esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Calculating Fluxes Using the Gradient Flux Method
#### 2.2.2 Estimation of Soil Gas Diffusion Coefficients  
Soil gas diffusion coefficients are needed to calculate soil gas fluxes from soil gas profiles. \(D_{S}^{i}\) represents the exchange coefficient of a specific gas \(i\) in the soil and depends on the pore structure of the air-filled pore space \(\varepsilon\) (m\({}^{3}\) m\({}^{-3}\)) and the characteristics of the diffusing gas \(i\). The relative gas diffusion coefficient \(D_{S}/D_{0}\) is a dimensionless measure of the quality of the structure of soil which allows the quantification of how \"easily\" a gas is able to diffuse through the pore structure, and which is independent of the gas species. Multiplying this dimensionless coefficient with gas specific diffusion coefficient in free air \(D_{0}\) (m\({}^{2}\) s\({}^{-1}\)) allows calculating the gas specific soil gas coefficient \(D_{S}\) (Equation (4)), which is needed to calculate fluxes.  
\[D_{S}=\frac{D_{S}}{D_{0}}D_{0}, \tag{4}\]  
\(D_{S}/D_{0}\) depends on the air-filled pore structure, which is often addressed as a combination of the air-filled pore volume and their effectivity to allow gas exchange. Different models have been developed that can be used to calculate \(D_{S}/D_{0}\), from simple general functions [85; 86; 87; 88] to functions that require knowledge of many parameters or measurements of diffusivity [89; 90; 91]. We used site-specific models since it is impossible to predict the best diffusivity model _a priori_[44]. The site-specific diffusivity models were derived from laboratory measurements of soil samples from the different sites. Several soil and humus samples were taken at different depths using 100 cm\({}^{3}\) soil cores. The porosity of the cores was measured by vacuum pycnometry. Bulk density of samples was measured gravimetrically by drying at 105 \({}^{\circ}\)C after all measurements. Soil gas diffusivity was measured using a one-chamber method similar to the one described by [PERSON] et al. [65]. We used exponential functions as described by [PERSON] et al. [85] to parameterize our site-specific and soil-layer specific gas diffusivity functions [92]. Using these site-specific gas diffusivity functions allowed calculating the time series of \(D_{S}/D_{0}\) from the air-filled pore-space, which in turn can be calculated as the difference between the total pore-volume and the soil water content. Using Equation (4) allows deriving \(D_{S}\) from \(D_{S}/D_{0}\). Since the speed of diffusion is affected by temperature \(T\) (K) and barometric pressure \(p\) (Pa) \(D_{0}\) has to be corrected for \(T\) and \(p\).  
\[D_{0}(T,p)=D_{0}^{norm}\left(\frac{p_{norm}}{p}\right)\left(\frac{T}{T_{norm}} \right)^{\alpha}, \tag{5}\]  
where \(D_{0}^{norm}\) is \(D_{0}\) at normal barometric pressure \(p_{norm}\) and normal temperature \(T_{norm}\), and \(\alpha\) is a gas specific exponent between1.5 and 2 [93]. Including Equation (5) in Equation (4) allows accounting for the changing environmental conditions on molecular diffusion.
## 4 Conclusions  
We have measured the indoor radon concentrations at 15 locations in various objects (basements and ground floor/1 st floor rooms) in the area of northern Croatia. Since most radon comes from the ground, the highest concentrations of radon are found in the basement rooms compared to values measured in the ground floor and first-floor rooms. The arithmetic mean (AM) and geometric mean (GM) of basement rooms were \(70.9\pm 38.8\) Bq/m\({}^{3}\) and \(61.2\pm 2.2\) Bq/m\({}^{3}\) compared to ground floor and first-floor rooms of \(42.5\pm 30.8\) Bq/m\({}^{3}\) and \(32.8\pm 2.9\) Bq/m\({}^{3}\).  
The distribution of indoor radon has been found to be largely dependent on a building's occupation and ventilation. Generally, it can be concluded that the ventilation of the rooms has a strong potential to mitigate radon pollution from sources within buildings as well as a considerable reduction of radon concentration when ventilation actions were undertaken by occupants. Furthermore, the values of radon concentration obtained in Varazdin and Medimurje counties are below the maximum allowable concentration of 300 Bq/m\({}^{3}\) (proposed by the Euroatom Directive) and do not pose a threat for human health.  
