esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 2 Materials and Methods
### Phantom Acquisition
#### 2.3.2 Slide Device  
Constrained SSA was achieved without robotic control or external tracking using a 3-D printed slide fixture designed for the P4-2v ultrasound transducer shown in Figure 3. A track was designed allowing up to 10 cm motion (center to center) of the array along a linear path. A sliding block was designed to ride in a cutout track, fitting tightly to prevent tilting or twisting of the array but loose enough to slide freely when moved by hand. A 3-D scan of the P4-2v transducer was used to create a hole the appropriate size and shape in the sliding piece, enlarged to allow for the dimension tolerance of the printed PLA filament. The sliding piece was divided into two pieces to allow for easy insertion and removal of the transducer. The sweep was oriented along the array elements (i.e., within the imaging plane). A handle was added to assist in holding the device still during the transducer sweep but was removable. The pieces were printed using a Lulzbot Taz 5 desktop 3-D printer (Aleph Objects, Loveland, CO, USA).
# Mobility-Multihoming Duality
## 4 ILNP Implementation in Linux
### L64 State Management  
An L64 value is an IPv6 routing prefix. L64 values for an end system are stored in the ILCC. When a new prefix is discovered from IPv6 RA messages, e.g., when a physical interface is enabled, the prefix can be used as an L64 value. This can be used for mobility and muldhoming: the local ILCC is updated, and this L64 needs to be signalled to the correspondent node (CN) via LU messages. The replacement of one set of L64 values with a new set of L64 values can be either _soft_ or _hard_.  
With _soft replacement_ or _soft handover_, the changes are applied in stages; that is, the sender of the LU waits for an LU acknowledgement from the CN before applying the change in L64 values locally. This is in contrast to a _hard replacement_ or _hard handover_, where the sender would apply the change straight away, without waiting for an LU acknowledgement from the CN. We only use the soft approach.  
When conducting soft handovers in mobility-multihoming scenarios, the communication session must first transition to the _'new path(s)'_ before the _'old path(s)'_ is (are) decommissioned. This reduces the disconnection time between the CN and MN, and it minimises packet loss during transmission. In the context of mobility-multihoming, one or more interfaces (PoAs) or L64 values are changed within the ongoing communication. Such a state change must be handled by the ILNP mobility-multihoming ILCC mechanism itself, and it must be coordinated between the two hosts participating in the communication session.  
The implementation of ILNP includes a 'disabled interface list' to facilitate the successful transfer of communication away from unused interfaces before they become disabled. Specifically, a network interface could be added to such a list, which triggers the LU message to be sent to the CN to notify it that one or more L64 values for the session are being changed.  
For the purposes of the evaluation, the interface was not turned off completely. Instead, it was only recorded in the disabled interface list. This allowed the evaluation to remain simpler in terms of the procedure, and it eliminated the time required for lower layer (layer 1/2) initialisation beyond the beginning of the communication session. This is because the lower layer initialisation is a variable that can change depending on the lower layer technology, and so it is not a factor in considering the operation of ILNP. In our testbed, we found that such initialisation took around \(\sim\)7-10 s with the equipment we used.
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
###### Abstract  
The growth of location-based services (LBS) has increased rapidly in last years, mainly due to the possibility to exploit low-cost sensors installed in portable devices, such as smartphones and tablets. This work aims to show a low-cost multi-sensor platform developed by the authors in which an ultra-wideband (UWB) indoor positioning system is added to a classical global navigation satellite systems-inertial navigation system (GNSS-INS) integration, in order to acquire different synchronized data for further data fusion analysis in order to exploit seamless positioning. The data fusion is based on an extended Kalman filter (EKF) and on a geo-fencing approach which allows the navigation solution to be provided continuously. In particular, the proposed algorithm aims to solve a navigation task of a pedestrian user moving from an outdoor space to an indoor environment. The methodology and the system setup is presented with more details in the paper. The data acquired and the real-time positioning estimation are analysed in depth and compared with ground truth measurements. Particular attention is given to the UWB positioning system and its behaviour with respect to the environment. The proposed data fusion algorithm provides an overall horizontal and 3D accuracy of 35 cm and 45 cm, respectively, obtained considering 5 different measurement campaigns.  
