esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of Magnetic Mill Productivity
## 3 Problem Statement  
The productivity of a mill performing the functions of grinding the working substance with the help of a magnetic field is determined by efficiency of conversion of the energy consumed into energy spent on commuting this substance. The transmission mechanism and stages of energy conversion, as well as the type and intensity of dynamic interactions of the substance with the millstones, depend on the design of this equipment and the method of forming the dispersive force. Therefore, in order to theoretically assess the productivity of the MM, it is necessary to establish a deterministic interrelationship between the design parameters of the mill (dimensions, magnetic properties of materials, etc.) and the quantitative indicators of the above-mentioned effort.  
The analysis of publications showed that theoretical research methods have particular shortcomings, namely:  
* existing MM project design methods either do not relate structural parameters to the performance of the grinding/mixing process at all or do it using empirical dependencies obtained by processing experimental data and for a rather narrow range of variations of structural parameters;
* optimization methods of technological process control systems using MM are based on the results of mathematical modeling that consists of statistical processing of experimental data;
* insufficient attention is paid to developing online methods for evaluating the quality of grinding products directly during processing or immediately after it. Among the design flaws inherent in EMMs, the following ones can be noted:
* salient-pole EMM inductors are characterized by an increased level of magnetic field dispersion, at the level of 20-40%, which negatively affects the efficiency of the energy conversion process as a whole;
* the EMM inductor works with a low power factor; consequently, it requires the use of compensating devices.  
**The purpose of the research** is to identify indicators directly affecting the productivity of the grinding/mixing process; to develop a method of quantitative assessment of these indicators depending on the size of the mill working chamber, the number and size of the millstones and parameters of the inductor.  
**The object of the research** is the processes of dynamic interaction between ferromagnetic mills, the substance being ground/mixed and the working chamber, in the rotating magnetic field inside this chamber.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Evaluation Metrics
#### 4.3.2 Detection Time  
The notion of detection time plays a critical role in the context of data stream drift detection since it directly impacts the ability of a detector to process incoming data streams in real-time. Applications requiring the timely detection of concept drifts need the use of fast and efficient detectors. The detection time for a data stream drift detector corresponds tothe time required to process each incoming instance, and it is influenced by various factors, such as data complexity, data stream size, detector implementation specifics, and available computational resources. Given the trade-off between accuracy and detection time, careful consideration of both factors is vital in selecting and optimizing the data stream drift detectors. Therefore, an evaluation of the detection time of detectors is essential to ensure their ability to operate in real-time and fulfill the requirements of the given application.  
For our studies, the detection time of each algorithm was measured by assessing the amount of time it takes for the algorithm to process all the bits of each data stream.
# Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment
## 4 Numerical Results and Discussion  
All the calculations in this chapter are performed by the UQLab module in Matlab [26]. The sample data points are obtained from simulations by CST Studio Suite [27]. In general, one group with 5 variables (radius of bone \(B\), reflector to skin distance \(R\), thickness of fat \(F\), thickness of muscle \(M\), and implantation depth \(I\)) 243 samples in total are analyzed. The chosen observable output in the electromagnetic problem is the gain of the antenna. The ranges for each variable are presented in the following Table 3. In these variable ranges, the S11 value is almost immune to changes and stays at around \(-16\) dB.
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
## 3 Forward Error Correction  
Appropriate techniques are necessary to overcome the problem of errors that are introduced during the transmission which occur due to inter-symbol interference (ISI), the multipath phenomenon, and noise. FEC is used for combatting this issue in a communication system. FEC techniques add some redundancy to the data which enables the receiver to detect and correct errors. BCH and RS codes are FEC techniques which work by appending extra data at the end of each message, known as a codeword (see Figure 1). This section shows some preliminaries of BCH and RS codes.
