esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 A 4-Bit SNN Working as an Episodic Memory
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**(a)**} & \multicolumn{3}{c}{**Single Junction Ratio: 0.7\(-\)0.3**} & \multicolumn{3}{c}{**(b)**} & \multicolumn{3}{c}{**Single Junction Ratio: 0.8\(-\)0.2**} & \multicolumn{3}{c}{**(c)**} & \multicolumn{3}{c}{**Single Junction Ratio: 0.9\(-\)0.1**} \\ \hline \multicolumn{1}{c}{**channel**} & **1** & **2** & **3** & **4** & **channel** & **1** & **2** & **3** & **4** & **channel** & **1** & **2** & **3** & **4** \\ \hline \multicolumn{1}{c}{**INPUT**} & 1 & 0 & 0 & 1 & **INPUT** & 1 & 0 & 0 & 1 & **INPUT** & 1 & 0 & 0 & 1 \\ \hline \multicolumn{1}{c}{**OUTPUT**} & 0.5747 & 0.4122 & 0.4285 & 0.5846 & **OUTPUT** & 0.6641 & 0.3066 & 0.3345 & 0.6948 & **OUTPUT** & 0.7849 & 0.1639 & 0.1977 & 0.8536 \\ \hline \multicolumn{1}{c}{**INPUT**} & 0 & 1 & 1 & 0 & **INPUT** & 0 & 1 & 1 & 0 & **INPUT** & 0 & 1 & 1 & 0 \\ \hline \multicolumn{1}{c}{**OUTPUT**} & 0.4212 & 0.586 & 0.5717 & 0.4211 & **OUTPUT** & 0.3236 & 0.6887 & 0.666 & 0.3218 & **OUTPUT** & 0.1851 & 0.827 & 0.8063 & 0.1816 \\ \hline \multicolumn{1}{c}{**INPUT**} & 1 & 0 & 1 & 0 & **INPUT** & 1 & 0 & 1 & 0 & **INPUT** & 1 & 0 & 1 & 0 \\ \hline \multicolumn{1}{c}{**OUTPUT**} & 0.3902 & 0.336 & 0.6588 & 0.615 & **OUTPUT** & 0.3314 & 0.2209 & 0.7648 & 0.7029 & **OUTPUT** & 0.1933 & 0.1017 & 0.8785 & 0.8266 \\ \hline \multicolumn{1}{c}{**INPUT**} & 0 & 1 & 0 & 1 & **INPUT** & 0 & 1 & 0 & 1 & **INPUT** & 0 & 1 & 0 & 1 \\ \hline \multicolumn{1}{c}{**OUTPUT**} & 0.6121 & 0.6637 & 0.3377 & 0.3865 & **OUTPUT** & 0.6999 & 0.7731 & 0.2227 & 0.3043 & **OUTPUT** & 0.8264 & 0.8869 & 0.1029 & 0.1839 \\ \hline \multicolumn{1}{c}{**INPUT**} & 1 & 1 & 0 & 0 & **INPUT** & 1 & 1 & 0 & 0 & **INPUT** & 1 & 1 & 0 & 0 \\ \hline \multicolumn{1}{c}{**OUTPUT**} & 0.508 & 0.792 & 0.2054 & 0.4946 & **OUTPUT** & 0.3635 & 0.8366 & 0.1482 & 0.6518 & **OUTPUT** & 0.1944 & 0.9057 & 0.0787 & 0.8212 \\ \hline \multicolumn{1}{c}{**INPUT**} & 0 & 0 & 1 & 1 & **INPUT** & 0 & 0 & 1 & 1 & **INPUT** & 0 & 0 & 1 & 1 \\ \hline \multicolumn{1}{c}{**OUTPUT**} & 0.4946 & 0.2054 & 0.7704 & 0.5296 & **OUTPUT** & 0.6518 & 0.1482 & 0.8086 & 0.3915 & **OUTPUT** & 0.8213 & 0.0787 & 0.8804 & 0.2196 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the simulation outputs of the mathematical model for 2-digit, 4-bit numbers and for different SJR values: (a) SJR = \(0.7\!-\!0.3\), (b) SJR = \(0.8\!-\!0.2\), and (c) SJR = \(0.9\!-\!0.1\). configurations for this case and increasing SJR values. As can be seen, all outputs show 3 channels with high values and one with a low one. The contrast follows the SJR value. The evolution of signals in the network follows specific patterns (Figure 6), whose contrast increases as the SJR parameter increases too.
