esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principle of DASH Interferometer
### Principle of DASH
\[\varphi(x)=\tan^{-1}\frac{\Im(I_{D})}{\Im(I_{D})}, \tag{5}\]  
From Equation (3), the phase \(\varphi(x)\) is given as follows:  
\[\varphi(x)=2\pi[4(\sigma_{0}+\Delta\sigma-\sigma_{L})\tan\theta_{L}x+2(\sigma_ {0}+\Delta\sigma)\times\Delta d]=\varphi_{0}(x)+\Delta\varphi(x), \tag{6}\]  
where \(\varphi_{0}(x)=2\pi[4(\sigma_{0}-\sigma_{L})\tan\theta_{L}x+2\sigma_{0}\times \Delta d]\) is the zero-wind phase, and \(\Delta\varphi(x)=2\pi[4\tan\theta_{L}x+2\Delta d]\Delta\sigma\) is the phase shift due to Doppler shift. The zero-wind phase \(\varphi_{0}\) can be removed using the phase-retrieval algorithm from Equation (6), and the Doppler phase shift \(\Delta\varphi\) can then be obtained. The Doppler velocity \(v\) can be calculated as follows [22]:  
\[v=\frac{\Delta\varphi\cdot c}{4\pi\Delta d\cdot\sigma_{0}}, \tag{7}\]  
where \(c\) is the speed of light. As expressed in Equation (7), an increase in the optical path offset can increase the phase shift caused by the same Doppler velocity. Similarly, increasing the optical path offset can increase the size of the interferometer. As expressed in the equation for phase shift, the spectral resolution \(\cdot\sigma\) is proportional to the phase shift, and this can be expressed as follows:  
\[\Delta\sigma=\frac{1}{4W\sin\theta_{L}}, \tag{8}\]  
where \(W\) is the beam width measured along each grating. The grating size can be increased to improve the spectral resolution, which also increases the size of the DASH interferometer. This is exemplified using the NIR DASH interferometer in this study. Compared with the visible DASH interferometer (size of 91.5 \(\times\) 85.5 \(\times\) 40 mm), the NIR DASH interferometer requires a higher spectral resolution to measure the same Doppler velocity. The size of the utilized NIR DASH interferometer is 110.5 \(\times\) 101.5 \(\times\) 50 mm.
## 4 Proposed Energy-Efficient Relay Mechanism  
In multihop wireless sensor networks, packets are transmitted from sources to the sink following the ad hoc communication paradigm. Intermediate nodes are deployed to relay packets in a hop-by-hop fashion, especially due to the expected limited communication range imposed by the employed wireless radio hardware, the energy constraints of the nodes and the characteristics of physical and MAC protocols. Incoming uncorrupted packets will be typically inserted into a FIFO queue and processed for relaying purposes by different protocol layers or following a cross-layer approach. Commonly, intermediate nodes will employ the same techniques for every incoming packet.  
We propose an energy-efficient packet relaying mechanism, where intermediate nodes relay packets according to their current energy resources and the packets' priorities. The proposed mechanism is composed of two different approaches, according to the use of DWT coding as a local QoS parameter.
## 4 NFV and SFC Frameworks Taxonomy
### Resource Allocation and Service Orchestration Frameworks
#### 4.1.9 Openbox Framework  
The OpenBox framework proposed by [PERSON] et al. [14] is a framework for the management, development, and deployment of vNFs. OpenBox abstracts network functions as packet processing graphs, which represent the behaviour of typical network functions, such as a firewall, DPI, and NAT. Processing graphs are implemented using the elements of the click router framework [77], specifically for firewall, web cache, load balancer, and IPS.  
To improve performance, OpenBox introduced a graph merging algorithm to merge the abstracted NF processing graphs, which reduces per-packet latency by minimising the number of processing blocks that need to be traversed by packets. The graph merging process starts by normalising the graphs into trees to avoid the convergence of paths, and the resulting trees are concatenated in the right order of packet processing by the network functions (to ensure chain-wide correctness of the processing pipeline).  
