esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Independent Verification of Model Prediction Capability  
To rigorously evaluate the best trained and tested model on unseen data, a separate dataset from a well drilled in one of the two studied fields, consisting of ninety-nine data points, was evaluated with the RBFNN-GO model. The statistical characterization of the FD, MFV, and FV variables for the unseen dataset is summarized in Table 7. A comparison of these values with those in Table 1 indicates that the range of changes in the unseen data parameters is entirely covered by the range of changes in the data applied to develop the FV prediction models.  
The best-performing model, RBFNN-GO, was applied to predict the FV for the normalized unseen data records. Figure 15 illustrates a comparison between the measured FV values and those predicted by the RBFNN-GO model for the unseen ninety-nine data points. The trained model effectively captures the fluctuations in the FV across the dataset, indicating its strong predictive capability. The RMSE for these FV predictions is 0.3227 mL, reflecting the model's ability to predict the FV with low errors. The R\({}^{2}\) value for these predictions is 0.9624, further confirming the model's excellent fit to the data and its ability to replicate the observed trends. Considering the model's reliable FV prediction performance on unseen data from another well, it can be concluded with confidence that the RBFNN-GO model is generalizable and can be applied as a promising predictive tool to provide credible semi-real-time FV predictions for future wells drilled within the studied fields.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Properties** & **Maximum** & **Minimum** & **Average** & **Skewness** & **Kurtosis** \\ \hline FD, pcf & 87 & 74 & 79.9091 & 0.1960 & \(-\)1.5008 \\ \hline MFV, sec/quart & 48 & 30 & 40.5354 & \(-\)0.2419 & 1.0585 \\ \hline FV, mL & 9 & 2.5 & 5.01 & 0.1200 & \(-\)0.6400 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical characterization of ninety-nine data records from a separate well from within the studied fields not involved in the HML model training and testing.  
Figure 14: The (**a**) 3D and (**b**) 2D heat map partial dependence plots showcasing the interplay between pairs of input features in the predictions of the FV as generated by the RBFNN-GO model applied to the training subset.
## 2 The Algorithms: MC, MCQ, MCR, MCS and BBMC
### Bbmc
#### 2.4.1 BBMC in Java
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**instance** & **Fais** & **Daleview** & **Cyprus** \\ \hline brock200-1 & 1.26 & 0.70 & 2.03 \\ brock200-4 & 1.17 & 0.72 & 2.35 \\ hamming10-2 & 3.10 & 3.24 & 2.21 \\ hamming8-4 & 1.16 & 0.86 & 1.24 \\ johnson16-2-4 & 1.23 & 0.61 & 1.66 \\ MANN-a27 & 2.62 & 1.93 & 4.12 \\ p-hat1000-1 & 1.01 & 0.79 & 1.80 \\ p-hat1500-1 & 0.86 & 0.72 & 1.52 \\ p-hat300-3 & 1.28 & 0.85 & 2.36 \\ p-hat500-2 & 1.32 & 0.90 & 2.56 \\ p-hat700-1 & 1.38 & 0.88 & 1.86 \\ san1000 & 1.63 & 1.63 & 1.76 \\ san200-0.9-1 & 1.17 & 0.71 & 1.93 \\ san200-0.9-2 & 1.61 & 0.88 & 2.36 \\ san400-0.7-1 & 1.23 & 0.94 & 2.04 \\ san400-0.7-2 & 1.18 & 0.84 & 2.12 \\ san400-0.7-3 & 1.14 & 0.87 & 2.10 \\ san200-0.7 & 1.24 & 0.70 & 1.95 \\ san200-0.9 & 1.48 & 0.86 & 2.30 \\ san400-0.5 & 1.08 & 0.69 & 1.85 \\ san400-0.7 & 2.70 & 0.68 & 1.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Calibration experiment part 2, does hardware affect relative algorithmic performance? Values greater than 1 imply BBMC is faster than MCSa, and values less than 1 imply MCSa is faster. ## Appendix

