esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Thermal Hydraulic Performance Optimization
#### 6.1.3 Sensitivity Analysis of the Geometric Design Parameters on the Thermal Hydraulic Performance  
Figure 3 shows the global sensitivity analysis of design variables. The apex angle \(\theta\), channel height \(H\), and exchanger height \(L_{\text{h}}\) have an influence on the thermal hydraulic performance of the air-to-air heat exchanger with cross-corrugated triangular ducts.  
To understand how these significant design parameters affect the thermal hydraulic performance indexes of the air-to-air heat exchanger, Figure 4 shows how the \(j\) factor, \(f\) factor, and \(JF\) factor vary with the three key design parameters. For the three results, only one input parameter is varied, while the other input parameters remain constant. This indicate that all the indexes, \(j\) factor, \(f\) factor, and \(JF\) factor, decrease with an increasing apex angle \(\theta\) and channel height \(H\) and increase with an increasing heat exchanger height \(L_{\text{h}}\). A bigger apex angle \(\theta\) or a larger channel height \(H\) results in a bigger cross-sectional area and reduce the heat transfer area when all the other parameters are fixed. These changes create lower heat transfer efficiency and a smaller pressure drop. A larger \(L_{\text{h}}\) leads to more air inlets when the volume flow rate and all of the other parameters are fixed, resulting in a reduction of the air flow in per channel. In addition, the heat transfer area is increased. These changes create a larger heat transfer area and a greater pressure loss.  
Figure 4: \(Cont.\)  
Figure 3: Global sensitivity analysis of the geometric design parameters on the thermodynamic performance.
## 4 Limitations and Challenges  
While addressing the high accuracy achieved in classifying segmented brain images in our research, it is crucial to acknowledge and address the several challenges and limitations encountered during our study. These include dealing with data imbalances, enhancing model interpretability, and diversifying datasets by incorporating different image modalities and real patient cases.  
Data imbalances pose a significant challenge in training machine learning models as they can lead to biased predictions. To overcome this, future research should focus on strategies such as data augmentation, oversampling, or using specialized algorithms designed to handle imbalanced datasets.  
Enhancing model interpretability is another key aspect. While achieving high accuracy is important, understanding why a model makes certain predictions is equally crucial, especially in medical applications like Alzheimer's disease diagnosis. Techniques such as model explainability methods, feature importance analysis, and visualization tools can aid in interpreting model decisions.  
Diversifying datasets by including various image modalities (e.g., MRI and CT scans) and real patient cases from different demographics and conditions can improve the robustness of the model. This ensures that the model generalizes well across diverse scenarios and demographics, making it more reliable and applicable in real-world clinical settings.  
By addressing these challenges and limitations, future research can further enhance the accuracy, interpretability, and robustness of machine learning models for brain image classification in Alzheimer's disease diagnosis.
## 2 Generalities and Particular Features of FK Phases
### Results on Different FK Phases  
A systematic study of the site occupancies for different FK phases has been conducted as a function of composition. The existing data has been reviewed in the following papers: \(\upmu\)[21], \(\sigma\)[22], \(\chi\)[23]. The comparison between these different phases indicates that these three phases behave differently as far as the Kasper rules are concerned.  
The \(\upmu\) phase presents two sites of equal coordination numbers (\(3a\) and \(18h\) with CN12) that have very different behaviors. Site \(3a\) may be occupied by either \(A\) or \(B\). Two stoichiometric (ordered) compositions can therefore be defined \(A_{6}B_{7}\) in which site \(3a\) is occupied by \(B\) and \(A_{7}B_{6}\) in which site \(3a\) is occupied by \(A\). Even more interesting is the fact that in certain system (e.g., Mo-Co) site \(3a\) may undergo a reversal of its occupancy within the homogeneity domain of the phase [21]. The non-stoichiometry in this phase is therefore mainly accommodated by the site exchange in this particular site. Site \(18h\) with the same coordination has a completely different behavior, since it is found to be mainly occupied by \(B\) in all the studied systems, independently of the composition, except at very high \(A\) content.  
On the other hand, the \(\sigma\) phase presents sites of distinct coordination (CN14 and CN15) that behave rather similarly as shown in Figure 3. For this phase, it has been shown that a single ordered composition \(A_{2}B\) exists. The ideal scheme for atom replacement drawn in the same figure is therefore significantly different from that assumed by applying the Kasper rules (Figure 2).  
