esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Various surface-wave exploration methods have become increasingly important tools in investigating the properties of subsurface structures. Inversion of the experimental dispersion curves is generally an indispensable component of these methods. Accurate and reliable calculation of partial derivatives of surface-wave dispersion curves with respect to parameters of subsurface layers is critical to the success of these approaches if the linearized inversion strategies are adopted. Here we present an open-source MATLAB package, named SWPD (Surface Wave Partial Derivative), for modeling surface-wave (both Rayleigh- and Love-wave) dispersion curves (both phase and group velocity) and particularly for computing their partial derivatives with high precision. The package is able to compute partial derivatives of phase velocity and of Love-wave group velocity analytically based on the combined use of the reduced delta matrix theory and the implicit function theorem. For partial derivatives of Rayleigh-wave group velocity, a hemi-analytical method is presented, which analytically calculates all the first-order partial differentiations and approximates the mixed second-order partial differentiation term with a central difference scheme. We provide examples to demonstrate the effectiveness of this package, and demo scripts are also provided for users to reproduce all results of this paper and thus to become familiar with the package as quickly as possible.  
Keywords:phase velocity; group velocity; surface waves; love waves; rayleigh waves; partial derivatives; dispersion curves; open-source code; MATLAB package +
Footnote †: journal: Applied Physics Letters
# Estimating Significant Wave Height from SAR with Long Integration Times
## 2 Estimating Significant Wave Height from SAR with Long Integration Times
### Wavelength and Propagation Direction Calculation of \(n\)th Ocean Waves  
Since the parameters such as wavelength and propagation direction are required in estimating significant wave height, the equations of the \(n\)th wavelength and propagation direction are introduced below. The 180\({}^{\circ}\) ambiguity of wave propagation direction is removed through the optimum focus setting directly without any additional processing.
## 3 Results and Discussion
### Spray Area  
Figure 18 shows the comparison of SA between circular and elliptical nozzles in XZ and YZ planes. In the XZ plane, the SA of elliptical nozzles exhibited minimal variation from that of the circular nozzle at the beginning and end of spraying. During the majority of the spraying process, elliptical nozzles demonstrated a larger SA than their circular counterparts. Additionally, the average SA of elliptical nozzles with aspect ratios of 1.25 and 1.5 demonstrated an increase compared to the SA of the circular nozzle, reaching 9.39% and 13.05%, respectively. In the YZ plane, the SA of elliptical nozzles was consistently less than that of a circular nozzle. Furthermore, the average SA of elliptical nozzles with aspect ratios of 1.25 and 1.5 decreased by 17.28% and 14.38%, respectively, compared to that of the circular nozzle during the spray development phase. At the end of spraying, the elliptical nozzle with a larger aspect ratio had the largest SA in the XZ plane and was second only to the circular nozzle in the YZ plane. As illustrated in Figure 19, at equivalent STP, compared with circular nozzle, the SA of both elliptical nozzles was greater in the XZ plane. Conversely, in the YZ plane, the SA of the elliptical nozzle with a smaller aspect ratio was smaller, but the SA of the elliptical nozzle with a larger aspect ratio remained larger, compared with the circular nozzle.  
Figure 15: Average SCA of circular and elliptical nozzles in different planes.  
Figure 16: Pressure distribution in the XZ plane for circular and elliptical nozzles.  
Figure 17: Vorticity distribution in the XZ plane for circular and elliptical nozzles at ASOI of 1.2 ms.  
Figure 19: _Cont._  
Figure 18: Spray area (SA) for circular and elliptical nozzles: (**a**) SA in the XZ plane; (**b**) SA in the YZ plane.
