esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method to Measure the Powder Consumption of Dental Air-Polishing Devices
## 4 Discussion
This device does not have any power regulation settings; it always works at maximum consumption. 2. This device uses a'swirling' picking-up mechanism, so the powder consumption cannot be regulated. Consequently, this device will likely over-consume powder, as the user is not able to compensate for the faster powder delivery with this device by sufficiently speeding up the treatment. After nearly nine minutes of treatment with this device, the powder consumption reduced drastically; this is barely enough time to treat the full mouth of one patient. Therefore, Table-top M3 needs to be refilled after each treatment. In addition, the device likely generates more dust, which is more difficult to master with a high-speed suction unit. The case of Table-top N2 was slightly different. The powder picking-up mechanism of this device used the powder-air interface and the empty volume above the powder limit. As the empty powder volume increased with the powder consumption, it was also observed that the powder consumption decreased accordingly [13]. During the emptying of the powder chamber, the user will, therefore, experience a change in the cleaning capacity at a factor higher than two, if we assume that the cleaning capacity is proportional to the consumed powder amount [12]. Supposing that the device could last for two full mouth treatments of 10 min each, both treatments would be very different in terms of functionality. For Table-top E1, the results were completely different. The reduction in the powder consumption over time was quite low (max 20%), such that--under intensive usage--the user has to refill the powder chamber after four full-mouth treatments (assuming a 10 min treatment for a full-mouth), which represents nearly half a day of work. This powder consumption consistency was mainly due to the working mechanism of the powder chamber, which picks up the powder from the chamber bottom. As a result, the powder volume does not impact the resulting powder consumption. These findings were corroborated by the results of the study of [PERSON] et al. [13]. The working behavior of the Handy devices is far less controlled than that of the Table-tops:  
1. The autonomy of the Handy devices is much shorter;
2. The powder consumption is less controlled;
3. They have a lack of reactivity due to additional powder delivery after the pedal is released. It should be highlighted that, during a longer powder delivery after the pedal's release, the device is not more active. Therefore, this consumed powder is pure powder waste for the system. Considering a 12 s pedal press, a 3 s stop-delay represents about 25% overconsumption due only to this working mode. Therefore, the Handy devices consume more powder than the Table-top devices, consequently entailing a greater running cost.
## 3 Experiments and Analysis
### Experiment 1: Case Western Reserve University (CWRU) Bearing Dataset  
#### 3.1.1 Dataset Introduction and Experiment Description  
The data used in this paper for experimental validation were provided by Case Western Reserve University (CWRU), Cleveland, Ohio, USA. As shown in Figure 6, the test stand consists of an electronic motor, a torque transducer, a dynamometer, and control electronics. Single point faults were introduced to the test bearings using electro-discharge machining. Faults were set on the rolling elements, the outer races, and the inner races, and each fault bearing was installed in the test ring, which was run at a constant speed for motor loads of 0, 746, 1492, and 2238W, with motor speeds corresponding to 1797, 1772, 1750, and 1730 rpm. A more detailed introduction can be found on the CWRU Bearing Data website [39].  
Figure 5: The construction and training of the stacked sparse denoising autoencoder (SSDAE). (**a**) the shallow sparse autoencoder (SAE), (**b**) the shallow denoising autoencoder (DAE), (**c**) the constructed SSDAE network, and (**d**) the proposed supervised learning SSDAE network.  
In this experiment, sample data were collected at 12 kHz, and detailed information is listed in Table 1. A motor load of 746W with corresponding motor speeds of 1772 rpm was selected. Datasets, including a normal condition (N), an outer race fault (ORF), a ball fault (BF), and an inter race fault (IRF), with fault diameters of 0.18, 0.36, and 0.54 mm. and a fault depth of 0.3 mm, were selected. We chose 150 samples for each defect diameter with the same fault type, in which 100 samples at a motor load of 746W were used for training, and the remaining 50 samples were used for testing. Therefore, each fault type included 450 samples; 300 samples and 150 samples of which were respectively used as training and testing samples. Detailed information on the bearing datasets is provided in Table 2.
