esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Aroma Profiling  
Plants cultivated on different media were subjected to headspace solid-phase microextraction (HS-SPME) with 2 cm DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA). Briefly, 1 \(\pm\) 0.05 g of leaves was placed in a 10 mL headspace vial with 10 \(\upmu\)L of 2-undecanone (10,000 ppm) as an internal standard (IS). The vial was then placed on a heating plate at 70 \({}^{\circ}\)C for 5 min of conditioning, and the fiber was then exposed above the sample for 25 min. Analyte desorption was carried out for 2 min at 220 \({}^{\circ}\)C, split 1:100. The GC-MS analysis was carried out on a Varian CP-3800/Saturn 2000 (Varian, Walnut Creek, CA, USA) with a Zebron ZB-5 MSI column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) (Phenomenex, Torrance, CA, USA). The temperature program started at 40 \({}^{\circ}\)C for 3 min, which increased to 110 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and then to 270 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min. Scanning was carried out from 38 to 310 \(m/\)z at 70 eV. Helium was used as carrier gas at a flow rate of 1.0 mL/min. The procedure was repeated for plant material without the addition of growth regulators derived directly from the in vitro culture, without acclimatization and after acclimatization (two plant variants from the control medium). The analyses were carried out in triplicate.  
Compounds were identified by comparing experimentally obtained linear retention indices (LRIs) calculated against a C6-C30 \(n\)-alkane mix (Sigma-Aldrich, Saint Louis, MO, USA), and mass spectra with those accessed in libraries (NIST 17 Mass Spectral and Retention Index Libraries (NIST17) and NIST WebBook) and the literature [52]. Compound semi-quantification was performed by normalizing the peak area and calculations based on the amount of added IS. For semi-quantification, no standard curves were used for any of the compounds found in the samples. IS concentrations were compared with those of the other compounds, by supposing a general equipment response factor of 1 for all volatile compounds.
# The Impact of an Electric Machine Body on EM Wave Propagation in RTMS
## 1 Introduction
Similarly, [PERSON]'s study examined the influence of vehicle components on electromagnetic (EM) propagation within the tire pressure monitoring system (TPMS). The study suggested an antenna design that leverages vehicle components to enhance power efficiency. Thus, optimizing antenna design to harness surface electromagnetic waves (EMWs) induced on EM bodies is essential for achieving reliable communication. Our research explores the influence of various factors on signal transmission between RTMS sensors positioned within a rotor and a central receiver located outside the electrical machine. It evaluates how the structure of the electrical machine impacts the transmission and propagation of signals from rotor sensors. Additionally, it illustrates that RTMS antennas designed to capitalize on surface waves induced on the electrical machine's body can surpass the performance of RTMS antennas optimized for free space transmission and reception. The final section of this paper encompasses the experimental results obtained from the implementation of the RN171 antenna on the electrical machine, providing valuable insights into the propagation characteristics and contributing to a comprehensive understanding of the electromagnetic dynamics within the system. The paper is composed of five sections. Section 1 is the introduction. Section 2 presents the RTMS (rotor temperature monitoring system). Section 3 investigates EM propagation in an electric machine. Section 4 presents the experiment's results. Finally, Section 5 represents the conclusion.
# Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
## 2 The Evolution of Biodegradable Polymeric Drug Delivery Systems
### Polyamides, Polydopamine, and Polyphenol  
Polyamides were introduced for prolonged delivery of drug cargos, with amide acid derived polymers being the most widely used [34]. Although they display biocompatibility in animals and produce relatively non-toxic products after metabolism [35; 36], applications are restricted due to the intrinsic hydrolytic stability of the amide bond and cleavage rates rely highly on the presence of amidase enzymes. However, by modifying the amide acid group and conjugating the drug via a carboxylate bond [37], polymer-drug conjugates can be designed as insoluble particles [38], which can penetrate into cells and release the drug payload by enzymatic degradation over a long period [39; 40]. In this decade, there has been interest in the application of materials such as polydopamine (PDA) and polyphenol. PDA is a nature inspired polymeric pigment that exhibits excellent photo-responsive properties and it has active surface functionality for drug loading [41; 42]. Polyphenols are widely used in tissue engineering and drug loading based on their unique polyphenic structures and inherent biocompatible, bioadhesive, antioxidant, and antibacterial properties [43].
