esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
From our state-of-the-art review, it appears that using a control structure with an NN architecture as a feedforward component for the inner-loop dynamic compensation allows for reducing the errors in position and velocity during the trajectory tracking without any prior knowledge of the dynamic model [4; 5; 11]. However, there is a lack of studies optimizing the design process of this model-free control structure. This gap necessitates experimentally selecting learning parameters, often leading to unnecessarily complex architectures. Particularly, the current models still require a large number of parameters--ranging from \(10^{3}\) to \(10^{4}\) parameters--resulting in non-convex loss functions and higher computational costs during the training process. The minimum number of parameters needed for the NN architecture to provide an effective dynamic compensation is also absent from the literature, as well as the amount of data required to train these architectures. Moreover, there are no established guidelines on the best NN architecture for the use as feedforward compensation--whether a global NN for the whole system (the GNN method) or smaller NNs for each joint (the INN method)--and the necessary conditions to achieve optimal performances for these methods remain unclear. The objective of this study is to design a minimal model-free control structure based on a neural network for the fast and accurate trajectory tracking of robotic arms. The subsequent objective is to compare 1. one NN per individual joint (the INN method) and 2. one global NN for all joints (the GNN method). To meet this objective, both real 3D-printed robotic arms (3- and 5-DOF) and simulated robotic arms (3-DOF) were used. We focused on the fast trajectories, i.e., trajectories where the PD is unable to accurately track the trajectories due to the response time limitations. Figure 3 presents the design of the studied robots. The planar 3-DOF setup allows straightforward results while accounting for external forces like gravity, while the 5-DOF setup demonstrates the architecture's applicability to a widely used topology in industry settings.
## 2 Basic Analysis of Prediction Models and Model Verification
### Prediction Models of Surface Tension
#### 3.3.2 Comparison and Discussion of Different Models of Surface Tension  
The surface tensions of FC-72, Novec 649 and HFE-7100 were predicted, respectively, by using the above six prediction models (shown in Table 7), and the relative deviations between the predicted results and the reference values from the literature [62; 70; 71] are shown in Figures 8-10. The combined expanded uncertainties in measurements (level of confidence = 0.95, coverage factor = 2) of FC-72, Novec 649 and HFE-7100 were 0.45% [62], 1.5% [70] and 1.5% [71], respectively. The MRDs and ARDs between the predicted and reference values of different models are shown in Table 8.  
As [PERSON] et al. [101] found, for most studied compounds, the GST model had the lowest average relative deviations compared with the other two investigated models (by [PERSON] et al. [99] and [PERSON] et al. [100], named the PST model in this paper) in predicting surface tension; the results from Table 8 also indicate that the GST model has the best universality out of all the models, and it has a good accuracy for electronic fluoride liquids.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Name** & **Model Expression** \\ \hline \hline \(\mathrm{PST}\) & \(\sigma=p_{c}^{2/3}T_{c}^{1/3}\frac{186+1.186}{19.05}\bigg{[}\frac{3.75+0.94 }{0.291-0.08\omega}\bigg{]}^{2/3}(1\,-\,T_{r})^{11/9}\) \\ \(\mathrm{GST}\) & \(\sigma=p_{c}^{2/3}T_{c}^{1/3}[7.728729\,*10^{-4}T_{br}+2.476318\,*10^{-4}(T_{br} ^{3}+V_{c})](1\,-\,T_{r})^{11/9}\) \\ \(\mathrm{MST}\) & \(\sigma=kT_{c}\left(\frac{N_{c}}{V_{c}}\right)^{2/3}(4.35+4.14\,\omega)(1\,-\,T_{ r})^{126}(1+0.19\,(1\,-\,T_{r})^{0.5}-0.25(1\,-\,T_{r}))\) \\ \(\mathrm{DST}\) & \(\sigma=0.57088p_{c}^{2/3}T_{c}^{1/3}(1+0.7467\,\omega)(1\,-\,T_{r})^{1.26}\,\,(1 +0.0973\,(1\,-\,T_{r})^{0.5}-0.1885(1\,-\,T_{r}))\) \\ \(\mathrm{NST1}\) & \(\sigma=0.813p_{c}^{0.565}T_{c}^{0.364}(4.259\,\omega\,-\,0.645\frac{4300\mu^{2}}{T _{r}V_{c}})^{0.143}(1\,-\,T_{r})^{1.266}\) \\ \(\mathrm{NST2}\) & \(\sigma=0.658p_{c}^{0.618}T_{c}^{0.34}(1+\omega)^{0.77}\,(1\,-\,T_{r})^{1.262}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The prediction models of surface tension.
# Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN
## 3 The Design and Implementation of DSCSD
### Dynamic Traffic Scheduling  
We classified the flows among multiple data centers into different types. As for data duplication flows, we set a lower priority for them, while the other flows of high quality requirement had a higher priority. The higher priority flows traverse the shortest path, and the lower priority can select a path depending on the real-time link states. The concrete link states on the shortest path are categorized into the following four cases.  
State 1: The time when a low priority flow arrives. In this state, we still have several different possibilities. First, if bandwidth remains on the shortest path (S1, S4, S5), then it will directly select this path. If the shortest path (S1, S4, S5) is fully occupied by a high priority flow, then it will have no choice but be transmitted on the second shortest path (S1, S2, S3, S5). The last possibility is that this flow should go into the congestion control with multilevel feedback queues, given all paths have no bandwidth to be used.  
State 2: The time when a high priority flow arrives. In this state, we also have several different possibilities. First, if bandwidth remains on the shortest path (S1, S4, S5), then it will directly select this path. If the shortest path (S1, S4, S5) is fully occupied by a high priority flow, then it will have no choice but be transmitted on the second shortest path (S1, S2, S3, S5). The last possibility is that this flow should go into the congestion control with multilevel feedback queues, given all paths have no bandwidth to be used.  
State 3: The time when a high priority flow is transmitting on the link. Due to the high priority, we do nothing with it.  
State 4: The time when a low priority flow is transmitting on the link. From state 1, we can learn that the low priority flow can select any one of the two paths adapting to data fluctuation. Therefore, if there is no available bandwidth on the shortest path link when a new flow with high priority arrives at this point, it will vacate the shortest path link and be scheduled to the second shortest path link. Otherwise, if the new flow has an equally low priority and no subsequent low priority flow needs transmitting, the shortest path is allotted to the newly arrived flow.  
Figure 2: System model.  
According to the aforementioned analysis of the link states, we can realize dynamic traffic scheduling to improve the utilization of bandwidth resources.
## 3 Results
### Correlation between GPPEC, Vegetation Indices, and Air Temperature  
The comparisons between GPPEC and VIs (EVI and NDVI) show that EVI has a better relationship with GPP in terms of the phase and amplitude (Figure 4). In order to further evaluate the biological significance of VIs in GPP predictions, the simple linear relationships between GPPEC and VIs were investigated (Figure 5). Correlation analyses show that the dynamics of GPPEC correlated well with that of the VIs (EVI, NDVI, and LSWI), both in 2009 and 2010 (Figure 5). However, EVI (\(R^{2}\) = 0.65, \(p\) < 0.0001) has a better correlation with GPPEC than NDVI does (\(R^{2}\) = 0.48, \(p\) < 0.0001). EVI can account for 65% of the variance in GPPEC, while NDVI only 48%. Above results indirectly prove that the improved vegetation index EVI has a higher predictive power to simulate the GPP of the coastal wetland ecosystem. The LSWI also has a strong relationship with GPPEC (\(R^{2}\) = 0.41, \(p\) < 0.0001, Figure 5), showing that the land surface water content is important for the estimation of GPP during growing season.  
We also analyzed the relationships between VIs (EVI, NDVI, and LSWI), GPPEC, and air temperature (Ta). The regression analysis shows that Ta was positively correlated with NDVI (\(R^{2}\) = 0.55, \(p\) < 0.0001), EVI (\(R^{2}\) = 0.62, \(p\) < 0.0001), and GPPEC (\(R^{2}\) = 0.78, \(p\) < 0.0001), respectively (Figure 6a,b,d). NDVI, EVI, and GPPEC gradually increased as the temperature rose, and the temporal variation in NDVI, EVI, and GPPEC fluxes could be largely explained by air temperature. LSWI had a significant U-shaped relationship (\(R^{2}\) = 0.28, \(p\) < 0.01) with air temperature. As temperature rose, LSWI decreased first, reached its bottom, and then increased. The demarcation point is about 10 \({}^{\circ}\)C (Figure 6c).  
Figure 4: Seasonal dynamics of (**a**) Normalized Difference Vegetation Index (NDVI); (**b**) Enhanced Vegetation Index (EVI); (**c**) Land Surface Water Index (LSWI); and (**d**) estimated gross primary production from EC tower measurements (GPPEC). Three vegetation indices come from MODIS imagery during 2009–2010 in the targeted wetland.
