esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Electrical Conductivity  
The electrical conductivity (\(\sigma\)) is related to the imaginary component of dielectric permittivity, according to Equation (3). A conductivity sensor inserted into a soil matrix measures the bulk electrical conductivity dependent on the percentage of the various phases and on the structure itself. An empirical relation between the bulk electrical conductivity and the conductivity of the water solution in soil was suggested by [PERSON] [28]:  
\[\sigma_{t}\ =\ \frac{1}{a}\sigma_{w}\varphi^{n}S_{w}^{m} \tag{9}\]  
where \(n\) is the cementation exponent, ranging from 1.3 in sand to 2 in consolidated rocks; \(a\) is the tortuosity factor or lithologic factor; \(m\) is the saturation exponent, equal to 2 in non-polluted soils, up to 3-4 in presence of hydrocarbons. Electrical conductivity is also a complex quantity:  
\[\sigma\ =\ \sigma^{\prime}\ +\ i\sigma^{\prime\prime} \tag{10}\]  
As can be observed in Equation (3), \(\sigma^{\prime}\) (= \(\sigma_{dc}\)), proportional to energy loss per second, is related to the imaginary component of dielectric permittivity (energy loss per cycle). Besides, the imaginary part of electrical conductivity is proportional to the real part of dielectric permittivity [29]:  
\[\sigma^{\prime\prime}\ =\ \omega\varepsilon_{r} \tag{11}\]
## 4 Discussion
### Discussions on the Theoretical Causes and Distribution of Cracks  
Figures 17 and 18 present a comparison between the maximum tensile and compressive stress of the secondary lining of the lower tunnel and the standard value of concrete's tensile and compressive strength. The results indicate that the unloading effect of the upper tunnel excavation results in a change in the structural stress of the lower tunnel. This change causes the maximum tensile stress of the secondary lining of the lower tunnel to exceed the standard value of the concrete tensile strength. As a result, the rock mass at the arch waist enters a plastic state, leading to the formation of cracks in the secondary lining.  
Figure 17 further highlights that section 2 has the highest maximum tensile stress value among all the sections. This finding indicates that the unloading of the upper tunnel near this specific mileage has the most significant impact on the surrounding rock of the lower tunnel, causing the most noticeable trend of concentrated distribution of cracks. Moreover, the maximum tensile stress corresponds to working condition 11, wherein the temporary cross bracing of the upper tunnel has not been removed, and the secondary lining has not yet formed a ring. Therefore, this suggests that the lower tunnel is most prone to cracks during this phase.  
Figure 17: The maximum tensile stress of each section.  
Figure 18: The maximum compressive stress of each section.  
In addition, Figure 19 demonstrates that, except for section 3, the maximum tensile stress occurs at the right arch waist, suggesting a higher risk of crack formation near the right arch waist. Conversely, the maximum tensile stress of section 3 occurs at the left arch waist. Additionally, the maximum tensile stress at sections 2 to 4 is relatively high, implying that cracks may develop at both the left and right arch waists of sections 2 to 4.  
After comparing the on-site monitoring feedback and the lining crack situation with Figures 15 and 19, it can be concluded that the theoretical distribution law of cracks in time and space is consistent with the existing crack distribution law. This indicates that the theoretical analysis is reliable and can be used to guide the design and construction of similar tunnel projects in the future.
## 2 Outline and Objective of Robust Optimal Design  
### Outline of the Proposed Method  
A chiller plant generally consists of several identical chillers (i.e., equal capacity) for the convenience of control and maintenance. However, a chiller plant consisting of chillers with different capacities can make the chiller plant work at a more efficient operating point, particularly at low part load conditions [39]. For instance, two chillers (rated cooling capacity of each chiller is 375 kW) provide cooling capacity for a commercial building (e.g., its peak cooling load is 750 kW). To supply 100 kW cooling capacity, the chiller operates at 26.7% of its rated cooling capacity, which is not within its efficient operating region (i.e., above 30%). When it is served by a big chiller (rated cooling capacity is 600 kW) and a small chiller (rated cooling capacity is 150 kW), the smaller chiller would operate at a very efficient operating point (i.e., 66.7% of its rated cooling capacity).  
Our previous study [9] also showed that the optimum design of chiller plant should consist of several larger chillers and one smaller chiller based on the assumption that larger chillers usually have a higher COP (coefficient of performance) compared to smaller chillers. In operation, larger chillers generally operate at their highest efficient points with a nearly full load. When the total cooling load in operation is more than the total cooling capacity provided by all larger chillers, the smaller chiller would meet the rest cooling load and operate at a very efficient operating point.  