Figure 3: Experimental site occupancies for the \(\sigma\) phase in all the studied systems (redrawn from [22]). The ideal scheme to describe the experimental data is drawn with lines.  
Finally, the \(\chi\) phase, contrary to the other phases, obeys in a rather strict manner the Kasper rule with sequential \(A\) occupancy of site CN16, CN13 and CN12 as a function of \(A\) composition. Two ordered compositions may therefore be defined \(A_{5}B_{24}\) and \(A_{17}B_{12}\) and are indeed observed experimentally in certain systems (Zr-Re for the first, Mg-Al for the latter).
# An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care
## 3 Sensor Setting
### Sensor Kit  
As previously stated, the AAL sensor kit should consist of WiFi-based devices that can monitor the five different types of activities, namely bed, chair, toilet usages, door activities and motion presence, as well as a 4G WiFi router. Consequently, the following sensing technologies are considered:  
* Bed and chair sensors use pressure-sensitive pads to detect presence. Whenever a sufficient pressure is applied (i.e., a person sitting on the chair or lying over the mattress), the sensor exhibits very low impedance, whereas absence implies high impedance.
* Toilet sensor attempts to measure toilet bowl usage by means of an active Infra-Red (IR) illuminator and a photodetector. The IR sensitivity can be trimmed to detect presence only within a given proximity (60 cm in our study), which makes this a selective sensor. In other words, this sensor is specifically triggered when the user is quite near it: proper placement allows the precise counting of toilet bowl uses.
* The room activity sensor is a wide-range PIR (Passive IR) sensor that detects motion within the environment. The higher the activity level in the room, the higher the sensor firing counts throughout the day.  
* Door opening is detected by means of a magnetic contact sensor, featuring a magnetically sensitive switch that closes when a permanent magnet comes in strict proximity. By placing one element on the door and the other one on the door frame, the sensor can detect open/close conditions.  
Each of the above-mentioned sensors triggers event-based binary signals, at 1 Hz frequency. A person interacting with such sensors leaves a trace of events that are logged by each of them.  
Hardware and firmware were specifically designed to fulfill interoperability and data security constraints as well as ensuring that the information is stored in national servers, hosted by authorized operators. Our selected sensors are low-power and low-quiescent devices powered by batteries. Power saving is achieved by the following strategies. First, the use of super-capacitor technology, allowed to meet the demanding dynamic power range requirements, to fully exploit the battery capacity. Furthermore, sensors triggering are collectively transmitted once per hour, rather than at each occurrence, which provides acceptable latency for long-term data analytics.
# Efficient Gene Transfection by Electroporation--In Vitro and In Silico Study of Pulse Parameters
## 4 Discussion  
Electroporation with long pulses in vitro and in vivo is a well-established and efficient method for GET [55,66]. Most often, a train of pulses in the range of few to several tens of millisecond is used, although even shorter, i.e., 100-500 us, pulses are used. With expanding the use of GET from in vitro to in vivo, the need to avoid pain and undesirable muscle contractions emerged. It was also suggested that electrochemical reactions could negatively affect GET efficiency [27, 28, 29, 30, 31, 32, 33]. The approval of cell therapies, where cells are transfected ex vivo, further strengthen the need to reduce electrochemical reactions during treatment because, usually, the number of available cells is low; consequently, it is important that most of the cells survive the treatment. Currently, when GET is performed ex vivo, the high voltage required for electroporation poses risks of cytotoxicity and loss of cytoplasmic content, which can adversely affect GET efficiency [67].  
The use of shorter pulses in GET provides a potential mitigation of the above-mentioned drawbacks of long pulses, but the HF-BP and nanosecond pulse range remains poorly explored. Currently, there are only a few studies reporting the use of HF-BP and nanosecond pulses for GET [15,40,41]. Therefore, the aim of this study was to further explore the nanosecond range of pulse durations and HF-BP pulses. We aimed to find optimal parameters for GET with 200 ns and 500 ns pulses in two different cell lines and compare efficiency and expression dynamics of different pulse durations, up to 5 ms, including HF-BP. Since the electrophoretic effect of electric pulses on pDNA in GET is considered important, we also developed a theoretical framework of the diffusive and electrophoretic movement of pDNA during different pulse protocols and compared the results to those obtained experimentally.
