esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Wavelet-Domain Classification Results  
Table 19 shows the accuracy ranking of all classifiers on the wavelet-domain features with \(p\)-values < 0.05. The random forest classifier ranked the best, with an accuracy of 77.45%.  
Table 20 reports more classification metrics for different BAC classes for the best-performing random forest classifier on wavelet-domain features with \(p\)-values < 0.05.  
Figure 6 visualizes the confusion matrix performance of the random forest classifier on these wavelet-domain features. The classifier showed high classification ability, with leading values in the diagonal of the confusion matrix.  
Table 21 shows the model configuration for the best-performing random forest model for the wavelet-domain features.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline BAC = 0 & 0.865 & 0.073 & 0.714 & 0.865 & 0.783 & 0.905 \\ BAC = 0.05 & 0.600 & 0.054 & 0.632 & 0.600 & 0.615 & 0.740 \\ BAC = 0.12 & 0.807 & 0.066 & 0.742 & 0.807 & 0.773 & 0.879 \\ BAC = 0.2 & 0.658 & 0.038 & 0.714 & 0.658 & 0.685 & 0.789 \\ BAC = 0.3 & 0.829 & 0.043 & 0.920 & 0.829 & 0.872 & 0.910 \\ \hline Weighted Avg. & 0.779 & 0.054 & 0.785 & 0.779 & 0.779 & 0.865 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Performance metrics for different BAC classes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Classifier & \multicolumn{1}{l}{weka.classifiers.trees.J48} \\ \hline Confidence Factor for Pruning (C) & 0.25 \\ \hline Minimum Number of Instances per Leaf (M) & 2 \\ \hline Unpruned & False \\ \hline Reduced Error Pruning & False \\ \hline Binary Splits & False \\ \hline Collapse Tree & True \\ \hline Subtree Raising & True \\ \hline Use Laplace for Smoothing & False \\ \hline Seed & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 18: J48 configuration for frequency-domain features.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Classifier Type** & **Accuracy** \\ \hline
**Random Forest** & **77.85\%** \\ J48 & 75.84\% \\ JRip & 70.81\% \\ Decision Table & 53.36\% \\ Naive Bayes & 42.62\% \\ SMO (SVM in WEKA) & 37.25\% \\ \hline \hline \end{tabular}
\end{table}
Table 19: Classifiers ranked by accuracy for wavelet-domain features with \(p\)-values < 0.05.
## 3 Numerical Results and Discussion
### BER versus Layers  
To verify whether the proposed dual-driven MIMO detector will outperform the OAMP-Net detector with different network layers, we conducted the tests using a channel estimation MSE of \(-30\) dB and an SNR of \(20\) dB. In this context, the number of layers corresponds to the number of iterations, denoted as \(T\).  
A performance comparison between the dual-driven detector and the OAMP-Net detector is shown in Figure 6 as the number of layers ranges from \(1\) to \(7\). We can see that the performance of OAMP-Net hardly improves, while the performance of the dual-driven detector improves with increasing network layers, which means the detection error can effectively be reduced iteratively in the dual-driven network.  
Figure 7 depicts the results for the dual-driven MIMO detector with varying numbers of layers and different learning rates for the Adam optimizer, maintaining an SNR of \(20\) dB and a channel estimation MSE of \(-30\) dB. It is evident that as the number of layers increases, the BER generally decreases across all three different learning rates. However, when the learning rate is set to \(0.00001\), the BER with seven layers is actually higher than that with six layers. This discrepancy could be attributed to overfitting. Consequently, in our implementation, we choose to set \(T\) to seven as a suitable number of layers. Different learning rates control the convergence speed. It is noticeable that the differences among the three learning rates (\(0.01\), \(0.0005\), and \(0.00001\)) are not significant. Therefore, we opted for a variable learning rate approach. Specifically, we set the learning rate to \(0.001\) initially until convergence and then adjusted it to \(0.005\) or \(0.0001\) to prevent overfitting.  
To sum up, this test verifies the hypothesis the that the proposed dual-driven MIMO detector outperforms the OAMP-Net with different layers. With different layers and different learning rates, the proposed dual-driven detector can converge well and maintain stability.  
Figure 5: BER versus MSE for the dual-driven MIMO detector and the other three detectors when SNR = 20 dB.