## 2 Results and Discussion
### Advantages of an MIR Waveform Synthesizer for Generating an Isolated Attosecond Pulse
The spectral modulation is due to interference between short and long trajectories, where the trajectory can be selected by placing the medium after or before the laser focus [46]. Comparing the HHs generated by the electric field \(E\!f_{s}\) with \(E\!f_{1}\) (one color 1), the cutoff is markedly extended. For HHs with the same photon energies (below 350 eV), the conversion efficiency (from the driving laser to the HHs) is one order higher. Compared with the HH generated by the field \(E\!f_{2}\) (one color 2), which generates the same cutoff as the two-color synthesizer, the conversion efficiency is \(\sim\)2.5 times higher for \(E\!f_{s}\), which is due to the higher instantaneous ionization rate caused by the increased electric field near \(-\)2.6 fs for \(E\!f_{s}\). From these results, it is clear that a single-cycle laser field is not an optimum waveform for IAP generation from the viewpoints of reducing the attochirp and increasing the conversion efficiency. In contrast, a waveform synthesizer is helpful for efficiently generating shorter IAPs by decreasing the attochirp.
# SPME Method Optimized by Box-Behnken Design for Impact Odorants in Reduced Alcohol Wines
## 3 Results and Discussion
### Effect of Ethanol Content Reduction  
The overall effect of dealcoholization by RO-EP treatment on the wine's volatile composition includes two distinct effects: (i) the RO-EP effect and (ii) the ethanol content reduction effect [7]. To quantify the effect arising from the ethanol content reduction, a comparison between the reconstituted and 5% dealcoholized wines, was carried out. GC-MS analyses on reconstituted samples were performed after 24 h and 14 days of storage at 1.8 \({}^{\circ}\)C. If the ethanol content had either a suppressing or enhancing the effect on analyte responses, the addition of exogenous ethanol to the 5% \(v/v\) dealcoholized samples should result in a significant difference between the GC-MS results of reconstituted and 5% reduced alcohol wines.  
As indicated by Tables 3 and 4 for the Shiraz and Chardonnay wines, respectively, the concentration of many analytes was lower in the reconstituted wines compared to that of the 5% \(v/v\) ethanol samples. These values must be viewed in terms of the minor dilution of around 7-8% arising from the ethanol addition to reconstitute the wine to the original ethanol concentration. The concentration of ethyl decanoate in the reconstituted Shiraz and Chardonnay wines significantly decreased after 24 h storage by 88% and 79%, respectively, compared to the corresponding 5% \(v/v\) ethanol samples. ANOVA, however, shows that the number of volatile compounds that decreased in the headspace of reconstituted wines was different between the Chardonnay and Shiraz wine varieties, and slightly higher after 24 h (10/23 and 12/23 for the Shiraz and Chardonnay wines, respectively) than after 14 days (8/23 and 11/23 for the Shiraz and Chardonnay wines, respectively) storage. A negative relationship between ethanol and other volatile compounds is consistent with the previous studies for other wines or model solutions [15; 16; 34]. Differences in the number of affected analytes between Shiraz and Chardonnay wine varieties likely arise from a different composition in wine non-volatile matrix, including an expected higher presence of phenolics in red than in white wine.  
While several volatile compounds exhibited a negative relationship with ethanol, many others did not show any appreciable interaction. For example, the concentration of ethyl butyrate, ethyl-2-methyl butyrate, and ethyl-3-methyl butyrate did not change at any storage extent (i.e., 24 h, 14 days) for both Shiraz and Chardonnay reconstituted wines compared to the 5% \(v/v\) ethanol samples. In the case of vanillin only, its concentration in the wine headspace of the reconstituted Shiraz and Chardonnay wines increased after 24 h storage by 60% and 72%, respectively. However, no differences were observed after 14 days of storage.  
Overall, these results indicate that the reduction of the ethanol content in the Shiraz and Chardonnay wines significantly affected the headspace composition of wine, however, the nature of changes on the different classes of analytes is not specific.
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 2 V2X Communications in 3 GPP
### V2X Communication Modes and Entities  
Four V2X communication modes are identified by 3 GPP [5], with different scope and encompassing different communication end-points, as illustrated in Figure 1 and summarized in Table 1.  
_Vehicle-to-vehicle_ (V2V) and _vehicle-to-pedestrian_ (V2P), respectively, cover direct communication between vehicular user equipments (VUEs) and between VUEs (for the sake of simplicity, in the following, we refer to VUEs as UEs only) and vulnerable road users (VRUs), such as pedestrians, bikers, and motorcyclists. _Vehicle-to-infrastructure_ (V2I) refers to communications between vehicles and the roadside infrastructure, e.g., a road-side unit (RSU). _Vehicle-to-network_ (V2N) allows UEs to interact with a server specifically introduced in the 3 GPP architecture to support V2X applications.  
