esa-sceva/satcom-chunk-collection huggingface dataset:
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 3 Model Establishment
### Fuzzy Neural Network  
Fuzzy neural network combines the advantages of fuzzy logic and neural networks well, and this method is widely used in fault diagnosis [30; 31; 32]. The fuzzy set represented by the membership function value in the fuzzy theory transforms the information with certain similarities. Meanwhile, a neural network has a strong ability for self-learning and information storage. Therefore, the fuzzy set obtained by fuzzy theory can be trained by neural networks to realize fault diagnosis. Furthermore, the fuzzy theory changes the fuzzy fault information of the recognition into the deterministic information, which can efficiently obtain more accurate diagnosis results.  
The learning of fuzzy neural networks is a process of updating the network connection parameters constantly, and finally makes the network performance reach optimal effect. The learning error is calculated by the actual output value and the target value, then the backpropagation error is used to adjust the model condition parameters. The calculation steps of the fuzzy neural network used in this paper are as follows.  
Step 1. For the k-dimensional input vector \(x=[x_{1},x_{2},\ldots,x_{k}]\), the membership degree of each input variable \(x_{j}\) is calculated according to the fuzzy rules, and the membership function is Gaussian type.  
\[\mu A_{j}^{i}=\exp[-(x_{i}-c_{j}^{i})2/b_{j}^{i}] \tag{18}\]  
where,\(j=1,2,\cdots,k\), \(i=1,2,\cdots,n\), \(c_{j}^{i}\), \(b_{j}^{i}\) are the center and width of the membership function respectively; \(k\) is the dimension of the input parameter (the number of eigenvectors); \(n\) is the number of fuzzy subsets.  
Step 2. Each membership degree is fuzzy calculated, and the fuzzy operator is continuous multiplication operator.  
\[\omega^{i}=\mu A_{j}^{1}(x_{1})\mu A_{j}^{2}(x_{2})\cdot\cdot\cdot\mu A_{j}^{k}(x _{k}),\ i=1,2,\cdot\cdot\cdot,n \tag{19}\]  
Step 3. According to the result of fuzzy calculation, the output value of the fuzzy model is calculated.  
\[y_{i}=\sum\limits_{i=1}^{n}\omega^{i}(p_{0}^{i}+p_{1}^{i}x_{1}+\cdot\cdot\cdot +p_{k}^{i}x_{k})\bigg{/}\sum\limits_{i=1}^{n}\omega^{i} \tag{20}\]  
Step 4. Calculate the error between the expected output and the actual output of the network.  
\[e=\frac{1}{2}(y_{d}-y_{c})^{2} \tag{21}\]  
where, \(y_{d}\) is the expected output of the network; \(y_{c}\) is the actual output of the network.  
Step 5. Adjust the network connection parameters by Equation (22).  
\[\left\{\begin{array}{l}p_{j}^{i}(k)=p_{j}^{i}(k-1)-\alpha\frac{\partial e}{ \partial p_{j}^{i}},\frac{\partial e}{\partial p_{j}^{i}}=(y_{d}-y_{c})\omega^ {i}\bigg{/}\sum\limits_{i=1}^{n}\omega^{i}x_{j}\\ c_{j}^{i}(k)=c_{j}^{i}(k-1)-\beta\frac{\partial e}{\partial c_{j}^{i}},b_{j}^{ i}(k)=b_{j}^{i}(k-1)-\beta\frac{\partial e}{\partial b_{j}^{i}}\end{array}\right. \tag{22}\]
# Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification
## 3 Results and Discussion
### Comparison with State-of-the-art Methods  
In the literature [12; 16; 54; 55], the author noticed that many studies used the MIT-BIH arrhythmia dataset for the heartbeat classification, whereas this study used different datasets. Therefore, it is obvious that this study cannot present a fair comparison between the current study and state-of-the-art methods. To support a fair comparison analysis, this study used the MIT-BIH arrhythmia dataset for heartbeat classification using our proposed non-random ESN model. This method is focused on a single lead and ventricular heartbeat classification. Therefore, this technique considered only two classes: (N + SVB) or (A + VB). For MLI and V1, both leads were taken individually to classify the ventricular heartbeats from the ECG signals. This methodology outperforms the rest of the other studies that were discussed in Table 4. Moreover, some studies in the literature only focused on one lead classification and the computational cost of the proposed algorithm was not suitable for real-time application [9; 56]. However, this study used both leads for classification and the technique computational cost and performance of our technique is noteworthy for real-time implementation. Thus, the author compared this study with those state-of-the-art methods that used both leads for learning the algorithm and the results were extracted from their confusion matrix which is only related to ventricular heartbeats.  