There are three major components that make up the OpenBox framework: OpenBox Applications, OpenBox Controller (OBC), OpenBox Service Instances, and OBIs, which constitute the OpenBox data-plane. Packet processing graphs are sent down to the OBI data plane (which can be implemented in hardware or software) from the controller, which in turn receives information about the packet processing capabilities of the OBIs. The communication between the OpenBox controller and the data plane is handled by the OpenBox protocol, which defines packet processing blocks for building vNFs.  
The controller is used by OpenBox to achieve multi-tenancy, smart NF placement, and NF scaling, in addition to steering traffic to the right vNF. This component of the OpenBox framework provides network application developers with a layer of abstraction for creating applications that have a specific packet processing graph and logic. The OpenBox framework was evaluated using service chain configurations and pipelined network function scenarios, with both scenarios yielding much higher throughput when compared with scenarios without OpenBox. To cater to the resilience of network functions, the authors merged multiple NFs together to create a single processing pipeline, which provides higher throughput to one of the network functions at off-peak times of the second NF in the merged processing graph, yielding a throughput that is 20% better than the naive merge approach.
# Using Graph Partitioning for Scalable Distributed Quantum Molecular Dynamics
## 3 Algorithms for Graph Partitioning Considered in Our Study
### Edge Cut Graph Partitioning
#### 3.1.1 METIS  
_METIS_[14] uses a three-phase multilevel approach to perform graph partitioning:  
1. Starting from the original graph \(G=G_{n}\) (where \(n=|V|\)), _METIS_ generates a sequence of graphs \(G_{n},G_{n-1},\ldots,G_{n^{\prime}}\) for some \(n^{\prime}<n\) to coarsen the input graph. The coarsening ends with a suitably small graph \(G_{n^{\prime}}\) (typically with \(n^{\prime}<100\) vertices).
2. An algorithm of choice is used to partition \(G_{n^{\prime}}\).
3. Using the sequence \(G_{n^{\prime}},\ldots,G_{n}\), the partitions are expanded back from \(G_{n^{\prime}}\) to the full graph \(G_{n}\).  
Additionally, _METIS_ employs a refinement algorithm such as the one of Fiduccia-Mattheyses [15] to improve the partitioning after each projection. This is necessary since the finer the partition, the more degrees of freedom it has during the uncoarsening phase. Several tuning parameters can be set in _METIS_, including the size of \(G_{n}\), the coarsening algorithm, and the algorithm used for partitioning \(G_{n}\).
## 4 DC-DC Converter Topologies for LEV Charging
### Off-Board Charging
#### 4.2.1 Isolated Converter
In [60], a quasi-resonant flyback converter is presented for charging purpose, which is also interleaved to reduce component size. This interleaved converter forms part of a 10 kW three-port charger, which is interfaced with a solar PV power supply and grid power supply. In this three-port charger, there exists a common DC-link, which facilitates exchange of power between the three sub-converters, and the given interleaved flyback converter forms the interface between the EV and the common DC link. The interleaved flyback converter also facilitates bidirectional operation, like the dual active bridge converter--thus achieving V2G capability. The presented converter is operated in quasi-resonant mode in both charging and V2G mode, which has numerous advantages, such as reduction in switching losses due to zero or low voltage switching (ZVS/LVS), reduction in turn off losses from the use of resonant capacitors, as they absorb excess energy during turn-off operation, and the reduction in RMS current due to boundary conduction operation of the converter. Ultimately, the presented interleaved flyback converter displayed a good peak efficiency of 98.8%. However, the component count is equal to the dual active bridge converter, and there is a scope of reduction in the same for use at lower power levels. The configuration of the given converter is shown in Figure 17.  
Figure 16: Modular dual active bridge-based charger [63]. A similar topology for DC charging of LEVs is shown in [4], which uses the same quasi-resonant interleaved flyback structure, albeit for a lower power level. This converter was implemented as part of an on-campus charging station for e-bikes, e-scooters, and e-carts. A significant aspect of this charger was that it was primarily fed from a 48 V DC nano-grid, which constitutes a standalone PV power supply, a backup battery, and a grid connection. In times of lower solar irradiance, power is fed from the backup battery, otherwise it is fed from the grid. Since the charging power is much less compared to [60] at 2.6 kW, a dual interleaved flyback converter is used for this application. The primary and secondary currents of the flyback converter are controlled through current mode control, and quasi-resonant switching is imposed on the switches. While the circuit design is much simpler compared to [60], this work lacks data on charging efficiency, and the charging  
Figure 17: Quasi-resonant interleaved flyback converter [60]. times of the listed LEVs--which range from 3 to 5 h--may be further optimized through increase in charging current.