## 3 Results
### Time-Dependent Calculations for State Population  
The time-dependent approach employing the equations described in Section 2 allows for precise studies of the time evolution of populations in the ground, intermediate and final states during the course of an X-ray pulse. Figure 3 shows the time profiles of the population of the discussed states calculated for an incident X-ray flux of 1.5 \(\times\) 10\({}^{32}\) photons/cm\({}^{2}\cdot\)s, which was a typical value used in the experiment discussed in Figure 2. For comparison, the shape of the incident X-ray pulse is also shown in Figure 3.  
In Figure 3a, we show the population N\({}_{1}\) of the ground state versus the time, which is anticorrelated with the time evolution of the incident X-ray pulse. Consequently, both curves exhibit the same width and extrema positions. After the incident X-ray pulse ends, the population of the ground state completely recovers its initial value N\({}_{1}\) = N through decays from the N\({}_{2}\) and N\({}_{3}\) states. The population of the intermediate state, plotted in Figure 3b, is fully correlated with the incident X-ray pulse and anticorrelated with the ground state. The biggest difference, shown in Figure 3c, is observed for a continuum state, which is populated by the TPA process. While the peak value in time for the N\({}_{3}\) population state is at the same position as for the peak of the incident X-ray pulse, the width of the N\({}_{3}\) population state evolution is significantly narrower. We find that, while the incident X-ray  
Figure 3: Calculated time-dependent population of the N\({}_{1}\), N\({}_{2}\) and N\({}_{3}\) states. The dashed line represents the shape of the incidence X-ray pulse, and is plotted in **(a–c)** for comparison. In Figure **c**, we also plot the Gauss-square function (for details see text).  
pulse is 30 fs long, the population of the N\({}_{3}\) state can be characterized with a Gaussian shape of about 21 fs width. Finally, as noticed above for the N\({}_{1}\) state population recovery at the end of X-ray pulse, the populations of both the N\({}_{2}\) and N\({}_{3}\) states vanish to zero through the decays of these states to the ground state.  
Using the presented 3-level model approach, we extracted the full widths at half maximum for the populations of the intermediate (N\({}_{2}\)) and continuum (N\({}_{3}\)) states for X-ray fluxes varying in the range of 10\({}^{30}\)-10\({}^{33}\) photons/cm\({}^{2}\)s. The results are presented in Figure 4, which confirms our previous observations. The calculated population N\({}_{3}\) of the continuum state presents a narrowing effect, which leads to a shorter time distribution of the states populated through the two-photon absorption. The performed calculations demonstrate that at higher X-ray flux, i.e., above 10\({}^{32}\) photons/cm\({}^{2}\)s, the widths of the populations of the intermediate and continuum states systematically increase (Figure 4). However, it should be noted that the difference between the widths of these states amounts to about 30% and is constant over the studied incidence X-ray flux range.
# An Adaptive Control Based on Improved Gray Wolf Algorithm for Mobile Robots
## 3 Design of the Adaptive Controller
According to the Schwarz inequality, one can obtain  
\[tr\left[\overset{\sim}{\Lambda}^{T}\left(\Lambda-\overset{\sim}{\Lambda} \right)\right]\leq\left\|\overset{\sim}{\Lambda}\right\|_{F}\left\|\Lambda \right\|_{F}-\left\|\Lambda\right\|_{F}^{2} \tag{41}\]  
Since \(K_{emit}\left\|\xi\right\|^{2}\leq\xi^{T}K_{e}\xi\), the function \(\dot{L}\) will be negative if the following (42)-(45) are satisfied  
\[K_{emit}\left\|\xi\right\|-\varepsilon_{N}+n\left(\left\|\overset{\sim}{ \Lambda}\right\|_{F}^{2}-\left\|\overset{\sim}{\Lambda}\right\|_{F}\Lambda_{M }\right) \tag{42}\]  
\[=K_{emit}\left\|\xi\right\|-\varepsilon_{N}+n\left(\left\|\overset{\sim}{ \Lambda}\right\|_{F}-\Lambda_{M}\right)^{2}-\frac{\pi}{4}\Lambda_{M}^{2}\geq 0\]  
\[\left\|\overset{\sim}{\Lambda}\right\|_{F}\geq\frac{1}{2}\Lambda_{M}+\sqrt{ \frac{\Lambda_{M}^{2}}{4}+\frac{\varepsilon_{N}}{n}} \tag{43}\]  
\[\left\|\xi\right\|\geq\frac{\varepsilon_{N}+\frac{\pi}{4}\Lambda_{M}^{2}}{K_{ emit}} \tag{44}\]  
