esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Quality of Service Routing Protocol (QoSRP) in UWSNs
### Packets Forwarding Scheme (UIWPF)
#### 3.4.2 Routes Construction with Optimal Forwarders
Upon receiving the _rdfs_msg_, each receiving acoustic node located on the same or lower levels marks itself as a helper node. The more potential helpers in the same or low level increases the opportunity to find a relay node with the highest priority on the network. The receiving node repeats the same aforesaid procedure to find the most appropriate forwarder towards the sea surface sink. Finally, the route discovery message is delivered to the sea surface sink, which sends an _e_c_c_c_msg_ message to the downstream senders, including the identity of each forwarding relay node which is delivered to the source node. An acoustic relay node that receives an _e_c_c_c_msg_ message from the sea surface sink first it checks and decides either it was the selected next-hop forward along a distinct routing path in the upstream direction. If this is the case, the relay node confirms that it is on the reverse path to the source node and marks itself as a reverse forwarder candidate in the network. Then, the forwarder candidate updates its records and after setting the priority in the routing table forwards the received message to its downstream link candidates located on the lower levels. In this way, the reverse route construction information is propagated by each relay node until it reaches the data source node in the network. Finally, this process finds reverse paths with guaranteed upward routing paths from the source to the destination. In this entire process, if a receiver candidate receives the same request messages multiple times from the same sender node, the destination node shall only reply to the first received request message and neglect others. In addition, each forwarder maintains two tables of best forwarding candidates upwards with high priority and downwards with low priority, respectively. This entire mechanism ensures that the packets will travel over a set of optimal forwarders with the appropriate distances before getting to the sea surface sink. The packets forwarding along a restricted narrow pipe like routing path by considering the acoustic nodes minimum angle information significantly minimize the average routing path length as shown in Figure 5a,b. Thus, a set of high quality routes from source to the destination are constructed in both upward and downward directions for reliable data transmission in the network. Thus, the chance of data path loops and congestion occurrences are reduced notably in the network. Figure 5: Packets forwarding mechanism in QoSRP. **(a)** relaying data; **(b)** angle formation.
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 2 5G NR QC-LDPC Encoding  
### LDPC Encoding Specification in 5G NR  
In 5G mobile base station, PDSCH channel is used for information transmission at the base station transmitter, and its transmission data is composed of transport blocks (TBs). The information transmission process is shown in Figure 1.  
In a TTI, a transmission channel delivers up to two transport blocks to the physical layer, \(A\) bits TB attaches either 24 bits CRC or 16 bits CRC according to TB length, and TB can be further partitioned to code word (CW). It needs to be divided into \(C\) code block of \(B\) bits, each code block will attach 24 bits CB-CRC, and finally it becomes the transport block size (TBS) of \(K\) bits. \(K_{cb}\) is the maximum length of the code block with CRC, \(K_{cb}\) is 8448 for BG1, and 3840 for BG2. The base graph and TB length also specifies the number of columns \(K_{b}\) in the kernel matrix of the parity check matrix \(H\). So \(K=K_{b}\times Z\), where \(Z\) is lifting size and is also the length of each CW. Each \(C\) code block after segmentation consists of \(K_{b}\) CW. After LDPC encoding is completed, a code length of \(N\) bits is outputted. The encoding output result \(N\) bits is composed of \(n\)\(Z\)-length CW. The encoding of each \(C\) code block is independent. After the respective LDPC encoding, rate matching and interleaving are performed respectively. The 5G QC-LDPC encoding process is shown in Figure 2.