# A Network-Based Method for the EMU Train High-Level Maintenance Planning Problem
## 4 Path Set Generation
### Steps for Path Set Generation  
After clearing the subsequent arc set for each arc on the network, the path set \(R_{e}\) for each \(e\) can be generated. The processes of path generation depends on time based depth-first, and the specific steps are as follows:  
Step 1: \(G^{\prime}=G\);  
Step 2: According to the initial state of train-set \(e\), we assign the initial arc and clear the arc's type on the network \(G^{\prime}\);  
Step 3: Based on the connection sequence of arcs mentioned at Section 4.1, the time based depth-first search algorithm is applied to find the subsequent arc considering constraints (5) on the network \(G^{\prime}\). If no subsequent arc is found, then turn to Step 6. Otherwise, turn to Step 4;  
Step 4: Judge whether or not the subsequent arc reaches at the virtual hyper-node. If yes, turn to Step 5; otherwise, turn to Step 3;  
Step 5: Connect all arcs in the order and output the path \(r\). Add \(r\) into \(R_{e}\), and delete the \(a_{1j}^{k}\) (\(j=2,3,4\)) existing in path \(r\). Then reconstruct the network \(G^{\prime}\) and go back to Step 2;  
Step 6: End the path generation processes and output \(R_{e}\).  
Reapply the algorithm above and generate \(R_{e}\) on the network for each \(e\) (\(e\in E\)).  
Then, we can also get the universal set \(R\) of all train-sets for model I as follows:  
\[R=\underset{e\in E}{\cup}R_{e} \tag{18}\]  
Meanwhile, \(\lambda_{e}^{r}\) can be assigned by Equation (18).  
Based on the method of path set generation, the constraints (5) in model I and model II can be removed.
# Measuring Torque and Temperature in a Rotating Shaft Using Commercial SAW Sensors
## 2 Materials and Methods
### Calibration Setup  
To determine the gage factor \(S_{G}\) of the SAW strain gages, a static calibration setup was previously designed and implemented. The operation principle is represented in Figure 4a. A force \(\overset{\rightarrow}{F}\) is applied perpendicularly to a metallic arm that is connected to a steel shaft. The load cell is attached to this shaft, which, in turn, stands on two bearings. Under these conditions, the torque \(M\) applied to the load cell is given by the following equation:  
\[M=F\cdot\frac{L}{2}\cdot\sin\theta, \tag{1}\]  
where \(F\) is the applied force, \(L\) is the length of the moveable arm and \(\theta\) is the angle formed between the applied force vector and the arm. Since the force is applied perpendicularly to the moveable arm and the maximum vertical displacement of the arm is negligible when compared to its length, for practical considerations \(sin(\theta)\approx 1\) and this term can be neglected.  
The instrumented load cell was assembled in the calibration setup and the RF couplers were positioned with a relative orientation of 0\({}^{\circ}\). A Shimadzu Autograph AGS-10 kN Universal Testing Machine (UTM) (Kyoto, Japan) applied strain in the downward direction in the moveable arm; measurements were performed at room temperature. The values of applied strain varied between 0 and 47 \(\upmu\)m/m with a step of 4.7 \(\upmu\)m/m. The procedure was repeated afterwards but with strain applied in the upward direction using a steel cable connected to the arm. For each applied strain, the response of the gages was measured with the interrogation unit. The detail of the load cell, the strain sensors and the moveable arm can be seen in Figure 4b.