UWB; GNSS; indoor positioning; INS; pedestrian navigation; sensor integration; data fusion +
Footnote †: This paper is an extended version of the conference paper: [PERSON] [PERSON]; [PERSON]; [PERSON] Seamless Navigation using UWB-based Multisensor System. In Proceedings of the 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA, 20-23 April 2020; pp. 1079-1084.
# Estimation of Mental Distress from Photoplethysmography
## 3 Results
Figure 4: Tree-based discriminant models obtained by considering SDNN, MNN, SENN and RMSFD (the first derivative of root mean square) values from all PRV (pulse rate variability) signals. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Physiological Analysis**} & \multirow{2}{*}{**Feature Acronym**} & \multicolumn{3}{c}{**Learning**} & \multicolumn{3}{c}{**Test**} \\ \cline{3-8}  & & **Se (\%)** & **Sp (\%)** & **Ac (\%)** & **Se (\%)** & **Sp (\%)** & **Ac (\%)** \\ \hline Temporal & _SDNN_ & 61.30 & 84.02 & 72.34 & 58.82 & 76.47 & **67.41** \\ Temporal & _DRNN_ & 69.57 & 85.10 & 77.32 & 61.76 & 82.35 & **72.06** \\ Temporal & _SENN_ & 68.23 & 75.12 & 71.42 & 64.61 & 70.59 & **67.65** \\ Temporal & _SDSD_ & 66.73 & 75.20 & 70.54 & 67.65 & 64.71 & **66.18** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensitivity (Se), specificity (Sp) and accuracy (Ac) of significant parameters using ROC (receiver operating characteristic) analysis for training and test subsets. Figure 3: Receiver operating characteristic (ROC) curves for the four statistical significant parameters: (**a**) SDNN (standard deviation of NN intervals), (**b**) DRNN (dynamic range of NN intervals), (**c**) SENN (standard error of successive differences of adjacent NN intervals), (**d**) SDFD (the first derivative of standard deviation). AUC: area under curve. In the next step, the samples labeled as calmness are newly partitioned into two subgroups using _MNN_ and a threshold of 0.8068. Finally, in each remaining subgroup, _SENN_ and _SDFD_ are chosen to distribute the rest of samples by using thresholds 0.0508 and 0.2153, respectively. No further ramifications are formed with the criteria imposed. This tree-based model achieves a sensitivity, specificity and accuracy of 79.49, 85.29 and 82.35%, respectively. In this regard, the model improves global correctness more than 10% regarding the best single accuracy reported by _DRNN_. Finally, it is relevant to note that, while the single parameter with higher discriminatory power showed a limited sensitivity (i.e., ability to detect stress), when the parameters were combined by means of a tree-based classifier, sensibility increased more than 17%, achieving a more balanced discriminatory model.
# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
## 3 Methodology and Workflow
### Step 1  
**Evaluate and improve quality of seismic data**  
The quality of seismic data is crucial for accurate stratigraphic and sedimentological interpretation. Over the last several decades, academia and the industry have significantly advanced data acquisition and processing. Modern digital subsurface seismic data typically feature a 10 to 100 Hz frequency range, utilize a zero-phase wavelet, maintain precise 3D positioning in migrated data, and possess a satisfactory signal-to-noise ratio. We can  
Figure 3: Proposed workflow for high-resolution, subsurface sedimentary facies and reservoir prediction using seismic data.  
still perform various analyses even with imperfect data--such as numerous legacy 3D data sets collected in the 1980s and 1990s. Numerous published case studies of seismic geomorphology and seismic sedimentology from the last thirty years rely on these legacy data sets (e.g., [10; 26; 27]).  
Geologic interpreters can seek assistance from geophysicists to achieve cost-effective improvements in data quality through reprocessing and specialized processing techniques. For example, significant efforts have been made to enhance data processing for higher seismic resolution. A common approach is to extrapolate or to reshape the bandwidth using poststack data, for instance, spectral balancing [28; 29], spectral-decomposition-based inversion [30], bandwidth extension [31; 32], and pseudo deconvolution using inverse continuous wavelet transform [33].
