esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Routing Scheme
### Distributed Detour Routing Scheme
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## 6 Analyzing the Graph Database-Based and IFC-Based Construction Management Information Model
### Delay Analysis  
In addition to the automatic analysis of the schedule network, automatic delay analysis plays a critical role in the graph database-based information model. The analysis of delays includes two parts. The first part is to calculate the project schedule network's critical path and store the critical path's information in the extended IFC schema, which can be automatically complete by the graph-based algorithm. The graph-based algorithm proposed in this paper calculates the critical path by finding the path that has the longest duration. The IfcTask entity identified in the critical path will be associated with the extended property set Pset_CriticalPath to store the critical path's information. Furthermore, project managers are interested in the following properties of each construction task: Earliest Finish Time (EF), Earliest Start Time (ES), Latest Start Time (LS), Latest Finish Time (LF), Slack Time. These insightful properties will also be easily calculated through the graph and then added to the graph, which is infinitely better than manually typing functions into several Excel cells.  
The second part is to collect the construction information and analyze the delay. When the construction task is identified as its start time later than the last start time, its finish time later than the last finish time, or its delay time longer than the slack time, the IfcTask is associated with the extended property set Pset_DelayAnalysis. The result of the delay analysis is then be stored in the extended IFC schema, facilitating the managers to master the project progress. Furthermore, the programming developed by this paper will also analyze the impact on the overall construction duration due to the delayed task based on whether the delayed task occurs on the critical path or whether the duration of the delay exceeds the slack time of the task, thus assisting managers in their decision making.
## 3 Neural Network Based DPD Architecture
### Features of the NN Model
#### 3.1.6 Characteristics of the NN Models  
Based on the discussion made so far, we have built a foundation of all components of a Neural Network and established that the two neural network models we require here are, first, the DPD NN, used to predistort our original RoF link and second, the emulated RoF NN, which is essential for training this DPD NN. The NN employed here contains \(N\) hidden-layers, \(K\) neurons per hidden-layer and is a feedforward fully connected network. The symbolic structure of the employed NN is shown below in Figure 8. The NN has two inputs and two outputs, in view of the complex nature of baseband signals representing both the real and imaginary parts of the signal. The ReLu activation function has been utilized for at least one of the multiple hidden layers (owing to its lower complexity).  
Figure 8: Schematic diagram of the feedforward fully connected NN structure used, with one of the \(N\) number of hidden layers having \(K\) neurons per hidden layer. R(\(x\)) and R(\(y\)) are real I/P and O/P; I(\(x\)) and I(\(y\)) are imaginary I/P and O/P, respectively.  
Figure 7: Schematic diagram of complete densely connected in (**a**) NN and (**b**) representation of network upon selection and removal of random nodes, after any one iteration.  
We represent the O/P for the first hidden-layer as follows:  
\[l_{1}(n)=f\bigg{(}W_{1}\bigg{[}\begin{array}{c}\mathbb{R}(x)\\ \mathbb{I}(x)\end{array}\bigg{]}+b_{1}\bigg{)} \tag{5}\]  
where, \(l_{1}\) expresses the first hidden O/P layer, \(f\) is the activation function (nonlinear) and \(W_{1}\) is the weight and \(b_{1}\) is the bias for the first output layer in the network.  
We represent the general output for the \(i\)th layer as:  
\[l_{i}(n)=f(W_{i}h_{i-1}(n)+b_{i}) \tag{6}\]  
where, \(i\in\mathbb{N}\ :2\leq i\leq N\).  
Then, the final output after \(N\) hidden layers will be:  
\[\hat{x}(n)=W_{N+1}h_{N}+b_{N+1} \tag{7}\]  
Training Algorithm  
The algorithm used to train the NN DPD model is given below. The algorithm utilizes MSE as the loss function, the optimizer used is ADAM and backpropagation is used for updating the weights. The process is repeated for a certain number of iterations (Z) to refine performance.  
As mentioned earlier, we train our emulated RoF NN model using the I/P and O/P of the original RoF link and once this model is acquired, we connect the NN DPD model to it. Then, upon convergence of this training, we connect the actual RoF link with this DPD NN and proceed with its predistortion (see pseudocode in Algorithm 1).  
