esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
The Huber regressor has achieved the exact same correlation percentage on the _Conscientiousness_ dimension as the Elastic-Net model did when using the same features. Additionally, the Huber regressor yielded the best results when predicting _Neuroticism_ at 31.2% correlation, scoring 2.9% higher than the baseline approach. Finally, SVR model achieved an increase of 2.8% in correlation over the baseline when predicting the _Agreeableness_ trait, using _n_-grams, MBTI/Enneagram predictions and LIWC features selected for the _T-F_ type, with which it demonstrated a statistically significant correlation. When analysing the results achieved using the deep-learning model, it is a bit surprising to see it not perform as well as the other options, especially considering the popularity of deep-learning approaches for tasks of automatic personality recognition in recent years [58]. However, as [PERSON] et al. [7] also reported similar results when applying a deep-learning algorithm, we can conclude that linear regression models tend to be the better choice in leveraging this particular set of features for prediction. This is possibly due to the high linearity of features, as linear regression models tend to be designed to work best in these situations. Another possible contributing factor to ensemble and deep-learning approaches performing worse than expected could be that KerasRegressor and XGBoost require more data to be efficient. One final experiment that we conducted was the analysis of the effect Enneagram predictions on the task of predicting the Big Five traits (Table 14). Our analysis indicated that, despite a considerable increase in prediction scores for the _Conscientiousness_ and _Agreeableness_ traits on several different feature sets, the scores for _Neuroticism_ were worse every single time when Enneagram predictions were removed from the feature set. These results signal a possibility of a relationship existing between the Enneagram types and Big Five's _Neuroticism_ trait. This is significant due to the fact that _Neuroticism_ has not been previously found to correlate with any of the MBTI types [13; 17; 18].
## 3 Results and Discussion  
9-methoxyellipticine (C\({}_{18}\)H\({}_{16}\)N\({}_{2}\)O, 276.3) is a naturally occurring carbazole alkaloid isolated from _O. elliptica_, which displayed anticumoral, immunosuppressive, and trypanicidal activities [33]. In this study, 9-methoxyellipticine demonstrated favorable activities against STEC, MRSA, MDR _K. pneumoniae_, and MDR _B. cereus_ either alone or in combination with a commercial antibiotic when tested with the disc diffusion method. Regarding STEC O157, GEN showed a strong antibacterial activity with an inhibition zone of 20.0 \(\pm\) 1.00 mm (Figure 2A and Table 1), while penicillin (PEN) was almost inactive. Figure 2: **(A)**. The inhibition zone (mm) of 9-methoxyellipticine against _K. pneumoniae_ (1: DMSO as negative control; 2: gentamycin as positive control; 3: 9-methoxyellipticine) (x = 3). (**B**). The inhibition zones (mm) of 9-methoxyellipticine against MRSA (1: DMSO as negative control; 2: methoxy ellipticine alone, 3: vancomycin as positive control, 4: vancomycin–9-methoxyellipticine combination, and 5: penicillin–9-methoxyellipticine combination (x = 3). One of the highly beneficial methods to guard against the multidrug-resistant bacterial infection is the drug combination [34]. Captivatingly, an excellent activity was detected using 9-methoxyellipticine and both antibiotics--GEN and AK--providing 25.0 \(\pm\) 0.577 mm and 23.0 \(\pm\) 1.00 mm as zones of inhibition, correspondingly (Figure 2B and Table 1). Regarding the MDR _K. pneumoniae_, though the strain was resistant to GEN and AK, it was sensitive to 9-methoxyellipticine alone (21 \(\pm\) 0.577 mm) and in combination with GEN (23.0 \(\pm\) 1.00 mm) and AK (23.0 \(\pm\) 1.00 mm) (Figure 2B and Table 1). One possible explanation for that effect might be that 9-methoxyellipticine could result in bacterial cell membrane damage [21], thus improving the efficiency of the antibiotics. Likewise, 9-methoxyellipticine alone showed an antibacterial inhibition zone of (21 \(\pm\) 0.577 mm) against MDR _K. pneumoniae_ which was GEN resistant (Table 2), while against STEC O157, 9-methoxyellipticine alone showed an inhibition zone of (18 \(\pm\) 1 mm). On the other hand, 9-methoxyellipticine was ineffective against MRSA and MDR _B. cereus_ (3 \(\pm\) 1.00 mm and 5 \(\pm\) 0.577 mm, respectively). For MRSA, the combination of 9-methoxyellipticine and