esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis Results and Discussion  
### Additional Rail Axial Stress  
The additional rail axial stress of the slab track due to the track-bridge interaction should not exceed 92.0 MPa for both compression and tension [16]. A sequential analysis was performed for the additional rail axial stress due to thermal and train loads when the anchors were not installed on the QHT. Figure 7 shows the additional rail axial stress due to temperature and train loads depending on the friction coefficients between the track and the bridge.  
The additional rail axial stress due to temperature load increases with an increase in the friction coefficient (Figure 6(a)). The longitudinal bridge expansion due to a temperature change is delivered to the rail by friction and fastener springs, thereby causing additional rail axial stress. The additional axial stress on rails decreases as the longitudinal resistance from friction gets smaller, as transferred displacement from the friction layer gets smaller.  
Figure 6(b) illustrates the additional rail stresses generated by applying the train load while considering the deformed state due to the temperature load based on a sequential analysis scheme. When the friction coefficient equaled 0.3 or higher, the same level of rail stress was generated. When the friction coefficient was 0.1, the rail stress increased because the track segment slipped from the train load. When the friction coefficient was 0.1, the stress from the train load was greater than that from the temperature load. Hence, the effect of the train load was dominant.  
In a conventional analysis of track-bridge interactions, an increase in rail stress due to the traction and braking loads of a train is caused by the drift of a bridge pier. A bridge pier was not taken into account for this analysis; therefore, a change in stress due to the train load should be small because only the bending of a bridge due to train vertical load and end rotation causes such a change. However, as shown in the analysis results, a great amount of additional rail stress was generated due to traction loads when the friction coefficient of the underside of the track slab was 0.1, which is very small and unlikely to happen in practice.  
If the bridge span is longer than the targeted bridge or if multiple bridges are adjacent to each other, the effect of traction loads can increase significantly, Therefore, it is recommended that the friction coefficient under the QHT should be maintained at 0.3 or higher before anchor installation. When the frictional coefficient may become excessively small, a low-speed operation should be guided so as to not induce traction/braking loads.  
Figure 7: Additional rail axial stresses.
# Iron-Rich Magnetic Coal Fly Ash Particles Induce Apoptosis in Human Bronchial Cells
###### Abstract  
Svalbard is an arctic archipelago where coal mining generates all electricity via the local coal-fired power station. Coal combustion produces a waste product in the form of particulate matter (PM) coal fly ash (CFA), derived from incombustible minerals present in the feed coal. PM \(\leq\)10 \(\upmu\)m (diameter) may be \"inhaled\" into the human respiratory system, and particles \(\leq\)2.5 \(\upmu\)m may enter the distal alveoli to disrupt normal pulmonary functions and trigger disease pathways. This study discovered that Svalbard CFA contained unusually high levels of iron-rich magnetic minerals that induced adverse effects upon human lungs cells. Iron is a well-characterised driver of reactive oxygen species (ROS) generation, a driving force for cell death and disease. CFA physicochemical characterisation showed non-uniform particle morphologies indicative of coal burnt at inefficient combustion temperatures. The bioreactivity (ROS generation) of PM\({}_{2.5/10}\) fractions was measured using plasmid scission assay (PSA, DNA damage) and haemolysis assays (erythrocyte lysis), with PM\({}_{2.5}\) CFA showing significant bioreactivity. CFA leached in mild acid caused a significant increase in toxicity, which could occur in CFA waste-stores. The CFA and leachates were exposed to a surrogate model of human bronchial epithelia that confirmed that CFA induced apoptosis in bronchial cells. This study shows that CFA containing magnetic iron-rich minerals mediated adverse reactions in the human lung, and thus CFA should be considered to be an environmental inhalation hazard.  
