esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Process Parameters on Mode Conversion in Submicron Tapered Silicon Ridge Waveguides
## 2 Structure and Analysis
### Mode Hybridization Widths  
For any waveguide eigenmode, the mode polarization ratio (\(\gamma\)) can be defined as:  
\[\gamma=\frac{\int{\left|\mathrm{E_{X}}\right|^{2}\mathrm{dxdy}}}{\int{\left| \mathrm{E_{X}}\right|^{2}\mathrm{dxdy}}+\int{\left|\mathrm{E_{y}}\right|^{2} \mathrm{dxdy}}} \tag{1}\]  
where \(\mathrm{E_{x}}\) and \(\mathrm{E_{y}}\) are the components of the electric field in the x- and y-directions, respectively. This equation is true for any eigenmode. For a typical TE mode, the Ex-component is much stronger than the \(\mathrm{E_{y}}\) component, and consequently, the ratio \(\gamma\) is close to 100%. On the other hand, for a TM mode, ideally \(\gamma\) = 0. Therefore, modes are hybridized when 0 \(<\gamma<\) 100%. In other words, at mode hybridization widths, the \(\mathrm{E_{x}}\) and \(\mathrm{E_{y}}\) components of two different modes become comparable to each other. By calculating \(\gamma\) for all guided modes at different widths, mode hybridization regions can be identified. However, in this  
Figure 1: The cross-section for an SOI ridge waveguide (**a**) without sidewall angle (**b**) with sidewall angle.  
work, we have used commercial software to determine waveguide widths at which modes are hybridized. A Finite-Difference-Eigenmode (FDE) solver (from Ansys Lumerical) is used to identify the region where mode crossing occurs between TM\({}_{0}\) mode and TE\({}_{1}\) mode. At the core silicon width (W\({}_{\rm co}\)) of mode crossing, due to vertical asymmetry, TM\({}_{0}\) and TE\({}_{1}\) modes are hybridized [22]. We run FDE simulations, varying the ridge waveguide width (W\({}_{\rm co}\)) from 0.5 \(\upmu\)m to 3 \(\upmu\)m. At each step, the effective index (n\({}_{\rm eff}\)) for the first 4-5 modes are calculated, and we then subtract from it the n\({}_{\rm eff}\) of the slab mode (without the ridge). For a ridge waveguide, an optical mode is supported if its effective index is higher than the slab mode. We then plot this difference (\(\Delta\)n\({}_{\rm eff}\)) to determine the width (W\({}_{\rm h}\)) at which mode hybridization is observed.  
Figure 2a-c shows the \(\Delta\)n\({}_{\rm eff}\) for the SOI ridge waveguides for different etch depths for oxide-cladded ridge waveguide without any sidewall angle as the width of core silicon (W\({}_{\rm co}\)) varies from 0.5 \(\upmu\)m to 3 \(\upmu\)m. The mode hybridization between TM\({}_{0}\) and TE\({}_{1}\) is observed at silicon waveguide widths (W\({}_{\rm co}\) = W\({}_{\rm h}\)) of 1.47\(\upmu\)m, 1.37 \(\upmu\)m, and 1.23 \(\upmu\)m for the ridges with etch-depths of 0.6 H, 0.5 H, and 0.4 H, respectively. It can be seen that W\({}_{\rm h}\) gets narrower when the waveguide etch-depth is reduced. As the latter is reduced from 0.6 H to 0.4 H, the mode hybridization region shifts from W\({}_{\rm h}\) = 1.47 \(\upmu\)m to 1.23 \(\upmu\)m. The simulation shows no significant change in the mode hybridization width (W\({}_{\rm h}\)) due to vertical asymmetry introduced by asymmetric cladding and angled sidewalls.
## 6 Experimental Studies  
The proposed LTV MPC scheme described in Section 4 is applied to the two-link manipulator in two ways. In the first study, the performance and robustness of the implemented LTV MPC scheme is demonstrated. For this purpose, the state measurement is obtained directly from the encoders and allows a sampling interval of 20 ms. In order to demonstrate the performance with vision-based state measurement, the control loop is closed with visual feedback instead of encoders in the second scenario. This requires a sampling interval of 200 ms, which results in limited performance of the overall closed-loop system.  
