esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
\(F_{0}\) and speaking rate were selected as prosodic features to be modified because their modification is easy to implement and because their modification has shown promising results in previous studies [33; 34; 39]. In addition, modification of these factors can be done in a more robust manner from noisy speech compared to factors such as formants, whose estimation deteriorates in noisy environments. Figure 1 shows a flow diagram demonstrating how data augmentation and time-scale modification are used in the current investigation. Note that it would also be possible to modify the prosodic structure of adult speech in the training phase using time-scale modification, but this should be done separately for each noise condition and is therefore not feasible. Data augmentation and time-scale modification have been investigated separately in previous ASR studies (e.g., [40; 41; 42] for the former and [33; 34; 39; 43] for the latter). However, the effect of _combining_ these techniques has not been investigated in recognition of noisy children's speech before. Therefore, the main contribution of the current study is to investigate how the performance of a children's speech ASR system that suffers from the two challenges described in the beginning of this section is affected when using data augmentation, \(F_{0}\) modification, and speaking rate modification either separately or by combining these techniques one by one. The study shows encouraging results, indicating that while none of the three previously studied methods alone gives an adequate improvement in the recognition performance, the combination of the three approaches as implemented in the current study results in a considerable improvement in recognition of children's speech in noisy conditions. The remainder of the paper is organized as follows. The two main techniques studied, data augmentation and time-scale modification, are first described in Sections 2 and 3, respectively. Section 4 describes the speech databases and the ASR system used in the study. Figure 1: A flow diagram describing the ASR scenario studied in the current article to recognize noisy children’s speech using training based on clean adults’ speech. The two techniques studied, data augmentation and time-scale modification, are marked with red and blue color, respectively. The results of the ASR experiments are reported in Section 5 by describing in separate sub-sections how data augmentation, \(F_{0}\) modification, speaking rate modification, and finally the combination of the three affect the recognition performance. The results are discussed in Section 6, and the conclusions of the study are drawn in Section 7. The list of abbreviations used in this study are given in Table 1.
###### Abstract  
In situ pressure-preserved coring (IPP-Coring) is one of the most efficient methods for identifying the scale of the oil and gas content. However, the differential motion assembly of the IPP-Coring system often undergoes ball and ball seat seal failure and sticking due to surface erosion, and a greater pressure drop may unexpectedly trigger the assembly. This paper addresses these issues by improving the hydraulic structure of an assembly based on a deep understanding of the flow characteristics in the assembly, thus increasing the success rate of the IPP-Coring. Computational fluid dynamics (CFD) was employed to investigate flows in a differential motion assembly. The effects of the diameter and outlet structure of the ball seat on the fluid status, velocity, and pressure distribution were thoroughly analyzed. When the ball seat diameter increased from 30 to 40 mm, the maximum velocity and pressure drop decreased to 0.55 and 0.2 times their original values, respectively. There was a severe vortex area in the differential motion assembly due to the presence of the ball seat, but changing the outlet structure in the ball seat to an arc structure decreased the length of the vortex area and the fluid velocity near the wall to 0.7 and 0.4 times, respectively, compared with those with the original right-angled structure. In addition, the pressure drop decreased to 0.33 times the original value. Thus, the hydraulic structure of the assembly was improved, and a 40 mm diameter ball seat and an arc-shaped ball seat outlet were selected. Particle trajectory and erosion calculation results showed that the improved structure has a lower particle velocity and less impact on the wall, and the average erosion rate is only 0.42 times the value of the original structure. Due to the better erosion resistance and smaller pressure drop, the improved structure shows promise for field performance.  