# Process Optimization and Wear Performance of Plasma-Cladding Fe5 Coatings on Rotary Tillage Blades
## 3 Results and Discussion
### Structure and Properties of Fe5 Coatings Under Optimized Conditions
#### 3.2.2 Microhardness of Fe5 Coatings  
The wear of materials depends on the properties of their surfaces, such as their roughness and hardness [34]. Figure 7 shows the cross-sectional microhardness variation of the Fe5 cladding layer under optimal parameters. Analysis of the figure indicates that  
Figure 5: Positions of pot scanning for EDS in the SEM image of the plasma-cladding Fe5 coatings.  
Figure 6: Elemental distribution across the plasma-cladding Fe5 coatings; (**a**) Scanning Area. (**b**) Scanning Elements.  
the overall hardness of the cladding zone is significantly higher than that of the substrate. Higher hardness can reduce the severity of wear, and there exists a gradual transition zone between the two, which can prevent the coating from peeling off under stress [18; 35]. The average hardness of the cladding layer is 1171 Hv0.5, approximately 2.9 times that of the substrate. This higher hardness can better resist wear and extend the service life.
## 3 Proposed Methodology
### Clustering Management
#### 3.2.6 Convergence Criteria  
The water cycle stops when a feature is selected, which occurs when the standard-fit does not change at a predetermined value \(\varepsilon=di_{max}\) after several iterations or achieving the largest number of generations.  
The WC is illustrated along with the classifier algorithms for a WSN-DS dataset in Algorithm 2.  
```
1. Set Mopp, Msr, dimax, Max_Iteration.
2. Execute Equations (11) and (12)/to determine the number of streams flowing into rivers and sea
3. \(k=\) Features (WSN-DS)
4. Mopp (1:\(k\)) = random initial population
5. Set the classifier algorithm (DT, KNN, SVM, DL, NB)
6. Calculate the fitness function for Mopp (1:\(k\)) using Equation (10)
7. Sort fitness values in descending order
8. F_sat = best fitness for initial populations
9. F_rier = best next fitness after the F_sea
10. F_stream = Execute Equation (13)/to determine the number of streams flow to their corresponding rivers and sea, which is considered the best fitness after F_river
11. \(t=0\)
12.while (\(t<\) Max_Iteration) do
13. for\(i=1\): Mopp do
14. new_stream = Execute Equations (15) and (16)/to find new stream flows
15. \(F\_new\_stream = Execute Equation (10) for new_stream
16. if F_new_stream < F_river then
17. \(\text{fewier = new\_stream}\)
18. if F_new_stream < F_west then
19. \(\text{sau = new\_stream}\)
20. endif // line 16
21. endif // line 18
22. \(\text{new\_river = Execute Equation (17)//to find new river flows
23. \(F\_new\_river = Execute Equation (10) for new_river
24. if F_new_river < F_sen then
25. \(\text{sau = new\_river}\)
26. endif // line 24
27. endfor // line 13
28. for\(i=1\): Msr do
29. if (distance (Sear and River) < dimax) or (rand < 0.1) then
30. Nune_stream = Execute Equation (18)
31. endif // line 29
32. endfor // line 28
33. \(t=t+1\)
34. endwhile
35. print the performance accuracy and number of selected features
```  
**Algorithm 2** WC Feature Selection method  
The WC can be integrated with various machine learning classification algorithms such as DT, SVM, KNN, DL classifier, and Naive Bayes (NB). The integrated scheme that has the best performance metrics will be determined to be a WC approach in the CH node in the WSN simulation. Furthermore, the WC technique is benchmarked with various feature selection techniques such as PSO, SA, HS, and GA on the same classifier algorithms and same dataset. Based on [62], WC has been demonstrated to be a highly efficient statistical algorithm compared to many other feature selection techniques and has demonstrated superiority over them. This is in addition to the accuracy of the algorithm in terms of the number of evaluation functions for each problem. It has also been empirically proven that WC can offer competitive solutions compared to most metaheuristics. Therefore, since the WSN traffic data is statistical, it is expected to give the best results.