In both studies, the MPC cost function (22) is parameterized by diagonal weighting matrices to penalize the control error with respect to the desired trajectory, that is, \(\mathbf{Q}=\text{diag}([Q_{\alpha},Q_{\beta},Q_{\alpha},Q_{\beta}])=\text{diag}([1 00,100,0.1,0.1])\), whereas the changes in the input are weighted by \(R=\text{diag}([R_{\Delta I_{1}},R_{\Delta I_{2}}])=\text{diag}([1,1])\). A preliminary adjustment of the parameters has been obtained by simulations followed by re-adjusting the weights at the hardware. In order to not exceed the maximum currents of the rotatory modules, the constraints for the inputs are determined by \(I_{1,\text{min}}=I_{2,\text{min}}=-4\) A and \(I_{1,\text{max}}=I_{2,\text{max}}=4\) A. Moreover, a prediction horizon of \(N=5\) is chosen  
Figure 9: Doughnut chart for average runtime visualization of computation time for one sampling instance when visual control feedback from one camera is used.  
and the controller is initialized by \(\mathbf{x}(0)=\mathbf{x}_{\text{des}}(0)\) and \(\mathbf{u}(-1)=\mathbf{u}_{\text{ff}}(0)\). The repetitive linear-quadratic optimization problem from Equations (29) and (30) is solved by using the active set method applying the open-source implementation qpOASES [17].  
For interpreting the results, it is mentioned that the encoders can measure the angular velocity of the modules with a resolution of 0.04 rad/s = 2.29 deg/s.
## 3 Predicting the Signal Attenuation Level at Free Space (Section A)
### Collecting the Attenuation Levels  
In stage 2, we collected the attenuation levels during the satellite transition in orbit for 24 h. They correspond to the angle (\(\theta\)) changes between the ground station and the satellite position.  
Figure 3: SNR measurement collection of the attenuation levels.
## 4 Classification of Routing Protocols
### Postural-Movement-Based Routing Protocols  
The network topology of WBSNs or the link between two nodes often faces the problem of partitioning or disconnection because of the body postural movements. In this class of WBSNs routing protocols, different researchers have tried to solve the problem of link disconnection by defining a cost function, which is updated periodically. These protocols choose routes from the bio-medical sensor nodes to the base station with minimum cost to forward the data packets. Some of these routing protocols are discussed in the following sections.
## 3 Results and Discussion
### Analysis of PCA
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Groups** & **T\({}_{2}\)** & & **TVB-N** & **K Value** & \begin{tabular}{c} **Moisture** \\ **Content** \\ \end{tabular} & \
\begin{tabular}{c} **Fat** \\ **Content** \\ \end{tabular} &
\begin{tabular}{c} **Activity of** \\ **Ca\({}^{2+}\)-ATPase** \\ \end{tabular} & **MDA** \\ \hline CK2 & pT\({}_{21}\) & R & 0.56437 & 0.34126 & 0.39643 & 0.23168 & 0.41154 & 0.28746 \\  & R\({}^{2}\) & 0.31851 & 0.11646 & 0.15716 & 0.05367 & 0.16937 & 0.08263 \\  & P & ns & ns & ns & ns & ns \\  & pT\({}_{22}\) & R & 0.38324 & 0.29435 & 0.93293 & 0.97317 & 0.97224 & 0.89146 \\  & R\({}^{2}\) & 0.3705 & 0.08644 & 0.87036 & 0.94706 & 0.94525 & 0.7947 \\  & P & ns & ns & 0.01 & 0.01 & 