esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning for Evaluating Hospital Mobility: An Italian Case Study
## 4 Experimental Results and Discussions
### Experiments
Logistic regression, chosen for predicting hospital mobility levels (low, medium, high), is distinguished by an AUC of 0.969. This index, being the primary metric for comparing prediction algorithms, reflects the model's high ability to differentiate between the predicted classes, a critical aspect for ensuring precision in clinical and operational decisions. The AUC, by measuring the model's quality across the entire spectrum of classification thresholds, provides an assessment independent of the specific distribution of classes in the dataset, a fundamental aspect when considering multiple outcome categories. Logistic regression, with its probabilistic nature, offers a robust interpretative framework and flexibility in adapting to multiclass dependent variables, making it particularly suitable for addressing our tripartite target variable. The rigor in model validation was uniformly maintained for all, using 10-fold cross-validation and dividing the dataset into a proportion of 70% for training and 30% for testing, thus ensuring the robustness and generalizability of the predictive performances. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **AUC** & **CA** & **F1** & **Prec** & **Recall** & **MCC** \\ \hline Logistic Regression & 0.969 & 0.876 & 0.869 & 0.871 & 0.876 & 0.758 \\ \hline SVM & 0.959 & 0.876 & 0.866 & 0.874 & 0.876 & 0.760 \\ \hline Random Forest & 0.957 & 0.865 & 0.865 & 0.865 & 0.865 & 0.739 \\ \hline Neural Network & 0.953 & 0.865 & 0.859 & 0.858 & 0.865 & 0.735 \\ \hline kNN & 0.945 & 0.865 & 0.865 & 0.865 & 0.865 & 0.741 \\ \hline Gradient Boosting & 0.930 & 0.876 & 0.874 & 0.873 & 0.876 & 0.759 \\ \hline Naive Bayes & 0.924 & 0.787 & 0.791 & 0.814 & 0.787 & 0.629 \\ \hline AdaBoost & 0.870 & 0.854 & 0.855 & 0.858 & 0.854 & 0.726 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance parameters of prediction models.
## 4 The Proposed Two-Step Access and Handover Strategy
### Utility Function-Based GEO/LEO Satellite Network Selection
#### 4.1.2 Problem Formulation  
After obtaining the delay utility function and the availability utility function, the network selection utility function of user \(i\) to satellite \(s\) is expressed as:  
\[U^{i,s}(t)=U_{d}^{i,s}(t)U_{u}^{i,s}(t) \tag{9}\]  
Letting \(x^{i,s}(t)\) be the decision variable, \(I\) be the set of users, and function \(g(U)\) be set as:  
\[g(U)=\begin{cases}1&,U>0\\ 0&,U=0\end{cases} \tag{10}\]  
the network selection problem is formulated as follows:  
\[\begin{array}{ll}\max\limits_{x_{i,s}(t)}&\sum\limits_{i=1}^{l}\sum \limits_{s=1}^{2}x_{i,s}(t)U^{i,s}(t)\\ s.t.&\text{C1}:x_{i,s}(t)\in\{0,1\}&\forall i\\ \text{C2}:\stackrel{{}}{{\sum}}_{s=1}^{2}x_{i,s}(t)\leq 1&\forall i\\ \text{C3}:x_{i,s}(t)\leq g\big{(}U^{i,s}(t)\big{)}&\forall i,s\end{array} \tag{11}\]  
C1 is the constraint of \(x^{i,s}(t)\). If \(x^{i,s}(t)=1\), user \(i\) connect to satellite \(s\), while \(x^{i,s}(t)=0\) means user \(i\) does not connect to satellite \(s\). C2 restricts each user to connect at most one satellite at the same time. The C3 constraints that user \(i\) will not connect to satellite \(s\) when delay exceeds the tolerance or the satellite \(s\) has no available channel.
## Results  
### Molding  
The cores as well as the replicates of the tube and the manifold were created successfully. The silicone Elastosil(r) M 4601 has been found suitable for molding the core. With this material, it is possible to guarantee a detailed core reproducing the original internal geometry and being easily removable from the tube. For the second and decisive cast the transparent Elastosil(r) RT 601 has proved to be particularly good. The two silicones do not bond and can therefore be easily separated from each other. There are also no residues in the replicated part. The results of the experiments are depicted in Figures 4 and 5.  
The removal of the silicone core from the welded manifold also proceeded without any problems. Therefore, due to the high elasticity of the silicone this molding technique can also be used for replicating complex fluidic structures like manifolds with different branching angles.  