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Compound** & **Sample Type** & **LOD (\(\mu\)g/mL)** & **LOQ (\(\mu\)g/mL)** & **Linear Range (\(\mu\)g/mL)** & \(R^{2}\)** \\ \hline Basic Blue 26 & Tattooo lipstick & 0.08 & 0.24 & 0.24–12.00 & 0.9999 \\  & Tattooo eyebrow & 0.10 & 0.30 & 0.30–15.00 & 0.9997 \\  & Hair tint & 0.08 & 0.24 & 0.24–12.00 & 0.9999 \\ \hline Basic Red 2 & Tattooo lipstick & 0.30 & 0.90 & 0.90–45.00 & 0.9999 \\  & Tattoo eyebrow & 1.20 & 3.60 & 3.60–180.00 & 0.9997 \\  & Hair tint & 0.30 & 0.90 & 0.90–45.00 & 0.9999 \\ \hline Basic Yellow 28 & Tattooo lipstick & 0.20 & 0.60 & 0.60–30.00 & 0.9999 \\  & Tattoo eyebrow & 0.50 & 1.50 & 1.50–75.00 & 0.9998 \\  & Hair tint & 0.20 & 0.60 & 0.60–30.00 & 0.9999 \\ \hline Disperse Brown 1 & Tattooo lipstick & 0.02 & 0.05 & 0.05–2.50 & 0.9999 \\  & Tattooo eyebrow & 0.30 & 0.90 & 0.90–45.00 & 0.9999 \\  & Hair tint & 0.01 & 0.04 & 0.04–2.00 & 0.9999 \\ \hline Disperse Orange 3 & Tattooo lipstick & 0.01 & 0.04 & 0.04–2.00 & 0.9999 \\  & Tattooo eyebrow & 0.18 & 0.53 & 0.53–26.50 & 0.9999 \\  & Hair tint & 0.01 & 0.04 & 0.04–2.00 & 0.9999 \\ \hline Disperse Yellow 3 & Tattooo lipstick & 0.05 & 0.15 & 0.15–7.50 & 0.9999 \\  & Tattooo eyebrow & 0.40 & 1.20 & 1.20–60.00 & 0.9999 \\  & Hair tint & 0.05 & 0.16 & 0.16–8.00 & 0.9999 \\ \hline HC Blue No. 2 & Tattooo lipstick & 0.04 & 0.12 & 0.12–6.00 & 0.9999 \\  & Tattooo eyebrow & 0.24 & 0.72 & 0.72–36.00 & 0.9999 \\  & Hair tint & 0.04 & 0.11 & 0.11–5.50 & 0.9999 \\ \hline HC Yellow No. 5 & Tattooo lipstick & 0.01 & 0.03 & 0.03–1.50 & 0.9999 \\  & Tattooo eyebrow & 0.40 & 1.20 & 1.20–60.00 & 0.9999 \\  & Hair tint & 0.01 & 0.03 & 0.03–1.50 & 0.9999 \\ \hline Solvent Orange 4 & Tattooo lipstick & 0.07 & 0.20 & 0.20–10.00 & 0.9999 \\  & Tattooo eyebrow & 0.30 & 0.90 & 0.90–45.00 & 0.9999 \\  & Hair tint & 0.01 & 0.02 & 0.02–1.00 & 0.9998 \\ \hline \end{tabular}
\end{table} 
Table 1: LOD, LOQ, and linearity of colorants in cosmetic samples using HPLC-DAD. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Compound** & **Sample Type** & **LOD (\#g/mL)** & **LOQ (\#g/mL)** & **Linear Range (\#g/mL)** & \(R^{2}\) \\ \hline Solvent Orange 7 & Tattoo lipstick & 0.10 & 0.30 & 0.30–15.00 & 0.9999 \\ Tattoo eyebrow & 0.67 & 2.01 & 2.01–100.50 & 0.9999 \\ Hair tint & 0.10 & 0.30 & 0.30–15.00 & 0.9999 \\ \hline Solvent Red 24 & Tattoo lipstick & 0.15 & 0.45 & 0.45–22.50 & 0.9999 \\ Tattoo eyebrow & 1.00 & 3.00 & 3.00–150.00 & 0.9999 \\ Hair tint & 0.18 & 0.54 & 0.54–27.00 & 0.9999 \\ \hline Solvent Yellow 1 & Tattoo lipstick & 0.02 & 0.05 & 0.05–2.50 & 0.9999 \\ Tattoo eyebrow & 0.20 & 0.60 & 0.60–30.00 & 0.9999 \\ Hair tint & 0.01 & 0.03 & 0.03–1.50 & 0.9999 \\ \hline Solvent Yellow 3 & Tattoo lipstick & 0.15 & 0.45 & 0.45–22.50 & 0.9999 \\ Tattoo eyebrow & 0.30 & 0.90 & 0.90–45.00 & 0.9999 \\ Hair tint & 0.02 & 0.05 & 0.05–2.50 & 0.9999 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 4 Conclusions  
