esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Algorithm Flow  
The flowchart of TVR analysis method based on EDF-FCTP-Kriging is shown in Figure 3. Detailed steps are as follows:  
Step 1: All the random variables involved in the problem are transformed from space to standard normal space.  
Step 2: Initialize the number of iterations and define the convergence accuracies \(\varsigma_{2}\).  
Step 3: For \(j=0\), generate \(N_{0}\) initial training samples \(\mathbf{Z}^{initial}\) on the interval \([-3,3]\) based on US, and the corresponding FCTP \(\mathbf{ct}^{initial}\) is calculated by the LSF equal to zero.  
Step 4: Build initial surrogate model \(\phi^{initial}(\mathbf{Z})\) using Kriging model based on initial training sample set \(\left\{\mathbf{Z}^{initial},\mathbf{ct}^{initial}\right\}\).  
Step 5: Obtain integration points and the corresponding weight coefficients by LPSS method and calculate the first four order origin moments of initial surrogate model. Estimate the probability distribution of the FCTP using KDE based on the following.  
Step 6: Calculate the average \(\overline{d_{g}(x)}\) and minimum distance \(d_{m}(x)\) from training samples to all candidate samples.  
Step 7: \(j=j+1\), search for the best sample center among candidate samples using the EDF learning function.  
Step 8: Add \(d+1\) training samples \(\mathbf{Z}_{j}\) on hypersphere centered at \(\mathbf{Z}_{next}^{(j)}\) with radius \(r\) and calculate corresponding FCTP \(\mathbf{ct}_{j}\).  
Step 9: Update training samples, \(\mathbf{Z}^{(j)}=\left[\mathbf{Z}^{(j-1)};\mathbf{Z}_{j}\right]\), \(\mathbf{ct}^{(j)}=\left[\mathbf{ct}^{(j-1)};\mathbf{ct}_{j}\right]\), and build surrogate model \(\phi^{(j)}(\mathbf{Z})\) through Kriging.  
Figure 3: Flowchart of TVR analysis method.  
Step 10: Judge according to the convergence condition; if \(max\Big{(}\Delta M_{1}^{(j)},\Delta M_{2}^{(j)},\Delta M_{3}^{(j)},\Delta M_{4}^{ (j)}\Big{)}\)\(>\xi_{2}\) or \(1-\sqrt{f^{(j)}(x)f^{(j-1)}(x)}>\xi_{1}\), then return Step 7, otherwise output the optimal surrogate model \(\phi^{final}(\mathbf{Z})\) and the PDF of FCTP.  
Step 11: Calculate cumulative failure probability over the life cycle by the optimal surrogate model and the probability density function of the FCTP.
# Improving Response Time for Crowd Management in Hajj
## 6 Conclusions  
FANET applications require certain the PSNR and/or SSIM of received images at the base-station in a lossy network. Using simulation experimentation, this paper demonstrates the usefulness of PRoFFAN in improving the response time of FANET applications by prioritizing packets that contribute more to higher PSNR and SSIM. In this paper, we demonstrated that PRoFFAN delivers images of higher quality to base-stations for FANET applications in the same amount of time and network conditions as compared to a typical  
Figure 11: Comparison of PRoFFAN and Zigbee based on PSNR/SSIM vs. Deadline for UAV at 60 km/h. (**a**) Deadline based PSNR at 60 km/h, (**b**) Deadline based SSIM at 60 km/h.  
Figure 10: Comparison of PRoFFAN and Zigbee based on PSNR/SSIM vs. Deadline for UAV at 30 km/h. (**a**) Deadline based PSNR at 30 km/h, (**b**) Deadline based SSIM at 30 km/h.  
Zigbee-based network. It was also seen that deadline-based FANET applications receive an image of higher quality in the same time as the typical Zigbee based network.  
Some areas of PRoFFAN require improvements in future, especially Encoding and Transmission stages, outlined in Figure 2. More efficient image encoding techniques have to be explored in future. Morevoer, the current implementation of PRoFFAN was tested and compared with Zigbee-based networks. In future, other low-powered transmission techniques have to be considered.  
Finally, this paper demonstrated PRoFFAN's usefulness using simulations. In the future, we plan to implement a real-time FANET test-bed.  