## 6 CNN Model Architecture, Design, and Implementation
### SNR Prediction  
This study constructed the CNN layers using the Keras library with the Functional API model. The loss error for the training sample, or the difference between the predicted and actual values, was also calculated. The cost function, or the cost error function, is the cumulative total of all errors for the training set. The cost function, which measures the model's accuracy, essentially refers to how far the predicted value is from the real data. The cost function's minimum value is determined throughout the CNN model learning phase. The task is to identify the model weights resulting in the cost function having a minimum value. Gradient descent optimisation (GD), a fundamental approach for CNN model optimisation, is employed to achieve this [113; 114; 115]. The equation of GD is as follows:  
\[\theta_{j}:=\theta_{j}-\alpha\frac{\partial}{\partial\theta_{j}}J(\theta_{0}, \theta_{i}) \tag{21}\]  
By substituting the partial derivative of \(J(\theta_{0},\theta_{i})\) we obtain the following:  
\[\theta_{j}:=\theta_{j}-\alpha\frac{1}{m}\sum_{i=1}^{m}\Big{(}h_{\theta}\Big{(} x^{(i)}\Big{)}-y^{(i)}\Big{)}\cdot x_{j}^{(i)} \tag{22}\]  
where j = 0, 1, 2, \(\ldots\), n and m is the number of samples.  
This study used MAE as a loss function, while the RMSE was used as a metric function to measure the model's performance. MAE is the mean of absolute differences between predictions and real results where all individual deviations are even more critical, and the RMSE is measured as the average of the square root of the sum of squared differences between predictions and actual output. The mathematical formulas of them are as follows:  
\[\text{MAE}\Big{(}y_{true},y_{pred}\Big{)}=\frac{1}{n_{images}}\sum_{i=1}^{n_{ images}}\Big{|}y_{true}-y_{pred}\Big{|} \tag{23}\]  
\[\text{RMSE}\Big{(}y_{true},y_{pred}\Big{)}=\sqrt{\frac{1}{n_{images}}\sum_{i =1}^{n_{images}}\Big{(}y_{true}-y_{pred}\Big{)}^{2}} \tag{24}\]  
where \(n_{images}\) is the variable that represents the number of eye diagram images in the testing sample.  
This CNN programme retrieves the SNR (True) from the database and computes the predicted SNR value by processing the run-time-generated eye diagram images. The MAE  
Figure 11: The CNN architecture and implementation.  
is calculated by comparing SNR (True) and SNR (Predict). Moreover, the BER values are extracted from SNR, as shown in Figure 12.
## 3 Materials and Methods
### Data Preparation  
The inherent structure of most natural data often proves inadequate for optimal model training due to its complexity and variability. To address this, our study represents EEG signals in the form of a matrix, which is considered a best practice for handling such multidimensional data. EEG data underwent preprocessing to remove noise and artifacts, ensuring that the signals were clean and reliable for further analysis. Following this, \(Z\)-score normalization was performed to standardize the data, bringing all the values into a common scale with a mean of zero and a standard deviation of one.  
For the experiments, an 80/20 train-test split was employed, where 80% of the data were allocated for training the model and 20% were allocated for testing its performance. Additionally, 12.5% of the training data were set aside for validation purposes, allowing for an independent evaluation during the training phase. This resulted in a data split of 70% for training, 20% for testing, and 10% for validation. Considering all 48 participants in the analysis, the dimensions of the input data fed into the model were (921600, 15) for the low workload mode and (921600, 15) for the high workload mode. This extensive dataset ensures that the model has sufficient examples to learn from, ultimately enhancing its ability to generalize and perform well on unseen data.
## Appendix C RL Algorithm Posterior Updates and Posterior Sampling Action Selection
### Zero-Inflated Poisson Regression Model  
For the zero-inflated Poisson regression model, the posterior distribution of the weights \(\theta_{i}=\{\alpha_{i,b},\beta_{i,b},\alpha_{i,p},\beta_{i,p}\}\) given data \(H_{i,t-1}\), \(p(\theta_{i}|H_{i,t-1})\) does not have a closed form. Therefore, we use Metropolis-Hastings (MH) with a normal proposal distribution as an approximate posterior sampling method.  