Keywords:bioreactivity; cytotoxicity; coal fly ash; particulate matter; leachate; iron-rich minerals; Svalbard +
Footnote †: journal: Appl. Sci. 2020, 10, 8368; doi:10.3390/app10238368
# Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
## 1 Introduction
[50] used SVM and Artificial Neural Networks (ANN) to classify hyperspectral images based on nitrogen application rates and weed management practices over a corn field and the results showed that SVM provided very low misclassification rates compared with ANN. In addition, they recommended the SVM technique for early stress detection in corn, which could aid in effective early application of site-specific remedies. [PERSON] et al. [51] applied MR, ANN, SVM, and RF algorithms on UAV RS data to predict the above ground maize biomass and their results showed that RF provided the most balanced results with low error. Monitoring within-field variability requires sufficient spatial and temporal resolutions according to crop type and targeted application [6; 18]. In the case of corn, which represents about 20% of Italian cereal cultivated area, more than 80% of corn fields have an area over 10 ha [52]. The Sentinel-2 satellite constellation provides images with a 10 m spatial resolution for red, green, blue, and near-infrared (NIR) bands, which is equivalent to 1000 pixels in a 10 ha field. Moreover, the corn growing seasons are spring and summer (April to September), which have fewer clouds compared to other seasons, and Sentinel-2 provides images with a 5-day revisit frequency [53]. Thus, Sentinel-2 provides images that have sufficient spatial and temporal resolutions for monitoring within-field variability in Italian corn fields. The purpose of this paper was to evaluate the potential of using different VIs derived from archived Sentinel-2 satellite images for identifying corn yield spatial variability within-field and compare results with three regression methods that have been proven to perform best in the recent literature: random forests (RF), support vector machines (SVM), and multiple regression (MR). The specific objectives were  
1. to examine different vegetation indices for corn yield estimation;
2. to define the suitable crop age for predicting yield variability within the field scale;
3. to develop a corn yield prediction model based on Sentinel-2 images and the best performing machine learning technique and compare the results with VIs.
# Computational Flow Dynamic Analysis in Left Atrial Appendage Thrombus Formation Risk: A Review
## 1 Introduction
The computational modeling process is carried out with the assistance of numerical analytic techniques and computational fluid dynamics (CFD). It can provide more quantitative data regarding blood flow and evaluate thrombosis risk [32]. Additionally, it can assess blood flow in specific regions containing LAA lobes. CFD has proved to be a valuable complementary tool for recommending ideal device sizing and placement for LAA occlusion device implantation [32]. In addition, CFD can be applied to better comprehend blood flow patterns following the LAA occlusion procedure and to estimate the risk of device-related thrombosis (DRT) for a given patient and device configuration. This study aims to review the CFD simulation studies that analyzed hemodynamic parameters and thrombus formation risk. To the authors' knowledge, there has been no published literature review on this subject to date.
## 5 Results and Discussions
### Influence of Impeller Speed  
Figure 18 suggests the three-dimensional view of solid volume fraction iso-volume and the solid volume fraction of vertical cross-section for different impeller speeds. Figure 19 exhibits the 3D solid volume fraction contour plots and cloud height. At a low impeller speed (i.e., 180 rpm), solid particles are basically deposited at the bottom of the tank, and the upper parts of the stirred tank are almost liquid. An increase in impeller speed decreases the accumulation region at the bottom of the stirred tank gradually. More and more particles are suspended and dispersed in the stirred tank, and the distribution of the solid particles is uniform. Only a few solid particles accumulate below the impeller and around the baffles at impeller speeds of 540 rpm, and more solid particles are pumped to a higher position. As can be seen from Figure 19, the cloud height gradually increases with an increase in impeller speed. According to [PERSON]'s criterion [38], the state of complete suspension is defined as no solid remains stationary on the vessel base for more than 1-2 s. [PERSON] [39] indicates that the cloud height reaching 90% or more of the liquid height can be considered to have reached the state of complete suspension. When the  
Figure 16: Influence of aperture parameters on cloud height. (**a**) Influence of aperture diameter, (**b**) influence of aperture ratio.  
Figure 17: Effect of liquid viscosity on local axial solid concentration profile.  
impeller speed reaches 420 rpm, the cloud height has reached 90% of the liquid height, which can be considered the state of complete suspension. There is no significant increment in cloud height with a further increase in the impeller speed.
# SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives
## 3 Features Quantification
### Calculation of the Feature Added Value  
The added value of a feature (\(O^{\prime}\)) is the product of the Feature's Category Importance (\(FCI\)) and the summation of the products of the Scores of the Quantified properties (\(SQp\)) and Importance (\(Io\)) (see Section 4.1) of the corresponding objectives. The algorithm allows the user to give priority to features that exist in another product of the same SPL, by adding a bias value (\(b\)) of 0.1 to the total score of the feature.  
\[O^{\prime}=FCI*\sum_{i}SQp*Io+\ b \tag{4}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Value** & **FSE Generated Results** \\ \hline Cost of Feature 1 & 10 & 10.30 \\ \hline Cost of Feature 2 & 97 & 100 \\ \hline Cost of Feature 3 & 1 & 1.03 \\ \hline Cost of Feature 4 & 100 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Example of values returned by Feature Selection Engine (FSE) for “Upto (Higher Better)”.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Quantification Procedure** & **Procedure Call** \\ \hline Equal and Above (Higher Better) & Proximity (\(max_{p}\), \(t_{p}\), \(v_{i}\), \(h\), \(b\)) \\ \hline Equal and Above (Lower Better) & Proximity (\(max_{p}\), \(t_{p}\), \(v_{i}\), \(l\), \(b\)) \\ \hline Between (Higher Better) & \begin{tabular}{c} \(Proximity\big{(}max_{p}\), \(t_{p}\), \(v_{i}\), \(h\), \(b\)) \\ \(Proximity\big{(}min_{p}\), \(t_{p}\), \(v_{i}\), \(h\), \(f\)) \\ \end{tabular} & \begin{tabular}{c} \(Proximity>t_{p}^{1}\) \\ \(Proximity\big{(}min_{p}\), \(t_{p}\), \(v_{i}\), \(h\), \(f\)) \\ \end{tabular} & \
\begin{tabular}{c} \(Proximity<t_{p}\) \\ \(Proximity>t_{p}^{1}\) \\ \(Proximity>t_{p}^{1}\) \\ \(Proximity\big{(}min_{p}\), \(t_{p}\), \(v_{i}\), \(l\), \(f\)) \\ \end{tabular} &
\begin{tabular}{c} \(Proximity>t_{p}^{1}\) \\ \(Proximity>t_{p}^{1}\) \\ \(Proximity<t_{p}^{2}\) \\ \end{tabular} \\ \hline Upto (Higher Better) & Proximity (\(min_{p}\), \(t_{p}\), \(v_{i}\), \(h\), \(f\)) \\ \hline Upto (Lower Better) & Proximity (\(min_{p}\), \(t_{p}\), \(v_{i}\), \(l\), \(f\)) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Proximity based formulas.  
For each Objective \"O\", the Importance Level values are represented as Multipliers \"Low = 0.25, Medium = 0.5, High = 0.75, and Critical = 1.0\".  
One exception to the above is when the quantified property score is 0 and the importance of the corresponding objective is Critical (4). In that case, the added value of the feature is set to \(-1\). This exception ensures that features that do not meet critical objectives are disqualified. For example, if we are looking for 3D models, we set the objective that the sub-string \"3D\" should exist in the property \"model type\". Then if the score of the quantified \"model type\" property is 0, it means that this is not a 3D model, hence this feature should be disqualified irrespective of the rest of its properties.
# Cybersecurity Risk Assessment: A Systematic Mapping Review, Proposal, and Validation
## 4 Analyze and Discuss the Results
### Coverage of Tools  
The risk assessment was divided into three sub-stages and six activities to compare the tools identified in the primary studies. These sub-stages and activities were selected based on the ISO/IEC 27005:2018 standard.  
* Risk identification sub-stage:
* Activity 1 was defined as identifying of the assets that must be part of critical business or organizational processes;
* Activity 2 was defined as the identification of the threats and the identification of existing controls;
* Activity 3 was defined as the identification of vulnerabilities and the identification of consequences;
* Risk analysis sub-stage:
* Activity 4 was defined as the assessment of the consequences and the assessment of the incident likelihood;
* Activity 5 was defined as the level of risk determination. It is the most important activity of the entire risk assessment stage;
* Risk evaluation sub-stage:
* Activity 6 was defined as the selection of an acceptable risk value. The acceptable or tolerable risk value defined in the context establishment stage (see ISO/IEC 27005:2018) was compared with the risk value obtained in Activity 5, and each risk was prioritized.  