0.01 & 0.05 \\  & pT\({}_{23}\) & R & 0.90213 & 0.97568 & 0.82514 & 0.28246 & 0.87412 & 0.93562 \\  & R\({}^{2}\) & 0.81384 & 0.95195 & 0.68086 & 0.07978 & 0.76173 & 0.87539 \\  & P & 0.01 & 0.01 & 0.05 & ns & 0.05 & 0.01 \\ \hline C1 & pT\({}_{21}\) & R & 0.54873 & 0.28534 & 0.42674 & 0.32143 & 0.37892 & 0.29836 \\  & R\({}^{2}\) & 0.30111 & 0.08142 & 0.18211 & 0.10332 & 0.14358 & 0.08902 \\  & P & ns & ns & ns & ns & ns & ns \\  & pT\({}_{22}\) & R & 0.88412 & 0.38423 & 0.96612 & 0.97412 & 0.97733 & 0.88911 \\  & R\({}^{2}\) & 0.78167 & 0.14763 & 0.93339 & 0.94628 & 0.94731 & 0.79052 \\  & P & 0.05 & ns & 0.01 & 0.01 & 0.01 & 0.05 \\  & pT\({}_{23}\) & R & 0.93215 & 0.97346 & 0.79354 & 0.58421 & 0.74278 & 0.96542 \\  & R\({}^{2}\) & 0.8689 & 0.94762 & 0.62971 & 0.3413 & 0.55172 & 0.93204 \\  & P & 0.01 & 0.01 & 0.05 & ns & 0.05 & 0.01 \\ \hline C2 & pT\({}_{21}\) & R & 0.32145 & 0.31238 & 0.50156 & 0.16973 & 0.45672 & 0.37829 \\  & R\({}^{2}\) & 0.10333 & 0.0969 & 0.25156 & 0.02881 & 0.20859 & 0.1431 \\  & P & ns & ns & ns & ns & ns & ns \\  & pT\({}_{22}\) & R & 0.82356 & 0.49324 & 0.94582 & 0.96374 & 0.91214 & 0.62569 \\  & R\({}^{2}\) & 0.67825 & 0.24329 & 0.89458 & 0.9288 & 0.832 & 0.39149 \\  & P & 0.05 & ns & 0.01 & 0.01 & 0.01 & ns \\  & pT\({}_{23}\) & R & 0.96739 & 0.95678 & 0.94718 & 0.50047 & 0.81248 & 0.45768 \\  & R\({}^{2}\) & 0.93584 & 0.91543 & 0.89715 & 0.25047 & 0.66012 & 0.20947 \\  & P & 0.01 & 0.01 & 0.05 & ns & 0.05 & ns \\ \hline C3 & pT\({}_{21}\) & R & 0.45362 & 0.37288 & 0.51118 & 0.19463 & 0.47284 & 0.32145 \\  & R\({}^{2}\) & 0.20577 & 0.13904 & 0.26131 & 0.03789 & 0.22358 & 0.10333 \\  & P & ns & ns & ns & ns & ns & ns \\  & pT\({}_{22}\) & R & 0.73246 & 0.30123 & 0.94378 & 0.94178 & 0.89236 & 0.64113 \\  & R\({}^{2}\) & 0.53649 & 0.09074 & 0.89072 & 0.88695 & 0.79631 & 0.41105 \\  & P & 0.05 & ns & 0.01 & 0.01 & 0.01 & 0.05 \\  & pT\({}_{23}\) & R & 0.98275 & 0.95467 & 0.79932 & 0.5101 & 0.80213 & 0.95467 \\  & R\({}^{2}\) & 0.9658 & 0.9114 & 0.79389 & 0.2602 & 0.64341 & 0.9114 \\  & P & 0.01 & 0.01 & 0.05 & ns & 0.05 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont.
# Mechanical Performance of Polymer Materials for Low-Temperature Applications
## 3 Results and Discussion
### Application under Low-Temperature Conditions in Ships and Offshore Structures
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temp.**} & \multicolumn{2}{c}{**PVDF**} & \multicolumn{2}{c}{**PE**} & \multicolumn{2}{c}{**ABS**} \\ \cline{2-7}  & \(\sigma_{\text{T}}\) **(MPa)** & **E (MPa)** & **\(\sigma_{\text{T}}\) (MPa)** & **E (MPa)** & **\(\sigma_{\text{T}}\) (MPa)** & **E (MPa)** \\ \hline \(-\)100 & 148 (1.52) & 7700 (2.11) & 47 (1.92) & 2550 (2.86) & 75 (2.41) & 4100 (2.53) \\ \hline \(-\)50 & 93 (1.38) & 3400 (1.82) & 45 (1.53) & 1750 (3.12) & 66 (1.89) & 2600 (1.45) \\ \hline
0 & 55 (1.36) & 1700 (1.58) & 30 (1.62) & 1250 (2.52) & 51 (1.59) & 2230 (2.05) \\ \hline
25 & 42 (1.25) & 1600 (1.55) & 16 (1.12) & 300 (2.21) & 39 (1.75) & 2160 (1.62) \\ \hline \hline \end{tabular} Note: Values in parentheses 0 represent standard deviation.  
\end{table} 
Table 2: Tensile strength and Young’s modulus from temperature-dependency tests.