## 3 Dataset Description
### HFAVD Dataset Structure
#### 3.3.4 Oxygen Saturation  
The estimation of oxygen saturation in this study was conducted based on the method proposed by [43]. The procedure involved the following steps: (a) Extraction of face region: The face region was initially extracted by using the 3 DDFA_V2 framework. This facilitated the isolation of the relevant area for subsequent analysis. (b) Input to VGG19: The extracted face region was then used as input for the VGG19 model, which had been pre-trained on the ImageNet dataset. This allowed for the extraction of relevant features from the face region. (c) Utilization of XGBoost: The output obtained from VGG19 was subsequently fed into an XGBoost algorithm. XGBoost is a machine learning framework that combines multiple decision trees to make predictions. In this case, it was employed to obtain the estimation of the oxygen saturation value. Following the training phase, the model was subjected to testing by using two distinct datasets. The derived mean absolute error (MAE) for the first test set was found to be 1.17%, while for the second test set, it was marginally lower, at 0.84%. The primary challenge encountered in training this model pertained to the limited number of samples depicting SpO2 levels below 85. This scarcity poses a significant obstacle for the models, particularly when examining subjects with specific health conditions that are associated with low SpO2 levels.
## 2 Model Description
### Wind Speed Extrapolation Method Based on Atmospheric Stability  
The power law does not fully consider the effect of atmospheric stability on wind shear. According to the characteristics of atmospheric stability, the wind speed extrapolation (WSE) method based on the atmospheric stability is proposed. The measured dataset by wind towers is named as dataset \(Q\), including time, wind speed, wind direction and wind direction standard deviation. The low wind speed data less than the cut-in wind speed in the dataset \(Q\) is first removed to obtain the filtered dataset \(W\). Then the dataset \(W\) is classified into different stability conditions using the RG, MO, WSR, and WDSD methods. After that, Equation (3) is used to calculate the average wind shear exponent using the dataset \(W\) under each atmospheric stability condition. Finally, using the wind speeds at the lower heights in dataset \(Q\), the higher level wind speed is predicted by Equation (2).  
Four kinds of atmospheric stability classification methods, RG, WDSD, WSR and MO are adopted to develop four new methods, namely WSE-RG, WSE-WDSD, WSE-WSR and WSE-MO, for the wind speed extrapolation.
# Evidence-Based Investigation of Coronary Calcium Score in Cardiac Computed Tomography
## 4 Discussion
The occurrence of changes from Mild to Moderate in both groups implied that noise in overweight and obese patients may introduce inaccuracies into the calcium score, although there were no changes at higher levels. This inaccuracy can affect the interpretation, possibly leading to unnecessary additional examinations. The results of the phantom study showed a tendency for the calcium scores to be higher with a bolus than without, which is consistent with the results of the patient study. The calcium scores of the phantoms below 130 HU erroneously indicated the presence of calcium, as in the patient study. The calcium scores of phantoms above 130 HU showed a tendency to increase in the presence of a bolus. In addition, calcium scores tended to increase as the applied tube voltage increased. However, the rate of increase decreased as the effective thickness increased. This suggests that using a tube voltage of 120 kV may result in a smaller change in the calcium score due to effective thickness. In addition, thinner slices were associated with higher calcium scores, regardless of effective thickness. Slice thickness had a greater impact than tube voltage on calcium scores. When obtaining automated calcium score measurements, more detailed anatomical information can be collected from thinner slices [38], while simultaneously minimizing the impact of noise through denoising techniques. Furthermore, as the effective thickness increases, higher tube voltages can be employed to reduce the uncertainty associated with calcium scoring. With ongoing technological advancements, it is to be expected that the precision of calcium scoring will be enhanced through the aforementioned methods, particularly in the context of automated calcium scoring, which employs deep learning technology. This study has some limitations. First, the noise used to generate hypothetical overweight and obese patient images in the patient study may have been caused by factors other than effective thickness. This is because we could not arbitrarily increase the effective thickness of a single patient, and it is against radiation ethics to irradiate a single patient multiple times. Second, the study does not include demographic and body composition data, such as age, sex, and body fat distribution. The current simple calcium scoring method is limited by its inability to fully take into account pathophysiologic information. Finally, only one calcium measurement program was used. Different programs vary in the measurement of calcium, which may lead to differences in calcium scores. Evaluation of these different programs would increase the generalizability of the results of this study.
# Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods
## 5 Conclusions  
Given the urgent need for user-located applications in indoor environments, especially very large and nested environments such as department stores, hospitals, and universities, researchers are now increasingly studying indoor location without the need to use satellite signals. Reviewing the results obtained by other researchers showed that the existence of high error in different algorithms has created the need to reduce this error by further investigation using creative methods, and by providing an operational example in the real environment. As shown in this study, the use of combined methods could obtain better results; in the final result in the HYBRID-MAPPED method, the error rate was reduced by about 18% compared to the samples performed in the world.  
One way that can reduce the location error in indoor environments in the fingerprint method is to improve the training data over time using the system. This means that users who are in the environment can add the results obtained from the algorithms in the online section to the training data to increase the amount of training data. Applying the most frequent filter based on subscription can provide users with more reliable results. It is also recommended to use mobile phone motion sensors to improve the location of indoor environments in order to predict the user's movement and increase the accuracy of location.  
Conceptualization, J.A.; Methodology, F.K.; Software, F.K.; Validation, J.R.; Formal analysis, F.K.; Investigation, F.K. and J.R.; Resources, J.R. and J.A.; Data curation, F.K.; Writing--original draft, F.K.; Writing--review & editing, J.R. and J.A.; Supervision, J.R. and J.A.; Project administration, J.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Link Pruning for Community Detection in Social Networks
## 3 Our Proposed Framework
### Algorithm for Clustering Using Link Pruning
#### 3.2.2 Theoretical Analysis  
The stochastic block models (SBMs) are graph generation models that reflect community structure [27; 28]. This model creates a block that comprises \(N\) nodes based on the numbers of \(N/K\) nodes and \(K\) blocks (communities). Subsequently, it generates a link of nodes in the same block based on the probability of \(p_{in}\) and that of nodes in different blocks based on the probability of \(p_{out}\). Mixing is utilized to evaluate the degree of strength of the community structure. The mixing process is defined by a rate of nodes connected in the same community among the entire links. A simple linear system can adjust the values of \(p_{in}\) and \(p_{out}\) to enable the generated graph to contain the average number \(\langle k\rangle\) of connections and mixing \(\mu\). The result of the theoretical analysis is presented as follows. When \(p_{in}=MP_{out}\), the average value of \(|N(u)\cap N(v)|\) is \(\frac{K}{N}p_{out}^{2}(M^{2}+K-1)\) for links connected in the same community and \(\frac{K}{N}p_{out}^{2}(2M+K-2)\) for links connecting different communities. Therefore, the former value is higher by approximately \(\frac{M}{2}\) times the latter value. Because mixing is low, \(M\) increases. Basically, a difference in the number of common triangles will increase as the strength of the community structure increases. The same conclusion can be deduced for other link attributes in the same way.
# Development of a Radio-Frequency Quadrupole Accelerator for the HL-2A/2M Tokamak Diagnostic System
## 3 RF Conditioning  
### Coupling Factor for Setup  
The RFQ requires a total power of about 71 kW, the cavity power (\(P_{cav}\)) is 56 kW (20% redundancy has been considered), the beam power (\(P_{beam}\)) is 15 kW. The RF power is fed by a magnetic loop coupler and a 120 kW RF amplifier. According to the definition of the coupling factor, the factor is given by:  
\[\beta=\frac{Q_{0}}{Q_{ext}}=\frac{P_{ext}}{P_{cav}}=\frac{P_{cav}+P_{beam}}{P_{ cav}} \tag{1}\]  
Therefore, the coupling factor needed to be set as 1.27, and the input reflection coefficient S\({}_{11}\) is \(-\)18.55 dB.  
