esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis and Experimental Verification  
### Sideband Features  
The steady-state response of the motor current in one phase is shown in Figure 7. In Figure 7a, the time-domain waveform of the current signal shows that the periodic signal with the supply frequency dominates the content. However, in the frequency domain, there were numerous spectral lines, as shown in Figure 7b. This system has a speed resonance frequency of 127 Hz. The spectrum shows that the system resonates at 177 Hz.  
To examine the details of the current and speed spectra, the rotation frequency of interest, such as the motor rotating frequency \(f_{r}\), spindle rotating frequency \(f_{s}\), and belt-passing frequency \(f_{b}\), were calculated as follows:  
\[f_{r}=\frac{(1-s)f_{e}}{P} \tag{19}\]  
\[f_{s}=\frac{f_{r}\,r_{1}}{r_{2}} \tag{20}\]  
\[f_{b}=\frac{2\pi r_{1}f_{r}}{l_{b}}=\frac{2\pi r_{2}f_{s}}{l_{b}}, \tag{21}\]  
where \(l_{b}\) is the circumference of the belt, and \(s\) is the motor rotor slip.  
In an ideal system, it is impossible to observe \(f_{s}\), \(f_{r}\), or \(f_{b}\). However, incomplete balance systems cause periodic fluctuations in the spindle speed or torque owing to high dynamic loads between the workpiece and the tool, insufficient accuracy during machining and installation, misalignments of the belt or pulley, aging of the belt, and other factors.  
To suppress the sidelobes in a data segment in the FFT calculation, a Hanning data window was used. A 0-100 Hz frequency spectrum signal was extracted for analysing the current and speed frequency modulation. Figure 8 shows a typical result when the static load is at its baseline value of P\({}_{0}\) = 300 N. In power transmission belt drives, rotational vibrations are often caused by periodic changes in the rotational speed of the drive pulley (similar to that of the motor shaft) and cyclic fluctuations in the torque loads on the spindle. \(f_{s}\) sidebands denoted by spindle rotational fluctuation in the figure are present on both  
Figure 6: Steps for numerical analysis.  
sides of the current spectrum at 50 Hz, which is consistent with the velocity spectrum. The second-order sidebands also have small amplitudes.  
When transverse vibration is considered in the simulation system, it adds two excitations, \(b_{r}\cos\left(\dot{\theta_{r}}t\right)\) and \(b_{s}\cos\left(\dot{\theta_{s}}t\right)\), and \(b_{r}=b_{s}=8.5\times 10^{-4}\). Transverse vibrations appear to be responsible for the appearance of second-order frequency conversion in the driving pulley, as shown in Figure 9. Meanwhile, the transverse vibration modulated the \(f_{r}\) and \(f_{s}\) couplings. In addition, the transverse vibration had no effect on the first-order sidebands.  
When the motor shaft rotates eccentrically owing to an eccentric pulley or motor rotor, \(T_{r0}=A_{r}\cos\dot{\theta_{r}}t\) is introduced into the dynamic system. For this case, the excitation amplitude Ar is set to 0.1 N.m. \(f_{r}\) in Figure 9 results from this excitation source and does not affect the frequency spectrum \(2f_{r}\) caused by the transverse vibration of the belt.
# Reliability-Based Optimization of Impeller Structure Using Exponential Function Approximation
## 2 Reliability-Based Optimization  
Reliability-based optimization design methods attempt to minimize structural costs by optimizing design variables while satisfying the given reliability and other deterministic constraints [2]. This is accomplished using the following mathematical expressions:  
\[\begin{array}{llll}Min&C(\theta,d)\\ s.t.&P_{FI}(\theta)\leq P_{FI}^{tol}&(l=1,2,\ldots,n_{P})\\ &L_{j}(\theta,d)\leq 0&(j=1,2,\ldots,n_{D})\\ &\underline{\theta}_{j}\leq\theta_{i}\leq\bar{\theta}_{i}&(i=1,2,\ldots,n_{ \theta})\\ &\underline{d}_{k}\leq d_{k}\leq\bar{d}_{k}&(k=1,2,\ldots,n_{d})\end{array} \tag{1}\]  
where \(C(\theta,d)\) is the objective function, in which \(\theta=[\theta_{1},\ldots,\theta_{n_{\theta}}]\) are the design parameters related to the distribution of the system's basic random variables (e.g., mean values) and \(d=[d_{1},\ldots,d_{n}]\) are the other deterministic design variables; \(L_{j}(\theta,d)\) represents the \(j\)-th deterministic constraint; \(P_{FI}(x,\theta)\) is the \(l\)-th failure probability constraint (i.e., reliability constraint), which is a function of \(\theta\) and \(P_{FI}(\theta)\), and \(P_{FI}^{tol}\) is the corresponding failure probability constraint threshold; and \(n_{P},n_{D},n_{g}\), and \(n_{d}\) are the number of probabilistic constraints, number of deterministic constraints, number of design parameters, and number of deterministic parameters..  
