esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
## 5 Conclusions  
This paper presents a parametric analysis on a NACA-8412 airfoil with the aim to optimize three AFC parameters, momentum coefficient, jet inclination angle and pulsating frequency, associated to a SJA. The procedure followed to perform a parametric optimization of any airfoil is established and presented. A maximum airfoil efficiency increase, measured respect to the baseline case, of 276% is obtained for \(C_{\mu}=0.003\); \(F_{*}^{+}=4\) and \(\theta=40^{\circ}\), the groove was located from \(x/C=0.08\) to \(x/C=0.09\) being its width of 0.01C. The maximum airfoil lift was obtained for the same AFC parameters except the pulsating frequency which was of \(F_{*}^{+}=3\). The efficiency increase with respect to the baseline case  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Cases** & \(\mathbf{\alpha}^{\circ}\) & \(\mathbf{U_{max}}\) [m/s] & \(S_{j}\) [m\({}^{2}\)] & \(\theta^{\circ}\) & \(W_{j}\) [W] & \(W_{G}\) [W] & \(W_{G}\)/\(W_{j}\) \\ \hline max efficiency & 15 & 0.6832 & 0.01 & 40 & 5.3277 \(\times\) 10\({}^{-4}\) & 0.0889 & 166.81 \\ max lift & 15 & 0.6832 & 0.01 & 40 & 5.3277 \(\times\) 10\({}^{-4}\) & 0.0780 & 146.35 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Power ratio values characterizing the maximum efficiency and maximum lift configurations.  
was of around 218%. From the comparison of the AFC parameters for maximum lift and maximum efficiency cases, it is proved that the pulsating frequency is capable of highly improving the airfoil efficiency.  
Conceptualization, J.M.B. and N.C.; methodology, N.C. and J.M.B.; software, N.C.; validation, N.C.; formal analysis, N.C. and J.M.B.; investigation, N.C. and J.M.B.; data curation, N.C.; writing--original draft preparation, N.C. and J.M.B.; writing--review and editing, J.M.B.; visualization, N.C. and J.M.B.; supervision, J.M.B.; project administration, J.M.B. All authors have read and agreed to the published version of the manuscript.  
No funding was associated to this project.  
Not applicable.  
The authors declare no conflict of interest.  
Appendix A Lift, Drag and Efficiency Results for the Different \(\theta\) and \(C_{\mu}\) Values Studied
## 5 Discussion
Obviously, the MLP model is the most appropriate and stable model because of its ability to constructs and weight the conditioning factors using a nonlinear projection, unlike the LR model that analyzes the conditioning factors as linear functions [21]. The MLP model, for each training sample subset, calculates the neuron output from each layer and makes a prediction with the final layer (forward pass). The prediction is based on how fast the variation between the predicted and the actual output is calculated to get the prediction error, which is subsequently used to vary the weights of the neurons in all of the previous layers (backpropagation) until it reaches the optimal prediction accuracy [43; 85]. This intricate procedure allows it to handle both linear and nonlinear datasets accurately. The result of the SVM model based on the standard evaluation [86; 87] is normally acceptable, but in this study, it is observed that the classification map appears more suitable than it is in the actual situation. This is traceable to the influence of the neighboring training points of the optimal hyperplane using the radial basis function which employs a nonlinear kernel function to project a linear model [22; 88; 89; 90], even though not all of the conditioning factors exhibit a linear feature. As a result, error is introduced in the classification result, particularly in the very high and very low suitability classes. Similarly, the LR model employs a linear function [52], contrary to the characteristics of the conditioning factors. For instance, it is observed that some conditioning factors which have a high relative influence, in turn have a negative effect on determining hospital site suitability. In the Gaza Strip, road, river, and population density influenced the LR model most but contrary factors such as altitude and slope, both of which have a low relative influence, indicate a relatively high sensitivity in the resulting suitability map. Perhaps, testing SVM with other kernels may improve the accuracy but caution needs to be observed with the LR model.
