esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Scenarios and Intervention Stages
#### 2.3.2. Risk Assessment: Intervention Stages  
Since the degree of harm to health depends on the air quality level, the content of the alerts must be sensitive to these levels. Therefore, we constructed scenarios to guide the utilization of the collected data (temperature, humidity, fine dust, and ultrafine dust) according to consistent criteria, using the range of air quality categories defined by governmental agencies. The differentiation of temperature and humidity was based on the \"Living Weather Index\" provided by the Korea Meteorological Administration's website [38] and a systematic review [39]. The classification of fine dust concentration was based on the criteria used by the Korea Air Pollution Information Forecasting website [40].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Indoor Air Quality**} & \multicolumn{3}{c}{**Outdoor Atmosphere**} \\ \cline{2-6}  & **Standards** &
\begin{tabular}{c} **Spring** \\ **and Fall** \\ \end{tabular} & **Summer** & **Winter** & **Standards** & **Four Seasons** \\ \hline \multirow{7}{*}{Temperature} & Very hot & \textgreater{}32 \({}^{\circ}\)C & Hot & \textgreater{}32 \({}^{\circ}\)C \\ \cline{2-6}  & Hot & 28–32 \({}^{\circ}\)C & Good & 10–32 \({}^{\circ}\)C \\ \cline{2-6}  & Good & 22–28 \({}^{\circ}\)C & Cold & 10 \({}^{\circ}\)C \\ \cline{2-6}  & Cold & 18–22 \({}^{\circ}\)C & & & \\ \cline{2-6}  & Very cold & \textless{}18 \({}^{\circ}\)C & & & \\ \hline \multirow{7}{*}{Humidity} & Very humid & \textgreater{}80\% & \textgreater{}90\% & \textgreater{}70\% & Humid & \textgreater{}80\% \\ \cline{2-6}  & Humid & 60–80\% & 70–90\% & 50–70\% & Good & 20–80\% \\ \cline{2-6}  & Good & 40–60\% & 50–70\% & 30–50\% & Dry & \textless{}20\% \\ \cline{2-6}  & Dry & 20–40\% & 30–50\% & 10–30\% & & \\ \cline{2-6}  & Very dry & \textless{}20\% & \textless{}30\% & \textless{}10\% & & \\ \hline \multirow{7}{*}{PM10} & Good & 0–30 \(\textgreater{}\text{}\textgreater{}\text{}\textgreater{}\text{}\textWe categorized the intervention stages into four levels to assess the risk level of each scenario and provide corresponding alerts. Intervention Stage 1 occurs when both indoor and outdoor air are deemed harmless. For instance, if the indoor and outdoor temperatures are at the 'good' level, this state is considered ideal, requiring no warning about air quality. Hence, we designate this as IAQ intervention Stage 1. Intervention Stage 2 emerges when IAQ is satisfactory but the external environment is unfavorable. Stage 3 pertains to cases where IAQ has deteriorated beyond the 'normal' or 'good' level, posing risks, particularly for older COPD patients, if exposure is prolonged. Stage 4 is assigned to scenarios where both indoor and outdoor air quality reach the most dangerous levels. For example, in Scenario 1, where the indoor temperature is'very hot' and the outdoor temperature is 'hot', immediate action might be necessary to prevent potential harm.