If the diameter of the fluidic components is too small, the silicone core can break when pulled out. If the diameter of the test samples is too big, the removal of the core can also become more troublesome, because it will be more difficult to deform the material.  
Figure 4: (**a**) Welded tube and silicone core made of Elastosil® M 4601; (**b**) silicone replication made of Elastosil® RT 601 inside the aluminum frame with fluidic connections for \(\upmu\)-PIV analysis.  
Figure 5: (**a**) Welded manifold with 90\({}^{\circ}\) angle; (**b**) silicone core made of Elastosil® M 4601; (**c**) silicone replicate made of Elastosil® RT 601 inside the aluminum frame with fluidic connections for \(\upmu\)-PIV analysis.  
The surface properties of the silicone replication, like roughness and surface energy, differ from those of the original component. Although this molding technique provides a very accurate reproduction of geometrical surface details that are sufficient for this investigation, it has to be kept in mind that microfluidic effects would increase as the geometries become even smaller (e.g., influence of surface roughness, wall slip, entrance effects) [19]. It would therefore be necessary to closely investigate the respective properties of the original component and its silicone replica: for example, surface roughness and surface energy.  
In the present study, the diameter of the tube is in the preferred range of several millimeters and the reproduction of the surface worked very well. The influence of the wall effects is negligibly small. Furthermore, experimental investigations were carried out with only low volume flows and an open fluid pathway, so no deformation of the silicone due to fluidic pressure could be observed.
## 1 Introduction
Even though recent research works are developing piezo technology in a variety of applications by using a wide range of materials [30; 31; 32; 33; 34; 35; 36; 37], we are presenting a new approach in the piezo technology systems that functions as a data pipeline for future research on a variety of artificial intelligence (AI) or machine learning (ML) modeling applications. This method integrates COMSOL's electromechanical principles with machine learning. An intelligent Piezoelectric-based wearable system for estimating calorie intake using ML was developed by [31]. However, our proposed research methodology and study details are distinct from theirs. The results of this study show that a load's speed, location, and mass may be deduced from voltage measurement matrices. When compared to on-site data collection, the time and money saved by using simulated data are substantial. The use of COMSOL Multiphysics in the planning stages of constructing a digital data pipeline for a smart sensor design is of critical relevance to the development of AI and ML in the future. Further studies [38; 39; 40] extend and provide specifics on many structural design characteristics, such as load mass, speed, and piezo sensor dimension, all of which are crucial to developing an effective ML/AI modeling pipeline for utilizing the data. Our proposed model architecture is comprised of several piezo sensors that measure the voltage produced by a variation in the speed and mass of a load's pressure on the system. This makes it possible to figure out the mass of a load and how fast it is moving.
# Limit Equilibrium Models for Tunnel Face Stability in Composite Soft-Hard Strata
## 4 Analysis of Numerical Results
### Deformation Analysis  
Figure 6 depicts the total displacement contours of composite strata at the limit state, with the interface between the soft and hard layers represented by a white dashed line. The legend is scaled from 0 to 1.1 m. The visualization highlights that the majority of soil displacement occurs within the soft soil, whereas the hard rock experiences relatively minimal displacement. As the SA progressively increases, the maximum displacement of the tunnel face shifts upward from its center located within the soft soil layer. For instance, when SA is 1.0, the largest displacement of the thrust surface is observed at 1/4 \(D\) (upper portion). Moreover, as the SA continues to increase, the disturbance originating from tunnel face instability propagates toward the surface ground, consequently amplifying ground deformation. These observed phenomena stem from the fact that, when maintaining constant values for tunnel diameter and cover depth, a larger SA indicates a larger proportion of soft soil within the tunnel face. This, in turn, increases the challenges associated with maintaining tunnel face stability and the radius of instability propagation, thus expanding the extent of disturbance within the soil layer.  
Figure 7 depicts the shear strain contours of composite strata at the limit state, with the legend range set from 0 to 0.3. Similar phenomena can be observed, where the shear strain within the strata is predominantly concentrated in the soft soil layer, whereas the shear strain of the hard rock layer is less prominent. This observation aligns with the findings shown in Figure 6. In general, tunnel face instability is primarily induced by shear failure in the soil layer. The small strain-hardening model adheres to the Mohr-Coulomb failure criterion, which indicates that strata deformation sharply increases and results in substantial displacement and instability of the tunnel face when the stress in the soil layer exceeds the shear strength. Consequently, the contours presented in both Figures 6 and 7 complement each other, providing a comprehensive understanding of the phenomena.  