# Image De-Quantization Using Plate Bending Model
## 4 Material and Methods
### Boundary Conditions  
The quantization error of the signal can be taken into account with an upper (\(U\)) and lower envelope (\(L\)). The original signal cannot be larger than the upper envelope, and cannot be smaller than the lower envelope:  
\[L(x,y)\leq f(x,y)\leq U(x,y). \tag{6}\]  
The distance between the envelopes does not have to be constant. If there is a priori knowledge about the signal, or the quantization levels are uneven, then appropriately chosen envelopes could take care of this problem. For instance, if the two envelopes touch each other, then it is equivalent with a Dirichlet boundary condition.  
Numerical solution becomes much more tractable if mirrored periodic boundary conditions are assumed. This is a frequent assumption which does not limit the generality of the approach. Both \"odd\" and \"even\" mirroring could be used. In our examples, we used \"odd\" mirroring, which subtracts the reflected values two times from the edge value. Odd mirroring is depicted in Figure 2.  
Non-Continuous Regions  
The envelopes should only be defined for the regions of interest. In irrelevant regions, the envelopes should take maximum/minimum allowed values. Using the plate analogy, supporting points (defined envelope values) can bend the plate. In irrelevant regions there is no external force;  
Figure 2: Two dimensional simplified example of mirrored periodic boundary conditions. The image is represented as a red P, and its edges are outlined with a black line. The reflected signals are in black.  
the plate is continuous as the biharmonic equation governs it. We use a binary mask to define regions which should not be processed and where the envelope limits should be removed. After solving the the numerical problem, these regions are kept intact from the low-resolution input image.
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 6 Performance Evaluation
### Simulations Environment  
In order to perform our simulations, we designed a custom simulator using the OMNet++ framework [54] building ad-hoc modules for the architecture implementation. Protocol messages and interfaces have been implemented in compound modules written in c++ language by extending _Veins_ and _Intet_ module. In particular, Inet framework has been utilized for creating the edge/fog layers and cloud services. These custom modules permit to realize the LRTM and HRTM modules, with the related communication interfaces. Vehicle environment has been realized by using the _Veins_ module [55] that implements the IEEE802.11p/WAVE protocol and DSRC model. For creating the mobility scenarios we connect SUMO framework [56] with _Veins_. Reference maps have been obtained from real road maps by importing City area from Open Street Maps service. They permit to realize real traffic scenario adopting dynamic behavior. Maps have been populated by choosing sources and sink nodes in the outer region of the maps. Table 4 shows the simulation parameters used in the proposed architecture. In the next section we considered the following parameters:  
* _Number of Collision_: it represents the number of collisions due to an accident event in the map;
* _Average Speed_: it is the average speed of the car during its travelling in the map;
* _Travelling Time_: it is the time spent by a car during its travelling;
* _Average Distance_: it is the average distance travelled by car during its path in the map;
* _Number of Packets_: it represents the total number of protocol messages sent during the simulation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Param. Name** & **Param. Value** & **Description** \\ \hline City Area size & 11 Km \(\times\) 12 Km & City map area size managed by ITS \\ Number of FLows & 4–6 & Regulate vehicle density in the monitored area \\ Flow 1 & 50–300 & From 8 a.m. to 12 p.m.: _Vehicle per Hour_ (Vph) \\ Flow 2 & 50–300 & From 8 a.m. to 2 p.m.: _Vehicle per Hour_ (Vph) \\ Flow 3 & 50–300 & From 8 p.m. to 3 p.m.: _Vehicle per Hour_ (Vph) \\ Flow 4 & 50–300 & From 8 p.m. to 6 p.m.: _Vehicle per Hour_ (Vph) \\ Flow 5 & 50–300 & From 14 p.m. to 7 p.m.: _Vehicle per Hour_ (Vph) \\ Flow 6 & 50–300 & From 14 p.m. to 7 p.m.: _Vehicle per Hour_ (Vph) \\ \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 3 Disruption Tolerant Distributed Routing Algorithm  
### Algorithm Overview  
Our routing algorithm is divided into four parts. The first part introduces the periodic topology discovery process of the satellite network. The second part and the third part are based on the established network topology. Each satellite node calculates its main path direction and alternate path direction to all nodes in the whole network in a distributed manner and forms the main path routing table and the alternate path routing table. In these two parts, the horizontal and vertical hop directions of each source-destination node pair are calculated first. The main path direction is to select from these two directions according to the shortest path criterion and the alternate path direction is the remaining one of the two directions. The fourth part introduces the transmission method of the link failure information, and the algorithm that the node receiving the information calculates the detour path of the failure area and forms the detour path routing table. In this part, the satellite node checks the routing information within a specified number of hops it maintains. For each failed link, it determines whether a detour is required according to whether the main path to each destination node passes through the link and forms a forwarding table. Finally, it combines all of the forwarding table to form the detour path routing table.  
