esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
They analyzed symmetric and asymmetric double-sided blind holes and concluded that the thickness and the type of the hole are relevant to the scattering behavior. This, in turn, reveals that conclusions on the size and type of defects can be drawn on the basis of characteristics of the scattering signals. In their paper, they provide a summary of further analytical solutions of the wave scattering phenomena for different types of defects. The investigation of the \(A_{0}\)-mode shows that the Scattering Directivity Pattern (SDP) is asymmetric for angled cracks [6] and that for some angles of incident waves, there are no reflections [62]. The latter seems to also be apparent for the circular defects investigated in the present paper. [PERSON] and [PERSON] [62] concluded that angles with no reflections need to be considered when designing and mounting an SHM system. Further, [PERSON] and [PERSON] [60] investigated the clapping phenomenon for cracks in an aluminum plate for the \(S_{0}\)-mode. The clapping leads to a nonlinearity in the scattering behavior, which creates higher harmonics. The paper focuses on the first two harmonics. It shows that the observed nonlinear effects are well suited for damage detection. If a setup where linear waves are investigated is used, they recommend using a pitch-catch configuration, where the actuator and the sensor are in different sides of the defect. In the present contribution, we will focus on linear waves that are indeed examined in such a pitch-catch setup. In their study on \(S_{0}\) waves with cylindrical holes, [PERSON] et al. [64] investigated relations between the size of the defect and the scattering coefficient, as well as between the distance from the defect and the scattering coefficient. The latter relation is described through a simple formula, reflecting geometrical damping, which is used herein to find the scattering waves for sensors at any distance to a scatterer. In the current paper, a novel approach is proposed, which combines several of the aforementioned aspects, to achieve flexible surrogate models that are able to predict the sensor response in plates with several flaws. An online-offline approach is adopted, where the scattering behavior for different flaws is firstly learned in the form of transmissibility functions and next stored in a dictionary during an offline phase. The created dictionary of transmissibility functions is integrated in a ray tracing approach, allowing for consequently evaluating the sensor response. The remainder of the paper is structured as follows. In Section 2, the theory underlying the surrogate is presented, followed by the description of the proposed approach in Section 3, pertaining to both the online and offline phase of the model, including algorithmic implementation details. In Section 4, the method is demonstrated on a numerical example and results for two different kinds of defects are investigated. The paper is concluded by a discussion on the advantages and limitations of the model and an outlook on further research.
## 3 Results and Discussion
### Oxidative Stability and Antioxidant Capacity
\begin{split}\text{AC}=46.739-0.007*\alpha\text{-TOCO}+0.053*\text{ HTY}-0.015*\text{OLEA}+0.005*\text{OLEA}\\ \text{AGL}+0.1004*\text{TY}+0.001*\text{OLEC}+0.118*\text{MH OLEC}+0.016*\text{LIG AGL}+\\ 0.250*p\text{-CA}+0.003*\text{TPC}\end{split}  \tag{4}\]  
With a coefficient of determination R\({}^{2}\) = 0.583. The abbreviations in the equation stand for \(\alpha\)-TOCO--\(\alpha\)-tocopherol, HTY--hydroxytrosol, Y--tyrosol, OLEA--oleacain, OLEC--oleacain, OLEC--oleacain, OLEC--oleacain, OLEC--sum of oleupreoin aglycones, LIG AGL--sum of ligtroside aglycones, MH OLEC--methyl hemiacetal of oleocanthal, \(p\)-CA--\(p\)-comamic acid, TPC--total phenols. The model for antioxidant capacity yielded an R\({}^{2}\) of 0.583 and a Q\({}^{2}\) of 0.515, indicating moderate predictive strength. VIP analysis (Figure 2a) highlighted ligtroside aglycones, oleupreoin aglycones, \(p\)-comamic acid, TPC, methyl hemiacetal of oleocanthal, and hydroxytrosol (VIP > 1) as the main contributors to AC. Compounds such as tyrosol, \(\alpha\)-tocopherol, oleacain, and oleocanthal showed VIP scores < 1, suggesting a lesser impact under the conditions studied. Interestingly, similar to the case of IP, both \(\alpha\)-tocopherol and oleacain demonstrated a negative impact on antioxidant capacity. In contrast, other phenolic compounds showed a positive effect. Previous studies have reported that hydroxytrosol and oleupreoin derivatives and ligtroside aglycones possess the strongest antioxidant activity among the phenolic compounds found in VOO [102; 103]. The predicted vs. actual values plot (Figure 2b) further supported the model's validity. However, some scatter was evident, particularly at higher antioxidant capacity values, indicating potential limitations in the model's accuracy across the full range of observations. These findings are consistent with the established roles of major phenolic compounds and tocopherols as potent antioxidants in olive oil, although the moderate R\({}^{2}\) indicates that additional factors may also contribute significantly. Figure 1: (**a**) Variable importance in the projection (VIP) of induction period—IP (min) with the variables SFA—sum of saturated fatty acids, MUFA—sum of monounsaturated fatty acids, PUFA—sum of polyunsaturated fatty acids, \(\alpha\)-TOCO—\(\alpha\)-tocopherol, HTY—hydroxytrosol, TY—tyrosol, OLEA—oleacain, OLEC—oleacenthal, OLEU AGL—sum of oleupreoin aglycones, LIG AGL—sum of ligtroside aglycones, MH OLEC—methyl hemiacetal of oleocanthal, \(p\)-CA—\(p\)-comamic acid, TPC—total phenols. The dashed black line represents the significance threshold for VIP score. (**b**) The predicted versus actual values of induction period. The gray solid line represents the 95% confidence interval.