The chiller plant generally works at part load conditions for a large proportion of the entire cooling season. Figure 1 presents typical COP curves of two types of chillers with constant-speed and variable-speed drivers, respectively. Constant-speed chillers have a higher operating COP when near the full load conditions compared to variable-speed chillers. On the contrary, when operating at part load conditions, especially low part load conditions, the variable-speed chillers perform much better.  
In order to take both the uncertainty and reliability into account of the chiller plant design, the following assumptions are made:  
* A chiller plant consists of several constant-speed chillers, and no more than two variable-speed chillers.
* The failure rate of a constant-speed chiller is smaller than that of variable-speed chillers.  
Figure 1: Coefficient of performance (COP) of a constant-speed chiller and a variable-speed chiller.  
All the constant-speed chillers are equally sized and have the same failure rate.
* All the variable-speed chillers are equally sized and have the same failure rate.
# Parallel Extreme Learning Machines Based on Frequency Multiplexing
## 3 Principle of Operation
### Dataset Preprocessing  
We proved in [19] that when working with a single comb, supplying the same input feature to multiple neurons may be beneficial for the network, both because it increases the mixing between input information and because it decreases the possibility that a feature gets lost by being assigned to a weak comb line. When working in single operation mode with multiple combs, redundancy is even more important since if two features are assigned to lines belonging to different combs, they do not mix in the hidden layer. Hence, when working in single operation mode, we select a random map of correspondence with repetitions, from the set of available dataset features to the set of available comb lines; instead, when working in parallel operations mode, following our previous work [19], we supply the same feature of the dataset to \(d\) consecutive comb lines, where \(d\) is optimized for each task.  
Once the vector \(\mathbf{u}\) of input features is built (eventually accounting for repetitions of features, as described above), its entries are linearly converted into attenuations spanning the range of \([0\text{ dB},\ 30\text{ dB}]\), constituting the vectors \(\mathbf{F}^{\text{in}}\) in Equation (4) supplied to the filter \(\text{SF}_{1}\). In the case of the parallel operations mode, the same procedure is applied to each vector \(\mathbf{u}^{j}\) (\(j\in[1,\ N_{S}]\)).  
The features are always encoded in the most central part of the combs, which are composed of approximately 30 lines. Central lines are preferred because they are the most powerful and can encode the inputs with the best contrast. The unused comb lines receive zero attenuation because this allows more power to take part in the mixing, which is beneficial for the system [19].
# An Image Processing Method for Extraction of the Stress Wave Reflection Period
## 3 Experimental Verification
### Experimental Results
#### 3.3.2 Buried Metal Piles with a Length of 2.00 m  
Experimental results of the buried metal piles with a design length of 2.00 m, with a burial depth of 1.20 m, are selected for display in Figure 8. Figure 8a shows the original sequence waveform of the reflected stress wave. Figure 8b shows the waveform after wavelet denoising. Figure 8c,d show first and quadratic self-correlation analysis, respectively. A time-frequency spectrogram is shown in Figure 9 by performing STFT on the signal after multiple iterations of self-correlation operations. The area marked as a black dotted box on the time-frequency spectrogram is selected as the region of interest. The time interval of the ROI region is \(\Delta=0.008s\), and the number of columns of the ROI is 420.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test No.** & **Calculated Length (m)** & **Error Rate (\%)** & **Average Error Rate (\%)** \\ \hline
1 & 1.0101 & 1.01 & \\
2 & 0.9892 & 1.08 & \\
3 & 0.9899 & 1.01 & \\
4 & 0.9880 & 1.20 & 1.10 \\
5 & 0.9846 & 1.54 & \\
6 & 0.9900 & 1.00 & \\
7 & 1.0092 & 0.92 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of the different depths of the 1.00 m buried metal piles.  
Figure 8: Signal denoising and enhancement. (**a**) Original sequence waveform; (**b**) waveform after wavelet denoising; (**c**) waveform after first self-correlation; (**d**) waveform after quadratic self-correlation.  
Figure 9: Time–frequency spectrogram (black dotted box indicates the ROI area).  