Methodology, A.N.; Literature, A.N. and E.F.; Experimentation and Results, A.A.S.; writing--original draft preparation, E.F., A.N. and A.A.S.; writing--review and editing, E.F.; funding acquisition, E.F. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research Innovation, Ministry of Education in Saudi Arabia for funding this research work through project number 0909.  
Not applicable.  
Authors can confirm that all relevant data are included in the article.  
The authors declare no conflict of interest.
# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 4 Results
### Performance of Height Measurement Model  
According to the official parameters of the Intel RealSense D455 camera and the transportation case, the accuracy of the depth value would affect the accuracy of the height measurement, which influenced the determination of the number of layers where the cargo was carried. We conducted an experiment in an orchard environment with natural light changes, considering the influence and error of the depth value obtained by the depth camera. We used the standardized baskets and divided the experiments into two groups according to height: 274 and 536 mm. For each group, the horizontal distances (D) from the camera to the test point were 200, 500, 800, 1100, 1400, and 1800 mm, as shown in Figure 13.  
As shown in Table 3, a gradual increase in error occurred in the height measurement model as the depth value increased, which was consistent with the trend in error as the distance changed. As shown in Figure 14, the height values in the two groups of experiments were distributed on both sides of the \(H_{1}\) and \(H_{2}\) values, but the bias in the height error calculated at the horizontal distances of 1100 and 1400 mm was high, which was caused by the abnormality of the loading platform. We regarded the values as anomalies. Therefore, the height error of the height measurement model was within 3 cm within a horizontal distance of \(<\)1.8 m, which met the requirements.  
Figure 12: Detection result visualization. (**a**) RetinaNet-Res50. (**b**) YOLOv3-tiny. (**c**) YOLOv5s. (**d**) The proposed method.  
Figure 14: Performance of height measurement model: (**a**) height value distribution; (**b**) height error distribution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Height** & \multicolumn{6}{c}{**Group 1 (\(H_{1}\) = 274 mm)**} & \multicolumn{6}{c}{**Group 2 (\(H_{2}\) = 536 mm)**} \\ \hline
**Horizontal Distance (mm)** & **200** & **500** & **800** & **1100** & **1400** & **1800** & **200** & **500** & **800** & **1100** & **1400** & **1800** \\ \hline Depth value (mm) & 691 & 891 & 1065 & 1169 & 1379 & 1472 & 558 & 692 & 850 & 1018 & 1167 & 1349 \\ Height value (mm) & 276.5 & 276.7 & 278.6 & 292.3 & 320.2 & 289.7 & 534.6 & 528.5 & 556.3 & 580.1 & 599.2 & 552.3 \\ Height error (mm) & 2.5 & 2.7 & 4.6 & 18.3 & 46.2 & 15.7 & 1.4 & 7.5 & 20.3 & 28.6 & 63.2 & 16.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of height measurement model.  
Figure 13: Experimental scene of the height measurement model: (**a**) lateral view; (**b**) overhead view.
## 3 Results
Figure 9: Scenario 5 with a supply ramp inside a construction and acoustic barriers on the roof—Lday with all noise sources (**left**: 4 m, **right**: 10 m). The noise values from the receiver points do not highlight any exceeding of the noise limits. The barrier solutions from Scenarios 1 and 2 do not lead to any significant noise reduction, the maximum attenuation being 0.8 dB. At 4 m height, the noise is lower than at 10 m due to the masking effect of the building on which the air conditioner units are placed. Therefore, a study of the noise level only at 4 m is not conclusive and inhabitants at higher floors can face noise level problems. To predict the noise impact, namely the noise exposure of the inhabitants, a facade calculation was adopted in accordance with the VBEB method. This method is according to the German national method, in which the inhabitants are proportionally allocated to the receiver points where noise is calculated; the sum over all receiver points of a building represents the total number of inhabitants [23; 24]. The result of the VBEB analysis is presented in Table 1.  
