esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Research of Photonic Reservoir Computing for Optical Channel Equalization
## 3 Results and Discussion
### Equalization Performance for Different Types of Noise
This is because when ASE noise is high and comparable to signal power, it is difficult to separate the noise from the signal, limiting the equalization capabilities of PhRC chips. When ASE noise decreases to a level where PhRC chips can fully exert their equalization effects, it is observed that the number of nodes and the equalization capability exhibit a positive correlation, suggesting that reservoir layouts with more nodes tend to exhibit superior equalization performance in the presence of ASE noise. In practical optical fiber communication system links, noise is a mixture comprising dispersion, power attenuation, and nonlinear noise. We investigate the equalization performance of PhRC across various fiber transmission lengths, as illustrated in Figure 8a. At a transmission length of 10 km, the 24-node layout achieves the best equalization result, BER = 1.651 \(\times\) 10\(-\)12, and yields a seven-orders-of-magnitude BER reduction after equalization. Given the relatively low difficulty of the equalization task at this point, the 12-node layout delivers a suboptimal outcome, whereas the equalization performances of the 36-node and 48-node layouts are inferior. As the transmission length increases to 20 km, the noise equalization task becomes more challenging. The equalization capabilities of the 12- and 24-node layouts begin to falter, resulting in higher BERs for their equalized signals compared to those of the 36-node and 48-node layouts. At a fiber transmission length of 30 km, the BER of the equalized signals decreases sequentially with increasing node count. In addition to transmission length, signal speed also affects noise equalization. With the transmission length fixed at 20 km, we examine the equalization performance of PhRCs at different signal speeds, results as shown in Figure 8b. At the lower signal speed of 10-20 Gb/s, the BER of the equalized signal decreases with increasing node count. However, as the signal speed rises to 30-50 Gb/s, while the 12-node and 24-node layouts still  
Figure 8: The equalization effect of PhRC with different (**a**) fiber lengths and (**b**) signal speeds. Figure 7: The equalization effect of PhRC with (**a**) dispersion noise and (**b**) ASE noise. follow the positive correlation between node count and equalization capability, the equalization results for the 36-node and 48-node layouts exhibit opposite trends. This is because a reservoir layout with 36 nodes can meet the equalization requirements and achieve an optimal trade-off between the number of nodes and insertion loss. This demonstrates that, for specific tasks, there exists an optimal number of reservoir nodes, which is not necessarily the maximum within the selectable range.
# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 6 Evaluation Measures for Visual Saliency Prediction
In saliency prediction, EMD represents the minimum cost of converting the probability distribution of the saliency map into human-eye attention maps called the fixation map. * Kullback-Leibler (KL) Divergence: KL divergence is a general information theory measurement corresponding to the difference between two probability distributions. The calculation formula of KL is \[\text{KL}(\text{P},\text{G})=\sum_{\text{i}}\text{G}_{\text{i}}\log(\varepsilon+ \frac{\text{G}}{\varepsilon+\text{P}_{\text{i}}})\] (3)  
Similar to other distribution-based measures, KL divergence takes the predicted saliency map (P) and the true value view (G) as the input and evaluates the loss of information where P is used to approximate G, \(\varepsilon\) is the regularization constant. Furthermore, KL divergence is an asymmetric dissimilarity measure. A low score indicates that the saliency map is close to the true value. * (6) Similarity Metric (SIM): SIM measures the similarity between two distributions. After normalizing the input map, SIM is calculated as the sum of the minimum values at each pixel. The calculation formula of SIM is \[\text{SIM}(\text{P},\text{G})=\sum_{\text{i}}\min(\text{P}_{\text{i}},\text{G }_{\text{i}})\] (4)  
Given the predicted significance map (P) and the true value view (G), a SIM of 1 means that the distribution is the same, whereas a SIM of 0 means no overlap. SIM can penalize predictions that fail to consider all true densities. In general, these evaluation measures are complementary. A good model should be good under a variety of evaluation measures, because these measures reflect different aspects of the saliency map. Usually, a variety of evaluation measures are selected when evaluating the model. As a widely used measure of location based, AUC is essential. At the same time, a variety of other measures such as CC, SIM and other distribution-based measures should be selected to reflect other salient map factors such as relatively saliency region or similarity. Thus far, we have summarized the abovementioned six common evaluation measures based on whether they are appropriate as probability distribution, similarity, and continuous GT tools for statistics and classification. The details are shown in Table 1.