## 3 Results
### Analysis of PP Beverage Cups  
The developed method was used for the analysis of the migrated NCAs from some FCM samples: PP beverage cups. Simulant D1 was used, and the specific migration testing conditions were the ones defined in the EU regulation for this type of item [3]. Results are presented in Table 5.  
The results showed that the migrated mass fractions of the NCAs ranged from 1.1 mg kg\({}^{-1}\) up to 2.6 mg kg\({}^{-1}\). The samples were compliant with the existing legislative framework [3]. Moreover, it shall be highlighted that no limits are in place for FCM no. 674, 752 and 766. Only FCM no. 808 has an SML of 5 mg kg\({}^{-1}\), but n mean no analytical standard of high purity is available commercially. This additive may originate via hydrolysis of the compound 4-propyl benzaldehyde, an FCM non-intentionally added substance (NIAS) [5]. Neither this substance nor the degradation products of the additives FCM no. 674, no. 752 and no. 766 (benzaldehyde, 3,4-dimethyl benzaldehyde and 4-methyl ben  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Analyte** & \multicolumn{4}{c}{**mg kg\({}^{-1}\) of Food \({}^{2}\)**} \\ \hline  & & **Sample 1 (PP1)** & **Sample 2 (PP2)** & **Sample 3 (PP3)** & **Sample 3 (PP3)** \\ \hline
1 & FCM no. 674 & n.d. & 1.1 & n.d. & n.d. \\
2 & FCM no. 752 & n.d. & n.d. & n.d. & n.d. \\
3 & FCM no. 766 & n.d. & n.d. & 1.2 & n.d. \\
4 & FCM no. 808 & 1.9 & n.d. & n.d. & 2.6 \\ \hline \end{tabular} \({}^{\dagger}\) migration testing 70 \({}^{\circ}\)C for 2 h with food simulant D1. \({}^{2}\) For the transformation of concentrations (ng mL\({}^{-1}\)) to mass fractions of food (ng g\({}^{-1}\)), conversion surface-to-volume of 6 dm\({}^{2}\)/kg of food was used, according to Commission Regulation (EC) no. 10/2011 [3]; n.d. = below limit of detection (LOD).  
\end{table}
Table 5: Method application to real FCM samples, after performance of migration testing \({}^{1}\).  
zaldehyde, respectively) were detected in the migration solutions. This seems to indicate that these NCAs are quite resistant in ethanol-water simulants, even at harsh migration conditions (70 \({}^{\circ}\)C \(\times\) 2 h).
## 2 Methodology
### Initial and Boundary Conditions of the Model  
Torque coefficient \(\mathrm{C_{m}}\) and power coefficient \(\mathrm{C_{p}}\) are the main aerodynamic parameters characterizing the efficiency of a vertical-axis wind turbine. The torque coefficient \(\mathrm{Cm}\) reflects the ability of the rotor to generate torque under the action of aerodynamic forces, \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Meaning** \\ \hline Aerodynamic profile & NACA 0021 \\ \hline Rotor diameter D, m & 1.5–4 \\ \hline Chord length c, m & 0.2–0.8 \\ \hline Number of blades & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Geometric parameters of the model. Figure 2: 2D geometric model. and the power coefficient Cp determines the proportion of the kinetic energy of the wind converted into useful mechanical energy. The torque coefficient and power coefficient of the vertical axis wind turbine are calculated from Equations (1) and (2), respectively. \[C_{m}=\frac{2T}{\rho ARU^{2}}, \tag{1}\]  
\[C_{P}=\frac{2 Tw}{\rho ARU^{3}} \tag{2}\]  
where \(A\)--swept area (m\({}^{2}\)), \(R\)--turbine radius (m), \(U\)--wind speed (m/s), \(\rho\)--air density (kg/m\({}^{3}\)), \(T\)--torque (N\(\times\)m), \(w\)--angular velocity of the turbine (rad/s). From Equations (1) and (2) it follows that  
\[C_{P}=\frac{Rw}{U}C_{m} \tag{3}\]  
where \(\frac{Rw}{U}=\lambda\)--tip speed ratio. The flow is incompressible and isothermal; therefore, the equations for continuity and impulse can be expressed as follows:  
\[\
abla\times\overset{\rightarrow}{v}=0, \tag{4}\]  
\[\frac{\partial\overset{\rightarrow}{v}}{\partial t}+\overset{\rightarrow}{v }\times\
abla\times\overset{\rightarrow}{v}=-\frac{1}{\rho}\
abla P+ \overset{\rightarrow}{F}+v\