VAN revealed a synergetic antibacterial impact as implied from the growth in the inhibitory zone (from 18 mm to 23 mm) (Table 2). However, the combination of 9-methoxyellipticine with PEN was ineffective in reversing the resistance of both microbes. The MIC quantities of 9-methoxyellipticine were 512 ug/mL (for _E. coli_) and 256 ug/mL (for MDR K. _pneumiae_) (Table 2). The compound showed weak activity on Gram-positive bacteria, with an MIC higher than 2048 ug/mL for both MRSA and _B. cereus_ (Table 2). Fortunately, the results are promising for the in vitro bacterial susceptibility test, where against STEC O157 and _Klebsiella pneumoniae_ (KPC), 9-methoxyellipticine showed potent bactericidal activity either alone or in combination with commercial antibi
_applied sciences_  
_Article_  
**Selective Extraction and Determination of Hydrocortisone and Dexamethasone in Skincare Cosmetics: Analytical Interpretation Using Statistical Physics Formalism**  
**[PERSON] 1,*\({}^{\copyright}\)**, [PERSON] 2, [PERSON] 3, [PERSON] 1 and [PERSON] 2  
1 Department of Physics, College of Science, Princess Nourah Bint Abdulrahman University,  
Riyadh 11564, Saudi Arabia; [EMAIL_ADDRESS]  
2 Laboratory of Quantum and Statistical Physics, LR18 ES18, Faculty of Sciences of Monastir, Monastir University, Monastir 5000, Tunisia; [EMAIL_ADDRESS] (N.B.); [PERSON]L_ADDRESS] (A.B.L.)  
3 Department of Physics, College of Science, Jout University, Sakaka PO. Box 2014, Saudi Arabia; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract:** Molecularly imprinted polymers (MIPs), as magnetic extraction adsorbents, are used for the selective, rapid determination and extraction of dexamethasone and hydrocortisone in skincare products. Therefore, in this paper, magnetic molecularly imprinted polymers (MMIPs) and magnetic non-molecularly imprinted polymers (MNIPs) were utilized as adsorbents to describe the adsorption phenomena of dexamethasone and hydrocortisone. This interpretation, based on a statistical physics theory, applies the multilayer model with saturation to comprehend the adsorption of the drugs. Results obtained via numerical simulation revealed that dexamethasone and hydrocortisone adsorption happens via a non-parallel orientation on the surfaces of MMIPs and MNIPs, and they also showed that the adsorption amount of the MMIPs for the template molecule was notably greater than that of the MNIPs at the same initial concentration. The adsorption energy values retrieved from the data analysis ranged between 7.65 and 15.77 kJ/mol, indicating that the extraction and determination of dexamethasone and hydrocortisone is a physisorption process. Moreover, the distribution of a site's energy was calculated to confirm the physical nature of the interactions between adsorbate/adsorbent and the heterogeneity of the surfaces of the MMIPs and MNIPs. Finally, the thermodynamic interpretation confirmed the exothermicity and spontaneous nature of the adsorption of these drugs on the tested adsorbents.  
**Keywords:** cosmetic samples; dexamethasone; hydrocortisone; MIIPs; statistical physics
## 1 Introduction
Practically, it is rather cumbersome to require two measurements at different locations to be defined as 'data'. This work extends the previous work [28; 29] by the same research group, which used deep learning techniques on dynamic pressure signals in horizontal pipes to identify different flow regimes. Continuous wavelet transform was used to convert pressure signals into images and then ShuffleNet and ResNet-50 architectures were applied for classification. The objective of the present work is to identify flow regime by post-processing dynamic pressure data using Fast Fourier Transform (FFT) and Probability Density Function (PDF). The different characteristics of dynamic pressure signals (like dominant frequency, FFT amplitude, PDF location and PDF magnitude) in the time and frequency domain are studied to develop a benchmark for flow regime identification. The dynamic pressure is obtained from simulation of gas-liquid flow in a horizontal pipe using the Level-Set (LS) method coupled with Volume of Fluid (VOF). This work also explains the numerical model and methodology to simulate two-phase flow and its validation in detail. The developed methodology herein will be a steppingstone for implementation of algorithms into instrumentation hardware. The presented results show that the use of post-processing of dynamic pressure fluctuation has the potential to identify flow patterns in industrial applications where visual access to flow is not possible.