## 4 Performance Evaluation, and Result Analysis and Discussion
### Discussion  
In performance experiments, we had a problem when assuming a congested network between the cloud and the edge. Due to the congested network, the edge node was unable to smoothly convey the current cache utilization to EDCrammer, and the EDCrammer also had difficulty in data caching in the edge node. To this end, we conducted further studies, confirmed that the probability of cloud-edge congestion is low in the existing network, and excluded this scenario from the experiment. However, this problem could occur in densely crowded environment as the traffic is concentrated to an access point (AP) of Fog, MEC, etc. The authors of [50, 51] solved this issue by network slicing. Therefore, it is necessary to consider network slicing technique for data caching in a densely crowded environment.  
In this paper, we consider an environment that the cloud node transmits data (data caching) to the edge node with consideration of user's data consumption. As a characteristic of the edge node that the computing resources are limited, an efficient caching rate-control algorithm, EDCrammer, that can store the data quickly in the edge node without requiring a lot of computing power has been proposed, and showed its necessity. However, since the proposed algorithm mainly considers mitigation of latency and load congestion, further studies on trust, security, privacy, forensic, etc. [52, 53, 54, 55] are needed, such as intrusion awareness [56, 57, 58, 59] and secure data aggregation [60]. We expect that applying those would provide more secure data caching. Meanwhile, since AI-based data caching [61, 62, 63] and AI-based edge management [59, 64] are computationally intensive, this paper assumes that it is not appropriate for edge nodes. However, it can be effective for data caching when multiple factors are considered. Therefore, a lightweight AI or ML method for edge nodes is necessary. We expect that applying those would provide more precise and efficient data caching.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 6 Conclusions and Perspectives
### Constructing the Fluorescence Validation Network  
Due to the limited development of fluorescence remote sensing, the availability of fluorescence ground measurement data is very limited, making it difficult to use fluorescence data to validate the airborne or space-borne fluorescence results. The fluorescence retrieval methods based near the ground without considering atmospheric effects have been successfully applied in numerous studies. Thus, the fluorescence values near the ground can be thought of as 'true' values and can be utilized to validate other fluorescence retrieval results. Using standard spectral measurement and fluorescence retrieval technology, the ground fluorescence measurement network was constructed in order to obtain the 'true' fluorescence value. With the emergence of more fluorescence satellite data and products, it is urgent that we validate the fluorescence satellite data in order to improve fluorescence satellite retrieval methods and accuracy.  
Chlorophyll fluorescence varies with vegetation biochemical parameters and canopy structure; thus, it has a different response to vegetation species under different environmental conditions. The fluorescence validation network construction covering a large number of measured samples will ensure the global validity of SIF data. In the FLEX fluorescence project, the \"bottom-up\" scheme was proposed to validate the FLEX fluorescence products, which start from tower-based canopy fluorescence measurements to the landscape level including the different vegetation types and the non-vegetated surfaces. Several factors, such as measured sites, vegetation types, structures, phenology, the range of photosynthetically active radiation (PAR), and so on, should be considered. Based on the existing data sites, such as FLUXNET, the deployment of fluorescence measurements may be carried out [138; 139].  
[PERSON] supervised this paper. [PERSON] collected the materials and wrote this paper. [PERSON] revised the paper. [PERSON] sorted out the materials and formatted the paper.  
This work was funded by the National Natural Science Foundation of China (41701421) and the Open Fund of the State Key Laboratory of Remote Sensing Science (Grant No. OFSLRSS201719).  
The authors declare no conflict of interest.
## 4 Experiment and Analysis
### Evaluation Metrics  
To evaluate the cyberbullying detection performance of the model, precision, recall, and \(F_{1}\) score are used as the primary evaluation metrics:  
\[Pre=\frac{TP}{TP+FP} \tag{12}\]  
\[Rec=\frac{TP}{TP+FN} \tag{13}\]  
\[F_{1}=2\times\frac{Precision\cdot Recall}{Precision+Recall} \tag{14}\]  
where \(TP\) denotes instances correctly identified as offensive, while \(TN\) signifies correctly identified normal instances. Conversely, \(FN\) refers to offensive instances mistakenly classified as normal, and FP represents normal instances incorrectly classified as offensive. The \(F_{1}\) score, a holistic assessment metric, integrates precision and recall for a comprehensive evaluation.  