The noise levels computed in receiver points did not identify any exceeding of the noise limits, but the VBEB method identified a number of 16 persons in Scenario 1, 13 persons in Scenario 2 and 5 persons in Scenario 3, that are exposed at noise levels higher than 55 dB(A). To have an overview of the noise propagation of the most exposed buildings, Figure 11 presents the acoustic prediction for Scenario 2 and the solution with the best results from Scenario 5. In Scenario 2, the closest building is exposed at a noise level higher than 55 dB(A). The above predictions were considered for the day period with all noise sources, including the supply ramp noise. Considering that the heat pumps and air conditioning units are running continuously, even in the night period and especially during the summer, a new study was performed by removing the noise generated by the supply ramp. This evaluation is needed in order to verify if the night noise limit of 45 dB(A) is exceeded. The assesment was made by computing the noise level in receiver points and also by using the VBEB method. For night-time, in the predictions of the Scenarios 1 to 4, the same noise levels are obtained due to the fact that the proposed technical solutions have an insignificant influence in the propagation of the chiller noise. Only in Scenario 5, the acoustic barriers from the roof will act as an absorbent obstacle with attenuation effects. Thus, the noise levels computed in virtual receivers are presented only for Scenarios 1 and 5. From Figure 12, it is observed that at 1.5 m height in receivers R6 and R7, a noise reduction of almost 10 dB is obtained between the two scenarios.
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 3 Results
### Experimental Results
Other images appeared to be reproduced without problems. TP improved spectral distortion more than BP. In the results shown in Tables 7 and 8, the method using GIHS (WLS and SCMP) was better than the existing methods using SR. In addition, it was found that the proposed method, the linear combination of SR and SCMP, gave better results than SCMP alone. One of the problems to be solved in PS processing is the independence of the processed image. In other words, it is important to obtain stable and good results rather than obtaining good results only on a specific image. Although there are some methods shown in Tables 7 and 8 that gave better results than the proposed method, comparing the other evaluation results shows that the results were inconsistent. The reason why the evaluation results were so different could be that these processing methods depend on the processed image. As shown in Figures 7 and 8, it was found that the reproduction of vegetation area by the GS was unstable. Since the image quality differs depending on the size of the local estimation on the distinct blocks used in BDSD, we evaluated the visibly good images with good numerical values, but the resolution of the image was low. The WLS gave good numerical results with two images, but the images were blurred. Both ISSR and SRayas using BP generated ringing artifacts. Other images seemed to be reproduced without problems in resolution and color. Among them, the proposed method gave the best results in the numerical evaluation. In this method, resolution enhancement was achieved by using the visible and NIR regions. On the other hand, it can be applied only to the resolution enhancement of RGB images, and not NIR images.
###### Abstract  
Nanomedicine has been used as a precise treatment for many diseases. The advantage of using nanodrug is that they have more permeability and less toxicity to cells, which enhances the drug delivery system. Graphene is well known for its potential biological applications in drug, food, and pharma industries. This study aimed to assess the productivity and potentiality of nitrogen-doped graphene (NDG) and to evaluate their anticancer, antimicrobial, and biofilm inhibition activity. Nitrogen-doped graphene was synthesized by using a one-pot facile synthesis of NDG, wherein the NDG was prepared by the reduction of graphene oxide (GO) in the presence of hydrazine hydrate as a reducing agent, while ammonium hydroxide was used as a source of nitrogen on the surface of graphene. As-synthesized NDG was characterized by various characterization techniques such as UV-Vis, FT-IR, XRD, XPS, TEM, and N\({}_{2}\) sorption studies analysis. Antimicrobial, anticancer, and biofilm inhibition assays were performed by standard protocols. N-doped graphene (NDG) showed better activity against Gram-positive bacteria, such as methicillin-resistant _Staphylococcus aureus_ (MRSA), _Bacillus subtilis_, _Streptococcus pneumoniae_, and _Streptococcus mutans_ (\(p\leq 0.05\)), whereas there was no activity against Gram-negative strains in _Klebsiella pneumoniae_ and _Pseudomonas aeruginosa_. Biofilm inhibition was also improved with NDG compared to the standard ampicillin. NDG showed better results in both MCF-7 and Hela cell lines with IC\({}_{80}\) of 27.15 ug/mL and 30.85 ug/mL, respectively. In conclusion, NDG has the best ability for use as a biomolecule, and research studies focusing on proteomics, metabolomics, and in vivo studies are needed to increase the impact of NDG in the drug and pharma industry.  