differential motion assembly; in situ coring; erosion; flow characteristics; computational fluid dynamics +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Process Conditions for the Ultrasonic Field (US)  
Ultrasonic irradiation experiments were performed at a bench scale using a reactor that included an ultrasonic processor, a reactor cell, and a water jacket for temperature control. The ultrasound source was a SONOPULS HD 3200 system from Bandelin (Berlin, Germany), operating at a nominal power of 200 W and a frequency of 20 kHz. A titanium probe with a diameter of 13 mm and a maximum vibration amplitude of 170 \(\upmu\)m was utilized. Temperature regulation of the sonochemical reactor was achieved through the water jacket, maintaining the water solution at 20 \(\pm\) 1 \({}^{\circ}\)C. To evaluate the ultrasonic energy dissipated within the sample along with other energy-related characteristics of the ultrasonic field, a calorimetric method was employed. This approach, similarly used in studies such as those by [PERSON] et al. (2003) [PERSON] et al. (2003) and [PERSON] et al. (2011) [PERSON] et al. (2011), involved analyzing four vibration amplitudes (\(A\)) set to 20% and 50%. The dissipated acoustic power (\(N\)) in the liquid was calculated by monitoring the rate of temperature increase caused by the conversion of ultrasound energy into heat using Equation (2) to determine \(N\):  
\[N=m\cdot C_{p}\frac{dT}{dt}\ [W] \tag{2}\]  
\(m\) is the mass of the liquid (g), \(C_{p}\) is the heat capacity of the liquid (J/g\(\cdot\)\({}^{\circ}\)C), and (\(dT/dt\)) is the initial slope of the curve of temperature versus time (\({}^{\circ}\)C/s). The values for the energetic parameters of the ultrasonic field are as presented in Table 1.  
In turn, to determine the operational parameters of the ultrasonic field, experiments were conducted during which the energy input into the sample was monitored. This measurement was essential to calculate the acoustic power and other derived energy-related parameters of the ultrasonic field. The intensity of the sound wave was calculated according to Equation (3):  
\[I=\frac{N}{S}\left[\frac{W}{cm^{2}}\right] \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Amplitude [\%]** & **Acoustic Power [W]** & **Acoustic Intensity [W/cm\({}^{2}\)]** & **Electricity Efficiency [\%]** \\ \hline
20 & 16.36 & 3.02 & 40.9 \\ \hline
50 & 27.91 & 5.16 & 27.9 \\ \hline \end{tabular}
\end{table}
Table 1: Influence of vibration amplitude on selected energy quantities of the ultrasonic field at 20 kHz.  
\(S\) is the area of the surface through which the wave passes (cm\({}^{2}\)).  
The electrical energy efficiency was calculated based on Equation (4):  
\[n_{e}=\frac{N}{N_{W}}[\%] \tag{4}\]  
\(N_{w}\) is the output power (W).  
The first stage of the research involved testing the effectiveness of the oxidation process of the studied dyes by applying ultrasonic waves at two different intensities of acoustic waves. The solution volume was 50 mL. In the next stage, this process was supported by adding 30% hydrogen peroxide in the form of perhydrol at a dose of 15 \(\upmu\)L to each solution. All experiments were conducted in triplicate, with an observed deviation of less than 5%. For this reason, the average values are shown in the graphs.
## 3 Results
### Direct Analysis
Figure 7: Parametric analysis of the effect of the slenderness ratio on example 2: (**a**) bending (\(w_{b,1}\)) and shear (\(w_{s,1}\)) rotations at node 1 and (**b**) percentage error of rotations in node 1 when \(w_{s,1}\) was neglected. the ACI Committee 318, the errors of neglecting the shear rotations reached 23.4% and 14.7%, respectively. The results of the analyzed cantilever beam are summarized in Figure 8. The boundary conditions of the structure referred to all the movements and rotations restricted at the left support. The loading case corresponded with a concentrated load of 100 kN applied at the free end of the beam (\(V=100\) kN). Figure 8a,b include the same information as Figure 7a,b, respectively. Figure 8a shows that the shear rotations at the free end did not depend on the \(L/h\) ratio, as they remained constant with a value of 0.010 rad. In the case of the bending rotations, they increased nonlinearly with the slenderness changing from 0.020 rad when \(L/h=1\) to 4.501 rad when \(L/h=15\). The analysis of Figure 8b shows that in this example, shear rotations represented 34.1% of the rotations when \(L/h=1\). This figure also shows that for the geometrical limitations of the deep beams presented in the Eurocode EN and the ACI Committee 318, the errors of neglecting the shear rotations reached 5.3% and 3.0%, respectively. It is important to highlight that increasing the value of the imposed load increased the shear rotation value. Nevertheless, in this case, the bending rotation also increased simultaneously. For this reason, in slender beams, the value of the shear rotation remained practically negligible in comparison to the bending one. Therefore, as the beam became slender, it behaved more like an Euler-Bernoulli beam than a Timoshenko one. This same conclusion was presented by [PERSON] and [PERSON] [49]. It is also important to highlight that, as indicated in [50], the effect of prestressing plays an important role in higher-order shear deformations. These effects on shear rotations will be addressed in future studies.