# Age and Gender Impact on Password Hygiene
## 2 Materials and Methods
### Dataset of Leaked Passwords
#### 2.1.2 Data Description and Validity Checks  
The leaked dataset contains 110,302 records in total. Each record includes a unique sequence ID, the user's first and last name, a password hash, the state-issued personal identification number (similar to the social security number used in the US), and an email. The sequence ID ranges from 1 to 111,052. Gaps in the sequence are small, and there are only 750 deleted records (below 1% of all records). Some of the first records were clearly used for testing purposes with incorrect hashes (5 records) and emails of the developers of the car-sharing company itself (17 records).  
The Lithuanian personal identification number consists of 10 digits, revealing the person's gender and date of birth followed by a control digit. Furthermore, most male Lithuanian names and surnames end with the letter \(s\), giving us an additional criterion for the validity check. Records with empty, invalid (foreign or inconsistent), or duplicate personal codes were excluded from gender and age analysis.  
We found over 91% of the 110,302 records containing a valid Lithuanian state-issued personal code revealing the user's gender and date of birth. Passwords were stored using a fast SHA1 hashing algorithm [27], giving us the possibility of recovering most of them (92%) in a reasonable time. This created a unique opportunity to analyse password hygiene and habits of a large set of technologically savvy Lithuanian users of various ages and genders.
## 2 Materials and Methods
### Code Selection for Encoding  
Algorithm 2 is responsible for selecting the most suitable code \(k_{j}\) for a given state \(s_{i}\). The algorithm starts by initializing the minimum cost to a very high value. It then iterates through each code \(k_{t}\) in the set \(K\). For each code, a trial assignment is made to \(s_{i}\) and the resulting cost in terms of FSM area is computed. If the computed cost is lower than the current minimum cost, the minimum cost is updated, and \(k_{j}\) is set to \(k_{t}\). After evaluating all codes, the algorithm returns the code \(k_{j}\) that resulted in the lowest cost.  
In Algorithm 2, step 4 is crucial as it calculates the FSM area when \(k_{t}\) is assigned to \(s_{i}\). It has been experimentally found that FSM state encoding primarily affects the area of transition functions, not output functions. Therefore, in step 4 of Algorithm 2, the area is calculated only for FSM transition functions.
## 4 Results
### Second Validation Cycle
#### 4.2.2 Findings Based on Second validation Cycle Results  
Once the \(C^{*}_{D,\text{OF},\mu}\) was optimized based on the AS drag calculation, the OF model was updated, and the results from these simulations were plotted together with GPGP measurements (data points) and the 15% increase in the mean load, explained in Section 3.4 (colored area above the curves), to produce Figure 19. Based on the latter and using the same validation criteria as for the first validation cycle, the following findings can be identified:  
* The optimized \(C^{*}_{D,\text{OF},\mu}\) for a narrow span was definitely more accurate than the first cycle value. That demonstrated the influence of the shielding effect on the drag coefficients. Nevertheless, the 15% increase area was still below the data around \(u_{\text{stw}}=1.0\) m/s and \(u_{\text{stw}}=1.1\) m/s. This means that the DT was slightly underestimating for narrow span. The mean deviation in this case was \(-10\%\).
* The optimized \(C^{*}_{D,\text{OF},\mu}\) also produced a more-accurate and more-conservative nominal span curve, in comparison with the first cycle, since all data points between \(u_{\text{stw}}=0.75\) m/s and \(1.0\) m/s were inside the 15% increase area or below the curve. Having points below the curve might mean that the second cycle curve was slightly overestimating the lowline tension for nominal span. Indeed, the mean deviation was about 14%.