The activities covered by the tools are shown in Table 8. Tools are listed in descending order by coverage. As a result, we identified 11 tools that cover all the risk assessment activities (and, thus, sub-stages).  
The most common activities covered by the risk assessment tools were Activity 5 (define a risk value), which was covered by all tools, and Activity 3 (vulnerability identification), which was covered by all the tools, except for the asset assessment methodological tool. Regardless of their focus, all risk assessment models identify the vulnerabilities to perform a cybersecurity risk assessment. Therefore, these two activities are the most common and relevant in cybersecurity risk assessment tools.  
When implementing a cyber risk assessment tool, it is necessary to identify which sub-stage or activity is targeted. Identifying sub-stage and activity coverage shows how to address the risks. Using a tool that only partially covers the risks can create a false sense of security certainty.  
Summary of the results of this subsection: (1) Eleven tools covered all the sub-steps of risk assessment. (2) Activity 5 (risk determination) was the most covered by the tools. (3) Activity 3 (vulnerability assessment) was the second most relevant to the tools. (4) Activity 6 (comparison of acceptable risk value and calculated risk) was the least considered by risk assessment tools.
# Metabolic Rate Measuring with Indirect Calorimetry for Thermal Comfort Evaluation
## 2 Methodologies
### Physiological Measurements  
The portable metabolic device (Figure 3a, MetaMax 3B, Cortex Ltd., Leipzig, Germany) was used to measure the MET of the subjects. Table 1 displays the internal sensor type, measurement range, and precision. The O\({}_{2}\) and CO\({}_{2}\) concentration and flow rate were calibrated before the experiments. When using the device, a specialized mask (Figure 3b) was required to cover the subject's mouth and nose, in order to collect the oxygen consumption (V\({}_{\text{O2}}\)) and carbon dioxide output (V\({}_{\text{CO2}}\)) of each breath. In general, energy in the body comes mainly from the oxidation of carbohydrates and fats. The amount of oxygen consumed and carbon dioxide produced by the oxidation of different energy substances varies. Therefore, the amount of carbohydrates and fats involved in oxidation can be obtained by measuring V\({}_{\text{O2}}\) and V\({}_{\text{CO2}}\), thus determining MET using the following equations [22]:  
\[\text{RQ}=\text{V}_{\text{CO2}}/\text{V}_{\text{O2}} \tag{2}\]  
\[\text{EE}=5.88\times(0.23\text{RQ}+0.77) \tag{3}\]  
\[\text{MET}=\text{EE}\times\text{V}_{\text{O2}}/\text{A}_{\text{DU}} \tag{4}\]  
where V\({}_{\text{O2}}\) is oxygen consumption, L/h; V\({}_{\text{CO2}}\) is carbon dioxide output, L/h; RQ is the respiratory quotient; EE is the energetic equivalent, W \(\times\) h/L; A\({}_{\text{DU}}\) is the subject's body surface area, m\({}^{2}\); MET is the metabolic rate, W/m\({}^{2}\).  
Figure 1: Climate chamber.  
Figure 2: (**a**) Testo 416, (**b**) UX100-011A.  
A heart rate tape (Figure 3c, Polar H10, Polar Ltd., Beijing, China, accuracy \(\pm\)1 bpm) was used to record the HR of the subjects every 10 s during the experiments.  
The temperature recorders (Figure 3d, DS1992L, Maxim Ltd., Wilmington, MA, USA, accuracy \(\pm\)0.5 \({}^{\circ}\)C) were used to measure the skin temperature of the subjects every 10 s during the experiments. Four temperature records were placed at the right upper arm, left chest, right thigh, and calf, respectively. The mean skin temperature (mTsk) is calculated by the following equation [23]:  
\[\text{mTsk}=0.3\times\text{T}_{\text{arm}}+0.3\times\text{T}_{\text{chest}}+0.2 \times\text{T}_{\text{thigh}}+0.2\times\text{T}_{\text{calf}} \tag{5}\]  
where \(\text{T}_{\text{arm}}\) is the skin temperature of the right upper arm, \({}^{\circ}\)C; \(\text{T}_{\text{chest}}\) is the skin temperature of the left chest, \({}^{\circ}\)C; \(\text{T}_{\text{thigh}}\) is the skin temperature of the right thigh, \({}^{\circ}\)C; \(\text{T}_{\text{calf}}\) is the skin temperature of the right calf, \({}^{\circ}\)C; mTsk is the mean skin temperature of the subject, \({}^{\circ}\)C.