graphene; nitrogen; anticancer; physicochemical; antimicrobial; biofilm +
Footnote †: journal: _crystals_
# Modeling and Optimizing the System Reliability Using Bounded Geometric Programming Approach
## 3 Geometric Programming Problem: Basic Concepts
### Geometric Programming Problems
Consider that the geometric programming problem (2) is the primal problem, then its dual problem can be presented in the geometric programming problem (4). For this purpose, we formulate an auxiliary geometric programming problem (3) by dividing the constraint co-efficient by its RHS value \(B_{i}\), which can be depicted as follows:  
\[ \begin{array}{ll}\text{F}=\underset{x}{\text{Minimize}}&\sum_{k=1}^{l_{0}} c_{0k}\prod_{j=1}^{m}x_{j}^{a_{0 kj}}\\ \text{s.\ x.}&\\ &\sum_{k=1}^{l_{i}}\frac{c_{jk}}{B_{i}}\prod_{j=1}^{n}x_{j}^{\beta_{kj}}\leq 1,\quad i=1,2,\ldots,m,\\ &x_{j}\geq 0,\quad\ j=1,2,\ldots,n.\end{array}  \tag{3}\]  
The derivation for the dual formulation of the geometric programming problem (2) can be carried out using the concept of [1; 2; 3]. Furthermore, the potential complexity in obtaining and solving the dual geometric programming problem (4) can be realized by the research work in [6; 16]. Thus, the dual formulation of the geometric programming problem (2) is presented in the geometric programming problem (4). \[ \begin{array}{ll}\textbf{Dual}&\\ \text{F}=\underset{y}{\text{Maximize}}&\prod_{k=1}^{l_{0}}\left(\frac{c_{0k} }{y_{0k}}\right)^{y_{0k}}\prod_{i=1}^{m}\prod_{k=1}^{l}\left(\frac{c_{ik}y_{i }}{B_{i}y_{ik}}\right)^{y_{ik}}=F(y)(say)\\ \text{s.\ x.}&\\ &\sum_{k=1}^{l_{0}}y_{0k}=1,\quad i=1,2,\ldots,m,\\ &\sum_{k=1}^{l_{0}}a_{0 kj}y_{0k}+\sum_{i=1}^{m}\sum_{k=1}^{l_{i}}\beta_{ikj}y_ {ik}=0,\ \ j=1,2,\ldots,n,\\ &y_{ik}\geq 0,\ \ \forall\ i,k.\end{array}  \tag{4}\]  
**Theorem 1**. : _If \(\delta\) is a feasible vector for the constraint posynomial geometric programming (2), then \(F_{0}(x)\geq n\sqrt[n]{F(y)}\)._  
Proof.
## 3 Instrumentation  
The test rig is specifically designed to replicate realistic working conditions on the PV surface, with the aim of assessing the slip resistance of the track groove material designed for the belt track of the PV cleaning robot. As depicted in Figure 3a, the apparatus consists of a chassis (a) supporting a PV panel (b) with an adjustable tilt angle. A displacement mass (c) of known weight is associated with a weight-bearing frame (d), which is equipped with multiple rubber sheets acting as the slip specimen. The weight-bearing frame is positioned on the PV panel to ensure the uniform distribution of gravitational forces through the displacement mass. An electronic load cell (e) is connected to the weight-bearing frame via an inelastic link to measure the x-component force of gravity.  
To mimic a more realistic working environment, adjustable water sprayer nozzles (f) are installed to create a water curtain between the PV panel surface and the slip specimen surface, a primary factor contributing to track groove slippage. The water flow is controlled by a PID controller that incorporates feedback from a flow sensor, maintaining consistent trial conditions. Furthermore, the water flow is adjusted to a specific value, simulating PV panels being cleaned to evaluate its impact on the coefficient of static friction. The spray angle of the nozzle is adjusted to closely resemble actual working conditions.
## 3 Results and Discussion  
### Comparisons Among the Predicted Ms Temperature Results  
Eight random samples from the testing dataset, detailed in Table 1 with their chemical compositions and corresponding experimental Ms temperatures, are selected to evaluate the efficiency of the ANN model.  