Furthermore, to introduce error analysis metrics in OLD, we use the Brier Score (BS) to measure the accuracy of probability predictions. The calculation of BS for cross-lingual OLD is:  
\[BS=\frac{1}{N}{\sum_{i=1}^{N}{(p_{i}-y_{i})}^{2}} \tag{15}\]  
where \(N\) is the total number of samples. \(p_{i}\) represents the model's predicted probability that sample \(i\) is offensive. \(y_{i}\) is the ground truth label for sample \(i\), where \(y_{i}=1\) indicates the text is offensive, and \(y_{i}=0\) indicates it is non-offensive. The BS measures the mean squared error between the model's predicted probabilities and the actual labels, evaluating the performance of the cross-lingual OLD model. BS ranges from 0 to 1, with lower values indicating more accurate probability predictions; 0 represents perfect predictions, and 1 represents the worst predictions.  
Log Loss (\(LL\)) is used to measure the discrepancy between the predicted probabilities of a model and the actual labels. Although the BS focuses more on the accuracy of predicted probabilities, \(LL\) places greater emphasis on the divergence between predicted probability distributions and the true labels. LL is calculated as:  
\[LL=-\frac{1}{N}{\sum_{i=1}^{N}{[y_{i}\log(p_{i})+(1-y_{i})\log(1-p_{i})]}} \tag{16}\]_LL_ calculates the logarithmic loss between the predicted probability distribution of the model and the actual labels. A lower value indicates that the model's predictions are closer to the true labels, with 0 representing perfect prediction accuracy.
## 3 Data Sources and Research Methods
### Research Methodology
#### 3.2.3 Disturbance Model  
The disturbance model was conducted on the urban network using Python to analyze the variation characteristics of the network structure resilience, and the strengthening strategy of urban network resilience was proposed.  
In an urban network, the stronger the connection between nodes is, the greater the reliability will be when dealing with network shocks, which means that it is easier to make corresponding adjustments and reduce the damage caused by disturbance. According to the classical definitions, the network resilience can be defined as follows:  
\[R(e)=\sum degree/\sum degree0 \tag{3}\]  
where \(R(e)\) represents network resilience and \(0\leq R(e)\leq 1\); where \(\sum degree0\) represents the initial total degree of the network, and \(\sum degree\) represents the total degree of the network after the attack, which can be used to measure the ability of the urban network to withstand damage and continue to operate.  
Generally, interruption simulation is based on five common failure strategies. The first strategy is called node-degree-value-based disturbance strategy. In this strategy, target nodes are selected in descending order according to the degree of nodes, the network with the highest degree of nodes is attacked, the node with the highest degree of nodes is removed from the network, and then the network robustness after the removal of nodes is calculated. Finally, the variation of network characteristics before and after the attack is obtained, that is, the \(R(e)\) value. The second method is based on node random elimination strategy. As the name implies, this strategy is to randomly select a network node, remove the node from the network, calculate the network robustness after the removal, and then obtain the change value of network robustness. The third strategy is called edge-based random disturbance strategy. Different from the second strategy, the third strategy no longer starts from the point of view but randomly selects an edge in the network to remove, calculates its network robustness, and finally obtains the changed value. The fourth strategy is similar to the fifth one, but has a different state based on the network. It can be divided into a static network betweenness centrality disturbance strategy and a dynamic network betweenness centrality disturbance strategy. Both strategies start from the node with the highest betweenness centrality in the network then remove the target node from the network, and, finally, they calculate the network robustness to obtain its variation. The dynamic network node betweenness centrality will be changed while the nodes were removed, so it will be different from the static network. The dynamic network each time shall be recalculated and finally get a different network robustness value.