We try to locate radon-prone areas by dividing our measurements according to geological criteria and allocating measurements randomly within the selected areas and buildings. A short-term detector was used for the purpose of undertaking initial radon screening. These short-term measurements were taken with the objective of identifying different types of object with increased radon concentrations in a short period (3-16 days). Short-term measurements for low-level radon concentrations (less than 100 Bq/m\({}^{3}\)) as shown in this study generally should be a good indicator of long-term average values if standard deviation is considered, but it should not be taken as a basis for long-term radon exposure estimates. Future long-term measurements will allow us to assess representative averages in buildings that are interesting for the general population and potential radon health risk as well for seasonal radon variations.  
Conceptualization, A.P.S. and D.S.; methodology, D.S.; software, D.S.; validation, N.S.; formal analysis, A.P.S., D.S. and D.D.; investigation, T.T., A.P.S., D.S., N.S. and D.D.; resources, A.P.S.; data curation, N.S.; writing--original draft preparation, A.P.S.; writing--review and editing, A.P.S., N.S. and D.S.; visualization, D.S.; supervision, A.P.S.; project administration, A.P.S. and N.S.; funding acquisition, A.P.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. APC was funded by D.S.  
We would like to thank to the Laboratory for Environmental Geochemistry and Department for Hydrotechnics for technical and financial support.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Methodology and Data Sources
### Model Formulation
#### 3.5.1 Estimating SOB-Caused Inflow PM\({}_{2.5}\) Concentration
So, the PM\({}_{2.5}\) horizontal transport mass along the wind direction per unit of time is written as:  
\[M_{i}^{Trans}=C_{i-1}\cdot u_{i-1}b_{i-1}h_{i-1}-\ C_{i}\cdot u_{i}b_{i}h_{i} \tag{5}\]  
where \(C_{i-1}\) is the inflow PM\({}_{2.5}\) concentration into sub-box \(i\), that is, the average PM\({}_{2.5}\) concentration in sub-box \(i-1\) (\(\mu\)g/m\({}^{3}\)); \(u_{i-1}\) is the wind speed at the upwind cross-section of sub-box \(i\), that is, wind speed at the downwind cross-section of sub-box \(i-1\) (m/s); \(b_{i-1}\) and \(h_{i-1}\) are the width and height, respectively, of the upwind cross-section of sub-box \(i\), that is, the width and height of the downwind cross-section of sub-box \(i-1\) (m); \(u_{i}\) is the wind speed at the downwind cross-section of sub-box \(i\) (m/s); and \(b_{i}\) and \(h_{i}\) are the width and height of the downwind cross-section of sub-box \(i\) (m). Compared with primary emissions and horizontal transport, chemical reactions within 1 h in the sub-boxes could be negligible. Therefore, after bringing in Equations (2)-(5), Equation (1) can be rewritten and converted to a differential form as follows:  
\[\frac{\mathrm{d}C_{i}}{\mathrm{d}t}=\frac{S_{i}^{c}\gamma_{i}^{c}\rho^{c} \eta^{c}\delta^{c}\delta^{c}+S_{i}^{r}\gamma_{i}^{r}\rho^{r}\eta^{r}\delta^{r} \delta^{r}\eta^{r}}{S_{i}H_{i}}-\frac{v_{d}}{H_{i}}C_{i}+\frac{u_{i-1}b_{i-1}h_{ i-1}}{S_{i}H_{i}}C_{i-1}-\frac{u_{i}b_{i}h_{i}}{S_{i}H_{i}}C_{i} \tag{6}\]  
As the model is based on HWFGs, the residence time for air parcels moving across sub-box \(i\) is 1 h (\(t\) = 3600 s). We assume that the air mass flowing into the first box is clean (\(C_{0}\) = 0 \(\mu\)g/m\({}^{3}\)). Thus, in a given initial condition of \(C_{i}(t)|_{t=0}=C_{i}(0)\), the average PM\({}_{2.5}\) concentration in each sub-box \(C_{1}\), \(C_{2}\),..., \(C_{n}\) can be calculated in sequence by Equation (6). \(C_{n}\), the average PM\({}_{2.5}\) concentration of the sub-box closest to the receptor city, is the PM\({}_{2.5\text{-inflow}}\) concentration.