## 3 Theoretical Formulation of Turbulent Gas Flow Modeling
### Turbulence Models
#### 3.2.1 \(k\)-\(\epsilon\) Turbulence Model  
The turbulence kinetic energy (\(k\)) and the rate of dissipation of the turbulent kinetic energy (\(\epsilon\)) are obtained from the following equations:  
The turbulent kinetic energy equation:  
\[\
abla\bullet(\rho\mathbf{u}k)=\
abla\bullet\left(\left(\mu+\frac{\mu_{t}}{ \sigma_{k}}\right)\
abla k\right)+G_{k}-\rho\epsilon \tag{5}\]  
The turbulent kinetic energy dissipation rate equation:  
\[\
abla\bullet(\rho\mathbf{u}\varepsilon)=\
abla\bullet\left(\left(\mu+\frac{ \mu_{t}}{\sigma_{\epsilon}}\right)\
abla\varepsilon\right)+C_{1\epsilon}\frac {\varepsilon}{k}G_{k}-C_{2\epsilon}\frac{\varepsilon^{2}}{k} \tag{6}\]  
where \(\mu_{t}\) is given by:  
\[\mu_{t}=\rho C_{\mu}\frac{k^{2}}{\epsilon} \tag{7}\]  
In the standard \(k\)-\(\epsilon\) turbulence model proposed by [PERSON] [26], all the model coefficients \(C_{\mu}\), \(C_{1\epsilon}\), and \(C_{2\epsilon}\) are considered to be constant; however, the standard model is incapable of capturing the subtler relationships between the turbulent energy production and the turbulent stresses caused by the anisotropy of the normal stress, and therefore results in poor performance in cases of near-wall flow [27]. In the other two turbulence models (realizable and RNG) from the \(k\)-\(\epsilon\) turbulence model family, these shortcomings of the standard \(k\)-\(\epsilon\) model are solved by introducing a wall damping function for each of the coefficients \(C_{\mu}\), \(C_{1\epsilon}\), and \(C_{2\epsilon}\). This allows it to perform better in the near wall boundary condition, where the viscous sublayer is persistent. Detailed information about realizable and RNG \(k\)-\(\epsilon\) models can be found in the research works of [PERSON] et al. [28] and [PERSON] et al. [29].
# A Simplified Analysis to Predict the Fire Hazard of Primary Lithium Battery
## 1 Introduction
Federal Aviation Administration (FAA) had completed a series of tests to determine the flammability characteristics of primary lithium batteries and the dangers associated with shipping them in bulk on commercial transport category aircraft [25]. Scholars have conducted experimental and theoretical investigations to analyze the thermal runaway and fire behaviors of multiple lithium batteries. [PERSON] et al. studied a three-dimensional (3D) thermal runaway propagation model for a large format lithium-ion battery module and presented four recommendations to postpone or prevent thermal runaway propagation [26; 27]. [PERSON] predicted cell-to-cell fire propagation in a five lithium-ion battery pack by simulating the temperature distribution in neighboring cells, which is important for designing and validating safe systems [28]. [PERSON] et al. found that the higher the number of batteries the worse the battery pack fire, and thermal failure appeared earlier for packs with more heaters [29]. Electric vehicles and plug-in hybrid electric vehicles use large batteries pack to store energy. The energy flows into the battery pack as it is charged, either from regenerative braking or from the grid, and discharges from the pack to power the vehicle. The charging and discharging current causes heating in the batteries; the higher the current the greater the heating effect. The performance of lithium battery is greatly impacted by its temperature, which can lead to thermal runaway. Special cooling systems may be required to prevent damage or even fire during charging and discharging [30; 31; 32; 33]. In order to develop reliable fire protection strategies for the mass storage and transport of lithium batteries, more studies are needed to understand the fire behaviors of multiple lithium batteries. However, studying large-scale lithium battery fires with experiments is costly. A simulation method is needed to calculate the magnitude of fires for multiple large batteries in order to provide awareness and recommendations for safety protection. In this study, experiments and simulations were conducted to investigate the burning behaviors of different numbers of lithium batteries. The purpose was to establish a baseline set of analyses that characterize specific quantities of primary lithium batteries involved in an unsuppressed fire.