## 4 Experiment, Results & Discussion
### Optimization of Benchmark Problems
#### 4.1.1 Number of Agents
Figure 3 further illustrates the results of the experiment. The results are converted into error values for better graph representation. The error value is the difference between the fitness of the solution found with the ideal fitness. At the top left of the figure is the close-up of the graph. Similar to what is observed in Table 2, it can be seen that the number of agent equal to 50 gives the least error in most of the functions, but not in all functions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Number of Agents**} \\ \hline
**Function** & **10** & **20** & **30** & **40** & **50** \\
**Name** & & & & & \\ \hline f4 & 400.0557 & 400.0003 & 400.001 & 400.0008 & 400.0013 \\ f5 & 519.997 & 519.996 & 519.9923 & 519.983 & 519.9913 \\ f6 & 601.5922 & 601.0678 & 600.3217 & 600.3587 & 600.0771 \\ f7 & 700.0595 & 700.027 & 700.0418 & 700.0591 & 700.0517 \\ f8 & 801.9904 & 800 & 800 & 800 & 800 \\ f9 & 905.9708 & 905.9698 & 904.9748 & 905.9698 & 904.9748 \\ f10 & 1007.031 & 1000.375 & 1000.25 & 1003.602 & 1000.25 \\ f11 & 1247.431 & 1226.78 & 1140.238 & 1222.493 & 1131.949 \\ f12 & 1200.059 & 1200.006 & 1200.012 & 1200.01 & 1200.017 \\ f13 & 1300.138 & 1300.132 & 1300.1 & 1300.043 & 1300.137 \\ f14 & 1400.171 & 1400.144 & 1400.123 & 1400.079 & 1400.052 \\ f15 & 1500.94 & 1500.575 & 1500.499 & 1500.578 & 1500.417 \\ f16 & 1602.263 & 1602.072 & 1602.032 & 1601.511 & 1601.108 \\ \hline Friedman & 5 & 2.9615 & 2.3462 & 2.5769 & 2.1154 \\ Rank & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effect of Number of Agents. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(i\) & **Number of Agents** & \(p\) & **Holm** \\ \hline
4 & 10 & 0.00003 & 0.0125 \\
3 & 20 & 0.172447 & 0.016667 \\
2 & 40 & 0.45675 & 0.025 \\
1 & 30 & 0.709815 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Holm Post Hoc for Number of Agents.
## 4 Discussion  
In many cases, due to the complexity of reliability analysis problems, it is hard to establish an effective response surface to assess the \(P_{\rm f}\) of concrete gravity dams. A reliability analysis method based on the IPSO-LSSVM was proposed to calculate the reliability of concrete gravity dams when explicit nonlinear limit-state functions are difficult to obtain accurately. In the method, the hyper-parameters of the LSSVM model were tuned by the IPSO algorithm to reflect the relationship between the influencing factors and safety  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Samples** & \(P_{\rm f}\) & **Coefficient of Variation** \\ \hline
1 & 0 & - \\
10 & 0 & - \\ \(10^{2}\) & 0 & - \\ \(10^{3}\) & 0 & - \\ \(10^{4}\) & \(9.00\times 10^{-5}\) & 1.05 \\ \(10^{5}\) & \(7.45\times 10^{-5}\) & 0.44 \\ \(10^{6}\) & \(8.87\times 10^{-5}\) & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Calculation results of the probability of stress exceedance at the toe of the dam.  
Figure 11: The curve of \(P_{\rm f}\).  
indexes. After that, the IPSO-LSSVM model was utilized in conjunction with the MC method to evaluate the failure probability.  
Accurate expression of limit state function is the key to obtain reliable calculation results. This paper focused on the improvement method for the accuracy of the model used for reliability analysis of concrete dams. The RSM models were used to test the degree of nonlinearity between the influencing factors and safety indexes and the results show a highly nonlinear relationship between the influencing factors and the safety indexes. The accuracy of the IPSO-LSSVM model was compared with that of the PSO-LSSVM model, and the result shows that the IPSO-LSSVM has higher accuracy, indicating that the IPSO-LSSVM model significantly improves the accuracy of the PSO-LSSVM model.  
In the case study, the number of samples required for \(P_{\text{f}}\) calculation is \(10^{6}\). If the traditional Monte Carlo-finite element method is used to calculate \(P_{\text{f}}\), then it consumes more than 2000 h. However, the method proposed in this paper can effectively shorten computing time; it only takes about 20 min, so its computing efficiency is greatly improved. Therefore, the method proposed in this paper can effectively improve computing efficiency on the precondition of accuracy maximization.  
The main contribution of this paper is its improvement of the established model [32] and short computing time compared with the traditional reliability analysis method. The proposed reliability analysis method is shown to be an efficient scheme with respect to both computational effort and accuracy.
## 3 The Extraction of Offset, Gain, Time Skew, and Bandwidth Mismatch  
The input signals in the proposed methods are wide-sense stationary. Hence, the statistical characteristics for each sub-ADC output are the same when there is no mismatch in the TI-ADCs system.
