esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Simulated Datasets  
The goal of the last part of the investigation was to explore the performance of AS (and Cmax/Tmax), as well as the other PK measures, under various kinetic scenarios. To achieve this goal, simulations were performed with absorption rates that were faster (2\(\times\)), slower (0.5\(\times\)), and equal (1\(\times\)) to the actual absorption rate. Thus, simulated 2 \(\times\) 2 crossover BE datasets, with N = 1000 subjects each, were generated, and machine learning was applied to them. The remaining population pharmacokinetic parameters were those obtained through non-linear mixed effect modeling and reported in the [PERSON] 2023 study [23]. In addition, the three kinetic conditions (slow, typical, and fast) were combined with the three sampling schemes (see Table 2). Overall, nine conditions of BE studies were simulated, and the simulated C-t data for these datasets are depicted in Figure 5. The impact of the sampling scheme is highlighted in Figure 5a, where the absorption rate constant is set equal to the typical estimate from the population pharmacokinetic modeling. The role of absorption rate can be seen in Figure 5b, where the C-t profiles generated from three levels of absorption rate constant are shown in the case of the \"typical\" sampling scheme. Using different sampling schemes (i.e., with different sampling densities) allows exploring the impact of the dataset on the estimation of the PK parameters and mainly on the estimation of single point parameters like Cmax, Tmax, and Cmax/Tmax. Following that, all PK parameters (see Table 1) for each scenario were calculated, and the analysis was continued by re-applying the two ML algorithms (PCA and RF) on each BE dataset.  
Figure 4: Variable importance scores for the random forest models using Tmax as the response variable. The Cmax/Tmax ratio is used in (**a**), while the average slope (AS) is utilized in (**b**). All pharmacokinetic parameters were calculated from the actual data. The quartiles of the Tmax distribution were used to define its four categories.
## 2 Results and Discussion
### Evaluation of the Effect of Independent Variables on the Extraction of VOCs  
#### 2.1.1 Extraction Temperature  
Temperature is one of the main factors influencing the yield of volatile compounds' extraction, especially in plant matrices [7; 9; 26]. This condition is due to its ability to maximize or reduce the extracted volatiles due to the potential degradation of analytes that exacerbated temperature values can trigger [33]. For the levels used in this study, the temperature significantly affected the experiment performed with DVB/CAR/PDMS and PDMS/DVB fibers.  
As for DVB/CAR/PDMS fibers, the temperature was the only variable that exerted a significant effect, maximizing the area of extracted volatiles (Figure 3). It was observed that the largest extraction areas were obtained at temperatures from 80 \({}^{\circ}\)C. Related to the region with the most significant area, the use of larger amounts of pulp is noted, indicating that, in order to obtain significant values for the volatile area, it is necessary to use high temperatures and a larger quantity of pulp. A possible explanation is the lower affinity of the compounds with the fiber coating, making it demand high values of the system variables to obtain a better response [22].  
Observing the graph of the response surface of the PDMS/DVB fiber (Figure 3), which depicts the behavior of temperature in the extraction of volatiles, it appears that temperature had a positive effect on the area of extracted volatiles. Thus, the response was maximized in the presence of heat, especially from 50 to 107 \({}^{\circ}\)C, where the region of the response surface was identified with a larger area. However, temperatures higher than 107 \({}^{\circ}\)C reduced the volatile area, indicating that although it is effective and important for the HS-SPME system in the analyzed sample, exacerbating values can negatively affect volatile extraction. A possible explanation for this is the occurrence of degradation of volatiles due to the use of high temperatures [33], which in the studied system was temperatures higher than 107 \({}^{\circ}\)C. From 50 \({}^{\circ}\)C, a positive effect on the extraction yield is already observed, indicating that applying high temperatures is unnecessary.  
Figure 2: Percentage of peak areas of volatile chemical compounds extracted by SPME-HS using different types of coating.  
In assays carried out with PA fibers, the temperature conditions had no significant effect on the extraction of volatiles. The lowest temperature values were enough to release analytes from the plant matrix and their adsorption by compatibility with the polyacrylate coating from the headspace. This reinforces that it is not necessary to use high-temperature values to maximize the yield of volatile extraction from _E. klotzschiana_ in the studied systems.