## 5 Capacity Trends Analysis
### Dynamics of Nodes
For satellites with low inclinations, the percent coverage curves in Figure 2 generally move towards higher latitudes when the satellite altitude increases. The range of latitudes with zero coverage decreases about \(15^{\circ}\) with the increase in inclination. Figure 2: Percentage of satellite passes that can cover the sensors for different satellite inclinations and sensor node latitudes with \(e_{\min}=10^{\circ}\). **(a)** Satellite altitude: 550 km; **(b)** Satellite altitude: 880 km; **(c)** Satellite altitude: 1450 km; **(d)** Satellite altitude: 8050 km. To compute total visibility duration, we first calculate the satellite's position vector \(W\left(t\right)\) in the Earth-centered Earth-fixed (ECEF) frame with the initial satellite orbit parameters (semimajor axis, eccentricity, inclination, argument perigee, right ascension of the ascending node (RAAN) and true anomaly) using the geometric algorithm introduced in [47], where \(t\in\left[t_{0},t_{0}+T\right]\). The position vector of the satellite in the ECEF frame is time-varying, but \(W\left(t\right)\) in each moment can be easily calculated based on the initial orbit parameters. Then, we calculate the position vector \(U\) of the sensor node in the ECEF frame with the longitude and latitude parameters. Obviously, the position vector of the sensor node in the ECEF frame is static. Hence, the visibility between the satellite and the sensor node can be expressed as:  
\[\psi\left(t\right)=e_{\min}-\arccos\left(\frac{W\left(t\right)\cdot U}{\left|W \left(t\right)\right|\left|U\right|}\right) \tag{5}\]  
If \(\psi\left(t\right)\geq 0\), there is visibility between the satellite node and the sensor node. Additionally, the visibility duration \(\Delta T\) can be expressed as:  
\[\Delta T=\int_{t_{0}}^{T+t_{0}}\varepsilon\left(\psi\left(t\right)\right) \mathrm{d}t \tag{6}\]  
In Equation (6), \(\varepsilon\left(t\right)\) is the unit step function, where, if \(t\geq 0\), \(\varepsilon\left(t\right)=1\), otherwise \(\varepsilon\left(t\right)=0\). From the visibility duration trends in this section, optimal satellite orbits and sensor node locations for maximizing path availability, _i.e._, capacity, can be selected using the equations and plots presented.
## 2 Related Works
### Nearest Neighbors-Based Adaptive DPC with an Optimized Allocation Strategy (NADPC)  
Assume a dataset \(X=\left\{x_{1},x_{2},\ldots,x_{i},\ldots,x_{n}\right\}\) and any point \(x_{i}=[x_{i1},x_{i2},\ldots,x_{im}]\), where n and m are the number and dimension of \(X\), respectively.  
The set of mutual nearest neighbors of point \(x_{i}\) is called the mutual neighborhood (\(MH_{i}\)) of point \(x_{i}\). \(MH_{i}\) is shown in Equation (12):  
\[MH_{i}=\left\{x_{j}|\forall x_{j},f(i,j)=1\right\}, \tag{12}\]  
where for any point \(x_{i}\), if its KNN contains \(x_{j}\), and the KNN of \(x_{j}\) also contains \(x_{i}\), then \(f(i,j)=1\).Otherwise, \(f(i,j)=0\).  
The local density of any point \(x_{i}\) is shown in Equation (13):  
\[\rho(x_{i})=\sum_{x_{j}\in\text{MH}_{i}}d_{max}-d_{ij}, \tag{13}\]  
where \(d_{max}\) is the maximum distance between points in \(X\).  
The relative density of \(x_{i}\) is shown in Equation (14):  
\[rnd(x_{i})=\left\{\begin{aligned} 0,&|\mu_{1}(i)| =0\\ \frac{\sum_{\forall x_{j}\in\mu_{1}(i)}\frac{\rho(x_{j})}{\rho(x_{ j})}}{|\mu_{1}(i)|},&|\mu_{1}(i)|>0\end{aligned}\right., \tag{14}\]  
where \(\mu_{1}(i)\) is a set of all points with local density greater than 0 in \(KNN_{i}\).  
The candidate cluster center based on mutual neighborhood is shown in Equation (15):  
\[fm(i)=\begin{cases}1,\forall x_{j}\in MH_{i},\rho(x_{i})\geq\rho(x_{j})\\ 0,\hskip 28.452756 ptotherwise\end{cases}, \tag{15}\]  
where \(fm(i)=1\) indicates that \(x_{i}\) is a candidate cluster center; otherwise, it is not. Assuming that there are \(g\) candidate cluster centers, then \(ct=\left\{ct_{1},ct_{2},\ldots,ct_{g}\right\}\) is the set of candidate cluster centers.  
The credibility of any candidate cluster center is shown in Equation (16):  
\[crd(ct_{i})=rnd(ct_{i})\delta(ct_{i}), \tag{16}\]  
\[\delta_{i}=\begin{cases}\begin{array}{c}min\big{(}d_{ij}\big{)}\ \ \rho(ct_{i})<\rho \big{(}ct_{j}\big{)}\\ max(\delta(j))\,\quad otherwise\end{array},\end{cases} \tag{17}\]  
The details of the neighborhoods of points based on high-density nearest neighbors, shared nearest neighbors, mutual nearest neighbors, and similarity refer to Ref. [36].
