esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Model Training
By iteratively dividing the training set data into training and testing folds, multiple validations of the model's performance for a given hyperparameter setting were conducted to accurately assess the effectiveness of hyperparameter adjustments. Drawing from previous research experience, the optimization effect of the meta-heuristic optimization algorithm on the target model is significantly influenced by the population size. Therefore, in this study, five different population sizes (10, 20, 40, 80, 160) will be employed to enable the optimization algorithm to accurately search for high-performance hyperparameter settings. Figure 8 illustrates the curve of model fitness with the number of iterations for different population size settings, and it can be seen that the fitness of the model stabilizes as the number of iterations increases. The ultimate hyperpa  
Figure 7: Framework for predicting borehole breakout size. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Pop Size**} & \multicolumn{5}{c}{**Training**} \\ \cline{2-9}  & \multicolumn{2}{c}{**R\({}^{2}\) (Score)**} & \multicolumn{2}{c}{**MAE (Score)**} & \multicolumn{2}{c}{**VAF (Score)**} & \multicolumn{2}{c}{**RMSE (Score)**} \\ \cline{2-9}  & \(\theta\) & \(\pi_{h}\)/\(\tau\) & \(\theta\) & \(\pi_{h}\)/\(\tau\) & \(\theta\) & \(\pi_{h}\)/\(\tau\) & \(\theta\) & \(\pi_{h}\)/\(\tau\) \\ \hline
10 & 0.9936(4) & 0.9943(1) & 0.0084(3) & 0.0096(1) & 0.9862(4) & 0.9877(1) & 0.015(4) & 0.0176(1) \\ \hline
20 & 0.9934(3) & 0.9948(5) & 0.0083(4) & 0.0093(5) & 0.9857(3) & 0.9885(5) & 0.0153(3) & 0.017(5) \\ \hline
40 & 0.9932(2) & 0.9947(4) & 0.0085(2) & 0.0095(2) & 0.9852(2) & 0.9884(4) & 0.0155(2) & 0.0171(4) \\ \hline
80 & 0.9938(5) & 0.9946(3) & 0.0083(5) & 0.0093(4) & 0.9864(5) & 0.9881(3) & 0.0149(5) & 0.0173(3) \\ \hline
160 & 0.9929(1) & 0.9944(2) & 0.0086(1) & 0.0094(3) & 0.9848(1) & 0.9879(2) & 0.0158(1) & 0.0174(2) \\ \hline \multicolumn{9}{c}{Test} \\ \cline{2-9}  & \multicolumn{2}{c}{R\({}^{2}\) (score)} & \multicolumn{2}{c}{MAE (score)} & \multicolumn{2}{c}{VAF (score)} & \multicolumn{2}{c}{RMSE (score)} \\ \cline{2-9}  & \(\theta\) & \(\pi_{h}\)/\(\tau\) & \(\theta\) & \(\pi_{h}\)/\(\tau\) & \(\theta\) & \(\pi_{h}\)/\(\tau\) \\ \hline
10 & 0.9699(4) & 0.9642(1) & 0.018(3) & 0.0226(1) & 0.9382(4) & 0.9195(1) & 0.0314(4) & 0.0449(1) \\ \hline
20 & 0.9689(3) & 0.9661(5) & 0.0177(4) & 0.0218(5) & 0.9367(3) & 0.9223(4) & 0.0318(3) & 0.0441(4) \\ \hline
40 & 0.968(1) & 0.9651(2) & 0.0184(1) & 0.0224(2) & 0.9349(1) & 0.9201(2) & 0.0323(1) & 0.0448(2) \\ \hline
80 & 0.9704(5) & 0.9657(3) & 0.0174(5) & 0.0218(4) & 0.9395(5) & 0.9219(3) & 0.0311(5) & 0.0443(3) \\ \hline
160 & 0.9685(2) & 0.966(4) & 0.0181(2) & 0.0221(3) & 0.9358(2) & 0.9237(5) & 0.0321(2) & 0.0438(5) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal parameters obtained by WAOA for different Models.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 6 Performance Evaluation and Benchmarking
### Metrics for Evaluating VLC-Based IPS
#### 6.1.3 Robustness  
Robustness can be quantified by identifying and counting the number of outliers in the localization results. Outliers are data points that significantly deviate from the expected value and can negatively impact the accuracy of the system. Various statistical techniques,such as clustering, filtering, or robust regression, can be employed to detect and mitigate the effect of outliers.  