At the top of MCQ's search, [PERSON] [12] sorts vertices in non-increasing degree order and the first \(\Delta\) vertices are given colours 1 to \(\Delta\) respectively, where \(\Delta\) is the maximum degree in the graph, thereafter vertices are given colour \(\Delta+1\). This is done to _prime_ the candidate set for the initial call to EXPAND. Thereafter [PERSON] calls NUMBER-SORT immediately before the recursive call to EXPAND. A simpler option is taken here: colouring and sorting of the candidate set is done only at the start of \(expand\). Using graph g10-50 as an example, in Figure 2 of [12] vertices would initially be selected in the order [7,9,8,5,2,1,6,4,0,3] with colours respectively [6,6,6,6,6,5,4,3,2,1], _i.e._, using 6 colours. Here vertices are selected in order [9,8,5,7,2,1,6,4,0,3] with colours [4,3,3,2,2,2,2,1,1,1], _i.e._, using 4 colours, a tighter upper bound and vertices no longer in degree order. Listing 13 presents a Java implementation of MCQ as described in [12] but in our framework. Lines 18 to 20 give an initial colour to the sorted vertices. Method \(numberSort\) is now called _after_ the selection of a vertex (line 40). A similar change was made to MCSa. Figure 11 shows the effect of the initial colour ordering, using \(G(100,p)\) on calls to \(expand\) (nodes). We see on the left that [PERSON]'s MCQ is marginally better than MCQ1 and on the right that MCSa1 is better than [PERSON]'s equivalent (and we see a similar improvement in BBMC1). In conclusion, the approach adopted here is simpler, using a single colour-ordering procedure. In MCQ1 the effect on performance is detrimental but small, and in MCSa1 (and BBMC1a) it is beneficial. ## Acknowledgements

I would like to thank [PERSON], [PERSON], [PERSON] and my reviewers. Figure 11: The effect of [PERSON]’s initial colour ordering. ```
1 importjava.util.
# The Genetic and Imaging Key to Understanding Bicuspid Aortic Valve Disease
## 4 Linking Genetics and Imaging  
Integrating genetic and imaging data offers a comprehensive approach to understanding BAV and its complications. Genetic mutations in BAV, such as those in NOTCH1, GATA, and SMAD6, can influence imaging findings. For example, NOTCH1 mutations are often associated with calcification and valve abnormalities that can be visualized using echocardiography or cardiac MRI (CMR) [37; 38; 39]. These imaging modalities can detect structural changes in the valve and aorta, providing insights into the extent and severity of the disease caused by specific genetic mutations.
# Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography
## 4 Conclusions  
This paper proposes a nanotomography-based geometric parameter self-calibration method based on feature matching of mirror projection images. The method takes full advantage of an image-matching algorithm based on feature points. We design a special feature-point screening algorithm according to the characteristics of the system. This allows some mismatched feature points to be removed and improved accuracy and stability of rotational axis fitting. Simulation and actual data experimental results and numerical analysis prove the effectiveness and practicality of our proposed method. Blurring and ghosting of the reconstructed image caused by geometric artifacts are significantly suppressed. The feature-point-based method exhibits better stability than the self-calibration method that directly constructs the objective function from the gray value of the projected image. In addition, the method utilizes the object's projection information to achieve correction without the need for additional correction models and iterative reconstruction.  
Conceptualization, S.Y. and Y.H.; methodology, Y.H.; software, S.Y.; validation, S.Y., Y.H., X.X. and L.L.; formal analysis, S.Y. and Y.H.; investigation, S.Y.; resources, B.Y.; data curation, X.X., S.T. and L.Z.; writing--original draft preparation, S.Y. and Y.H.; writing--review and editing, S.T., X.X., L.L. and B.Y.; visualization, S.Y. and M.L.; supervision, B.Y.; project administration, X.X.; funding acquisition, L.L. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2020 YFC1522002) and the National Natural Science Foundation of China (62201618).  
Not applicable.  