# Variable Tap-Length Algorithm with Mixed Parameter
## 2 Variable Tap-Length Algorithm  
### Least Mean Square Algorithm  
The LMS algorithm is composed of an input signal \(\mathbf{X}\), an error signal \(e(n)\), an output signal \(y(n)\) and a weight coefficient vector \(\mathbf{W}\). The full equation is as follows:  
\[y(n)=\mathbf{W}(n)^{\mathsf{T}}\mathbf{X} \tag{1}\]  
\[e(n)=\left(\mathbf{W}_{opt}-\mathbf{W}(n)\right)^{\mathsf{T}}\mathbf{X}+v(n) \tag{2}\]  
\[\mathbf{W}(n)=\mathbf{W}(n-1)+\mu e(n)\mathbf{X} \tag{3}\]  
where \(\mathbf{W}_{opt}\) is the optimal weight. \(v(n)\) is the noise signal, and \(\mu\) is the step-size. When \(\mu=\mu^{\prime}=\frac{\mu}{X^{\mathsf{T}}\mathbf{X}+\beta}\), it becomes the normalized least mean square algorithm (NLMS). \(\beta\) is a small positive number multiplied by the unit matrix. The length of the weight coefficient vector \(\mathbf{W}\) is the tap-length. The lengths of the optimal weight \(\mathbf{W}_{opt}\) and actual weight \(\mathbf{W}\) are \(L_{opt}\) and \(L\), respectively. When the tap-length \(L\) is much smaller than \(L_{opt}\), the algorithm diverges. Similarly, when the tap-length \(L\) is much larger than \(L_{opt}\), we can infer from Equation (2) that \(e(n)\) increases as well. As the computational complexity of the LMS algorithm is proportional to the tap-length \(L\), a large tap-length \(L\) also requires a large amount of computation. To maintain the same performance level, a smaller tap-length requires less computation. Therefore, the tap-length \(L\) is a tradeoff among stability, computation, and convergence speed.
## 3 Results
### Printing Resolution Effects  
#### 3.4.1 Mathematical Modelling of Permeability  
Previous studies have proposed various mathematical equations to describe the relationship between permeability and void space in rocks. In this study, we used the cylindrical tube model to estimate fluid flow, assuming that the porous medium consists of a bundle of tubes tightly packed together [73]. Based on [PERSON]'s equation (Equation (1)), fluid flow through a cylindrical tube is determined by several factors, including the tube radius, fluid viscosity, pressure difference between the two ends of the tube, and the tube length.  
\[Q=\frac{\pi r^{4}}{8\mu}\times\frac{\Delta P}{\Delta L} \tag{1}\]  
where Q is the flow rate (cm\({}^{3}\)/s), r is the radius of the tube (cm), \(\mu\) is the fluid viscosity (poizes), \(\Delta\)P is the pressure difference between two ends of the tube (dyne/cm\({}^{2}\)), and \(\Delta\)L is the length of the tube (cm).  
Considering [PERSON]'s law,  
\[Q=\frac{KA}{\mu}\times\frac{\Delta P}{\Delta L} \tag{2}\]  
where the permeability of the one-tube system (\(A=\pi r^{2}\)) can be written as:  
\[K=\frac{r^{2}}{8} \tag{3}\]  
where K is the permeability (cm\({}^{2}\)).  
Figure 7: Three orthogonalized surfaces of the porous basalt in the seepage test.  
If the flow medium is assumed to contain several parallel tubes (n), the total flow rate is:  
\[Q_{total}=nQ=n\frac{\pi r^{4}}{8\mu}\times\frac{\Delta P}{\Delta L} \tag{4}\]  
The corresponding porosity \(\Phi\) of this system is:  
\[\Phi=\frac{n\pi r^{2}}{A_{total}} \tag{5}\]  
and the corresponding [PERSON]'s law can be revised to:  
\[Q_{total}=\frac{KA_{total}}{\mu}\times\frac{\Delta P}{\Delta L} \tag{6}\]  
Therefore, the permeability of the multi-straight-tube system with the porosity \(\Phi\) can be represented by:  
\[K=\Phi\frac{r^{2}}{8} \tag{7}\]  
In nature, the flow tube is normally not straight but has some tortuosity. Here, we define the tortuosity \(\tau\) as the ratio of the actual flow path (L\({}_{\text{e}}\)) to the length (L) of the porous medium along the macroscopic flow direction (Figure 8). Then, the equation about permeability should be revised accordingly.  