abla^{2}\overset{\rightarrow}{v}. \tag{5}\]  
Equation (5) can be further derived to obtain the Reynolds-averaged Navier-Stokes (RANS) equation. Using the Reynolds-averaging method, the turbulent fluctuations are averaged and decomposed into fluctuation and mean parts to reflect the influence of turbulent flow [30]. As a result, the RANS equation becomes as follows:  
\[\frac{\partial\Big{(}\overset{\rightarrow}{\rho v}\Big{)}}{\partial t}+\
abla \times\Big{(}\rho\overset{\rightarrow}{v}\overset{\rightarrow}{v}\Big{)}=- \
abla P+\overset{\rightarrow}{F}+\
abla\Big{(}\mu\
abla\overset{ \rightarrow}{v}\Big{)}+\
abla\times\Big{(}-\rho\overset{\rightarrow}{v} \overset{\rightarrow}{v}\Big{)}. \tag{6}\]  
The last term \(\Big{(}-\rho\overset{\rightarrow}{v}\overset{\rightarrow}{v}\overset{ \rightarrow}{v}\Big{)}\) represents a turbulent fluctuation [31], which can be modeled using turbulence models shear stress transfer (SST) k-\(\omega\). In this study, the k-\(\omega\) SST turbulence model [32] was used to solve the governing equations of turbulent flow. The k-\(\omega\) SST turbulence model has the advantages of the k-\(\omega\) model for boundary layer flow and the advantages of the k-\(\epsilon\) model in free flow, where \(\epsilon\) is the dissipation rate of turbulent kinetic energy [33].
# Study on the Bearing Characteristics of Composite Foundations with Permeable Concrete Piles
## 2 The Best Mix Ratio Design of Permeable Concrete Piles
### Experiment Method
#### 2.1.3 Test Method  
In this experiment, the compressive strength, porosity, and permeability coefficient of pervious concrete were primarily measured. The specific measurement methods are as follows.  
(1) Compressive Strength  
According to the \"Standards for Testing Methods for Physical Mechanical Properties of Concrete\" (GB/T 50081-2019) [17], the test specimen was dried in an oven at 45 \({}^{\circ}\)C for 24 h, and then the compressive strength test was performed. The size of the specimen is \(100\:\mathrm{mm}\times 100\:\mathrm{mm}\times 100\:\mathrm{mm}\). The final compressive strength is the average of the three samples.  
(2) Porosity  
Pervious concrete contains both connected and closed pores. Based on Archimedes' principle [18], specimens were immersed in water for 24 h, and their submerged mass (\(M_{1}\)) was recorded. After drying, the dry mass (\(M_{2}\)) was recorded [10]. The porosity was calculated using the following formula:  
\[\mathrm{Porosity}(\%)=(1-\frac{M_{2}}{M_{1}})\times 100 \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Particle Size** & **Apparent Density** & **Bulk Density** & **Water Absorption Rate** & **Crush Value** \\
**(mm)** & **(kg/m\({}^{3}\))** & **(kg/m\({}^{3}\))** & **(\%)** & **(\%)** \\ \hline
4.75–9.50 & 2700 & 1520 & 0.6 & 8.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical parameters of aggregate.  
(3) Permeability Coefficient  
According to the \"Technical Code for Pervious Cement Concrete Pavements\" (CJJ/T 135-2009) [16], specimens with dimensions of \(\varphi\)100 mm \(\times\) H50 mm were used (\(\varphi\) is the diameter of the sample; H is the height of the sample). Before testing, the specimens were immersed for 24 h, and their sides were sealed with paraffin wax. After fixing the specimens, water was added, and once the flow stabilized, the volume of water passing through in 5 min was recorded to calculate the permeability coefficient. The experimental setup is shown in Figure 1. Figure 1a is a diagram of the unlimited compressive strength test device through which the sample's compressive strength can be measured. Figure 1b is a water permeability test device that can measure the permeability coefficient of a concrete sample, and Figure 1c is a schematic diagram of the principle of the permeability test.  
Figure 1: Experimental procedure: (**a**) compression testing machine; (**b**) principle of permeability test; (**c**) permeation schematic.