# DNA Barcoding as a Plant Identification Method
## 9 Meta Barcoding  
Meta barcoding is a method for identifying numerous species in a single environmental sample by using universal PCR primers to simultaneously amplify multiple DNA barcodes. The discovery and development of NGS (next-generation sequencing), which allows for parallel reading of DNA sequences without the requirement of cloning, contributed to the development of meta barcoding techniques. In contrast to traditional DNA barcoding, which attempts to identify intact specimens up to the species level, metabarcoding aims to identify in various samples or in degraded DNA samples, like environmental samples (eDNA) up to the family level or higher. Metabarcoding represents a versatile and precise approach to analyzing food matrices, to detect adulterants or contaminants, in forensics and ecological analyses [31; 46; 68; 69; 70].
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 3 Results
### Comparison of the Statistical Parameters of Methods  
Figure 9(a) shows the cumulative relative occurrence of \(R^{2}\) of individual 10 min data for the different methods. The _PRESS_ method has a very concentrated distribution with all \(R^{2}\) greater than 0.85, and over 78% of the \(R^{2}\) data greater than 0.95. The _CLS_ method in combination with the SWE Scanner has the widest distribution of the systems and methods that was investigated. The results when using the Galion with an opening angle of \(\phi=20^{\circ}\) are similarly wide, the differences of the shape of the distribution is very similar to the distribution of the SWE Scanner.  
A comparison of the ranges of quantiles for the individual methods against a reference value gives a good impression of the relative dispersion of the individual methods. For this purpose the width of the 5-95 th quantile is calculated for each method (Figure 9(b)). The results of the SWE Scanner with the _CLS_ method were taken as reference because this trajectory is very similar to commercial lidar wind profilers. The narrowest quantile is observed using the _linear_ model and the _CLS_ model for the Galion Lidar with all 18 trajectory points. These procedures achieve a reduction between 20% and 60%.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Lidar System** & **Method** & **Slope [-]** & **Offset [m\(\cdot\)s\({}^{-1}\)]** & \(R^{2}\) \\ \hline SWE Scanner & _CLS_ & 1.074 & 0.219 & 0.949 \\ SWE Scanner & _PRESS_ & 1.103 & 0.385 & 0.938 \\ SWE Scanner 2 nd position & _CLS_ & 1.046 & 0.537 & 0.942 \\ SWE Scanner 2 nd position & _PRESS_ & 1.09 & 0.674 & 0.936 \\ Galion 18 Pts. & _CLS_ & 1.033 & 0.02 & 0.974 \\ Galion 20\({}^{\circ}\) & _CLS_ & 1.00 & 0.293 & 0.93 \\ Galion 39.2\({}^{\circ}\) & _CLS_ & 1.013 & 0.067 & 0.962 \\ Galion 55\({}^{\circ}\) & _CLS_ & 1.032 & 0.03 & 0.960 \\ Galion & _linear_ & 1.026 & 0.03 & 0.976 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the OLS (Ordinary least-squares) results.  
Figure 10: Overview of the distribution of \(R^{2}\) and the reduction of the 5 th and 95 th percentile.  
In comparison, the _PRESS_ method raises the dispersion by up to 20%. The range is reduced for the other procedures, but not in order as for the _linear_ model.