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 3 Results
### Back End and Front End  
The DSS is a comprehensive framework that leverages multiple technology layers to access data and computational resources. It provides users with advanced analytical tools and delivers results through a web-based interface (Figure 3).  
The front end consists of several applications built with HTML and JavaScript, utilizing various libraries and frameworks to present tools tailored for users. Maps are generated using MapWall (vers. 0.05+4), a framework developed by CRS4 for creating web-GIS dashboards based on the OpenLayers library (vers.6.5.0), while charts are created with the Chart.js library (vers. 4.3.0).  
The back end is built on a Node.js infrastructure with the Express framework and provides a set of REST APIs to the front end, acting as a bridge between user interfaces and the Data Processing Layer. This layer manages data handling and model execution, using ETL (extract, transform, load) procedures to generate input datasets for AERMOD. Data are stored in a PostgreSQL database and a file system repository, with ETL procedures implemented in Python (vers. 3.6.9).  
When a user designs a new scenario via the web interface, the back-end processes the request, initiating a pipeline to create the necessary input data for AERMOD based on user specifications. Once the data preparation phase is complete, the input environment is transferred to the HPC layer to execute the AERMOD model. Upon completion, the output is processed through another ETL procedure to load the simulated data into the database and generate heatmaps, which are stored in the file system repository. Finally, the  
Figure 3: The AERQ platform integrates robust hardware and software components designed for advanced air quality analysis. Its physical infrastructure consists of a web server for user access, a high-performance computing (HPC) cluster for executing computationally intensive tasks, and a data warehouse that securely stores large datasets, including environmental, meteorological, and traffic data. The platform’s processing stack leverages the HPC environment to efficiently access and process these datasets, enabling the simulation and analysis of complex air quality scenarios. This setup allows for rapid data handling and high-resolution modeling, supporting both real-time and historical scenario execution and analysis. The platform accommodates two types of users with different access privileges. Only authorized users can utilize the job-on-demand interface to prevent unintentional resource wastage or disruption to other users’ tasks. Public users, like citizens, can access the platform to view pre-run scenarios and reports.  
scenario catalog is updated, making results available for visualization (GIS maps, graphs, and reports) in the front end.  
The front end of the system features both the Job-demand App and the Visualization App.
# Numerical Simulation of Natural Gas/Hydrogen Combustion in a Novel Laboratory Combestor
## 2 Materials and Methods  
### Combustion Chamber  
The combustion chamber investigated in the present work is shown in Figure 1. It is a vertical quartz-glass cylinder wrapped in a thick glass-fiber blanket to provide insulation. The inner diameter and the length of the chamber are equal to 100 mm and 300 mm, respectively. The thickness of the wall of the cylinder is 10 mm, and the insulation is 30 mm thick. The burner features a central tube, coaxial with the cylindrical chamber, surrounded by an annular swirler with vanes inclined at 45\({}^{\circ}\) to the axial direction. The burner head is elevated, positioned inside the chamber at a height of 25.8 mm above the base, resting atop a truncated cone.  
The chamber is designed for both primary and secondary air injections. Primary air enters through the central tube of the burner and secondary air is supplied via a top-mounted plenum. This plenum contains eight holes, each 3 mm in diameter, positioned 2 mm from the chamber's inner wall, as illustrated in Figure 1. During operation, primary air and fuel are introduced through the swirler and the central tube of the burner, respectively. The fuel is a mixture of natural gas and hydrogen. The properties of methane (the main component of natural gas) and hydrogen relevant to the present work are listed in Table 1.  
Figure 1: Schematic of the combustion chamber.  
Limited experimental data are available for this combustor chamber. After ignition, the chamber operated for 1 h to achieve stable conditions and minimize transient heat losses before measurements were conducted. The thermal input (5 kW) was maintained unchanged during the experiments. The measurements of temperature and species concentration were conducted using probes mounted on a movable arm, inserted through the exit section of the chamber. Local mean temperatures were recorded using 76 \(\upmu\)m diameter fine-wire platinum/platinum-13% rhodium (type R) thermocouples. Gas concentrations of O\({}_{2}\), CO\({}_{2}\), CO, and NO were measured using a stainless-steel, water-cooled probe. The gas species data exhibited high repeatability, with an average standard deviation of 5% from the mean. The experimental setup, the temperature measuring system, and the gas sampling system are identical to those described in [PERSON] et al. [36]. Further details on the measurement procedures can be found in [PERSON] [37].
