esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Further Discussion
### Optimization of the Damping Layer's Position and Thickness  
Employing the optimization technique for the damping layer thickness function, as detailed in the preceding section, Figure 18 depicts the fitting accuracy of the BP network post-training with Matlab software. The BP neural network has determined the following optimal thickness variation function for the damping layer:  
\[h_{dp}(x)=\frac{9}{16}(|x-0.22|-0.04)^{2},\quad x\in[0.18,0.26]. \tag{35}\]  
Figure 18 displays the graphical depictions of the functions \(h_{dp}\) and \(h_{d12}\), which are specified by Equations (34) and (35), respectively. Figure 19 presents a comparison of the impact on the ABH effect exerted by damping layer thicknesses varied according to the functions \(h_{dp}\) and \(h_{d12}\). It is evident that with the increase in resonance order, the modal loss factors for the system with the damping layer thickness following the function \(h_{d12}\) tend to stabilize at approximately 0.036, while for the system with the damping layer thickness following the function \(h_{dp}\), these factors stabilize at around 0.038. The mean modal loss factor for the first fifteen modes of the system with the damping layer thickness set by the function \(h_{d12}\) is 0.0323, whereas for the function \(h_{dp}\), it is 0.0338, which is roughly 4.6% higher compared to the scenario with \(h_{d12}\).  
Figure 17: The effect of varying the thickness of the damping layer on the system’s modal loss factor. The damping layer is confined to the interval between \(x_{d1}=0.16\,m\) and \(x_{d3}=0.28\,m\), with \(h_{d}(x)\) taking on the values of 0.0002, \(h_{d10}\),..., \(h_{d13}\). The values for \(h_{d10}\) through \(h_{d13}\) are specified in Equation (34).  
Comparing Figure 20 with Figure 13, it can be observed that when the same mass of damping layer material is applied (after optimizing the thickness of the damping layer using the BP algorithm), the average value of the first fifteen modal loss factors of the modified ABH beam structure (as shown in Figure 15) increases by approximately 15% compared to before the modification (as shown in Figure 1). This indicates that when the truncation thickness \(h_{0}\) of the ABH beam shown in Figure 15 cannot be reduced, the ABH effect can be enhanced by adding a segment with constant thickness \(h_{0}\) within \([x_{y1},x_{y2}]\). To  
Figure 19: The graph of functions \(h_{dp}(x)\) and \(h_{d12}(x)\), as defined in Equations (34) and (35), respectively. The regions bounded by these two function curves and the \(\mathrm{x-axis}\) are identical.  
Figure 18: The BP neural network regression analysis graph.  
achieve better effects, the length of the segment \([x_{y1},x_{y2}]\) can be further optimized using the BP algorithm.
## 2 Experimental Design
### Temperature Measurement in Thermal Conduction and Convection  
As shown in Figure 4, a total of nine precise and calibrated thermocouples (accurate to \(\pm\)0.02 \({}^{\circ}\)C) are strategically placed at various locations in the experimental beaker. The experimental cup is then placed in the center of a second cup, which serves as the heating cup. Water is used as the heating fluid, and six thermocouples are also placed in this heating beaker to ensure a uniform temperature of the heating fluid. To achieve controlled heating, the temperature of the heating fluid is gradually increased at a constant rate of 5 \({}^{\circ}\)C per minute, which is controlled by a 2500-watt resistor at the bottom and four 200-watt resistors (one at each corner) of the heating cup, as shown in Figure 4. It is expected that the use of five resistors can produce more homogeneous temperature fields, minimizing viscosity measurement errors. Throughout the experiments, the temperature values of all thermocouples are recorded simultaneously to analyze the temperature distribution in the fluid.  
Figure 4: (**a**) Experimental setup for analysis of thermal conduction and convection. (**b**) Schematic illustration of the experimental setup and placement of all sensors.  
Figure 3: Viscosity temperature graphic of Fluid A, B, and C.  