Such a server is referred to as _V2X Application Server_ (AS) and gathers information about the road and traffic conditions from several sources (UEs, RSUs, infrastructure modes such as traffic lights, etc.). The V2X AS could be deployed outside the 3 GPP network (e.g., by the transportation authority) or at the Radio Access Network (RAN) premises, according to the Multi-Access Edge Computing (MEC) paradigm [6].  
In the 3 GPP V2X architecture, a central role is undertaken by the _V2X Control Function_, implemented in the core network, which is responsible for enforcing V2X network-related actions. In particular, it provides the UEs with specific configuration parameters that allow them using V2X communications in a given network, under coverage of the eNodeB as well as when they are not served by the cellular infrastructure, i.e., in _out-of- coverage_ scenarios.
# Specific Detection of PE-Included Vesicles Using Cyclic Voltammetry
## 1 Introduction  
Cinnamycin, a 19-amino acid tetracycline peptide, is a globular electrically neutral peptide capable of forming an equramolecular complex with phosphatidylcholine-ylethanolamine (PE) specifically [1, 2]. The specific formation is generated due to its unique structure: most of the hydrophobic amino acids are positioned at one side of the peptide, whereas the hydrophilic one is located on the other side. Both the headgroup and the hydrocarbon chains of PE are crucial for interaction with the peptide [3]. The hydrophilic side of the peptide binds to the headgroup, and then the binding is enhanced by hydrophobic interaction [2]. The specificity has led the cinnamycin to be used for the investigation of PE-related mechanisms such as apoptosis, cell division, migration and tumor vasculature [4, 5, 6, 7, 8]. Furthermore, this peptide has been considered as not only a potential probe for disrupting PE-containing membranes, such as those of cancer cells and bacteria, but also an alternative treatment for atherosclerosis [9, 10]. The physical characteristics of its specificity, i.e., binding affinity, thermodynamic properties and structural changes, have been investigated using enzyme-linked immuno-sorbent assay (ELISA), isothermal titration calorimetry, small-angle X-ray scattering, transmission electron microscopy and surface plasmon resonance [2, 11, 12, 13].  
Spherical phospholipid bilayers, named vesicles, are widely-used as a model of the cell surface and also for investigating molecular events in membranes because the preparation methodology for the lipid bilayers has been well established and highly sensitive analytical techniques can be applied to investigate the events [14, 15, 16, 17, 18]. The vesicles have also been important for biomedical research of cell recognition, antimicrobial peptide activity, drug delivery and disease diagnosis [19, 20, 21, 22]. Cyclic voltammetry (CV) has proved a valuable tool for direct, label-free, and noninvasive detection of surface binding in real-time at pM-scale sensitivity [23]. Surface binding includes the molecular adsorption, nucleic-acidhybridization and antibody-antigen interaction [24; 25; 26]. In this work, we aim to determine the characteristics of the specific binding between the cinnamycin and the vesicle with an outer layer of PE inclusion using CV. These characteristics may provide a platform for the cinnamycin-based development of therapy or diagnosis.
## 5 Analysis of the Numerical Results
### Transverse Settlement of the Ground Surface
#### 5.2.2 When C/H = 0.606  
With the decline of the C/H ratio to 0.606, the difference in the settlement caused by the \"hty\" and \"htm\" conditions was also widened, and the maximum surface deformation at line 1 could be up to 13.4 mm, as shown in Figure 13(a). However, under a fixed C/H ratio, the maximum settlements at lines 1-3 decreased sequentially with the increasing spacing between the tunnels. As the overburden of the new tunnel became shallower, the intersection area between the existing and new tunnels shrunk. Thus, the difference in settlement resulting from the existing tunnel being backfilled for line 2 was weakened compared to Figure 10(b). Moreover, as shown in Figures 13(b), 13(b) and 13(b), the symmetry axes of the settlement curves were no longer obviously affected by the existing tunnel. The settlement differences in Figures 13(c), 13(c) and 13(c) were even less apparent due to the tunnel spacing. Figure 15 shows a similar law to Figure 13, despite the symmetry axes of all settlement curves coinciding with the tunnel centerline, and their deviation distance from the overlying road center was 0.75 m. In Figure 16, such a deviation increased to 1.5 m.  