###### Abstract  
Quantification of water carbon fluxes in rice paddies and analysis of their causes are essential for agricultural water management and carbon budgets. In this regard, two tree-based machine learning models, which are extreme gradient boosting (XGBoost) and random forest (RF), were constructed to predict evapotranspiration (ET), net ecosystem carbon exchange (NEE), and methane flux (FCH\({}_{4}\)) in seven rice paddy sites. During the training process, the k-fold cross-validation algorithm by splitting the available data into multiple subsets or folds to avoid overfitting, and the XGBoost model was used to assess the importance of input factors. When predicting ET, the XGBoost model outperformed the RF model at all sites. Solar radiation was the most important input to ET predictions. Except for the KR-CRK site, the prediction for NEE was that the XGBoost models also performed better in the other six sites, and the root mean square error decreased by 0.90-11.21% compared to the RF models. Among all sites (except for the absence of net radiation (NETRAD) data at the JP-Mse site), NETRAD and normalized difference vegetation index (NDVI) performed well for predicting NEE. Air temperature, soil water content (SWC), and longwave radiation were particularly important at individual sites. Similarly, the XGBoost model was more capable of predicting FCH\({}_{4}\) than the RF model, except for the IT-Cas site. FCH\({}_{4}\) sensitivity to input factors varied from site to site. SWC, ecosystem respiration, NDVI, and soil temperature were important for FCH\({}_{4}\) prediction. It is proposed to use the XGBoost model to model water carbon fluxes in rice paddies.  
Keywords:evaptranspiration; net ecosystem carbon exchange; methane flux; extreme gradient boosting (XGBoost); random forest (RF) +
Footnote †: journal: _Sustainability_
# Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement
## 4 Results
### Model-Configuration Study
#### 4.2.3 Effectiveness of Different Components  
Since weakly supervised localization and deep metric learning driven by fuzyzing similarity based on triplet loss are time-consuming, we analyzed the influence of different components of our model in Table 4. When weakly supervised localization is used alone, our approach achieves the accuracy of 84.63% and 91.84% on CUB-200-2011 and Cars-196, respectively. Considering that neither object nor part annotations is used, it is a promising result. This suggests that our weakly supervised localization is effective since we obtained better accuracy with only 0.4 M parameter increment. The \"B-CNN+loss function\" improved classification accuracy and mAP by distinguishing highly similar subcategories, which yielded 0.97% and 0.89% improvement compared with the result of \"B-CNN\" on CUB-200-2011 and Stanford Cars-196, respectively; the number of parameters also increased by 15.05 M as the triplets brought a lot of extra parameters. The computational complexity of the network also increased significantly when we combined these two methods, which can be seen intuitively from the last column of Table 4. Through analysis of classification results, we find that our method has an advantage in distinguishing subcategories with significant similar appearance. However, we still had a high classification error rate for some subcategories. This was caused by two factors: some subcategories in CUB-200-2011 could only be distinguished by habitat and voice rather than the appearance, and some images in the dataset were incorrectly labelled. The large amount of computational overhead is another shortcoming of our model, which needs to be solved in the next stage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CUB-200-2011**} & \multicolumn{2}{c}{**Cars-196**} \\ \hline
**Method** & **Accuracy** & **mAP** & **Accuracy** & **mAP** & **Parameter Increment** \\ \hline B-CNN & 84.00\% & 81.70\% & 91.20\% & 88.90\% & 0 \\ B-CNN + localization & 84.63\% & 83.90\% & 91.84\% & 91.04\% & 0.4 M \\ B-CNN + loss function & 84.97\% & 85.60\% & 92.09\% & 92.75\% & 15.05 M \\ Ours & **85.31\%** & **86.75\%** & **92.43\%** & **93.64\%** & 16.6 M \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification accuracy and mAP of different models on Cars-196 and CUB-2011, as well as the additional computation of these methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **CUB-200-2011** & **Cars-196** \\ \hline softmax & 84.63\% & 91.84\% \\ SVM & **84.70\%** & **91.92\%** \\ k-NN (k = 3) & 65.73\% & 71.49\% \\ k-NN (k = 4) & 67.88\% & 73.96\% \\ k-NN (k = 5) & 67.96\% & 74.08\% \\
## 3 Results
### Sfm vs. TLS  
Figure 12 shows an overlay between the 3D model generated by SfM and the point cloud obtained with TLS (in red). It reveals areas in which there is no representation of the points obtained with TLS or, more precisely, that they remain hidden under the SfM model. It is observed in Figure 12 that all the points measured by TLS on the smooth marble surface corresponding to the sump of the mosaic are not shown in the model. Figure 13A shows an enlargement of this area. Figure 13B shows an elevation showing that the points taken with TLS are located below the model, with GCP406 located on the surface of the sink as a reference.  