Figure 4 presents the predicted Ms temperature results for these samples using the above-mentioned formulae and the ANN model. The results demonstrate that the ANN model, with an AAE of 6.79 degrees and an MPE of 1.26%, outperforms the alternative methods. The line fitting of the data points yields a Pearson correlation coefficient of 0.97, indicating a high degree of correlation with the actual values. In addition, among the entire set of 313 steel samples, the ANN model achieves an AAE of \(-0.93\) degrees and \(-0.097\)% in MPE.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Data No.** & **C** & **Mn** & **Si** & **Cr** & **Ni** & **Mo** & **V** & **AI** & **W** & **Cu** & **Experimental Ms (K)** \\ \hline
206 & 0.41 & 1.42 & 1.42 & 0.78 & 1.37 & 0.03 & 0 & 0 & 0 & 0 & 522 \\
221 & 0.51 & 0.72 & 0.27 & 0.94 & 0.15 & 0.05 & 0.2 & 0 & 0.11 & 0.15 & 548 \\
236 & 0.45 & 0.8 & 0.25 & 1.15 & 0.55 & 1 & 0.05 & 0 & 0 & 0 & 560 \\
244 & 0.3 & 0.8 & 0 & 0.55 & 0.54 & 0.21 & 0 & 0 & 0 & 0 & 644 \\
246 & 0.43 & 0.74 & 0 & 0.92 & 0 & 0 & 0.16 & 0 & 0 & 0 & 594 \\
266 & 0.35 & 0.65 & 0.13 & 0.55 & 1.27 & 0 & 0 & 0 & 0 & 0 & 619 \\
290 & 0.5 & 0.73 & 0.22 & 0.83 & 0.04 & 0.41 & 0.15 & 0.07 & 0 & 0 & 568 \\
308 & 0.39 & 0.82 & 0.31 & 0.96 & 0.13 & 0.2 & 0 & 0 & 0 & 0.08 & 610 \\ \hline \end{tabular}
\end{table}
Table 1: Medium-carbon steel sets for predictive result comparisons.  
Figure 3: The graphical user interface of the ANN model.
## 3 Simulations and Experiments
### Numerical Simulations
#### 3.1.1 Verification of PC Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RESOLUTION** & \(d\) **(mm)** & **RECONSTRUCTION METHOD** & **PSNR (dB)** & **SSIM** \\ \hline \multirow{2}{*}{\(64\times 64\)} & \multirow{2}{*}{691.0} & \multirow{2}{*}{PS} & 59.66 & 0.999 \\ \cline{3-4}  & & & PC & 57.70 & 0.998 \\ \hline \multirow{2}{*}{\(128\times 128\)} & \multirow{2}{*}{345.5} & \multirow{2}{*}{PS} & 55.08 & 0.995 \\ \cline{3-4}  & & & PC & 56.58 & 0.997 \\ \hline \multirow{2}{*}{\(256\times 256\)} & \multirow{2}{*}{172.8} & \multirow{2}{*}{PS} & 46.05 & 0.983 \\ \cline{3-4}  & & & PC & 20.67 & 0.923 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Propagation distance \(d\), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of PS and PC methods for object planes with different resolution. Figure 5: Reconstruction results of object plane with different resolution. Panels (**a**,**c**,**e**) are generated by PC and reconstructed by PS method; panels (**b**,**d**,**f**) are generated by PS and reconstructed by PC method. Figure 6: PSNR curve of different flare angles of wavefront recording plane (WRP).