## 3 Materials and Methods
### Data Conversion  
In traditional fMRI classification methods, a two-dimensional correlation coefficient matrix is often established by calculating the Pearson correlation coefficient of the time series between two ROIs, and then the correlation coefficient matrix or its upper/lower triangular matrix is generated for further classification processing. As we know, convolutional neural networks (CNNs) have been successfully applied to image classification, image segmentation, and other fields, and it can also be used to model local neighborhood relations. However, if the convolution operation is directly performed on the preprocessed fMRI two-dimensional sequence \(X_{TS}\), the convolution kernel 3 \(\times\) 3 can only establish the relationships among three time-step sequences and three adjacent ROIs. Therefore, the modeling area is too small.  
The data conversion module was introduced to convert the two-dimensional sequence matrix, \(X_{TS}\), into a three-dimensional image \(X_{IMG}\). As shown in Equation (1), the one-dimensional vector \([a_{j1},a_{j2},\ldots,a_{jm}]\), noted as \(v_{j}\), represents the signal values of \(m\) ROIs at the \(j\)th time point. Hence, according to the row-first method, the vector, \(v_{j}\), can be transformed into a two-dimensional matrix, \(b_{j}\), with a size of \(r\times r\) by a reshaping function, where \(r=\left\lceil\sqrt{m}\right\rceil\) and the insufficient bits are filled with 0. Therefore, the preprocessed fMRI two-dimensional sequence, \(X_{TS}\), composed of vectors \(v_{j}\) (\(j=1,\,2,\,\ldots,s\)) at \(s\) time points, can be converted into a three-dimensional image \(X_{IMG}\) of \(r\times r\times s\), which is stacked by \(s\) two-dimensional matrices \(b_{j}\) (\(j=1,\,2,\,\ldots,s\)) according to the time axis. Obviously, the third dimension of the three-dimensional image \(X_{IMG}\) is the same as the second dimension of the two-dimensional sequence \(X_{TS}\), which refers to the time dimension. A diagram of the data conversion is presented in Figure 2. The specific conversion method was as follows: the BOLD values of all 2843 ROIs at a time point in the dotted box on the left were converted into a two-dimensional image of 54 \(\times\) 54 on the right.
## 3 Results  
### Multiple Linear Regression (MLR) Predictions  
The results from the MLR model show its limitations in predicting \(C_{c}\), especially for the testing database. Figure 4 shows that the training data aligns reasonably well with the 1:1 reference line and indicates a decent fit for the known data. Many points fall outside of the \(\pm 10\%\) boundaries and show that MLR struggles to generalize new data. This is because MLR relies on linear relationships. This linear approach is not enough to capture the complex, nonlinear interactions often seen in soil compressibility behavior.  
The boxplots and violin plots in Figure 4 further illustrate MLR's shortcomings. The boxplots show wider interquartile ranges and more outliers in both the training and testing predictions compared to the actual values, and thus indicate less precision. The violin plots reveal broader and misaligned distributions for predicted values, especially in the testing dataset. This reflects MLR's inability to handle the variability and complexity of real data. These results show the need for more advanced methods, such as machine learning models like XGBoost or GP, which are better suited to capture nonlinear relationships and improve prediction accuracy in complex geotechnical datasets.  
Table 5 shows the performance of the MLR method in predicting \(C_{c}\) values for both training and testing databases. The model performs well and achieved an \(R^{2}\) of 0.879 for training and 0.843 for testing and indicated strong predictive accuracy and good generalization to new data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Metrics** & **Training Database** & **Testing Database** \\ \hline MAE & 0.054 & 0.059 \\ MSE & 0.006 & 0.008 \\ \(R^{2}\) & 0.879 & 0.843 \\ _RMSE_ & 0.065 & 0.070 \\ _MAPE_ & 25.901 & 28.184 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance of the MLR method to predict \(C_{c}\) for training and testing databases.  
Figure 4: Evaluation of actual vs. predicted \(C_{c}\) using MLR through various visualizations.  
The error metrics further support these results. The MAE values are 0.054 for training and 0.059 for testing, while the MSE values are 0.006 and 0.008, respectively. These low error rates highlight the MLR method's ability to provide reliable predictions.
## 3 Result and Discussion
### CO\({}_{2}\) Concentration Measurement Experiment
#### 3.6.2 Measurement Result  
During the field experiment, the sensor node worked properly by acquiring indoor CO\({}_{2}\) concentration and transferring the data to the cloud over the IoT system to be visualized on the Adafruit dashboard, as shown in Figure 18a for the measurement in an indoor room and Figure 18b for outdoor.  
