esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Cooling
At the moderate pump power of \(\simeq\)13 W, where thermal lensing does not greatly affect the beam quality, the SNR is similar for the cooled and the uncooled cases. With a repetition rate of 100 kHz, SNRs of \(\simeq\)270 and \(\simeq\)265 were calculated for the cooled and uncooled conditions respectively, while at a 200 kHz repetition rate (with the same pump power), the SNRs were \(\simeq\)109 and \(\simeq\)110, respectively. On the contrary, a noticeable difference in SNRs was observed between measurements with the cooled and uncooled crystal at the higher \(\simeq\)28 W pump power, where the thermal effects play a major role. For the 100 kHz repetition rate case shown in Figure 7, cooled and uncooled SNRs of \(\simeq\)505 and \(\simeq\)265, respectively, were recorded. For the 200 kHz case, the measured SNRs were \(\simeq\)187 and \(\simeq\)60. It is worth noting that the pump and the probe energies at 200 kHz are half those at 100 kHz; hence the measured THz field decreases, as expected, by a factor of \(\simeq\)2\({}^{3/2}\), resulting in a decreased SNR. On the other hand, the SNR does not increase as expected with increasing power (and consequently pump and probe energies). This is likely  
Figure 5: Efficiency of THz generation for cooled (blue) and uncooled GaP (red). due to the increase in the scattering of the spatially degraded pump beam, which is picked up by the detector. Indeed, its contribution cannot be rejected by the lock-in amplifier. Figure 6: Comparison of cooled vs. uncooled GaP crystal. (**a**) Thermal image of crystal surface after 7 min of exposure to 25 W average pump power. (**b**) Thermal image of crystal surface in water-cooled mount after 7 min of exposure to 25 W average pump power. (**c**) Far-field image of 23.5 W pump beam after transmission through uncooled crystal. Substantial thermal lensing is evident, resulting in beam aberrations and an increase of beam size. (**d**) Far-field image of 23.5 W pump beam after transmission through water-cooled crystal. Minimal thermal lensing is observed. Figure 7: THz traces taken with cooled (blue) and uncooled (red) GaP at a pump power of 28 W. The scattered light resulting from thermal lensing leads to a reduction in the SNR in THz traces taken under these conditions.
## 5 Conclusions  
In this study, we have used the known techniques of IR thermographic nondestructive testing and the scanning heating of cylindrical objects. However, these techniques are innovatively applied to a new class of test objects. Also, the implementation of heating devices and data processing using a concept of maygrams is novel.  
TNDT has been used for performing evaluations of reinforced concrete poles on operating railways. This technique is safe for personnel and fully nondestructive since the excess pole temperature does not exceed 15 \({}^{\circ}\)C.  
IR imaging allows the visualization of both longitudinal and circumferential rebar reinforcement, and whether corrosion has caused a separation between the rebar and the concrete. By heating poles inductively (released power: about 10 MW/m\({}^{3}\)) or applying electric current (up to 400 W per linear meter of a pole), surface temperature signals in defect areas may reach 3-10 \({}^{\circ}\)C depending on reinforcement depth, as well as a grade of rebar thinning and presence of corrosion products characterized by high thermal resistance. The same defects diminish the rate of temperature changes (temperature derivative) by 2-4-fold, which can be used for defect identification.  
It has been experimentally determined that galvanic corrosion in concrete pole reinforcement can be detected in pole sections located higher than 20 cm above the ground level. To evaluate underground sections, a pole should be unearthed for about 20-30 cm below the ground level, although in some cases it is not necessary. It is worth mentioning that, until now, corrosion damage could only be detected just prior to the failure of a pole. However, this technique can detect damage well before failure.  
TNDT surveys can detect reinforcement displacement and predict the residual strength of poles by analyzing concrete cracking in the near-ground pole section. This is a goal of the ongoing research.  
This work was supported by a Russian Scientific Foundation grant #17-9-01047 (3D numerical modeling) and in part by a State Order of the Russian Ministry of Higher Education for 2017-2019, NIR #9.5966.2017/BY (experimental implementation).  