## 3 Results and Discussion  
### Morphology and Chemical Composition  
#### 3.1.1 FESEM Analysis  
With spatial resolution up to 1.5 nm or better, FESEM creates smoother and less electrostatically blurred pictures, many times better than the traditional SEM. Good resolution, low-voltage pictures were accomplished with marginal sample electric charge (acceleration voltages vary between 0.5 and 30 kV). The FESEM images of the synthesized MXene nanoparticles at different magnifications are shown in Figure 2. From the image, it was clear that the three-dimensional Ti\({}_{3}\)AlC\({}_{2}\) MAX powder was converted to multilayer Ti\({}_{3}\)C\({}_{2}\) which demonstrated the grafted Al layer from Ti\({}_{3}\)AlC\({}_{2}\) using NH\({}_{4}\)HF\({}_{2}\) etchant. One of the significant parameters that affect the thermophysical properties of MXene nanofluid is the thickness of the synthesized MXene nanosheets, which consist of many layers. These layers also decide the suitability of 2D materials in functional devices such as supercapacitors, batteries, and semiconductors. In this study, the average thickness of each layer ranged from 70 to 90 nm, which was in line with previous literature results [16; 17]. On observing the thickness of the MXene layers, it could be noted that some layers showed loosely packed structures creating interspace between the MXene layers providing fluidic channels for base fluids.  
Figure 2: FESEM images of (**a**,**b**) Cross-sectional view of Ti\({}_{3}\)C\({}_{2}\) MXene after drying (**c**,**d**) Top view of the synthesized Ti\({}_{3}\)C\({}_{2}\) MXene after delamination.  
The FESEM image (Figure 2a,b) clearly illustrates the structure of MXene sheets. The chemical etching method exfoliated the MXene layers, and the corresponding sheets revealed a topography identical to the previous study [18]. Figure 2c,d indicates the synthesized MXene sheets formed because of the delamination process, which led to the production of 2D MXene sheets whose size was less than 2 \(\upmu\)m.
###### Abstract  
The Midwest wine industry has shown a marked increase in growers, hectares planted, wineries, and wine production. This growth coincides with the release of cold-hardy cultivars such as Brianna and Frontenac gris, in 2001 and 2003, respectively. These white grape varieties account for one-third of the total area grown in the state of Iowa. It is generally accepted that the wine aroma profile plays a crucial role in developing a local, sustainable brand. However, the identity of Brianna/Frontenac Gris-based wine aromas and their link to the grape berry chemistry at harvest is unknown. This study aims to preliminarily characterize key odor-active compounds that can influence the aroma profile in wines made from Brianna and Frontenac gris grapes harvested at different stages of ripening. Brianna and Frontenac gris grapes were harvested approximately 7 days apart, starting at 15.4 \"Brix (3.09 pH) and 19.5 \"Brix (3.00 pH), respectively. Small batch fermentations were made for each time point with all juices adjusted to the same \"Brix prior to fermentation. Odor-active compounds were extracted from wine headspace using solid-phase microextraction (SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS) and simultaneous olfactory (O). Over 30 odor-active compounds were detected. Aromas in Brianna wines developed from \"cotton candy\" and \"floral\", to \"banana\" and \"butterscotch\", then finally to \"honey\", \"caramel\" and an unknown neutral aroma. Frontenac gris wines changed from an unknown neutral aroma to \"fruity\" and \"rose\". Results from the lay audiences' flavor and aroma descriptors also indicate a shift with harvest date and associated \"Brix. To date, this is the first report of wine aromas from Brianna and Frontenac gris by GC-MS-O. Findings from this research support the hypothesis that aroma profiles of Brianna and Frontenac gris wines can be influenced by harvesting the grapes at different stages of ripening.  
Frontenac gris; Brianna; wine aroma; SPME-GC-MS; olfactory; cold-hardy grapes Article
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 4 Biological Signals for Wearable-Based HR
### Area features  
Area features correspond to the area under the bioimpedance curve in between two characteristic points. These features have shown to have a high correlation with total peripheral resistance, which is key to estimating blood pressure [18].  
* The area between the diastolic point and the maximum slope point \[A_{dia}=-\int_{t_{dia}}^{t_{slope}}a\cdot dt\] (5)
* The area between the maximum slope point and the systolic point \[A_{slope}=-\int_{t_{sys}}^{t_{slope}}a\cdot dt\] (6)
* The area between the diastolic and the systolic point \[A_{sys}=A_{dia}+A_{slope}\] (7)
## 5 Experimental Results
### Supffix Generation Results

Table 2: The suffix generation capability of top-k.  