## 4 Results and Discussion
### Comparison between Simulated and Measured Results of the Pipes with Pre-Existing Cracks
#### 4.3.2 Effect of Crack Depth  
Cracks with a depths of 25, 20, and 15 mm having a length of 300 mm are created at the crown of the concrete pipe to study the influence of crack depth on the mechanical response of the pipe (see Figure 18). An external load is applied to the pipes with the pre-existing cracks step by step. The stress nephogram, when the maximum principal stress of the pipe reaches the ultimate tensile strength (2.66 MPa), is illustrated in Figure 19.  
Figure 17: Circumferential strain curve of the pipes containing cracks with different lengths: (**a**) outer wall and (**b**) inner wall.  
Figure 18: Pipes containing cracks with different depths: (**a**) 25 mm, (**b**) 20 mm, and (**c**) 15 mm.  
(2) Circumferential strain  
Similarly, the circumferential strains on the pipes containing cracks with different depths are extracted under the destructive load at eight locations, i.e. 0, 45, 90, 135, 180, 225, 270, and 315\({}^{\circ}\), in the circumferential direction, and the circumferential strains on the pipes containing cracks with depths of 25, 20, and 15 mm are analyzed. The strain curves are shown in Figure 21.  
Figure 21: Circumferential strain curve of the pipes containing cracks with different depths: (a) outer wall and (b) inner wall.  
Figure 20: Load–strain curves of the pipes containing cracks with different depths: (**a**) outer wall at the springline and (**b**) inner wall at the crown.  
Based on Figure 21, the distribution of the circumferential strain on the inner and outer walls of the middle pipe is still highly symmetrical. At the four positions of the crown (0\({}^{\circ}\)), invert (180\({}^{\circ}\)), and springline (90\({}^{\circ}\) and 270\({}^{\circ}\)), the strains on the inner and outer walls of the pipe are relatively large and positive but opposite to each other. However, the strains at the four positions of the shoulder (45\({}^{\circ}\) and 315\({}^{\circ}\)) and the haunch (135\({}^{\circ}\) and 225\({}^{\circ}\)) are relatively small, indicating that, under the action of an external load, these four positions are under relatively small stress, so they are not easily damaged. This is also consistent with the failure mode of the concrete pipes with pre-existing cracks during the test. At the position of the crown (0\({}^{\circ}\)) in Figure 21a,b, cracks with different depths have a greater influence on the strain, and deeper cracks significantly lower the bearing capacity of the concrete pipe.
## 9 Experimental Assessment and Analysis
### Experiment 1: Analysis of the Transitions Generated by the Fuzzy-Genetic Model

Table 1: Mean squared errors \(MSE^{FG-GT}\) of \(Fuzzy-Genetic\) and the mean squared error \(MSE^{Bu-GT}\), of \(Burg\) where \(Bu\) stands for Burg and \(FG\) for \(Fuzzy-Genetic\) algorithms. In the following we report some graphical examples of _Burg_, _FuzzyGenetic_ and _GroundTruth_\(PL-VO\) transitions, precisely the /ti/, /pi/, /pi/ and /pa_ diphones, with spectrograms anf second formant tracks. Another test is performed on the consistency of the \(C-V\) transitions with the \(locus;\)_theory_ of formant transitions [66; 67]. According to the locus theory, the second-formant trajectory of one consonant towards vowels, by extrapolation, points back in time to the same frequency, called the \(locus\) of that consonant. This is a perceptual invariant of each consonant's transition toward the vowels, that must be necessary for correct perception of the consonants. A comparison of the \(locus\) estimated from the spectrograms and from the \(Fuzzy-Genetic\) algorithm for the plosive phonemes /p t q d b k g/ toward all the vowels results in a 100% accuracy. An example is shown in Figure 14 related to the second-formant transitions of the phonemes /pa/, /pi/. As shown in Figure 14 the obtained curves are consistent with the trajectories derived from spectrograms. In particular, we can estimate from this Figure that the second frequency locus for the transitions, is approximately 500 Hz, a value perfectly consistent with the literature (e.g., Reference [57]). As a further example, let's consider the following transitions: /ti/, /ta/. The trend of the second formant determined by the algorithm is shown in Figure 15, and corresponds to the spectrograms. In this case the locus is about 2000 Hz, which is again consistent with the literature (e.g., Reference [57]). Using the /ti/, /ta/ diphones, we construct by concatenation the word /\(patita\)/. To verify the validity of the generation, we compare the spectrograms and the generated second formant (F2) trajectory of spoken and artificial words respectively. At the top of the Figure 16 we show the spectogram of the word /\(patita\)/ spoken by the human tutor. At the bottom we show the spectrogram of the same word artificially generated. The two spectrograms are different in timing but similar from a frequency point of view. This similarity confirm that the articulator parameters are generated correctly. We therefore can expect that facial movements are properly generated. In conclusion, all the performed objective tests give very good results, and we now show some results of subjective tests. Figure 14: Second formant transitions of /pi/pa/ diphones. Figure 15: Second formant transitions of the /ti/, /ta/ diphones.