The trend of CO\({}_{2}\) recorded data was presented in a spreadsheet chart shown in Figure 19 that can be used for further analysis. The field experiment result shows that the sensor node can monitor and record indoor CO\({}_{2}\) concentration levels in real time and continuously within ranges of 286 ppm to 670 ppm, and outdoors in ranges of 129 ppm to 997 ppm. The average CO\({}_{2}\) concentration level measurement in an indoor room is 314 ppm, while the average CO\({}_{2}\) concentration level measurement outdoors is 423 ppm. These results indicate that the outdoor CO\({}_{2}\) concentration level is higher than the indoor concentration level. The total of people in the room, the people's activity, and the ventilation condition contribute to the high indoor CO\({}_{2}\) concentration.  
The field experiment results revealed that the sensor node provides accurate measurement results in different environmental conditions (indoor and outdoor) with various and fluctuating CO\({}_{2}\) concentrations.  
Figure 17: CO\({}_{2}\) sensor node configuration for the field experiment.  
Figure 19: The CO\({}_{2}\) concentration trend in the field experiment.  
Figure 18: Visualization of CO\({}_{2}\) measurement on a dashboard: (**a**) indoor environment; (**b**) outdoor environment.
# IDSMatch: A Novel Deployment Method for IDS Chains in SDNs +
## 5 Evaluation
### The Ratios of Redundant Packet Inspection  
With the proposed SDN-based IDS architecture, a crucial aspect we examined is the prevalence of redundant packet inspection across varying numbers of deployed IDSs. Redundant packet inspection refers to the scenario where network traffic traverses multiple IDSs in a chain, leading to duplicated efforts in packet analysis. We conducted a series of simulations to quantify the ratios of redundant packet inspection under different configurations, considering both the fixed and dynamically adjusted numbers of IDSs within the detection chain. The goal is to discern how the system's capacity limitations on individual IDS units may contribute to an increase in redundant inspections, particularly in situations of high network flow.  
To present our simulation results, we have compiled Table 3, detailing the impact of redundant packet inspection across different scenarios. The table includes the number of deployed IDSs in each chain, the corresponding volume of network traffic, and the resultant ratios of redundant packet inspection. This analysis aims to provide insights into the trade-offs between increased accuracy through multiple IDS layers and the associated costs in terms of redundant packet inspection, aiding in the optimization of the proposed SDN-based IDS architecture for real-world deployment. The table below summarizes the key findings:  
Table 3 contains the calculated ratios of redundant packet inspection for each combination of IDSs and traffic volumes. The ratio reflects the proportion of packets that undergo redundant inspection as they traverse multiple IDSs in the detection chain. These results highlight the dynamic nature of redundant packet inspection in relation to the IDS deployment strategy and network conditions, providing valuable insights for system optimization and resource allocation in SDN environments. For example, in the cell at the intersection of \"6 IDSs\" and \"Moderate Traffic Volume,\" the value of 0.26 suggests that, under these conditions, approximately 26% of the packets undergo redundant inspection.  
Figure 14: Comparison of different strategies for IDS based on detection rate. (**a**) Data center topology. (**b**) 4-node USNET topology. (**c**) 75-node CORONET topology.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of IDSs** & **Network Traffic Volume** & **Redundant Packet Inspection Ratio** \\ \hline \multirow{2}{*}{2} & Low & 0.15 \\  & Medium & 0.20 \\  & High & 0.25 \\ \hline \multirow{3}{*}{4} & Low & 0.18 \\  & Medium & 0.23 \\  & High & 0.28 \\ \hline \multirow{3}{*}{6} & Low & 0.21 \\  & Medium & 0.26 \\  & High & 0.32 \\ \hline \multirow{3}{*}{8} & Low & 0.25 \\  & Medium & 0.30 \\ \cline{1-1}  & High & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of redundant packet inspection with different numbers of IDSs and different network traffic volume.
## 3 Result
### Complex Network Clustering Results  
In this study, multi-sample weighted undirected networks were clustered and analyzed. The Louvain algorithm was used for clustering analysis to identify communities within the weighted undirected graph. The Louvain community detection algorithm, a popular method for network structure analysis, identifies community structures in networks by optimizing modularity. Widely applied in the clustering detection of complex networks, the Louvain algorithm aids researchers in identifying and understanding clustering structures within networks.  
Figure 8: Schematic Diagram of the Stacked Classification and Regression Prediction Framework.  