The objective function \(C(\theta,d)\) typically includes the material, manufacturing, and maintenance costs [22]. In general equipment systems, material costs \(\theta\) and \(d\) are directly related to design parameters and can be calculated using the volume and density of the material [23]. Manufacturing costs are related to the processing technology employed and complexity of the intended design; they can be estimated using the process parameters. Maintenance costs are related to structural reliability and expected life and can be predicted using a reliability analysis [24].  
Critically, the deterministic constraints \(L_{j}(\theta,d)\) include geometric and mechanical constraints that must be satisfied during the design process. These constraints can be mathematically expressed in forms such as \(L_{j}(\theta,d)=g_{j}(\theta,d)-g_{j}^{lim}\leq 0\), where \(g_{j}(\theta,d)\) is a function of the design variables and \(g_{j}^{lim}\) is the corresponding upper limit constraint [25].
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 3 Design and Simulation of Particle Damper
### Analysis of the Influence of Particle Damper Materials and Structural Parameters
#### 3.4.5 Contact Body Friction  
The friction coefficient is another important parameter affecting the total energy dissipated by the pellet system. The friction coefficients between particles and particles were set at 0.15, 0.35, 0.55, 0.75, and 0.95, and the friction coefficients between particles and the container wall were set at 0.17, 0.37, 0.57, 0.77, and 0.97. The influence of these friction coefficients between different contact bodies on the total energy dissipated by the particle system is shown in Figure 13.  
As shown in Figure 13, the total energy dissipated by the particle system tended to increase as the friction coefficients between particles and between particles and the container walls increased. Since the total energy dissipated by the particle system includes collision energy dissipated and friction energy dissipated, under the same working conditions, a larger friction coefficient between particles resulted in a larger friction energy dissipated by friction. The total energy dissipated by the particle system also increased.  
Figure 12: Normal energy dissipated with different particle recovery coefficients.  
Figure 13: Total energy dissipated with different friction coefficients between contact bodies.
## 3 Results and Discussion
### Microbiological Activity  
_Cannabis_ EOs (distilled from leaves and infloresences) have been tested on Gram + and Gram--bacteria and on a _Candida albicans_ strain. Overall, the essential oils from infloresences exhibited no detectable antibacterial activity at the tested concentrations(MICs are in general >4 mg/mL), while a slight antifungal activity has been shown for _Candida_ (MIC of 4 mg/mL) (Table 4). The same mild activity against _Candida_ was found for EOs obtained from leaves that, however, also show fair activity against _S. aureus_ (MICs 0.5 mg/mL and 1 mg/mL for Tisza and Kompolti EOs, respectively). This activity is bactericidal, as demonstrated by the values of MBCs that are equal to the MICs values. Flower resins have no inhibitory activity against Gram-strains but exhibited interesting activity on _S. aureus_ (MIC = MBC 0.015-0.031 mg/mL) (Table 4; Figure 4). It is logical to assume that this activity depends on the high CBD content (84-86%) of resins, since, as already mentioned in the Introduction, the inhibitory activity of CBD against Gram+ pathogens is well documented [6, 8, 28].  
The negligible antimicrobial activity of our EOs should not surprise, because the studies on _Cannabis_ EOs demonstrate that their antimicrobial activity is extremely variable. For instance, [PERSON] et al. [17] reported the very good activity of six hemp essential oils against Gram-positive bacteria (MIC 1-32 mg/mL), while the same oils proved to be ineffective towards Gram-negative strains. In these EOs the presence of cannabinoids, especially CBD, was also observed, in some samples in quantities up to 1 mg/mL. Since the antimicrobial properties of cannabinoids are well documented [6, 8, 28], [PERSON] et al. concluded that the antimicrobial activity of the analyzed hemp EOs probably arose from a synergism between volatile components and cannabinoids. On the other hand, the EOs of _Cannabis_ with different chemical profiles can exhibit modest or poor antibacterial activity [16, 29]. [PERSON] et al. [16] tested the antimicrobial properties of a _Cannabis_ EO and reported very high MIC and MBC values (8-16 mg/mL) against different _S. aureus_ strains and no activity against yeasts (_Candida_ spp., _Malassezia_ spp.; MIC > 12,460 mg/mL), while against clinical _Helicobacter pylori_, that EO showed MIC values of 16-64 mg/mL. In general, the high variability of the composition of EO of _C. sativa_ documented in the literature may explain the many contradictory data on their antimicrobial activities, even toward the same bacterial species, even if typically, the antibacterial effect is more marked on Gram+ microorganisms compared to Gram\(-\) microorganisms, with variable activity on mycetes [30]. In particular, the activity of _C. sativa_ extracts against _S. aureus_ attracts the attention of researchers because this species not only causes a wide spectrum of infections but can also develop resistance to beta-lactam antibiotics used in hospitals (methicillin-resistant _Staphylococcus aureus_ or MRSA) [31, 32].
