esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### FTIR Analysis  
To explore the impacts of ultrasound and the combination of ultrasound and quercetin on the short-range crystal structure of broken rice starch (C), FTIR spectroscopy was adopted for the determination of the infrared spectra of the three above samples, as illustrated in Figure 3a. All three starch samples exhibited a broad and strong absorption  
Figure 2: **(a)** The X-ray diffraction patterns of broken rice starch (C) (black line), ultrasound-modified starch (US) (red line), and modified starch composite (US-Q) (blue line), as well as quercetin (**b**).  
peak around 3300 cm\({}^{-1}\), primarily due to the absorption caused by the stretching vibration of the -OH bond [42]. The absorption peak at 2930 cm\({}^{-1}\) resulted from the stretching vibration of -CH and -CH\({}_{2}\) in the starch. In comparison with the infrared spectrum of C, after the ultrasonic treatment, there was no significant alteration in the shape or position of any characteristic group within the C. Meanwhile, no characteristic absorption peak disappeared, nor did any new one emerge, indicating that the short-range crystal structure remains unchanged during the ultrasonic process, thereby making it a physical modification method [38]. When compared with the unmodified broken rice starch, its relative absorbance decreased, suggesting that ultrasonic treatment affects the absorption intensity of the hydroxyl groups of broken rice starch molecules. This may be because the ultrasonic treatment induces mechanical action and cavitation effects that change the ordered structure of the starch crystalline structure, thereby causing the alteration of the intensity of the starch absorption peak. The absorption peak of quercetin (Figure 3b) at 3414 cm\({}^{-1}\) is the stretching vibration of the hydroxyl group. After compounding with quercetin, the absorption peaks appearing at 1610 cm\({}^{-1}\), 1560 cm\({}^{-1}\), and 1515 cm\({}^{-1}\) could be attributed to the stretching vibration of the benzene ring bond. In the wavelength range of 1665-1022 cm\({}^{-1}\), several absorption peaks of different intensities emerges, and these absorption peaks were consistent with the positions of the absorption peaks in the FTIR spectrum of quercetin, indicating that quercetin is bound to the starch and suggesting the existence of an interaction between starch and quercetin [43].
## 1 Introduction  
Vehicular networking serves as a technology enabler for various multi-purpose mobile applications that fully integrate the vision of the future Internet [1]. Intelligent transportation systems (ITSs) [2] are envisioned to play a significant role in the future, making transportation safer and more efficient. With respect to these expectations, vehicle-to-infrastructure interactions have evolved to include various types of applications, safety-related and user-oriented.
## 2 Results and Discussion
### UV-Vis Titration  
The anion binding abilities of 12 compounds **(1-12)** were investigated in detail through UV-vis spectroscopy in DMSO. Results indicated that only six compounds **(1**, **2**, **3**, **6**, **9** and **11**) showed binding ability for anions. The UV-vis spectral changes of six compounds (4.0 \(\times\) 10\({}^{-5}\) mol\(\cdot\) L\({}^{-1}\)) upon the titration with acetate anion are shown in Figure 2. As seen in Figure 2, the spectra of free **1** exhibited a strong absorption band centered at 350 nm and a weak absorption band centered at 437 nm. With the stepwise addition of acetate anion to the solution of compound **1**, the strong absorbance band at 350 nm decreased in intensity and the sharp peak thoroughly disappeared. At the same time, the weak wide absorbance band centered at 437 nm increased gradually and developed a sharp peak. In general, the red-shift phenomenon in absorption spectra was induced by the addition of acetate anion to compound **1**. The reason may be that the conjugative effect between the host and guest was strengthened after the hydrogen bond formed between compound **1** and acetate anion [23,24]. One clear isosbestic point at 375 nm appeared, which indicated that a stable complex having a certain stoichiometric ratio had formed between **1** and acetate anion [25]. Particularly, the presence of F\({}^{-}\) and H\({}_{2}\)PO\({}_{4}\)\({}^{-}\) induced similar changes in the UV-vis spectra of **1** compared with AcO\({}^{-}\) (the spectra listed in the supplementary information Figure S1). However, the addition of excess equivalents of Cl\({}^{-}\), Br\({}^{-}\), and I\({}^{-}\) resulted in slight changes in the absorption spectrum. The above results indicated that compound **1** showed a different binding ability with F\({}^{-}\), AcO\({}^{-}\), and H\({}_{2}\)PO\({}^{-}\), and almost no or a very weak binding ability with Cl\({}^{-}\), Br\({}^{-}\), and I\({}^{-}\). For compounds **2** and **3**, similar spectral changes were induced by the addition of an acetate anion due to the similar structures (1,5-diaza-fluorenone derivatives). However, compounds **6** (1,10-phenanth-5,6-dione), **9** (ferrocene-1,1\({}^{-}\)-dione), and **11** (anthracene-9-carbaldehyde) showed a different spectral response dueto the different framework, while a similar red-shift phenomenon appeared in the host-guest interaction process.  