# Soft Sets Extensions: Innovating Healthcare Claims Analysis
## 4 Soft Sets Extensions
### IndetermSoft Set
#### 4.2.2 Example  
Consider a dataset comprising healthcare claims from various patients.  
1. Indeterminacy with respect to the function:(1a) You inquire from a source:  
--\"Which patients have been diagnosed with diabetes?\"  
The source responds:  
--\"I'm uncertain; it could be patients Patient1 or Patient2\".  
Thus, _F_(diabetes) = Patient1 or Patient2 (an indeterminate/uncertain response).  
(1b) Another query:  
--\"And which patients have undergone surgery?\"  
The source replies:  
--\"I'm not certain; all I can confirm is that Patient5 has not had surgery because I have their records\".  
Thus, _F_(surgery) = not Patient5 (again, an indeterminate/uncertain response).  
(1c) Further inquiry:  
--\"Then, which patients have high blood pressure?\"  
The source asserts:  
--\"It's either Patient8 or Patient9 for sure\".  
Thus, _F_(high blood pressure) = either Patient8 or Patient9 (yet another indeterminate/uncertain response).  
II. Indeterminacy with respect to the set \(P\) of patients:  
You ask the source:  
--\"How many patients are included in the dataset?\"  
The source replies:  
--\"I haven't counted them, but I estimate the number to be between 100-120 patients\".  
III. Indeterminacy with respect to the set \(C\) of medical conditions:  
You inquire:  
--\"What are all the medical conditions diagnosed in the patients?\"  
The source states:  
--\"I'm certain there are patients diagnosed with diabetes, high blood pressure, and heart disease, but I'm unsure if there are patients with other conditions\".  
The IndetermSoft Set addresses the inherent indeterminacy present in healthcare claims data by introducing a flexible framework that accommodates varying degrees of uncertainty. Through the incorporation of indeterminacy measures, the IndetermSoft Set offers researchers the ability to effectively manage and quantify uncertainty, facilitating more robust decision-making processes and knowledge discovery.
## 3 Secrecy Performance Analysis
### MRC/MRT-SC with HD Scenario
\begin{split} F_{7 RB,1}\left(R_{S}|G_{1}\right)&= \int_{0}^{\infty}F_{7 RB,1}\left(2^{R_{S}}\left(1+y\right)-1\right)f_{7 RE,1} \left(y\right)dy\\ &=\sum\limits_{i=0}^{N_{R}}\left(\begin{array}{c}N_{B}\\ i\end{array}\right)(-1)^{i}\exp\left(-\frac{i\left(2^{R_{S}}-1\right)\sigma_{ B}^{2}}{P_{R}\lambda_{RB}}\right)\Theta_{N_{R},i}\frac{\sigma_{E}^{2}}{P_{R} \lambda_{RE}}\left(\frac{\sigma_{B}^{2}}{P_{R}\lambda_{RB}}\right)^{\phi}\\ &\times\sum\limits_{j=0}^{\phi}\left(\begin{array}{c}\phi\\ j\end{array}\right)\left(2^{R_{S}}-1\right)^{\phi-j}2^{jR_{S}}j!\left(\frac{P_ {R}\lambda_{RB}P_{R}\lambda_{RE}}{\sigma_{B}^{2}\left(i2^{R_{S}}P_{R}\lambda_{ RE}+P_{R}\lambda_{RB}\right)}\right)^{j+1}.\end{split}  \tag{35}\]  
The random variable \(G_{1}=\left|h_{RP}\right|^{2}\) also obeys the exponential distribution, and the PDF expression of \(G_{1}\) can be written as \(f_{G_{1}}\left(g\right)=\frac{1}{\lambda_{RP}}\exp\left(-\frac{1}{\lambda_{RP }}g\right)\). Thus, by using (35) and \(f_{G_{1}}\left(g\right)\), we can write the CDF of \(\gamma_{RB,1}\) for the MRT-SC scheme with the HD mechanism in the following theorem. Theorem 3.2: _By using MRT-SC scheme we can calculate the CDF of \(\gamma_{RB,1}\), which can be derived as_  