\[K=\Phi\frac{r^{2}}{8\tau} \tag{8}\]
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 2 Background and Threat Model
### SDN and OpenFlow Basics  
SDN simplifies network management by separating the control and data planes [22]. As the leading southbound protocol, OpenFlow [2] defines the communication between the control and data plane. In OpenFlow networks, the controller installs flow rules in switches to dictate forwarding policies. Figure 1 gives an example of the process. When receiving an incoming packet, the switch looks up the flow table to find an entry matching the packet. If there is a match, it handles the packet according to the rule actions, e.g., forwards the packet to a switch port. Otherwise (i.e., table-miss), the switch sends a packet-in message to the controller. Then, the controller installs a flow rule in the switch to define its forwarding behavior, which is known as reactive installation. The reactive installation method supports flexible and dynamic control of forwarding behaviors, and is widely adopted in most SDN applications [23].  
Once deployed, the lifetime of a flow rule is defined by the idle timeout and hard timeout fields. A hard timeout deletes a flow rule after a given time, while an idle timeout deletes a flow rule if it matches no packets for some time. When a flow rule is deleted, the switch sends a Flow-Removed message to the controller.  
Previous works revealed the side-channel probing attack based on the transmission time difference [24]. An attacker can send a probing packet and check whether it matches existing flow rules by its delay. By leveraging this, the attacker can generate probing packets to infer whether the switches have matched flow rules.  
Figure 1: Reactive flow rule installation.
## Materials and Methods
### Statistical Analyses  
All variables were tested for normal distribution (Shapiro-Wilk Test) and the assumption of variance homogeneity (Levene's Test for equality of variances). Descriptive statistics (median, mean, standard deviation (SD), minimum, maximum, and 95% confidence intervals (95% CI)) were ascertained for all variables. The mean differences of anthropometric and performance parameters between (adjacent) age groups (U10 vs. U11 vs. U12) were tested using the Kruskal-Wallis H test and the Mann-Whitney U test for post-hoc testing, because data were not normally distributed. Pearson's product moment correlations determined relationships between anthropometric (independent variables) and performance parameters (dependent variables). The criteria adopted for interpreting the magnitude of correlations (r) between measures were: \(<\)0.1: trivial; 0.1-0.3: small; 0.3-0.5: moderate; 0.5-0.7: large; 0.7-0.9: very large; and 0.9-1.0: almost perfect [24]. A power calculation (nQuery Advisor 4.0; StatisticalSolutions, Saugus, MA, USA) showed that with a sample size of 7 in each group, the study had 80% power to detect a difference between means of 3.0 cm (main outcome: CMJ height) using a two-sided _t_-test with a significance level of \(p\) < 0.05 while assuming a common standard deviation of 2.0 cm [25]. All statistical analyses were performed using SPSS version 25.0 for Windows (IBM, Armonk, NY, USA).
## 5 Biosynthetic Pathway Engineering for Novel C\({}_{30}\) Carotenoid Generation
Host strains also play a role in the biosynthetic pathway and final carotenoid structure, although the reason for this has not yet been determined. The acyclic C\({}_{30}\) carotenoids diaponeurosporene and diapolycpene were produced by seven _E. coli_ host strains (Top10, MG1655, MDS42, JM109, SURE, DH5a, and XL1-Blue). However, monocyclic diapotorulene was heavily strain-dependent and favored the SURE _E. coli_ strain in a manner similar to that of heterologous lycopene [57]. With regard to bioengineered pathways, engineered microbial hosts designed for high carotenoid yields require optimization of their isoprenoid precursor pool. Moreover,  
Figure 5: Gene clusters of 4,4\({}^{\prime}\)-diapocarotenoids and apo-8\({}^{\prime}\)-carotenoids from various microbial sources. White arrows represent putative genes in the biosynthetic pathway, including new and novel carotenoids. microbes must be able to contain lipophilic carotenoids and the expression of carotenoid genes need to be modulated for the efficient transformation of precursors into target compounds. Optimization of the precursor pool requires increasing the isoprenoid flux via overexpression of enzymes in the nonmevalonate isoprenoid pathway. Balancing isoprenoid expression and carotenoid-synthesizing enzymes enhances carotenoid production by alleviating the effects of growth inhibition caused by enzyme overexpression.