The data and the code used for the manuscript are available for researchers on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Viscosity Measurement, the Average Speed (Vavr) and Reynolds Calculations of Nanofluids  
The viscosity and density of nanofluids obtained by adding 0.1%, 0.2% and 0.3% CuO nanomaterials were calculated with an SV-10 viscometer at different pH values and different temperature values. The viscometer is used for liquids that cannot be identified with a single viscosity value. Therefore, more parameters should be set and measured than for a viscometer. The viscometer determines the viscosity of the nanofluids and allows the viscosity values to be examined in the face of temperature variations.  
In order to create a more precise model of computational intelligence methods used in the viscosity estimation, the number of data values must be increased. In order to increase the number of data, the Reynolds number and average speed values, which are the most important parameters in determining the flow type, were selected. The average velocity and Reynolds numbers were calculated with the help of the experimental setup in Figure 2. In the experimental setup, the temperature of the in-pipe nanofluids obtained by the heat band and the temperature values where the viscosity measurements are made are the same.  
As shown in Figure 2, the flow rate of nanofluids is adjusted by means of a flow rate control valve. Temperature measurements were taken from 4 different points on the surface of the pipe with the help of the thermocouples of the nanofluids passing through the copper pipe through laminar flow. The inlet and outlet temperatures of the nanofluids to the copper tube were measured by means of a fluid thermometer. The volumetric flow rate of the nanofluids was determined by means of the flow meter.  
In heating and cooling applications, the average speed may vary slightly due to density and temperature changes. In practice, however, we evaluate fluid properties by taking fluid properties at an average temperature and treat them as fixed. The suitability of working with fixed features often compensates for a slight deviation from accuracy. The Reynolds number for the internal flow in the circular pipe is defined as follows [21].  
\[Re=\frac{IntertiaForces}{ViscosityForce}=\frac{V_{avr}.D}{\upsilon}=\frac{\rho.V_ {avr}.D}{\mu} \tag{1}\]  
In Equation (1), \(\mu\) is viscosity (kg/m\(\cdot\)sn), \(\upsilon\) is kinematic viscosity (m\({}^{2}\)/sn), \(D\) is pipe diameter (m), \(\rho\) is density (kg/m\({}^{3}\)) and \(V_{avr}\) is mean flow velocity (m/s).  
Figure 2: The experimental setup (**1**) Pump; (**2**) T Connection reducer elbow pipe; (**3**) Heat Band; (**4**) Copper pipe; (**5**) Plastic pipe; (**6**) Flow Measurement Electronic device sensor; (**7**) Flow Measurement Electronic device; (**8**) Fluid reservoir; (**9**) Flow Adjustment Valve; (**10**) Fluid Thermometer; (**11**) Thermocouple.  
We have already calculated the density and viscosity values. After calculating the average speed of the nanofluids with device 7 in Figure 3, the Reynolds number is calculated with the help of Equation (1).  
A diagrammatic representation of the measurements taken in the experimental setup is given in Figure 3.
## 4 Clustering, Classification, and Quantification of Coal Based on Machine Learning
### Clustering Models
#### 4.1.2 DBSCAN Clustering  
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering is a density-based method. It first selects a sample point randomly from the dataset and then  
Figure 5: Elbow rule descent curve for K-means clustering (**a**) and clustering results for best K values (**b**).  
find out all the available points under the combination of the radius (\(\epsilon\)) and the minimum number of neighboring points (minPts). If the sample number in the circle with the radius of \(\epsilon\) is higher or equal to the minPts, the circle will be transferred to the next sample points; if the sample point is on the edge of the dataset, the clustering center will be re-selected until an appropriate clustering center appears. There is no limitation for DBSCAN clustering on the shape and number of clusters, and it is not sensitive to anomalous data. However, DBSCAN clustering has some requirements of the data density and sample balance, and different parameters have different results.  
The key parameters of DBSCAN clustering are the circle radius (\(\epsilon\)) and the minimum number of neighboring points in the circle (minPts). If the circle radius is too large or small, the clustering performance will be poor and unreasonable, and it will generate too few clusters or too many clusters. The minPts value refers to the threshold for the number of samples in the circle, and if the number of samples in the circle is greater than or equal to the threshold, the sample is considered to be the clustering center. The minPts value is usually related to the feature dimension.  