The cavity power was measured by reading the scope amplitude of the RF pick-up (50 \(\Omega\) impedance). The coupling factor of the pick-up can be measured by a two-port network composed of a coupler and a pick-up, as shown in Figure 6. The coupling factor of the pick-up is given by:  
\[\beta_{pickup}=\frac{P_{pickup}}{P_{cav}} \tag{2}\]  
The coupling factor \(\beta_{pickup1}\) that we measured by the network analyzer is \(6.15\times 10^{-7}\), which is \(-\)62.2 dB. In the case of neglecting coaxial cable line loss, the dependence of the cavity power on the scope amplitude is given by:  
\[P_{cav}=\left(\frac{V_{pp}}{2}\right)^{2}\frac{1}{2\times 50}\times 10^{6.22} \times 10^{-3} \tag{3}\]  
where \(P_{cav}\) is in kW and \(V_{pp}\) is the peak-to-peak voltage of the scope.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Ion species & D\({}^{+}\) \\ RFQ type & 4-vane \\ Frequency [MHz] & 162.5 \\ Beam current [mA] & 10 \\ Input normalized \(\varepsilon_{t}\) [\(\tau\cdot\)mm\(\cdot\)mrad] & 0.20 \\ Input energy [keV] & 40 \\ Output energy [MeV] & 1.52 \\ Duty cycle [\%] & 1 \\ Voltage [kV] & 70 \\ Length [m] & 2.191 \\ Peak field & 1.52 kp \\ Minimum aperture radius [cm] & 0.270 \\ Average aperture radius [cm] & 0.460 \\ Vane tip radius [cm] & 0.345 \\ Synchronous phase & \(-90^{\circ}\) to \(-25^{\circ}\) \\ Maximum modulation factor & 2.2 \\ Output normalized \(\varepsilon_{t}\) [\(\tau\cdot\)mm\(\cdot\)mrad] & 0.208 \\ Output longitudinal normalized \(\varepsilon_{t}\) [MeV\(\cdot\)deg] & 0.060 \\ Transmission efficiency [\%] & 99.0 \\ Radius of cavity [mm] & 173.82 \\ RF power consumption [kW] & 45 \\ Specific shunt impedance [k\(\Omega\cdot\)m] & 240 \\ Mode separation [MHz] & 3.1 \\ Intrinsic Q-value & 14,722 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main physics parameters of the RFQ.
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 5 Conclusions  
This paper presents the localization method of an AUV in environments, such as harbors or ports, where passing ships can exist. Assume that source levels and trajectories of the passing ships are known a priori. This paper introduces the AUV localization method based on bearing-only measurements of passing ships moving along known trajectories (planned routes). The RPEKF is used to reduce the range estimation error in bearing-only measurements.  
As far as we know, this paper is novel in presenting the underwater localization method using RAM and sound measurements of ships moving along known routes. If we do not use the proposed localization, the AUV needs to depend on IMU-only localization. It is inevitable that the IMU-only localization leads to the integration of localization error as time goes on. The simulation results in Sections 4.1 and 4.3 show that using the measurements of a single ship can improve the localization accuracy, compared to IMU-only localization.  
Moreover, the simulation results in Sections 4.1 and 4.2 show that using measurements from multiple ships gives better localization accuracy, compared to the case where the measurement of a single ship is used. As our future works, we will verify the effectiveness of the proposed localization method using experiments with a real AUV.  
There may be a case where there exist many ships of the identical type (for example, container ships). In this case, it is not trivial to identify a ship by processing the sound of the ship. In the case where a ship is not correctly identified, we may generate false measurements, similar to clutters in BOTMA problems [43]. In the future, we will tackle the case where there exist many ships of the identical type.  
The proposed localization scheme can be further applied for localization of various autonomous vehicles, such as an Unmanned Aerial Vehicle (UAV). The UAV can measure the directions of signals emitted from airplanes (or sites) of which trajectories (locations) are known a priori. Then,  
Figure 6: Consider the case where the Inertial Measurement Units (IMU)-only localization is used. We plot \(meanE_{k}\), \(maxE_{k}\), and \(minE_{k}\) with respect to sample-index \(k\). Since no ship is used to fix long-term drift, the estimation error does not decrease as time goes on. Compared to Figures 2 and 4, the estimation error increases.  
bearing direction measurements can be used for localization of the UAV. This localization scheme can be used when Global Positioning Systems are not available for the UAV.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019 RIF1A1063151).  