For instance, the authors in [117] propose a two-phase indoor positioning algorithmic framework, namely _coarse phase_ and _fine phase_, when subject to insufficient anchor luminaries. In [118], a continuously adaptive meanshift algorithm is performed to improve the robustness. Even if most of the LED is shielded or damaged, the system can still achieve high-precision tracking by utilizing color recognition and local detection, regardless of changes in shape. This ensures robust positioning capabilities without affecting the real-time ability of the positioning algorithm.
## 3 Background and Previous Related Work
### Dlt
#### 3.3.3 Digital Signatures  
Digital signatures enable the sender of a message to sign it with their private key, proving authenticity and integrity. In DLT, digital signatures are used for transaction authorization and ownership verification. Commonly used digital signature algorithms in DLT include ECDSA and the Edwards-curve Digital Signature Algorithm (EdDSA).
## 4 Clustering and Information Content
### Connection between Cluster Variation and Probability Distribution  
To build the connection, we need to make the following assumptions:  
1. The data are drawn independently from a mixture of multivariate Gaussian distributions corresponding to the clusters. Each Gaussian distribution is characterized by its mean \(\mu_{k}\) and a covariance matrix scaled by \(W_{y}\).  
Clusters with larger variation occupy a more significant volume in the data space, implying that they are more likely to encompass a randomly drawn data point. The volume occupied by a cluster in data space can be loosely tied to its spread or variation. For a multivariate Gaussian distribution, the volume it occupies is directly related to the determinant of its covariance matrix. In simple terms, if a cluster's data points are widely dispersed, its \"spread\" or \"variation\" is large, and it occupies a more significant volume in the space. As we have tied the cluster variation \(W_{y}\) to the variance of our Gaussian distribution, clusters with larger \(W_{y}\) will have a larger determinant of their covariance matrix, thereby occupying a larger volume. Thus, the probability of a randomly drawn point falling within such a cluster is higher, validating our assumption.  
Given that we are considering our data to be drawn from a mixture of multivariate Gaussian distributions (each corresponding to a cluster), the volume occupied by each cluster becomes pivotal in understanding the likelihood of a random sample falling within a particular cluster.  
The volume \(V_{y}\) of the region that is captured by cluster \(y\) is influenced by the square root of its variance (or in multidimensional space, the determinant of its covariance matrix). Given that \(W_{y}\) is essentially a measure of the squared distances (variances) from the centroid, it stands to reason that the volume \(V_{y}\) is proportional to \(W_{y}^{d/2}\), where \(d\) is the dimensionality of the data.  
Assuming that data points are uniformly and randomly sampled, the probability \(p(y)\) that a point lies within cluster \(y\) is proportional to the volume \(V_{y}\) of cluster \(y\).  
Given the volumes of all clusters, the probability that a randomly drawn data point lies within cluster \(y\) can be determined by the ratio of the volume of cluster \(y\) to the sum of the volumes of all clusters:  
\[p(y)=\frac{V_{y}}{\sum_{k=1}^{K}V_{k}}\]  
Substituting our earlier derived relationship for volume in terms of \(W_{y}\):  
\[p(y)=\frac{W_{y}^{d/2}}{\sum_{k=1}^{K}W_{k}^{d/2}}\]
## 3 Results
### Multi-Coil Arrangement
For equal lift-offs, the signal recorded in two coils arrangement is symmetric. Because of the plate tilt, the signals recorded in the single coil configuration are asymmetric and the maximum amplitudes recorded in both cases are nearly equal (the difference comes from imperfection in coils alignments with respect to the bias magnetic field). The method  
Figure 10: (**a**) Measurement of the angular dependence of the rf signal amplitude with the primary rf field produced by two coils (green points) and a single coil on the same (navy diamonds)/opposite (red triangles) side of the plate with respect to the sensor. Each point represents the mean of 30 measurements and the error bars show the standard deviation. Different gradients of the signal angular dependence result from different lift-offs of the primary rf field coils. (**b**) Normalised dependence of the signal’s angular dependence gradient on the difference in lift-off of the coil on the same (navy diamonds)/ (red triangles) opposite side of the plate. Lift-off is measured from the point at which the two coils lift-off is equal and the distance between the two coils is the same for all measurements. discussed in the previous paragraph referred to measurements conducted