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 3 Results
### Time Course and Kinetics for Production of Cellulase and Xylanase  
The _Streptomyces_ strain was cultured under optimized conditions and monitored for growth, in addition to cellulase and xylanase production, for a period of 96 h. Production was initiated at 12 h of incubation, corresponding to early logarithmic growth phase (Figure 5a,b). The result also indicated that the _Streptomyces_ strain attained optimum xylanase production at 48 h with an activity of 18.54 \(\pm\) 0.58 U/mL and specific productivity of 7417 \(\pm\) 232 U/g (Figure 5a), which also corresponded with the late logarithmic growth of the strain. Growth and xylanase production were in tandem up until 60 h, after which there was a decline in xylanase production, with specific productivity of 5688 \(\pm\) 124 U/g (Figure 5a). On the other hand, optimal cellulase production by the _Streptomyces_ strain was attained at 60 h with activity of 0.27 \(\pm\) 0.01 U/mL, and a specific productivity of 205 \(\pm\) 14 U/g also at late logarithmic growth phase. A decline in cellulase production was observed at 72 and 84 h with specific productivity of 175 \(\pm\) 9 and 151 \(\pm\) 12 U/g, respectively; however, a slight increase was recorded at 96 h (185 \(\pm\) 2.8 U/g) corresponding to stationary growth phase (Figure 5b).  
Figure 5: Growth and kinetics of xylanase production (**a**) dots: time vs. xylanase activity; cross: time vs. specific productivity; closed circle: time vs. biomass; and cellulase production (**b**) closed circle: time vs. cellulase activity; cross: time vs. specific productivity; asterisk: time vs. biomass.
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 2 Methodology  
The randomness of the wind speed, along with the possible failures in the data collection stage, does not allow users to get the power production easily and makes the calculation method a complex problem. The study of the simultaneity of a generation when there is wind energy injection in the system reveals a stochastic process, which includes uncertainty at each of the locations, and converts it into a deterministic one by assigning a value of probability to each place and making it possible to obtain a solution to the problem [23]. The degree of accuracy of the proposed algorithm increases when spatial and temporal correlations are considered. Taking this into account, in this paper, both kinds of correlations are considered. In the case of temporal correlation, a value of lag 1 is used.  
As is detailed later, the process starts by considering that the random behavior of the wind speed at every location can be cumulatively described by a Weibull probability distribution function, an assumption generally accepted in wind energy studies. This enables the simulation of the wind speed randomness as a stochastic process with known probabilities. The Monte Carlo method is used with this aim [10].  
Two major stages are developed. The first one consists of modeling, and in this stage, the correlations between the wind speeds of the different locations are obtained. The second one consists of simulating, and here the randomness of the wind speed sequences that can be generated in a certain period is reproduced, and in this stage, it is possible to cover the errors produced during the collection. As can be seen in Figure 1, each stage has its own steps, developed progressively until the objectives are achieved.
