esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study and Algorithm Validation
### Effects of Iterations
Based  
Figure 20: Effects of iterations on path length. on the data provided in Figure 21, the proposed BSO method is 18.57%, 59.66%, 58.30%, and 32.63% better than PSO, GA, FA, and standard BAS. The effects of the iterations on the generated UAV paths are provided in Figure 18.0 From the curves, the path generated by the proposed BSO method is the smoothest, besides the fewest turns on it. It proves that it is quite suitable for the UAVs to follow. Also, combined with the previous results, the cost of the UAV path given by the proposed BSO method is the lowest. All in all, the number of iterations could generate quite limited effects on the proposed BSO method, which, as a result, proves its accuracy and robustness. It would converge as soon as possible without too many iterations. But, oppositely, the number of iterations affects the other four algorithms a lot. For example, for PSO, more iterations would make the path smoother. Especially in Figure 22c, when the iteration is 250, PSO could generate a similar path to the proposed BSO method but with more turns on the path. If the particles in PSO evolve more generations, the solutions are much closer to the true global best. But more generations introduce more time consumption correspondingly. Standard BAS is highly dependent on the iterations, too, because if the beetle makes more moves, it becomes closer to the optimum position. There are a great many turns on the paths produced by GA and FA, regardless of the number of iterations. Of course, the iterations have positive effects on GA and FA. More iterations make the two algorithms obtain relatively optimum solutions, which makes the generated paths easy for the UAVs to follow. But as a whole, neither GA nor FA would give the perfect flying paths as the proposed BSO method. Figure 18.1 Effects of iterations on path cost.
# A Survey of Graphene-to-Phoneme Conversion Methods
## 2 Grapheme-to-Phoneme Methods
### Methods Based on Deep Learning
#### 2.2.3 Transformer  
The Transformer model consists of an encoder and a decoder, both of which are composed of multiple stacked self-attention layers and fully connected layers. The attention mechanism is an attention mechanism that associates different positions of a single sequence to calculate its internal representation. Add positional encoding to input and output embeddings without using any loop layers. Location information provides the order of input and output sequences for Transformer networks. Ref. [29] utilized the attention mechanism in English-German translation that achieved a state-of-the-art result. The Transformer network structure is illustrated in Figure 8.  
Figure 7: The structure of CNN and BiLSTM G2P converters.  
Ref. [30] first introduces the Transformer to G2P conversion to obtain better performance. Ref. [31] proposed a transformer structure for grapheme-to-phoneme conversion, which improves the performance of the model by increasing the number of encoder-decoder layers. This work studied 3 \(\times\) 3 (3-layer encoder and 3-layer decoder), 4 \(\times\) 4, and 5 \(\times\) 5 models based on Transformer and evaluated them. The 4 \(\times\) 4 model achieved the best performance, with a PER of 5.23% and a WER of 22.1%, which illustrates appropriately increasing the number of encoder and decoder layers can improve model performance, but the model will have relatively more training parameters; a large number of parameters will increase the training time of the model and may also reduce its performance. The model structure is illustrated in Figure 8.  
Compared with other deep learning G2P models, the model with the best performance in Transformer has competitiveness, small model parameters, short training time, fewer training resources required, and higher accuracy. Ref. [32] proposed A novel Transformer G2P model architecture (NN-KoG2P) for the Korean language, which reflects the characteristics of Korean pronunciation and has fast inference speed. It can generate pronunciation sequences in real-time services.
## 4 Prediction of Side Wall Rock Mass Mechanical Parameters
### Estimation of Rock Mass Parameters  
#### 4.2.1 The Relationship between Rock Mass Wave Velocity and Uniaxial Compressive Strength of Rocks  
[PERSON] [18] proposed a relationship between the rock mass longitudinal wave velocity and the uniaxial compressive strength of rocks based on more than 20,000 sets of rock test data collected from 11 railway construction projects in southern China.  
\[V_{\rm pr}=1150\sigma_{\rm ci}^{\frac{1}{3}} \tag{1}\]  
In the equation: \(V_{\rm p}\) represents the longitudinal wave velocity of rocks in m/s, and \(\sigma_{\rm ci}\) represents the uniaxial compressive strength of rocks in MPa. It should be noted that \(\sigma_{\rm ci}\) needs to satisfy the condition of 0.5 MPa \(\leq\sigma_{\rm ci}\leq\) 300 MPa.  