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 4 Parametric Influence Study
### Effect of Current Velocity  
Figure 7 shows the structural dynamic response under different current velocities, with the same working conditions as given in Figure 3. The reduced velocity \(U_{r}\) is defined as: \(U_{r}=v_{0}/f_{id}\); \(A_{ci}/d_{i}\) is introduced as the dimensionless maximum amplitude of the cable at its mid-section; \(A_{\Omega}\) is the maximum vibration amplitude of the tube at its mid span; \(A_{\Delta\sigma}\) is the maximum stress variation in the cables.  
In Figure 7a, the largest amplitude of the cable occurs when \(U_{r}=5.00\)(flow velocity \(v_{0}=1.18\) m/s), which illustrates a notable resonance phenomenon, called as lock-in. The VIV suppression measures should be given enough attention in the stages of design. However, the wave force acting on the SFT tube has a finite influence on the anchor cables.  
As shown in Figure 7b, in the case that the current velocity is far away from the lock-in region, the calculation results are similar to those shown in Figure 6, which means the current effect is weak. Further comparing Figure 7a with Figure 7b, the lower inertia of the cables than of the tube will lead to a wider flow velocity range, which can excite intensive vibrations of the cables.
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 9 Conclusions and Future Trends  
Electrospun PLA-based fibrous structures have shown considerable potential in biomedical applications (wound healing, controlled drug delivery, scaffolds for tissue engineering of different tissue, such as bone, nerve, and cardiac tissues) as a result of their specific properties, such as the high surface area to volume ratio, high porosity, biocompatibility, biodegradability, and sustainability. The electrospinning technique has advantages since some physical properties of nanofibers, such as fiber diameter, surface morphology, and porosity can be optimized, simply by controlling electrospinning process parameters to mimic the specific tissue in the body. Nevertheless, this technique has some limitations for biomedical applications including difficulties in the fabrication of 3D scaffolds with macropores and scaling up into industrial-scale. New strategies are used to overcome such limitations including the development of novel electrospinning systems.  
For example, conventional electrospinning uses a needle spinneret for the generation of nanofibers and is associated with many limitations and drawbacks (i.e., needle clogging, limited production capacity, and low yield); needleless electrospinning (NLES) has been proposed to overcome these problems. Ultrasound-enhanced electrospinning (USES) is a novel NLES approach as a continuous orifice-less technique for the fabrication of nanofibers in which high-intensity focused ultrasound is used for the fabrication of nanofibers.  
Edge electrospinning is another needleless technique that provides a straightforward approach for scaled-up production of high-quality nanofibers through the formation of many parallel jets. Near-field electrospinning (NFES) is a micro-additive manufacturing technique that uses DC electric fields to continuously print micro- and nanofibers onto moving collectors. Several notable examples of electrospinning in combination with other techniques, such as microfluidics and additive manufacturing have been performed but these combinations and their innovative products are still in their infancy.  
