esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Flow Characteristics of Unsteady Cloud Cavitation
In order to have a deeper understanding of the re-entrant jet as it progresses and the shedding mechanism with the three cavitation models, Figure 9 gives the formation and evolution of the re-entrant jet calculated by using the three different cavitation models for a whole cycle. Meanwhile, Figure 10 gives the re-entrant jet and vortex shedding results that were observed in the experiment. Figure 8: Time evolution of cloud cavitation for the numerical and experimental results (\(\sigma\) = 0.65, \(\alpha\) = 60\({}^{\circ}\)). Figure 9: The formation and evolution of the re-entrant jet calculated by three different cavitation models (\(\sigma=0.65\), \(\alpha=60^{\circ}\)). From the results of the Zwart model, it can be seen that the mixture of water and vapor at the cavity tail moves toward the leading edge near the wall under the action of local high pressure. The vortex is generated in the re-entrant jet area and expands with the forward movement of the re-entrant jet, and then moves downstream with the shedding bubbles. It is not obvious enough for the Zwart model to simulate the vortex motion inside the cavitating area. The full cavitation model (FCM) not only predicts the formation of the re-entrant jet in the cavity and the separation of attached bubbles caused by re-entrant jet, it also predicts the vortex flow. The Kunz model also predicts the vortex motion inside the cavitation area. However, the vortex cannot move to the lower and rear part of the hydrofoil. Moreover, the Kunz model does not predict the shearing action of the re-entrant jet on the attached cavity. The attached cavity is compressed and then shears under the action of the tail vortex; then, it is further compressed by the re-entrant jet, which is inconsistent with experimental phenomena. Considering the three cavitation models, the full cavitation model (FCM) successfully predicts the formation of the re-entrant jet and its shearing on the attached cavity. In addition, the FCM predicts the vortex shedding of bubbles, which is consistent with the experimental observations.
## 3 Results
### Prediction and Validation of Temperature Profile and Part Distortion
#### 3.2.2 Validation of Distortion  
Figure 11 shows the measured and predicted distortion in a color plot after the completion of the first and last layers at the edge of the DB-4 build plate. The thicker red color represents a higher distortion in the plate. The two red circles at the plate center are the clamp bolts that were  
Figure 10: Measured temperature history and model prediction at TC-13 (=right), -15(=left) and -17(=center) during the deposition at center clamp condition. Two interlayer cooling times of (**a**) DB4 = 4 min and (**b**) DB5 = 0 min.  
Figure 9: Measured temperature history and model prediction at TC-13 (=right), -15(=left) and -17(=center) during the deposition at full clamp condition. Two interlayer cooling times of (**a**) DB1 = 0 min and (**b**) DB2 = 2 min.  
placed on the top of the plate, because the laser scanner measured a high distortion value for the bolts. Approximately 3.3 mm of distortion was observed at both side edges of the plate, as shown in Figure 11a,b. The distortion progressively decreased towards the center of the plate, and became almost zero at the center region, because of the bolt clamps. This trend was well captured by the prediction in Figure 11c,d.  
The predicted distortions for DB-1, DB-2, DB-4, and DB-5 are compared with the measured values using the FaroArm scanner in Figures 12 and 13. The blue marker and red solid line represent the predicted and measured values, respectively. The plate bends upwards by approximately 1.0 mm and 1.5 mm in DB-1 and DB-2, respectively, and 3.3 mm and 2.6 mm in DB-4 and DB-5, respectively. This upward bending can be explained by the temperature gradient mechanism [32; 33]. The deposited layer expands during heating and contracts during cooling. The contraction of the upper layer is constrained by the previously deposited layer and generates tensile residual stress, resulting in the upward bending. Larger distortion was found in DB-2 (2 min. interlayer cooling) and DB-4 (4 min.) than in DB-1 (0 min.) and DB-5 (0 min.). This shows that the level of distortion increased with interlayer cooling time. This is consistent with the findings of [PERSON] et al. [7] on blown powder direct energy deposition of Ti-6 Al-4V. Also, larger distortion was observed in center clamp conditions (DB-4 and DB-5) than full clamp conditions (DB-1 and DB-2). This indicates that the full clamp constraint effectively decreased the distortion. The model prediction captured all the characteristic features described above for the center and full clamp conditions. This implies that the model can predict the evolution of residual stress at various conditions with a reasonable accuracy.  