Edge detection of the target image in the X direction is performed using the Sobel operator to obtain an edge image, as shown in Figure 10a. Figure 10b shows an image obtained by gradation of the maximum value method, and binarized to obtain Figure 10c. Figure 11a is a statistical waveform of the column pixel mean value in the binary image, and Figure 11b is obtained after the self-correlation operation. FFT is performed in Figure 11c, and the dominant frequency is obtained to be 19 Hz. Bringing \(\mathrm{F}=19\) Hz into Equation (6) to obtain the time interval between the reflected signal and the direct signal, and then according to Equation (7), the approximate length of the buried metal piles are calculated to be 2.021 m with an error of 1.05% from the true length of 2.000 m.  
Table 5 lists the test results for different depths of the 2.00 m buried metal piles. Among them, 15 sets of data were collected for each working condition, and the average value was obtained to obtain the measured length. The depth of the buried metal piles has little effect on the extraction length. The error range fluctuates between 0.50% and 1.05%, and the average error is 0.70%.  
Figure 11: Extracting the image period from the ROI. (**a**) Statistical waveform; (**b**) self-correlation operation result; (**c**) spectrogram.  
Figure 10: Extraction of the edge information of the ROI. (**a**) Edge detection result; (**b**) grayscale image; (**c**) binarized image.
## 3 Results and Discussion
### Determination of Optimal Bacterial Growth Conditions  
The optimal growth conditions at several temperatures (4, 8, 20, 25, 29, 32, 34, 35, 36, 37, and 45 \({}^{\circ}\)C), the pH (4, 7, and 9), and the growth inhibition caused by increasing the saline concentration (NaCl) at 1, 2, 4, 6, 8, 10, 12, and 13% for both strains are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **E. faecalis** & **Media Blank** & **K. lactis** & **Media Blank** \\ \hline
4 & \(-\) & \(-\) & \(-\) & \(-\) \\
8 & \(-\) & \(-\) & \(-\) & \(-\) \\ Room temperature (21 \({}^{\circ}\)C) & \(++\) & \(-\) & \(++\) & \(-\) \\
25 & \(++\) & \(-\) & \(++\) & \(-\) \\
28 & \(++\) & \(-\) & \(++\) & \(-\) \\
29 & \(++\) & \(-\) & \(++\) & \(-\) \\
32 & \(++\) & \(-\) & \(++\) & \(-\) \\
34 & \(++\) & \(-\) & \(++\) & \(-\) \\
35 & \(++\) & \(-\) & \(+\) & \(-\) \\
36 & \(-\)\(+\) & \(-\) & \(-\) & \(-\) \\
37 & \(-\) & \(-\) & \(-\) & \(-\) \\
45 & \(-\) & \(-\) & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal bacterial growth conditions of the strains isolated from cheese whey (_Enterococcus faecalis_ and _Kluyveromyces lactis_).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & _E. faecalis_ & _K. lactis_ \\ \hline Pigmentation & White & White \\ Size & 0.1–1 mm & 0.5–4 mm \\ Form & Circular & Circular \\ Elevation & Flat & Convex \\ Margin & Entire & Entire \\ Texture & Viscose & Dry \\ Gram & \(+\) & \(+\) \\ Catalase & \(-\) & \(+\) \\ Microorganism & Cocci & Yeast \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of strains isolated from cheese whey (_Enterococcus faecalis_ and _Kluyveromyces lactis_).  
The optimal growth temperatures were determined to be 32 \({}^{\circ}\)C for _Enterococcus faecalis_ and _Kluyveromyces lactis_, and they were also neutrophil and alkalophilic. It is important to note that _Kluyveromyces lactis_ has a greater tolerance to acidic pH, which has been detailed as an advantage of yeasts over LAB [6]. _Enterococcus_ spp. are considered along with other bacteria as alkalophilic LAB, that is, they can grow at 7.0-11.5 pH [6]. This high tolerance of _Kluyveromyces lactis_ was also observed against salinity, reaching up to 12% compared to 4% for _Enterococcus faecalis_, which corroborates the greater resistance to extreme conditions of yeasts compared to bacteria.