# Intrusion Detection Method Based on Preprocessing of Highly Correlated and Imbalanced Data
## 3 Results
### Model Selection, Training, and Evaluation
#### 3.4.2 Evaluation the Effectiveness of Using the Feature Space SP_PCA Reduction Procedure
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Model of** \\ **Classifier** \\ \end{tabular} } & \multirow{2}{*}{**Performance Metrics**} & \multicolumn{2}{c}{**Balanced**} & \multicolumn{2}{c}{**Feature-Reducing Methods**} \\ \cline{3-6}  & & **Data Without** & \multirow{2}{*}{**SP\_PCA**} & **Variance** & \multirow{2}{*}{**Random Forest**} \\ \cline{3-6}  & & **Feature Reduction** & & **Threshold** & **Random Forest** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Decision Tree \\ Classifier \\ \end{tabular} } & Average F1 score & 0.91 & 0.95 & 0.95 & 0.95 \\ \cline{2-6}  & Train Time, s & 1.579 & 1.412 & 1.919 & 1.481 \\ \cline{2-6}  & Recognition Time, s & 0.015 & 0.01 & 0.009 & 0.012 \\ \hline \multirow{3}{*}{\begin{tabular}{c} KNN \\ \end{tabular} } & Average F1 score & 0.89 & 0.94 & 0.94 & 0.93 \\ \cline{2-6}  & Train Time, s & 0.024 & 0.021 & 0.02 & 0.019 \\ \cline{2-6}  & Recognition Time, s & 20.628 & 19.961 & 19.12 & 19.299 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Logistic \\ Regression \\ \end{tabular} } & Average F1 score & 0.83 & 0.83 & 0.84 & 0.83 \\ \cline{2-6}  & Train Time, s & 18.735 & 15.357 & 16.187 & 13.823 \\ \cline{2-6}  & Recognition Time, s & 0.013 & 0.01 & 0.011 & 0.017 \\ \hline \multirow{3}{*}{\begin{tabular}{c} SVM \\ \end{tabular} } & Average F1 score & 0.77 & 0.84 & 0.85 & 0.85 \\ \cline{2-6}  & Train Time, s & 123.201 & 93.34 & 81.508 & 108.484 \\ \cline{2-6}  & Recognition Time, s & 32.33 & 32.708 & 23.8 & 25.256 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Gradient \\ Boosting \\ \end{tabular} } & Average F1 score & 0.86 & 0.92 & 0.92 & 0.92 \\ \cline{2-6}  & Train Time, s & 239.169 & 209.948 & 266.502 & 228.647 \\ \cline{2-6}  & Recognition Time, s & 0.455 & 0.306 & 0.33 & 0.37 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Random Forest \\ \end{tabular} } & Average F1 score & 0.95 & 0.97 & 0.97 & 0.97 \\ \cline{2-6}  & Train Time, s & 16.507 & 15.575 & 20.359 & 16 \\ \cline{1-1} \cline{2-6}  & Recognition Time, s & 0.958 & 0.689 & 0.696 & 0.825 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of applying feature-reducing methods.
# TDM-PON PAM Downstream Transmission for 25 Gbit/s and Beyond
## 3 Simulation Results
### 50 Gbit/s PAM4  
In this part, the electrical bandwidths of the optical components are enhanced in order to upgrade the rate from 25 Gbit/s to a target bit rate of 50 Gbit/s. The results are therefore akin to 25 Gbit/s and better than the optimized 37.5 Gbit/s PAM8 (Figure 7).  
Compared to 25 Gbit/s PAM4, a small change in the eye diagram is noticeable in terms of rising and falling time because of the higher limiting bandwidth of the transceivers: at 25 Gbit/s (12.5 Gbaud), the photodiode used in the experimental setup had a 8.5 GHz bandwidth (68% of the baud rate), whilst in the simulated 50 Gbit/s (25 Gbaud), the laser is the limiting bandwidth element at 20 GHz (80% of the baud rate).  
Contrary to the 25 Gbit/s PAM4 simulations, which were based on actual measurements from our experimental setup, it should be noted that here we rely only on the datasheets of the 20 GHz optics that we took as a reference.  
Another approximation in our model regards chromatic dispersion. Although weak, depending on the emitting wavelength in the O band, it could reach up to \(\pm 3\) ps/nm/km. The recent  
Figure 6: PAM8 eye diagrams for standard deviations of the decoder’s Gaussian noise of (**a**) 500 a.a.u., (**b**) 300 a.a.u., and (**c**) 100 a.a.u. (**d**) Simulated and polynomial approximations of BER versus Optical Budgets for different standard deviations of the simulated decoder’s noise.  
Figure 7: BER of 37.5 Gbit/s PAM8 and 50 Gbit/s PAM4 versus Optical Budgets for 0, 10, and 20 km.  
normalization recommendation [3] states that for chirpless 25 Gbit/s NRZ transmission, the dispersion tolerance is around 190 ps/nm, which means that wavelengths between 1260 and 1410 nm are usable over up to 20 km of fiber without dispersion compensation. For 50 Gbit/s PAM4, the usable spectrum might be restrained due the higher number of amplitude levels. This means that the simulated results might be valid on a narrower wavelength spectrum.