Posterior Density:  
The log-likelihood of the zero-inflated Poisson regression model is:  
\[\log f(R_{i,t}|S_{i,t},A_{i,t};\theta_{i})=\begin{cases}\log((1-p)+p\exp(- \lambda))&R=0\\ \log p-\lambda+R\log\lambda-\log R!&R=1,2,3,...\end{cases}\]  
where \(p=1-\text{sigmoid}\big{(}m(S_{i,t})^{T}\alpha_{i,b}+A_{i,t}\cdot f(S_{i,t})^ {T}\beta_{i,b}\big{)}\) is the probability of the user intending to brush, and \(\lambda=\exp\big{(}m(S_{i,t})^{T}\alpha_{i,p}+A_{i,t}\cdot f(S_{i,t})^{T} \beta_{i,p}\big{)}\) is the expected Poisson count.  
Therefore, the log posterior density is:  
\[\log p(\theta_{i}|H_{i,t-1})\propto\sum_{n=1}^{N}\log f(R_{i,t}|S_{i,t},A_{i, t};\theta_{i})+\log p(\theta_{i})\]  
Proposal Distribution:  
We choose a normal distribution for our proposal distribution. At each step of MH, we propose a new sample given the old sample, \(\theta_{\text{prop}}^{k}\sim\mathcal{N}(\theta_{\text{old}}^{k},\gamma^{2}I)\), where \(\theta^{k}\) denotes the \(k\)th value of \(\theta\).  
Metropolis-Hastings Acceptance Ratio:  
The Metropolis-Hastings acceptance ratio given a proposed sample \(\theta_{\text{prop}}\) and an old sample \(\theta_{\text{old}}\) is defined as:  
\[\alpha(\theta_{\text{prop}},\theta_{\text{old}}):=\min\left(1,\frac{p(\theta_ {\text{prop}})/q(\theta_{\text{prop}}|\theta_{\text{old}})}{p(\theta_{\text{ old}})/q(\theta_{\text{old}}|\theta_{\text{prop}})}\right)\]  
Since our proposal distribution is symmetric, the log acceptance ratio becomes:  
\[\log\alpha(\theta_{\text{prop}},\theta_{\text{old}}):=\min(0,\log p(\theta_{ \text{prop}})-\log p(\theta_{\text{old}}))\]
## 3 Results and Discussion
### Construction Stage
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**H\({}_{2}\)S (ppm)**} & \multicolumn{2}{c}{**SO\({}_{2}\) (ppm)**} & \multicolumn{2}{c}{**NO (ppm)**} & \multicolumn{2}{c}{**CO (ppm)**} & \multicolumn{2}{c}{**CO\({}_{2}\) (ppm)**} & \multicolumn{2}{c}{**Oxy (\%)**} \\ \cline{3-14}  & & **Range** & **Ave.** & **Range** & **Ave.** & **Range** & **Ave.** & **Range** & **Ave.** & **Range** & **Ave.** & **Range** & **Ave.** \\ \hline  & 1 January 2009 & 0.0 to 0.2 & 0.1 & 0.0 to 0.0 & 0.0 & 0.0 to 8.1 & 3.7 & 1.8 to 128.3 & 65.6 & 1684.0 & 10 7632.0 & 5906.0 & 29.6 to 30.0 & 29.9 \\  & 3 January 2009 & 0.0 to 0.0 & 0.0 & 0.0 0 to 0.0 & 0.1 to 1.7 & 1.4 & 0.6 to 56.6 & 29.9 & 1755.0 & 10 8600.0 & 29.5 to 30.0 & 29.8 \\  & 5 January 2009 & - & - & - & - & - & 0.7 to 52.7 & 38.4 & 643.0 to 1433.3 & 7083.9 & 293.0 to 30.0 & 29.4 \\  & 6 January 2009 & 0.0 to 0.0 & 0.0 & 0.0 & 0.0 to 2.5 & 1.5 & 7.8 to 15.6 & 33.2 & 882.0 to 1271.0 & 1528.3 & 297 10 29.9 & 29.8 \\  & 7 January 2009 & 0.0 to 0.1 & 0.0 & 0.0 to 0.0 & 0.0 & 0.2 to 3.2 & 2.5 & 0.0 to 78.2 & 48.4 & 473.0 to 2886.0 & 2220.5 & 29.2 to 30.0 & 29.5 \\  & 9 January 2009 & 0.0 to 0.1 & 0.0 & 0.0 & 0.0 & 0.0 to 3.9 & 1.5 & 2.4 to 149.0 & 30.7 & 1046.0 to 2792.0 & 16864.8 & 293.0 to 29.9 \\  & 20 January 2009 & 0.0 to 0.2 & 0.1 & 0.0 to 0.0 & 0.0 & 0.2 to 2.5 & 1.4 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Figure 2: Gas concentrations in Lowari Tunnel for 1 h during blasting and mucking activities. (**a**) Concentration of nitrogen oxide; (**b**) concentration of carbon monoxide; (**c**) concentration of carbon dioxide.