## 3 Proposed Methodology
### Modified Hybrid Power Converter  
The hybrid power converter approach proposed in [31] aims to minimize input-current ripple. However, they considered two optimization parameters (\(D\) and \(k\)) for the power converter model. The power converter model proposed in [31] considers two periods. Considering these periods, the input-current ripple may attain a maximum value. Therefore,it is necessary to formulate the objective function in order to minimize the maximum value of the input-current ripple. If \(D>D_{Z}\)[31], this procedure minimizes a maximum ripple in the input-current. Similarly, the largest input-current ripple is minimized when \(D<D_{Z}\)[31]. Furthermore, they considered several restrictions, including achieving the desired voltage gain; evaluating the switch time, and searching the domain, which includes valid values for \(D\) and \(k\). However, the objective-function estimation is highly sensitive to \(D_{Z}\). Hence, in our proposed model, we aim to optimize three variables (\(D\), \(k\), and \(D_{Z}\)) simultaneously and analyze its performance. The range of the three variables is between 0 and 1, according to the below equations. The modified hybrid power converter can be expressed as:  
\[\min_{D,k,Dz\in R}f(D,k,Dz)=\left\{\begin{aligned} & f_{1}(D,k,Dz), \quad D>Dz\\ & f_{2}(D,k,Dz),\;\textit{otherwise}\end{aligned}\right. \tag{21}\]  
Subject to;  
\[G\leq\frac{1}{1-kD}+\frac{D}{1-D}\leq G+t \tag{22}\]  
\[0\leq D\leq 1 \tag{23}\]  
\[0\leq k\leq 1 \tag{24}\]  
\[0\leq D_{Z}\leq 1 \tag{25}\]  
Thus,\(f_{1}(D,k,D_{Z})\) and \(f_{2}(D,k,D_{Z})\) are formulated as follows:  
\[f_{1}(D,k,Dz)=\left[\begin{aligned} &\left|\Delta_{igA_{1}}\right|,&\left|\Delta_{igA_{1}}\right|>\left|\Delta_{igB_{1}}\right|\\ &\left|\Delta_{igB_{1}}\right|,&\textit{Otherwise.} \end{aligned}\right. \tag{26}\]  
\[f_{2}(D,k,Dz)=\left[\begin{aligned} &\left|\Delta_{igA_{2}}\right|,&\left|\Delta_{igA_{2}}\right|>\left|\Delta_{igB_{2}}\right|\\ &\left|\Delta_{igB_{2}}\right|,&\textit{Otherwise.} \end{aligned}\right. \tag{27}\]  
\(D_{Z}\) was maintained at a constant of 0.6 in [31]. It plays a significant role in the calculation of the objective functions. Consequently, its optimization would yield better results. The effectiveness of optimizing \(D_{Z}\) is verified in the simulation section.
# How Biological and Environmental Factors Affect the Quality of Lavender Essential Oils
## 2 Lavender Essential Oil Production
### Biosynthesis of Lavender Essential Oil Compounds  
Lavender EOs are primarily monoterepenes and sesquiterpenes obtained from the reduction of isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) [22] (Figure 1). Prenyltransferase, including geranyl diphosphate synthase (GPPS), reduce the IPP and DMAPP to synthesise geranyl diphosphate (GPP) [23]. Moreover, the action of a cis-prenyl transferase, lavandulyl diphosphate synthase (LPPS), results in the synthesis of lavandulol [24]. Terpene synthases (TPSs) catalyse GPP into cyclic and non-cyclic monoterepenes within the lavender glandular trichomes' secretory cells [25]. Five of the seven TPS gene subfamilies are present in lavender, accounting for 100 genes [6]. The expression of the _TPS-b_ gene subfamily that encodes for monoterepenes synthases is high in Lavender [26]. The first two monoterepene synthases characterised by lavender plants were (R)-linalool synthase (LaLINS) and (R)-limonene synthase (LaLIMS) [27], responsible for producing linalool and limonene, respectively. The enzymatic activity of 1,8-cineole synthase (LaCINS) converts GPP into 1,8-cineole [28].  
BPP synthase (BPPS) catalysis produces bornyl diphosphate (BPP) from GPP and initiates the synthesis of Borneol and camphor [29]. Borneol oxidation by borneol dehydrogenase (BDH) results in the formation of camphor [30]. Another secondary modification of the monoterepenes is acetylation; for example, the acetylation of lavandulol by lavandulylacetyltransferase produces Lavandulyl acetate [31]. Furthermore, linalyl acetate, the primary compound of lavender EO, is catalysed from linalool by linalool acetyltransferase [32].  
Linalool and linalyl acetate are the major compounds of lavender EO, followed by camphor and 1,8-cineole. The latter compounds weaken the EO quality used in perfume and aromatherapy [33]. Plant developmental stages and biotic factors regulate the biosynthesis of monoterepenes and display important physiological and ecological roles in _Lavandula_ plants [6; 34; 35].