Figure 13: Longitudinal surface settlement of model C-C with D/W = 0.124.  
Figure 14: Longitudinal surface settlement of model D-D with D/W = 0.378.  
Figure 15: Longitudinal surface settlement of model E-E with D/W = 0.251.  
According to the above research results, the line 1 region was a sensitive zone affecting the surface settlement. Thus, a comparison of Figures 13(a), 14(a) and 15(a) revealed that the settlement changed indistinctly with various surface loads in the \"htm\" condition of C/H = 0.606, showing a trend opposite to that at C/H = 0.764. This was because the backfilling of the existing tunnel only affected the span of new tunnel when C/H = 0.764. In contrast, when C/H = 0.606, the spatial effect of the existing tunnel backfilling on the new tunnel changed from the mere effect on the span to the joint effect on the height and span. The effective height of the new tunnel could be considered invariant when the existing tunnel was not backfilled such that the change in settlement was indistinct. In the \"hty\" condition, however, the height turned into a sensitive influential factor on the line 1 region. Further comparison of Figure 13(b,c, Figure 14(b),c and Figure 15(b),c found the largest settlement with model E-E under the same conditions. Thus, it could be inferred that the surface settlement was controlled jointly by the C/H and D/W ratios, and the difference in the control weight between the two meant that the settlement results presented different laws. Therefore, the correctness of the settlement law described in this section needs further verification by integrating the simulation results into the C/H = 0.449 condition.
## 3 Results
### Technical Performance Results
#### 3.1.6 Porosimetry  
The pore structure of the hydrated paste mixtures (per Table 5) was characterised using mercury intrusion porosimetry with the aim of learning more about the effects of partial replacement of Portland cement with the studied slags. Figure 10a,b shows the pore volume of the pastes as a function of the pore entry diameter for the 28- and 90-day old samples, respectively. Figure 10a clearly indicates that the partial replacement of Portland cement with either slag type or quartz powder resulted in both coarsening of the pore structure and increase in porosity up to the age of 28 days. However, a considerable refinement took place in the slag-blended mixtures at later ages, resulting in a decrease in their porosity and pore size parameters, while the mixture containing quartz powder (P_QP) experienced significantly less refinement in pore structure between 28 and 90 days. The pore structure parameters of the 28- and 90-day pastes are tabulated in Table 8. It was observed that the slag-blended mixtures had lower porosity compared to P_QP both at 28 days (despite their coarser particle size according to Table 2), and the difference became  
Figure 8: The bound water (**a**) and portlandite (**b**) contents of the paste mixtures up to the age of 90 days.  
more pronounced by the age of 90 days. This can be interpreted as the long-term reactivity of the slags as cementitious materials. The least refinement between 28 and 90 days took place for P_TS2, which could be associated with the considerably higher crystalline content of TS2 compared to TS1 and TS3.  
Figure 10: Pore volume of paste mixtures as a function of intruded pore diameter (**a**) at 28 days and (**b**) at 90 days.  
Figure 9: The slag degree of reaction in different slag-blended pastes up to the age of 91 days.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline
**Parameter** & **F.PC (St days)** & **F.Q (P/G days)** & **P.T (St days)** & **P.T (St days)** & **P.T (St days)** & **P.T (St days)** & **P.T (St days)** & **P.T (St 0 days)** & **P.T (St 00 days)** & **P.T (St 00 days)** & **P.T (St 00 days)** & **P.T (St 00 days)** \\ \hline Total cumulative volume (\(m^{2}/g\)) & 71 & 114 & 105 & 107 & 94 & 70 & 96 & 67 & 86.5 & 88 \\ Total specific surface area (\(m^{2}/g\)) & 163 & 244 & 201 & 195 & 166 & 129 & 21.4 & 11.1 & 17.3 & 95.5 \\ Average point (\(m^{2}/g\)) & 159 & 231 & 180 & 192 & 16.2 & 127 & 16.9 & 124.4 & 16.9 & 11.9 \\ The Small peak energy radius (\(m\)) & 159 & 274 & 270 & 258 & 263 & 181 & 170 & 230 & 1.85 & 21.9 \\ Small peak energy radius (\(m\)) & 127 & 125 & 220 & 29.9 & 22.2 & 127 & 12.1 & 16.5 & 2.23 & 18.5 \\ \hline \end{tabular}
\end{table}
Table 8: Characteristic pore structure parameters of the hardened paste mixtures at 28 and 90 days.