Figure 6: Total displacement contours under the limit conditions.
## 4 Carotenoids Analysis in Plant-Based Foods
### Vegetable Oils and Fats  
Vegetable oils and fats, especially from avocado, palm, and olive, are rich sources of non-polar carotenoids such as lutein, lycopene, and \(\beta\)-carotene. However, their lipophilic nature complicates carotenoid extraction and quantification, requiring efficient solvent extraction methods as those discussed in the previous section followed by saponification to remove interfering lipids before chromatographic separation [98]. Advances in detection methods, such as DAD and MS, have improved the identification of carotenoids in complex oil matrices with greater specificity and sensitivity [99; 100].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Carotenoid** & **Food Source** & **Determination** & **Refs.** \\ \hline \multirow{3}{*}{Lycopene} & Tomatoes, Microalgae, rosehip & HPLC-DAD & \\  & Tomatoes, Microalgae, rosehip & UHPLC-DAD & [8; 45] \\  & & SFC-PDA-QTOF MS & \\ \hline \multirow{3}{*}{\(\beta\)-Carotene} & Tomatoes, Sweet Pepper, Soybean, & HPLC-PAD-MS/MS & \\  & Peach Palm, Maize (_Zea mays_ L.), & HPLC-UV-VIS & [8; 21; 37; 40; 45; 92; 93; 94] \\  & Microalgae, rosehip, orange peel, & SFC-PDA-QTOF & \\  & Phoenix sylvestris fruit epicarp & MSHPTLC-UV & \\ \hline \multirow{3}{*}{Lutein} & Tomatoes, Soybean, Goji Berry, & HPLC-DAD & \\  & Durum Wheat Pasta, Microalgae, & HPLC-UV-VIS & \\  & rosehip, Phoenix sylvestris fruit & SFC-PDA-QTOF MS & [12; 21; 37; 40; 45; 95; 96] \\  & epicarp & HPTLC-UV & \\ \hline \multirow{3}{*}{Zexanthin} & Sweet Pepper, Soybean, Goji & HPLC-PAD-MS/MS & \\  & Berry, Microalgae, rosehip & HPLC-UV-VIS & [21; 45; 92; 95] \\  & & SFC-PDA-QTOF MS & \\ \hline \multirow{3}{*}{Capsanthin} & Sweet Pepper, Chili Peppers & HPLC-PAD-MS/MS & \\  & HPLC-UV/Vis & [92; 97] \\ \hline \multirow{3}{*}{Astaxanthin} & Microalgae, rosehip & HPLC-MS & \\  & SFC-PDA-QTOF MS & [45; 95] \\ \hline \multirow{3}{*}{\(\gamma\)-Carotene} & Peach Palm & HPLC-DAD & [93] \\ \hline \end{tabular} Ref., reference; HPLC, high-performance liquid chromatography; UHPLC, ultra-high-performance liquid chromatography; DAD, diode array detector; PAD, photodiode array detector; MS/MS, tandem mass spectrometry; HPLC-MS, high-performance liquid chromatography-mass spectrometry; UV-VIS, ultraviolet-visible spectroscopy; SFC, supercritical fluid chromatography.  
\end{table}
Table 4: Analytical methods and food sources of carotenoids.  
Moreover, the structural instability of carotenoids due to oxidation and thermal processing poses additional challenges, as degradation products may interfere with quantification. In this context, C30 columns are particularly useful for enhancing the separation of geometrical isomers, which frequently occur due to oil refining and storage conditions [101]. Studies focusing on olive oil analysis have demonstrated how optimizing chromatographic conditions is essential to accurately measure carotenoid content throughout processing and storage [102].
## 4 Performance Evaluation and Comparison
### Evaluation Metrics  
In the process of rigorously evaluating the performance of our LSTM-based intrusion detection system (IDS), we rely on a set of standard evaluation metrics that facilitate a comprehensive assessment of the model's effectiveness in detecting network intrusions. These metrics play a pivotal role in gauging the accuracy and reliability of our proposed IDS, thereby guiding us toward valuable insights regarding its performance.