# Modelling of a Non-Transferred Plasma Torch Used for Nano-Silica Powders Production
## 4 Results and Discussion
### Structure of Nanosilica Particles  
X-ray diffraction is an interesting technique to identify the purity, size, and crystalline nature of the materials. Figure 14 depicts the X-ray diffraction patterns of the silica sample. This figure contains, around the angle 2 theta 22-23, a strong broad, which is the characteristic of silica. This result confirms that the state of the synthesized nanosilica was amorphous. The Scherrer formula and the full width at half maximum (FWHM) were used to determine the average particle size of the silica samples. The average size of nanoparticles was calculated using the following formula:  
\[G=\frac{K\lambda}{Bcos\Theta_{B}} \tag{24}\]  
where \(K\) is a dimensionless shape factor with a typical value of about 0.9, \(\lambda\) is the X-ray wavelength (\(\lambda=1.78901\) A for Co Ka), \(\theta_{B}\) is the maximum of Bragg diffraction peak (in rad), and \(B\) is the line width at half maximum. Using this formula, the calculated average grain size of nanoparticles was 24 nm.  
Figure 14: XRD of nanosilica.  
Figure 13: The radial profiles of the temperature (**a**) and velocity (**b**) plasma jet at 80 mm from the nozzle torch exit.  
The Debye-Scherrer formula was obtained from a model in which the size distribution of the crystallites was ignored; hence, the average grain size obtained from this formula was not precise compared to the size of the nanoparticles measured by TEM. Different images at variable magnifications (50,000-60,000) were recorded for the sample to investigate its average grain size. The recorded TEM images of nanosilica are shown in Figure 15. These images show that the morphology of the nanoparticles had a nearly spherical shape with a diameter ranging from 10 to 20 nm. These results confirm that the average particle dimension of the obtained silica was in nanoscale size and that they are in accordance with the XRD results.
## 1 Introduction
### Motivation and Novelty  
Existing studies have employed various numerical analysis and simulation techniques to predict and control the electromagnetic, thermal, and structural deformations that occur during induction hardening. However, these approaches face significant limitations, such as insufficient consideration of structural deformations that arise after heat treatment and the exclusion of phase transformations induced by the process. Moreover, most analyses have been conducted on two-dimensional (2D) models, which fail to fully capture the complex characteristics of three-dimensional (3D) geometries.  
To address these limitations, this study proposes an induction hardening analysis process specifically designed for 3D geometries. Unlike previous studies that primarily focused on simplified models, this research aims to develop a predictive methodology applicable to induction hardening and quenching processes for workpieces with complex geometries. Furthermore, the proposed methodology is validated through a comparison with actual product measurements. By applying induction hardening to specific regions of the CTBA, this study seeks to improve the accuracy of localized strength and distortion predictions.  
Additionally, this study leverages the advantages of 3D analysis by simulating key phenomena such as deformation caused by hardness changes and the quenching phase, replicating real-world induction hardening conditions more accurately. Ultimately, this research presents a validated approach that enhances the reliability of induction hardening simulations for complex geometries.
## 2 Proposed Method
### Spatial-Channel Mutual Attention-Guided Transformer Module (SCATM)
#### 2.3.1 Spatial-Channel Mutual Attention Guiding Block-Top (SCAGB-T)  
The SCAGB-T aims to locate and guide both local facial details and global facial structures for the following CMTB, the detailed architecture of which is shown in Figure 3a. First, the Hourglass Block [54], which has already proven its effectiveness in generating spatial attention maps [41], is utilized to capture facial landmark features such as the eyes, nose, and mouth. Moreover, aiming to guide the weights of different feature map channels, the channel attention (CA) network [29] is applied to select and pay more attention to feature map channels that are rich in features. Thanks to a carefully designed structure that mutualizes spatial and channel attention wisely, the SCAGB can guide the following CMTB to capture the essential part of the face images of all channels for better reconstruction results with more details. Afterwards, a \(3\times 3\) convolutional layer followed by a sigmoid function is applied to generate the spatial-channel mutual attention map. Then, the input feature maps are multiplied element-wise by the attention map and fed to the subsequent transformer block with better extracted spatial features and promoted channel information.  
Aiming to eliminate the gradient vanishing problem, the residual connection with a full connection layer is applied between the input feature maps and the output of the channel attention network.