Figure 11: Graphical representation of the performance Analysis of the ESN model.
# Design and Development of an Omnidirectional Three-Wheeled Industrial Mobile Robot Platform
## 6 Experiments and Results
### Experiments and Results with Mobile Robot Platform
#### 6.2.3 Analysis of Limiting Factors in Automatic Mode  
As described in Section 6.1, curvilinear (linear and circular) trajectories are generated from predefined motion commands characterized by three parameters: final position, velocity, and acceleration. These parameters are used to calculate the required angular velocities (in steps) for each motor through the inverse kinematics model. However, the definition of these parameters is carried out manually and intuitively, without prior dynamic or kinematic validation.  
This approach, although valid for exploratory purposes, leads to the following potential issues:  
* The generation of step commands that are physically unfeasible under real load conditions.
* The exceedance of motor torque limits during certain phases of the trajectory.
* The loss of motor synchronization (step loss), especially during curves with abrupt tangential acceleration changes.  
As a result of these inaccuracies, the platform is reliably able to transport up to 150 kgf in automatic mode. However, when the load exceeds 150 kgf, the motors are unable to execute the planned movements, as the computed steps from the inverse kinematics demand more torque than available.  
Conversely, in teleoperated mode, the human operator continuously adjusts the movement parameters in real-time, intuitively compensating for variations in load and trajectory dynamics. This capability enables the stable transportation of loads up to 300 kgf without the need for predefined kinematic profiles.  
Based on these findings, the following improvement lines are proposed for future developments:  
* Implementation of a dynamic trajectory planner capable of automatically optimizing velocity and acceleration parameters according to the load and the desired trajectory;
* Integration of a kinematic feasibility validation module to detect commands that may saturate motor torque or speed limits before execution;
* Enhancement of the control system through closed-loop feedback using position and torque sensors, enabling adaptive responses to dynamic changes during operation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Load [kgf]** & **Speed Regulation [\%]** & **Observation** \\ \hline
100 & 80 & Pass \\
200 & 60 & Pass \\
300 & 20 & Pass \\
350 & — & Cannot move \\ \hline \end{tabular}
\end{table}
Table 6: Load performance and speed regulation.
## 3 Numerical Results
### Thin-Walled Single-Cell Composite Box Beam  
In this section, the geometric properties of the thin-walled composite box beam are taken into account as in Figure 1, while the material distribution of that beam is shown in Figure 5. Orthotropic materials with alignment angle as [0\({}^{\circ}\)/90\({}^{\circ}\)/0\({}^{\circ}\)] cross-ply are used here. The properties of these materials are detailed above.  
Large Deflection of Cantilever Thin-Walled Single-Cell Composite Box Beam for Post-Buckling  
In this section, the behaviour of the cantilever thin-walled single-cell composite box beam subjected to large deflection due to axial loading is examined. Figure 6 shows the equilibrium curves in NL regimes in beam models with polynomial degrees L4 and L9 subjected to axial loading effect. Accordingly, the displacement distribution of the box beam at L4 and L9 is similar up to \(u_{z}/L=0.5\). Afterwards, L4 and L9 create a similar curve, but the L4 model is effective with slightly larger levels than L9.  
Figure 7 shows the distribution of axial and shear stress components for two different P* values of L4 and L9 of the composite box beam subjected to axial loading for the NL regime. Here, the distribution along \(z\)-axis is analyzed for \(x=-a_{2}/2\) and \(y=L/2\). The variation of the axial stress component is similar for both loads at L4 and L9. The distribution of the shear stress component along the \(z\)-axis at different load effects is similar for L4, but varies for L9.  