Figure 2: Comparison of suffix generation strategies. Typical-p sampling tends to select words with low conditional entropy, thereby producing texts with high typicality. As illustrated in Table 4, the results generated by typical-p sampling are comparable to those of top-p sampling, although they are marginally less effective. We visually compare the above four suffix generation strategies in Table 5. The comparison is based on three aspects: text quality, text diversity, and runtime. The beam search generation strategy demonstrates a distinct advantage in text quality, while the top-k generation strategy exhibits a clear advantage in text diversity. Due to the high diversity of text generated by top-k sampling and the high quality of text generated by beam search, this experiment aims to impartially and accurately assess the impact of temperature on suffix generation. We evaluate the effect of temperature on suffix generation by setting top-k = 3 (Table 6) and beam-search = 3 (Table 7). The suffix generation strategy used therein is a loss-based membership inference attack. It can be seen that an appropriate temperature parameter can improve the accuracy of training data extraction. Repetition_penalty is a mechanism designed to reduce repeated texts by penalizing repetitions when its value exceeds 1. Observing the impact of repetition_penalty under the condition of num_beam = 3, as shown in Table 8, reveals that its impact is minimal and predominantly negative. This is due to the model's tendency to overfit to the training data during extraction, where penalizing repetition may result in the selection of incorrect tokens. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Typical-p** & \(M_{1p}\) & \(M_{20p}\) & \(M_{p}\) & \(D_{edit}\) \\ \hline
0.4 & 0.407 & 0.491 & 0.470 & 15.584 \\
0.5 & 0.405 & 0.535 & 0.494 & 15.393 \\
0.6 & 0.404 & 0.543 & 0.498 & 15.201 \\
0.7 & 0.395 & 0.573 & 0.502 & 15.351 \\
0.8 & 0.365 & 0.601 & 0.506 & 16.562 \\
0.9 & 0.330 & 0.597 & 0.492 & 18.058 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The suffix generation capability of typical-p. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Text Quality** & **Text Diversity** & **Runtime** \\ \hline beam-search & ✓ ✓ ✓ & ✓ & ✓ \\ \hline top-k & ✓ & ✓ ✓ ✓ & ✓ ✓ \\ \hline top-p & ✓ ✓ ✓ & ✓ ✓ & ✓ ✓ \\ \hline typical-p & ✓ ✓ & ✓ ✓ & ✓ ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different suffix generation strategies. Use the number of “✓” to indicate good or bad performance, with more “✓” indicating better performance.
## 6 Prediction of the Ground Settlement of Pipe Jacking
### Statistical Analysis of the Peck Correction Coefficient  
Based on Equations (16) and (17), the sections of three sections of well 7-4, well 11-9, and well 13-14 of the Bailonggang sewage connection pipe project in Zhuyuan, Shanghai were selected. A total of 30 sets of tunnel construction monitoring data were collected, and many correction coefficients \(\alpha\) and \(\beta\) were statistically analysed, as shown in Figure 17a,b.  
Figure 17a,b shows that when the correction coefficient \(\alpha\) of the maximum surface settlement value is between 0.6 and 3.0, the distribution ratio is 86.67%, and when the correction coefficient \(\beta\) of the settlement trough width is between 1.6 and 4.0, the distribution ratio is 90.00%. This shows that when the values of the correction coefficients \(\alpha\) and \(\beta\) are distributed in these two intervals, it is reasonable to use the Peck formula to predict surface subsidence.  
To further prove the feasibility of the modified parameter interval, two typical cases were selected for testing. The lower limits of \(\alpha\) and \(\beta\) are 0.6 and 1.6, and the upper limits are 3.0 and 4.0. The curves obtained using other values are between the upper and lower limits. Six sets of measured data were randomly selected from the four intervals of wells 7-4, 9-7, 11-9, and 13-14 to compare with the upper and lower limit curves predicted by the Peck formula. As shown in Figure 18, 88.90% of the measured data fall between the upper and lower limit correction prediction curves, indicating that the \(\alpha\) and \(\beta\) correction parameter intervals are feasible for this project and that the obtained correction curve can effectively predict the surface subsidence caused by the construction of the pipe jacking section of well 9-7. The value is also applicable to the prediction of surface subsidence in the other three intervals of the project.  
Figure 17: Distribution intervals of the \(\alpha\) and \(\beta\) data: (**a**) \(\alpha\) data distribution diagram; (**b**) \(\beta\) data distribution diagram.  
Figure 18: Comparison of the corrected Peck upper and lower limit curves with the measured data.