Figure 2: UV-vis spectral changes of various compounds upon the addition of an acetate anion compounds (**1**, **2**, **3**, **6**, **9** and **11**). (compound) \(=4.0\times 10^{-5}\) mol\(\cdot\)L\({}^{-1}\); (acetate anion) \(=-1.6\times 10^{-3}\) mol\(\cdot\)L\({}^{-1}\). Arrows indicate the direction of increasing anion concentration.
## 2 Results and Discussion
### Effects of Rootstocks on Berry Volatile Compounds
In the O2 PLS-DA model of CS/Riparia Gloire vs. CS, 23 volatile compounds with VIP > 1.0 were filtered (Supplementary Table S3). Typically, the concentrations of (\(Z\))-2-penten-1-ol in CS/Riparia Gloire remained lower compared to CS after E-L 36 stage. Statistically lower (\(Z\))-2-penten-1-ol concentrations were observed at E-L 36 and 37 stage in 2015. Besides, Riparia Gloire significantly decreased the concentrations of total esters at harvest in the two seasons. Riparia Gloire inhibited the accumulation of \(\gamma\)-terpinene prior to harvest, except at E-L 33 stage in 2016. Notably, \(\gamma\)-terpinene concentrations were significantly lower in CS/Riparia Gloire in comparison to those in CS at E-L 31 stage. Inversely, at harvest, Riparia Gloire markedly increased the concentration of \(\gamma\)-terpinene in 2015 (Figure 4f). Overall, 110R, Riparia Gloire, and SO4 induced reductions in the concentrations of total esters in mature berries. Besides, there were decreases in terpenids, especially \(\gamma\)-terpinene and \(\alpha\)-terpineol, in CS/SO4 compared to CS. Interestingly, 101-14, Ganzin 1, 110R, and 5 BB led to increases in the concentrations of C\({}_{13}\)-norisoprenoids at harvest. Esters and terpen both greatly contribute to the  
Figure 4: Effects of 101-14 (**a**), 110R (**b**), 5 BB (**c**), Ganzin 1 (**d**), Riparia Gloire (**e**), and SO4 (**f**) on the volatile biomarkers in two seasons (2015-2016). Each data represent the concentration difference of volatile biomarkers between each grafted and own-rooted vines. The asterisk * in each bar indicates significant differences between grafted and own-rooted vines in the same season at the basis of student’s \(t\)-test at \(p<0.05\). fruity and floral aromas. In addition, C\({}_{13}\)-norisoprenoids are characterized as important volatile compounds due to their low olfactory thresholds and pleasant smell. Therefore, with respect to the grape aroma profiles, SO4 may induce adverse effects, whereas CS/101-14, Ganzin 1, and 5 BB can impart a positive influence.