```
\(\hat{x}(n)\gets x(n)\) for\(i\leq Z\)do \(y(n)\gets I(\hat{x}(n))\) //RoF-Transmission \(\hat{I}\leftarrow\) Train on \(\hat{x}(n)\), \(\frac{y(n)}{\hat{G}}\) //Updating RoF NN //Freeze NN weights of \(\hat{I}\) \(\hat{I}\leftarrow\) Train on \(x(n)\). //Use \(\hat{I}^{-1}(\hat{I}(x(n)))\) \(\hat{x}(n)\leftarrow\hat{I}^{-1}(x(n))\). //Predistort endfor
```  
**Algorithm 1** DPD training
## 3 Results
### Spread-Out Bragg Peak RBE  
Figure 6 shows the proton RBE for three LET\({}_{\text{D}}\) values, namely, 1 keV/\(\upmu\)m, 2.5 keV/\(\upmu\)m and 8 keV/\(\upmu\)m, which correspond approximately to different regions of a spread-out Bragg peak (SOBP), namely, the proximal SOBP (\(\sim\)1 keV/\(\upmu\)m), the center of the SOBP (\(\sim\)2.5 keV/\(\upmu\)m), and the distal SOBP (\(\sim\)8 keV/\(\upmu\)m) [1]. Note, however, that the exact LET\({}_{\text{D}}\) values at different SOBP regions may depend on SOBP width and depth, as lower LET\({}_{\text{D}}\) values are expected in wide modulations and deep targets [1]. For each SOBP region, RBE values are calculated as an average of the empirical models (\(\overline{\text{RBE}}_{\text{emp}}\)), the MKM and the TDRA for the examined cell types at 2 Gy, 5 Gy and 7 Gy dose fractions. Figure 7 shows, for each SOBP region, the absolute relative percentage difference between the examined RBE models and the conventional value of RBE = 1.1.  
Figure 5: Relative percentage difference (Equation (37)) between MCDS-based and analytic-based calculations of proton RBE for five cell types at a 2 Gy dose fraction.  
Figure 6: Calculated proton RBE for three LET\({}_{\text{D}}\) values corresponding (approximately) to the proximal SOBP region (\(\sim\)1 keV/\(\upmu\)m), the center of the SOBP (\(\sim\)2.5 keV/\(\upmu\)m) and the distal edge of the SOBP (\(\sim\)8 keV/\(\upmu\)m) for five cell types at 2 Gy, 5 Gy and 7 Gy dose fractions.
## 6 Simulations Results
### Effect of Polarization Error on Success Rate and RMSEs  
The simulation conditions were the same as in Section 6.1. Among them, the auxiliary polarization angle and polarization phase difference of polarization parameter error changed from 0 to 8 degrees, and the step size was 0.5 degrees, separately. The relationship curve between the success rate and the auxiliary polarization angle error is shown in Figure 14, and the relationship curve between RMSEs and the auxiliary polarization angle error is shown in Figure 15. The relationship curve between the success rate and the polarization phase difference error is shown in Figure 16, and the relationship curve between RMSEs and the polarization phase difference error is shown in Figure 17.  
A6 and A7 had low success rates and almost failed, and RMSEs had no reference significance. A5 was almost unaffected by the estimation error of polarization parameters. Without the assistance of time-frequency information, A5 was still affected by residually strong interference, which weakened the parameters' estimation error influence. The success rate of A8 and A9 decreased, and the RMSEs increased with the increase in the auxiliary polarization angle estimation error. For the auxiliary polarization angle estimation error, A9 had the same approximate success rate as A8 and lower RMSEs than A8. For the polarization phase difference estimation error, A9 and A8 were almost identical. The effect of the polarization phase difference estimation error was little. It can be seen from \(\mathbf{c}_{n}=\left[c_{nv},c_{nth}\right]^{\mathrm{T}}=\left[\cos(\gamma_{n} ),\sin(\gamma_{n})e^{\left|\eta_{n}\right|}\right]^{\mathrm{T}}\) that the polarization phase difference estimation error affected the exponential part and had little effect on solving the noise subspace of the SPTDs matrix.  
Figure 14: The curve between the success rate and the auxiliary polarization angle error.  
Figure 13: The relationship curve between RMSEs and SNR and epsilon.  
All of the above simulations are summarized below. (1) The cross terms can be extracted. Algorithm 9 is feasible. (2) In many aspects, such as environmental SNR, JSR and snapshot number, A9 is the best overall, with a high success rate and small RMSEs. (3) The cross terms extraction parameters have little impact on the algorithm. When it is small, A9 has better performance. (4) Among the polarization parameter estimation errors, the cross terms extraction parameters are very similar. (5) The cross terms extraction parameters are very similar. (6) The cross terms extraction parameters are very similar. (7) The cross terms extraction parameters are very similar. (8) The cross terms extraction parameters are very similar. (  
the polarization auxiliary angle estimation error has a greater impact on the algorithm than the polarization phase difference estimation error. Even if there is an estimation error, A9 still performs well.
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 1 Introduction
* Second, in addition to utilize the feature differences between distorted and pseudo-reference images, quality restoration features are also employed in the model to leverage rich semantic information in image restoration features, enabling the model to exploit not only differences between pseudo-reference and distorted images, but also the semantic information within quality restoration features. * Third, a multi-scale feature fusion module is proposed to fully fuse quality restoration features and multi-scale content features of distorted images, enabling the model to extract both global and local features simultaneously. The rest of this paper is organized as follows. Section 2 introduces related studies for NR-IQA. Section 3 provides a detailed description of MT-IRN. Section 4 reports the experimental results. Section 5 concludes this paper.