## 3 Results and Discussion
### Accuracy of the Method and Sample Extraction Efficiency  
We applied LLE to avoid any possible loss caused by other frequently used extraction methods (e.g., solid phase extraction (SPE)), and used SIM for detection to reduce interference from impurities as well as to enhance the sensitivity. As a result, the recoveries for 0.5 \(\mathrm{\SIUnitSymbolMicro g}\)/L solutions containing 13 target metabolites were 89.3%-94.5% (average recovery rate: 92.3%), and that for 5 \(\mathrm{\SIUnitSymbolMicro g}\)/L (93.4% on average) were even better (Table 3). The high recovery rates associated with low RSDs (<7%) for 13 target metabolites plus one IS indicate the accuracy and consistency of the method; the low relative errors (<5%) for comparison between Groups A and B also confirm the excellent LLE efficiency. The merit may have been, in part, attributable to the autoprogrammed vacuum concentration system (miVac Duo concentrator), which is able to reduce the cross-contamination usually caused by the traditional  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metabolite** & **RT (min)** & **DI (\(m\)/z)** & **LOD (\(\mathrm{\SIUnitSymbolMicro g}\)/L)** & **LOQ (\(\mathrm{\SIUnitSymbolMicro g}\)/L)** \\ \hline
7-phenol & 9.34 & 245.0 & 0.050 & 0.2 \\ DMP & 9.63 & 306.1 & 0.050 & 0.2 \\ DEP & 12.81 & 334.7 & 0.100 & 0.3 \\ DMTP & 14.54 & 322.2 & 0.050 & 0.2 \\ DMDTP & 16.73 & 338.2 & 0.100 & 0.3 \\ DETP & 17.38 & 350.4 & 0.100 & 0.3 \\ DEIDTP & 19.21 & 366.0 & 0.050 & 0.2 \\ _trans_-CDCA & 10.63 & 331.0 & 0.050 & 0.2 \\ _cis_-DCCA & 10.86 & 323.0 & 0.025 & 0.1 \\ _trans_-DCCA & 11.08 & 323.0 & 0.025 & 0.1 \\ _cis_-DBCA & 11.15 & 368.9 & 0.025 & 0.1 \\ FPBA & 20.37 & 364.0 & 0.025 & 0.1 \\
2-PBA & 21.28 & 364.0 & 0.025 & 0.1 \\
3-PBA & 21.92 & 382.0 & 0.025 & 0.1 \\ \hline \hline \end{tabular}  
* RT: retention time; DI: detected ion; LOD: limit of detection; LOQ: limit of quantification.  
\end{table}
Table 2: Analytical data of GCMS for tested insecticide metabolites.  
Figure 2: GCMS chromatogram of 13 insecticide metabolites plus one IS in a spiked urine sample (0.1 \(\mathrm{\SIUnitSymbolMicro g}\)/L).  
drying method (i.e., manual use of rotary evaporator and nitrogen drying). Additionally, no significant difference in concentration between Groups A and B (identical to standard solutions) also eliminates any possibility of matrix interference in the analysis.
## 4 Outage Probability Analysis
Finally, the effect of quantization bits of the low-resolution ADC on the outage probability and ergodic rate is illustrated in Figures 9 and 10. It can be seen that both the outage and ergodic rate performances are degraded by higher channel estimation errors. Channel estimation errors also lead to a floor in high SNR areas. When the quantization bits of the ADC are very low, the system performance is poor even if the channel estimation error is small. It can be observed that the outage and ergodic rate gradually improve as the number of quantization bits increases from 1 to 6. However, there is no significant performance gain as the number of quantization bits continues to increase. Instead, performance can be improved by reducing the strength of the loopback interference. This result provides insight for system design, as appropriate quantization bits can be chosen to achieve the desired resolution and balance device cost and performance enhancement benefits.