Figure 8 shows the distributions of axial and shear stress components in L and NL regimes for different P* values in L9. The distributions of the axial stress components for \(P_{6}^{*}\) are close to each other for L and NL regimes. For P*\({}_{18}^{*}\) and P*\({}_{30}^{*}\), the distribution of the axial stress component is similar along the \(z\)-axis in the L regime. In the NL regime, the axial stress along the \(z\)-axis changes the behaviour of the beam towards the upper parts at \(P_{18}^{*}\) and \(P_{30}^{*}\) loads. As seen in Figure 8, when the shear stress is examined, for all load values in  
Figure 5: Thin-walled composite beam cross section.  
Figure 6: The equilibrium curves for L4 and L9 of the single-cell composite beam subjected to axial loading, P* = \(4 PL^{2}/\pi^{2}EI\).  
linear behaviour, the distribution along the \(z\)-axis occurs similarly. In the NL behaviour, as the load increases, the intensity of the stress increases at the lower parts of the beam and decreases at the upper parts.
# Distributed Reliable and Efficient Transmission Task Assignment for WSNs
## 5 Reliable and Efficient Task Allocation Algorithm for WSNs
### Distributed Task Assignment
#### 5.2.2 Distributed Reliable and Efficient Task Allocation in Planar Topology
\begin{tabular}{l l}
**Symbol** & **Description** \\ \(s_{i}\) & \(i\)-th source node \\ \(m_{i}\) & The task to be assigned, which is the element in the set \(M(t)\) \\ \(e_{ij}\) & Energy consumed by the task \(m_{i}\) on the node \(N_{j}\) \\ \(e_{comp}\) & Calculated energy consumption of \(N_{j}\) \\ \(e_{j}\) & Average processing energy consumption of the nodes in the network \\ \(t_{ij}^{comp}\) & Calculation time of the task \(m_{i}\) \\ \(e_{ij}^{comm}\) & Communication energy consumption of the node \(N_{j}\) \\ \(e_{ij}^{t}\) & Energy consumption of transmitting data of length \(l_{ij}\) bit at the distance \(d\) \\ \(e_{dec}\) & Energy consumption of operating the radio model for each bit \\ \(e_{amp}\) & Coefficient of the transmit amplifier \\ \(e_{ij}^{t}\) & Energy consumption of receiving data of length \(l_{ij}\) bit at the distance \(d\) \\ \(P_{i}\) & Task period of \(m_{i}\) \\ \(S_{i}\) & Start time of task \(m_{i}\) \\ \(C_{i}\) & Deadline for task \(m_{i}\) \\ \(R_{WSN}\) & Mission reliability of the network \\ \(R_{s}\) & Target reliability \\ \(ClusterR_{s_{i},sink}(t,i)\) & \(i\)-th transmission Reliability of clustered WSNs from \(s_{i}\) to the sink at time t \\ \(R_{A}^{c}(t,i)\) & Reliability of the \(i\)-th transmission performed by cluster \(c\). \\ \(R_{T}^{c}(t,i)\) & Reliability of the head of cluster \(c\) which successfully sends the collected data of \(i\)-th transmission to sink at time \(t\). \\ \(MeshR_{s_{i},sink}(t,i)\) & \(i\)-th transmission reliability in mesh WSNs from \(s_{i}\) to the sink at time t \\ \(R_{s_{i},sink}^{i}(t,i)\) & Probability that there exists at least one path whose packet delivery rate is greater than the threshold of \(i\)-th transmission in the disjoint minimal path sets (from \(s_{i}\) to sink) \\ \(R_{path(s_{i},sink)}^{j}(t,i)\) & \(i\)-th transmission reliability in mesh WSNs of \(j\)-th path from \(s_{i}\) to the sink at time t \\ \(TG(V,E)\) & Physical topology of the network \\ \end{tabular}
## 3 An Autoencoder and Partial Least Squares Regression Based Extreme Learning Machine Model
### Partial Least Squares Regression  
PLSR developed by [PERSON] is a multivariate statistical analysis method that takes advantage of both principal component analysis and the least square method [33]. Compared with the least square method, the PLSR method can deal with the multicollinearity problem. The PLSR method allows for regression modeling when the number of samples is smaller than the number of independent variables. In addition, the PLSR model considers information of both the independent and responsible variables, which makes it easier to identify system information and noises.  