The biodegradationomer produced from the microbial culture had antimicrobial activity against filamentous microorganisms that were present in the PSW and AS mixture. The biodegradation were assessed for their antimicrobial activity in terms of volumetric zones of inhibition. The lowest VZI of 1.32 L/mL was observed when the co-culture of _Aeromonas veronii_ and _Klebsiella grimontii_ was used. The highest VZI of 1.39 L/mL was achieved when the consortium culture was used, meaning that the microorganisms were more competent as a community than as co-cultures. Furthermore, the results illustrated that the Bio-AS aeration tank could remove up to 94% FOG, 99% of soluble proteins, 93.3% TSS, and 85.4% COD, whereas the Syn-AS removed 74% FOG, 79% soluble proteins, 83.2% TSS, and 61% COD; moreover, the CAS achieved the removal of up to 72.3% FOG, 68% soluble proteins, 87% TSS, and 50.5% COD. The contaminants were further removed in the secondary clarifier, which uses solid-liquid separation by gravity mechanism to separate the sludge from the effluent. The secondary clarifier step of the Bio-AS removed 85% FOG, 99% soluble protein, 86% TSS, and 67% COD. The secondary clarifier for Syn-AS removed 67% FOG, 92.2% soluble proteins, 86% TSS, and 44.1% COD. The CAS secondary clarifier removed 64.3% FOG and 89.3% soluble proteins, 77% TSS, including 47% COD. This indicates that Bio-AS performed better compared to Syn-AS and CAS in terms of the nutrient's removal.  
Conceptualization, C.D., S.K.O.N. and M.B.; Data curation, C.D.; Funding acquisition, C.D., S.K.O.N. and M.B.; Investigation, C.D.; Project administration, S.K.O.N.; Resources, S.K.O.N., M.B., B.S.C. and B.I.O.; Supervision, S.K.O.N., M.B., B.S.C. and B.I.O.; Validation (Method), S.K.O.N., M.B., B.S.C., B.I.O. and C.D.; Writing--original draft, C.D.; Writing--review and editing, S.K.O.N., M.B., B.S.C. and B.I.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Cape Peninsula University of Technology university research fund (URF RK16). The National Research Foundation of South Africa (NRF) cost center R017.  
Not applicable.  
The authors would like to thank the Bioresource Engineering Research Group (_BioERG_) students and my supervisors for their unwavering support, and the Chemical Engineering lab Technician [PERSON], Faculty of Applied Sciences, CPUT for their technical support.  
The authors declare no conflict of interest.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 2 GNSS-RTK Technology  
In this section, the GNSS-RTK technology is introduced. GNSS is the common name used for satellite navigation systems, which include GPS (North American system), Galileo (European system), GLONASS (Russian system), BeiDou (Chinese system), and QZSS (Japanese system).