## 3 Results and Discussion
### Visual Observation of H\({}_{2}\)O\({}_{2}\) Pre-Oxidation Effects on PVA Removal  
Figure 4 compares the reaction phenomena after ferrite treatment at different PVA concentrations (3-7 g/L) with and without H\({}_{2}\)O\({}_{2}\) pretreatment. In systems without H\({}_{2}\)O\({}_{2}\) pretreatment, increasing PVA concentrations led to substantial non-uniform iron oxide deposits at the reaction vessel walls and solution interfaces, particularly pronounced at higher concentrations of 6-7 g/L. This non-uniform deposition phenomenon suggests interference with ferrite formation in high-concentration PVA environments, potentially reducing treatment effectiveness. In contrast, systems with H\({}_{2}\)O\({}_{2}\) pretreatment demonstrated significantly improved reaction characteristics. First, wall deposition was notably suppressed, with deposits remaining relatively uniform even under high PVA concentrations. Second, the solution phase exhibited more distinct stratification, with darker and more compact precipitates at the bottom, indicating that H\({}_{2}\)O\({}_{2}\) pretreatment promoted the formation of more complete ferrite structures. This improvement likely stems from reactive species generated during H\({}_{2}\)O\({}_{2}\) pre-oxidation, which not only regulated system pH but potentially facilitated partial PVA molecular degradation, enabling more effective subsequent reactions with iron ions. Notably, samples in the low-to-medium concentration range (3-5 g/L) with H\({}_{2}\)O\({}_{2}\) pretreatment showed optimal reaction performance, characterized by clear phase separation and uniform precipitate formation. This indicates that within an appropriate concentration range, H\({}_{2}\)O\({}_{2}\) pre-oxidation can effectively enhance the stability and efficiency of the ferrite treatment process. This finding provides important experimental evidence for optimizing high-concentration PVA wastewater treatment processes.
## 5 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Device/** & **Attack** & **Algorithm** & **Accuracy** & **TP** & **FP** & **FN** & **TN** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline \multirow{9}{*}{TCP} & RF & 0.999943 & 11973 & 3 & 2 & 2012 & 0.999849 & 0.999833 & 0.999891 & 0.999954 \\  & DT & 0.999714 & 11942 & 2 & 2 & 2044 & 0.999833 & 0.999833 & 0.999833 & 0.999876 \\  & kNN & 0.999571 & 11919 & 2 & 4 & 2065 & 0.999832 & 0.999665 & 0.999748 & 0.999744 \\  & XGBOOST & 0.999786 & 11903 & 1 & 2 & 2084 & 0.999916 & 0.999832 & 0.999874 & 0.999962 \\  & SVM & 0.99857 & 11885 & 7 & 13 & 2085 & 0.999411 & 0.998907 & 0.999159 & 0.998787 \\ \cline{2-11}  & \multirow{4}{*}{UDP} & RF & 0.999815 & 4494 & 4 & 5 & 1997 & 0.999889 & 0.999993 & 0.999972 \\  & & DT & 0.998923 & 4459 & 2 & 5 & 2034 & 0.999552 & 0.99888 & 0.9999216 & 0.998947 \\  & & kNN & 0.999538 & 4435 & 2 & 1 & 2062 & 0.999549 & 0.999775 & 0.999662 & 0.999642 \\  & XGBOOST & 0.998923 & 4400 & 3 & 4 & 2093 & 0.999319 & 0.999092 & 0.999205 & 0.999741 \\  & SVM & 0.996769 & 4420 & 6 & 12 & 4420 & 0.99849 & 0.997548 & 0.998019 & 0.998866 \\ \cline{2-11}  & \multirow{4}{*}{UDP} & RF & 0.999784 & 21087 & 3 & 2 & 2008 & 0.999858 & 0.999905 & 0.999881 & 0.999862 \\ \cline{2-11}  & \multirow{4}{*}{TCP} & DT & 0.99961 & 21042 & 4 & 5 & 2049 & 0.99981 & 0.999762 & 0.999786 & 0.999649 \\ \cline{2-11}  & \multirow{4}{*}{TCP} & kNN & 0.99974 & 21025 & 1 & 5 & 2069 & 0.999952 & 0.999762 & 0.999857 & 0.999824 \\ \cline{2-11}  & \multirow{4}{*}{GT} & XGBOOST & 0.99974 & 20983 & 1 & 5 & 2111 & 0.999952 & 0.999762 & 0.999857 & 0.999743 \\ \cline{2-11}  & & SVM & 0.999351 & 20992 & 8 & 10 & 20992 & 0.998409 & 0.998182 & 0.998295 & 0.999371 \\ \hline \multirow{9}{*}{DNS} & RF & 0.999931 & 3187 & 3 & 1 & 2009 & 0.99976 & 0.999886 & 0.999773 & 0.999842 \\ \cline{2-11}  & \multirow{4}{*}{TCP} & DT & 0.999231 & 3170 & 2 & 2026 & 0.999369 & 0.999369 & 0.999369 & 0.9993636 \\ \cline{1-1}  & \multirow{4}{*}{TCP} & kNN & 0.999231 & 3125 & 1 & 3 & 2071 & 0.99968 & 0.999401 & 0.99936 & 0.999362 \\ \cline{1-1}  & \multirow{4}{*}{TCP} & XGBOOST & 0.998654 & 3084 & 2 & 5 & 2109 & 0.999352 & 0.998381 & 0.998866 & 0.998948 \\ \cline{1-1}  & \multirow{4}{*}{TCP} & SVM & 0.995962 & 3137 & 7 & 9 & 3137 & 0.998649 & 0.995688 & 0.999716 & 0.998563 \\ \cline{1-1}  & \multirow{4}{*}{TCP} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{1-1}  & & & & & & & & & & & & & & \\ \cline{1-1}  & & & & & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 15: Classification results (thermostat—Gafgyt).
# Operational Resilience Metrics for Complex Inter-Dependent Electrical Networks
## 2 Operational Resilience Metric for Electrical Distribution Networks
### Assessing the Impact of Improved Distribution Automation Systems (DAS) on the EDN Resilience  
In this case, RecSIM was extended to simulate the operator operational procedures for fault detection, isolation (FDIR) [19]. The proposed framework was used to assess the performance of the EDN during normal and daily operational conditions. In particular, the extended tool was used to assess the performances of the Distribution Automation System (DAS) and to evaluate the improvements that could be achieved by adopting new communication technologies for the DAS network. Similar to the works in [31; 32; 33], ENEA and Areti SpA are currently working on an optimization framework to support the optimal placement and composition of automatic switches (ASs) to improve the distribution power grid reliability. Indeed, as noted in [32], a greater number of automatic switches allows for better operation; however, there are practical limitations because of the increasing of installation costs and more strict maintenance requirements for the ASs. This work shows preliminary results that were obtained using the improved RecSIM to assess the benefits of an improved DAS schema (currently under testing in Areti SpA), where the ASs collaborate using the 4G communication network.
# Simulation and Experimental Research on the Energy Loss of Confluence Pipelines
###### Abstract  
Compared with T- and Y-shaped confluence pipelines, arc confluence pipelines have a smaller energy loss coefficient. In the study presented herein, numerical simulation analyses of the flow fields of T-shaped, Y-shaped, bifurcated, and arc confluence pipelines were carried out. An experimental system was designed to study the energy loss coefficient of the arc confluence pipeline by using five different pipe diameters and nine different radii with water as the working fluid and analyze the influence of pipe diameter, radius, and Reynolds number on the energy loss of the arc confluence pipeline. As the radius of the arc confluence pipeline increases, the energy loss coefficient of confluence first decreases and then increases, and the minimum energy loss corresponds to a radius of 65 mm; with an increase in Reynolds number, the energy loss coefficient of confluence first decreases gradually and tends to a certain value. With an increase in diameter, the energy loss coefficient of confluence decreases gradually.  