## 4 Extension to Multiple SWIPT AF Relaying Systems: Performance Analysis
### Approximation SWIPT Relay to AF Relay
#### 4.5.1 PDF and CDF of \(\gamma^{\rm AF}_{\rm id}\) and \(\gamma^{\rm AF}_{\rm cb}\)  
By approximating the SWIPT relay as a general AF relay, the indirect and combined link SNRs can be approximately expressed, based on (31) and (37), as  
\[\begin{split}\gamma_{\rm id}&=\sum_{r=1}^{R}\gamma _{\rm id,r}\simeq\sum_{r=1}^{R}\gamma^{\rm AF}_{\rm id,r}=\gamma^{\rm AF}_{ \rm id}\\ \gamma_{\rm cb}&=\gamma_{0}+\gamma_{\rm id}\simeq \gamma_{0}+\gamma^{\rm AF}_{\rm id}=\gamma^{\rm AF}_{\rm cb}.\end{split} \tag{38}\]  
The PDFs of \(\gamma^{\rm AF}_{\rm id}\) and \(\gamma^{\rm AF}_{\rm cb}\) can then be expressed as  
\[\begin{split} f_{\gamma^{\rm AF}_{\rm id}}(x)&=\sum _{r=1}^{R}\frac{\pi_{\rm id,r}}{\tilde{\gamma}^{\rm min}_{r}}\exp\left(-\frac{ x}{\tilde{\gamma}^{\rm min}_{r}}\right)u(x)\\ f_{\gamma^{\rm AF}_{\rm cb}}(x)&=\sum_{r=0}^{R} \frac{\pi_{\rm cb,r}}{\tilde{\gamma}^{\rm min}_{r}}\exp\left(-\frac{x}{\tilde{ \gamma}^{\rm min}_{r}}\right)u(x)\end{split} \tag{39}\]  
where \(\pi_{\rm id,r}=\prod_{i=1,i\
eq r}^{R}\frac{\tilde{\gamma}^{\rm min}_{r}}{ \tilde{\gamma}^{\rm min}_{r}-\tilde{\gamma}^{\rm min}_{r}}\), \(\pi_{\rm cb,r}=\prod_{i=0,i\
eq r}^{R}\frac{\tilde{\gamma}^{\rm min}_{r}}{ \tilde{\gamma}^{\rm min}_{r}-\tilde{\gamma}^{\rm min}_{r}}\), and \(\tilde{\gamma}^{\rm min}_{0}=\tilde{\gamma}_{0}\). Then, the cumulative distribution functions (CDFs) of \(\gamma^{\rm AF}_{\rm id}\) and \(\gamma^{\rm AF}_{\rm cb}\) can be obtained as  
\[\begin{split} F_{\gamma^{\rm AF}_{\rm id}}(\gamma)& =\int_{0}^{\gamma}f_{\gamma^{\rm AF}_{\rm id}}(x)dx=\sum_{r=1}^{R} \pi_{\rm id,r}\left(1-\exp\left(-\frac{\gamma}{\tilde{\gamma}^{\rm min}_{r}} \right)\right)\\ F_{\gamma^{\rm AF}_{\rm cb}}(\gamma)&=\int_{0}^{ \gamma}f_{\gamma^{\rm AF}_{\rm cb}}(x)dx=\sum_{r=0}^{R}\pi_{\rm cb,r}\left(1- \exp\left(-\frac{\gamma}{\tilde{\gamma}^{\rm min}_{r}}\right)\right).\end{split} \tag{40}\]
# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
## 3 Imaging Method Based on MAF-MDF
### Motif Difference Field  
The motif difference field (MDF) is a methodology that encodes one-dimensional time series into two-dimensional images, effectively conveying structural information inherent in the time series. Compared to the Gramian angular field (GAF), MDF boosts a simpler implementation and is more straightforward to construct [22].  