## 3 Results and Discussion
### Radon/Thoron Exhalation Rates vs. Radionuclide Activity
Similarly, the moderate to strong correlation between Average Rn-222 Surface Emission and Average Rn-220 Surface Emission (r = 0.71) reflects a partial association between the emissions of these gases, though this relationship is weaker compared to their exhalation rates. Moreover, the exceptionally strong correlation between the I Index and the Dose Rate (r = 0.99) underscores a significant relationship between this index--likely representing a composite measure of radon and thoron activities--and the radiation dose rate. This indicates that fluctuations in the I Index are closely tied to variations in the detected radiation dose. In contrast, the weaker correlations between radon and thoron exhalation rates with uranium (U mg/kg) and thorium (Th mg/kg) activities (generally below 0.5) suggest a less direct relationship when considering each element individually. This weaker association implies that other factors might play a more substantial role in influencing surface emissions and exhalation rates. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Radionuclide Activity (Min, Max, and Average)**} \\ \hline
**Rock Type** & \# & \multicolumn{3}{c}{**U mg/kg**} & \multicolumn{3}{c}{**Th mg/kg**} & \multicolumn{3}{c}{**K \%**} & \multicolumn{3}{c}{**I Index**} & \multicolumn{3}{c}{**Exhalation Rates of \({}^{220}\)Rn**} & \multicolumn{3}{c}{**Exhalation Rates of \({}^{220}\)Rn**} \\  & & & & & & & & & & & & & & & & & & \\ \multicolumn{11}{c}{} \\ Breccia & 1 & & & & & & & & & & & & & & & & & & & & & \\ Brick & 2 & 1.76 & 1.90 & 1.83 & 2.52 & 3.11 & 2.81 & 0.75 & 0.78 &  
Furthermore, the correlations between radon and thoron exhalation rates with uranium and thorium suggest some degree of interference between these elements (i.e., Th is a  
Figure 4: Box plot showing the statistics of radon (**bottom**) and thoron (**top**) exhalation rates for different rock types and building materials. significant input in radon prediction, and similarly, U is significant when predicting thoron exhalation rates. This interference indicates that interactions among these radionuclides could be affecting the observed exhalation rates. To better understand and model these interactions, advanced modeling techniques could be applied. By incorporating the effects of thorium and uranium interference, it may be possible to enhance the accuracy of predictions for radon and thoron exhalation rates. Such advanced models [27] could account for the combined influences of multiple radionuclides, leading to more refined assessments of radiation levels and improved monitoring methods.
## 3 Results and Discussion
### Electrochemical Stability  
The anodic stability of the FTFSI ionic liquid electrolytes (IL:LiTFSI mole ratio = 4:1) was investigated through cyclic voltammetries (CVs) carried out using carbon working electrodes. The results, depicted in Figure 5, evidence very low current flows (normalized with respect to the geometrical area of the carbon working electrode) in the first anodic scan. A remarkable reduction inthe current density (<10 \(\mu\)A cm\({}^{-2}\)) is observed in the following cycles up to the massive degradation (oxidation) of the electrolyte samples occurring around 4.8 V vs. the Li\({}^{+}\)/Li\({}^{\circ}\) redox couple), and no other feature is observed. This indicates an excellent anodic stability up to 4.8 V and suggests that the irreversible oxidation processes taking place during the first anodic scan are likely associated to impurities, even if in very low content, rather than to the ionic liquids and/or the LiTFSI salt [30]. The drastic decay of the current flow through the cell after the first anodic sweep also supports a high purity content of the investigated samples. Overall, the FTFSI electrolytes show electrochemical robustness against oxidation up to 4.8 V, this making them appealing for high operative voltage lithium battery systems.
## 3 System Model and Problem Formulation
### Overview  
As shown in Figure 2, LEO satellites fly above the surface of Earth at high speed, and connect satellite terminals (STs) of remote smart grids to the ground station. The urban terrestrial cloud (TC) is directly connected to the ground station via an optical fiber, and its transmission delay can be ignored. We assume that the access satellite is always available, and consider \(N\) STs denoted by \(\mathcal{N}=\{1,2,\ldots,N\}\) and a TC within the coverage of the same access satellite. Each ST sends their computational tasks to a visible LEO satellite via the terrestrial-satellite link. Particularly, aiming to protect their usage pattern privacy, STs send not only real tasks but also some random dummy tasks to confuse adversaries. After receiving tasks, the satellite decides whether to execute them directly by the local SatEC server or transmit them to the urban TC. For simplicity, we denote the wireless signal traveling from ST to its access satellite as the 1 st hop, and the 2 nd hop from access satellite to TC. We assume that the access satellite can measure channel states before deciding the offloading locations and allocating the bandwidth. The notations used throughout the paper are listed in the Nomenclature.  
Figure 2: Task offloading with privacy protection in satellite–terrestrial edge computing networks.