Figure 11: Distortion at the edge of the build plate for DB-4 (=center clamp and 4 min interlayer cooling time). The magnitude of the measured (**a**,**b**) and predicted (**c**,**d**) distortions are well agreed at layer 1 and layer 47. Laser line scanner was used for the measurement.
# Accelerating FVM-Based Parallel Fluid Simulations with Better Grid Renumbering Methods
## 2 Numerical Fluid Simulations Based on Finite Volume Method
### The Basic Idea of a Finite Volume Method  
The finite volume method is also called the control volume method. Its basic idea is to divide the computational area into discretized non-repeating control volumes. A set of discrete equations is obtained by integrating the differential equations (governing equations) to be solved over each control volume. The coefficients of discrete equations are calculated by values situated in the cell center and its neighbors, which is why it is important to keep the neighboring cell numbers as close as possible.  
In a concise summary, the basic idea of FVM can be expressed in Figure 1. The FVM acts like a transformer in fluid dynamics simulations, taking the control equations with boundary and mesh configurations as inputs [20]. The resulting outputs contain discretized sparse linear systems [21].  
In order to assemble a linear system \(\mathbf{Ax}=\mathbf{b}\), the continuous governing equation has to be solved discretely on a grid. Usually, the input mesh file contains the unstructured grids. It means that the internal meshes in the interesting domain do not have exactly the same adjacent cells. As shown in Figure 2, (a) represents a structured grid and (b) represents an unstructured grid. The unstructured grid is used for this paper's examples and experimental cases.  
After the domain discretization process using unstructured grids, the following four geometric elements can be obtained:  
* Node: The geometrical position of the unknown quantity is to be solved;
* Control volume: The smallest geometric unit to which a governing equation or conservation law applies;
* Face: Interface positions are used to segment control volumes corresponding to different nodes;
* Grid line: A cluster of curves formed by joining two adjacent nodes.  
In order to identify each element, a number or id has to be assigned to every node, volumes/cell, and face. The number determines the sequence of assembling and the resulted matrix structure.
# Efficient Removal Of U(VI) Ions from Aqueous Solutions by Tannic Acid/Graphene Oxide Composites
## 2 Materials and Methods  
### Synthesis and Purification of Graphene Oxide (GO) and Tannic Acid/Graphene Oxide (TA/GO)  
Graphene oxide was prepared according to the modified Hummer's method [28]. First, 120 mg of graphene oxide were added to 80 mL of deionized water and ultrasonically dispersed for 30 min. Subsequently, 32 mg of tannic acid were added to the resultant dispersion and stirred for 30 min. Finally, 5 mL of epichlorohydrin were added for crosslinking, and the solution was continuously stirred for three hours. The synthesized material was washed several times with deionized water and then lyophilized.