## 4 Material and Methods
### Solid-Phase Microextraction (SPME) Procedures  
Volatile flavour compounds were collected using the headspace SPME technique ([PERSON] et al., 2016). Briefly, 50 \(\mathrm{\mu L}\) of 1,2-dichlorobenzene as ISTD and 5 mL of sample were mixed in 20 mL glass vials (CNW Technologies, Germany) fitted with a PTFE/silicone septum. The final concentration of ISTD in each sample was 10 \(\mathrm{\mu}\)g/L. All extractions were performed using SPME fibres (50/30 \(\mathrm{\mu}\)m DVB/Carboxer/PDMS; Supelco, Inc., Bellefonte, PA, USA), the glass vials were kept at 50 \({}^{\circ}\)C for 60 min and the samples were stirred continuously with a magnetic stirring bar at 300 rpm. Subsequently, the fibre was immediately inserted into the injection port of a 7890B GC (Agilent Technologies, Inc., Palo Alto, CA, USA) for 5 min at 270 \({}^{\circ}\)C to desorb volatile compounds into the GC.  
A 7890B gas chromatograph equipped with a 5977A mass selective detector (MSD; Agilent Technologies) was used to analyse the volatile flavour compounds of the samples.  
Details: column, HP-5 MS column (30 m length, 0.25 mm inside diameter, 0.25 \(\upmu\)m film thickness; California, USA, Agilent Technologies Inc.); carrier gas, helium at 1 mL/min; oven temperature, 35 \({}^{\circ}\)C maintained for 5 min, increased to 140 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min and maintained for 5 min at 140 \({}^{\circ}\)C, increased further to 250 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min, with a final 5 min extension at 250 \({}^{\circ}\)C; ion source temperature, 230 \({}^{\circ}\)C; transfer line temperature, 250 \({}^{\circ}\)C; scan range, 40-400 \(m/z\); electronic impact (EI) mode, 70 eV.  
Mass spectra in the National Institute of Standards Technology Mass Spectral Database 11 (accessed using software from Agilent Technologies, Inc.) were used to identify all of the volatile flavour compounds. The MassHunter workstation was used to compare the MS peaks. The concentration of each compound was calculated using the following formula:  
\[\mathrm{c_{i}}=\frac{\mathrm{A_{i}}}{\mathrm{A_{s}}}\times\mathrm{c_{s}} \tag{3}\]  
where '\(\mathrm{c_{i}}\)' is the concentration (\(\mathrm{\mu g/L}\)) of each volatile flavour compound, '\(\mathrm{c_{s}}\)' is the concentration (\(\mathrm{\mu g/L}\)) of the ISTD, '\(\mathrm{A_{i}}\)' is the chromatographic peak area of each compound, and '\(\mathrm{A_{s}}\)' is the chromatographic peak area of the ISTD.  
The retention index (RI) was calculated from the retention time by automatically retrieving the mass spectral results of each component using the random carry MassHunter workstation Standards Technology Mass Spectral Database 11. RI was calculated using the following formula:  
\[\mathrm{RI}=100\times\left[Z+\frac{\mathrm{RT_{(X)}-RT_{(Z)}}}{\mathrm{RT_{(Z+ 1)}-RT_{(Z)}}}\right] \tag{4}\]  
where 'RT' is the retention time (min); retention times for n-alkane carbon atom numbers follow the \(\mathrm{RT_{(Z)}<RT_{(X)}<RT_{(Z+1)}}\) order.