## 5 Proposed Methods
### Estimation Phase Design
In their paper, they show that the tracking error converges with an exponential rate, equal to \((1-\mathbf{q})\lambda_{\min}(\Gamma)/2\sigma_{2}\|\mathbf{X}\|^{2}\) to the ball with radius \(2\kappa\sigma_{2}\|\mathbf{X}\|^{2}/\mathbf{q}\lambda_{\min}(\Gamma)\) where \(0<\mathbf{q}<1\) for all \(\Delta\tau_{d}(0)\in R^{n}\). According to Theorem 1, the NDO design reduces to finding a constant invertible matrix \(\mathbf{X}\) that satisfies the inequality (24). The following theorem, reported by [PERSON] et al. [34], states how (24) can be formulated as a linear matrix inequality (LMI) problem. It is important to mention that this theorem is useful to design the NDO for the estimation phase. **Theorem 2** ([PERSON] et al. [34]). : _Define the matrix \(\mathbf{Y}=\mathbf{X}^{-1}\) and assume that an upper bound of \(\|\hat{\mathbf{M}}(\mathbf{q})\|\) is \(\zeta\). The inequality (24) holds if the following LMI is satisfied:_  
\[\left[ \begin{array}{cc}\mathbf{Y}+\mathbf{Y}^{T}-\zeta\mathbf{I}&\mathbf{Y} ^{T}\\ \mathbf{Y}&\mathbf{\Gamma}^{-1}\end{array} \right]\geq 0 \tag{25}\]  
Please refer to [PERSON] et al. [34] for a detailed proof of Theorem 2.
## 4 Conclusions  
In summary, we studied mid-IR OPA with simultaneous wavelength and temperature insensitivity in a LiGaS\({}_{2}\) crystal through theoretical analysis and numerical simulations. A strict WTIPM solution of \(\lambda_{\mathrm{s}}\) = 5 \(\upmu\)m and \(\lambda_{\mathrm{p}}\) = 2.06 \(\upmu\)m was found in noncollinear Type-I OPA in the XZ plane of the LiGaS\({}_{2}\) crystal; this could be tuned by varying the pump wavelength. Such a WTIPM solution can support 3.5-cycle 5 \(\upmu\)m OPCPA with a temperature bandwidth beyond 200 K. In addition to the strict WTIPM solutions, two collinear WIPM solutions with large temperature bandwidths were also discovered for the Type-I OPA in the XZ plane of LiGaS\({}_{2}\) crystal: one was the first-order WIPM solution of \(\lambda_{\mathrm{s}}\) = 4.12 \(\upmu\)m and \(\lambda_{\mathrm{p}}\) = 2.06 \(\upmu\)m, and the other was the third-order WIPM solution of \(\lambda_{\mathrm{s}}\) = 3.3 \(\upmu\)m and \(\lambda_{\mathrm{p}}\) = 1.65 \(\upmu\)m. The two collinear solutions with a large temperature bandwidths could be treated as approximate WTIPM solutions, and the third-order WIPM solution was numerically verified for building a sub-two-cycle 3.3 \(\upmu\)m OPCPA. The WTIPM solutions given in this study will pave the way towards achieving high-power ultrafast mid-IR OPA and OPCPA with the semiconductor LiGaS\({}_{2}\) crystal.  
Figure 6: Simulation results for mid-IR OPCPA with the third-order WIPM given in Figure 4b. The lateral surface of the crystal is assumed with a constant temperature of 293 K. (**a**) Temperature distribution in the xz plane of the crystal. (**b**) Conversion efficiency versus crystal length with (red) and without (blue) considering the thermal effect. Pink, the crystal’s lateral surface is in contact with a heat sink with a constant temperature of 293 K under \(h_{1}\) = 400 W\(\cdot\)m\({}^{-2}\cdot\)K\({}^{-1}\). (**c**) Signal spectra before OPCPA (black) and after OPCPA with (red) and without (blue) considering the thermal effect. Small-signal gain profile with \(L\) = 8 mm is shown as a green curve. (**d**) Compressed pulse profile.  
**Author Contributions:** Conceptualization, [PERSON]; funding acquisition, [PERSON]; investigation, [PERSON]; software, [PERSON]; supervision, [PERSON]; Writing--original draft, Z.H.; writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from the National Natural Science Foundation of China (grant no. 62122049, 91850203, and 61705128) and the Shanghai Rising-Star Program (grant no. 21 QA1404600).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Simulation Result Analysis
#### 4.1.1 Step Response  
Set channel 1, channel 2 and channel 4 of the MCCS system to work, load the simulation ball valve opening to 100% with no interference, and only open the main controller. The set value of the pressure in the main pipe is set at a step from 0.025 MPa to 0.035 MPa, and the step response curve of the pressure in the main pipe is obtained, as shown in Figure 13. It can be seen from the figure that, when the set value of the main pipe changes in a step, the control effect of the MCCS system using the variable universe fuzzy PID is compared with the traditional PID control and adaptive fuzzy PID control, with a shorter adjustment time and higher control accuracy.