# Formation of Conductive Networks in Polysulfone Filled with Graphite-Derived Materials
## 3 Results and Discussion
### Thermal Conductivity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Filler** & \multicolumn{2}{c}{**NG**} & \multicolumn{2}{c}{**AG**} \\
**Content wt. \%** & & **Thermal** & **Thermal** & **Thermal** \\  & **Electrical** & **Conductivity in** & **Conductivity in** & **Electrical** & **Conductivity in** \\  & **Conductivity S/cm** & **Normal to Plane** & **the In-Plane** & **Conductivity S/cm** & **Normal to Plane** \\  & & **Direction, W/m\(\cdot\)K** & **Direction W/m\(\cdot\)K** & & **Direction W/m\(\cdot\)K** \\ \hline
0 & \(1\times 10^{-11}\) & 0.05 & & \(1\times 10^{-11}\) & 0.05 \\ \hline
30 & 0.0305 \(\pm\) 0.009 & 1.6 \(\pm\) 0.12 & 6.8 \(\pm\) 0.8 & 0.0005 \(\pm\) 0.0001 & 0.65 \(\pm\) 0.1 \\
40 & 8 \(\pm\) 0.9 & 2.9 \(\pm\) 0.19 & 5.6 \(\pm\) 0.9 & 0.0024 \(\pm\) 0.0016 & 0.89 \(\pm\) 012 \\
50 & 37.5 \(\pm\) 1.9 & 3.6 \(\pm\) 0.2 & 11.4 \(\pm\) 0.9 & 0.013 \(\pm\) 0.002 & 1.15 \(\pm\) 0.13 \\
60 & 49.6 \(\pm\) 3.5 & 9.5 \(\pm\) 0.6 & 20.1 \(\pm\) 1.5 & 0.4 \(\pm\) 0.01 & 2.10 \(\pm\) 0.19 \\
70 & 52.9 \(\pm\) 4.2 & 17.4 \(\pm\) 1.2 & 27.6 \(\pm\) 1.8 & 3.846 \(\pm\) 0.2 & 3.47 \(\pm\) 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Electrical and thermal conductivity of PSU-based composites filled with NG and AG. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Filler Content wt. \%** & **Electrical Conductivity S/cm** & \
\begin{tabular}{c} **Thermal Conductivity in Normal** \\ **to Plane Direction, W/m\(\cdot\)K** \\ **to Plane Direction, W/m\(\cdot\)K** \\ \end{tabular} &
\begin{tabular}{c} **Thermal Conductivity in the** \\ **In-Plane Direction, W/m\(\cdot\)K** \\ **to Plane Direction, W/m\(\cdot\)K** \\ \end{tabular} \\ \hline
28 wt. \% NG + 2 wt. \% CB & 0.07 \(\pm\) 0.01 & 0.88 \(\pm\) 0.1 & 1.7 \\
26 wt. \% NG + 4 wt. \% CB & 0.14 \(\pm\) 0.01 & 1.19 \(\pm\) 0.11 & 2.8 \\
68 wt. \% NG + 2 wt. \% CB & 53.2 \(\pm\) 4.3 & 5.46 \(\pm\) 0.3 & 30.6 \\
66 wt. \% NG + 4 wt. \% CB & 55.5 \(\pm\) 4.5 & 8.13 \(\pm\) 0.9 & 37.1 \\
28 wt. \% AG + 2 wt. \% CB & 0.0025 \(\pm\) 0.0005 & 0.46 \(\pm\) 0.05 & - \\
26 wt. \% AG + 4 wt. \% CB & 0.0009 \(\pm\) 0.0002 & 0.50 \(\pm\) 0.05 & - \\
68 wt. \% AG + 2 wt. \% CB & 2.2 \(\pm\) 0.16 & 5.23 \(\pm\) 0.3 & - \\
66 wt. \% AG + 4 wt. \% CB & 1.9 \(\pm\) 0.12 & 8.55 \(\pm\) 0.9 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Electrical and thermal conductivity of PSU-based composites filled with NG + CB and AG + CB.
# Optimal Energy Integration and Off-Design Analysis of an Amine-Based Natural Gas Sweetening Unit
## 3 Heat Integration Model
### First Loop: Direct Heat Integration  
Before heat integration, the required heat of the sweetening reboiler was completely provided by a fired heater (i.e., external heating source). In order to alleviate fuel consumption, a direct heat integration loop was first added to recycle the waste heat that is available above the pinch.  
It should be noted that only the turbine exhaust was found to be at a temperature higher than the pinch point, and thus, it was the only source considered for direct heat integration. The energy balance of the first waste heat recovery exchanger from the turbine's exhaust, noted E0 in Figure 2, was solved by manipulating the outlet temperature of the exhaust, noted T0 in Figure 2, to maximize the heat recovery under three constraints:  
1. An overall minimum temperature approach of 10 \({}^{\circ}\)C;
2. A minimum temperature of 160 \({}^{\circ}\)C assumed as a cooling limit of the exhausts in order to prevent sulfur condensation;
3. A maximal temperature for the heating oil of 177 \({}^{\circ}\)C to prevent amine degradation at the skin of reboiler tubes.