Two same principal components were input as the K-means clustering to reduce the dimension. The circle radius \(\epsilon\) of the DBSCAN clustering is determined by the elbow point of the k-distance plot, which is the same as K-means clustering. The k-distance values between all sample points were arranged in descending order in the k-distance plot as shown in Figure 6a. Thus, the circle radius \(\epsilon\) can be determined as three according to the elbow method. The minPts is usually recommended as the feature dimension multiplied by two, hence, the minPts value is considered to be four. The results of DBSCAN clustering are shown in Figure 6b.  
The silhouette coefficient of the training and test sets was 0.604, and the silhouette coefficient of the prediction set was 0.437, lower than the K-means clustering. The low silhouette coefficient of the prediction set is strongly related to poor data density and data imbalance. For example, the number of the sample in the training set is three, which is lower than the minPts value. On this condition, the DBSCAN clustering considered these three samples as noise and then filtered them. Although some imbalanced data were filtered, the remaining results were still meaningful. The results indicated that the 100 coal samples could be split into three different clusters only according to LIBS spectra.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 2 Notation and Preliminaries
### Evaluation Criteria of Sparse Time-Series Clustering
#### 2.3.3 Evaluation  
In this work, we resort to ARI to quantify the performance of the clusterings computed by our framework. More precisely, for every instance \((\mathcal{X},k)\), with a set \(\mathcal{X}\) of time series to be partitioned into a predefined number \(k\) of clusters, we calculate the ARI for the clustering computed by \(k\)-means on the original instance \((\mathcal{X},k)\) and on the instance obtained by applying SGPR to the time series in \(\mathcal{X}\).  
Comparing the ARI of the two clusterings indicates the performance loss (or sometimes benefit) due to the sparsity of \(\mathcal{X}\)'s representation in our framework. Moreover, we use the _spread_ and the _average difference_ (which can be gain or loss), computed with respect to the ARI of the two algorithms on a family of instances, as a summary indicator of the performance of our Sparse Time-Series Clustering framework.
# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 2 Review on Time-Series Forecasting Studies
In addition, Deep Learning (DL) models, as a subcategory of the AI models, including Artificial Neural Network (ANN) and Recurrent Neural Network (RNN), were introduced in the early 2000s, although not much attention was given to the application of DL models until the 2010s. From the review, the application of DL models in the 2000s was less than the application of the regression and stochastic models, as shown in Figure 1(a). During that time, researchers were investigating new ways to improve the model performance. Thus, different combinations of hybrid models have been proposed and presented. More attention was given to the hybrid models from the 2010s due to their robustness and the availability of computing resources. Notably, the stock and electricity markets have always been a focus of time-series forecasting, as shown in Figure 1(b). Different models have a relatively higher application count in these two fields, which is likely due to the market complexity that is highly volatile. It is also noted that the application of time-series forecasting is extending to other domains for dynamic pricing recently. The following section introduces the statistical model, AI model, and hybrid model for dynamic pricing solutions.
# Analysis of a Bistatic Ground-Based Synthetic Aperture Radar System and Indoor Experiments
## 1 Introduction  
In recent years, technical advancement in synthetic aperture radar (SAR) has changed the system configurations from monostatic to bistatic/multistatic modes with multiple antennas [1]. There have been several conventional bistatic SAR operations: the single-pass interferometric missions to obtain topography such as the shuttle radar topography mission (SRTM) [2], TanDEM-X [3], and along-track interferometry (ATI) [4]. Advanced SAR concepts have been widely tested onboard airborne platforms and, for a few cases, with satellites [5]. Such configurations include the use of SAR satellite constellation for multistatic operations or one-stationary and one-moving antenna configurations [6]. For the former configurations, multiple SAR satellites fly on the same (or nearby) orbital plane taking roles of transmitter (Tx) and/or receiver (Rx). By doing so one can enhance the resolution or investigate bidirectional scattering property of targets [7].  