When the satellite node forwards data packets, it first uses the main path routing table, and when the link directly connected to it fails, it switches to the alternate path routing table. After receiving the link failure information, the satellite node switches to the detour path routing table. The details of the priority of these three routing tables is introduced in Section 4.2. \"Forwarding Process and Loop Avoidance\".
## 4 Discussion
The performance metrics were calculated for each model, and statistical significance tests were employed to determine if the differences in performance were due to the model's predictive capability rather than random variation. Our findings revealed that while certain models, such as the Random Forest, demonstrated comparable predictive accuracy, our GBM model, interpreted through SHAP values, provided additional interpretability which is indispensable in clinical decision making. Particularly, the ability to elucidate the intricate interactions between features like 'Acuity', 'Hours', and 'Age' with patient outcomes is a distinct advantage that supports healthcare professionals in understanding the model's rationale, a qualitative benefit that goes beyond numerical performance measures. Moreover, the integration of SHAP plots for individual-level interpretation aligns with personalized medicine objectives, offering insights that are not available through traditional global importance measures. Future research will be aimed at furthering these comparisons, considering not only performance metrics but also aspects such as computational efficiency, ease of model update, and interpretability, to reinforce the practicality of our GBM-SHAP methodology in emergency healthcare settings. The use of GBMs in conjunction with SHAP values thus offers an innovative solution to the trade-off between predictive power and interpretability. This methodology can be applied in various healthcare settings to derive meaningful insights from complex data, thereby supporting evidence-based clinical decision making [14,29,33]. While our findings have practical implications for emergency departments, they also contribute to broader discussions around the interpretability of machine learning models in healthcare. Future research could explore other feature interpretation methods and model types, as well as apply these methods in different healthcare contexts. While our approach uses the same machine learning algorithm (GBM) and explanation method (SHAP) as [PERSON] et al. [28], our study is distinguished by the use of a different dataset (MIMIC-IV) and focuses on predicting hospital admissions in an emergency department setting. This provides unique insights into patient outcomes specific to our dataset. In conclusion, this study has demonstrated the value of using GBMs and SHAP values for interpreting complex machine learning models in healthcare. These methods offer a powerful tool for understanding and predicting patient outcomes, thereby supporting evidence-based practice in healthcare settings.
# Hybrid CMP Slurry Supply System Using Ionization and Atomization
## 3 Results  
### Global Planarization  
Figure 5 illustrates the MRR and uniformity of the 300-mm Cu NPW CMP using four types of slurry supply system. As indicated in the figure, the three slurry supply systems exhibit higher MRR and lower NU than those of the conventional system. The hybrid and ionization systems demonstrate 23% higher MRRs, whereas the NUs of the hybrid and atomization systems are approximately four times lower than those of the conventional system. Thus, we concluded that the efficiencies of the material removal and planarization were improved by the ionization system and the atomized slurry, respectively. Therefore, the proposed hybrid system is a combination of these two systems, achieving high MRR and uniformity.  