* The optimized \(C^{*}_{D,\text{OF},\mu}\) led to the majority of the data points falling inside the 15% increase area. The mean deviation was computed as 6%. Then, the DT can be considered as validated and sufficiently overestimating for a wide span. This also means that the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimization Case** & **Span Type** & **Angles** \\ \hline
1 & Narrow & \(0^{\circ}\leq\theta\leq 2^{\circ}\) \\
2 & Nominal & \(30^{\circ}\leq\theta\leq 90^{\circ}\) \\
3 & Wide & \(45^{\circ}\leq\theta\leq 90^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimization cases.  
Figure 20: Distribution of the angles of attack.  
AS calculation was enough to calibrate the OF axial drag coefficient \(C^{*}_{D,\text{OF},a}\) and to obtain a proper estimation of the towline tension in a wide span.
# Mixed Method for Isogeometric Analysis of Coupled Flow and Deformation in Poroelastic Media
## 2 Governing Equations
### Boundary Conditions  
The governing linear momentum and mass balance equations in (7) and (14), respectively, are accompanied by the usual boundary conditions in the formulation of boundary value problems. Let \((\Gamma_{\text{D}}^{\intercal},\Gamma_{\text{D}}^{\intercal})\) and \((\Gamma_{N}^{\intercal},\Gamma_{N}^{\intercal})\) be two partitions of the boundary \(\partial\Omega\) of domain \(\Omega\), for representing Dirichlet and Neumann boundary conditions, respectively.  
The Dirichlet boundary conditions for the equilibrium (7) and mass balance (14) equations are  
\[\begin{cases}\mathbf{u}=\overline{\mathbf{u}}&\text{on }\Gamma_{D}^{u},\\ p^{\text{f}}=\overline{p}^{\text{f}}&\text{on }\Gamma_{D}^{\text{p}},\end{cases} \tag{15}\]  
where \(\overline{\mathbf{u}}\) and \(\overline{p}_{\text{f}}\) are the prescribed displacement and pressure, respectively.  
The Neumann boundary conditions are  
\[\begin{cases}\mathbf{\sigma}\cdot\mathbf{n}=\overline{\mathbf{t}}&\text{on }\Gamma_{N}^{u},\\ \mathbf{w}\cdot\mathbf{n}=\overline{\mathbf{q}}&\text{on }\Gamma_{N}^{\text{p}},\end{cases} \tag{16}\]  
where \(\mathbf{n}\) is the outward pointing normal vector, \(\overline{\mathbf{t}}\) is the surface traction and \(\overline{\mathbf{q}}\) is the fluid flux on the boundary.
# MT-VTON: Multilevel Transformation-Based Virtual Try-On for Enhancing Realism of Clothing
## 4 Approach
### Cloth Refinement Process  
While our cloth warping process effectively generates warped clothes with rich contextual features, such as cloth folding, we observed a loss in the detail of logos or textures. To address this, we appended the cloth refinement process to the end of the cloth warping process. Unlike most previous works [4; 6; 7; 8], which use generative models to reconstruct the contextual features, our refinement step specifically focuses on restoring the logos.  
To restore logo details, we calculated the pixel-level-flowed clothes. Instead of calculating pixel-level appearance flow from scratch, we utilized the feature-level flow fields from GFLA and upscaled them to match the dimensions of the clothes. Given the warped cloth and the pixel-level-flowed cloth, we mended the logos by replacing the damaged area in the warped cloth with their counterparts from the pixel-level-flowed cloth. However, this procedure poses another challenge: How can we identify the regions containing logos?  
Generally, logo regions exhibit significant changes in pixels. Leveraging this observation, we computed the variance map in the warped clothing image to identify potential logo regions, as demonstrated in the second image of the first row in Figure 2. The variance map comprises the variance calculated for every pixel, resulting in a map with identical dimensions to the clothing image. To calculate variance for the \(i\)-th pixel, we define a \(7\times 7\) window, \(\mathcal{W}_{i}\), centered on the pixel. The variance of this pixel is then computed as follows:  
\[v_{i}=\frac{1}{|\mathcal{W}_{i}|}\sum_{j\in\mathcal{W}_{i}}(I(i)-I(j))^{2}, \tag{7}\]  
where \(v_{i}\) is the variance of the \(i\)-th pixel and \(I(i)\) represents a pixel value in position \(i\). If the variance of the pixel exceeds \(\mu(v)+1.2\sigma(v)\), where \(\mu(v)\) and \(\sigma(v)\) denote the mean and the standard deviation values in the variance map for one image \(I\), we consider it a candidate pixel for logo regions. Through this procedure, we can obtain the variance map mask containing potential logo pixels. We set the threshold to \(\mu(v)+1.2\sigma(v)\) empirically.  