\[\left\|f\right\|\geq\frac{\delta_{b}^{2}}{4}\frac{\alpha}{\lambda_{min}\{F\}} \;or\left\|\overset{\sim}{\delta}^{T}\right\|_{F}\geq\delta_{b} \tag{45}\]  
In accordance with the Lyapunov stability theorem [35], this proves that \(\left\|\xi\right\|\), \(\left\|f\right\|\), and \(\overset{\sim}{\delta}^{T}\) are uniformly ultimate boundedness and \(\dot{V}\leq 0\). In the case where \(\alpha\) is relatively small or zero, \(\dot{V}\leq-\lambda_{min}\{F\}\|f\|^{2}-\xi^{T}K_{e}\xi++M+n\|\xi\|tr\left[ \overset{\sim}{\Lambda}^{T}\left(\Lambda-\overset{\sim}{\Lambda}\right)\right]\), which indicates that the tracking error tends towards zero using the adjustment \(\xi\) and \(f\). Conversely, when \(\alpha\) is not zero, the trajectory error is observed to converge to an arbitrarily small neighborhood around the zero defined by (42)-(45), which is a function of the unknown upper bound for all subsystems. This convergence can be achieved through adjustments to the \(\alpha\), \(f\), and \(\xi\). \(\square\)  
It is worth noting that, in contrast to other robust control methods already in use for robots, the robust term, which can be capable of being generalized to a wide range of robotic systems that are sensitive to local external perturbations, and effectively compensate for unknown upper limits corresponding to each subsystem, is proposed in the paper.
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 4 Experiment
### Parameter Settings  
Based on multiple experiments, the CNN model was stacked with the convolution layer, BN layer, pooling layer, expansion layer, and smoothing layer. In addition, the BILSTM model was set as a two-layer BILSTM layer, and the number of hidden units was set to 128, with 32 dropout units. The layer was set to 0.25, and finally, the output layer dimension was adjusted with a fully connected layer. The parameters were set to optimize the Adam function through 100 iterations of model training, with a batch size of 2400 and an initial learning rate of 0.005. The learning rate decreasing period was set at 20, and the learning rate was adjusted with a weight factor of 0.8. After adjustment, the learning rate  
Figure 3: CNN\(-\)BILSTM algorithm framework diagram.  
was evaluated as the product of the current learning rate and weight factor. Moreover, the dropout value was set to 0.2, and L2 regularization was employed to prevent overfitting.
# Stability Reinforcement of Slopes Using Vegetation Considering the Existence of Soft Rock
## 2 Materials and Methods
### Numerical Model and Simulation
#### 2.2.3 Simulation Scenarios  
To investigate the effect of vegetation type and reinforcement on the stability of the slope, in this case, a total of 15 simulation cases with various rainfall situations were conducted. Those simulations can be divided into two groups: one for stability analysis with rainfall while the other with various intensities of rainfall. The vegetation reinforcement was set by increasing the cohesion with various values for the six sub-layer soils. Comparisons of reinforcing effect by different vegetation types were also conducted based on the simulation results for both dry and rainfall conditions. For the cases with and without vegetation reinforcements, only additional cohesion was applied in the case with vegetation reinforcement and other input parameters are the same.  
Before the simulation, a constant gravitational load is applied to generate geostatic stress. For a slope with a constant groundwater level and no rainfall/evaporation on the surface, the distribution of matric suction above the groundwater level is linear, but the matric suction head of the natural slope generally has an upper limit. Generally, a minimum infiltration rate is given on the surface of the slope to obtain a steady-state, which can ensure a relatively reasonable suction distribution and can be set as the initial state of the rainfall cases. Thus, for the cases with rainfall in this example, we first prescribe virtual precipitation of 1 mm/day on the upper surface of the slope and calculate the steady-state of the seepage. Then, the steady-state was taken as the initial state for the rainfall-induced deformation of the slope.  