\[ \begin{split} F_{\gamma_{2}^{MRT-SC}}\left(R_{S}\right)& =\sum\limits_{i=0}^{N_{R}}\left(\begin{array}{c}N_{B}\\ i\end{array}\right)(-1)^{i}\Theta_{N_{R},i}\sum\limits_{j=0}^{\phi}\left( \begin{array}{c}\phi\\ j\end{array}\right)\left(2^{R_{S}}-1\right)^{\phi-j}\left(\frac{1}{\varepsilon \tilde{\gamma}_{B}}\right)^{\phi}2^{jR_{S}}j!\\ &\times\left(\frac{i\left(2^{R_{S}}-1\right)\lambda_{RP}+\mu \epsilon\tilde{\gamma}_{B}}{\varepsilon\tilde{\gamma}_{B}\lambda_{RP}}\right) ^{-(\phi-j+1)}\Gamma\left(\phi-j+1,\frac{i\left(2^{R_{S}}-1\right)\lambda_{RP }+\mu\epsilon\tilde{\gamma}_{B}}{\varepsilon\tilde{\gamma}_{B}\lambda_{RP}} \right)\right].\end{split}  \tag{36}\]  
In this phase, the proof process of \(F_{\gamma_{2}^{MRT-SC}}\left(R_{S}\right)\) for the MRT-SC scheme is similar with _Theorem 1_, and will not be elaborated here. Substituting (32) and (36) into (27), the closed-form expression of SOP of MRC/MRT-SC for \(P_{out}^{MRC/MRT-SC}\left(R_{S}\right)\) is attained after some simple mathematical manipulations. In (27), replacing \(\zeta\), \(\zeta\) with MRC and MRT-SC, respectively.
## 3 Proposed Scheme  
In this section, we explain our designed scheme for encrypting text using elliptic curves and the max-plus algebra-based wavelet transform. The proposed method utilizing elliptic curves offers comparable security to traditional cryptosystems but with significantly shorter key lengths, resulting in improved efficiency in terms of computational resources and storage requirements. The transformation works within the framework of max-plus algebra, which possesses characteristics that strengthen the confusion and diffusion properties of encryption schemes. The Type IVa MP wavelet transform possesses the capability to effectively manage extensive amounts of data. By employing a hierarchical breakdown, it performs operations on sequences or signals, enabling parallel processing and decreasing the computational complexity of the encryption procedure. The combination of these techniques is a powerful tool in modern cryptography and possesses a diverse range of applications in various fields, such as finance, healthcare, and government [37]. Figure 1 illustrates the flowchart of our designed text encryption scheme.
## 4 Results and Discussion
### Acceptable Collapse Margin Ratio Values  
Given the known values of the total system uncertainty and the established acceptable probabilities of collapse, it was possible to determine the acceptable collapse margin ratios. Figure 18 presents the values of the acceptable collapse margin ratio \(ACMR_{10\%}\) and \(ACMR_{20\%}\), which were based on the total collapse uncertainty and the target collapse probabilities of 10% and 20%, respectively.  
By interpolating with \(\beta_{TOT}=0.53\) in the curves shown in Figure 18, the following values were obtained: \(ACMR_{10\%}=1.97\) and \(ACMR_{20\%}=1.56\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Archetype** & **x** & **y** \\ \hline
0\({}^{\circ}\) & 3.915 & 4.219 \\
15\({}^{\circ}\) & 1.428 & 0.361 \\
30\({}^{\circ}\) & 8.643 & 0.786 \\
45\({}^{\circ}\) & 2.031 & 0.006 \\ \hline \end{tabular}
\end{table}
Table 11: Probability of collapse (%) for \(MCE\)-level demand.