Furthermore, we applied the proposed system to other materials. Figure 6 illustrates the 300-mm SiO\({}_{2}\) NPW CMP results, wherein the trend is similar to that observed in the Cu CMP test. Although the MRR profiles of the three systems cannot be distinguished clearly, the performances exhibited are superior to the conventional system. The hybrid and ionization systems exhibit high MRRs of 36% and 39%, respectively. Additionally, the NUs of the hybrid and atomization systems decreased by approximately 23%. In comparison with the conventional system, the effect of improved slurry supply systems on MRR is similar to that of the Cu CMP. However, the NU did not reduce when the ionization system was applied as the effect of the lubrication mechanism on the uniformity of the SiO\({}_{2}\) CMP is higher than that of the chemical mechanism.  
Figure 5: 300-mm Cu non-patterned wafer (NPW) CMP results based on the types of slurry supply system. (**a**) MRR profiles and (**b**) average MRR and NU.  
Figure 6: 300-mm SiO\({}_{2}\) NPW CMP results based on the types of slurry supply systems. (**a**) MRR profiles and (**b**) average MRR and NU.
## 4 Relay Scheme based on FAHP
### Fuzzy Analytical Hierarchy Process  
Fuzzy AHP is a technique used to solve many complex problems, and it is efficient in decision-making problems, which usually are complicated to manage. In AHP, the decision is made based on priorities, and the best decision is selected among the alternatives. AHP consists of three levels: the main goal, the evaluation criteria, and the alternatives. During a decision-making process, the alternatives are compared with each other, which helps in the selection of the best choice. Pairwise AHP is helpful in reducing complicated decisions and becomes helpful for decision makers, allowing them to reduce decision biases. In AHP, every criterion has a weight assigned that indicates its importance. The proposed criteria are then applied to the alternatives, assigning a priority to each of them [31] to select the best candidate. The objective of AHP is to select the alternative (node) that best describes the set of criteria, calculating the weights of each criterion.  
Fuzzy pairwise comparison is applied to criteria and alternatives through linguistic variables which are represented by real numbers named triangular fuzzy numbers (TFNs). A membership function is the linear representation of the TFNs. We will use a triangular shape function which can be defined as follows:  
\[\mu(x)=\left\{\begin{array}{ll}0&\quad x<l\\ \frac{x-l}{m-l}&\quad l\ \leq\ x<m\\ \frac{u-l}{u-m}&\quad m\ \leq\ x<u\\ 0&\quad x\ \geq\ u\end{array}\right. \tag{4}\]  
where \(l,\ \ m,\ \ u\), are the TFNs: \(l\) is the lower bound value, \(m\) is the middle value, and \(u\) is the upper bound value. Many operations can be performed on TFNs, and in our case, we will employ three of them: addition, multiplication, and reciprocal. These operations can be introduced by the following equations:  
\[(l_{1},m_{1},u_{1})\oplus(l_{2},m_{2},u_{2})=(l_{1}+,l_{2},m_{1}+m_{2},u_{1}+ u_{2}) \tag{5}\]  
\[(l_{1},m_{1},u_{1})\otimes(l_{2},m_{2},u_{2})=(l_{1}*l_{2},m_{1}*m_{2},u_{1}*u_ {2}) \tag{6}\]\[(l_{1},\ m_{1},u_{1})^{-1}=\left(\frac{1}{u_{1}},\frac{1}{m_{1}},\frac{1}{l_{1}}\right) \tag{7}\]  
It can be noted in (7) that in the reciprocal TFN, an increasing order of the components is needed to fulfill the definition of a membership triangular function.  
In this work, we will use both the [PERSON] [32] and [PERSON] [33] methods to calculate the final weights for selecting the best alternative. The steps of applying FAHP are given in the next subsections.