Figure 5: Mesh distributions with the root reinforcement of the numerical model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Layers** & **Saturated Moisture Content** & \(\mathbf{\alpha}\) & \(\mathbf{l}\) & \(\mathbf{n}\) \\ \hline Unit & \% & m\({}^{-1}\) & / & / \\ Sandy loess & 47\% & 1.360 & \(-\)0.8030 & 1.342 \\ Silt & 46\% & 0.94 & \(-\)1.382 & 1.400 \\ Mudstone & 55\% & 2.24 & \(-\)0.300 & 2.286 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for Van Genuchten function of the three soils.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 6 ML Applications in Energy Prediction
### Load Prediction and Monitoring  
To optimize energy production, save operating costs, and maintain supply and demand balance, load prediction is essential. Support Vector Machines (SVMs) and neuralnetworks are two examples of ML models that utilize historical consumption data to predict future demand. The efficiency of SVM in power load forecasting was shown by [101], highlighting the precision of ML in predicting peak and off-peak needs. A more recent work in [102] used variational autoencoders and tree-based regression models to predict power consumption in warehouses with a coefficient of determination exceeding 97%.
## 5 Computational Cost Reduction Methods
### Clustering Techniques
#### 5.2.1 K-Means Clustering  
In general, the K-means clustering method divides the AOI into subareas to confine the location process within a small area instead of an entire area [44]. Therefore, the computational cost is divided by \(K\), where \(K\) represents the number of clusters. In this technique, the center point of a cluster is determined by minimizing the signal distance between exemplar \(C_{j}\) and the members of the same clusters. Given a set of input patterns, \(X=\left(x_{1},\ldots,x_{j},\ldots,x_{M}\right)\), where M represents number of FP, and \(x_{j}=\left(x_{j1},\ldots\ldots,x_{j1}\right)\in R^{L}\), we have \(C=\left(C_{1},\ \ldots\ \ldots\,\ C_{k}\right)\ K\leq M\) exemplars for AOI. By minimizing an objective function, J, in this case a squared error function, the objective function is given in Equation (9):  
\[J=\sum_{j=1}^{K}\sum_{i=1}^{M}\|r_{i}^{j}-C_{j}\|^{2} \tag{9}\]  
where, \(\|r_{i}^{j}-C_{j}\|\) represents a measured distance between data points \(r_{i}^{j}\) and exemplar of cluster \(C_{j}\), it is an indicator of the distance of the n data points from their respective cluster centers. Figure 6 shows the distribution of FPs on \(K\) clusters \(C=\left(C_{1},\ \ldots\ \ldots,\ C_{k}\right)\). The steps of K-means are summarized in Algorithm 1.  
```
Input: Reference Points of Area of Interest (RM), K (number of clusters)  Output: Exemplars vectors \(C=\left(C_{1},\ \ldots\ \ldots,\ C_{k}\right)\), each RP is designated to a certain cluster  Mechanism of K-means
```  
**Algorithm 1** K-means clustering  
Figure 6: K-means clustering Technique.
## 6 Conclusions
By including the temperature and similar day effect as additional variables of the load fed to an FF-ANN model, the developed model successfully achieved a minimum MAPE of 2.99% for the weekdays and 4.53% for the weekends. The comparison showed that the MAPE obtained on the weekdays was improved by 0.34% and 8.04% for the weekends with an FF-ANN with respect to the conventional time series models. From this study, it was concluded that an FF-ANN is an acceptable forecasting model for both weekdays and weekends when compared with conventional time series load forecasting models with proper input variables available. However, in the case of a lack of data on the influencing variables, the time series model itself can be used to forecast the electricity load for weekdays, whereas a different approach for forecasting should be adopted for weekends. Conventional time series methods lack flexibility in addressing the effect of exogenous variables, which is a major drawback to the time series model, especially when addressing weekends, holidays, days of the month, and temperature, which all hold important information on the electric load pattern of the day. The weekdays and weekends resulted in different human activities, similar to special days, public holidays, and festive seasons also resulting in changes in human activities, resulting in changes to the electric load. Even with the ANN model's capability to track nonlinear relations to multi-variable inputs, the model is solely dependent on the input variable selected; thus, input error can result in major error in the performed forecasting. The proposed model is developing the use of one year of data as it lacks information on special events within the country, but with correct data on the events, the model has further room for improvement. This study can be further expanded upon to understand the relation of the load with the festivals and special events of Nepal, which are a major cause of electric load fluctuation. Conceptualization, Y.R., A.S. and A.G.; methodology, Y.R., A.M., B.G. and K.C. ; software, Y.R. and B.G. ; validation, A.S., A.G., A.T., K.C. and P.K. ; formal analysis, Y.R., A.M. and B.G. ; investigation, Y.R., A.M. and B.G. ; resources, A.S., A.G. and A.T.; writing--original draft preparation, All; writing--review and editing, All; supervision, A.G. and P.K. ; project administration, A.S. and A.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the EnergizeNepal Project Office of Kathmandu University, grant number ENEP-RENP-II-19-03. The data will be available upon request. The authors are thankful to the Department of Electrical and Electronics Engineering and the Center for Electric Power Engineering (CEPE) at Kathmandu University for providing the requested information, laboratory supports, and kind cooperation during this research work. A special thanks to Binamra Adhikari and the Nepal Electricity Authority Engineering Company of Nepal for sharing the data of the electrical load demand of the Baneshwore. There is no conflict of interest.
