esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Contact Angle  
The contact angle results are presented in Table 2. The reported values indicate that the hydrophilic nature of chitosan predominates is consistent with findings from previous studies. It is important to note that these values (69.18\({}^{\circ}\)) fall within the range of hydrophilic compounds responding to short analysis times (30 s) [52]. However, the water absorption (AW) results differ significantly between the two films, increasing the absorption of TPS-CH-M-A by 90% compared to the TPS-CH-M control. This effect may be attributed to the uniform distribution of acetaminophen particles within the polymer matrix, which likely reduces intermolecular interactions between polymer chains. Consequently, a greater  
Figure 8: SEM micrographs of (**a**) TPS-CH-M and (**b**) TPS-CH-M-A films.  
Figure 7: Tensile strength and elongation at break of the films with and without acetaminophen.  
number of water molecules can penetrate and interact with the material during submersion. The TPS-CH-M exhibited a contact angle of 62.18\({}^{\circ}\)\(\pm\) 0.12\({}^{\circ}\), confirming its hydrophilic nature, as materials with contact angles below 90\({}^{\circ}\) are considered hydrophilic [53]. The incorporation of the API into the edible film led to a slight increase in the contact angle to 62.91\({}^{\circ}\)\(\pm\) 0.03\({}^{\circ}\). However, despite this increase, both films remain within the hydrophilic category. The results suggest that the incorporation of API does not have a significant effect on the wettability of TPS-CH-M. However, regarding AW, it is evident that the presence of API in the film enhances water uptake. The TPS-CH-M film exhibited an AW of 321.28%, whereas TPS-CH-M-A showed an increased AW of 410%, representing a 28% rise. This result is favorable, as higher water absorption can facilitate faster film disintegration, a phenomenon promoted by water interaction.
# Blind Interleaver Parameters Estimation Using Kolmogorov-Smirnov Test
## 2 Materials and Methods
### Bernoulli Trial Based Method  
By using the rank deficiency factor \(s\) as an interleaver period estimator, the strict failure of interleaver period prediction can be determined when \((s<2)\) is observed base on Table 1. This simplifies the problem of interleaver parameters estimation to counting the success events that follows the binomial distribution [4].  
The constructed \(\Lambda\) from intercepted symbols can be considered as a random square matrix, a failure case, when the predicted interleaver period is not equal to the real interleaver period. On the contrary, when the estimated period matches the real one, the number of success cases increases owing to the high probability of rank deficiency.  
Finally, the predicted interleaver size \(l\) is declared only when the maximum number of success events exceeds a certain threshold for false alarm control.
# Predicting the Performance of Solar Power Generation Using Deep Learning Methods
## 3 Method and Model
### Time-Sequential Predictive Algorithm (RNN, LSTM, GRU, and Related Models)
In this study, the Bidirectional-simpleRNN, the Bidirectional-LSTM, and the Bidirectional-GRU are utilized to predict the generated solar power and the efficiency and accuracy are evaluated and compared.
## 2 Materials and Methods
### Stage-2 LUT for Quantifying Perfusion Properties  
We performed forward modeling of tissue absorption at two wavelengths, 660 and 850 nm. Hemoglobin concentration, \(CHb\), was varied from 0.075 to 22.5 g/L with an increment of 0.075 g/L, which corresponds to 0.05% and 15% of the total hemoglobin concentration of 150 g/L in the whole blood. This \(CHb\) range reflects the majority types of organs except for the kidney, the liver, and the exposed vasculature [25]. \(CHb\) in those tissues is well above 30% [25]. The range of the optical and physiological properties may need to be expanded before the two-stage LUT method can be applied to characterize those tissues. Oxygen saturation, \(StO2\), was varied from 50% to 100% with an increment of 0.25%. A hemoglobin molecular weight of 64,500 g/mol was used in the simulation [26]. The absorption coefficient at both wavelengths can be analytically described by Equations (5) and (6), respectively, where \(\varepsilon_{HbO_{2}}\) and \(\varepsilon_{Hb}\) are molar extinction coefficients of oxygenated and de-oxygenated hemoglobin. The values of the molar extinction coefficients were obtained from the tabulated data compiled by Dr. [PERSON] [27]. With a similar technique described in  
Figure 2: Monte Carlo simulation workflow. (**A**). Schematic of Monte Carlo simulation over a large volume. For each setting, the simulation was performed with phase shifts of 0, 120, and 240 degrees. The central region (red dash box) of the diffuse reflectance image (gray-scale image) was averaged along the direction of yellow arrows to obtain reflectance plots. (**B**). Reflectance plots at three phase shifts. (**C**). Demodulated AC and DC intensity plots.  