## Abbreviations
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**HH** & \multicolumn{6}{c}{**[PERSON]**} & \multicolumn{6}{c}{**[PERSON]**} \\ \hline
**(m)** & **WS** & **ZP** & **RP** & **AEP** & \(R_{i}\) & **WS** & **ZP** & **RP** & **AEP** & \(R_{i}\) \\ \hline
50 & 6.08 & 9.42 & 0.47 & 4,552,426 & 0.448 & 4.99 & 16.17 & 0.08 & 2,982,459 & 0.368 \\
60 & 6.24 & 8.95 & 0.59 & 4,876,812 & 0.453 & 5.11 & 15.39 & 0.12 & 3,213,212 & 0.364 \\
70 & 6.37 & 8.58 & 0.72 & 5,161,005 & 0.467 & 5.23 & 14.78 & 0.16 & 3,417,237 & 0.365 \\
80 & 6.49 & 8.25 & 0.84 & 5,413,711 & 0.484 & 5.33 & 14.25 & 0.21 & 3,600,277 & 0.384 \\
90 & 6.60 & 7.98 & 0.96 & 5,641,045 & 0.505 & 5.41 & 13.8 & 0.27 & 3,766,569 & 0.426 \\
100 & 6.70 & 7.74 & 1.09 & 5,847,676 & 0.530 & 5.49 & 13.41 & 0.33 & 3,919,029 & 0.480 \\
110 & 6.79 & 7.53 & 1.20 & 6,037,218 & 0.542 & 5.57 & 13.07 & 0.39 & 4,059,755 & 0.532 \\
120 & 6.87 & 7.34 & 1.33 & 6,211,605 & 0.553 & 5.64 & 12.75 & 0.46 & 4,190,603 & 0.584 \\
130 & 6.95 & 7.18 & 1.45 & 6,373,608 & 0.555 * & 5.70 & 12.49 & 0.53 & 4,312,877 & 0.615 \\
140 & 7.02 & 7.03 & 1.56 & 6,524,469 & 0.552 & 5.76 & 12.23 & 0.61 & 4,427,608 & 0.632 * \\ \hline \hline \end{tabular}
\end{table} 
Table 9: TOPSIS analysis for turbine Hanjin HJWT2000-93. The best choice for each site is marked with an asterisk. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**HH** & \multicolumn{6}{c}{**Turair**} & \multicolumn{6}{c}{**Wejh**} \\ \hline
**(m)** & **WS** & **ZP** & **RP** & **AEP** & \(R_{i}\) & **WS** & **ZP** & **RP** & **AEP** & \(R_{i}\) \\ \hline
50 & 6.08 & 9.39 & 0.96 & 6,632,186 & 0.439 & 4.99 & 16.12 & 0.27 & 4,418,179 & 0.391 \\
60 & 6.24 & 8.92 & 1.18 & 7,095,683 & 0.441 & 5.11 & 15.33 & 0.38 & 4,746,076 & 0.390 \\
70 & 6.37 & 8.55 & 1.40 & 7,503,843 & 0.446 & 5.23 & 14.73 & 0.50 & 5,037,027 & 0.398 \\
80 & 6.49 & 8.22 & 1.62 & 7,868,771 & 0.457 & 5.33 & 14.20 & 0.64 & 5,298,941 & 0.423 \\
90 & 6.60 & 7.95 & 1.87 & 8,199,107 & 0.478 & 5.41 & 13.75 & 0.77 & 5,537,503 & 0.456 \\
100 & 6.70 & 7.71 & 2.11 & 8,500,607 & 0.501 & 5.49 & 13.36 & 0.91 & 5,756,929 & 0.500 \\
110 & 6.79 & 7.51 & 2.37 & 8,777,953 & 0.524 & 5.57 & 13.02 & 1.06 & 5,959,892 & 0.546 \\
120 & 6.87 & 7.32 & 2.64 & 9,034,599 & 0.543 & 5.64 & 12.70 & 1.22 & 6,149,030 & 0.582 \\
130 & 6.95 & 7.15 & 2.93 & 9,273,298 & 0.555 & 5.70 & 12.45 & 1.38 & 6,325,976 & 0.602 \\
140 & 7.02 & 7.00 & 3.23 & 9,496,276 & 0.561 * & 5.76 & 12.19 & 1.54 & 6,492,396 & 0.609 * \\ \hline \hline \end{tabular}
\end{table} 
Table 10: TOPSIS analysis for turbine Leitwind LTW70-2000. The best choice for each site is marked with an asterisk.
## 3 Results and Discussion
### Single-Factor Experiments
#### 3.1.5 Effect of Liquid/Solid Ratio  
A series of extractions were performed with different liquid/solid ratios to evaluate the effect of the liquid/solid ratio on the yields of the polysaccharides and essential oil. Figure 3D shows that the yields of the polysaccharide and essential oil significantly increased when the liquid/solid ratio was increased from 10 to 20 mL/g. When the liquid/solid ratio was varied from 20 to 30 mL/g, there was no discernible difference in the yields of target compounds. Based on these results, the optimal liquid/solid ratio was determined to be 20 mL/g and used for the subsequent experiments.