[PERSON] conceived and designed the experiments. [PERSON] performed the experiments. [PERSON] did the numerical modeling of defect situations. [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 16: Experimental temperature profiles at the ground level: pole excess temperature vs. time (1—+50 cm, 2—0 cm, 3—30 cm).
# Continuous Wavelet Transform and CNN for Fault Detection in a Helical Gearbox
## 3 Test Rig, Gearbox States, and Data Processing
### Data Processing with CWT  
The information on each axis of the analyzing records (7710 samples acquired from about 18 pinion rotations) is processed by the Morlet wavelet transform (resulting in a wavelet amplitude and phase map). Figure 5 shows such a record of raw vibrations on the \(Y\)-axis. A filter bank was generated using the analytic Morlet wavelet with frequency limits from 0.4 kHz to 4 kHz. This frequency band is supposed to capture the most important dynamic characteristics of the meshing process of the gear pairs. The gear mesh frequencies, their six to eight harmonics, and their two associated pairs of sidebands often dominate the vibration response. Four octaves of interest with the associated frequency intervals 4 kHz-2 kHz, 2 kHz-1 kHz, 1 kHz-0.5 kHz, and 500 Hz-250 Hz, are considered. A portion, namely 500 Hz-400 Hz, of the lowest octave is used. Ten filters per octave are generated, resulting in a total of 34 filters. The filter bank is calculated once and is applied to each analyzing record of the vibration signal.  
The magnitude matrix of the CWT complex coefficients is transformed into a real-valued matrix in the range [0, 1]. Zero (black) corresponds to the lowest amplitude, and one (white) represents the highest amplitude. This new matrix is used to create a grayscale image in png format. For individual axes, the images are of size [34, 7710]. For all three axes, the CWT of the \(X\), \(Y\), and \(Z\) axes are concatenated on the height dimension (top to bottom), resulting in an image of size [102, 7710].  
The images are much wider than they are high, due to the length of the raw signal recording (1.8 rotations of the reducer output, meaning about 18 rotations of the faulty pinion). The CNN model will be adapted to the image size. Samples of images corresponding to the 2D CWT-based representations of the signals on all three axes for diverse states, loads, and rotational speeds are presented in a matrix format in Figure 6. Each image in the table contains the first 10% of the width of the actual image.  
Four 2D grayscale CWT-based images, one for the \(X\)-axis, another one for the \(Y\) -xis, the third for the \(Z\)-axis, and the fourth for all three axes, were generated for each observation (in a csv file). Accordingly, four datasets of images were created and used further in defect detection. Each such dataset contains 2593 instances for state D1 and 2440 instances for each of the other three states, D2, D3, and D4.  
The defect detection task proposed in the current paper is modeled as a four-class classification problem, with the four classes (D1, D2, D3, and D4) corresponding to the gearbox health states under observation.  
Figure 5: Raw vibration of an analyzed record (amplitude vs. 7710 samples) on the \(Y\)-axis.
## 2 Geological Settings  
The South China Sea is considered a unique marginal sea in the western Pacific Ocean. The reason for this is that since the Cenozoic it has experienced multiple periods of seafloor expansion [11], forming typical geomorphic units, such as continental shelves, continental slopes, and deep ocean basins, which have controlled the evolution of carbonate platforms. The carbonate platform in the South China Sea began to develop in the Late Oligocene, evolved extensively in the Miocene, and began to decline after the Late Miocene [10]. The carbonate platform has variable scales, forms, and properties throughout the area of study [12; 13]. Affected by the tectonic evolution of the South China Sea, the carbonate platform in the south developed earlier than in the north. As a special shelf-slope break in the northern South China Sea, the study area contains different geological units, such as the Qiongdongnan Basin, Xisha Trough, Xisha Uplift, Zhongjiannan Basin, and Zhongsha Trough (as shown in Figure 1), which determine the particularities of the development and evolution of carbonate platforms. A combination of well and seismic data studies indicate eight sequential boundaries from top to bottom in the area, as shown in Figures 2 and 3. Among them, T6 (23.3 Ma) is an essential structural interface. This interface divides the structural evolution of the whole area into two stages: the rifting stage and the post-rifting stage [13]. Between these stages, the rifting period (65.0-23.3 Ma) was dominated by northeast-trending faults, forming rift basins around the uplift. For instance, the Qiongdongnan Basin and Zhongjiannan Basin contain massively thick Eocene and Oligocene strata, which are also the primary source rocks in the region [14; 15; 16]. The post-rifting period (23.3 Ma to present) has been dominated by thermal subsidence, which has promoted the deepening of the water in this area, leading to the gradual submergence of the Xisha Uplift and Guangle Uplift. During the submergence process, the Xisha Uplift and its periphery have had little terrigenous debris input; as a result, the ceasing of fault activities has created conditions for the development of reefs and carbonate platforms [9; 17; 18; 19].  