# Analysis on the Isostatic Bipod Mounts for the HERA Mission LIDAR
## 5 Thermoelastic Simulations
### Thermal Modeling and Materials  
The isostatic bipod mounts were simulated in simple conditions to assess their behavior. The simulation only contemplated a circular base, to maintain the system symmetric, the three isostatic bipod mounts, and the mirror. In Figure 7, the simulation performed for the first design is depicted, and in Figure 8, the simulation performed for the optimized flexure design. The boundary conditions applied to the model were to prevent axial and tangential displacement, which means the base only has freedom radially, and a fixed-point support on the base center, which is coincident with the optical axis. The temperature was defined in the bottom surface of the base.  
Figure 6: Final step on the bonding procedure. The protective sheets are removed before the end of the work life of the adhesive (schematic).  
Figure 7: The boundary conditions of the first design.  
Both designs were subjected to the qualification non-operational temperature limits, i.e., +80 \({}^{\circ}\)C and \(-\)60 \({}^{\circ}\)C. The materials chosen were also the same: titanium grade 5 (Ti-6 Al-4V) for the isostatic bipod mounts, aluminum 7075-T7351 for the base, Zerodur for the mirror, and Epoxy 2216B/A Gray for the adhesive pads. The properties of the chosen materials are presented in Table 1. The mechanical screw behavior was not assessed at this stage.  
The Poisson's ratio and the Young's Modulus were given by the supplier and are confidential. The remaining properties are based on documentation [9; 10]. The properties presented in Table 1 are the inputs needed for the thermo-elastic simulation.  
The tolerance of the system is given by an optical study (i.e., tolerance analysis in ZEMAX). Regarding the telescope, it was identified that the worst tolerance offenders are the mirror radius and the mirror displacement. The tolerances that are assessed in this paper is the displacement on XZ plane of the primary mirror, the distance of the primary mirror to the base, and the curvature radius of the primary mirror (see Table 2).
## 2 Numerical Method
For the numerical simulations, we use the parameter set displayed in \[\overrightarrow{\mathbf{U}}_{i,j,k}^{n+1}=f(i,j,k,\overrightarrow{\mathbf{X }}_{l}^{n+1})\cdot\overrightarrow{\mathbf{U}}(\overrightarrow{\mathbf{X}}_{ l}^{n+1}). \tag{5}\]0 \begin{table}
\begin{tabular}{l} _Step 1_: Calculate the cilia velocity at grid points \(\overrightarrow{\mathbf{U}}_{i,j,k}^{n+1}\) using Equations (5) and (6). \\ _Step 2_: Spread singular force \(\overrightarrow{\mathbf{F}}\) excerted by cilia by solving Equation (4). \\ _Step 3_: Solve Equation (7) to get intermediate velocity field \(\vec{\mathbf{u}}^{*}\). \\ _Step 4_: Compute the pressure increment \(\phi^{n+1}\) by solving Poisson Equation (9). \\ _Step 5_: Update the velocity and pressure fields by Equations (10) and (11). \\ \end{tabular}
\end{table} 
Table 2: The numerical procedure for each time step of the present method. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Description** & **Symbol** & **Units** & **Value** & **Ref.** \\ \hline Fluid grid size & \(h\) & \(\mu\)m & 0.5 & - \\ No. of control points & \(N\) & - & 20 & - \\ No. of cilia & \(N_{cilia}\) & - & 72 & - \\ Ciliary beat frequency & \(CBF\) & Hz & 10 & [29] \\ Cilium length & \(L\) & \(\mu\)m & 6 & [29] \\ Computational domain length & \(L_{\Omega}\) & \(\mu\)m & 48 & - \\ Computational domain width & \(W_{\Omega}\) & \(\mu\)m & 48 & - \\ Computational domain height & \(H_{\Omega}\) & \(\mu\)m & 8 & [51] \\ Fluid density & \(\rho\) & g/cm\({}^{3}\) & 1 & [29] \\ Fluid dynamic viscosity & \(\mu\) & g/cm\({}^{\cdot}\) s & 0.01 & [29, 52] \\ Time step size & \(\Delta t\) & s & \(1\times 10^{-6}\) & - \\ Phase difference between cilia & \(\Delta\Phi\) & radian & \(\pi/6\) & - \\ Interciliary spacing along wave fronts & - & \(\mu\)m & \(3\sqrt{2}\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters used in the simulations. \(L\), \(\rho\), \(\mu\), and o of cilia are the standard parameter set, which can have different values depending on the type of our parametric studies. Figure 3: Code validation at \(Re=0.01\). (**a**) Results for fluid velocity for the Couette flow comparing the present simulation results with the analytical solution. (**b**) Results for streamlines for a lid-driven cavity comparing the present code results (’\(-\).’) with the works of [PERSON] [48] (’\(-\)’) and [PERSON] [49] (’\(\triangle\)’).