## 1 Introduction  
Additive manufacturing (AM) is a new technology, with which a structure can be created by depositing successive layers. This approach has been considered to be an alternative, economic process for producing metallic components due to its potential benefits of saving lead time and costs [1]. Owing to its intrinsic characteristics, each AM process is naturally suitable for certain applications [2; 3; 4; 5; 6; 7]. Compared with powder-based AM techniques, which usually employ laser beams and electron beams as heat sources, wire + arc additive manufacturing (WAAM) has shown its advantages in manufacturing large-scale components thanks to its high deposition rate, high material utilization rate, low production and equipment cost, and high equipment flexibility and scalability [4; 7; 8]. High strength aluminum alloys have been widely used in aeronautics and aerospace applications for their excellent mechanical properties [9]. In recent years, manufacturing aluminum alloy components with the WAMM process is gaining more and more interest from the aerospace industry due to its advantages as described above. Among all the different aluminum alloys, Al-6.3%Cu alloy is particularly favored because of its wide aerospace applications such as cryogenic tanks, fuselage or shells for space vehicles [10].  
So far, only a few research papers have addressed the topic of WAAM Al-6.3%Cu alloy. [PERSON] et al. [11] reported their work on the mechanical properties of the additively manufactured Al-6.3%Cu alloy with Al2319 wire (nominally Al, 6.3 wt % Cu) and variable polarity gas tungsten arc welding (VP-GTAW) process, and discussed the microstructure evolution of this alloy during the deposition [12]. WAAM Al-6.3%Cu alloy was also studied with the cold metal transfer (CMT) process [13], with a focus of the effect of inter-layer cold working and post-deposition heat treatment on the material strengthening of this alloy [8] and their effect on porosity control [14]. [PERSON] et al. [15] found that by using pulse-advanced cold metal transfer (CMT-P-ADV), pore-free deposition with refined equiaxed grains of the WAAM Al-6.3%Cu alloy can be produced.  
However, all of the abovementioned research projects are based on the thin wall structure. The porosity and microstructure characterization of block structure is not studied. In this paper, thin walls and blocks of WAAM Al-6.3%Cu alloy samples were built with CMT in its pulsed (CMT-P) and advanced (CMT-ADV) variants, respectively. The porosity, microstructure, and micro hardness characteristics for these deposits were investigated in detail.
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 3 Methodology
### Overview of the Proposed Model  
The AMGC-AT model architecture is shown in Figure 3. The model framework consists of two parts: the graph representation module and the traffic flow prediction module. The pictorial representation is a pre-defined set of four schemas based on a priori knowledge. Macroscopically, the predefined scheme considers location information, functional location splitting, network topology information, and the flow of traffic between stations in the metro network. The second part is intended to predict traffic volumes throughout the metro system. The system consists of a multi-view convolution module, a spatial-temporal self-attention module, and a gated convolution network. Firstly, multi-view convolution networks can capture the correlation in depth of each metro station point in space. Second, the spatiotemporal self-attention mechanism can be used to learn the dependency of spatial embeddings on different epochs of a road segment. Finally, the output goes into the complete connection layer through the gated convolution network. To reduce the dimensionality of the planar layer, we use a fully connected layer and capture the non-linear relationship between high-level features and predicted outcomes. The final step is to reconstruct the output of the full connection layer as the target shape.
## 3 Results  
### Absolute Biases of Sentinel-3A  
During the campaign in October 2016, Sentinel-3A passed over the tracks 666 and 707 with a time shift of one day (Figure 2). We had followed these two tracks. Based on the procedure of water height measurements described in Section 2.4, we have studied the surface water along the two satellites tracks, with the _Ocean_ and _Ice-1_ retrackers, respectively (Figures 5 and 6). Comparisons have been conducted with the water height calculated using GPS data processing, for static and kinematic sessions, which were processed using a PPP method (2.3). The altimeter bias has then been calculated using the methodology presented in Section 2.5. The same procedure has been used for Jason-3, with the track 131.  
The two sets of measurements on the two tracks, from the GPS on the boat (and from the altimeter onboard the Sentinel-3A, with _Ocean_ and _Ice-1_ retrackers, were in a remarkable agreement (Figures 5 and 6). From these data, we have calculated that the absolute bias of Sentinel-3A with _Ocean_ retracker was \(-14\)\(\pm\) 20 mm StD. For _Ice-1_, the offset was 285 \(\pm\) 20 mm StD. These values represented the mean of both tracks. Owing to the improvement in the instrumental design, and to the very limited dispersion in the Sentinel-3A data, the estimate of the altimeter bias of Sentinel-3A was derived with a small uncertainty, namely 20 mm StD, a result much better than the ones obtained in previous studies (37 mm StD in [PERSON] et al. [11] for [PERSON]/AltiKa, and 50 mm StD in [PERSON] et al. [9] for Jason-2).