Conceptualization, H.M., B.A., S.I. and S.D.; methodology, H.M.; investigation, H.M., B.A., S.I.; writing--original draft preparation, H.M., B.A., S.I; writing--review and editing, S.D.; visualization, H.M., B.A.; supervision, S.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Italian Ministry of University and Research (MIUR) under the framework of the project \"BIONUTRA\" (CUP B14I20001320005).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Simulation Scope** & \multicolumn{2}{c}{**10 \(\div\) 280 s**} & \multicolumn{2}{c}{**130 \(\div\) 185 s**} & \multicolumn{2}{c}{**136 \(\div\) 146 s**} \\ \hline  & \(W_{g\text{-}lo}\) & \(W_{g\text{-}est}\) & \(W_{g\text{-}lo}\) & \(W_{g\text{-}est}\) & \(W_{g\text{-}lo}\) & \(W_{g\text{-}est}\) \\ \hline CF \(T_{c}\) = 0.1 s & 0.621498 & 0.576179 & 1.349837 & 1.236206 & 1.963671 & 1.769485 \\ CF \(T_{c}\) = 0.5 s & 0.621498 & 0.401644 & 1.349837 & 0.780036 & 1.963671 & 1.061259 \\ CF \(T_{c}\) = 0.75 s & 0.621498 & 0.331582 & 1.349837 & 0.580706 & 1.963671 & 0.777876 \\ CF \(T_{c}\) = 1.0 s & 0.621498 & **0.318516** & 1.349837 & **0.520676** & 1.963671 & **0.693720** \\ CF \(T_{c}\) = 1.25 s & 0.621498 & 0.359823 & 1.349837 & 0.619029 & 1.963671 & 0.830859 \\ CF \(T_{c}\) = 1.5 s & 0.621498 & 0.420162 & 1.349837 & 0.754573 & 1.963671 & 1.010642 \\ CF \(T_{c}\) = 2.0 s & 0.621498 & 0.551540 & 1.349837 & 1.028800 & 1.963671 & 1.369436 \\ NCF & 0.621498 & 0.478083 & 1.349837 & 1.090888 & 1.963671 & 1.608529 \\ CCF & 0.621498 & 0.521293 & 1.349837 & 1.202050 & 1.963671 & 1.756727 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of computation of performance indices for delay = 1 s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Simulation Scope** & \multicolumn{2}{c}{**10 \(\div\) 280 s**} & \multicolumn{2}{c}{**130 \(\div\) 185 s**} & \multicolumn{2}{c}{**136 \(\div\) 146 s**} \\ \hline  & \(W_{g\text{-}lo}\) & \(W_{g\text{-}est}\) & \(W_{g\text{-}lo}\) & \(W_{g\text{-}est}\) & \(W_{g\text{-}lo}\) & \(W_{g\text{-}est}\) \\ \hline CF \(T_{c}\) = 0.1 s & 0.510954 & 0.491201 & 1.048553 & 0.995981 & 1.391555 & 1.304261 \\ CF \(T_{c}\) = 0.5 s & 0.510954 & 0.416304 & 1.048553 & 0.766302 & 1.391555 & 0.925406 \\ CF \(T_{c}\) = 1.0 s & 0.510954 & 0.424071 & 1.048553 & 0.702773 & 1.391555 & 1.053646 \\ CF \(T_{c}\) = 1.5 s & 0.510954 & 0.513063 & 1.048553 & 0.872885 & 1.391555 & 1.456678 \\ CF \(take into account different delay values between the reference signal and the estimated signals. Table 2 presents the obtained values of the performance index for a delay of 1 s. With the time constant \(T_{c}=1\) s, the best (smallest) performance index values for the CF were obtained (these values are distinguished by bold). It should be added that the analyses were also carried out for lower and higher values of delays, but no better results were obtained. For the NCF and CCF, the best fit values in the entire range of the analysed simulation were obtained taking into account the delay of 1.5 s (Table 3). In the case of limiting the scope of analyses to only one characteristic headwind gust, a slightly better fit was obtained when taking into account the delay of 1.75 s (Table 4).
# The Comparison of Electron Density between CSES In Situ and Ground-Based Observations in China
## 4 Comparing with Ionosonde Data
### Latitudinal Analysis
In general, the difference between the two datasets is a little larger in December compared to other months, when the correlation coefficient is relatively low.
## 3 The Effect of the Rootstock on Thinning  
The use of dwarf rootstocks such as 'Gisela 5' and Tabel(r) Edabriz in the production of commodity sweet cherries in the United States increased yield despite induced precocity and reduced tree vigour compared to the use of standard Mazzard rootstock. The increase in yield, however, resulted in smaller fruit, which was explained as due to inadequate crop load management [17; 34]. This effect was particularly acute in the cultivation of self-fertile cultivars such as 'Lapins', 'Sweethart', and 'Summit' [62; 93]. The best solution used to improve the fruit size on trees growing on dwarf rootstocks was to remove of 30 to 50% of fruiting spurs [65]. Thinning can also be optimised by measuring the number of fruit to the branch cross-sectional area ratio. The best fruit quality was obtained with 10 sweet cherries per cm\({}^{2}\) of branch cross-sectional area [19; 35]. The selection of the rootstock and thinning have a positive effect on many fruit quality parameters [36; 40].  