## 7 Risk Analysis
### Edge Gateway
#### 7.6.5 Repudiation  
1. **Component**  
_Security risk_: If the edge gateway has multiple legitimate accounts, the attacker might gain access to one of them and compromise the non-repudiation principle. This attack may compromise the whole system and also cause a major privacy impact, as important data can be stolen.  
_Security measures_: For mitigation, comprehensive logging of all relevant activities should be enabled on the edge gateway. Also, a trusted execution environment can be used, as well as multiple-factor authentication.
# Study on the Coupling Relationship of Low Temperature Fluidity and Oxidation Stability of Biodiesel
## 4 Conclusions  
When the biodiesel addition ratio increases, the value of CFPP of biodiesel blending fuel increases accordingly. Although the biodiesel blending oil (B10, B20, B50) has the best point for the addition of CFI, but the CFPP of biodiesel (B100) is not affected by CFI.  
Biodiesel (B100) has poor oxidation stability, but it can satisfy the national standard for not less than 6 h by adding a small amount (0.1%) of antioxidants. The combination of low proportion (B10) and 0 PD can effectively improve the oxidation stability. However, the oxidation stability of biodiesel blended oil is worse with the increase of the ratio of blending with 0 PD. Biodiesel blending oil with a blending ratio of more than 50% can no longer meet national standards. Nevertheless, national standards can be achieved by adding a small amount (0.1%) of antioxidants.  
The fatty acid methyl ester composition generally contains a small amount of saturated fatty acids (\(<\)20%), a large amount of unsaturated fatty acids (\(>\)20%), and a certain amount of polyunsaturated fatty acids (\(<\)70%). The low temperature fluidity and oxidation stability of biodiesel have a mutually constraining coupling relationship. It is feasible to simultaneously improve the low temperature  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Ratio**} & \multicolumn{3}{c}{**Categories**} \\ \cline{2-4}  & **SFAME** & **SUFAME** & **PUFAME** \\ \hline RME & 7.32 & 56.14 & 29.13 \\ R90P10 & 11.648 & 54.545 & 27.138 \\ R80P20 & 15.976 & 52.95 & 25.146 \\ R70P30 & 20.304 & 51.355 & 23.154 \\ R60P40 & 24.632 & 49.76 & 21.162 \\ R50P50 & 28.96 & 48.165 & 19.17 \\ R40P60 & 33.288 & 46.57 & 17.178 \\ R30P70 & 37.616 & 44.975 & 15.186 \\ R20P80 & 41.944 & 43.38 & 13.194 \\ R10P90 & 46.272 & 41.785 & 11.202 \\ PME & 50.6 & 40.19 & 9.21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Composition of fatty acid methyl esters of blended fuel.  
Figure 11: Effects of blending ratio on coupling relationship between low temperature fluidity and oxidation stability of biodiesel.  
fluidity and oxidation stability of biodiesel through adjusting the composition and content of biodiesel fatty acid.
## 3 Challenges and Related Recommendation
### Future Research Challenges
#### 3.2.1 Hybrid RF-Optical Networks
Other problems that were studied and investigated in hybrid LiFi networks include optimization of network throughput and data rate provided to the user mainly in load balancing and selecting the best possible access point [74; 85; 86; 87]. On the other hand, machine learning has been used in such networks for problem solving and network optimization [29; 88; 89; 90]. Various simulation results were obtained by the existing studies including increasing the network throughput and/or data rate, reducing the switching/outage probability and complexity. Optimizing LiFi hybrid network performance is essential for user quality in indoor scenarios and in other applications such as vehicle communication.