Figure 1: (**A**) Distribution of sedimentary basins and the study area in the northern South China Sea. The sedimentary basins are mapped with black dotted lines. The location of the study area adjacent to the Qiongdongnan and Zhongjiannan Basins is marked by the red rectangle. (**B**) Tectonic divisions of the study area and the locations of seismic lines and wells mentioned in the text. The red lines are the locations of seismic profiles showing different carbonate platforms.
## 2 Materials and Methods
### Data Collection and Preparation (Sampling Campaigns)
#### 2.1.3 Delays Estimation  
Delays interactions are a ubiquitous feature of many dynamical systems, including the cement industry. Therefore, time delays played a fundamental role in our experiment. Noteworthy, the time delay was non-linear and caused complex dynamic behavior, which could not be explained by just looking at the constituents of a system. These delays were between the mill and separator parameters and the coarse return. To simplify the non-linear delays, a cross-correlation analysis time delays estimation [27] was conducted between the mill and separator parameters and coarse return. This technique was used to determine the temporal relationship between process variables. By finding the highest absolutes cross-correlation for various lag times, an average delay time between the two variables could be determined. For example, the time lag between mill parameters (i.e., FRF, MIF, MOF, and BEA), and coarse return varied between 1 to 60 min, but in minute 23, the cross-correlation was in the maximum. This time lag for separator parameters (i.e., SEA and SES) and coarse return is almost null. Figure 5 shows the applied cross-correlation technique results between the mill and separator parameters and coarse return variable with different time lags.
# A Multi-Objective Hybrid BESSA Optimization Scheme for Parameter Extraction from PV Modules
## 10 Conclusions and Future Scope of Work  
Our main conclusions are as follows:  
* The Hybrid BESSA has better exploitation capabilities, as the Hybrid BESSA is able to find global optima for all the unimodal functions when compared to the BES, CamWOA, WCMFO, ABC, CS, PSOGSA, MFO, GSA, WCA, DA, and WOA.
* The Hybrid BESSA has better exploration capabilities, as it produced better results for all of the multimodal functions except function \(f_{13}(p)\).
* When tested on fixed dimensional multimodal functions, it was found that the Hybrid BESSA provided better results for six functions out of the ten functions.
* The multi-objective parameter extraction scheme based on the Hybrid BESSA yielded a more accurate and robust modeling output as compared to that of the BES, as was evident from the lower percentage error of power, lower percentage error of the open circuit voltage, and lower percentage error of the short circuit current obtained as compared to the modeling conducted with the parameters using the BES-based extraction scheme. Again, the combined objective function of the Hybrid BESSA-based scheme was lower than that of the BES-based scheme.
* To validate the theoretical analysis, the Hybrid BESSA was also tested on the hardware PV Module AS-M3607-S.The Hybrid BESSA was a better fit and was more accurate when compared against the hardware-obtained results as compared to the BES results.  
From all of these studies, it has been concluded that the Hybrid BESSA-based multiobjective parameter extraction scheme is more accurate than the BES-based multi-objective parameter extraction scheme. The Hybrid BESSA is more accurate and robust as compared to the BES.  