## 4 Experiments
### Experimental Setup
#### 4.1.3 Systematic Workflow for Data Acquisition and Processing
As summarized in Table 4, the camera used had a field of view (FOV) of 85\({}^{\circ}\), and the robot's motion remained within roughly \(\pm\)30\({}^{\circ}\) of the center of the frame. Under such conditions--even with a moderately wide-angle lens--lens distortion errors typically remain below 2%. Consequently, a dedicated camera calibration was deemed unnecessary, and we were able to achieve sufficiently meaningful results without additional distortion compensation. To verify that the error is indeed negligible, we compared the marker-tracked CoR trajectory with the simulated CoR trajectory of the curved-spoke wheel ascending the 300 mm \(\times\) 160 mm stairs, as shown in Figure 12. The mean error in the \(y\)-direction between the two trajectories was 5.96 mm, with an RMS error of 7.08 mm. Over the 0-585 mm range of motion in the \(y\)-direction, these values correspond to error rates of approximately 1.02%, respectively. This minor discrepancy may arise from slight lens distortion, minor inaccuracies in marker placement, or the purely kinematic assumptions of the simulation--excluding inertia, friction, and motor torque variations. Nonetheless, we considered the error sufficiently small for evaluating the suspension's impact and performance.
## 2 Numerical Methods
### Boundary Conditions and Grid Independence  
The computational domain consisted of a rotating domain of the sweeping rod and a static domain of the integrated AITD, as shown in Figure 4. Its inlet, located 1.5H upstream of the sweeping rod, was prescribed as Re = 7.16 \(\times\) 10\({}^{4}\) (based on the inlet annulus height) and FSTI = 3%. The outlet, located 2.5C\({}_{x}\) downstream of the integrated LPT-GV, was set at uniform atmospheric pressure. No-slip and no-heat transfer conditions were imposed at the solid boundaries. The rotation speed of the sweeping rod was set to 220 RPM, and the reduced frequency was 1.07. In the unsteady calculation, the total duration was 0.389 s, and the minimum physical time step (\(\Delta t\) = 6.5 \(\times\) 10\({}^{-5}\) s) was set to 20 times the Karman vortex street frequency downstream of the round rod calculated by classical boundary layer theory.  
Using the commercial software Numeca/AutoGrid, the computational domain of the integrated AITD was meshed with an HOH-type structured grid. Numerical simulations with 12.2, 6.3 and 3.1 million nodes were conducted to investigate the grid independence. As the results with 12.2 million nodes were only slightly different from those with 6.3 million nodes, the latter grid was chosen for further calculations. In the chosen grid, the numbers of circumferential, spanwise and streamwise nodes were 73, 133 and 552, respectively, and the numbers of O-type nodes around the LPT-GV and rod were 369 and 81, respectively. In the boundary layer, the grid thickness of the first layer was 0.002 mm, and the growth ratio was 1.15, which ensured that y+ <1 for all cases studied in this paper.
## 2 Materials and Methods
### Analysis of Volatile Compounds  
The analysis of volatile profiles was performed by solid phase microextraction (SPME) and their qualification and quantification on GC/MS following the method by [PERSON] et al. (2011) [23]. For sample preparation, 5 g of minced BF or SM muscle was mixed with 25 mL of distilled water saturated with NaCl in a commercial blender. A 10 mL aliquot was added to 20 mL vials with the addition of 100 \(\upmu\)L 4-methyl-2-pentanol (1.2 mg/kg) (internal standard) and magnetic stirrer, and then sealed with a PTFE septum. An SPME fiber coated with 2 cm of 50/30 \(\upmu\)m DVB/Carboxen/PDMS (Supelco, Bellefonte, PA, USA) was placed over the sample mixture (previously heated at 240 \({}^{\circ}\)C for 2 min). Extraction of triplicate 20-mL vials was performed at 40 \({}^{\circ}\)C for 180 min with constant stirring. After extraction, the SPME fiber was transferred to the injection port of the 6890N gas chromatograph coupled to a 5975i mass selective detector (Agilent Technologies, Santa Clara, CA, USA). Helium was used as the carrier gas with a constant flow of 1 mL/min (9.59 psi). Thecolumn used for volatile separation was a DB-5 MS capillary column (30 m \(\times\) 0.25 mm, film thickness 0.25 \(\upmu\)m (Agilent Technologies, Santa Clara, CA, USA)). Injection was performed in splitless mode, with an injector temperature of 230 \({}^{\circ}\)C and a desorption time of 5 min. The chromatographic conditions were: The temperature was set at 40 \({}^{\circ}\)C, isothermal for 10 min, then increased to 200 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, and then to 250 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min. The final temperature was held for 5 min and the transfer line temperature was maintained at 280 \({}^{\circ}\)C. Mass spectra were recorded at 70 eV at a rate of 1 scan/s over the \(m/z\) range of 50-450.  