In this experimental setup, four thermocouples are located near the liquid surface at a depth of 5 cm, while another set of four thermocouples is located near the liquid bottom at a depth of 15 cm. In addition, a single thermocouple is placed in the center of the liquid at a depth of 10 cm. This detailed arrangement allows for a comprehensive analysis of the thermal fields within the sample cup. The thermocouple sensors within the sample cup are referred to as internal sensors (_IS_), while the sensors within the heating cup are referred to as external sensors (ES). In addition, the thermocouple in the center of the fluid is referred to as the central sensor. The sensors _IS_1, _IS_3, _IS_5 and _IS_7 are located near the surface, the sensors _IS_2, _IS_4, _IS_6 and _IS_8 are located near the bottom and the sensor _IS_9 is located in the center of the liquid.
# Evaluation of Urban Transportation Resilience under Extreme Weather Events
## 2 Dataset
### Data Pre-Processing  
The bus trajectory dataset in Fuzhou presented the following problems: (1) redundancy and noise in trajectory data; (2) the drift of trajectory data points; (3) trajectory data outliers; (4) small amounts of trajectory data were missing. In order to improve the accuracy of data, the data were pre-processed before analysis. Firstly, according to the data usage instructions provided by Fujian Beidou Big Data Center, it was known that the DATATYPE column values of 12 and 55 were removed due to on-board GPS device failures. Secondly, the latitude and longitude values of 0, NAN, and duplicate entries in the dataset, totaling about 32 million data, were deleted to improve data quality. Thirdly, outliers were removed based on bus speed exceeding 80 km/h [40]. Fourth, the ROUTEID, PRODUCTID, ACTDATETIME, and BUSRDID columns in the bus GPS trajectory dataset were sorted in ascending order to obtain the running track of the bus during operation. By pre-processing the Fuzhou bus trajectory data, 30 million high-quality data were finally used to quantify the transportation resilience in this study. The pre-processed bus trajectory data are shown in Figure 3.
## 1 Introduction
Current transfer strategies often rely heavily on experimental experience while lacking universal guiding principles. This paper provides relevant recommendations by examining how migration paths influence final prediction accuracy. The remainder of this paper is organized as follows: In Section 2, an analytical solution is presented for the vibration problem of an infinite beam on a foundation subjected to a moving load. The analytical approach yields a limited dataset of displacement responses for the beam under prescribed parameter conditions. These displacement data are subsequently perturbed with noise to emulate sensor measurements. Additionally, the analytical solution is employed to generate response data across a broader range of arbitrary parameters, which serve as benchmark values for validating the surrogate model results in subsequent sections. In Section 3, three PINN models are developed to learn displacement data across various parameter conditions, aiming to identify the relationship between coordinates and beam displacement under specific parameters with limited measured data. In Section 4, transfer learning is utilized to adjust the weights of the initial PINN models for specific parameters. By leveraging the mapping capabilities of these PINN models, a large dataset of beam displacement responses is generated across several targeted regions within the parameter space. This dataset is then utilized in Section 5 to train a PINN-based surrogate model. Numerical examples show that the surrogate model accurately predicts beam displacement over a wide parameter range. A comparison between the prediction results of this study and those from traditional PINN-based surrogate models is presented in Section 6. Section 7 examines the impact of sensor measurement noise and transfer quantity and paths in transfer learning on the prediction accuracy of the surrogate model. The conclusions are presented in Section 8. For easy understanding, a list of symbols used in this paper along with their definitions is placed at the end of the text. The Acquisition of Displacement Response Data for an Infinite Beam on a Foundation Subjected to a Moving Load