These current research findings are limited to a comparison of the BESSA and BES algorithms with experimental results which achieved less than 1% error rates. However, the inclusion of [PERSON]'s rank-sum test would be a better tool for proving the reliability of the results at a higher level of accuracy and is one of our suggestions for future work. This future scope of work consists of deducing and analyzing results based on different types of PV modules, i.e., thin film modules, polycrystalline modules, and monocrystalline modules, working in different weather conditions. It is also necessary to complete performance comparisons between a dual-diode PV model and a single-diode PV model both with regards to actual hardware results and their theoretical bases.  
Conceptualization, N.S. and P.C.M.; methodology, N.S.; software, N.S.; validation, N.S. and P.C.M.; formal analysis, N.S.; resources, N.S.; data curation, N.S. and P.C.M.; writing--original draft preparation, N.S. and P.C.M.; writing--review and editing, N.S. and P.C.M.; visualization N.S.; supervision, N.S.; project administration, N.S.; funding acquisition, N.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is included in the manuscript.  
The authors
# Practical Estimation of Machine Tool Spindle Dynamics for Maintenance Decision Making
## 3 Results
### Static Spindle Test  
The average time-series accelerometer data from the \(-\)x axis accelerometer is shown in Figure 10 for 50 tap test trials under each of the three static test conditions. The acceleration profiles displayed strong agreement across the static test conditions.  
Figure 11 shows the average input force spectrum for each test condition. Beyond the range of 2000 Hz, the magnitude of the input spectrum decays below 50% of the peak value. Degrading coherence was also observed beyond 20  
The magnitude plot for the three FRFs is shown in Figure 13. Close agreement is observed in the FRFs calculated for each of the three conditions in the first half of the frequency range up to 1000 Hz. Beyond this range, the results show some minor deviations in magnitude, with similar overall trends. The FRF peak at 1080 Hz displayed the most notable difference among the tests. The freely vibrating tool of Condition 1 displayed a high magnitude peak at this frequency, while the tool-IG contact found in Condition 2 and Condition 3 displayed a notably lower magnitude. However, Condition 2 and Condition 3 both show good agreement with one another, which suggests that the tool-IG contact is the cause of this difference and not the method of applying the force. Further discussion on the effect of this contact is found in Section 4 of this article.  
Figure 11: Comparison of the average input force spectrum recorded from 50 trials for each of the three static spindle test conditions.  
Figure 12: Comparison of the coherence value recorded from 50 trials for each of the three static spindle test conditions.  
The real and imaginary components of the FRFs were considered to determine modal characteristics of the static spindle. A detailed view of the real and imaginary components of the FRF between 100 and 300 Hz is shown in Figures 14 and 15, respectively. The peak picking method was used to identify the modal characteristics at 180, 190, and 230 Hz to illustrate the capability for the tracking of spindle dynamics. Table 1 summarizes the characteristics for the three modes calculated using Equations (7)-(9). Close agreement was observed across the three conditions, with the largest differences observed in the stiffness parameters for the three modes. These results demonstrate the capacity of the proposed system to accurately track the modal characteristics of a machine tool spindle unit with comparable accuracy to the conventional impact hammer test.  
Figure 14: Detailed view of the real components of the FRF for each of the three static spindle test conditions. Dashed lines indicate critical features for modal characterization.  
Figure 13: Average magnitude plot of the FRF recorded from 50 trials for each of the three static spindle test conditions.
## Materials and Methods
### Data Processing  
The PubChem [80] and ChemSpider [81] chemistry databases were used for the identification of the molecular formula of each annotated peak, the class of each chemical compound, and the CAS-registered number. The proportion of chemical compounds by class in the profile of _K. parvifora_ samples was calculated based on the peak intensities of the number of counted annotated compounds in each class, for a total of 100%. The mass spectral peaks where retention indices did not match with those on the reference libraries were described as unknown compounds.  