at the centre of the plate. The data shown in Figure 11b confirm that the amplitudes recorded only at this point with the single coil arrangement are equal (blue diamonds, and red triangles), when each coil has the same lift-off. However, the general behaviour of the rf signal recorded at any location over the plate is still valid. Figure 11a,c shows the same signal dependencies when recorded with different lift-offs, e.g., Figure 11a where the lift-off for the coil on same side as sensor is (red triangles) and the opposite coil is (blue diamonds). The data confirm that in these cases all dependencies show asymmetries and the maximum values recorded in single coil arrangements are not equal. It also shows that difference in the signal recorded in any location is minimised for the case of the equal lift-offs. Figure 11: _Cont._
## 3 Results and Discussion
### Effect of the Key Operating Conditions
#### 3.4.3 Effect of Pressure on Purity  
With the optimal adsorption time of 250 s obtained in the previous step, we then investigate the effect of pressure inside the bed on the purity of CH\({}_{4}\). Taking into account that the real process's operating pressure is 6.5 bar, we decided to vary the pressure between 5 and 8 bar. Note that the higher the operating pressure, the larger the energy consumption. As can be seen from Table 7, an increase in pressure with the same duration of the adsorption step increases the purity but, at the same time, also decreases the recovery of CH\({}_{4}\). Therefore, the pressure was lowered as much as possible to reach a purity of 92% of the biomethane at the outlet with maximum recovery in order to reduce greenhouse gas emissions. Reducing the pressure of the input biogas can reduce the compression costs and energy consumption, however, the purity is also decreased, as illustrated in Figure 14. In addition, we have seen that the extent of purity increase is less significant when pressure increases from 6.5 to 8 bar. Therefore, the optimal pressure is estimated at 6 bar with a corresponding pressurization time (PR) of 23 s, for a total cycle time of 556 s. The speed of the incoming gas stream is 0.270 m/s, the level of purity of the biomethane obtained after 94 cycles is 92.01%, and the level of recovery is 84.11%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Case 1** & **Case 2** & **Case 3** \\ \hline Pressure (bar) & 6.5 & 6.5 & 6.5 \\ PR (s) & 25 & 25 & 25 \\ AD (s) & 200 & 250 & 300 \\ DPE (s) & 5 & 5 & 5 \\ BL (s) & 25 & 25 & 25 \\ PU (s) & 200 & 250 & 300 \\ PPE [s] & 5 & 5 & 5 \\ Recovery of CH\({}_{4}\) (\%) & 78.85 & 81.63 & 83.79 \\ Purity of CH\({}_{4}\) (\%) & 92.35 & 92.08 & 91.86 \\ Number of cycles & 116 & 94 & 81 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Biomethane purity, recovery and number of cycles necessary to reach the CSS as a function of the adsorption time.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 4 Experimental Results
### Comparing Algorithms
#### 4.1.5. Bagged Decision Tree Results  
Bagged Decision Trees involve creating multiple decision trees through bootstrap aggregating (bagging), which enhances the stability and accuracy of the model by reducing variance.  
Figure (a)a illustrates the training performance, with actual vs. predicted 'Time Factor' values. Figure (b)b depicts the testing phase performance, showing the model's accuracy in predictions, while Figure (c)c shows the validation performance, emphasizing the model's generalizability.  
The Bagged Decision Tree model produced promising results in our analysis. It achieved a Mean Squared Error (MSE) of 2589.08 and a Root Mean Squared Error (RMSE) of 50.88, indicating fair predictive accuracy. Additionally, the model displayed a low Mean Absolute Error (MAE) of 36.89 and a Median Absolute Error (MedAE) of 28.54, reflecting its reliability. The Mean Bias Deviation (MBD) near zero (2.57) indicates that the model maintains a balanced prediction.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Training** & **Testing** & **Validation** \\ \hline Mean Squared Error (MSE) & 1194.496 & 2404.466 & 2589.089 \\ Root Mean Squared Error (RMSE) & 34.562 & 49.035 & 50.883 \\ Mean Absolute Error (MAE) & 26.168 & 36.749 & 36.897 \\ Median Absolute Error (MedAE) & 19.792 & 26.250 & 28.546 \\ Mean Squared Logarithmic Error (MSLE) & 0.003 & 0.006 & 0.007 \\ Root Mean Squared Logarithmic Error (RMSLE) & 0.055 & 0.077 & 0.081 \\ Mean Bias Deviation (MBD) & \(-\)0.173 & \(-\)6.595 & 2.575 \\ Mean Absolute Percentage Error (MAPE\(-\)Validation) & 4.07\% & 5.74\% & 5.73\% \\ Huber Loss (Validation) & 25.671 & 36.254 & 36.402 \\ Angle between Perfect Fit and Best Fit (degrees) & 34.165 & 29.025 & 27.121 \\ C-index & 0.88 & 0.85 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Gradient Boosting Machine analysis.  
Figure 10: GBM Time Factor performance.  