# A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Year** & **Method** & **Classifier** & **Accuracy(F-measure)** & **Category** \\ \hline Alvarenga [2] & 2010 & GLCM & FLDA & 83\% & M,B \\ [PERSON] [3] & 2011 & SRAD, RG & NLM & 94\% & Segmentation \\ [PERSON] [6] & 2012 & PCA+GA & SVM+RBF & 92\% & M,B \\ [PERSON] [4] & 2012 & GLCM & FLDA+632 & 87\% & C,B \\ [PERSON] [5] & 2013 & GLCM & SVM+632 & 87\% & M,B \\ [PERSON] [7] & 2013 & Backward & BL & 88\% & M,B \\ [PERSON] [8] & 2016 & SRAD, RG & DT, ANN, RF, SVM & 77.7\%, 78.1\%, 78.5\%, 77.7\% & M,B \\ [PERSON] [14] & 2018 & HOG & LeNet, U-Net, FCN-AlexNet & (91\%), (89\%), (92\%) & M,B \\ [PERSON] [16] & 2019 & LSDA, MR8 & DT, ANN, PNN, SVM & 89.3\%, 92.3\%, 96.1\%, 95.3\% & M,B \\ \hline \hline \end{tabular} Notes. [PERSON]; [PERSON]; [PERSON]; 632: 0.632\({}^{\circ}\) bootstraps; RG; Region Growing DT Decision Tree; NLM: Neutrosophic L-Means; LDA: Linear Discriminant Analysis; BL: Binary Logistic Regression; FLDA: Fisher Linear Discriminant Analysis; GLCM: Gray-Level Co-occurrence; LSDA: Locality Sensitive Discriminant Analysis; HOG: Histogram of Oriented Gradients; PNN: Probabilistic Neural Network.  
\end{table} 
Table 3: Literature reviews of breast ultrasound CAD system. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BI-RADS Categories** & **Features** & **Total** \\ \hline \multirow{8}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } & (F1) Circularity \\  & & (F2) Form Factor \\  & & (F3) Roundness \\  & & (F4) Long axis to Short axis Ratio \\ \cline{1-1}  & & (F5) Aspect Ratio \\ \cline{1-1}  & & (F6) Elliptic-Normalized Circumference & 10 \\ \cline{1-1}  & & (F7) Convexity \\ \cline{1-1}  & & (F8) Standard Deviation of Normalized Radial Length \\ \cline{1-1} morphology & & (F9) Entropy of Normalized Radial Length \\ \cline{1-1}  & & (F10) Extent \\ \cline{1-1} \cline{2-4}  & Orientation & (F11) Orientation Angle & 1 \\ \cline{1-1} \cline{2-4}  & & (F12) Roughness Area Ratio \\ \cline{1-1}  & & (F13) Margin Roughness1 \\ \cline{1-1}  & & (F14) Solidity \\ \cline{1-1}  & & (F15) Normalized Residual Value & 6 \\ \cline{1-1}  & & (F16) Number of Substantial Protubareances and Depressions \\ \cline{1-1}  & & (F17) Indistinction \\ \hline \multirow{4}{*}{Textural} & \multirow{2}{*}{Echo Pattern} & (F18) Multi-resolution Wavelet Transform + GLCM \\ \cline{1-1}  & & (F19) Multi-resolution Ranklet Transform + GLCM \\ \cline{1-1}  & & (F20) ROI + GLCM \\ \cline{1-1} \cline{2-4}  & Posterior Features & (F21) Difference of the average of gray level that between the upper and lower area \\ \cline{1-1}  & & (F22) Difference of the average of gray level that between the right and left area \\ \cline{1-1} \cline{2-4}  & & & \\ \hline \hline \end{tabular} Notes: GLCM represent Gray-Level Co-Occurrence Matrix.  
\end{table} 
Table 4: BI-RADS features corresponding list.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 3 Methodology
### Aircraft Weight Breakdown  
Weight is a crucial factor for any aircraft, especially for UAS. Minimizing weight enables take-off and maximizes range and endurance, though the focus of this study is a functional aircraft. Table 2 includes the weight breakdown of the major subsystems. Notably, the empty aircraft includes the airframe, propellers, motors, wiring harness, servo motor, and avionics. The total flight-ready aircraft weight is 378 N (84.9 lbf), which is below the manufacturer specified maximum take off weight of 391 N (88 lbf). The turboelectric system includes the weights of the turboprop, system casing, rectifier, fuel pump, and ECU, as shown in Figure 4. There were components, such as the engine casing, with potential for weight reduction by a few pounds [18]. Overall these percentages are a good starting point for future aircraft. However, it's likely that future aircraft would have a larger fuel weight and a lighter turboelectric system weight. Additionally, this technology demonstrator does not have a payload. Future aircraft would likely include a mission determine payload, which would impact the weight breakdown (battery size, fuel weight, and center of gravity).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Weight (N)** & **Weight (lbf)** & **Percentage** \\ \hline Power Battery Weight & 33.4 & 7.5 & 9\% \\ Avionics Battery Weight & 12.0 & 2.7 & 3\% \\ Fuel Weight & 26.7 & 6.0 & 7\% \\ Turboelectric System & 78.7 & 17.7 & 21\% \\ Empty Aircraft & 227.2 & 51.0 & 60\% \\ Total Flight Weight & 378.0 & 84.9 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Turboelectric Aircraft Weight Breakdown.  