## 3 Results and Discussion
### Optimization of Single Extraction Condition
#### 3.3.3 Effect of Microwave Power  
Optimization of the microwave power is crucial in ensuring efficient extraction, because the microwave energy significantly affects molecular interactions between the target compounds and ILs. Figure 5c shows that the yields of CPT, HCPT, VCS-LT, and EOs increased rapidly with increasing microwave power from 300 to 500 W. The yields of the target compounds decreased slightly at microwave powers higher than 500 W. A microwave power of 500 W was therefore used for further optimization experiments.  
Based on the above experiments, the optimum IL-UMASDE conditions were 0.75 M [C\({}_{\textrm{s}}\)mim]Br as the extraction solvent, a liquid-solid ratio of 12 mL/g, an extraction time of 30 min, and a microwave power of 500 W.
###### Abstract  
The objective of these studies was to determine the dependence of the distribution of the microwave field into a grain layer and the efficiency of energy transfer from the magnetron into the grain layer, via a waveguide, on the moisture content. The subject of the research was a semicircular waveguide with slot radiators that supply a microwave field to the grain surrounding the waveguide. The change in the directional diagram of the microwave field in the longitudinal direction of the waveguide was studied using CST Microwave Studio 2019 software. It was found that the change in the moisture content of the processed grain leads to a significant change in both the radiation and total efficiency values of the waveguide. For instance, the radiation efficiency of the waveguide decreases by 15.1% (from 68.729 dB to 58.294 dB) when the moisture content of the processed grain increases from 14% to 26%. The total radiation efficiency also decreases (from 11.27 dB to 21.7 dB). In this case, not only the value of the radiation efficiency but also the shape of its dependence on the magnetron radiation frequency change. The results of this study enable the formulation of a requirement for a selective approach to the alignment of emitting waveguides depending on the moisture content of the processed grain. Data were obtained on the change in the Q-factor for resonators in which grain layers are processed, depending on the variation in grain moisture content. The research results suggest that when designing microwave convective zones for grain processing, the quality factor of the resonator should not be considered as the primary parameter. The main focus should be on maximizing the radiation efficiency of the waveguides.  
microwave field; microwave-active zone; grain drying; moisture content in grain; directional diagram; radiation efficiency
## 3 Methodology
### Standard Deviation Elliptic Model  
The standard deviation ellipse model (SDE) can accurately reveal the overall characteristics of the spatial distribution of geographic elements. It is based on the spatial location and spatial structure of the research object, quantifying the spatial distribution of geographic elements in terms of centre of gravity, transfer direction, and other characteristics, and can reveal the spatial distribution and spatial and temporal evolution of the construction land's centre of gravity [34; 35]. The SDE model was first proposed by [PERSON] to reveal the spatial distribution characteristics of geographical elements [36; 37; 38; 39] and is now widely used in sociology, urban planning, ecology, and other fields [40; 41; 42]. The parameters of the standard deviation ellipse mainly include the centre of gravity of the ellipse, the long and short axes of the standard deviation ellipse, and so on. The centre of gravity of the standard deviation ellipse is chosen to represent the centre of gravity of urban construction land, and the centre of gravity of the standard deviation ellipse (\(\overline{X}_{w}\), \(\overline{Y}_{w}\)) is as follows.  
\[\overline{X}_{w}=\frac{\sum_{i=1}^{n}w_{i}x_{i}}{\sum_{i=1}^{n}w_{i}},\overline {Y}_{w}=\frac{\sum_{i=1}^{n}w_{i}y_{i}}{\sum_{i=1}^{n}w_{i}} \tag{5}\]  
where (\(x_{i}\), \(y_{i}\)) denotes the spatial location of the study object, and \(w_{i}\) denotes the corresponding weights.