Bagged Decision Trees demonstrated strong predictive performance, benefiting from the reduced variance and increased stability provided by the bagging technique, as seen in Table 7. This ensemble method effectively handled variability in the data, enhancing model reliability. Despite these advantages, the interpretability of the final model was somewhat diminished due to the aggregation of multiple decision trees.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.4.6. Weighted Intersection over Union (WIoU) [41]  
WIoU accounts for class imbalance by weighting the IoU of each class based on its frequency in the dataset, offering a more nuanced evaluation of segmentation performance in scenarios with uneven class distributions.  
\[WIoU=\sum_{i=1}^{N}\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}}\times\frac{Total\ pixels\ in\ class\ i}{Total\ pixels}, \tag{5}\]
# Chaotic Characteristic Analysis of Spillway Radial Gate Vibration under Discharge Excitation
## 2 Methodology
### Reconstruction of Phase Space  
#### 2.1.1 Time Delay  
The time series of the acceleration data of the gate, \(x_{t}\) (\(t=1,2,3,\ldots,N\)), is assigned to a new \(m\)-dimensional state vector, \(Y_{n}\) (\(n=1,2,3,\ldots,N-(m-1)\tau\)), based on the appropriate delay-coordinate method:  
\[Y_{n}=\left\{x_{n},x_{n+\tau},x_{n+2\tau},L,x_{n+(m-1)\tau}\right\} \tag{1}\]  
where \(\tau\) is the time delay and represents the multiple sampling time intervals for the measured data, while m represents the embedding dimension.  
By choosing a reasonable embedding dimension, m, and time delay, \(\tau\), an equivalent phase space can be reconstructed and the attractor of the prime dynamic system can be obtained. In present work, the average mutual information (AMI) [16] method with a strong anti-noise ability was used to determine the time delay, \(\tau\). And the AMI function \(I(\tau)\) is defined as:  
\[I(\tau)=\sum_{i,l+\tau}P(x_{i},x_{i+\tau})lnP(x_{i},x_{i+\tau})\ -\sum_{i}P(x_{i})lnP(x_{i})-\sum_{i+\tau}P(x_{i+\tau})lnP(x_{i+\tau}) \tag{2}\]  
where \(P(x_{i})\) and \(P(x_{i+\tau})\) are the independent probability densities of \(x_{i}\) and \(x_{i+\tau}\), respectively, and \(P(x_{i},x_{i+\tau})\) is the combined probability densities of \(x_{i}\) and \(x_{i+\tau}\). The optimal time delay was determined as corresponding to the first minimum of \(I(\tau)\).
# Maintenance Prediction through Sensing Using Hidden Markov Models--A Case Study
## 4 Case Study
### Data Preparation
As is well known, outliers can be classified into two different subgroups: natural and interesting ones, and those caused by defective instruments. The first group will contribute with data that may improve the analysis or construction of the subsequent model, while the second will contribute with errors that make the information extraction less accurate [21; 23; 56]. Therefore, in this case, as we are carrying out a study of the failure diagnosis of drying presses, we chose not to remove what could be the outliers, as in this case, because these values may represent equipment malfunctions and, by consequence, an invalid number.
Applying a cost sensitivity fitness function in an ensemble learning paradigm is different from existing cost-sensitive methods because it reduces the effects of the bias toward the minority classes, which is caused by the costs that are associated with the misclassification of minor class instances in the classical cost-sensitive methods. Moreover, the evolutionary nature of the utilized metaheuristic algorithms provides the accuracy and diversity that are required by ensemble learning to achieve a high prediction capability that exceeds the prediction capability of a single learner. The reason for selecting a PSO and a CSO as the optimization techniques in this work was that, compared to other metaheuristic algorithms, a PSO requires a small number of parameters and a correspondingly lower number of iterations [16]. On the other hand, a CSO is a relatively recent variation of a PSO that was designed to be used for large-scale optimization problems because half of the population is updated during each iteration [17]. To validate this, we used three different datasets from Spanish, Taiwanese, and Polish companies to evaluate the proposed method. The dataset of Spanish companies was considered very challenging, owing to its highly imbalanced distribution in which insolvency cases only formed 2% of the whole sample. In the datasets of Taiwanese companies and Polish companies, insolvency cases formed approximately 3% and 2% of the samples, respectively. When applying the cost sensitivity fitness function, we noticed a significant improvement in the number of true positive (TP) predictions but an increase in the number of false positive (FP) predictions. To overcome this problem, we used majority voting ensemble learning to maintain the high TP prediction rate and reduce the number of FP predictions. This work proposes a framework for solving financial distress prediction problems for extremely imbalanced datasets. The framework uses a cost sensitivity fitness function to reduce the number of FN predictions. Moreover, it relies on ensemble learning to compensate for the faults of individual learners and reduce the number of FP predictions. All of the steps in the framework are internal and do not affect the data; hence, it can be a helpful tool in financial distress prediction. To the best of our knowledge, our work is the first to combine a cost-sensitive MHOANN with majority voting ensemble learning for financial distress prediction. Another contribution of this work is the comparison of a PSO and CSO as optimization techniques for the MHOANN. The remainder of this paper is organized as follows. In the following section, we review the related works. Then, in Section 3, we explain the optimization algorithms that were used in our study. In Section 4, we describe the considered datasets. Section 5 describes the proposed method and in Section 6, we describe the evaluation metrics that were used. The experiments that were conducted and the obtained results are explained in Section 7. Finally, the conclusions and future work are discussed in Section 8.
