esa-sceva/satcom-chunk-collection huggingface dataset:
# Mutational Chemotaxis Motion Driven MdpI for Engineering Applications
## 4 Discussion of Experimental Results
### Impact of Optimization Strategies on MFO
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **F1** & \multicolumn{2}{c}{**F2**} & \multicolumn{2}{c}{**F3**} \\ \cline{2-6}  & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** \\ \hline HMCMMFO & \(7.45008\times 10^{3}\) & \(7.73709\times 10^{3}\) & \(2.08253\times 10^{2}\) & \(1.54018\times 10^{1}\) & \(3.00010\times 10^{2}\) & \(1.92062\times 10^{-2}\) \\ CLSGMFO & \(9.77833\times 10^{3}\) & \(9.27666\times 10^{3}\) & \(1.54528\times 10^{12}\) & \(5.62911\times 10^{12}\) & \(4.41481\times 10^{3}\) & \(2.46828\times 10^{3}\) \\ LGCMFO & \(9.54047\times 10^{3}\) & \(7.48239\times 10^{3}\) & \(3.17117\times 10^{12}\) & \(1.05059\times 10^{13}\) & \(7.63227\times 10^{3}\) & \(3.40084\times 10^{3}\) \\ NMSOLMFO & \(1.00029\times 10^{2}\) & \(1.19294\times 10^{-1}\) & \(2.00000\times 10^{2}\) & \(2.52539\times 10^{-7}\) & \(3.00000\times 10^{2}\) & \(7.53173\times 10^{-10}\) \\ QSMFO & \(7.76493\times 10^{5}\) & \(4.67365\times 10^{5}\) & \(1.65263\times 10^{14}\) & \(8.76662\times 10^{14}\) & \(1.65277\times 10^{3}\) & \(5.43654\times 10^{2}\) \\ CMFO & \(3.12151\times 10^{8}\) & \(7.85453\times 10^{8}\) & \(4.03821\times 10^{38}\) & \(2.20987\times 10^{39}\) & \(1.07744\times 10^{5}\) & \(3.18599\times 10^{4}\) \\ WEMFO & \(1.26782\times 10^{4}\) & \(8.16879\times 10^{3}\) & \(1.30928\times 10^{16}\) & \(4.91342\times 10^{16}\) & \(9.34514\times 10^{3}\) & \(4.04733\times 10^{3}\) \\ \hline  & **F4** & **F5** & \multicolumn{2}{c}{**F6**} \\ \cline{2-6}  & **AVG** & **STD** & **AVG** & **STD** & **AVG** & **STD** \\ \hline HMCMMFO & \(4.83921\times 10^{2}\) & \(1.00659\times 10^{1}\) & \(5.67565\times 10^{2}\) & \(1.58153\times 10^{1}\) & \(6.05821\times 10^{2}\) & \(1.49235\times 10^{0}\) \\ CLSGMFO & \(4.92717\times 10^{2}\) & \(3.18428\times 10^{1}\) & \(6.44607\times 10^{2}\) & \(3.75846\times 10^{1}\) & \(6.22085\times 10^{2}\) & \(1.14138\times 10^{1}\) \\ LGCMFO & \(4.96847\times 10^{2}\) & \(3.48442\times 10^{1}\) & \(6.49694\times 10^{2}\) & \(3.90103\times 10^{1}\) & \(6.15888\times 10^{2}\) & \(9.33071\times 10^{0}\) \\ NMSOLMFO & \(4.17466\times 10^{2}\) & \(2.80568\times 10^{1}\) & \(6.10793\times 10^{2}\) & \(3.63679\times 10^{1}\) & \(6.06714\times 10^{2}\) & \(2.26585\times 10^{0}\) \\ QSMFO & \(4.81483\times 10^{2}\) & \(4.03995\times 10^{0}\) & \(7.06033\times 10^{2}\) & \(2.19196\times 10^{1}\) & \(6.41868\times 10^{2}\) & \(1.87151\times 10^{1}\) \\ CMFO & \(5.76424\times 10^{2}\) & \(6.19701\times 10^{1}\) & \(7.29219\times 10^{2}\) & \(6.06530\times 10^{1}\) & \(6.52386\times 10^{2}\) & \(8.92716\times 10^{0}\) \\ WEMFO & \(4.86804\times 10^{2}\) & \(2.04131\times 10^{1}\) & \(6.69370\times 10^{2}\) & \(5.71347\times 10^{1}\) & \(6.30716\times 10^{2}\) & \(1.11558\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of MFO-variant algorithms.