## 3 Methodology
### Time Domain Feature Extraction Module
#### 3.2.2 Spatial Feature Extraction Based on GAT
The attention coefficient for each node pair \((i,j)\) in a single attention layer is computed as follows:  
\[\alpha_{ij}=\frac{exp\big{(}LeakyReLU\big{(}a^{T}\left[Wh_{i}\big{|}|Wh_{i} \big{]}\right)\big{)}}{\sum\limits_{k\in N_{i}}exp\big{(}LeakyReLU\big{(}a^{T }[Wh_{i}]|Wh_{k}\big{)}\big{)}} \tag{5}\]  
where:\(N_{i}\) is the neighbor node of node \(i\); the input feature of the node is: \(h=\{h_{1},h_{2},\ldots,h_{N}\},h_{i}\in R^{M}\), where \(N\) is the number of nodes and feature dimension, and the output of the node's feature is:\(h^{\prime}=\big{\{}h^{\prime}_{1},h^{\prime}_{2},\ldots,h^{\prime}_{N}\big{\}},h^{ \prime}_{i}\in R^{M^{\prime}}\); \(W\in R^{M*M^{\prime}}\) is the transformation weight matrix applied at each node; \(a\in R^{2M^{\prime}}\) is a weight vector that maps the inputs to \(R\); \(LeakyReLU\) is a nonlinear activation function; and \(\mid\mid\) is a join operation. The output of each node of each GAT layer, i.e., the feature output of the final node is represented as:  
\[h^{\prime}_{i}=\sigma\big{(}\sum\
olimits_{j\in N_{i}}\alpha_{ij}Wh_{i}\big{)} \tag{6}\]  
where: \(\alpha_{ij}\) is the attention coefficient of node \(i\) and node \(j\); and \(\sigma\) is a nonlinear activation function. The multi-head attention mechanism processes the input features in parallel through multiple graph attention mechanisms and splices the outputs of multiple attention heads,applying \(k\) independent attention mechanisms to compute the hidden states and then joining their features to obtain the following output representation:  
\[h_{i}^{\prime}=\sigma\left(\frac{1}{k}\sum_{k=1}^{k}\sum_{j\in N_{i}}a_{ij}^{k} W^{k}h_{i}\right) \tag{7}\]  
where: \(a_{ij}^{k}\) is the normalized attention coefficient of the k-th attention head.
# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
## 5 Experimental Validation and Results  
The difficulty of obtaining images of mobile ground targets from high altitudes is well known, and there are few existing datasets for the damage assessment of mobile ground targets. Therefore, we conducted experiments based on a simulation environment.  
To simplify our model while making it more applicable to the real world, we added object shadows as well as the strike effects of sparks, craters, and thick smoke on the targets. Furthermore, we introduced environmental noise, interference from ground buildings, and dynamic lighting changes and divided the simulation scenario into two parts: before and after the strike.
# MPC for Optimized Energy Exchange between Two Renewable-Energy Prosumers
## 1 Introduction
### Case Study  
Figure 2 below illustrates the main components of the two prosumers used in this study and power flows between them. These two prosumers are envisioned as two prosumers within a larger network of prosumers. The structure of both prosumers is the same, consisting of a load, renewable energy supply, battery storage, and connections to other sources of power, such as the other prosumers in the larger network and/or a retail energy supplier. Transactions between the two prosumers are shown as power flows through the peer-to-peer transactions block.  
The differences between the prosumers are captured in the two load profiles and the renewable energy profiles, the example used throughout this work imagines one load as residential and the other as commercial, although the system applies to any two loads that are complementary to one another. The residential and commercial load profiles are modeled based on real data, and renewable energy resource (RER) supplies are based on typical meteorological year (TMY) weather data and models for PV and wind turbines. The modeling presented here does not include currents or voltages, just discrete hourly energy flows. Variable energy pricing is not modeled, although the MPC objective function contains parameters that could depend on energy prices. Although there are several MPC formulations possible, here, a state space approach is used because it allows for handling many variables easily.  
The cost function is formulated as a constrained quadratic, whose complexity depends on the number of variables and the length of the control horizon. Referring again to Figure 2, it is possible to consider MPC operation with or without the energy sharing. This can be easily achieved by adjusting constraint parameters within the algorithm. This allows for exploration of the potential benefit of coupling two prosumers within a larger network of prosumers. Energy sharing between the two coupled prosumers is prioritized over energy sharing with other prosumers in the network.  
Figure 1: General model predictive control (MPC) control flow [9].
# Influence of Angiopoietin Treatment with Hypoxia and
## 3 Results
### Effect of Oxygen Tension and Angiopoietin-1/2 on ECM Related-Genes  
#### 3.3.1 Analysis of Aggrecan Relative Gene Expression  
Concerning the relative gene expression of \(ACAN\) for the NP cells mixed population, we did not observe any significant difference for normoxia, compared to the control condition (Figure 3a). No effects were detected in a dose-dependent manner for angiopoietin on the relative gene expression nor for specific variation in the use of Ang-1 or Ang-2. Nonetheless, all treated samples, either with Ang-1 or Ang-2, showed an equal amount of \(ACAN\) as the control samples, or at least a 2- to 3-fold increase in their ACAN relative expression. However, Ang-2 samples at 100 ng/mL in normoxia presented a downregulation in their \(ACAN\) gene expression.  