# NMR Analysis of Pulegone in Food Products
## Appendix A  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Pulegone Content [mg/kg] (n = 2)** & **Extraction Method** \\ \hline Corn mint EO 1 & 3094 \(\pm\) 290 & - \\ Corn mint EO 2 & 14,672 \(\pm\) 569 & - \\ Corn mint EO 3 & 4602 \(\pm\) 65 & - \\ Corn mint EO 4 & 13,196 \(\pm\) 890 & - \\ Peppermint EO 1 & 9850 \(\pm\) 633 & - \\ Peppermint EO 2 & 7396 \(\pm\) 163 & - \\ Peppermint EO 3 & 16,233 \(\pm\) 443 & - \\ Peppermint EO 4 & 18,733 \(\pm\) 170 & - \\ Peppermint EO 5 & 8713 \(\pm\) 77 & - \\ Peppermint EO 6 & 5581 \(\pm\) 171 & - \\ Peppermint EO 7 & 18,786 \(\pm\) 416 & - \\ Peppermint EO 8 & 12,582 \(\pm\) 41 & - \\ Peppermint EO 9 & 4051 \(\pm\) 393 & - \\ Peppermint EO 10 & 17,398 \(\pm\) 999 & - \\ Peppermint EO 11 & 12,768 \(\pm\) 35 & - \\ Peppermint EO 12 & 8216 \(\pm\) 442 & - \\ Peppermint EO 13 & 11,720 \(\pm\) 519 & - \\ Peppermint EO 14 & 5149 \(\pm\) 82 & - \\ Peppermint EO 15 & n.d. & - \\ Chinese Mint EO & 9566 \(\pm\) 478 & - \\ Pennyroyal EO 1 & 630,557 \(\pm\) 46,305 & - \\ Muna EO 1 & 559,410 \(\pm\) 17,506 & - \\ Mufa EO 2 & 288,603 \(\pm\) 76,824 & - \\ Buchu EO 1 & 30,181 \(\pm\) 2,487 & - \\ Buchu EO 2 & n.d. & - \\ Water mint EO & 13,154 \(\pm\) 530 & - \\ Lesser calaminit EO & 271,276 \(\pm\) 9,476 & - \\ Bergamot mint EO & n.d. & - \\ Balm Mint Bush EO & n.d. & - \\ Spearmint EO 1 & n.d. & - \\ Spearmint EO 2 & n.d. & - \\ \hline \hline \end{tabular}
\end{table}
Table 10: Overview of the pulegone content in various essential oils, teas, herbs, and confectioneries determined via \({}^{1}\)H NMR spectroscopy. The extraction methods used for tea, herbs, and confectioneries are also stated. Essential oils were directly analyzed. The reported data are based on replicate measurements, including sample preparation.
# A Combined Electromagnetic and Mechanical Approach for EU-DEMO Toroidal Field Coils
## 6 Fatigue Life Assessment--(S-N) Approach
On the contrary, the values of the equivalent alternating stress (Salt) and the equivalent mean stress (Smean) computed above represent a generic load cycle where the ratio between the minimum and the maximum stress value is not \(-1\). It is therefore necessary to define a relation to correct the equivalent stresses obtained. Following the ITER criteria, a good compromise is provided by the Goodman relation, which gives the most accurate result for cryogenic applications. The equivalent stress amplitude \(S_{eq}\) is thus obtained as  
\[S_{eq}=\frac{S_{alt}}{1-\frac{S_{mean}}{\sigma_{u}}} \tag{5}\]  
where \(\sigma_{u}\) is the ultimate strength of the material. For the (S-N) fatigue life assessment, the allowable values are specified in the ITER design criteria: either a safety factor of 2 on the equivalent stress amplitude \(S_{eq}\), or a safety factor of 20 on the cycles to failure, must be applied to the best fit of the material fatigue data. For the AISI 316 LN (S-N) curve [14], a safety factor of 2 on the equivalent stress amplitude always gives a more conservative limitation, and it has been chosen in our fatigue assessment. The (S-N) relation (best fit of the experimental data) that we employed reads  
\[\sigma_{alt}=\left(\frac{3.90\times 10^{27}}{N}\right)^{\frac{1}{8.74}} \tag{6}\]  
For N = 20,000 cycles, the resulting allowable value is \(S_{eq}\) = 462 MPa. As reported in Figures 19 and 20, there are a few localized regions where the \(S_{eq}\) reaches the allowable value. In particular, in the analysis performed by considering the residual stress on the base metal (Figure 19