The expression for the relationship between the longitudinal wave velocity of rocks \(V_{\rm p}\) and the uniaxial compressive strength of rocks \(\sigma_{\rm ci}\) can be derived by substituting the rock mass integrity index into Equation (1) [18].  
\[V_{\rm p}=1150k_{\rm v}^{\frac{1}{3}}\sigma_{\rm ci}^{\frac{1}{3}} \tag{2}\]  
In the equation: \(V_{\rm p}\) represents the longitudinal wave velocity of rocks in m/s, \(\sigma_{\rm ci}\) represents the uniaxial compressive strength of rocks, and \(K_{\rm v}\) represents the rock mass integrity index. The rock mass integrity index can be obtained using the rock mass volumetric joint number \(J_{\rm v}\) according to the relationship shown in Table 2 [19]. The fitted expression for the rock mass integrity index is given by Equation (3) [20].  
\[K_{\rm v}=-0.01834J_{\rm v}+0.7618 \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Forecasted Station Number** & **Forecast of Sidewall Conditions** & **Actual Conditions after Excavation** \\ \hline \multirow{4}{*}{TFD 1 + 260\(\sim\)TFD 1 + 280} & After TFD 1 + 270, there is an increase in negative reflection interfaces, indicating poor integrity of the surrounding rock at this location. & The intersection of three sets of fractures in the same stratigraphic plane (interlayer fractures) locally forms potential unstable blocks, and the width of the fracture surfaces is mostly slightly widened. \\ \cline{1-1}  & After TFD 1 + 300, the development of structural fracture zones is observed, and the integrity of the surrounding rock is poor. & The surrounding rock in this section mainly exhibits a laminated structure, with poor integrity. \\ \hline \end{tabular}
\end{table}
Table 1: TGP results compared with actual results.
## 2 Experimental Program
### Study and Characterization of the SCC
#### 2.1.2 Mix Proportions of the SCC  
The mix design of the mortar phase of SCC was performed based on the methodology proposed by [PERSON] et al. (1988), which considers the volumetric ratio of each fine aggregate (s1, s2,.., sn) in the total volume of fine aggregates (Vs), the powder mixture proportions (cement replacement by the addition), the ratio between the volume of powder and fine aggregates (Vp/Vs), the ratio between the volume of water and powder (Vw/Vp) and the percentage mass ratio between the superplasticizer and the powder (Sp/p%). Thus, considering the selected cement type and the intended average compressive strength, a water to cement ratio W/C (in mass) of 0.35 was estimated. Next, parameter Vp/Vs was set to be 0.80 and, based on the W/C ratio, cement type, and addition selected, the percentage of cement replacement by the addition was estimated as 30%. Parameters Vw/Vp and Sp/p% were obtained experimentally using the procedure described by [PERSON] et al. (1988). The following values were obtained: Vw/Vp = 0.77 and Sp/p% = 0.70. The volumetric ratio of each fine aggregate, defined in Section 2.1.1, is 0.5 of Sand 0/2 and 0.5 of Sand 0/4.  