To validate the degree of network modularity, the Louvain algorithm was executed 10 times with modularity parameters ranging from 0.1 (extensive detection) to 1.0 (classical cluster size detection). The resulting modularity indices are shown in Table 2.  
According to Table 2, when the modularity detection parameter in the Louvain algorithm is set to 0.1, that is, when extensive clustering is detected, the network modularity index reaches a significant peak of more than 0.6. Then, the modularity detection parameter gradually increases, the network modularity index is gradually narrowed, and is finally stabilized in the range of 0.2 to 0.4, which is the result of an index with a more significant modularity. This also indicates that, at first, the Louvain algorithm integrates all sample points into one cluster when performing extensive modularity detection. However, as the detection parameters gradually increase, this detection structure evolves into smaller, more defined clusters. This pattern suggests that the network displays distinct clustering properties and maintains a relatively cohesive cluster structure.  
Following this result, the study conducted clustering validation on multiple clustering partitions, yielding a modularity index of 0.351 and dividing the complex network into four clusters. Through comparative analysis, Cluster 1 was designated as high Hb, Clusters 2 and 3 as normal Hb, and Cluster 4 as low Hb. This result underscores the capability of the clustering approach in delineating distinct ranges of Hb levels. The statistical histogram illustrating the clustering outcomes is presented in Figure 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Modular Parameter** & **Cluster Index** \\ \hline
0.1 & 0.9 \\
0.2 & 0.8 \\
0.3 & 0.7001 \\
0.4 & 0.6001 \\
0.5 & 0.5073 \\
0.6 & 0.420 \\
0.7 & 0.352 \\
0.8 & 0.315 \\
0.9 & 0.283 \\
1.0 & 0.259 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clustering metrics table.  
Figure 9: Statistical Histogram of Clustering Results. **(A)** Clustering Distribution of four different types, with Cluster I noted as high Hb, Clusters II and III noted as normal Hb, and Cluster IV as low Hb. And Figure B demonstrates the histogram of the overlap between the clustering results and the hemoglobin distribution.
# Workflow Trace Profiling and Execution Time Analysis in Quantitative Verification
## 4 Model Building
### SMC Model
#### 4.4.2 Estimating the SMC Quantitative Parameters  
The initial distribution \(\alpha\) is a distribution over the states in the intersection of \(S\) and \(\{w_{\mathrm{ini}}\}\times Q_{0}\). If this intersection is not a singleton, we usually let \(\alpha\) be a uniform distribution. But in general, \(\alpha\) can be any discrete distribution over the state space.  
Recall that the statistics of execution traces is given as \(\texttt{Summ}=(\texttt{Freq},\texttt{PSum})\), where \(\texttt{Freq}\) includes the frequencies of transitions, and \(\texttt{PSum}\) includes the power sums of state holding times. We use \(\texttt{Freq}\) to estimate the transition matrix \(\mathbf{P}\) of the SMC. For each \(s\in S\), let  
\[\mathbf{P}[s,r]:=p_{s,r}=\begin{cases}\frac{\texttt{Freq}(s[0],r[0])}{\sum_{r \in\mathrm{Post}(s)}\texttt{Freq}(s[0],r[0])}&\text{if }r\in\mathrm{Post}(s)\\ 0&\text{otherwise.}\end{cases} \tag{1}\]  
Clearly, if \(\mathrm{Post}(s)=\{r\}\), then \(\mathbf{P}[s,r]=1\).  
It is very difficult to determine continuous probability distribution functions from data. We use \(\texttt{Freq}\) and \(\texttt{PSum}\) to estimate the moments of the CDFs in \(\mathbf{F}\). For each \(s\in S\) and \(k\geq 1\), let  
\[\mathbf{F}[s,r]:=\mu_{s,r}^{(k)}=\begin{cases}\frac{\texttt{PSum}(k,s[0],r[0])} {\texttt{Freq}(s[0],r[0])}&\text{if }r\in\mathrm{Post}(s)\\ 0&\text{otherwise.}\end{cases} \tag{2}\]  
We briefly summarise the model building stage in our approach. The fixed workflow model \(\mathcal{W}\) and an updatable execution summary \(\texttt{Summ}\) form a workflow profile. One or more DFAs \(\mathcal{A}\) are defined to formalise the trace components of \(\mathcal{W}\). The workflow profile \((\mathcal{W},\texttt{Summ})\) and the DFA \(\mathcal{A}\) are used together to build an SMC model \(\mathcal{M}\) for time analysis (which is detailed in the next section).