Rootstock can affect the yield regardless of vigour [42], but the rule according to which higher yield is associated with smaller fruit is rootstock-independent [10; 43; 94; 95; 96]. In a situation where the effectiveness of chemical thinning varies from year to year, a rational solution, especially for self-fertile cultivars, is the use of an appropriate rootstock that allows achieving acceptable yields of good quality cherries [17]. Comparative studies carried out on self-fertile 'Lapins' sweet cherry trees showed that the highest yield per tree and yield efficiency with relatively large fruit (-7 g) were obtained on trees grown on Gi 154/7 and Gisela 4 rootstocks. Large cherries were harvested from trees growing on P-HL-A and Gi 523/02 rootstocks--7.7 and 7.6 g, respectively, but the total yield for the research period was significantly lower. Small fruit (6.1 g) and low yield were obtained on trees growing on the popular Gisela 5 dwarf rootstock [97]. Other studies show that the size of fruit from trees grafted on Gisela 5 depends on crop size and weather conditions [98; 99]. In addition, subsequent experiments with the use of various rootstocks indicate that in addition to the rootstock and cultivar, soil, and climatic conditions are very important factors shaping the fruit quality [100; 101]. Rootstock affects the number of bouquet buds and the number of flowers in the inflorescence [102]. The use of Gisela 5 dwarf rootstock significantly increased the number of flower buds compared to the more strongly growing Colt rootstock [103]. In addition, a comparison of the Edabriz and F12/1 rootstocks indicated that the number of flowers and spurs on branches of trees grown on Edabriz was larger, whereas the size of fruit was smaller than on trees grown on the strongly growing F12/1 [104].
## 3 3D High-Speed Wheel/Rail Rolling Contact Finite Element Model  
### Overview of the Finite Element Model  
Figure 7 shows a 3D high-speed wheel/rail rolling contact finite element (FE) model and simulates a vehicle-track system on the high-speed railway line. Only half of a wheelset and track are simulated since the relative longitudinal and vertical planes of a vehicle-track system are symmetric. All components above the primary suspension of a vehicle are simplified as mass points and connected with wheels through the primary suspension. The high-frequency feature between the wheelset and track was fully considered through 3D modeling. It can be approximately assumed that the acceleration of the outer end of the wheel axle is consistent with that of the axle box. The track is a non-ballast track, which was modeled with consideration of the rails, fasteners, slab, and cement asphalt (CA) mortar. The fastening system contains support stiffness and damping that are relevant to the dynamic behavior. The track length of 15.6 m was considered in the model to eliminate the influence of boundary conditions.  
Figure 6: Cumulative distribution function (CDF) of 17 measured ABAs induced by rail welds.  
Figure 7: 3D wheel/rail rolling contact fine element model.  
The 3D wheelset and rail in real geometry were meshed by an 8-node hexahedral element. The rail was the CHN60 model with the rail cant set as 1:40. \"Face-to-face\" contact algorithm simulation based on the penalty function method was used for solving contact behaviors among rails. A non-uniform grid was used when meshing the wheel and rail in order to reduce the calculation time without losing precision in the solution. The finest contact zone (element size was 1 mm) was used for solving wheel/rail contact; the farther the contact zone, the larger the grid. The track slab and CA mortar were also stimulated by the 8-node hexahedral elements. The primary suspension and rail fastener of the vehicle were represented by a distributed spring and damping unit. The total number of nodes and elements were 1.46 and 1.29 M, respectively.  
The parameters of the model are listed in Table 1. Taking into account the elastic shakedown response of the wheel and rail's material in the field, a linear elastic material model was used for the wheel and the rail, though more accurate material models can be easily introduced. For an effective computation, the slab and mortar materials were also assumed to be linear elastic.