## 2 Plasticity Formulation
### Return Mapping Algorithm  
In plasticity problems, the constitutive laws linking stresses to strains, as well as the internal hardening law or that of the plastic multiplier (Equations (4) and (5)) are given as rates and therefore depend on the loading history. Thus, all these laws will have to be integrated over time with a numerical scheme. However, there are several numerical integration methods, including implicit and explicit Euler methods and Runge-Kutta methods. The choice of one of these methods should be simple; any one of them should provide accurate and robust results. The implicit Euler method also called the return-mapping method for elastoplastic problems and is often selected because of its stability [1; 4; 14; 15]. This integration technique was first developed by [PERSON] [16] for plane strain and elastoplasticity problems for low loads. Its extension to problems of large strains has been proposed by various authors, including [PERSON] [17] and [PERSON] and [PERSON] [18]. The iterative procedure often adopted for solving the constitutive problem is the classic Newton-Raphson scheme, which proves to be an optimal choice because of its quadratic convergence rate, generally leading to very efficient procedures in terms of the number of iterations required for convergence [1]. However, before applying this procedure, it is necessary, from the consistency condition, to express an equation dependent on the incremental plastic multiplier \(\Delta\gamma\), as presented in [1; 4]. The implementation of the return mapping resolution algorithm is laborious and often analytically impossible when the yield function \(f\) is nonlinear. Therefore, we are developing a method for solving a nonlinear system based on the fixed-point algorithm. This system (Equation (7)) is generally defined by [1]:  
\[\left\{\begin{array}{rl}\varepsilon_{n+1}^{e}-\varepsilon_{n+1}^{e\;trial}+ \Delta\gamma\;\mathbf{N}(\mathbf{\sigma}_{n+1},\mathbf{A}_{n+1})&=0\\ \alpha_{n+1}-\alpha_{n}-\Delta\gamma\;\mathbf{H}(\mathbf{\sigma}_{n+1},\mathbf{A}_{n+1})&= 0\\ f\big{(}\mathbf{\sigma}_{n+1},\mathbf{A}_{n+1}\big{)}&=0\end{array}\right. \tag{7}\]  
where at time \(n+1\), \(\varepsilon_{n+1}^{e}\) represents the elastic strain tensor, \(\varepsilon_{n+1}^{e\;trial}\) is the elastic strain test tensor; \(\alpha_{n+1}\) is the internal hardening, \(\mathbf{N}=\frac{\partial f}{\partial\varepsilon}\) the plasticity flux vector, \(\mathbf{A}_{n+1}\) is all the combined thermodynamic forces associated with \(\alpha\), \(\mathbf{H}=\frac{\partial f}{\partial\mathbf{A}}\) is the generalized hardening module \(f\) represents the yield function and \(\Delta\gamma\) the incremental plastic multiplier.
## 2 Key Factors Contributing to Spatial Resolution in PET Imaging
### Detector Element Width  
The detector element dimension also critically influences the PET scanner's spatial resolution [18; 27; 28]. Wider crystal elements within the detector arrays used to build the PET ring lead to increased uncertainty in locating the interaction points of 511-keV photons in the system, degrading the spatial resolution of the resultant images. Figure 3 demonstrates the impact of scintillation crystal width on the system's spatial resolution. The quantity \(\frac{d}{2}\) FWHM represents the theoretical limit of spatial resolution under the presumption that other influencing factors like the decoding process, sampling discrepancies, and detector penetration are minimal.