## 4 Principle and Operation of the Metamaterials
As discussed, metamaterial or patch-array-based technologies can be used to realize RISs structures. Metamaterial-based RISs can be deployed at different locations and can work as reflecting/refracting RIS between the user and base station (BS) or waveguide RIS operating at the BS [77]. A typical geometry layout of the patch-array smart surfaces based on RIS can be represented as a substrate with periodic (or quasi-periodic) unit cells. RISs are based on a local design, in which the cells do not interact with one other for clarity (referred to as a non-local design). Typically, a local design results in the creation of sub-optimal RISs. Further, RISs can be reconfigured electrically, mechanically, or thermally based on the tuning mechanisms. The electromagnetic properties of the RIS, such as phase discontinuities, can be controlled by tuning the surface impedance through various techniques. In addition to electrical voltage, other processes for tuning are thermal excitation, optical pump, and physical stretching. However, electrical control is the most suitable way as the electrical voltage is easier to quantize and control with FPGA chips. Metasurfaces based on three important RIS structure working conditions: reflection [78], refraction [79], and waveguide [76] are presented:  
1. Reflecting RIS: A digital coding reflective metasurface design is discussed in [80]. Varactor diodes with a configurable biassing voltage are contained in each element of metasurfaces. Each metasurfaces element can conduct discrete phase shifts and achieve beamforming for the reflected wave by predesigning various digitized biased voltage values. 2. Refracting RIS: A theoretical design of a perfectly reflecting/refracting metasurface is presented in [80]. An equivalent impedance matrix model is introduced to optimize the tangential field components on the two sides of the metasurface. Self-oscillating teleportation metasurfaces formed entirely of lossless components and nonlocal metasurfaces, the three possible device realizations, are discussed. Additionally, the importance of omega-type bianisotropy in constructing lossless-component implementations of perfectly refractive surfaces is discussed. 3. Waveguide RIS: A theoretical concept of waveguide-fed metasurfaces is presented in [76]. The metasurface's elements are characterized as uncoupled magnetic dipoles. The product of the reference wave and each element's polarizability measures the magnitude of each dipole element. The metasurface antenna can conduct beamforming by adjusting its polarizability. Each metasurface element functions as a micro-antenna. The small waveguide metasurface takes up less space than traditional antenna arrays and can broadcast a large range of radiation patterns.
## Appendix B Interference with Arbitrarily Polarized Beams  
The expression for the total electric and magnetic fields that arise from the coherent superposition of the electric and magnetic fields (106) and (107) are  
\[\mathbf{E}(\mathbf{r}_{\perp};z) = E_{0}\Bigg{\{}\Big{[}\alpha_{1}\psi_{1}(\mathbf{r}_{\perp};z)+ \alpha_{2}\psi_{2}(\mathbf{r}_{\perp};z)\Big{]}\hat{\mathbf{x}}+\Big{[}\beta_ {1}\psi_{1}(\mathbf{r}_{\perp};z)+\beta_{2}\psi_{2}(\mathbf{r}_{\perp};z) \Big{]}\hat{\mathbf{y}}\] \[+\frac{e^{-i(\varphi_{+}+\pi/2)}}{2k\sigma_{0}\sigma_{z}}\,\, \Big{[}\Big{(}\alpha_{1}\Delta x_{1}+\beta_{1}\Delta y_{1}\Big{)}\psi_{1}( \mathbf{r}_{\perp};z)+\Big{(}\alpha_{2}\Delta x_{2}+\beta_{2}\Delta y_{2} \Big{)}\psi_{2}(\mathbf{r}_{\perp};z)\Big{]}\hat{\mathbf{z}}\Bigg{\}}\] \[= E_{0}\Bigg{\{}\Big{[}\alpha_{1}\psi_{1}(\mathbf{r}_{\perp};z)+ \alpha_{2}\psi_{2}(\mathbf{r}_{\perp};z)\Big{]}\hat{\mathbf{x}}+\Big{[}\beta_ {1}\psi_{1}(\mathbf{r}_{\perp};z)+\beta_{2}\psi_{2}(\mathbf{r}_{\perp};z) \Big{]}\hat{\mathbf{y}}\] \[+\frac{e^{-i(\varphi_{+}+\pi/2)}}{2k\sigma_{0}\sigma_{z}}\,\, \Big{[}x\Big{(}\alpha_{1}\psi_{1}(\mathbf{r}_{\perp};z)+\alpha_{2}\psi_{2}( \mathbf{r}_{\perp};z)\Big{)}-x_{c}\Big{(}\alpha_{1}\psi_{1}(\mathbf{r}_{ \perp};z)-\alpha_{2}\psi_{2}(\mathbf{r}_{\perp};z)\Big{)}\] \[+y\Big{(}\beta_{1}\psi_{1}(\mathbf{r}_{\perp};z)+\beta_{2}\psi_{1} (\mathbf{r}_{\perp};z)\Big{)}\Big{]}\hat{\mathbf{z}}\Bigg{\}},\]\[\mathbf{H}(\mathbf{r}_{\perp};z) = H_{0}\Bigg{\{}-\left[\beta_{1}\psi_{1}(\mathbf{r}_{\perp};z)+\beta_ {2}\psi_{2}(\mathbf{r}_{\perp};z)\right]\mathbf{\hat{x}}+\left[\alpha_{1}\psi_{ 1}(\mathbf{r}_{\perp};z)+\alpha_{2}\psi_{2}(\mathbf{r}_{\perp};z)\right]\mathbf{ \hat{y}}\] \[+\frac{e^{-i(\varphi_{+}+\pi/2)}}{2k\sigma_{0}\sigma_{2}}\left[ \left(\beta_{1}\Delta x_{1}-\alpha_{1}\Delta y_{1}\right)\psi_{1}(\mathbf{r}_{ \perp};z)+\left(\beta_{2}\Delta x_{2}-\alpha_{2}\Delta y_{2}\right)\psi_{2}( \mathbf{r}_{\perp};z)\right]\mathbf{\hat{z}}\Bigg{\}}\] \[= H_{0}\Bigg{\{}-\left[\beta_{1}\psi_{1}(\mathbf{r}_{\perp};z)+ \beta_{2}\psi_{2}(\mathbf{r}_{\perp};z)\right]\mathbf{\hat{x}}+\left[\alpha_{1 }\psi_{1}(\mathbf{r}_{\perp};z)+\alpha_{2}\psi_{2}(\mathbf{r}_{\perp};z) \right]\mathbf{\hat{y}}\] \[+\frac{e^{-i(\varphi_{+}+\pi/2)}}{2k\sigma_{0}\sigma_{2}}\left[ x\left(\beta_{1}\psi_{1}(\mathbf{r}_{\perp};z)+\beta_{2}\psi_{2}(\mathbf{r}_{ \perp};z)\right)-x_{c}\left(\beta_{1}\psi_{1}(\mathbf{r}_{\perp};z)-\beta_{2} \psi_{2}(\mathbf{r}_{\perp};z)\right)\right.\] \[\left.-y\Big{(}\alpha_{1}\psi_{1}(\mathbf{r}_{\perp};z)+\alpha_{2 }\psi_{1}(\mathbf{r}_{\perp};z)\Big{)}\Big{]}\mathbf{\hat{z}}\Bigg{\}},\]  
from which, after some algebra, retaining terms up to the lowest order in \(\Delta x\) and \(\Delta y\), the expression for the intensity distribution, Equation (108), is obtained, with the visibility factor associated with the polarization states of each beam as given by Equation (109).
###### Abstract  
This study aims to enhance pavement crack detection methods by integrating unmanned aerial vehicles (UAVs) with deep learning techniques. Current methods encounter challenges such as low accuracy, limited efficiency, and constrained application scenarios. We introduce an innovative approach that employs a UAV equipped with a binocular camera for identifying pavement surface cracks. This method is augmented by a binocular ranging algorithm combined with edge detection and skeleton extraction algorithms, enabling the quantification of crack widths without necessitating a preset shooting distance--a notable limitation in existing UAV crack detection applications. We developed an optimized model to enhance detection accuracy, incorporating the YOLOv5s network with an Efficient Channel Attention (ECA) mechanism. This model features a decoupled head structure, replacing the original coupled head structure to optimize detection performance, and utilizes a Generalized Intersection over Union (GIoU) loss function for refined bounding box predictions. Post identification, images within the bounding boxes are segmented by the Unet++ network to accurately quantify cracks. The efficacy of the proposed method was validated on roads in complex environments, achieving a mean Average Precision (mAP) of 86.32% for crack identification and localization with the improved model. This represents a 5.30% increase in the mAP and a 6.25% increase in recall compared to the baseline network. Quantitative results indicate that the measurement error margin for crack widths was 10%, fulfilling the practical requirements for pavement crack quantification.  