The data processing and provisional VOC identification were performed by following the workflow scheme described by [PERSON] et al. (2016), with some modifications [82]. The raw data acquired by GC-TOF-MS analysis were exported in a network common data form (NetCDF) file format using Leco ChromaTOF software v4.71.0.0 (LECO, St. Joseph, MI, USA). All data-preprocessing procedures were conducted using the hierarchical multi-curve resolution (H-MCR) method by [PERSON] et al. (2006) and MATLAB 7.0 (MathWorks, Natick, MA, USA). The areas of the mass spectral values of the internal standards were calculated by MATLAB R2011b (MathWorks) [83]. The data matrix peaks were then normalized using the method of cross-contribution-compensated multiple standard normalization (CCMN), which eliminates systematic variations and allows for subsequent analysis [71]. The resolved mass spectra generated from the H-MCR method were matched against reference mass spectra from commercially available libraries by applying the NIST mass spectral search program (version 2.2) [84] and the peak annotation-customized software, following the method of [PERSON] et al. [71]. Reference libraries such as the [PERSON]'s FFNSC Library (Mass Spectra of Flavors and Fragrances of Natural and Synthetic Compounds, 3 rd edition), the Terpenoids Library [85], NIST14 [86], the VocBinBase Library [87], and the Adams Library (4 th edition) [88] were used for the identification and estimation of the VOCs. The same or very similar compounds were extracted from the referenced libraries and NIST05 based on the similarity (850 or 900) and the RI difference (<130 units) [71]. To determine whether the peaks were from a putatively annotated compound, a similarity of 800, with differences of less than 20 units, was applied when the standard deviation (SD) of the absolute RI difference between these compounds was less than 8.8 units.
## 1 Introduction
[34; 35] added basalt fiber into asphalt materials and evaluated their fatigue resistance by using direct tension, as well as fatigue tests. By means of X-ray tomography technology (i.e., CT technology) and the finite-element method, basalt fiber can release stress concentrations in critical areas and reduce fatigue damage. [PERSON] et al. [36] compared and discussed basalt fiber and commonly used fibers and found that basalt fiber has a superior reinforcement effect on the high-temperature anti-rutting ability of bitumen mastic. [PERSON] et al. [37] tested the reinforcement effects of basalt fibers with lengths of 3 mm, 6 mm, and 9 mm for asphalt mastics, with respect to lignin fiber and polyester fiber. Through leakage, penetration, strip-tensile, and dynamic shear rheometer (DSR) tests, basalt fiber, especially with length of 6 mm, has excellent comprehensive performances, due to a steady 3D networking structure in bitumen mastics. [PERSON] et al. [38] carried out repeated and multi-stress creep tests and used Abaqus software for analyzing the high-temperature performance of an asphalt mastic. Then [PERSON] et al. [39; 40] conducted numerical simulations in Abaqus for compressive creep and bending creep tests, for the purpose of analyzing the distribution effect and reinforcement mechanism of basalt fiber. [PERSON] et al. [41] explored the optimization design of SBS modified asphalt mixtures containing basalt fiber with the assistance of the central composite design method. Test results indicated that asphalt mixtures with a basalt fiber content of 0.34% and a length of 6 mm exhibited superior Marshall properties. Previous studies indicated that basalt fiber was effective in improving the mechanical properties of asphalt materials. Nevertheless, efforts undertaken for asphalt mixtures with basalt fiber under freeze-thaw cycles are still limited in this area. In this present paper, freeze-thaw cycles were performed for asphalt mixtures reinforced with eco-friendly basalt fiber. The volume and mechanical properties (i.e., air voids, splitting tensile strength and indirect tensile stiffness modulus) of asphalt mixtures were adopted for freeze-thaw damage analysis. Furthermore, this study developed the logistic damage models of asphalt mixtures. in terms of the damage characteristics of the volume and mechanical properties. in order to quantificationallyanalyze the damage degree and damage grow rate. Subsequently, multi-variable grey models (GM) (1,N) were established for modelling the damage characteristics of the asphalt mixtures under the action of freeze-thaw cycles.