###### Abstract  
The optical vortex (OV) has drawn considerable attention owing to its tremendous advanced applications, such as optical communication, quantum entanglement, and on-chip detectors. However, traditional OV generators suffer from a bulky configuration and limited performance, especially in the ultraviolet range. In this paper, we utilize a large bandgap dielectric material, niobium pentoxide (Nb\({}_{2}\)O\({}_{5}\)), to construct ultra-thin and compact transmission-type metasurfaces to generate and detect the OV at a wavelength of 355 nm. The meta-atom, which operates as a miniature half-wave plate and demonstrates a large tolerance to fabrication error, manipulates the phase of an incident right-handed circular polarized wave with high cross-polarized conversion efficiency (around 86.9%). The phase delay of \(\pi\) between the orthogonal electric field component is attributed to the anti-parallel magnetic dipoles induced in the nanobar. Besides, focused vortex generation (topological charge \(l\) from 1 to 3) and multichannel detection (\(l\) from \(-\)2 to 2) are demonstrated with high efficiency, up to 79.2%. We envision that our devices of high flexibility may have potential applications in high-performance micron-scale integrated ultraviolet nanophotonics and meta-optics.  
Keywords:dielectric metasurface; polarization conversion; optical vortex; niobium pentoxide +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
Current cardiovascular magnetic resonance (CMR) metrics (regurgitation volume and fraction) can assess MR accurately; however, they have notable limitations, such as variability in the metrics due to patient-specific anatomical differences and inconsistencies in imaging protocols, leading to measurement discrepancies that affect clinical decision-making. It is important to note that determining the timing of mitral valve surgery is still complicated by the lack of good markers of LV deterioration in patients with mitral valve surgery. Many investigators recommend watchful waiting, particularly in asymptomatic patients with preserved LV function. Thus, it is concerning that we cannot predict when the LV function (LVEF and end-systolic dimension) will decline. Traditional metrics (regurgitant volume and fraction) often fail to account for the complex hemodynamic forces and flow patterns involved in MR, limiting their ability to provide a comprehensive assessment and prognostic information [23; 24]. To the best of our knowledge, there is currently no research evaluating HDF among the MR cohort. Consequently, our study intends to utilize 4D-flow-derived HDF in MR patients with preserved EF to assess the MR severity as well as the relationship between LV function and derived HDF. The method used for HDF analysis in this study is based on the approach developed by [PERSON] et al. [21]. Here, we extend this methodology by introducing advanced parameters and applying them to MR patients with preserved EF. We hypothesize that HDF could serve as a biomarker to differentiate between MR patients and healthy controls and potentially aid in stratifying MR severity levels. We tested the hypothesis with a retrospective cross-sectional study design.
# Continuous-Thrust Circular Orbit Phasing Optimization of Deep Space CubeSats
## 3 Spacecraft Dynamics and Trajectory Optimization
### Numerical Results: Classical Case of the Circular Orbit Raising or Lowering
Black line \(\to m\); magenta line \(\to\tau\); blue line \(\to\hat{\mathbf{a}}\cdot\hat{\mathbf{r}}\); green line \(\to\hat{\mathbf{a}}\cdot\hat{\mathbf{\theta}}\); black circle \(\to\) start point; red square \(\to\) arrival point. (**a**) Case of an orbit lowering with \(r_{f}=0.8\,\mathrm{AU}\); (**b**) case of an orbit raising with \(r_{f}=1.2\,\mathrm{AU}\). Figure 7: Optimal circle-to-circle coplanar orbit transfer of a MTC when the radius of the parking orbit is \(r=1\,\mathrm{AU}\). Radial distances are in astronomical units. Black line \(\to\) rapid transfer trajectory; blue line \(\to\) parking circular orbit; red line \(\to\) target circular orbit; black circle \(\to\) start point; red square \(\to\) arrival point; orange circle \(\to\) the Sun. (**a**) Case of an orbit lowering with \(r_{f}=0.8\,\mathrm{AU}\); (**b**) case of an orbit raising with \(r_{f}=1.2\,\mathrm{AU}\).