pavement crack detection; YOLOv5s; U-Net++; binocular vision; unmanned aerial vehicle +
Footnote †: journal: Applied  
Article
# Evaluation of Unfilled Sheath in Concrete Structures Using Response Waveform in Time Domain
## 2 Difference Value Analysis (DVA)
### Normalization of Measured Waveform  
Figure 7 shows an example of the variation of the response waveform that is measured at the same point. Since the input for IEW uses the artificial excitation with the steel ball, measurement errors occur due to the difference of the input energy and small deviations of the input point. This input method is also used for the measurements in this study. For this reason, the amplitude of the response waveform changes because the input energy slightly varies at each excitation. Due to the nature of the excitation method, it is difficult to precisely control the input energy. In addition, the time from the start of the recording to the arrival of the elastic wave generally varies in each measurement. The recording normally starts if the amplitude exceeds a threshold. In the case of measurement using a steel ball, recording starts when the elastic wave reaches the receiving point, and it is impossible to observe the time of the excitation. Therefore, DVA mainly evaluates internal defects using the change of the shape of the time history after the arrival of the elastic wave, and the adjustment of the initial time is conducted in DVA. These errors have a significant impact on the difference values, and it is necessary to normalize the amplitude of the measurement waveform and perform time correction. Hence, with the objective of reducing these sources of error, this study uses the following method for amplitude normalization and compensate for the time. For this reason, the difference value \(D_{V}\) used in this study is a dimensionless quantity.  
In this study, the amplitude of the response waveform is normalized by the absolute value of the maximum amplitude as following Equation (8):  
\[f_{ni}=\frac{f_{i}}{\mid f_{max}\mid}, \tag{8}\]  
where \(\mid f_{max}\mid\) is the maximum amplitude of \(f_{i},f_{i}\) is the \(i\)th component of the time history of the amplitude and \(f_{ni}\) is the normalized amplitude of the time history. Figure 8a,b show an example of waveform normalization. The time shift is corrected by taking the first downward peak as the reference time and subtracting half of the contact time between the concrete and the steel ball as following Equation (9):  
\[T_{WS}=T_{DP}-\frac{T_{C}}{2}, \tag{9}\]  
Figure 7: Measurement waveform.  
where \(T_{WS}\) is the wave start time after correction, \(T_{WD}\) is the downward peak time and \(T_{C}\) is the contact time of the steel ball. The contact time of the steel ball is calculated from Equation (10):  
\[T_{C}=0.0043d_{s}, \tag{10}\]  
which is a numerical solution of the contact time with the steel ball diameter \(d_{s}\) based on the theory and physical behavior of colliding solids proposed by [PERSON] et al. [4; 30]. The contact time \(T_{C}\) used in this study are all calculated by Equation (10).
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 5 Description of the Considered Signaling Schemes
### Computational Complexity  
From a practical implementation point of view, an important point is the computational complexity of the Tx and the Rx for a given signaling scheme. At the Tx side, DCO and ACO use an \(N\)-point IFFT with the computational complexity of \(\mathcal{O}(N\log_{2}(N))\). For LACO, since an \(N\)-point IFFT is used for  
Figure 3: Iterative LACO-OFDM signal detection at the Rx with \(L\) layers.  
each layer, the computational complexity is on the order of \(\mathcal{O}(LN\log_{2}(N))\). However, as for \(\ell>1\), most of the corresponding symbols are equal to zero, and the computational complexity per layer can be reduced by preforming an \(N/2^{\ell-1}\)-point IFFT for the \(\ell^{\text{th}}\) layer. Then, the overall effective computational complexity can be written as \((2-\frac{1}{2^{L-1}})\mathcal{O}(N\log_{2}(N)))\)[25; 42].  
At the Rx side, DCO and ACO both need an \(N\)-point FFT with the computational complexity of \(\mathcal{O}(N\log_{2}(N))\). For LACO, an \(N\)-point FFT is needed per each layer as well as a convolution with the aggregate CIR for \(\ell>1\). Similar to that explained above, we can perform \(N/2^{\ell-1}\)-point FFT for \(\ell>1\). The computational complexity of the convolution is \(\mathcal{O}(N_{\text{ch}}N)\), where \(N_{\text{ch}}\) stands for the approximate length of the aggregate CIR. Overall, the computational complexity of the Rx will be \((5-\frac{1}{2^{L-3}})\mathcal{O}(N\log_{2}(N)))+(L-1)\mathcal{O}(N_{\text{ ch}}N)\)[25].