To calculate the retention indices (RI) of the detected compounds, an in-house mixture of C8-C20 n-alkanes was run under the same chromatographic conditions. The program AMDIS 3.2 version 2.62 was used to identify the components using the spectral library NIST 2005 version 2.0 (NIST, Gaithersburg, MD, USA) and to compare the retention indices obtained with literature values ([24] and the in-house library).
## 2 Materials and Methods
### Lateral Stability  
With reference to the lateral stability of the FLT, and in combination with interchangeable equipment, it is necessary to acquire the following additional data from the instruction handbook and/or the marking plate on the interchangeable equipment:  
* The maximum lateral displacement S [mm];
* The total mass (PCAS) of the elements FOR the interchangeable equipment which takes part in the lateral displacement [kg]. On the basis of the acquired data, it is possible to:
* Evaluate the maximum operating transversal moment (OTM) of the truck as per Equation (5) according to the lift height (H\({}_{\text{j}}\) in Figure 2). With reference to Figure 4, the load is supposed to be on the fork arm of the FLT and is positioned according to the test condition for lateral stability (Test 3) required by ISO 22915-2 at a slope of 6% (\(\alpha\) = 3.4\({}^{\circ}\)).  
* It is important to evaluate the total transversal moment of the interchangeable equipment (MTA) according to the lift height (H\({}_{\text{j}}\)) as per Equation (6). The FLT is supposed to be positioned according to the test condition for lateral stability required by ISO 22915-2 (Test 3) at a slope of 6% (\(\alpha\) = 3.4\({}^{\circ}\)). The value of lateral displacement should be the maximum allowed by the interchangeable equipment by the nature of the load being handled or by the load handle device employed. The total load at the interface (CI\({}_{\text{L}}\)) should be evaluated as the sum of the maximum load permitted by the manufacturer (Q\({}_{\text{i}}\) in Figure 3) and the total mass of the elements of the interchangeable equipment which take part in the lateral displacement (PCAs). \[\text{MTA}=\text{CI}_{\text{L}}\times\text{S}+\text{CI}_{\text{L}}\text{ sin}\alpha\times\text{H}_{\text{j}}.\] (6)  
Figure 4: Evaluation of OTM: nomenclature. Key: H\({}_{\text{j}}\)—lift height; Q\({}_{\text{j}}\)—rated capacity according to D\({}_{\text{i}}\) and H\({}_{\text{j}}\) Q\({}_{\text{m}}\)—rated capacity component normal to tilting platform; Q\({}_{\text{i}}\)—rated capacity component tangent to tilting platform; \(\alpha\)—slope.  
The last step is to verify for each condition the lift height and load distance as foreseen in the FLT load chart:  
\[\text{MTA}\leq\text{OTM}. \tag{7}\]  
Thus, the actual capacity of the truck coupled with the interchangeable equipment should be evaluated by considering the loads which allow Equation (7) to be satisfied. For this purpose, it could be necessary to reduce the value of the maximum load permitted for the interchangeable equipment (Q\({}_{i}\)).  
Interchangeable equipment which does not satisfy Equation (7) for any combination of loads is not compatible with FLT.
# Robust Grey Relational Analysis-Based Accuracy Evaluation Method
## 3 Robust Grey Relational Analysis-Based Accuracy Evaluation Method
### Accuracy Assessment Process Based on Strong Robust Grey Correlation Analysis  
The accuracy assessment steps based on strong robust grey correlation analysis are summarized as shown in Figure 8. Firstly, calculate the expected through-valve rate and the through-valve interpolation point data sequence, merge the interpolation point sequence with the original data sequence, select the perturbation tolerance and tolerance benchmark sequence according to the actual engineering application, dimensionless them to the bias acceptability, and construct the grey correlation coefficient-mean squared distance model and the mean squared distance-accuracy model based on the time series data from the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Traditional Method** & & **RGRA-AEM** \\ \cline{2-5}  & **No Additional** & **No Additional** & & \\
**Statistical Indicators** & **Interpolation Points** & **Interpolation Points** & **Increase Penetration Valve Interpolation Points** \\  & **Through the Valve** & **Through the Valve** & & \\ \cline{2-5}  & **No Increase in** & **Increase in Bias** & **No Increase in Bias** & **Increase in Bias** \\  & **Bias Tolerance** & **Tolerance by 2\%** & **Bias Tolerance** & **Tolerance by 2\%** \\ \hline Grey Correlation/(\%) & 54.315 & 58.0955 & 62.048 & 68.439 \\ Accuracy/(\%) & 68.312 & 74.760 & 80.504 & 87.602 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of evaluation results between two grey relational analysis methods under disturbance conditions.  