confluence pipelines; energy loss of confluence; experimental research; numerical simulation +
Footnote †: journal: applied sciences
# Performance Analysis of the Supercritical Carbon Dioxide Re-compression Brayton Cycle
## 2 Methodology  
### Cycle Processes  
Figure 1 shows a cycle layout and temperature-entropy (T-s) diagram of the system considered in the study. As shown in Figure 1a, the system consists of a turbine, a primary heat exchanger, high and low-temperature recuperators, the main compressor, a re-compression compressor and a gas cooler. In comparison with the recuperated cycle, the re-compression Brayton cycle includes an added intermediate compressor, an additional recuperator and split/mixing flow values. A fraction of mass is taken from the mainstream and fed to the re-compression compressor bypassing the low-temperature recuperator (LTR). This fraction of flow enters the mainstream again increases its temperature before entering into the high-temperature recuperator (HTR). This arrangement in turns increases the thermal efficiency of the cycle (42% to 50% for cycles operating with the lowest and highest temperatures, 37 \({}^{\circ}\)C and 700 \({}^{\circ}\)C respectively [10]) by reducing heat rejection in the pre-cooler.  
The stream exiting the re-compressor in State 5 and the stream exiting the recuperator in State 3 are combined in State 4. This stream then passes the HTR where its temperature further increases to State 6. After this, the heat addition process takes place at the main heat exchanger and the stream reaches \(T_{7}\) in State 7. In the turbine, the expansion process takes place until State 8. From State 8 to State 9, the HTR recuperates heat, and then, from State 9 to State 10, the LTR recuperates heat. After this, cooling takes place in the gas-cooler in State Point 1. The gas cooling process from State 10 to State 1 is used to transfer heat to the coolant (water) which is input to the gas cooler at the monthly temperature value (see Figure 2) and has to be heated to 40 \({}^{\circ}\)C for domestic us  
Figure 1: Schematic of re-compression \(sCO_{2}\) Brayton cycle.
## 3 Results and Discussion
### Local Search Algorithm Applied for Shape Optimization  
Program for pre- and postprocessing of the finite element model written in Visual Basic language contains values of parametric geometric variables. This language facilitates implementation of local stochastic search algorithm whose pseudocode follows:  
1. Select a starting point \(\mathbf{D}_{0}\in\Omega\) randomly;
2. \(\mathbf{D}_{m}\to\mathbf{D}_{m+1}\)--generate a random direction \(\mathbf{a}_{m}\in\mathbb{R}^{n}\), if \(\mathbf{D}_{m}+\mathbf{a}_{m}\
otin\Omega\) then generate a new \(\mathbf{a}_{m}\) direction and repeat it. If \(\mathbf{D}_{m}+\mathbf{a}_{m}\in\Omega\) then \(\mathbf{D}_{new}=\mathbf{D}_{m}+\mathbf{a}_{m}\);
3. If \(E(\mathbf{D}_{new})_{SVM}<E(\mathbf{D}_{m})_{SVM}\), then \(\mathbf{D}_{m+1}=\mathbf{D}_{new}\), otherwise \(\mathbf{D}_{m+1}=\mathbf{D}_{m}\), further \(m=m+1\);
4. Stopping criterion--if the maximum iteration number is reached.  
To generate \(\mathbf{a}_{m}\) direction, a normally distributed random number with mean zero is selected in all directions of the design variables. This algorithm contains two parameters to be determined, which are the \(\sigma\) standard deviation belonging to random number and \(m_{max}\) maximum iteration number should be provided as a precondition of stopping criterion, meaning cost of the algorithm. To investigate the operation of the algorithm, \(\sigma=20\) mm and \(m_{max}=5000\) values were selected. The search method was run for the two-variable shape optimization task for the rubber bumper as outlined in the previous section. The program written was operated for running the shape optimization task in an automatized closed system by providing the parameters. As a result, it determined the optimal objective function value as well as its other relevant values.  
Table 3 contains better function values accepted in \(m\) iteration by the algorithm and its related geometric variable values. Figure 6 visualizes the change in the location of the better function accepted by the optimization method in design space, as well as the starting and end points. Considering the results, the efficiency of the algorithm was low, as it was not suitable for identifying better value than \(E\big{(}\mathbf{D}_{opt}\big{)}_{FEA}=0.051\) kN\({}^{2}\) even after passing \(m=1250\) iterations. The optimum revealed, based on Figure 6, could not approach the global optimum even after \(m=5000\) function calculation, although it managed to approach it.  