## 2 Results and Discussion
### Validation of the Quantitative Method
#### 2.2.3 Accuracy  
The majority of the spiked recoveries fell within 80%-120%, although there were some exceptions (Table 2). Compounds 7, 8, 11 and 12 in PG dry style were about 70% and 5 and 9 in most wine styles was approximately 130%. As mentioned before in Table 1, compounds 7, 8, 11 and 12 had greater retention in L base, which may explain the low calculated recovery. Concentrations of 7, 8, 11 and 12 from Pinot Gris dry white wine were adjusted to 100% by a recovery factor (multiply by 1.4 for 7, 8 and 12; by 1.3 for 11). A possible explanation for the spiked recoveries for 5 and 9 (\(\sim\)130%) may be due to the very low concentrations measured. Reports show that systematic errors may be present for some monoterenes at very low concentration, especially close to the LOQ if the internal standard was not analogous to that compound being quantified, thus leading to non-linear responses [33] and recovery values higher than 100% [34]. Compounds 5 and 9 (rose oxide isomers) were calculated using d-linalool as the internal standard as a deuterium labeled rose oxide isotope was not available.
## 2 Complex-Image Layout Analysis Methods
### Layout Analysis Method Based on Machine Learning  
#### 2.3.1 Layout Analysis Method Based on Support Vector Machine (SVM)  
Before neural networks became very popular, traditional machine learning methods were the mainstream methods for layout area localization and recognition. Among them, the method of the support vector machine (SVM) is the more classical machine-learning method. The SVM algorithm is a sample learning technique established by [PERSON] [33] based on statistical learning theory. Based on the principle of structural risk minimization, the SVM algorithm uses kernel function technology to complete nonlinear mapping in low-dimensional to high-dimensional space, avoiding overfitting and further improving the generalization ability of the learning machine. At present, the SVM algorithm is gradually being applied to a wide range of functions, from the field of pattern recognition to character recognition, layout and image classification, target detection, and other needs; the SVM algorithm has better performance than the traditional method, and the effect is more ideal. [PERSON] [34] proposed a new SVM-based image-segmentation method, which combined the advantages of a mean clustering algorithm to automatically obtain training samples, and then extracted image color and texture features, respectively, to serve as SVM training samples. [PERSON] [35] proposed a segmentation method based on a support vector machine to manually select a sample-point set. Through artificial observation of color-feature changes, sample points were selected at the peak of pixels, so that the color difference between the background and the target sample points was obvious, thus achieving the purpose of simplifying sample points and realizing fast segmentation of color images. At the same time, the influence of different kernel function parameters and sample points on the segmentation effect is compared and analyzed. [PERSON] [36] proposed a complex-image-segmentation algorithm based on a support vector machine for the complex-layout-segmentation problem. The method obtains a new combined-feature vector by combining the statistical features of phase consistency and the texture features of the improved grayscale covariance matrix. Finally, the combined vector is used as a training sample to train the algorithm. [PERSON] [37] proposed an algorithm for blade image
## 5 Numerical Results
### The Influence of User Density and MEC Server Density  
Figure 6a,b shows \(P_{b}\) and \(P_{r}\) as a function of \(B_{2}\) for different user density \(\lambda_{u}\) and MEC server density ratio \(\lambda_{m,1}/\lambda_{m,2}\), respectively. Equation (2) indicates that \(\lambda_{u}\) directly correlates with the arrival rate. High \(\lambda_{u}\) overloads the first-tier MEC servers and the second-tier MEC servers. It can be seen from Figure 6a,b that when \(\lambda_{m,1}/\lambda_{m,2}\) is the same, the Probability  
Figure 4: \(E(T_{total})\) of HetMECNet with different service rate.  
Figure 5: Experimental results of different service rate. (**a**) \(P_{r}\) of HetMECNet as a function of \(B_{2}\) for different service rate. (**b**) \(P_{b}\) of HetMECNet as a function of \(B_{2}\) for different service rate.  
Figure 3: Performance of HetMECNet with different bias factors \(B_{2}\).  
of Immediate Service \(P_{r}\) is relatively high when \(\lambda_{u}\) is low, and the Blocking Probability \(P_{b}\) is, otherwise, due to the decrease in total user tasks. When the density of first-tier MEC servers \(\lambda_{m,1}/\lambda_{m,2}\) increases, \(P_{r}\) becomes larger and \(P_{b}\) becomes smaller as the number of the first-tier MEC server increases.