# Classification of Human Motion Data Based on Inertial Measurement Units in Sports: A Scoping Review
## 2 Results
### Data Sources and Sample  
All articles analysed captured acceleration, and 71 also used gyroscopes (angular velocity) in data collection. Thirty-five papers used IMU that included magnetometers, although not all of them used this data. In addition to IMU sensors, some papers used other data sources, such as GPS [28; 70; 80] or barometric pressure sensors (e.g., [30; 60; 79; 80]). Ref. [57] also included 3D pose data, ref. [28] combined acceleration data with GPS and heart rate data, and Ref. [109] combined and compared smartphone IMU data with data from the Microsoft Kinect.  
In most papers (79), the IMU type was a wearable or specialised hardware, five analysed IMU data collected via smartwatches or activity trackers, and seven with smartphones or iPods. Further, Ref. [73] used inside sensors, and some papers did not specify the sensor type [36; 42]. IMU sensors were either attached directly to the body or to clothing, e.g., belts, shoes, or gloves, or they were mounted on sports equipment (14). For some summer sports, particularly those involving rackets (tennis) or clubs (golf), and for some winter sports  
Figure 3: Disciplines that were analysed in the included articles.  
(skiing), IMU were often attached directly to the sports equipment (see Table A1 in the Appendix A for an overview of the type and position of IMU).  
The number of subjects ranged from 1 [54; 65; 109; 111] to 836 [75] subjects (mean = 21.7, median = 10, sd = 88.8) from whom data were collected. However, not all authors described the number of subjects. Oversampling techniques to increase sample size were used by [40; 46; 53; 65; 71]. Ref. [63] prepared random additional negative samples to ensure a balanced training set, ref. [102] also ensured a balanced data set, and [70; 75] down-sampled the majority class. In addition, the general challenge of unbalanced data sets was mentioned by some authors in the text (e.g., [25; 67; 78]). In the work of [96], the training data set (five stroke cycles) was much smaller than the test data set (more than 60 cycles). In the study by [80], the authors used data collected during another study. In addition, Ref. [25] used different data sets, one of which was based on simulated data. The effect of sample size on classification performance was investigated by [47], who tried different sample and window sizes, as well as different time intervals and sizes of training sets. Additionally, [107] analysed different combinations and numbers of sensors.
# New On-Line MFL Testing Method and Apparatus for Winding Mine Hoist Wire Rope
## 3 Analysis of the Novel Probe
### Signal Characterization  
For in-depth study and analysis of defect information, the features of the MFL signal are extracted, including the difference between the maximum and minimum values of the signal D (Dz and Dy), the span between the peak and the valley S (the span between the valley and the valley of the single peak Sz, the span between the peak and the peak of the double peak Sy), the envelope are \(A=\)\(\frac{1}{2}\sum_{i=1}^{m}(\)\(B_{i-1}+B_{i}\)\()\()\()\)\(d\), between the signal curve and the zero line (the absolute area Az and the absolute area Ay), where \(B_{i}\) is the flux density component of the ith data point, and d is the spacing between the two data points \(B_{i-1}\) and  
Figure 10: Changes in magnetic flux density with the changed inner diameter (ID) of the magnet: (**a**) axial component of magnetic flux density; and (**b**) radial component of the magnetic flux density.  
Figure 9: Changes in magnetic flux density with the changed outer diameter (OD) of the magnet: (**a**) axial component of magnetic flux density; and (**b**) radial component of the magnetic flux density.  
\(B_{i}\), shown in Figure 11, to analyze the variation patterns of the three eigenvalues under different variables.  
Figure 12a-d shows, in order, the variation law of signal characteristics (D, S and A) when the variables are H, L, OD and ID. As shown in Figure 12a, Dz, Dy, Az, and Ay first increase and then decrease with increasing L, and the defect signal characteristics are maximal when L is 30 mm, whereas Sz and Sy are almost unchanged. Figure 12b shows that Dz, Dy, Az, and Ay gradually increase with an increase in H, while Sz and Sy remain essentially constant. In Figure 12c, Dz, Dy, Az, and Ay generally increase with increasing OD, while Sz and Sy are essentially constant. In Figure 12d, Dz, Dy, Az and Ay first increase and then gradually decrease with the increase in ID, while Sz and Sy are basically unchanged. This means that the change in the dimensions of the magnetizers (permanent magnets and cores) will not have an effect on S (Sy and Sz), but primarily on D (Dz and Dy) and A (Az and Ay) for the same defect. For better detection of the leakage field of defects, the length L of the magnetic core should not be too large, while the values of the thickness H and OD of the magnet should be larger, and the ID of the magnet should be smaller.