## 3 Results
### Biomarker Concentrations  
Figure 1A-E provides an overview of plasma concentrations of the determined cardiovascular biomarkers depending on echocardiography measured sPAP (<40 mmHg vs. \(\geq\)40 mmHg). In Table 2, the numerical values of the collected biomarkers are given with the median \(\pm\) IQR for a better overview.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**sPAP \textless{} 40 mmHg**} & \multicolumn{2}{c}{**sPAP \textgreater{} 40 mmHg**} \\  & \multicolumn{2}{c}{**n = 32**} & \multicolumn{2}{c}{**n = 53**} \\ \hline \multicolumn{4}{c}{**sST2 (pg/mL)**} \\ \hline sST2 baseline—median \(\pm\) IQR & 10,750.80 & 5894.78 & 16,134.67 & 13,351.82 & **0.007** \\ sST2 24 h—median \(\pm\) IQR & 14,220.25 & 11,628.33 & 17,372.76 & 13,826.10 & **0.006** \\ sST2 96 h—median \(\pm\) IQR & 9919.84 & 4239.27 & 13,217.80 & 8591.19 & **0.014** \\ sST2 3 months—median \(\pm\) IQR & 10,120.52 & 4258.88 & 10,671.73 & 7354.71 & **<0.001** \\ sST2 12 months—median \(\pm\) IQR & 11,179.72 & 9233.51 & 12,142.09 & 10,029.74 & 0.092 \\ \hline \end{tabular}
\end{table}
Table 2: Tabular comparison of all collected cardiovascular biomarkers at baseline and 24 h, 96 h, 3 months, and 12 months after TAVR in patients with an sPAP < 40 mmHg and an sPAP \(\geq\) 40 mmHg. sST2: soluble suppression of tumorigenicity-2; GDF-15: growth differentiation factor-15; H-FABP: heart-type fatty acid binding protein; IGF-BP2: insulin-like growth factor binding protein 2; suPAR: soluble urokinase-type plasminogen activator receptor. Bold numbers: \(p\leq\) 0.050.  
Figure 1: Serum concentrations of several biomarkers in patients with an sPAP < 40 mmHg and with an sPAP \(\geq\) 40 mmHg. * \(p\leq\) 0.05; ** \(p\leq\) 0.01; *** \(p\leq\) 0.001. (A): sST2; (B): GDF-15; (C): H-FABP; (D): IGF-BP2; (E): suPAR.
## Materials and Methods
### Taguchi Orthogonal Array  
Taguchi orthogonal arrays are highly fractional designs, used to estimate main effects using only a few experimental runs. Taguchi full fractional trail requires a total of 3\({}^{\mathrm{k}}\) runs for k number of factors to be studied. In such an experimental design, k runs are used to study the \"effects\" of factors, while the remaining runs are used to track all possible interactions between these factors. When an investigation of every possible interaction becomes time-consuming and expensive, a Taguchi fractional factorial trial could be used to analyze only the \"main effects\" which give sufficient information from only a few experimental trials. The main effects refer to the contributing factors themselves as an individual. themselves as an, not effects due to interactions between the factors. Nine sets of laminate samples were produced using bagging techniques (SVB, MSVB, and DVB), curing profiles (MRCC, EMRCC, and DC) and laminate shapes (concave and convex). Each set of experiments is repeated three times to ensure the repeatability. Taguchi L\({}_{9}\) orthogonal array was used for both concave- and convex-cornered composite laminates, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & **Bagging Techniques (A)** & **Curing Profiles (B)** \\ \hline
1 & SVB & (A\({}_{1}\)) & MRCC & (B\({}_{1}\)) \\
2 & SVB & (A\({}_{1}\)) & EMRCC & (B\({}_{2}\)) \\
3 & SVB & (A\({}_{1}\)) & DC & (B\({}_{3}\)) \\
4 & MSVB & (A\({}_{2}\)) & MRCC & (B\({}_{1}\)) \\
5 & MSVB & (A\({}_{2}\)) & EMRCC & (B\({}_{2}\)) \\
6 & MSVB & (A\({}_{2}\)) & DC & (B\({}_{3}\)) \\
7 & DVB & (A\({}_{3}\)) & MRCC & (B\({}_{1}\)) \\
8 & DVB & (A\({}_{3}\)) & EMRCC & (B\({}_{2}\)) \\
9 & DVB & (A\({}_{3}\)) & DC & (B\({}_{3}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental plan for both concave- and convex-cornered composite laminates using Taguchi L\({}_{9}\) orthogonal array.  