According to the methodology proposed by [PERSON] et al. (1998, 2000), to complete the mix design of SCC, the following parameters are needed: the volumetric ratio of each coarse aggregate (g1, g2,..., gn) in the total volume of coarse aggregates (Vg), the volume of voids in concrete (Vv) and finally, the ratio between the volume of mortar and coarse aggregates (Vm/Vg). The following parameters were defined: Vv = 0.03 m\({}^{3}\), Vm/Vg was estimated to be 2.279 considering the required fresh properties. The volumetric ratio of each coarse aggregate, defined in Section 2.1.1, is 0.65 for Gravel 3/6 and 0.35 for Gravel 6/15. The SCC mix proportions are presented in Table 1.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 3 Results
### EO and Hy Chemical Composition

Table 2: Chemical composition (percentage mean value \(\pm\) standard deviation) of exocarp and mesocarp extracts. Qualitative and quantitative differences between the chemical composition of EO and HY were found. In detail, the volatile profile of EO was characterized by limonene (66.9%) and \(\gamma\)-terpinene (20.0%) as the most abundant compounds, while \(\alpha\)-terpineol (44.7%) and terpinen-4-ol (21.6%) were in the Hy. Furthermore, several minor monoterpene components, such as \(\alpha\)-thujene (0.2%), \(\alpha\)-pinene (0.6%), \(\beta\)-thujene (0.1%), \(\beta\)-myrcene (0.9%), \(\beta\)-pinene (0.8%), (+)-4-carene (0.2%), R-(+)-citronellal (0.1%), nerol acetate (0.2%), and geranyl acetate (0.1%) found in the EO were missing in the Hy. Instead, other compounds such as \(\beta\)-terpinene (1.0%), linalol (5.7%), thymol (1.9%), and piperitone (0.4%) were detected only in the HY.
## 2 Methods
### Detection of Wall Segments
#### 2.1.1 Wall Points Clustering  
An initial set of wall points are detected based on the idea that the normal direction of wall points tends to be horizontal. Suppose \(\vec{n_{p}}(n_{x},n_{y},n_{z})\) are the unit normal vectors of point P and \(n_{x}^{2}+n_{y}^{2}+n_{z}^{2}=1\), the first condition for the wall points is \(n_{z}<\arcsin(Th_{nv})\), where \(Th_{nv}\) is set as \(5^{\circ}\) in the tests. The Density-Based Spatial Clustering (DBSCAN) algorithm [27] is adopted to remove the small noise components. Once the points are separated, we project them to the 2D horizontal surface by simply omitting the z values of the points' coordinates, and the point normal vectors are also projected and normalized in the same way.
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 1 Introduction
### The Contribution  
In the development process of on-line Lamb wave detection equipment for microcracks in thin-walled tubes, the following work items have been accomplished.  
1. The dispersion characteristics of Lamb waves in thin plates with the same thickness were used to approximate the ultrasonic characteristics of thin-walled tubes, which reduced the dimensions of the model and laid a foundation for the engineering application of Lamb waves.
2. Based on the calculation and simulation of phase velocity, incident angle, and particle amplitude displacement, the Lamb wave mode with the highest sensitivity to the inner wall and outer wall cracks of a thin-walled cylinder was determined.
3. Multi-channel technology was adopted to solve the problem of rapid detection and improve the stability and reliability of the equipment.
4. The defect signal extraction circuit was designed, which greatly reduced the amount of data acquired and improved the data processing speed.
5. Rapid on-line detection equipment for microcracks in thin-walled tubes was developed, and the engineering application of rapid detection of microcracks by Lamb wave was realized.
6. Qualitative and quantitative non-destructive testing of the minimum depth crack of \(10\times 0.15\times 0.045\) (length \(\times\) width \(\times\) depth) was realized.
## 4 Numerical Results
### Methodology Comparison  
The most significant advantage of the proposed approach is the integration of both objective data and subjective opinions in the evaluation process. Furthermore, for subjective opinions, the prioritization considers the psychological behavior of the decision makers through the risk-aversion coefficient and the regret-aversion coefficient. This advantage makes decisions more consistent with the worldview of the decision maker.  
To verify the quality of the solution, the proposed method was compared with the proven powerful spherical fuzzy TOPSIS method, which is another distance-based method. In order for the results to be comparable, the risk-aversion coefficient and the regret-aversion coefficient in the proposed method were disabled (\(\varphi=1,\ \lambda=0\)). As shown in Figure 10, the ranking  
Figure 8: Risk-aversion coefficient sensitivity analysis.  
Figure 9: Regret-aversion coefficient sensitivity analysis.  
results were not significantly different. The difference mainly appeared in the adjacent ranks. In other words, if the psycho-behavioral consideration feature is omitted, the results of the proposed method are remarkably consistent with that of the proven similar method.