## 6 Discussion
### Hypothesis 2  
The second hypothesis to be validated was stated as follows:  
**H2.**: _The assessment of positive and negative emotions using AR does not vary when using psychometric questionnaire techniques and facial recognition techniques._  
The results showed significant differences between the measurements collected using the EAQ questionnaire and the Emolive system. Thus, at the end of the experience, the positive and negative emotions detected by the psychometric questionnaire were significantly greater than those automatically detected by facial recognition with Emolive. The same difference was found in the positive emotions at the beginning of the experience. Consequently, hypothesis H2 is rejected, and it cannot be stated that the assessment of positive and negative emotions using AR does not vary with different measurement techniques. It is unclear what may explain this divergence between the two types of emotion recognition. Some studies have identified limitations in the use of psychometric questionnaire techniques. For example, a student may express a false memory of an emotion, and emotional distortion may occur when responding based on memories of past emotions [12,38]. These phenomena could explain the divergence found in this study between the two emotion assessment techniques. However, the evidence found in this study is not conclusive enough to make this conclusion, and a more in-depth analysis of the specific emotions assessed by each technique is necessary.  
The differences observed between these techniques may be attributed to both psychological and technical factors. From the psychological perspective, questionnaires such as the AEQ can be subject to memory or social desirability biases [39]. In contrast, AI-based facial recognition tools like Emolive assess emotions in real time, but may be limited by technical conditions such as lighting or facial visibility [40]. Therefore, some researchersargue that both methods can be complementary and provide a more holistic understanding of learners' emotional states [41].
# GraphMS:Drug Target Prediction Using Graph Representation Learning with Substructures
## 4 Experiments and Results
### Ablation Experiment  
We visualized the embedding learned by Graph-M and Graph-S to see if Graph-S could capture the structure of an interactive network better than Graph-M. Graph-M uses only mutual information between node-level and graph-level representations, whereas Graph-S uses only mutual information between substructure and graph-level representations. Then we visualize the learned embedding in Figure 5. We could observe that there are potentially linked targets near some relatively marginal drug points in the embedded space which Graph-S learns. And the distribution of drug targets in the embedded space  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Multi-View**} & \multicolumn{2}{c}{**Single-View**} \\ \cline{2-5}  & **1:10** & **1:all** & **1:10** & **1:all** \\ \hline GraphMS & **0.847 \(\pm\) 0.002** & **0.622 \(\pm\) 0.001** & **0.760 \(\pm\) 0.002** & **0.594 \(\pm\) 0.002** \\ LightGCN & 0.834 \(\pm\) 0.001** & 0.608 \(\pm\) 0.001 & 0.734 \(\pm\) 0.001 & 0.582 \(\pm\) 0.001 \\ GAT & 0.832 \(\pm\) 0.002 & 0.608 \(\pm\) 0.001 & 0.731 \(\pm\) 0.001 & 0.581 \(\pm\) 0.001 \\ NeoDTI & 0.815 \(\pm\) 0.003 & 0.587 \(\pm\) 0.001 & 0.714 \(\pm\) 0.001 & 0.559 \(\pm\) 0.002 \\ DTINet & 0.743 \(\pm\) 0.001 & 0.452 \(\pm\) 0.001 & 0.693 \(\pm\) 0.002 & 0.313 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of AUPR scores for various methods under different experimental settings. The best results in each column are in bold.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Multi-View**} & \multicolumn{2}{c}{**Single-View**} \\ \cline{2-5}  & **1:10** & **1:all** & **1:10** & **1:all** \\ \hline GraphMS & **0.959 \(\pm\) 0.002** & **0.943 \(\pm\) 0.001** & **0.933 \(\pm\) 0.003** & **0.914 \(\pm\) 0.002** \\ LightGCN & 0.940 \(\pm\) 0.002** & 0.929 \(\pm\) 0.001 & 0.922 \(\pm\) 0.001 & 0.895 \(\pm\) 0.002 \\ GAT & 0.937 \(\pm\) 0.001 & 0.927 \(\pm\) 0.001 & 0.920 \(\pm\) 0.001 & 0.893 \(\pm\) 0.001 \\ NeoDTI & 0.929 \(\pm\) 0.003 & 0.919 \(\pm\) 0.002 & 0.908 \(\pm\) 0.001 & 0.880 \(\pm\) 0.001 \\ DTINet & 0.896 \(\pm\) 0.001 & 0.862 \(\pm\) 0.002 & 0.872 \(\pm\) 0.001 & 0.867 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of AUROC scores for various methods under different experimental settings. The best results in each column are in bold.  
of Graph-M learning is more concentrated. We further corroboration with AUROC and AUPR indicators.  