Figure 10: Results from SfM (**A–D**).  
Figure 11: Point cloud obtained with TLS.  
Figure 12: SfM model vs. TLS point cloud.  
Figure 13: TLS points representation on SfM model on marble sink area. (**A**) 2D representation; (**B**) Altimetric profile.  
It is clear that there is a difference in elevation in several areas, so it was considered necessary to perform a control measurement using a method that was not influenced by materials or geometric layout. For this purpose, the same TCR705 total station used for the georeferencing of the model was used. This total station was used to measure points of the mosaic with the help of a mini prism, so that the orientation or characteristics of the materials would not affect the measurement in any case. A total of 233 points were taken with a wide variety in location, material, degree of inclination, and color, due to the difference between shades represented in the tesserae of the mosaic. This method offered an accuracy within the range provided by the total station used, i.e., \(\pm\)2 mm + 2 ppm.  
The comparison was made with a selected reduced area of the mosaic, (see Figure 13), which contains all the elements where the main divergences were found (sump, baseboard, etc.).  
To obtain the required area from the data obtained with TLS, the area of interest was simply cut out of the point cloud obtained from the whole mosaic. In order to present the chosen mosaic area with SfM, a new project with a total of 69 photographs was made. Of the 233 GCPs measured with the total station, 18 were used, as they were marked with stickers, so that identification was quick and accurate in the photographs (see Figure 14).
# Low-Computational Extended Orthogonal Matched Filter Structure for Multiuser Detection
## 4 Performance Evaluation and Discussion
### Results and Discussion  
#### 4.2.1 Mudt  
The parameters of MUDT are shown in Table 3. In MUDT, it is assumed that desired users 1 and 2 have the same transmission power since each user has almost the same conditions. In addition, the signal arrival angle of desired user 1 is set to \(0^{\circ}\), and that of desired user 2 is set to \(180^{\circ}\), which is the opposite side of desired user 1. It is also assumed that the signal arrival angles of the interfering users are evenly spaced at \(\pm 180^{\circ}\) and their transmission powers are equal. Figure 4 shows a comparison of the squared errors of desired users 1 and 2 in the proposed system. The squared error decreases as the number of updates of the NLMS algorithm increases for both users. In addition, both squared errors are kept below \(10^{-2}\). Furthermore, both squared error performances are confirmed to be similar. In other words, both users can be detected while suppressing the squared error.  
Figure 4: Squared error of desired users 1 and 2 as a function of the number of normalized least mean square (NLMS) algorithm updates. The blue and green lines show the results of desired users 1 and 2, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Detail** \\ \hline Channel Model & Additive white Gaussian noise channel \\ Modulation & BPSK and DSSS \\ Spreading sequence & Gold sequence \\ Spreading factor & 31 \\ Carrier frequency & 760 (MHz) \\ Transmit power & 1 (desired users 1 and 2), DIR –30 (dB) equally divided \\  & (interference users) \\ Energy per bit to noise power spectral density ratio, \(E_{b}/N_{0}\) & 60 (dB) \\ Number of interference users & 30 \\ Number of antenna elements & 8 \\ Direction of arrival & 0 (degrees) (desired user 1), 180 (degrees) (desired user 2), \\  & equidistant to 180 (degrees) (interference users) \\   Information data length & 30,000 (bits) \\ Step size of NLMS algorithm & 0.01 \\ Array antenna weight vector & Winner solution \\ Synchronization, normalization & Ideal \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multiple Users Detection Test (MUDT) parameters.