## 3 Experimental Progress
### Tests on Mechanical Properties  
All samples were then cured at 100% humidity and room temperature for further tests, as shown in Figure 7, at 28 days. The IR- samples were polished into 40 \(\times\) 40 \(\times\) 160 cm cuboids for three-point bending tests at a loading rate of 300 N/s, as shown in Figure 7a. Compressive tests were then conducted on the IR- samples along the \(Z\)-axis and a load rate of 300 N/s, as shown in Figure 7b. The IL- samples were subjected to three-point bending tests, as shown in Figure 7c. The failing stresses and strengths were compared and discussed, and the failure modes and contact area between the inserted staples and cement were observed.  
As mentioned in Section 1 and illustrated in Figure 8a, there are interfaces between the layers and strips. The fabricated prisms and flexural loading can be A, B, or C, as shown in Figure 8b, Figure 8c, or Figure 8d, respectively. The IR- samples for flexural and compressive tests belong to type-A and type-E, as in Figure 8b,f, respectively. The IL- samples were for type-B (Figure 8b). Other studies on (Figure 8d) and (Figure 8e) are discussed in later sections.  
Figure 6: Quantification of the staple inserting results.  
Figure 7: Mechanical tests: (**a**) three-point bending for IR- samples along the \(Z\)-axis, (**b**) compressive test for IR- samples loading along the \(Z\)-axis, and (**c**) three-point bending for IL- samples.
# Compositional Analysis of Chalcopyrite Using Calibration-Free Laser-Induced Breakdown Spectroscopy
## 1 Introduction
The calibration method involves the drawing of calibration curves for reference elements and optical emission spectrum of the corresponding element is compared with the curve so drawn [16; 17]. Whereas in calibration free-LIBS (CF-LIBS) methodology, the intensity of the spectral lines and the plasma parameters are utilized for quantification [18; 19]. In the calibration method, the requirement of many calibration curves and uncertainties related to matrix effect makes it less attractive for analysis of multi-elemental geomaterials [20; 21]. Being more theoretical and free from errors associated with the matrix effect, the CF-LIBS technique is considered the most suitable approach for carrying out quantitative analysis of multi elemental materials such as minerals. Previously, the CF-LIBS technique had been adopted for compositional analysis of meteorites and other extraterrestrial objects [22; 23]. The quantitative analysis of quartz-bearing limestone using the CF-LIBS technique has been previously reported [24]. Malachite, a copper-bearing mineral, was also quantified using CF-LIBS methodology [25]. The same technique was adopted for the quantification of granite rock [26]. LIBS was applied in near vacuum ultra violet region (VUV) for the identification and classification of sulfide minerals, including pyrite, chalcopyrite, pyrrhodite, barite, sphalerite, calcite, and dolomite present in sulfur bearing drill core samples [27]. A combination of LIBS and Raman spectroscopy was chosen for studying the emission spectra from calcite, gypsum, sulfur, pyrite, and chalcopyrite, both in air and simulated martian atmosphere [28]. Recently, LIBS was employed for quantitative analysis of Cu bearing sulfide minerals including chalcopyrite by using the calibration method and chemometric techniques [29; 30]. To the extent of our knowledge, no study exists describing the quantitative analysis of chalcopyrite by CF-LIBS. The objective of this study was to utilize CF-LIBS methodology, which is easier, free from matrix effects and needs no reference sample, unlike the calibration method, to determine the elemental composition of industrially-important mineral chalcopyrite of local origin. This study seems to pave the way for the commissioning of the LIBS technique for the mineral exploration in the country. The largest known copper reserves in Pakistan are located in Baluchistan province [31] and reserves in the area of Neelum valley of Azad Jammu and Kashmir are not well known. This research work will also help in attracting the attention of the mining industry towards copper extraction from these areas.