Subsequently, we performed several rounds of erosion, followed by a few steps of dilation on the variance map mask to filter out false candidates, as illustrated in the third image of the first row in Figure 2. Since these masks often encompass areas larger than the actual logo regions, we employed the Active Contour algorithm [34], a morphological segmentation algorithm that identifies a segmentation contour by adjusting an initial contour. This algorithm is applied to the mask to determine the final logo regions, as depicted in Figure 2. Leveraging this final mask, damaged logos in the warped cloth are replaced with flowed logos, as illustrated in the second row in Figure 2.
## 5 Experiment
### Floor Classification and Landmark Detection  
For the feature extraction, the dimensionality of the Wi-Fi RSSI set is reduced from 193 to 10, i.e., \(d=193\) and \(r=10\). All the RSSI values are scaled into the range [0, 1]. Figure 5a compares FDA (supervised) to the semisupervised discriminant analysis for floor level estimation. Note that because PCA is an unsupervised method, the PCA alone cannot be used for the supervised floor and landmark detections. To estimate the floor level, the \(k\)-NN method with \(k=1\) is employed. In Figure 5a, the ratio of the used labeled data varies from 10% to 100%. The developed algorithm provides better accuracy than FDA. The most noticeable result appears when the ratio of the labeled data is small. Although 10% of the data is labeled, semisupervised analysis achieves 95% accuracy whereas supervised analysis results in 60% accuracy.  
Selecting a tuning the balancing parameter in Equation (35) depends on a cross validation. The optimal value is selected by minimizing the training error. In Figure 5b, the floor estimation accuracy is shown with respect to the variation of parameter values \(\alpha_{bal}\) introduced in Equation (35) and the ratio of the labeled training data. Setting a relatively large \(\alpha_{bal}\) value intends to focus more on FDA than on PCA in the semisupervised learning analysis. In this study, \(\alpha_{bal}=0.2\) is used in the rest of all experiments.  
Figure 4: Localization results of the compared algorithms when we vary the percentage of the labeled training data: (**a**) case with 283 training data points, (**b**) case with 93 training data points, and (**c**) running time of the compared semisupervised algorithms for pseudolabeling with respect to the percentage of used training data.  
Landmarks in an indoor environment, such as toilets, elevators, and rooms, create a particular pattern of Wi-Fi RSSI measurement sets so that their distinct features can be distinctly extracted. Figure 6a clarifies the extracted features at some landmarks using the semisupervised discriminant analysis algorithm. Figure 6b shows the distance on the signal space according to a user's path. The proposed algorithm detects the moment when the user arrives at each pre-defined landmark site with the threshold 0.3 in Figure 6b. Consequently, we can apply the landmark detection algorithm to calibrate any user's trajectory, by determining the start and end positions. Note that these trimmed trajectories are used for the trajectory learning in Section 4.
## 3 Results and Discussion
### Comparison of the Results of Different Partitioning Methods
The FVS separation results showed overestimation throughout the growing season, particularly at the end of the growing season, and were associated with underestimation of leaf WUE, a result identical to that of [PERSON] et al. 's study [38]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Parameters** & **Month** & **CEC** & **MREA** & **FVS** \\ \hline TR & 1:1 slope & 7 & 0.64 & 0.92 & 0.83 \\  & & 8 & 0.81 & 1.01 & 0.82 \\  & & 9 & 0.65 & 0.94 & 0.69 \\  & R & 7 & 0.90 & 0.95 & 0.92 \\  & & 8 & 0. The main reason for this discrepancy is that computational analyses based within the effective quadrant select more effective solutions, while ecosystem transpiration and evaporation may lack strong physical constraints; the FVS method based on flux similarity theory considers A Equations (A10) and (A11) in detail and therefore rejects more separation results, resulting in the smallest proportion of effective solutions. Nevertheless, more valid solutions do not represent the accuracy of the separation method, and future CEC methods should further consider adding transpiration-evaporation constraint equations to improve their valid estimates of ecosystem evaporation.