## 3 Materials and Methods
### Experimentation
It has been widely used in education research for comparing classification algorithms and models [33; 34] instead of other well-known evaluation measures such as Accuracy, F-measure, Sensitivity, Precision, etc. AUC can be defined as the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one (assuming 'positive' ranks higher than 'negative'). It is often used as a measure of the quality of the classification models. A random classifier has an area under the curve of 0.5, while AUC for a perfect classifier is equal to 1. In practice, most of the classification models have an AUC between 0.5 and 1. We have also calculated the AUC loss or difference between the two AUC values obtained when applying the model over the source dataset and when applying over the target dataset. The general procedure of our experiments has been summarized in Figure 1, where we graphically show the main steps of the experiments by using a flow diagram. An overall explanation of the main steps (see Figure 2) of our experiments is:  
* Firstly, Moodle logs have to be pre-processed (step 1) in order to obtain numerical and discretized datasets according to the format expected in the data mining tool to be used, Weka. * Then, for each course dataset (numerical and discretized), the algorithm J48 is run in order to obtain a general prediction model (step 2) to be used in portability experiments. * Next, courses are grouped according to 2 different criteria to conduct two types of experiments (step 3); for the first experiment (named \"Experiment 1\"), related courses are grouped by the area of knowledge (attribute \"Degree\" in Table 1); for the second experiment (\"Experiment 2\"), groups of courses are built according to the Moodle usage (\"Moodle Usage\" in Table 1). * In each experiment, each model is selected (step 4) and tested against the rest of the datasets of courses belonging to the same group (step 5), repeating this process for each course. * Finally, AUC values are obtained and AUC loss values are calculated when using the model against the rest of the courses of the same group (step 6). Figure 1: Experiments procedure steps. In the next two subsections, we provide a more detailed explanation of how we have grouped similar courses in the two experiments.
# SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning
## 1 Introduction
To demonstrate its effectiveness, the proposed method was applied to YOLOv4 [5] and YOLOv7 [8]. With the pig and vehicle datasets, the proposed method significantly improved the execution speed of YOLOv4 and YOLOv7 on a low-cost embedded board, TX-2, with acceptable accuracy. Compared to the baseline YOLO model, the proposed method can improve integrated performance (accuracy x speed) up to five times. The contributions of this work are summarized as follows:  
* Attention mechanisms have been used to improve the accuracy of deep learning models, while pruning techniques have been used to improve the execution speed. A step is taken forward from previous research by combining attention and pruning to achieve real-time YOLO on a low-cost embedded board with acceptable accuracy. To the best of our knowledge, this represents the first instance of concurrently considering both the accuracy and speed of YOLO by comparing the foreground-background information derived from GT box locations and the feature map difference before and after spatial attention. * To validate the feasibility of our proposed method, the experiments were conducted comparing the proposed method to baseline models in terms of accuracy and speed across various settings. The experiments with two datasets (pig and vehicle), two attention mechanisms (convolutional block attention network, CBAM and coordinate attention, CA), two detection models (YOLOv4 and YOLOv7), and two model sizes (medium and tiny) were conducted. Additionally, comparative experiments were carried out with conventional filter-pruning techniques based on magnitude and randomness. Figure 1: For important filters (shown as red rows), the comparison result (**c**) of the foreground–background information derived from GT box locations and the feature map difference before (**a**) and after (**b**) spatial attention can be used not to prune the filters. For unimportant filters (shown as blue rows), the comparison result (**f**) of the foreground–background information derived from GT box locations and the feature map difference before (**d**) and after (**e**) spatial attention can be used to prune the filters.