Given a set of \(N\) observed data samples, which were composed of \(p\) input and \(q\) output variables, i.e., \(S=(x_{i},y_{i})\) for \(i=1,2,\ldots,N\), where \(x_{i}\in R^{p}\) denotes the independent variable and \(y_{k}\in R^{q}\) denotes the responsible variable. The objective of PLSR is modeling the linear relationship between the \(p\) independent variables and \(q\) response variables. The modeling process of PLSR can be denoted as follows [26].  
First, the independent matrix \(X=\left(x_{1},x_{2},\ldots,x_{p}\right)_{n\times p}\) and response matrix \(Y=\left(y_{1},y_{2},\ldots,y_{q}\right)_{n\times q}\) are normalized into a zero mean and one variance. The normalized matrix of \(X\) and \(Y\) are denoted as \(E_{0}\) and \(F_{0}\), respectively. Second, the PLSR method is applied to \(X\) and \(Y\) to extract the first pair of score vectors \(u_{1}\) and \(v_{1}\), respectively. The score vectors \(u_{1}\) and \(v_{1}\) are the combination of the independent variable and responsible variables, respectively, and should contain the maximum variation information of them. The relationship between \(u_{1}\) and \(v_{1}\) should be as maximum as possible. Then the regression model between \(Y\) and \(u_{1}\) is deduced. Third, the residual matrix \(E_{1}\) and \(F_{1}\) are calculated to replace \(E_{0}\) and \(F_{0}\), respectively, to skip to the next step iteration until the residual matrix meets the stopping criteria.
# Effect of Structural Materials on Monopropellant Thruster Propulsion Performance in Micro Scale
## 3 Numerical Comparative Performance Analysis
### Grid Convergence Study  
Grid convergence studies were performed to ensure that steady solutions were not sensitive to grid refinement. In regard to the coarse computational domains, 394,000 hexahedral elements were used in the fluid domain, and 81,503 tetrahedral elements were used in the solid domain. In regard to the intermediate computational domains, 936,400 hexahedral  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Thermal Conductivity** & **Specific Heat** & **Density** & **Thermal Diffusivity** \\  & **(W m\({}^{-1}\) K\({}^{-1}\))** & **(J kg\({}^{-1}\) K\({}^{-1}\))** & **(kg m\({}^{-3}\))** & **(mm\({}^{2}\) s\({}^{-1}\))** \\ \hline Copper & 385 & 406 & 8960 & 106.8 \\ Silicon & 130 & 700 & 2330 & 79.7 \\ HTCC & 20 & 750 & 3280 & 8.1 \\ Stainless Steel & 15 & 502 & 7920 & 3.7 \\ LTCC & 3 & 729 & 3100 & 1.3 \\ Glass & 1 & 1200 & 2365 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of the solid materials used in the CHT simulation.  
elements comprised the fluid domain, and 116,419 tetrahedral elements comprised the solid domain. The dense computational domains were constructed using 1,736,000 hexahedral elements in the fluid domain and 399,672 tetrahedral elements in the solid domain. The glass microthruster with the gas mixture decomposed from 90 wt% hydrogen peroxide was used for the grid convergence study. The amount of thrust generated by the microthruster was first selected for comparison between the types of grid in the fluid domain, and then the averaged outside surface temperature of the solid was selected for comparison between grids in the solid domain. The results are summarized in Table 4. The variation in the amount of thrust produced from the intermediate grid to the coarse and to the dense grid was 0.081% and 0.067%, respectively, and likewise, the variation in the outside surface temperature was 0.031% and 0.020% respectively. It was apparent that the dense grid would yield more accurate results, but the dense grid cost almost twice as much computational time as the intermediate grid. After weighing the competing considerations of accuracy and practicality, the intermediate grid was selected and used throughout the simulations.