# Measurement of Structural Loads Using a Novel MEMS Extrinsic Fabry-Perot Strain Sensor
## 5 Conclusions  
In this work, the extrinsic FP strain sensor was fabricated by MEMS technology, and it was applied to the load measurement for a small structure. Before the experiment, the sensor should be confirmed as being located within the uniform strain field. Experimental results show the sensor has a good linearity for the applied load (adjusted r-squared), with results of 0.99367, 0.9952, 0.99506, 0.99684, 0.99767, and 0.99143. The proposed sensor also exhibited a good hysteresis feature, and the sensor signal remained stable when a constant load was applied onto the structure for a period of time. In addition, based on the consideration of the strain transfer coefficient, the load measurement accuracy of the small structure can reach 35.6 pm/N. In summary, compared with the fiber Bragg grating sensor, the sensor has higher repeatability and sensitivity. It can be concluded that this novel sensor with a high parallelism and good consistency cavity can easily achieve miniaturization and mass production, so it can be used as a distribution stability force sensor, and has potential application value in weak strain occasions.  
[PERSON] designed the experiment and wrote the manuscript; [PERSON] and [PERSON] gave some suggestions for an improved method; [PERSON] performed the experiment; [PERSON] and [PERSON] helped edit the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the fund of Aeronautics Science (Grant No. 20170252004), Shanghai Academy of Spaceflight Technology (Grant No. SAST2018-015), Jiangsu province key research and development plan industry foresight and generic key technology competition project (BE2018047).  
The author would like to thank the support from Shanghai Baiantek Sensing Technology CO., LTD Company.  
The authors declare no conflict of interest.
## 7 Conclusions  
In this paper, it was investigated whether clutters having similar power to the target power can be distinguished by using the logistic regression model. Therefore, the clutter signals considered in this study have such significant power as to lead to a false alarm with the threshold detection scheme. The target signals corresponding to the clutter signals are generated for various rotation angles and for assorted values of TCR. These signals are used to construct the feature vectors and the shuffle-split cross validation technique is applied to the trained model. The models are evaluated in terms of receiver operation characteristic, area under ROC curve, classification table and detection rate. According to the evaluation results, the use of the logistic regression model is very useful for underwater target discrimination. Furthermore, the experimental data are employed to verify the proposed approach. Note that the experimental data are collected for the purpose that is not related to this study. According to the application result, the model trained by the simulated data can discriminate the cylinder from the rock even though it is not perfect. In addition, note that the mathematical model can describe the scattering of the real cylinder well.  
In the simulation study, training and testing have been performed using every component of the magnitude spectrum of the received signal. Considering the spectral characteristics of the target and clutter, only the frequency bins carrying the main energy portion can be selected as feature components, which accelerates the processing time by reducing the size of the model. In the simulation study, various TCR conditions were considered since the spectrum is sensitive to magnitude. For further study, the cepstrum may be a good candidate for the feature vector component due to its scale invariance property. The application of the real experiment data reveals the need for the research on the reflection of the multipath components and modeling environments where a target is placed.  
**Acknowledgments:** The authors gratefully acknowledge the financial support provided by Defense Acquisition Program Administration and Agency for Defense Development under the contract (UD140004 DD). The authors thank the Applied Physics Laboratory at the University of Washington for providing the experimental data.  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON] performed the experiments related to the logistic regression model; [PERSON] performed the target signal and the clutter signal modeling; [PERSON] provided the discussion on the target detection problem in the shallow water; [PERSON] provided the description on the target signal and the multipath component generation; and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.