Figure 2: The mold with a concave corner and a convex corner inclined at 45\({}^{\circ}\), used for the manufacturing of laminated composite samples.
## 3 Results and Discussion
### Determination of Relative Recovery for Sampling Filter  
The RRs of four TSNAs in aerosol captured on the filters were measured by the RF of L-WS and MCT approach (Supplementary Table S1) [41; 46; 48]. The average RRs of four TSNAs that were collected onto CFP were relatively low: 78.0 \(\pm\) 2.04 for NNN, 61.3 \(\pm\) 3.36 for NNK, 50.2 \(\pm\) 2.05 for NAT, and 50.8 \(\pm\) 1.74% for NAB. The low recovery of CFP might be attributed to the high dilution factor (365 \(\pm\) 40.2, Supplementary Table S2) in the extraction procedure. In the process of puffing with the automatic sampling device a small amount of smoke did not pass through the filter and leaked out to the EC air hole.  
In all of the aerosol samples that were collected on the QW filter, NNN and NAB had high recovery relative to NNK and NAT. The RRs of four TSNAs for each concentration of spiked sample were more stable at low concentrations (92.2 \(\pm\) 6.98 for NNN, 82.4 \(\pm\) 4.00 for NNK, 82.7 \(\pm\) 9.40 for NAT, and 101 \(\pm\) 8.07% for NAB, in 100 ng mL\({}^{-1}\)) as compared to high concentrations (77.5 \(\pm\) 12.1 for NNN, 66.2 \(\pm\) 2.00 for NNK, 67.7 \(\pm\) 9.30 for NAT, and 88.5 \(\pm\) 10.2% for NAB, in 1000 ng mL\({}^{-1}\)). The concentrations that were calculated after pretreatment for each filter were 54.5 \(\pm\) 11.0 for CFP and 89.6 \(\pm\) 8.71% for QW. Therefore, high recovery and low relative standard error (RSE) values were observed for the QW filter (Figure 2).
# Reputation-Based Trust Approaches in Named Data Networking
## 2 Background  
### Reputation-Based Trust  
In reputation systems, users assign ratings to service or resource providers. Those ratings represent a judgment of their direct interactions' quality. Eventually, trust is computed from the aggregation of ratings concerning local experiences, taking into account the feedback that is being provided by other network entities. A prominent example of a reputation-based system implementation is electronic commerce. E-markets, such as eBay and Amazon, have adopted reputation as a means for trust enforcement as well as an incentive to encourage transactions with unknown providers. On these platforms, reputation management is achieved through a centralized framework. This requires the existence of a trusted third party which collects the ratings and computes the final trust scores.  
The deployment of trust management in commercial fields has triggered the extension to research in peer-to-peer (P2P) networking and multi-agent systems (MAS). The main feature of these networks that necessitates the existence of a mechanism for trust establishment, is the need of peers to identify if the intentions of other agents are reliable or malicious [9; 10]. Contrary to online reputation models, the decentralized design of peer-to-peer networks and multi-agent systems requires a distributed approach. PeerTrust [11] proposes a distributed mechanism aiming to effectively evaluate the trustworthiness of peers and to detect various malicious behaviors in a P2P eCommerce community.  
At the same time, trust has been investigated as an issue in Mobile Ad-hoc Networks (MANETs) and Wireless Sensor Networks (WSNs). In MANETs, multiple trust management schemes have been designed to address different goals, namely secure routing, authentication, intrusion detection, access control, key management and trust evidence [12]. More specifically, reputation-based approaches are typically used to secure routing, while credential-based approaches are preferred for the rest of the applicability domains. Characteristic of MANETs, is the fact that many trust-based protocols for secure routing derive trustworthiness from network-layer behavior and hence, networking parameters such as throughput or overhead are considered trust metrics.  
WSNs have been another field of related research [13]. Similar to MANETs, trustworthiness in WSNs can be measured either by considering networking aspects (e.g., throughput, delay etc.) or by using typical reputation- and policy-based approaches. As reputation-based models require a reputation exchange protocol, they introduce significant communication overhead in resource-constrained WSNs, also impacting node energy efficiency. Their implementation in such environments is therefore challenging. One solution that has been explored in this context, is piggybacking reputation values on the routing packets. The trade-offs between the desired level of trust and resource availability in a wireless sensor network, demand that protocol design is tailored to the specific application requirements.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 6 Open Research Challenges  
While much research has been carried out on IA in CR networks, there still exist some open challenges that are discussed in this section.