# Using Virtual Soundwalk Approach for Assessing Sound Art Soundscape Interventions in Public Spaces
## Materials and Methods
### Selected Urban Open Spaces
#### 2.1.1 Obala Petra Kresimira IV, Zadar (Location A)  
Obala Petra Kresimira IV seaside promenade is situated along the southern waterfront of the cca. 54 ha large Zadar peninsula which is continuously inhabited since the 9 th century BCE. The installation was designed within the promenade revitalization project and can be considered a revitalization of the northern top of the historical city center that was neglected during the reconstruction conducted after the World War II.  
The Sea Organ - Morske orgulje, is an architectural installation conceived as a wave driven organ. It is located at the northern end of the Obala Petra Kresimira IV seaside promenade. This mechanical sound art installation with musical features is based on sound sources--organ pipes which are hidden under the stone pavement, played by the sea waves similar to the way of the blues harp [59]. The resulting sound is highly dependent in dynamics on the movement of waves caused by the wind and the sea traffic, while its pitch is fixed by the design of the pipes.  
The Sea Organ project was developed as a cooperation between architectural studio [PERSON], acoustician and composer [PERSON], and organ restoration company Heferer [59]. It was built between 2005 and 2008 and it quickly became one of the city's landmarks [60,61]. The design was chosen at a public design competition held in 2004 by the City of Zadar and the Port authority of Zadar. One of the prime reasons for the project was the design of a new dock for cruise ships as a new attractive entry point into the historic city.  
The installation produces organ-like sounds designed to play continuous endless harmonic progression between G major and C major with added sixth in the range between 65 Hz and 250 Hz [45].  
This urban open space illustrates: (1) application of the principle of adding desirable soundscape components to an urban open space (acoustic sound art installation of the Sea Organ), (2) application of principles of promoting an existing desirable component of a sound environment (sounds of the sea and waves), (3) impact of laws and regulations on noise mitigation in Croatia and the standard ISO 1996-1: 2003 [62] on the integral design (orientation of organ pipes of the acoustic installation towards the sea instead to the city and shaping of the triangle resting area influenced by the nearby residential building) [63].
# Information Relaying Methods in VANET: Algorithms, Standards and Tests
## 3 System Model  
A V2V communication scenario is considered in this section, as shown in Figure 2. Assume that the number of vehicles in a traffic area is \(m\), which is denoted as \(V=\{v_{1},v_{2},...,v_{m}\}\). Among those that are randomly distributed exists a broadcasting vehicle we call the source vehicle, which broadcasts important messages (e.g., Emergency Situation Warning Message) with transmission power \(P_{0}\). Define two sets of vehicles, i.e., relay vehicles and free vehicles, which are denoted as \(R\) and \(N\), respectively. They satisfy \(V=R\cup N\). Then, we have the following description of the two vehicle sets mentioned above:  
\[R =\{v_{n}\ |\ \delta_{n}=1,v_{n}\in V\}, \tag{1}\] \[N =\{v_{n}\ |\ \delta_{n}=0,v_{n}\in V\}, \tag{2}\]where \(\delta_{n}\) is a binary variable, with \(v_{n}\) being a relay vehicle when \(\delta_{n}=1\) and \(v_{n}\) being a free vehicle when \(\delta_{n}=0\). When \(\sum\delta_{n}\geq 1\), at least one relay vehicle exists in the traffic area.
