I_{\rm m}\).  
(5) Count the amplitude of air-gap magneto motive force (\(F_{\rm m}=\frac{m_{1}}{2}\times 0.9\frac{N_{1}k_{\rm m}!I_{\rm m}}{p}\)) and the air-gap flux \(\Phi_{\rm g}=\frac{2\mu_{0}}{\pi\delta}F_{\rm m}\tau l_{ef}\).  
(6) Calculate the magnetic field density \(B_{\rm Fe}=\frac{2p\Phi_{g}}{\pi(D_{ii}-2\delta)l_{ef}}\frac{(I_{a}+d)(I_{c}+d) }{L_{a}L_{c}}\) and obtain the relative permeability \(\mu_{Fe}^{\prime}\) corresponding to \(B_{\rm Fe}\) by checking the \(B\)-\(H\) curve.  
Figure 8: Computational program diagram of ECM.  
(7) Calculate the error. If \(\left|\frac{\mu_{Fe}-\mu_{Fe}^{\prime}}{\mu_{Fe}}\right|>\varepsilon\), \(\mu_{Fe}=\mu_{Fe}^{\prime}\), return to step (3). Otherwise, proceed to the following steps.  
(8) Calculate other characteristics (i.e., output torque, efficiency, and power factor).
VMD has been widely employed to diagnose the train axlebox bearing faults [27; 28; 29], but its effectiveness in the wheel flat identification has not been deeply studied. Similarly, envelope spectrum (ES) is widely applied in the rotating machinery fault diagnosis, which is sensitive to the impact. It is reported that the ES has the potential to identify wheel faults [30]. On the basis of the above discussion, a VMD-ES combined method was proposed to detect the wheel flat faults by using the axlebox acceleration in this paper. The method can eliminate the end effect and mode mixing in signal decomposition, and extract the fault characteristic frequency more efficiently than the original signal spectrum domain. The structure of this paper is as follows: Section 2 established a three-dimensional vehicle-track coupled dynamics verified by field test to simulate the axlebox acceleration excited by the wheel flat. The VMD-ES method was presented in Section 3. The results of the wheel flat identification based on the method was investigated in Section 4, and the feasibility of the method was further validated by comparing the co-existence of the wheel flat and the wheel eccentricity in Section 5. Then the method validation is carried out with using the data measured from the field test in Section 6. Finally, some conclusions are given in Section 7.
## 3 Results
### Trunk Kinematics  
Performing repetitive bending while wearing the BSIE led to reduced upper-back and lower-back mean norm velocities by \(\sim\)10-15% and \(\sim\)50-60% during both the bending and retraction portions, respectively (Figure 7). However, the hip velocity was \(\sim\)13% higher during bending with assistance, and no difference was seen during retraction. Between the postures, asymmetry led to a higher mean norm velocity of the upper-back, lower-back, and hip by \(\sim\)5-15% (Table 5). With time, the mean velocity of the upper-back showed clear increases of up to \(\sim\)7% during the bending and retraction, both with and without the use of the BSIE. The highest velocity of 565 mm/s was seen without the BSIE at the end during retraction, while the lowest velocity of 401 mm/s occurred at the beginning of the trials during bending with assistance. While the upper-back speed varied, no difference was seen in the lower-back over time, and the velocities for the hip were almost similar (Figure 8).  
Figure 7: Comparison of movement of upper-back, lower-back, and hip regions between without assistance (N) and with assistance (E), categorized according to task portion as bending (BD), retraction (RT), and transition (TS) (Note: dissimilar letters/symbols within the same task portion denote statistical significance).  
Figure 8: Comparison of movement of upper-back, lower-back, and hip regions between without assistance (N) and with assistance (E) across time as beginning (B) and end (ED) and categorized according to task portion as bending (BD), retraction (RT), and transition (TS) (Note: dissimilar letters/symbols within the same task portion denote statistical significance).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & **UB** & & **LB** & & **Hip** & \\ \hline
**Portion** & **Assistance** & **Posture** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline \multirow{3}{*}{BD} & E & AS & 421.2 & 119.8 & 147.5 & 51.7 & 99.4 & 31.0 \\  & E & S & 404.1 & 123.4 & 127.6 & 46.4 & 85.3 & 29.5 \\ \cline{2-9}  & N & AS & 470.8 & 155.7 & 238.3 & 78.3 & 81.3 & 43.1 \\  & N & S & 457.9 & 163.5 & 211.8 & 83.3 & 80.9 & 52.2 \\ \hline \multirow{3}{*}{RT} & E & AS & 476.8 & 114.5 & 136.2 & 57.7 & 129.5 & 47.9 \\  & E & S & 471.4 & 132.8 & 138.5 & 57.8 & 100.3 & 34.6 \\ \cline{2-9}  & N & AS & 553.4 & 152.1 & 254.5 & 74.5 & 121.0 & 57.8 \\  & N & S & 539.8 & 155.3 & 227.3 & 79.7 & 110.9 & 55.1 \\ \hline \multirow{3}{*}{TS} & E & AS & 78.5 & 18.4 & 37.4 & 10.5 & 28.2 & 8.1 \\  & E & S & 78.6 & 23.6 & 35.0 & 9.8 & 22.5 & 6.8 \\ \cline{2-9}  & N & AS & 79.5 & 17.5 & 47.9 & 11.1 & 23.1 & 7.5 \\ \cline{2-9}  & N & S & 75.2 & 16.2 & 43.2 & 10.3 & 20.1 & 5.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean norm of velocity in the upper-back, lower-back, and hip regions with (E)/without (N) assistance across postures with asymmetry (AS) and symmetry (S) during the transition (TS) portion of the repetitive-bending task.