# An Outline of Fused Deposition Modeling: System Models and Control Strategies
## 2 Systems
### Deposition
#### 2.2.3 Global Structure  
The properties of the complete part depend on the interaction among the different layers and are often a function of the material only [8]. However, the process parameters that control the print locally have a strong effect on the global outcome. For instance, each layer must be able to sustain the stress induced by the extruded flow of the successive layer without deforming it excessively. This constraint translates into a limitation of the elastic strain of the substrate \(\sigma_{sub}\) as [8]  
\[\sigma_{sub}=\sigma_{com}+\sigma_{mot}<\sigma_{yield} \tag{18}\]  
where \(\sigma_{com}\) is the compression term provided by the weight of the next layer, and \(\sigma_{mot}\) represents the compression term given by the flow motion.  
Similarly, the lowest layers must bear the weight of all the successive deposits; hence [8]  
\[\sigma_{wall}=n_{l}\rho gh<\sigma_{yield}. \tag{19}\]  
The distortion ratio between structure deformation \(\sigma\) and layer height \(h\) is given by  
\[\Delta=\frac{\sigma}{h} \tag{20}\]  
and represents the deviation of the layer from the ideal plane of deposition, as shown in Figure 6. Such error is mainly induced by the shrinkage of the extrudate that occurs upon cooling down from glass temperature to room temperature.  
Figure 6: Scheme of the deformation phenomenon, quantified as \(\delta\).  
The maximum acceptable distortion ratio can be written as  
\[\Delta_{max}=\frac{4}{3A}\left(1-\cos(\frac{3}{8}AA)\right) \tag{21}\]  
where \(A\propto(T_{glass}-T_{room})\). The condition becomes \(\Delta_{max}<1\) to avoid interference between printing head and substrate.  
A crucial element for the mechanical properties of the printed part is the interlayer strength. It is defined as the ability to resist longitudinal stress (parallel to the deposition plane), and it is mainly affected by poor diffusion, which in turn depends on the temperature of both the substrate and new layer. A model of interlayer strength for plastic materials, validated through online measurements, has been presented [46] as  
\[\sigma_{pred}=\frac{w_{b}}{w}\sigma_{\text{inf}}\text{, for }\chi\simeq\chi_{ \text{inf}}\text{,} \tag{22}\]  
where \(w_{b}\) is the predicted bond width between layers, \(w\) the nominal road width, \(\sigma_{\text{inf}}\) the strength of the fully healed polymer, \(\chi\) the interlayer penetration distance of diffusion between layers, and \(\chi_{\text{inf}}\) the diffusion distance at which full strength is achieved and the healing process is complete.  
The interaction among layers and the resulting global part, including the effects of layer height \(h\) and strand-to-strand distance \(s\), has been studied through numerical simulations [47]. The results can be summarized as follows:  
* \(s<w\) can cause considerable distortions in new the layers;
* Smaller \(h\) and \(s\) lead to smaller porosity and better surface roughness.
# Forecasting Taxi Demands Using Generative Adversarial Networks with Multi-Source Data
## 4 Methodology
### Features Preparation
#### 4.3.3 Feature Concatenation  
To facilitate the dataset training, we embed the features in Table 4 into a \(1\times 11\) vector according to a time-step t. Figure 12 depicts Feature Concatenation.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Factor** & **Symbol** & **Data-Type** & **Description** \\ \hline Output (y) & & & \\ Taxi demands & dems & continuous & Summation of Taxi pickups and dropoffs \\ Inputs (x) & & & \\ Date & dt & time series & Date Format month-date \\ Time & tm & time series & Time Format hourmints \\ Is rush-time & rush & categorical & 1. Yes 0. No \\ Is weekend & weekend & categorical & 1. Yes 0. No \\ Is holiday & holiday & categorical & 1. Yes 0. No \\ Region & reg & categorical & Determining region by longitude and latitude \\ Temperature & temp & continuous & Temperature \\ Weather & wealth & categorical & 0. Clear 1. Light Rain 2. Heavy Rain 3. Fog \\ Wind & wind & continuous & Wind (km\textbackslash h) \\ Humidity & humid & continuous & Humidity by percentage \\ poi\_counts & poi & continuous & POI number in region \\ \hline \hline \end{tabular}
\end{table}
Table 4: Features in the dataset.  
Figure 11: Weather Influence on Taxi demands.  
After performing features normalization, transformation, and concatenation, the dataset was ready for forecasting by prediction models. The features included in the dataset are shown in Table 4.