Figure 5: Predicted points for the objective function values by the surrogate model in the design space.
## 3 Results and Discussion
### Response Surface and Contour Analysis
#### 3.3.3 Effect of PFA and SF Interaction  
When the SP is at an intermediate level, the response surface and contour in PFA-SF are shown in Figure 8. As can be seen from Figure 8a, the highest values exist across the response surface and are in the center, corresponding to a compressive strength of around 48 MPa for the manufactured sand concrete block. This indicates that in this study, the manufactured sand concrete exhibited a high compressive strength when the SP, PFA, and SF contents were all at intermediate levels. As can be seen in Figure 8b, the contours are centrosymmetric and equally spaced between the surrounding contours. This shows that equal variations in PFA and SF content have the same fluctuating effect on the compressive strength when the SP is at an intermediate level. In addition, the top leftmost corner of the contour in Figure 8b shows a compressive strength of less than 42 MPa, which is due to the high SF content and the low PFA content.  
Figure 7: Response surface and contour in SP-SF. (**a**) Response surface. (**b**) Contour.
## 3 Design of Control System
### Design of LESO  
Assume \(\dot{f}_{total}=h\). Rewrite (11) as  
\[\dot{\mathbf{X}}=\mathbf{A}_{1}\mathbf{X}+\mathbf{B}_{1}u+\mathbf{B}_{2}h, \tag{12}\]  
where \(\mathbf{A}_{1}=\begin{bmatrix}0&1&0\\ 0&0&1\\ 0&0&0\end{bmatrix}\), \(\mathbf{B}_{1}=\begin{bmatrix}0&b&0\end{bmatrix}^{\mathsf{T}}\), \(\mathbf{B}_{2}=\begin{bmatrix}0&0&1\end{bmatrix}^{\mathsf{T}}\), \(\mathbf{X}=\begin{bmatrix}x_{1}&x_{2}&f\end{bmatrix}^{\mathsf{T}}\). Construct an LESO as  
\[\dot{\mathbf{X}}=\mathbf{A}_{1}\mathbf{\hat{X}}+\mathbf{B}_{1}u+\mathbf{L}\big{(}\mathbf{X}-\mathbf{\hat{X}} \big{)}, \tag{13}\]  
where \(\mathbf{\hat{X}}=\begin{bmatrix}\hat{x}_{1}&\hat{x}_{2}&\hat{f}\end{bmatrix}^{ \mathsf{T}}\) is the estimated value of \(\mathbf{X}\) and \(\mathbf{L}\) is the gain matrix of LESO, which is given by  
\[\mathbf{L}=\begin{bmatrix}l_{1}&0&0\\ l_{2}&0&0\\ l_{3}&0&0\end{bmatrix}. \tag{14}\]  
Let \(\tilde{\mathbf{e}}\) represent the estimated error of the LESO. We have  
\[\tilde{\mathbf{e}}=\mathbf{X}-\mathbf{\hat{X}}. \tag{15}\]  
According to (12), (13), and (15), the following differential equation with respect to \(\tilde{\mathbf{e}}\) can be obtained as  
\[\dot{\tilde{\mathbf{e}}}=\mathbf{A}_{2}\tilde{\mathbf{e}}+\mathbf{B}_{2}h, \tag{16}\]  
where \(\mathbf{A}_{2}=\mathbf{A}_{1}-\mathbf{L}\).  
Assume that \(h\) is bounded, namely there exists a positive constant \(M_{1}\) such that \(|h|\leq M_{1}\). The estimated error of the LESO is always bounded, that is, there exists a positive constant \(M_{2}\) such that \(||\tilde{\mathbf{e}}||\leq M_{2}\) holds [21].