## 3 Microwave NDT Techniques
Initially regarded as \"emerging techniques\", microwave methods have gained substantial recognition over time due to their increasing applicability. As a testament to their established status, the American Society for Nondestructive Testing (ASNT) officially acknowledged MNDT as a distinct method in 2016 [38; 42]. Microwave inspection techniques depend on two propagation phenomena: reflection and transmission. Each behavior offers subtle and detailed insights into the material's structure and properties [38]. In reflection, the transmitted microwave signals are reflected from the structure, with varying amplitude and phase, revealing microstructural details about the inspected object [38]. Conversely, transmission involves microwave signals penetrating the specimen, where the data captured on the opposite side are analyzed for dielectric behavior, material loss, and the intricate interplay of porosity and permeability with frequency and temperature [38; 54; 55]. Advanced modeling and validation techniques link these characteristics to critical material properties, enhancing the precision and applicability of MNDT. Furthermore, MNDT techniques can be divided into five methods: far-field, near-field, resonance, microwave imaging, and reflectometry. The common thing with all these methods is that they utilize the reflection and transmission phenomena to inspect the object under test. Nevertheless, these methods are different in various aspects. A description of each MNDT method is given in the following sections.
# Freshness Monitoring of Packaged Vegetables
## 3 Factors Indicating the Freshness of Packaged Vegetables
### Oxygen and Carbon Dioxide Effects
#### 3.1.1 Oxidation  
The appropriate amount of oxygen inside the package is chosen depending on the type of vegetables. Oxidation is considered an important factor that can affect their shelf life. For example, the oxidation process induces the browning in vegetables by the action of polyphenol oxidase (PPO) [24]. PPO is a coper-containing metalloprotein enzyme that becomes exposed to air in fresh cut vegetables catalyzes the oxidation of phenolic compounds producing quinone, then further reaction with other amino acids produces brown pigment colors (Melanin) on exposed chloroplasts [16; 24; 25]. The summarized reaction is shown in Figure 3. Therefore, inhibiting this oxidation process requires adjustment of the oxygen partial pressure inside the package. For instance, for lettuce it was found that keeping the oxygen in the range of 0-1 kPa will reduce this process significantly. The problem of oxidation does not only lie in the brown color that might affect customers' desire to buy, but also leads to loss of the nutritional value of vegetables due to destruction of many nutrients such as vitamins and proteins. Moreover, the flavor may also change with this process, thus the quality deteriorates [23].
## 1 Introduction
However, this method could only consider two constraints: porosity and correlation function, resulting in poor connectivity of the generated model. Based on the process method proposed by [PERSON] et al., [PERSON] et al. further introduced elliptical rock particles while considering the filling effect of clay minerals on the pores [22; 23]. This approach better characterizes the true morphology and seepage properties of rocks. However, it is computationally complex and only applicable to rocks with simple components. [PERSON] et al. first introduced the multi-point geostatistical method into the study of rock pore structure models, extracting rock pore structure feature patterns from two-dimensional images of Berea sandstone and carbonate rocks and reconstructing the three-dimensional pore structure model of sandstone under the assumption of isotropy. The established digital core has good pore connectivity [24]. In 1997, [PERSON] et al. first applied the idea of simulated annealing to the reconstruction process of porous media, which can introduce any number of control functions to make the model closer to the real structure [25]. However, the algorithm complexity is high, and generating super-sized models is too time-consuming. Subsequently, a large number of studies combined the simulated annealing algorithm with the process method, Gaussian field method, and other methods to effectively reduce the computation time required for model reconstruction, but did not solve the problem of poor model connectivity [26; 27; 28]. The Markov chain Monte Carlo method has a fast reconstruction speed and good connectivity of the reconstructed digital core, but it cannot generate models in the presence of multiple pore structures simultaneously. The four-parameter random growth method (QSGS) simulates the growth process of rock minerals well and can effectively control the anisotropy of the model [29]. However, the constraint conditions only consider porosity as a single factor, and the connectivity of the model is relatively poor. From a petroleum geological engineer's perspective, current random generation algorithms are unable to simultaneously solve the following four problems: (1) clear physical significance, (2) low time complexity, (3) good pore connectivity, and (4) regional heterogeneity. This paper further develops the QSGS algorithm to simulate the mineral growth and dissolution processes, using the parameters porosity, coordination number, pore radius, and throat radius to control the generation of microscopic pore-throat networks. This approach enables rapid, super-sized, controllable, and heterogeneous pore-throat network modeling.