## 3 Results and Discussion
### Quantitative Analysis of 2,4-D in Zizania Latifolia  
In this paper, we further studied the feasibility of quantitative detection of 2,4-D in Zizania latifolia by THz technology. Figure 4 shows the frequency-domain spectrum of pure Zizania latifolia in the range of 0-4 THz. Absorbance spectra of Zizania latifolia mixtures at six different concentrations of 2,4-D (5%, 10%, 15%, 20%, 25%, and 30%) were detected, and the results are shown in Figure 4B.  
From Figure 4A, it can be seen that there was no absorption peak of Zizania latifolia at a usable detection range. Therefore, it would not influence quantitative analysis of 2,4-D. In the region with frequency greater than 2.5 THz, the SNR decreased significantly. Therefore, the usable range should be below 2.5 THz, which was smaller than for pure 2,4-D. From Figure 4B, it can be seen that only the absorption peak at 1.36 THz could be detected clearly. The second absorption peak could not be detected because its signal was too weak, and the third absorption peak was submerged in the noise. Moreover, it shows that the spectra had different degrees of baseline drift, which made it difficult for quantitative analysis. The baseline drift was mainly caused by a scattering effect, which might be related to sample particle size [38]. Therefore, baseline correction was conducted to these spectra for further analysis.  
It can be seen from Figure 4C, the 2,4-D absorption peak at 1.36 THz was detected obviously, and the absorption peak intensity at 1.36 THz had a corresponding increase trend with increase of 2,4-D concentration, which could be used as the main peak for quantitative analysis. At 1.36 THz, the LOD of 2,4-D was lower than 5%, which was close to the LOD of pesticide residues in food obtained by other researchers using terahertz. Figure 5 shows the fitting curve between 2,4-D concentration and absorbance at 1.36 THz. The fitting curve achieved a good linear regression relationship with an R\({}^{2}\) of 0.9854. To verify the accuracy of the model, 17%, 23%, 27%, and 33% concentrations of 2,4-D in Zizania latifolia were detected by THz-TDS, and the results are shown in Table 2. The result showed  
Figure 4: THz spectra of 2,4-dichlorophenoxyayacetic acid and Zizania latifolia mixtures. **(A)** Frequency-domain spectrum of Zizania latifolia; **(B)** Absorption spectra of Zizania latifolia mixtures with six different concentrations of 2,4-D from 0 to 3 THz; **(C)** Absorption spectra after baseline correction from 1.2 to 1.5 THz.  
Figure 3: Absorption peak vibration modes of 2,4-dichlorophenoxyayacetic acid. **(A)** Absorption peak vibration at 1.36 THz; **(B)** Absorption peak vibration at 1.60 THz; **(C)** Absorption peak vibration at 2.38 THz.  
that the recovery was 93.29%-98.75%, which fully demonstrated the great potential of THz technology in the detection of 2,4-D in Zizania latifolia. However, the LOD still could not reach the detection requirement for actual application, and further researches on improving apparatus sensitivity and designing usable signal enhancement materials need to be conducted.
# Methods of Atmospheric Coherence Length Measurement
## 3 Coherence Length \(r_{0}\) Direct Measurement Method
### Slope Difference Method
\]  
where \(\Delta x_{s}\), \(\Delta y_{s}\), \(\Delta x_{t}(z)\) and \(\Delta y_{t}(z)\) represents normalized spatial and temporal separation of subaperture slope measurements; \(x_{s}=(x-x^{\prime})/d\), \(\Delta y_{s}=(y-y^{\prime})/d\), \(\Delta x_{t}(z)=W_{x}(z)(t-t^{\prime})/d\), \(\Delta y_{t}\) (\(z)=W_{x}(z)\) (\(t-t^{\prime})/d\); \(tri(u)\) is a triangular function; \(\beta\) is the power law of the power spectrum of the refractive index fluctuations and \(\gamma_{\beta}\) is function of the power law. \(W(z)\) is a normalized profile. It is clear from the above equations that the slope structure function depends on these normalized separations as well as on the atmospheric parameters, \(\gamma_{\beta}\), \(r_{0}\) and \(W(z)\). The slope structure function is calculated based on the average slope measured by the SHWS, which is used to evaluate \(r_{0}\). It has been shown that the direct use of slope measurements to characterize atmospheric turbulence is feasible and less computationally intensive than wavefront phase reconstruction.