The author declares no conflict of interest.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Variables** & **Mean \(\pm\) SD** & **M** & **Min** & **Max** & **Skew** & **Kurt** & **MaxD** \\ \hline Trunk fat mass (kg) & 4.79 \(\pm\) 2.05 & 4.80 & 1.30 & 10.30 & 0.22 & \(-\)0.44 & 0.06 \\ Arms fat mass (kg) & 0.99 \(\pm\) 0.28 & 1.00 & 0.40 & 1.80 & 0.17 & \(-\)0.14 & 0.12 \\ Legs fat mass (kg) & 3.21 \(\pm\) 0.84 & 3.30 & 1.10 & 7.10 & 0.82 & 3.85 & 0.10 \\ Biepicondilar humerus width (cm) & 6.97 \(\pm\) 0.39 & 6.95 & 6.00 & 7.70 & 0.05 & \(-\)0.60 & 0.08 \\ Biepicondilar femur width (cm) & 9.33 \(\pm\) 0.50 & 9.35 & 7.95 & 10.65 & \(-\)0.31 & 0.45 & 0.08 \\ Upper arm girth flexed and tensed (cm) & 35.69 \(\pm\) 1.80 & 35.65 & 30.65 & 41.30 & 0.13 & 0.80 & 0.08 \\ Calf girth (cm) & 38.37 \(\pm\) 1.59 & 38.20 & 34.00 & 42.65 & \(-\)0.09 & 0.14 & 0.06 \\ Sum of skinfolds (mm) & 45.13 \(\pm\) 12.64 & 44.10 & 21.30 & 85.13 & 0.94 & 0.96 & 0.10 \\ Triceps skinfold (mm) & 9.31 \(\pm\) 2.65 & 8.90 & 4.90 & 18.70 & 0.96 & 1.11 & 0.09 \\ Subscapular skinfold (mm) & 11.32 \(\pm\) 3.70 & 10.40 & 6.10 & 28.20 & 1.95 & 5.68 & 0.13 \\ Supraspanale skinfold (mm) & 13.48 \(\pm\) 5.65 & 12.10 & 5.00 & 34.70 & 1.15 & 1.57 & 0.11 \\ Medial calf skinfold (mm) & 11.02 \(\pm\) 3.83 & 10.67 & 4.20 & 21.90 & 0.68 & 0.11 & 0.07 \\ Endomorphy rating & 3.23 \(\pm\) 0.99 & 3.03 & 1.35 & 6.31 & 0.78 & 0.50 & 0.08 \\ Mesomorphy rating & 4.81 \(\pm\) 0.90 & 4.70 & 2.74 & 7.56 & 0.43 & 0.35 & 0.08 \\ Ectomorphy rating & 2.25 \(\pm\) 0.86 & 2.25 & 0.56 & 4.46 & 0.17 & \(-\)0.46 & 0.05 \\ \hline \hline \end{tabular} Notes: SD—standard deviation, M—median, Min—minimum value, Max—maximum value, Skew—Skewness, Kurt—kurtosis, MaxD—maximum distance between relative cumulative theoretical frequency (normal) and relative cumulative empirical frequency obtained by measuring, and limit value of KS test for N = 97 is 0.14.  
\end{table} 
Table 1: _Cont._Table 3 presents the classification of the ILCA 7 sailors according to the somatotype category. The frequency and percentage of each somatotype category were calculated for the total sample. Out of the 13 possible somatotype categories, elite ILCA 7 sailors fit eight. Just over 75% of the total sample of elite sailors fit the somatotype categories with the dominant mesomorphic component, 41.24% of which fit the endomorphic mesomorph category. Figure 1 depicts a graphic representation of the somatotype ratings of ILCA 7 class sailors divided into three groups according to their level of general competitive success: _higher level_ (square), _medium level_ (rhomb), and _lower level_ (triangle).