Figure 4: Assembled Turboelectric System.
## 3 Proposed Method
### Cost-Sensitive Loss Function  
For the neural network, the smaller the loss function, the stronger the performance of the network model. Generally, loss functions are square loss function, exponential loss function and cross-entropy loss function. The neural network designed in this paper uses the cross-entropy loss function and improves it for multi-objective classification tasks of remote sensing images.  
Remote sensing images contain complex objects and unbalanced samples. For example, vegetation types account for a large proportion of remote sensing images, while roads and other objects often occupy only a small number of pixels in remote sensing images. In the process of neural network learning, the class with a small number of samples is prone to under-fitting. In order to solve this problem, we add a cost-sensitive matrix to the loss function. Thus, the network model has a larger penalty when it misclassifies a few sample categories, and it strengthens the attention of this class sample.  
For multi-classification, assuming that the total number of samples in the training data set is N, and the number of classes is M. The size of cost-sensitive matrix \(W\) is M \(\times\) M, which can be set to balance the penalty weights for different misclassified samples.  
\[W=\left[\begin{array}{cccc}\text{w}(1,1)&\text{w}(1,2)&\ldots&\text{w}(1, \text{M})\\ \text{w}(2,1)&\text{w}(2,2)&\ldots&\text{w}(2,\text{M})\\ \ldots&\ldots&\ldots&\ldots\\ \text{w}(\text{M},1)&\text{w}(\text{M},2)&\ldots&\text{w}(\text{M},\text{M}) \end{array}\right], \tag{6}\]  
where w(\(j\),\(k\)) represents the penalty when the class \(j\) is misclassified as the class \(k\). The elements on the diagonal of the matrix \(W\) are 0. The values of the cost-sensitive matrix are set according to the number of samples and the confusion matrix in the classification result. By introducing a cost-sensitive matrix to the loss function, the influence of samples imbalance on the performance of the network can be reduced. Then, the classification performance on a smaller number of categories can be improved.
## 2 Design Details of the Proposed System
### Software Details  
The embedded processing board we chose is based on Android, so we chose the Android Studio as the development platform for APP. The Android Studio is an Android Integrated Development Environment (IDE) from Google that provides integrated Android development tools for development and debugging. The software we installed on the server is Eclipse, MySQL, PyCharm, TensorFlow, and Sublime Text. Eclipse is an open, extensible IDE not targeted for any specific language or environment, which is used to develop an application to receive audio data from the embedded processing board. MySQL is a relational database management system for the storage and management of audio data. PyCharm and TensorFlow are used to build convolutional neural networks to recognize acoustic events. Sublime Text is an advanced code editor for the construction of the monitoring platform.