# Three-Dimensional Assessment of Upper Limb Proprioception via a Wearable Exoskeleton
## 4 Discussion  
Despite the paramount importance of proprioception in sensorimotor control, studies investigating proprioceptive acuity in coordinated multi-joint setups are still limited to single-joint or confined in the execution of tests involving planar workspace. Furthermore, not much evidence can be found in the literature about experimentally assessed physiological aspects that rise from the interconnection between distal and proximal joints in the perception of proprioceptive targets. With this in mind, we aim at providing insights into how proprioceptive acuity is encoded at the multi-joint level by means of a novel experimental paradigm involving proprioceptive assessment via a robotic device.  
We developed a protocol to compare \"single\"- versus \"multi-joint\" position matching in order to assess how perception of a proprioceptive target changes when multiple sensory information is encoded from the different sources or joints involved in the task.  
We used a robotic setup to overcome previous limitations of planar setups and test subjects' acuity of joints position across a 3D space.  
The proposed paradigm aims not only at extending proprioceptive assessment to a multidimensional manifold, replicating complex arm configurations, but provided also the unprecedented possibility of studying in detail the interconnection between those anatomical joints responsible for covering the whole arm workspace. To the best of our knowledge,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Body Side**} & \multirow{2}{*}{**DoF**} & \multicolumn{3}{c}{**Error Bias**} & \multicolumn{2}{c}{**Matching Error**} \\ \cline{3-6}  & & **SJ** & **MJ** & **SJ** & **MJ** \\ \hline \multirow{4}{*}{Right} & FE\({}_{\text{Elbow}}\) & & & & \\ \cline{2-6}  & FE\({}_{\text{Shoulder}}\) & 0.173 & 0.014 & \textless{}0.001 * & \textless{}0.001 * \\  & AA\({}_{\text{Shoulder}}\) & 0.289 & 0.006 & 0.197 & 0.002 * \\ \cline{2-6}  & FE\({}_{\text{Shoulder}}\) & & & & \\ \cline{2-6}  & AA\({}_{\text{Shoulder}}\) & 0.002 * & 0.082 & \textless{}0.001 * & \textless{}0.001 * \\ \hline \multirow{4}{*}{Left} & FE\({}_{\text{Elbow}}\) & & & & \\ \cline{2-6}  & FE\({}_{\text{Shoulder}}\) & 0.002 * & 0.001 * & \textless{}0.001 * & \textless{}0.001 * \\ \cline{1-1} \cline{2-6}  & AA\({}_{\text{Shoulder}}\) & 0.836 & 0.006 & 0.445 & 0.008 \\ \cline{1-1} \cline{2-6}  & FE\({}_{\text{Shoulder}}\) & & & & \\ \cline{1-1} \cline{2-6}  & AA\({}_{\text{Shoulder}}\) & 0.001 * & 0.127 & 0.001 * & \textless{}0.001 * \\ \hline \hline \end{tabular}  
* represents significant differences.  
\end{table}
Table 3: Statistical \(p\)-values for the Error Bias and Matching Error between the joints.  
there are no previous studies investigating such aspects involving robotic technology to increase measurement accuracy as well as contemplating a 3D testing paradigm.  
It has been observed that proprioceptive performance is influenced by the number of joints involved in the task as well as by the anatomical configuration of the tested degrees of freedom [20; 28; 42].