Figure 7: Grey relational coefficients of data points after adding 2% deviation acceptability: (**a**) Global distribution of grey relational coefficients, which shows the distribution of gray correlation coefficients for the sequence as a whole; (**b**) Some sample points with anomalous grey correlation coefficients, highlighting some of the anomalies with small gray correlation coefficients. Color blocks represent sample points and their corresponding grey correlation coefficients.  
simulation and test. Based on the simulation-test time series data, the grey correlation coefficient and grey correlation degree are calculated based on the acceptable deviation, and the grey correlation degree is mapped to the mean square distance by combining the grey correlation degree-mean square distance model. The mean square distance is mapped to the accuracy assessment result by combining the mean square distance-accuracy model.
## Appendix A
### Linear Density (LD)  
Enhanced image processing  
For completion of the radiometric calibration and atmospheric correction on selected high-quality images with less clouds and noise, PCA was used to enhance the extraction of geological information. Since the PC-1 images contain most of the information as they are based on maximum variance, they were implemented as the enhanced images and the line features had clear edges.  
Automatic lineament extraction and parameter test  
Based on the above-mentioned PC-1 images, edge filtering and line extraction techniques were performed in MATLAB or RS software. The most widely used software tool is the LINE module of PCI Geomatica. The parameters of concern were the radius of the filter in pixels (RADI), threshold for edge gradient (GTHR), threshold for curve length (LTHR), threshold for line fitting error (FTHR), threshold for angular difference (ATHR), and threshold for linking distance (DTHR). The parameters can determine the lineament forms and circular (e.g., curve) structures. The parameter tests were based on the given bedding (e.g., layer of stratum) and extracted lineaments. Thus, compared with the geological structures of a geological map (Figure 7), the results of this study and the literature are shown in Table 11.  
Post-processing of verification and density map  
Verification by in-situ data was needed to exclude artificial linear features, such as roads, power grids, rivers, and image backgrounds. In addition, automatically extracted lines with lower RADI values had more details (e.g., short lines) and noise, and those with DTHR values made the extracted lines linked with each other. Thus, in the 30 m \(\times\) 30 m spatial resolution Landsat imagery, extracted lines longer than 1000 m were considered as geological lineaments in post processing. Furthermore, the lineament density (LD) was analyzed with the spatial tools of ArcGIS software. The calculation of LD was defined by Equation (A1), and the results were ranked by JNB. A higher ranking and value of LD indicates the conditions of weathering or vulnerability of the bedrock, which present more potential for groundwater flow and storage.  
\[\text{LD}=\frac{\sum_{\text{i}=1}^{\text{i}=\text{n}}\text{L}_{\text{i}}}{ \text{A}}\] (A1)  
where \(\text{L}_{\text{i}}\) is the length of the lineament and A is the unit of area.
## 4 Challenges and Considerations
### Size Limitation  
One of the critical barriers for application of metal AM is the maximum build volume, which gives the maximum size of the printed part. While metal AM processes can be divided into different sub-processes such as DED, EBEAM, etc. (see Table 1), not all systems are suitable for producing large parts [170]. Industry-wide, metal AM manufacturers continue to produce novel technologies to remain competitive and overcome industrial adoption challenges. Table 2 lists some recent metal AM systems that are commercially available for production of large parts. It should be noted that DED processes are mainly used for repair applications and less commonly for production of a whole part, primarily due to the lower accuracy and minimum feature size, and require post processing treatments [171].
# Event Detection and Spatio-temporal Analysis of Low-Altitude Unstable Approach
## 2 Methodology
### Methods and Models
#### 2.2.2 Quantitative Risk Evaluation Model
We incorporated them in a toolkit for UA risk evaluation with the parameters in Table 1 as input, which will allow users to select the triggering reason and relevant parameters to obtain the UA risk score with QAR records. In this sense, the frequencies of UA events occurring around each airport can be calculated accordingly. In this study, all the UA events triggered by Chinese civil aircraft in January 2018 concern airports within mainland China.