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction
### Feature Selection
* Output Type--Feature selection methods may output either the feature subset that it considers best/most able to perform, or the feature ranking of the original data passed to it, meaning their relative importance according to the chosen approach. The most commonly used differentiation standard for feature selection seems to be its relation with the learning method. Each group has their advantages and disadvantages. Filter-based feature selection methods are computationally efficient and widely used across various domains. However, they typically assess each feature independently using statistical measures such as correlation with the target, chi-squared scores, or mutual information. This univariate approach overlooks interactions between features, meaning that features deemed irrelevant individually may be useful when combined [22]. Wrapper techniques address this limitation by searching much more thoroughly; however, this also means they are more computationally expensive [23; 24]. Performance degradation is an entire research subject itself [22]. Embedded techniques integrate the process of selecting relevant features directly into the model training phase. This approach allows for simultaneous feature selection and model optimization, which can lead to improved model performance. However, since their performance is dependent on the learning algorithm, each has its own drawbacks [25; 26].
# Diversity and Functional Evolution of Terpene Synthases in Rosaceae
## 4 Material and Methods
### Phylogenetic Tree Construction and TPS Classification  
The full-length sequences of TPSs protein sequence with both domains from Rosaceae species were used to perform sequence alignment and phylogenetic tree construction using MEGA7 program [34]. The ClustalW method was used for sequence alignment under default parameters, the Maximum Likelihood and Neighbor-joining methods based on the Jones-Taylor-Thormton (JTT) matrix-based model were used for phylogenetic tree construction, a Bootstrap method was used for the phylogeny test with 500 replications, rates among sites was Gamma distributed (G), and Partial deletion was used for gaps/missing data treatment, with site coverage cutoff at 90%. The produced tree was further embellished by the FigTree program ([[http://tree.bio.ed.ac.uk/](http://tree.bio.ed.ac.uk/)]([http://tree.bio.ed.ac.uk/](http://tree.bio.ed.ac.uk/)), accessed on 2 September 2021). TPS members were classified by their clustering relation with the query sequence mentioned above. For each TPS clade, their TPS member's sequences were further aligned by ClustalW method, and a sequence logo for the visualization of the highly conserved aspartate-rich motif in C-terminal domain was produced by TBTools [33].
## 2 Mechanism Analysis
### Calculation of Degree of Freedom  
\(O\) and \(O_{1}\) are the center points of the static and moving platforms, respectively. \(A_{i}\) (\(i=1,\,2,\,3,\,4\)) and \(C_{i}\) (\(i=1,\,2,\,3,\,4\)) are the intersections of the kinematic pairs of each branched chain with the static platform and the moving platform, respectively. The distance between \(A_{i}\) (\(i=2,\,3\)) and the \(O\) point along the \(X\)-axis direction is \(a\); the distance between \(A_{i}\) (\(i=2,\,3,\,4\)) and the \(O\) point along the \(Y\)-axis direction is \(b\); the distance between \(C_{i}\) (\(i=2,\,3\)) and the \(O_{1}\) point along the \(X_{1}\)-axis direction is \(c\); and the distance between \(C_{i}\) (\(i=2,\,3,\,4\)) and the \(O_{1}\) point along the \(Y_{1}\)-axis is \(d\). \(l_{a}\) is the length of \(B_{i}A_{i}\) (\(i=1,\,2,\,3,\,4\)), while \(l_{b}\) is the length of \(C_{i}B_{i}\) (\(i=1,\,2,\,3,\,4\)).  