# Shear Lag Effect of Ultra-Wide Box Girder under Influence of Shear Deformation
## 5 Correction of Cantilever Plate Displacement for Flexural Displacement Function
The equations for the cross-sectional parameters not listed are similar to Equations (53)-(57). To obtain more accurate values of the shear lag coefficients, the value of the correction parameter \(\varphi\) of the cantilever plate must be determined by comparing the results of the numerical analysis calculations obtained via MIDAS/FEA NX and those obtained via the theoretical method for different values of \(\varphi\), as well as the calculation of the upper-left wing plate of the finite-element model of a single-box, three-chamber box girder (\(x\leq 0\)). As shown in Figure 10, the parameter \(\varphi\) must be \(>\)1; thus, it was set as 1.0, and increased to 1.7 in steps of 0.1. The shear lag coefficients of the flexural box girder were corrected and calculated, and the results are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(x\) **Coordinate** & **Finite-Element** & \multicolumn{8}{c}{**Correction Parameter \(\varphi\)**} \\ \cline{3-10} **(mm)** & **Value** & **1.0** & **1.1** & **1.2** & **1.3** & **1.4** & **1.5** & **1.6** & **1.7** \\ \hline \(-\)8010 & 0.8275 & 0.9754 & 0.9614 & 0.9496 & 0.9262 & 0.9049 & 0.8764 & 0.8334 & 0.8014 \\ \(-\)7340 & 0.9107 & 0.9929 & 0.9855 & 0.9801 & 0.9665 & 0.9551 & 0.9398 & 0.9165 & 0.8994 \\ \(-\)6670 & 0.9889 & 1.0112 & 1.0093 & 1.0093 & 1. As shown in Table 3, when the value of the cantilever modification parameter \(\varphi\) is 1.6, according to the flexural displacement function derived in this study, the calculated shear lag coefficient values of the wing plate on the box girder at each point optimally coincide with the numerical simulation results of MIDAS/FEA NX and fully reflect the distribution law, indicating that the shear lag coefficient of the cantilever plate is smaller than that of the top plate. To more intuitively examine the influence of the correction parameter \(\varphi\), the shear lag coefficient values of the wing plate on the box girder at the \(y=1450\) cm section were compared, as shown in Figure 11.  
Comparing the stress distributions shown in Figures 10 and 11 reveals that when the cantilever plate correction parameter \(\varphi\) had a value of 1.6, the stresses on the upper-wing plate of the box girder calculated via the proposed theoretical method and the results obtained via the MIDAS/FEA NX numerical simulation agreed well, and the relative difference in the data was <2.00%. The proposed theoretical method can fully reflect the different constraint conditions of the cantilever plate and the top plate, as well as the transverse distribution of the stresses along the wing plate.
## 4 POMDP Model Formulations
### CSPOMDP Model
#### 4.1.2 Action  
Pipeline maintenance operations focus on mitigating the damage due to corrosion degradation. Some of the mitigating methods have been presented in Section 2. Adding inhibitors and applying the coating could reduce the degradation process efficiently at an earlier stage of the pipeline service life. As pipelines age, the damage becomes severe, requiring more invasive methods such as pigging. Pigging utilizes pipeline integrity gauge (pig) tools that flow with the product and repair pipeline defects. These pigs are not only used for repairs but also for cleaning and inspecting pipelines.  
The action element in the model can be based on the degradation state of the pipeline in order to attain the maximum result from the maintenance operation. This is achieved by restricting the implementation of each mitigation method discussed above to an interval degradation state. Overlap of application regions may arise in this formulation, where the model selects the best option based on the reward obtained. The action set can also  
Figure 3: Summary of maintenance implementation procedure and model formulation.  
include no action alternative (_do-nothing_) to perform only inspection. Replace actions can be recommended when the degradation of the pipeline goes beyond the allowed limit.
## 4 Evaluation over Use Cases
### ICT Gateway
#### 4.1.4 Risk Estimation and Evaluation  
This phase calculates the security level of the system based on the data collected from the testing tools, which can be in formats such as.json,.xml,.csv, etc., and the YAML system description created during the risk identification phase. To automate the process, we developed a scorer tool, the SecurityScorer (Figure 5), which performs the following sequence of actions:  
1. Reading and parsing the YAML system description file, including the TP;
2. Reading and parsing the outputs of the security testing phase tools, including non-binary results;
3. Using both sources of information to evaluate the risk value for each component based on the methodology defined in Section 3.4;
4. Combining the risks of the components to calculate the overall risk value of the whole system;
5. Using the tolerance profiles to certify the system properties (A, B, C, D, or not certified);
6. Generating the cybersecurity label according to the evaluation results.  
Figure 7: GraphWalker model for adapter layer of ICT GW.  
For the ICT GW, we obtained the results shown in Table 9. The last column shows the test result (failed/pass), while the sixth column shows the non-binary information collected from that test, if applicable. We discovered that some of the claims were not fulfilled by the ICT GW, such as the establishment of a limit on authentication attempts to prevent brute-force attacks or the protection against log removal. Among the non-binary information, we discovered the presence of 18 outdated libraries, a maximum of 4822 simultaneous connections supported in the service layer, and a session expiration time of 1 min. Based on the evaluation, the manufacturer was able to update the implementation to add additional security functionalities or suggest specific configurations to be enforced by the ecosystem, such as using a firewall, implementing limits on authentication attempts, or updating the identified libraries.