## 6 Results and Discussion
### Throughput Performance  
In Figure 6, we plot the mean network throughput versus the number of life-saving emergency nodes for the proposed FASBA protocol. We study the network throughput and validate our analytical model developed in Equation (8) using MATLAB 2024a-based simulation. We observe that both the analytical and simulation throughput of FASBA remain the same for emergency nodes up to 24. When the number of life-saving emergency nodes increases from 24 to 60, the analytical throughput (blue line) is slightly lower (up to 5%) than the simulation ones, but these differences are not very significant. A close match of analytical and simulation results validates our analytical model for FASBA life-saving emergency nodes. We consider Figure 6 to validate the simulation results such as throughput, MAC delay, and packet retransmission attempts presented next.  
The network throughput versus the number of life-saving emergency nodes of FASBA and MP-EDCA for an ad hoc and infrastructure network are shown in Figures 7 and 8, respectively. One can observe that FASBA provides higher throughput than MP-EDCA irrespective of network architecture, especially under medium- to high-traffic loads. For example, Figure 7 compares mean network throughput (ad hoc network) for N = 4 to 60 emergency nodes. We found that FASBA offers an improved network throughput of up to 10 Mbps for 60 nodes. We also observe that both MP-EDCA and FASBA provide similar throughput performance for N = 4 to 36 nodes. For 40 to 60, FASBA performs much better than MP-EDCA. For example, the proposed FASBA scheme achieved about 17% higher throughput than the MP-EDCA at N= 60 emergency nodes.  
Figure 8 exhibits the comparative average network throughput of an infrastructure network. The graphs shown in Figure 8 illustrate that the proposed FASBA offers an improved network throughput of up to 15 Mbps for 60 nodes. Similar to the ad hoc network 6.4(a), both schemes (MP-EDCA and FASBA) provide equal throughput for a number of nodes up to 28. When adding nodes from 32 to 60, FASBA outperforms MP-EDCA. The graph shows that FASBA achieves up to 17.5 % higher throughput than the standard MP-EDCA. Moreover, one can observe that the average network (ad hoc network) throughput constantly increases with the increasing number of nodes.  
Figure 6: Mean network throughput of the proposed FASBA.
## 5 Analysis of the Influence of Different Factors on the Adjacent Pile Foundation
### Analysis of the Influence of the Pit Angle Effect on the Adjacent Pile Foundation  
As mentioned in the previous section, the piles are inclined at an angle of 15\({}^{\circ}\), buried at a depth of 30 m, and the beam connection is 3 m long. Therefore, Figures 14 and 15 show the vertical pile bending moments and the pile displacement behavior at 5 m from the excavation surface, and the distance D from the pit angle is 1.75, 6.75, 11.75, 16.75, and 21.75 m, respectively.  
From Figure 14, it can be noted that the behavior of the bending moment for the pile is the same at different distances from the pit angle. In addition, the maximum bending moments are: \(-352.789\), \(-568.963\), \(-561.947\), \(-563.931\), and \(-671.931\), and the maximum  
Figure 14: Pile bending moments.  
Figure 15: Pile displacement behavior.  
bending moment appears on the pile foundation at D = 16.75 m. Furthermore, the pile closer to the pit corner, the smaller the pile bending moment, except for the piles near the pit corner and the centerline of the pit, the variation of the pile bending moment is small. Finally, the maximum bending moment increases by 61.28% from D = 1.75 m to D = 6.75 m and by 19.16% from D = 16.75 m to D = 21.75 m.  
Additionally, from Figure 15, it can be seen that the maximum pile displacement is 21.01, 32.93, 43.10, 49.33, and 51.43 mm respectively. Also, the maximum pile displacement occurs in the middle and lower part of the pile, and the further away from the pit angle, the greater the pile displacement and the smaller the displacement variation. The maximum pile displacement increases by 56.67% from D = 1.75 m to D = 6.75 m, and by 4.26% from D = 16.75 m to D = 21.75 m.  