## 2 Literature Review  
The identification of relevant papers regarding the application of explainable ML models to PL prediction tasks is carried out by filtering titles and content in the IEEE Xplore, ScienceDirect, ACM, and Google Scholar databases in the time between 1 January 2020 and 31 October 2024. The goal of our literature review was to identify the researches in the intersection of ML, XAI, and PL modeling (Figure 2). Thereby, we opted for broad coverage by using the search term \"path loss\" in the title as well as each of the search terms \"explainable\", \"interpretable\", \"feature importance\", and \"SHAPley\" in the full body of the papers' text (accessed on 31 October 2024) without using AI-related terms (e.g., \"machine learning\" or \"deep learning\"). The term \"SHAPley\" is applied as a search term as it addresses one of the most prominent XAI techniques, which is elaborated below. Databases, search terms, and the number of relevant results from searching explainable studies of PL prediction are presented in Table 1.  
After searching the relevant papers according to the search strategy presented in Table 1, we removed the duplicates, and scanned the remaining papers based on the criterion of whether the paper applies XAI to ML models to predict PL. We found 11 papers that jointly include ML, XAI, and PL modeling. The propagation context, model training, and model interpretation methods of these relevant studies are presented in Table 2.  
Figure 2: Scope of the literature review: identify the researches in the intersection of ML, XAI, and PL modeling. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Search Term** & **X = Path Loss** & **X = Path Loss** & **X = Path Loss** & **X = Feature** & **X = Path Loss** \\  & & **Y = Explainable** & **Y = Interpretable** & **Y = Feature** & **Importance** & **Y = SHAPley** \\ \hline \multirow{3}{*}{IEEE Xplore} & (“Document Title”: “X”) AND & & & & \\  & (“Full Text Only”: “Y”) Filters & 5 & 4 & 57 & 4 \\  & Applied: 2020–2024 & & & & \\ \hline \multirow{3}{*}{ScienceDirect} & Find articles with these terms “Y” & & & & \\  & Year: 2020–2024 Title, abstract, keywords: “X” & 23 & 10 & 14 & 1 \\ \hline \multirow{3}{*}{ACM} & [Title: path loss] AND [Full Text: & & & \\  & “Y”] AND [Title: “X”] AND & & & & \\  & [E-Publication Date: (1 January & & & \\  & 2020 TO 31 October 2024)] & 4 & 5 & 0 \\ \hline Google Scholar & intitle:“X” intext:“Y” since 2020 & 21 & 10 & 29 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of databases, search terms, and the number of relevant results by searching explainable ML studies of PL prediction along the literature scanning process.
# Energy Efficient Secure Communication Model against Cooperative Eavesdropper
## 2 System Model
### System Model Equations
The received signal at E2 is given as  
\[Y_{E2}=H_{AE2}X_{I}+\mathbf{H}_{JE2}\mathbf{X}_{I}+N_{E2}, \tag{7}\]  
where \(\mathbf{H}_{JE1}\mathbf{X}_{J1}\) is considered as a noise term. The SINR of \(Y_{E2}\) is given as  
\[ \begin{split}\Gamma_{E2}&=\frac{\mathbb{E}[|| \mathbf{H}_{JE2}\mathbf{X}_{I}^{T}||^{2}]/T_{I}}{\varepsilon_{H,JE2}\mathbb{E}[ ||\mathbf{X}_{I}|^{2}]+\mathbb{E}[|H_{AE2}X_{I}|^{2}]+\mathbb{E}[|N_{E2}|^{2}] }\\ &=\frac{\sigma_{X,I}^{2}\sigma_{H,E2}^{2}/T_{I}}{\varepsilon_{H, JE2}\sigma_{X,I}^{2}+\sigma_{X,I}^{2}\sigma_{H,AE2}^{2}+\sigma_{N,E2}^{2}}\\ &=\frac{P_{I,E2}}{T_{I}(\varepsilon_{H,JE2}+(1/\beta_{E2}))P_{I, E2}+\sigma_{N,E2}^{2}},\end{split}  \tag{8}\]  
where the SINR of each jamming signal stream is estimated separately. For the sake of simplicity, \(\sigma_{H,JE2}^{2}\) and \(\sigma_{H,AE2}^{2}\) are considered as \(1\). \(\Gamma_{E1}\) and \(\Gamma_{E2}\) can be improved by reducing the signal interference. We consider that E2 is closer to J and the information signal strength is lower than the jamming signal and, with this condition, by using IBDFE, the impact of signal interference can be reduced. The following section briefly explains IBDFE for MIMO model.
# Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
## 2 Materials and Methods
### Data Acquisition  
The multispectral images obtained through the snapshot camera have a resolution of 2048 pixels wide and 1088 pixels high, containing data for all wavelengths. The snapshot multispectral image in the visible (Vis) region consists of 16 wavelengths arranged in a 4 \(\times\) 4 format, while the snapshot multispectral image in the near-infrared (NIR) region consists of 25 wavelengths arranged in a 5 \(\times\) 5 format. Figure 2 illustrates a 4 \(\times\) 4 pixels component of a raw snapshot image in the Vis region and a 5 \(\times\) 5 pixels component of a raw snapshot image in the NIR region.  
To separate the wavelengths in the raw snapshot multispectral images, wavelength images were extracted. We obtained 16 wavelength images in the Vis region and 25 wavelength images in the NIR region. Subsequently, image preprocessing techniques were applied, including setting a region of interest (ROI) and removing the background. This preprocessing resulted in a spectrum composed of 16 wavelengths ranging from 462 nm to 624 nm in the Vis region and a spectrum composed of 22 wavelengths ranging from 631 nm to 871 nm in the NIR region. Thus, a spectrum consisting of a total of 38 wavelengths ranging from 462 nm to 871 nm was obtained.  
For each of the three stages, a total of 800 spectra was obtained from the garlic samples, resulting in 2400 data points on the 7 th day and 2400 data points on the 14 th day.
## 5 Results and Discussion
### Performance Comparison of Models for MFS Prediction
The red dashed line represents the best-fit line, indicating a perfect relationship between the predicted and the actual values. As shown in Figure 9a-c, all models demonstrate a strong relationship between the predicted and actual values during the training phase. Among them, the TPE-GB model exhibits a slightly better relationship with the best-fit line than the other models and achieves the highest R\({}^{2}\) value. Similarly, Figure 10a-c depict the relationship between the predicted and the actual values during the testing phase. All models exhibit a strong correlation between the predicted and actual values, although several outliers significantly deviate from the best-fit line, as indicated by the green circles in the figures. Figure 8: The performance score ranking results of the models: (**a**) training phase and (**b**) testing phase. Figure 9: Scatter plots of the models during the training phase: (**a**) TPE-GB model, (**b**) TPE-ET model, and (**c**) TPE-RF model.
## 4 Simulation Results
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Latency (ms)**} \\ \hline
**Platform** & **ALO** & **GA** & **ACO** & **PSO** & **GWO** & **MVO** & **EHO** & **SCEHO-IPSO** \\ \hline Storm & 2800 & 2914 & 3018 & 3920 & 2990 & 2560 & 1982 & 1630 \\ Flink & 2773 & 2888 & 2822 & 3892 & 2967 & 2521 & 1928 & 1626 \\ Spark & 2822 & 2880 & 2902 & 3620 & 2940 & 2490 & 1802 & 1550 \\ Kafka & 2754 & 2635 & 2772 & 3450 & 2829 & 2339 & 1820 & 1510 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of different optimization algorithms in light of latency.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 5 Management and Countermeasures  
In view of the non-negligible thermal hazards of LIBs involving components, a single battery and a battery pack, management or countermeasures regarding these thermal hazards must be conducted. In the present study, the following is discussed: (1) the use of safety devices within battery; (2) the application of fire retardant (FR) additives; (3) the thermal management of battery; (4) provision of a warning once hazard occurs and (5) the firefighting after thermal hazard forms. The former three countermeasures aim to enhance the inherent safety of LIBs and decrease the possibility of hazard, and the others are applied to constrain the deterioration of thermal hazard, thereby reducing the damage.