Suppose a discrete-time signal \(X=(x_{t},\)\(t=1,2,3,\ldots,T)\) of total duration \(T\) is given. The image set of signal \(X\) in the time domain can be defined as follows:  
\[\mathrm{M}_{d}^{n}=\left\{M_{d,s^{\prime}}^{n},s=1,2,\ldots,T-(n-1)\cdot d\right\} \tag{4}\]  
where \(M_{d,s}^{n}=(x_{t},\)\(t=s,\)\(s+d,\)\(s+2d,\)\(\ldots,\)\(s+(n-1)d)\); \(n\) represents the length of the subsequence sampled from \(X\) using a sliding window, satisfying \(1<n\leq T;s\) denotes the initial time index corresponding to the subsequence within \(X\); and \(d\) is the sampling interval size between two data points from \(X\), an integer that fulfills \(1\leq d\leq[(T-1)/(n-1)]\).  
\(\mathrm{M}_{d}^{n}\) measures the complexity of a time series only by comparing the values of adjacent points in the time series. However, the relative amplitude in the time series contains more structural information in the series, so the sequence of motif differences is further defined.  
\[\mathrm{dM}_{d}^{n}=\left\{dM_{d,s^{\prime}}^{n},s=1,2,\ldots,T-(n-1)\cdot d\right\} \tag{5}\]  
\[dM_{d,s}^{n}=\left\{x_{s+d}-x_{s},x_{s+2d}-x_{s+d},\ldots,x_{s+(n-1)d}-x_{s+(n -2)d}\right\} \tag{6}\]  
Due to the variation in the length of \(\mathrm{dM}_{d}^{n}\) with respect to \(d\), zero padding is necessary to ensure uniform sequence lengths. Consequently, a new sequence \(\mathrm{I}_{d}^{n}\) is defined as:  
\[\mathrm{I}_{d}^{n}=\left\{I_{d,s^{\prime}}^{n}s=1,2,\ldots,T-(n-1)\right\} \tag{7}\]  
\[\mathrm{I}_{d,s}^{n}=\left\{\begin{array}{l}dM_{d,s^{\prime}}^{n},1\leq s \leq T-(n-1)\cdot d\\ 0,\quad\quad T-(n-1)\cdot d<s\leq T-(n-1)\end{array}\right. \tag{8}\]  
Then, the motif difference field (MDF) is defined as the stack of \(\mathrm{I}_{d}^{n}\) with all possible delay values \(d\) from \(1\) to \(dmax\).  
\[\mathrm{MDF}^{n}=\left\{\mathrm{I}_{1}^{n},\mathrm{I}_{2}^{n},\ldots,\mathrm{ I}_{d_{\max}}^{n}\right\} \tag{9}\]  
\(\mathrm{I}_{d}^{n}\) denotes the set of \(n-1\) collections corresponding to when the spacing is \(d\), thereby generating \(n-1\) channel images. For the \(i^{th}\) channel, an image array is defined as follows:  
\[\mathrm{G}_{i}^{n}=[\overset{\rightarrow}{I}_{1}^{n}(i),\overset{\rightarrow }{I}_{2}^{n}(i),\ldots,\overset{\rightarrow}{I}
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 4 Numerical Analysis for an Optical Transmission System
### Single-Span Fiber System Setup
#### 4.1.1 AIR Results  
Results after propagation over single-span SSMF are shown in Figure 7 as AIR vs. launch power per WDM channel (\(P_{\text{ch}}\)). At the distance of 110 km, the GS gain, which is the difference (in AIR) between 64 QAM and 64-GS constellation, is 0.17 bits/sym at the optimal launch power (2.5 dBm). At the distance of 120 km, the GS gain increases to 0.21 bits/sym at the optimal launch power (3.5 dBm).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline WDM Channels & 11 \\ Symbol rate & 45 GBd \\ Root-raised-cosine roll-off factor & 1\% \\ Channel frequency spacing & 50 GHz \\ Center wavelength & 1550 nm \\ Attenuation & 0.2 dB km\({}^{-1}\) \\ Dispersion parameter & 17 ps nm\({}^{-1}\)km\({}^{-1}\) \\ Nonlinearity parameter & 1.2 W\({}^{-1}\)km\({}^{-1}\) \\ EDFA noise figure & 5 dB \\ Fiber span length (Multi-span) & 80 km \\ \hline \multicolumn{2}{c}{**Modulation Format**} \\ \hline Single-span system & 256 QAM/256-GS \\ \hline Multi-span system & 128 QAM/128-GS \\  & 64 QAM/64-GS \\ \hline \multicolumn{2}{c}{**FEC**} \\ \hline SCC & Standard/SABM decoding \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters of the simulated optical link.