# An Overview of Biopolymers for Drug Delivery Applications
## 2 Types of Biopolymers Used in Drug Delivery Systems
### Polysaccharides
#### 2.1.6 Dextran/Cyclodext  
Among the mentioned polysaccharides, dextran has earned great interest for nanoscale drug carriers due to its availability, hydrophilicity, biocompatibility, non-toxicity, non-immunogenicity, and biodegradability [82]. Dextran is biosynthesized intra- or extra-cellularly by several microorganisms. Commercial dextran is usually obtained from _L. mesenteroides_ or _L. dextrancium_ fermentation in a media with sucrose and is an important nitrogen source. For obtaining highly biocompatible DDSs, it is recommended that dextran obtained by fermentation to be minimally modified. Contrarily, if the l structure of dextran is affected, a decrease in the biocompatibility or an increase in cytotoxicity is observed. Thus, many DDSs containing acetylated, diethyl aminoethyl-dextran, carboxymethyl, or the sodium salt of dextran sulphate have been eliminated from in vivo or clinical studies. Many DDSs with dextran were developed in the form of microspheres, micro- and nanoparticles, micelles, liposomes, hydrogel, and medical adhesives for medical and pharmaceutical applications [83]. Lately, the formation of micelles using grafted dextran for DDSs in anticancer therapy has received great attention [84]. For obtaining amphiphilic polymers with the capacity to form micelles and trap chemotherapeutic agents, dextran is bound with lipids such as oleic acid, stearic acid, and cholesterol. These kinds of polymers have great stability and rapidly reach the target cell and avoid kidney extraction [85].  
Cyclodextrins are biocompatible and biodegradable materials produced by the enzymatic degradation of starch. In recent decades, cyclodextrins and derivates represent an important class of pharmaceutical excipients that contribute to increasing the therapeutic efficiency of many drugs. They are the smallest nanocarrier used in DDSs [86]. [PERSON] et al. developed a release system using \(\beta\)-cyclodextrinrin for the antiviral drug Acyclovir. They evaluated its hydrolysis in simulated physiological media to analyze the potentiality of this prodrug for its use through different ways of administration. The release of Acyclovir was tested in both acidic and neutral conditions and in the presence of porcine liver esterase. In all cases, the 100% release of free Acyclovir and Acicyclovir succinate at differing rates as a function of hydrolysis conditions was observed within 7 days [87].  
[PERSON] et al. evaluated the potential of novel \(\beta\)-cyclodextrintrin -dextran polymers for drug delivery. The results concluded that all the studied polymers had suitable sizes for parenteral administration. The presence of the dextran backbone structure did not influence the stability of the polymer/drug complex, in comparison to the native polymer and other commercially available derivatives. The drug release studies showed that the diffusion of the hydrocortisone drug was influenced by the solubilization induced by the developed polymer derivatives [88].
## 3 Methodology
### Visualizing Data
#### 3.2.4 eVTOL  
Figure 8 compares the bigram word clouds and frequency distributions for irrelevant and relevant document sets in the eVTOL corpus. Figure 8a illustrates the irrelevant documents, where terms such as \"control component\", \"connection element\", and \"optical sensor\" dominate. These terms reflect a broad range of general engineering and component-related topics but lack specificity to eVTOL technology. Conversely, Figure 8b focuses on the relevant documents, where bigrams like \"rotor assembly\", \"propulsion assembly\", and \"electrical power\" emerge as key themes. These terms emphasize critical eVTOL design concepts, such as propulsion systems, power distribution, and aerodynamic control. The frequency distribution further highlights the importance of these terms, emphasizing their prevalence in the relevant document set.  
Figure 9 illustrates the term co-occurrence networks for irrelevant and relevant document sets within the eVTOL corpus, highlighting the relationships and thematic structures of key terms. Figure 9a represents the irrelevant document set, where terms such as \"device\", \"element\", and \"aircraft\" dominate the network. The clusters are broad and loosely connected, encompassing general topics like \"sensor\", \"component\", and \"field\", which are not specific to eVTOL technology development. These dispersed connections reflect the lack of thematic focus within the irrelevant set. Figure 9b visualizes the relevant document set, showcasing a more cohesive network centered around terms directly associated with the design of eVTOL systems. Key nodes such as \"aircraft\", \"rotor\", and \"wing\" dominate, with tightly connected clusters focusing on critical concepts like \"propulsion system\", \"vertical takeoff\", and \"flight control\". The network reflects the specific design challenges and solutions within the eVTOL domain, emphasizing interrelations among propulsion, aerodynamics, and power systems.  
Figure 8: eVTOL top bigrams in (**a**) irrelevant and (**b**) relevant documents.