Finally, it can be concluded that the bending moment and displacement of the adjacent pile foundation are all significantly influenced by the pit angle effect. The reason for this is that the support structures at the corner of the pit are perpendicular to each other to provide additional constraints, compared with the rest of the location, the stiffness of the support structure at the corner of the pit is greater, which can better resist the deformation of the soil outside the pit, and the area of deformation of the soil outside the pit is smaller compared with other locations, the closer to the corner of the pit, the smaller the bending moment and displacement of the adjacent structure pile foundation by the excavation of the pit. Therefore, when designing a large area of deep foundation excavation, the pit angle can be pointed to the adjacent structure for safety reasons.
## Appendix A  
The individual problem \(G_{c}|t_{ij}=1|C_{\max}\) determined by mixed graph \(G^{*}=(V,A^{*},E^{*})\) is analogous to Example 1 of a job-shop unit-time problem \(J|t_{ij}=1|C_{\max}\), with the exception that there are given start-start constraints and completion-start constraints on the set of operations of different jobs of set \(J\). All the additional constraints are determined as follows: \(v_{1}\Rightarrow v_{4}\); \(v_{4}\Rightarrow v_{3}\); \(v_{3}\to v_{1}\); \(v_{8}\to v_{4}\) and \(v_{12}\to v_{9}\). Thus, the appropriate mixed graph \(G^{*}=(V,A^{*},E^{*})\) has to contain an additional subset of arcs and subset set of edges defined as follows:  
\[A^{*}=A\cup\{(v_{1},v_{4}),(v_{4},v_{3}),(v_{3},v_{1}),(v_{8},v_{4}),(v_{12},v_{9})\}\,;\,E^{*}=E\cup\{[v_{8},v_{4}],[v_{12},v_{9}]\}.\]  
Note that the operations \(v_{1},v_{3}\) and \(v_{4}\) have to be fulfilled simultaneously in ever schedule since circuit \((v_{1},v_{4},v_{3},v_{1})\) exists in digraph \((V,\varnothing,E^{*})\), which is a subgraph of the mixed graph \(G^{*}=(V,A^{*},E^{*})\) dipicted in Figure 11. At the right side of Figure 11, the colors of coloring \(c(G^{*})\) are presented instead of the natural numbers: \(c(v_{1})=2\), \(c(v_{2})=1\), \(c(v_{3})=2\), \(c(v_{4})=2\), \(c(v_{5})=3\), \(c(v_{6})=4\), \(c(v_{7})=5\), \(c(v_{8})=1\), \(c(v_{9})=3\), \(c(v_{10})=5\), \(c(v_{11})=1\), \(c(v_{12})=2\). The colouring \(c(G^{*})\) is optimal, \(\chi(G^{*})=5\), since directed graph \((V,A^{*},\varnothing)\) contains path \((v_{8},v_{4},v_{5},v_{6},v_{7})\) and graph \((V,\varnothing,E^{*})\) contains chain \((v_{8},v_{4},v_{5},v_{6},v_{7})\), whose lengths are equal to 5.  
Due to these path and chain, the following inequality holds: \(\chi(G^{*})\geq 5\). From Theorem 3, it follows that the optimal colouring \(c(G^{*})\) defines the following schedule \(S\) that is optimal for Example A1 of the unit-time problem \(G_{c}|t_{ij}=1|C_{\max}\), where the input data are determined by mixed graph \(G^{*}\) depicted in Figure A1.  
\[S=(C(Q_{1,1})=2,C(Q_{2,1})=1,(Q_{2,2})=2,C(Q_{3,1})=2,C(Q_{3,2})=3,C(Q_{3,3})=4,C(Q_{3,4})=5,\]  
\[C(Q_{4,1})=1,C(Q_{4,2})=3,C(Q_{4,3})=5,C(Q_{5,1})=1,C(Q_{5,2})=2)\]  
The correspondence of the operations \(Q_{ij}\in Q\) and the vertices \(v_{k}\in V\) is determined based on Remark 1. The equalities \(\chi(G^{*})=5=C_{\max}\) hold due to Theorem 3.