## 2 Experiment Setup  
A four-level ladder-type atomic configuration with \(5S_{1/2}-5P_{3/2}-57D_{5/2}-58P_{3/2}\) transition of \({}^{85}\)Rb atoms is employed in this experiment, which is shown in Figure 1a. The 780 nm probe laser drives \(5S_{1/2}(F=3)-5P_{3/2}(F^{\prime}=4)\) hyperfine transition, while the coupling laser excites atoms from \(5P_{3/2}(F^{\prime}=4)\) to \(57D_{5/2}\) Rydberg state. Then, a microwave E-field at a frequency of 11.36 GHz induces the \(57D_{5/2}\) to \(58P_{3/2}\) Rydberg transition.  
Figure 1b schematically depicts the experiment setup for the MCM scheme. The probe laser (780 nm) is provided by an external cavity diode laser (DL pro, Toptica, Munich, Germany), which is frequency locked on \(5S_{1/2}(F=3)-5P_{3/2}(F^{\prime}=4)\) transition by the saturation absorption spectroscopy (SAS) method. The frequency modulation (FM) is applied to the current modulation port of the probe laser. The coupling laser (480 nm) is generated by a frequency doubled amplified diode laser system (TA-SHG pro, Toptica, Munich, Germany), which is scanned across the \(5P_{3/2}(F^{\prime}=4)-57D_{5/2}\) hyperfine transition and real-time monitored by a wavelength meter (WS-7, Highfiness, Tubingen, Germany). The amplitude modulation (AM) of the coupling laser is achieved by an optical chopper (SR540, Stanford Research Systems, Sunnyvale, CA, USA) with frequency of 600 Hz before it is reflected by the dichroic mirror (DM). The coupling laser beam is overlapped with the main probe laser at a rubidium cylindrical vapor with 75 mm length and 25 mm diameter by the counter-propagating configuration, and the Doppler-broadening is effectively reduced.  
Then, a microwave E-field with 11.36 GHz is emitted by a standard gain horn antenna, and its direction is perpendicular to the propagation direction of the both lasers. The antenna is fed by a vector signal generator (SMB100A, Rohde & Schwarz, Munich, Germany), which is synchronized with a GPS disciplined oscillator with Rubidium timebase (GPSDO). The frequency of the microwave E-field can be precisely controlled with the accuracy of 0.1 Hz. The distance between the antenna and the Rb vapor cell is about 270 mm, which satisfies the far-field regime. A balanced detector (2307, New Focus, Santa Clara, CA, USA) is used to simultaneously detect the probe laser beam passing through the vapor and the reference beam, which can effectively cancel the noise from the laser itself. In addition, the output signal is demodulated by a lock in amplifier (SR830, Stanford Research Systems, Sunnyvale, CA, USA) with the corresponding modulation signal. With the help of these two methods, the \(1/f\) noise of the spectrum is significantly suppressed in this process, thus the signal-to-noise ratio of the spectrum has been effectively improved.
# An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level
## 4 NSGA-III Algorithm
### Conception Introduction
#### 4.1.3 Diversity Determination  
After selecting the reference point according to the method in Section 4.1.2, it is necessary to normalize the solution in the Pareto level to be selected so as to avoid the situation where an objective has too much influence on the result due to the difference in the number level of each objective. After normalization, the solutions at the Pareto level are connected with reference points, and the solutions are screened through the connection relationship so as to maintain the diversity of the solutions that enter the next iteration. The connection method between the reference points and the solutions at the Pareto level is shown in Figure 6:  
Figure 5: Reference point generation.  
Figure 6: Reference point selection.  
First, the reference point is connected with the origin to form a reference line (blue line). The point at the Pareto level selects the nearest reference line and connects (red lines and grey lines) with the reference point (green points) corresponding to the nearest reference line. As shown in Figure 6, point 1 is closer to reference point A than other reference points, so it is determined that point 1 is connected to reference point A, and point 2 is connected to reference point B.  
In the NSGA-III algorithm, diversity is the primary consideration for selecting the individuals for the next generation population, so when we select solutions for the next generation, the individuals of the reference point that have fewer connections can be selected preferentially.