## 4 Experimental Results and Discussion
### Comparisons with Best Known Results
[PERSON] and [PERSON] (2014) managed to produce high-quality solutions within 0.5 min and thoroughly presented data related to the computational time for other algorithms addressing this problem. [PERSON] and [PERSON] (2015) and [PERSON] et al. (2017), who also considered the VRPTW as multiobjective, did not present the computational time needed, while the proposed algorithm manages to produce high efficiency results in less than a minute (52 s on average). Of course, the CPU as well as the programming language are also important factors affecting the computational time needed. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Problem Type** & **MOLNSNV** & **MOLNSTD** & **BPNV** & **BPTD** & **Reference** & **Time (sec)** & **Deviation (\%)** \\ \hline C101 & 10 & 828.94 & 10 & 827.3 & [32] & 30.5 & 0.20 \\ C102 & 10 & 828.94 & 10 & 827.3 & [32] & 28.2 & 0.20 \\ C103 & 10 & 828.94 & 10 & 826.3 & [33] & 27.6 & 0.32 \\ C104 & 10 & 828.94 & 10 & 822.9 & [33] & 30.3 & 0.73 \\ C105 & 10 & 828.94 & 10 & 827.3 & [33] & 26.9 & 0.20 \\ C106 & 10 & 828.94 & 10 & 827.3 & [32] & 27.5 & 0.20 \\ C107 & 10 & 828.94 & 10 & 827.3 & [33] & 29.1 & 0.20 \\ C108 & 10 & 828.94 & 10 & 827.3 & [33] & 27.8 & 0.20 \\ C109 & 10 & 828.94 & 10 & 827.3 & [33] & 29.8 & 0.20 \\ R101 & 19 & 1654.93 & 18 & 1607.7 & [32] & 65.5 & - \\ R102 & 18 & 1475.33 & 17 & 1434 & [32] & 55.5 & - \\ R103 & 14 & 1240.44 & 13 & 1175.67 & [34] & 80.5 & - \\ R104 & 10 & 1010.72 & 10 & 974.2 & [22] & 65.2 & 3.75 \\ R105 & 15 & 1389.85 & 15 & 1346.12 & [35] & 78.5 & 3.25 \\ R106 & 13 & 1269.14 & 13 & 1234.6 & [36] & 85.6 & 2.80 \\ R107 & 11 & 1102.72 & 11 & 1051.84 & [35] & 87.8 & 4.84 \\ R108 & 10 & 991.57 & 10 & 942.9 & [24] & 95.2 & 5.16 \\ R109 & 12 & 1177.76 & 12 & 1101.99 & [37] & 99.2 & 6.88 \\ R110 & 12 & 1129.60 & 12 & 1068 & [36] & 85.0 & 5.77 \\ R111 & 12 & 1108.70 & 12 & 1048.7 & [36] & 86.3 & 5.72 \\ R112 & 10 & 964.15 & 10 & 953.63 & [38] & 101.6 & 1.10 \\ RC101 & 15 & 1662.56 & 15 & 1619.8 & [39] & 55.6 & 2.64 \\ RC102 & 14 & 1486.35 & 14 & 1461.33 & [24] & 48.6 & 1.71 \\ RC103 & 12 & 1291.95 & 12 & 1196.12 & [22] & 72.1 & 8.01 \\ RC104 & 10 & 1162.53 & 10 & 1135.48 & [40] & 46.3 & 2.38 \\ RC105 & 15 & 1604.53 & 15 & 1519.29 & [24] & 56.3 & 5.61 \\ RC105 & 16 & 1575.31 & 16 & 1518.6 & [41] & 56.3 & 3.73 \\ RC106 & 13 & 1400.09 & 13 & 1371.69 & [22] & 62.4 & 2.07 \\ RC107 & 12 & 1259.55 & 12 & 1212.83 & [41] & 47.9 & 3.85 \\ RC108 & 11 & 1205.13 & 11 & 1117.53 & [41] & 43.6 & 7.84 \\ \hline \end{tabular}
\end{table} 
Table 1: Solomon instances with short scheduling horizon: comparison of our MOLNS with the best-published results.