# A Comparison of Electrical Breakdown Models for Polyethylene Nanocomposites
## 3 Results
### Simulation Results of EBEG Model  
It is assumed that a certain scale of free volume exists in the LDPE nanocomposites. Charges in the extended states enter the free volumes and are accelerated by the electric field to gain energy. Figure 5 depicts the time-dependent changes in space charge distributions, electric field distributions, and electron energy distributions in pure LDPE and LDPE/Al\({}_{2}\)O\({}_{3}\) nanocomposites. As time passes, the voltage applied to the electrodes on both sides of the materials increases gradually. The space charge accumulation inside the materials increases, the electric field distortion becomes more serious, and the energy gained by the electrons in the free volume also increases. When the charges gain energy beyond the trapping ability of the deep traps, breakdown of the material will occur.  
Figure 4: Distributions of space charges (**a1**–**a5**) and electric fields (**b1**–**b5**) in pure LDPE and LDPE/Al\({}_{2}\)O\({}_{3}\) nanodielectrics with nanofiller contents of 0.1 wt%, 0.5 wt%, 2.0 wt%, and 5.0 wt%.  
When doped with a small amount of nanof fillers, the LDPE/Al2O3 nanocomposites produce more traps having deeper energy levels. More homogeneous space charges accumulate near the electrodes, weakening the electric field distortion inside the nanocomposites. In addition, as the traps become deeper, the trapping ability of the deep traps is increased. Two factors work together to increase the breakdown strength of the nanod dielectrics having lower nanofiller weight fractions. After doping a large amount of nanofillers, the trap density and energy levels of LDPE/Al2O3 nanocomposites decrease due to the overlapping of the interfacial regions. At this time, the electric field in the material is seriously distorted, and the trapping ability of the deep traps is weakened. Their combined effects lead to a decrease in the breakdown electric field of the nanocomposites having larger nanofiller contents.
# Multi-Layered Satellite Communications Systems for Ultra-High Availability and Resilience
## 5 Results: Findings from Simulations and Testbed Activities
### Simulation Studies
Routing in MLSS shall be further studied. * The benefits of GEO-LEO ISL were not present in the simulations since routing packets via both layers did not optimize either mean delay or jitter. However, we can think that it could be useful in certain cases, such as delivering packets to the LEO satellite nodes when heavy rain blocks the direct connection from the ground to visible LEO satellites. GEO in the L-band would still work, and it could forward the packets to the LEO layer from above the blocking rain/clouds. GEO could also provide backup routes if some LEO links were congested. However, we did not have this kind of scenario in these first simulations.
# Performance Evaluation of Laetitic Subgrade Soil Treated with Lime and Coir Fibre-Activated Carbon
## 3 Test Results and Discussion
### Deformability Index (I\({}_{\text{D}}\)) and Elastic Modulus (E\({}_{\text{50}}\))  
The influence of ACF on the flexibility and stiffness of soil is investigated with the deformability index. The deformability index is the strain at the peak strength of stabilised soil per strain at the peak strength of original soil. It is a factor utilised to explain the deformation behaviour of soil in this study [66]. This parameter has indicated the deformation behaviour of modified soil compared to unmodified soil [67]. UCS results show I\({}_{\text{D}}\) rises from 1.02 to 1.36 for 1% AC to 3%ACF. It confirms that fibre and AC improve soil behaviour from brittle to ductile [68]. However, I\({}_{\text{D}}\) for lime-stabilised specimens is less than one and reduces with increasing lime content, which shows that lime increases brittles in lateritic soil [69].  
The influence of AC, ACF, and lime on flexibility and stiffness of soil is also assessed with secant modulus. The secant modulus is regarded as half of the maximum UCS values, as in previous studies [69; 70]. Figure 8 shows the elastic modulus values for various combinations. The elastic modulus was raised in AC, ACF, and lime treated with rising content further in 3% ACF and 3% lime; the difference between elastic modulus is insignificant. It is clear that lime increases stiffness in soil due to hydration and pozzolanic processes in lime-treated soil [15].  
Figure 7: The compressive strength value for different lime and AC contents.