Ground-based synthetic aperture radar (GB-SAR) systems have been widely developed and used in recent years for multiple purposes such as a testbed for advanced SAR concepts and aotechnical equipment for surface displacement detection [8]. Limited by relatively short range and low grazing angle, GB-SAR has many advantages over airborne or satellite systems due to the precise motion control, repeatability and operability at the user's hands. A ground-based synthetic aperture radar (GB-SAR) system enhances its azimuth resolution by synthetizing signals from many observations from different antenna positions to extend the aperture length of the antenna beyond its physical size [9]. The motion of the antennas can be precisely controlled by ground-based vehicles such as a linear rail system [10] or an arc-scanning platform [11].  
A limited number of papers have reported the use of GB-SAR for bistatic configurations such as the use of a GB-SAR as a passive receiver and a satellite SAR as a transmitter [12] and the use of a ground-based transponder to redirect a transmitting signal to obtain the target's two-dimensional displacement vector [13].  
In this paper, we used a linear-scanning GB-SAR system as a bistatic configuration where two antennas (one Tx and the other Rx) were separated on a rail track by a baseline large enough to cause forward scattering. This can be considered as a simulation of a satellite bistatic SAR mission where Tx (or Rx) follows the Rx (or Tx) on the same orbit separated by tens or hundreds of kilometers. The range-Doppler algorithm (RDA) has been modified for this bistatic GB-SAR system, and its focusing performance has been evaluated by comparing the results with the ones obtained by the back-projection algorithm (BPA). Target scattering properties and image qualities were also discussed.
# Predictive Maintenance Based on Identity Resolution and Transformers in IIoT
## 5 Evaluation
### Experiments on State Awareness Model  
For the first experiment, the authors performed an initial evaluation of the perception capacity of the SAM. To comprehensively assess model performance, the authors employed varied performance metrics and utilized the CWRU dataset for both training and testing processes. The results presented in Table 2 and Figure 4(b) indicate that optimal model performance is achieved when the length of the sequence is 1024, as evidenced by the significantly higher accuracy, precision, recall, and F1-score. The authors additionally demonstrate that employing a sequence length of 1024 produces a lower test consumption time, as shown in Figure 4(b). Furthermore, our findings demonstrate a good convergence effect for the model, as illustrated in Figure 4(a), which reveals loss and accuracy during the training process. Finally, the authors show that the AUC values increase with the sequence length of 256, 512, 1024, 2048 and 4096, with better perception effects observed at the slightly elevated sequence length of 1024, as indicated in Figure 4(c).  
Relevant results about the state awareness model in comparison with current state-of-the-art methodologies are presented in Table 3 and Figure 6. The data presented in Table 3 showcase a strong accuracy for all compared methods. However, the authors show that state-aware models enable sequence-to-sequence prediction without feature extraction and optimization, significantly simplifying the construction of the model and providing acceleration for online execution of the model. These findings affirm the promising potential of the transformer structure as a viable PdM approach by balancing both optimal prediction performance and the efficient use of potential global correlations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Sequence Length**} & \multirow{2}{*}{**Data Volume**} & \multicolumn{3}{c}{**Time Consumption (s)**} & \multicolumn{3}{c}{**Metrics (Testing)**} \\ \cline{3-8}  & & **Train** & **Test** & **Accuracy** & **Precision** & **Recall** & **F1-score** \\ \hline
256 & 18,880 & 36,223,014 & 0.785 & 95.306\% & 95.252\% & 95.227\% & 0.952 \\
512 & 16,100 & 4538,319 & 0.830 & 97.696\% & 97.753\% & 97.705\% & 0.977 \\
1024 & 16,080 & **1511.606** & **0.738** & **99.062\%** & **99.049\%** & **99.053\%** & **0.991** \\
2048 & 16,050 & 1526,244 & 0.750 & 97.438\% & 97.448\% & 97.473\% & 0.974 \\
4096 & 15,980 & 2110,378 & 0.784 & 90.598\% & 90.613\% & 90.681\% & 0.906 \\ \hline \end{tabular}
\end{table}
Table 2: The evaluation results of state awareness model.