Like the NP cells mixed population, the control Day 0 and control Day 7 samples showed non-significant differences in their relative expression of \(ACAN\) in normoxia and hypoxia for the NP cells Tie2\({}^{-}\) population (Figure 3b). However, a remarkable effect in a dose-dependent manner was detectable for the Ang-1/2 sample at 10 ng/mL in normoxia, the Ang-1 sample at 100 ng/mL in normoxia and hypoxia, and the Ang-2 sample at 100 ng/mL in hypoxia.  
The NP cells mixed, NP Tie2\({}^{-}\) cells, and NP Tie2\({}^{+}\) cells populations showed no differences in the gene expression of \(ACAN\), compared to the control Day 0 (Figure 3c). We detected no specific variation in the use of Ang-1 or Ang-2 in normoxia and hypoxia but detected a dose-dependent increase in relative gene expression for hypoxia. In normoxia, we detected no effect in a dose-dependent manner of angiopoietin on the relative gene expression or specific variation in the use of Ang-1 or Ang-2. All the stimulated samples in normoxia were downregulated in their \(ACAN\) gene expression by 40 to 75%, compared to the control Day 0 sample.  
Figure 3: Relative gene expression of the three NP cells populations, comparing the effect of Ang1/2 at different concentrations in normoxia and hypoxia on the ACAN gene expression. The control (Day 0) samples were used as reference (set at 1). Ang-1 and Ang-2 were administrated in three concentrations: 10, 50 and 100 ng/mL. (**a**) ACAN relative gene expression of NP cells mixed population; (**b**) ACAN relative gene expression of NP cells Tie2\({}^{-}\) population; (**c**) ACAN relative gene expression of NP cells Tie2\({}^{+}\) population. Bars represent mean \(\pm\) SD.
## 3 Results
### Functional Connectivity Features Validation  
In order to further investigate the selected features in terms of feature value alterations, a post hoc Wilcoxon test was performed to indicate significant deviations between the ASD and TD groups (significance level set to 0.05). As such, 19 features presented significant differences (\(p\)-value < 0.05). Interestingly, all the resulting features included sFC and mean dFC, while no important distinction could be discerned in variance dFC values. These included frontal and occipital areas, the paracingulate, and the precuneus region, as well as the bilateral parahippocampal gyrus (Figure 4).  
In detail, sFC connections incorporated six pairs implicating frontal areas and one pair involving the precuneus and the middle occipital gyrus. On the contrary, the Wilcoxon test demonstrated mean dFC significant variations in eight connections in frontal areas, five in the parahippocampal gyrus, two in the paracingulate region, and one pair involving the right middle occipital gyrus. Although the importance of the common connections between the FC features was additionally verified by the statistical test, it is noteworthy that in all 19 connections, the Pearson correlation coefficient exhibited a significant increment from TD to ASD indicating autism-related over-connectivity patterns.
## 4 Material and Methods
### Simulation Steps  
A DVB-T2 system includes many processing blocks, and only the main blocks were implemented in our simulator, the native DVB-T2 system implemented was the light version of DVB-T2. Three main performance evaluation tools such as the BER, MER, and EVM were used in this work. The main simulation steps exploited to evaluate CFO performance are highlighted in Figure 9.  
As shown in this figure, the first step (1) was the development and simulation of the light version of the DVB-T2 system called native DVB-T2 and the Universal Filter Multicarrier (UFMC)-based DVB-T2 system and the CFO insertion. The second step (2) consisted in adding LDPC coding in both systems and evaluating the CFO impact. The third step (3) consisted in substituting the QAM mapping block for a 2D-NUC block in both the native DVB-T2 transmitter and the receiver. The fourth step (4) performed the same task as the previous one, but an NUC was inserted in the UFMC-based DVB-T2. The fifth step (5) consisted in comparing the CFO impact in the NUC-based DVB-T2 system to the CFO impact in Universal Filter Multicarrier/non-uniform constellation (UFMC/NUC)-based DVB-T2 system, and the last step (6) consisted in comparing the CFO impact on the native DVB-T2 system to the CFO impact on the UFMC/NUC-based DVB-T2 system. It is relevant to remember that simulations were performed when only Gaussian noise was considered and when both Gaussian noise and a fading (TU6) channel were used.