Our method uses the mutual information between the substructure and graph-level representations and the mutual information between the node-level and graph-level representations. It can be observed that the mutual information between the substructure and graph-level representations contributes more to the mutual information in a relatively sparse network. In a relatively dense network, the node-level and graph-level representations contribute more to the mutual information (see Figure 6).
## 4 Experiments
### Experimental Results and Analysis
#### 4.5.1 Comparison with Other Optimizers  
To evaluate the optimization performance of the proposed Improved IGWO, we compared it against three widely used metaheuristic optimization algorithms: Differential Evolution (DE) [57], Genetic Algorithm (GA) [58], and Bayesian Optimization (BO) [59]. These algorithms were selected due to their established effectiveness in hyperparameter tuning tasks across various domains, including time series forecasting and neural network training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MSE (\(p\))** & **MAE (\(p\))** & **MAPE (\(p\))** & **R\({}^{2}\) (\(p\))** \\ \hline CS-LSTM & 0.0012 & 0.0045 & 0.0031 & 0.0089 \\ PSO-LSTM & 0.0007 & 0.0023 & 0.0028 & 0.0102 \\ Transformer & 0.0154 & 0.0432 & 0.0305 & 0.0498 \\ LSTM-Transformer & 0.0271 & 0.0489 & 0.0364 & 0.0406 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical significance (\(p\)-values) between IGWO-MALSTM and baseline models across 10 runs.  
Figure 9: _Cont._  
In all experiments, the optimizers were assigned to tune the same set of MALSTM hyperparameters, including the lookback window size, number of neurons in the three LSTM layers, dropout rate, batch size, and number of training epochs. To ensure a fair comparison, identical search ranges were defined for all algorithms, and the fitness function was set as the Mean Absolute Percentage Error (MAPE) calculated on the validation set. Each optimizer was executed for up to 50 iterations or until convergence, and model performance was assessed on the same test dataset.  
The experimental results are summarized in Table 8 IGWO-MALSTM consistently achieved the best performance across all evaluation metrics, demonstrating superior predictive accuracy and faster convergence. Although BO also showed competitive results, its training process was more time-consuming due to the sequential nature of surrogate modeling. In contrast, both DE and GA exhibited higher error rates and slower conver  
Figure 9: Comparison between prediction results of each model.  
gence, which could be attributed to premature convergence or limited exploration in the high-dimensional parameter space.  
These findings indicate that IGWO not only enhances the predictive capability of the MALSTM model but also provides improved stability and convergence efficiency. The enhanced population initialization based on the Mersenne Twister generator further strengthens IGWO's global search ability and reduces the risk of being trapped in local optima.
# Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters
## 1 Introduction
As a result, the design complexity is greatly reduced, and the stability assessment becomes easier. However, it cannot satisfy the requirements of dynamic response in renewable energy generation [21]. To address the issues mentioned above, this paper presents a proportional multiple quasi-resonant-type repetitive control (PMQR-type RC) scheme for single-phase grid-connected H6 inverters with the LC filter. The proposed controller consists of a proportional component and a repetitive controller in parallel, resulting in a better harmonics suppression performance and faster dynamic response ability. A detailed system modelling process is used to guide the design of the RC. Stability analysis and specific design steps of the controller are performed to select the proper control parameters. Furthermore, a second-order generalized integrator-based phase-locked loop (SOGI-based PLL) is applied to this system for improving the robust performance against grid disturbances. The simulation results show that the designed current controller can guarantee an output current THD value less than 1%. The proposed control strategy is verified to achieve a better performance for grid-connected H6 inverter systems, by comparing with PI, PR, and conventional repetitive control schemes. The rest of this paper is organized as follows. In Section 2, the overall operation principle of the system is described. In Section 3, the PMQR-type RC scheme is analysed in detail. The simulation results are presented and discussed in Section 4, which verify the feasibility and superior performance of the proposed scheme. Section 5 concludes the paper.
## 3 Results
### Reliability
Figure 11: Bland–Altman analysis of CMJ MyJump test–retest agreement. Bland–Altman plot illustrated the agreement between CMJ measurements from MyJump test–retest. The central blue line represents the mean bias (0.153 cm), while the shaded region denotes the confidence interval for the mean difference. The dotted black lines define the limits of agreement (LoA), ranging from \(-\)2.142 cm to 2.449 cm. The green and red shaded regions highlight the upper and lower LoA zones, respectively. Each data point represents the difference between test and retest values plotted against their mean. between test and retest measures, reinforcing the repeatability and precision of MyJump in assessing SJ height. The narrow confidence intervals and minimal bias further indicate once again that measurement discrepancies are negligible and within expected variability limits for practical applications.