## 3 Results and Discussion
### Shoreline Changes  
The analysis of shoreline changes was conducted in the coastal areas adjacent to Areas B and C (Figure 6; Shorelines 1 and 2, respectively), which are heavily influenced by the main stream of the Nakdong River. Figure 7 provides a detailed representation of the range of the shoreline changes.  
In the Nakdong River estuary, the sandbars change position in response to differences in tidal currents, and changes in the shoreline can considerably affect the marine ecosystem. In particular, the Eulsuk Island, which is situated immediately above the river mouth and includes a sandbar, is a highly valuable environmental and ecological habitat for migratory birds.  
The analysis of Shorelines 1 and 2 performed using AutoCAD (Figure 8) showed that the range of the coastal indentations is changing owing to differences between high and low tides. For Shoreline 1, the shape of the terrain changed based on the tide level, with the reference point being October 2020. This is considered to be associated with changes in the terrain caused by the typhoons MAYSAK and HAISHEN that passed through Republic of Korea in 2020. As shown in Figure 9, in October 2020, it was confirmed that Shoreline 2 was interrupted and a channel was formed.  
Furthermore, by analyzing the height distributions and changes in the planar position of the shoreline from a total of nine shoreline surveys, it was found that the height values of the same location increased from 2018 to April 2020. As shown in Figure 10, the maximum height values of both Shorelines 1 and 2 increased. This observation indicates that sediment deposition occurred every year, leading to an increase in the height of the shoreline. Figure 10 is a graph of the height values of the shoreline, showing the deviation from the low to the high point. This represents the height value by time of data acquisition to determine whether sedimentation or erosion occurred at that time.  
Figure 7: Study area of shoreline survey. Yellow dotted shapes show the range of shoreline measurements.  
Figure 8: Changes in the shoreline owing to typhoons.  
After October 2020, both Shorelines 1 and 2 showed decreasing trends in height, followed by increasing trends. This phenomenon was attributed to erosion caused by typhoons, as observed in the previous analysis of shoreline range. However, in October 2022, Shorelines 1 and 2 showed different patterns. Shoreline 1, where the industrial complex is located, showed erosion, whereas Shoreline 2 exhibited deposition. The industrial wastewater may have affected Shoreline 1, and with the end of the COVID-19 lockdowns, the discharge of industrial wastewater increased, causing an increase in flow velocity in the narrow channel, leading to erosion rather than deposition. In contrast, Shoreline 2 showed relatively slow deposition as suspended sediment flowed slowly through the wider channel, probably facilitated by the increased discharge from the Nakdong River estuary.  
Figure 9: Shoreline surveying using GPS (October 2018–October 2022).
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 3 Modeling of Vehicle Power System  
### Intelligent Alternator Model  
The intelligent alternator model mainly consists of three interrelated variables: output voltage (\(V_{G}\)), output power (\(P_{G}\)), and rotational speed of the engine (\(n\)). Their relation can be expressed as follows:  
\[P_{G}\ =\ P_{G}(n,V_{G}). \tag{1}\]  
The model of an intelligent alternator can be obtained by an experiment. Figure 4 shows the relation between the output power of the intelligent alternator, the rotational speed of the engine, and the output voltage of the intelligent alternator [9; 10]. Figure 5 presents the mapping characteristic curve of the engine, which shows the correlation between the rotational speed \(n\) of the engine, torque \(T\) of the engine, and fuel consumption rate.  
Figure 4: Intelligent alternator model.  
Figure 5: Mapping characteristic curve of the engine.
## 3 Fiducial Reference Measurements (FRMs)
### FRMs in Practice
There will be many more examples of networks and/or data sets that meet the FRM criteria, and so some effort is being established to provide an endorsement and cataloguing process. Although the majority of FRM data sets are likely to be sub-orbital in origin, this is not a prerequisite requirement. Targets such as the moon using models such as GIRO [21], the GSICS Implementation of the Robotic Lunar Observatory (ROLO) model, and LIME [22], the Lunar Irradiance Model of ESA (where the uncertainties and methods are well-established and documented), can be considered FRMs for specific applications, particularly stability monitoring. Similarly, the future SITSat missions such as TRUTHS [23] and Clarreo [24] will provide Top of Atmosphere (ToA), and in the case of TRUTHS, Bottom of Atmosphere (BoA), FRM data of a quality to not only validate but also calibrate other satellite sensors.