The static coordinate system is established with the center \(O\) of the static platform as the origin. The \(Y\) axis is from the \(O\) point to the midpoint of \(A_{2}A_{3}\), the \(X\) axis is from the \(A_{3}\) point to the \(A_{2}\) point, and the \(Z\) axis is perpendicular to the static platform and its direction is upward. The moving coordinate system is established with the center \(O_{1}\) of the moving platform as the origin. The \(Y_{1}\) axis is from the \(O_{1}\) point to the midpoint of \(C_{2}C_{3}\), the \(X_{1}\) axis is from the \(C_{3}\) point to the \(C_{2}\) point, and the \(Z_{1}\) axis is perpendicular to the moving platform and pointing away from the mechanism, as shown in Figure 3.  
When branched chain 1 is in the PS mode and the vA pair is in the universal joint mode, the mechanism is in the 1T2R motion mode, as shown in Figure 3a; when branched  
Figure 3: Structural diagram and constraint screw system of the mechanism: (**a**) 1T2R motion mode, and (**b**) 2T1R motion mode.  
Figure 2: Two modes of vA pair: (**a**) universal joint mode and (**b**) revolute-pair mode.  
chain 1 is in the RPS mode and the vA pair is in the revolute-pair mode, the mechanism is in the 2T1R motion mode, as shown in Figure 3b.  
The constraint screw system is represented by the arrows shown in Table 1. Types and meanings of arrows adopted.
## 2 Theoretical Background
### Vibrations in Transformers  
Vibration signals have a direct correlation with the mechanical and electrical performance of transformers [4; 20]. These vibrations are predominantly generated within the transformer core and windings during its operation. Core vibrations are manly induced by the magnetostriction phenomenon, which describes the dimensional changes in ferromagnetic materials when exposed to magnetic fields [25; 26]. The fundamental vibration frequency in the core is twice the input power system frequency, and higher harmonics emerge due to the nonlinearity of the magnetostriction phenomenon. The vibration amplitude, \(a_{core}\), is directly proportional to the square of the voltage, \(U\), as follows [26]:  
\[a_{core}\kappa U^{2} \tag{1}\]  
The forces resulting from the vibrations caused by magnetostriction manifest in a perpendicular direction to the core [25]. On the other hand, winding vibrations, which have axial and radial components, result from electromagnetic forces acting on the windings due to the interaction between the leakage flux density and the current in the windings. Assuming the current is nearly sinusoidal, the primary harmonic, \(a_{winding}\), of winding vibrations is directly proportional to the square of the current, \(I\), as follows [26]:  
\[a_{winding}\kappa I^{2} \tag{2}\]As vibration signals are correlated with the mechanical and electrical performance of the transformer core and windings, a damage condition in the winding, e.g., a SCT condition, will change their frequency content, leaving the opportunity to develop methodologies based on signal processing techniques which can characterize such frequency changes. However, this task is not a straightforward process due to the high-level noise and nonstationary nature of the vibration signals.
# Extracellular Vesicles for Regenerative Medicine Applications
## 3 Pre-Clinical Applications of EVs in Tissue Engineering and Regenerative Medicine
### Nervous System  
A major focus in the field of TERM has been on the regeneration and repair of injuries to the central and peripheral nervous system. Like the cardiovascular system, the innate regenerative capacity of the nervous system is limited; consequently, there is a growing interest to implement novel strategies such as EV-based therapies to promote neural regeneration.  
There have been several studies showing the promise of EV-based therapy for neural regeneration in both central and peripheral nerve injury. In the central nervous system (CNS), exosomes derived from bone marrow MSC and stromal MSC containing miR-133b have been shown to promote neurite outgrowth and the re-myelination of CNS nerve fibers after injury [91; 92]. Exosomes derived from MSC are also understood to inhibit pericyte migration after spinal cord injury and, as a result, these exosomes improve motor function [8; 93]. Intravitreal administration of MBVs derived from porcine urinary bladder ECM protect against ischemia-induced retinal ganglion cell axon degeneration and death, as well as preserved visual function in an in vivo rodent model of retinal damage [94]. In the peripheral nervous system (PNS), Schwann cell-derived exosomes have been shown to promote axon growth and regeneration in a peripheral nerve injury model [95]. Broadly, there is a growing body of literature supporting the use of EVs in peripheral nerve repair and showing pre-clinical promise [96].