## Appendix B Parameter Estimation for Distribution Functions
### Constructing the Dependency Matrix  
To establish the correct computation order, the model constructs a binary dependency matrix. Each entry D_(i,j) indicates whether parameter i depends on parameter j:  
D_(i,j) = 1, if parameter i depends on parameter, otherwise D_(i,j) = 0 This matrix is built by analyzing user-provided formulas:  
* Hard Limit Rules--conditions defining when a parameter can take specific values.
* Linking Equations--expressions computing one parameter based on others.  
An example of a dependency matrix is as follows:  
\begin{tabular}{l l l l l l} \hline \hline
**Parameter** & **Age** & **Sex** & **Menopause** & **SBP** & **Hypertension** \\ \hline
**Age** & 0 & 0 & 0 & 0 & 0 \\ \hline
**Sex** & 0 & 0 & 0 & 0 & 0 \\ \hline
**Menopause** & 1 & 1 & 0 & 0 & 0 \\ \hline
**SBP** & 1 & 0 & 0 & 0 & 0 \\ \hline
**Hypertension** & 0 & 0 & 0 & 1 & 0 \\ \hline \hline \end{tabular}  
* Menopause depends on age and sex.
* SBP depends only on age.
* Hypertension depends on SBP.
# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 3 Material and Method
### Deep Learning Architectures
#### 3.3.2 Long Short Term Memory  
Long-Short term memory is seen as an advanced model of recurrent neural networks. Recurrent neural networks process the incoming data at once and remember the previous step [37]. RNNs are also an effective model for sequential tasks, and can give good results even in non-linear structures thanks to remembering previous information [38]. However, [PERSON] and [PERSON] [39] suggested LSTM to eliminate the existing Vanishing Gradient problem and deficiencies in RNNs. Thus, the existing problems in RNNs were resolved with LSTM.
## 3 Materials and Methods
### Augmented Reality (AR) Application  
Since the focus of the research was on evaluating users' perceptions of blended 3D models of artistic sculptures within a real public space environment, the primary purpose of the AR application was to provide an immersive experience that was easy to use. The main requirements for the AR application were:  
* Ability to create a mobile AR application;
* Able to access the AR app using a personal mobile device;
* Easy registration and tracking;
* No need to download or install the app;
* No manipulation with the 3D models required in order to simulate a real-world experience of static sculptures in space.  
For this purpose, we used Vectary AR [48], a 3D and augmented reality platform designed for creating Web AR experiences. Vectory AR runs on ARKit and ARCore, the frameworks developed by Apple and Google, respectively, to enable augmented reality experiences on mobile devices. It allows for the creation or import of 3D models and generates links or QR codes to activate the AR experience without requiring application downloads or installations. Due to its simplicity, Vectary AR is often used to embed 3D models or AR content into websites. It utilizes a WebXR (Web Extended Reality) system for registration [49], which is a set of web-based APIs that allow the creation of augmented and virtual reality experiences directly within a web browser, without the need for additional plugins. This registration process typically involves using a compatible web browser and an AR-capable device to interact with the AR content in real-time.  
In this study, Vectary Web AR was used to display each sculpture as a digital 3D model within the real environment, with access to the AR experience provided through links or QR codes generated for each model. The mobile AR application was built for both Android and iOS operating systems, ensuring compatibility with most mobile devices. A markerless system was used, while a physical concrete plate served as a plane to position and fix the 3D model of the sculpture within the predefined space in the real-world environment. The concrete plate was positioned at the center of the outdoor space designated for the AR exhibition, serving as a detection feature for overlaying the 3D models of the sculptures in AR. Users accessed the application on their personal mobile devices by scanning QR codes or opening provided links, which directed them to the specific 3D model of each sculpture. Once the 3D model was placed on the feature, users could move around it and view it from various angles and distances.