## 2 Materials and Methods
### Networks of Mean-Field Models  
Extending our previous work at the mesoscale [6] to model large brain regions, we define networks of mean-field models, representing interconnected cortical columns (each described by a mean-field model). For simplicity, we considered only excitatory interactions between cortical columns, while inhibitory connections remained local to each column. The equations of such a network, expanding the two-population mean-field model (Equation (4)), are given in the following equation:  
\[T\frac{dv_{e}(k)}{dt} = \mathcal{F}_{e}\big{[}v_{e}^{input}(k)+v_{aff}(k),v_{i}(k)\big{]} -v_{e}(k)\] \[T\frac{dv_{i}(k)}{dt} = \mathcal{F}_{i}\big{[}v_{e}^{input}(k)+v_{aff}(k),v_{i}(k)\big{]} -v_{i}(k)\] \[\tau_{w}\frac{dW(k)}{dt} = -W(k)+b\tau_{w}v_{e}(k)\] \[+a(\mu_{V}(v_{e}(k),v_{i}(k),W(k))-E_{L}),\]  
where \(v_{e}(k)\) and \(v_{i}(k)\) are the excitatory and inhibitory population firing rates at site \(k\), respectively, \(W(k)\) is the level of adaptation of the population, and \(v_{e}^{input}(k)\) is the excitatory synaptic input. The latter is given as follows:  
\[v_{e}^{input}(k)=v_{drive}(k)+\sum_{j}C_{jk}\;v_{e}(j,t-\left\|j-k\right\|/v_ {c}) \tag{6}\]  
where the sum runs over all nodes \(j\) sending excitatory connections to node \(k\), and \(C_{jk}\) is the strength of the connection from \(j\) to \(k\) (and is equal to 1 for \(j=k\)). Note that \(v_{e}(j,t-\left\|j-k\right\|/v_{c})\) is the activity of the excitatory population at node \(k\) at time \(t-\left\|j-k\right\|/v_{c}\) to account for the delay of axonal propagation. Here, \(\left\|j-k\right\|\) is the distance between nodes \(j\) and \(k\) and \(v_{c}\) is the axonal propagation speed.  
As detailed in a previous study [14], the spike-frequency adaptation parameter in the model can be linked to the neuromodulatory drive. From a biological perspective, during wakefulness, increased concentrations of neuromodulators--such as acetylcholine, NE,and 5 HT--diminish spike-frequency adaptation by down-regulating various K+ channels, which results in a prolonged depolarization of the neurons and enables the emergence of asynchronous and irregular firing [20]. On the contrary, during unconsciousness, the lower concentrations of neuromodulators leave K+ channels open. The adaptation build-up and its subsequent decay results in synchronous hyperpolarization and depolarization of the neuron's membrane potential, which, in turn, leads to the generation of slow-wave dynamics. \(b\) controls the strength of spike-frequency adaptation, so that augmenting \(b\) corresponds to a reduced neuromodulatory drive and switches the activity from asynchronous to slow waves (see details in [8,13,14]).
# Fractal Cardioid Slot Antenna for Super Wideband Applications
## 2 Proposed Antenna and Parametric Sweep
### Characteristics of the Proposed Antenna  
The simulated values of real and imaginary parts of the impedance of optimized antennas (Figure 1) are shown in Figure 6.  
The results show a very narrow range of variation of the real part of the impedance of only 28-87 \(\Omega\), which is very useful for wideband energy harvesting applications.  
The current density distribution at the first six antenna resonant frequencies, 2.1 GHz, 3.7 GHz, 5.2 GHz, 6.3 GHz, 8 GHz and 9.3 GHz, are shown in Figure 7.  
Figure 5: The simulated reflection coefficients for different values of distance \(g\).  
Figure 6: Simulated impedance of the proposed antenna.  
Simulated radiation patterns of the proposed antenna in the E-planes and H-planes are provided in the following frequencies: 1.8 GHz, 2.2 GHz, 2.4 GHz, 3.4 GHz, 5.8 GHz, 10 GHz, 13 GHz, 17 GHz, 19 GHz, 24 GHz, 28 GHz and 30 GHz. They are shown in Figure 8.  
Figure 7: Current density distribution over the antenna surface at a few resonant frequencies.  
Simulated 3D radiation patterns of the proposed antenna on frequencies 1.8 GHz, 2.2 GHz, 2.4 GHz, 3.4 GHz, 5.8 GHz and 10 GHz in the logaritmic scale are shown in Figure 9.  
Figure 8: Simulated radiation patterns of the antenna in the E-plane and H-plane.
# Lower-Latency Screen Updates over QUIC with Forward Error Correction
## 2 Background and Related Work
### QUIC and FEC
To the best of our knowledge, none of the previous studies considered a partial order delivery approach combined with FEC over QUIC to improve latency.