## 7 WiFi Sensing-Based Keyed Name Detection
### Empirical Results  
#### 7.2.1 Single Hand, Single Finger  
Figure 20 shows the CSI time series for five trials of typing the name \"Haoming\" using a single finger. Each trial displayed a consistent shape with distinct keystroke features, clearly separated by red dashed lines. These observations indicate that sequential key presses for a given name produce consistent waveform signatures, making them suitable for classification.  
Figure 20: Raw CSI time series for 5 trials of the name “Haoming”.  
Using the CSI time series as input features, our classifier achieved high accuracy in distinguishing among four names. As shown in Table 7, the true positive rate (TPR) for \"Haoming\" and \"Deepak\" was 100%, while \"Aryan\" and \"Aruna\" achieved 86.7%. The misclassification between \"Aryan\" and \"Aruna\" was attributed to their identical length and nearly identical key sequences, making their waveform shapes difficult to differentiate.
# Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging
## 2 Background
### Non-Adaptive Beamforming  
Delay-and-sum (DAS) is adopted to obtain the receiving focused echo signal. The sensor elements' response from the focus point will be added up in phase. Consequently, the desired signal from the focus point will be maximized. An arbitrary point \(\forall\overrightarrow{r}_{\text{p}}=(\text{x},\text{ z})\) is carried out by compensating for the propagation delay profile for this point. The delay is calculated as the propagation path from the nth  
Figure 1: The diagram of synthetic aperture.  
transmitting element to the focus point and back to the mth receiving element. For a phased transducer array of M elements, the propagation path is given by:  
\[\mathrm{k_{nm}}(\overrightarrow{r_{\mathrm{p}}})=\frac{\left\|r_{\mathrm{n}}^{ \rightarrow}-\overrightarrow{r_{\mathrm{p}}}\|+\|r_{\mathrm{m}}^{\rightarrow }-\overrightarrow{r_{\mathrm{p}}}\right\|}{\mathrm{c}}\times\mathrm{fs} \tag{1}\]  
where \(n\) and \(\mathrm{m}\) represent the transmitting and receiving element, respectively. N and M represent the number of elements of the transmitting and receiving array, which are usually equal to total element number of the array, respectively. \(n=1,2,\ldots,\) N; \(\mathrm{m}=1,2,\ldots,\) M. \(\overrightarrow{r_{\mathrm{m}}^{\rightarrow}}\) and \(\overrightarrow{r_{\mathrm{m}}^{\rightarrow}}\) are spatial positions of the transmitting element and the receiving element, respectively. \(\mathrm{c}\) is the ultrasonic velocity, fs is the sampling rate of ultrasonic system. The receiving focused echo image with low lateral resolution (LRI, low-resolution imaging) is given by:  
\[\mathrm{LRI_{n}}=\sum_{\mathrm{m}=1}^{\mathrm{M}}\omega_{\mathrm{m}}\times \mathrm{x_{nm}}(\mathrm{k_{nm}}(\overrightarrow{r_{\mathrm{p}}})) \tag{2}\]  
As all elements are excited in turn, N's LRI echo imaging will be obtained. Overlay all these LRIs with predefined, data-independent apodization weights, echo image with both transmitting and receiving focused is obtained. The newly achieved ultrasonic imaging is the response after synthetic aperture (SA), also known as the high-resolution imaging (HRI).  
\[\mathrm{HRI}=\sum_{\mathrm{n}=1}^{\mathrm{N}}\omega_{\mathrm{n}}\times \mathrm{LRI_{n}} \tag{3}\]