the previous section, maps of \(\mu_{a\_660}(StO2,CHb)\) and \(\mu_{a\_850}(StO2,CHb)\) were converted to maps of \(StO2(\mu_{a\_660},\mu_{a\_850})\) and \(CHb(\mu_{a\_660},\mu_{a\_850})\), and a scaling factor of 5000 was used.  
\[\mu_{a\_660}(StO2,CHb)=\frac{\log(10)}{64500}\left[\varepsilon_{HbO_{2}\_660} \cdot StO2\cdot CHb+\varepsilon_{Hb\_660}\cdot(1-StO2)\cdot CHb)\right] \tag{5}\]  
\[\mu_{a\_850}(StO2,CHb)=\frac{\log(10)}{64500}\left[\varepsilon_{HbO_{2}\_850} \cdot StO2\cdot CHb+\varepsilon_{Hb\_850}\cdot(1-StO2)\cdot CHb)\right] \tag{6}\]
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems
### Case Study 1: Coupling of Continuous Substructures
#### 3.1.5. Discrepancy Analysis  
The discrepancies observed in the modal method can be attributed to several factors. The modal method relies on analytical solutions to derive the mode shapes and compute the receptance by summing the contributions from selected modes. This approach, however, involves mode truncation, which limits the representation of higher-frequency contributions and, particularly, impacts the rotational receptance due to the complexity of the interactions between the rotational displacement and force excitation. In contrast, both the FEA and frequency-based substructuring (FBS) methods discretize the structure into elements or substructures, using the stiffness and mass matrices to calculate receptance. This discrete representation maintains consistent degrees of freedom and captures the dynamics more accurately, especially at higher modes.  
While all methods exhibit similar resonance trends up to 400 Hz, the modal method begins to show discrepancies at higher frequencies. Specifically, the modal method's peak resonance and anti-resonance shift for the higher modes indicate a tendency to present softer behavior in high frequencies due to the incomplete stiffness contributions. This limitation is more pronounced for the rotational receptance compared to the translational receptance. The FBS method, by dividing complex structures into smaller substructures,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & **Modulus of** & **Moment of** & **Length of** & **Cross-** & **Section** & \\  & **Elasticity** & **Inertia (I)** & **Beam** & **Sectional** & **Diameter (d)** & **Density (\(\rho\))** \\  & **(E)** & & **Element (L)** & **Area (A)** & **(d)** & \\ \hline value & \(200\times 10^{9}\) Pa & \(1.25\times 10^{-7}\) m\({}^{4}\) & \(1\) m & \(0.0013\) m\({}^{2}\) & \(0.04\) m & \(7850\) kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties and structural parameters used for modeling.  
Figure 5: The comparison of receptance components H11, H14, H22, and H33 for frequencies of up to 400 HZ.  
captures the interactions of local dynamics and higher modes more precisely. Therefore, it provides more accurate predictions across a broad frequency range compared to the modal method, which may oversimplify high-frequency dynamics and localized effects.
## 1 Introduction
Section 2 introduces the pseudorange Differential Correction (DC) expression generated by these RSs together with its second order statistics model. In addition, it introduces the linear LMMSE algorithm to estimate the true DC from the vector of noisy measurements relative to these RSs. Section 3 on the other hand, describes the pseudorange DC simulation process for a number of correlation distance values, and shows the sensitivity of the estimation algorithm to different modeling errors.
## 4 Application and Results
### CSE Corpus Building  
#### 4.1.1 Source Selection  
To create the CSE corpus, we first had to locate the sources to retrieve CSE attack dialogues. For that purpose, we initially started by gathering synonyms and similar search terms for the word _dialogue_. Using the _word embedding_[25] technique, similar terms were identified and selected based on a ranking of the most used words in context. The words that were selected were: _dialog_, _dialogue_, _chat_, _conversation_, and _discourse_. Additionally, the following terms in the CSE context were used: _discourse analysis_, _data leakage_, _sensitive data exposure_, _corpus_, _dataset_, _online chat_, _instant messenger_, _excerpts_, _conversation transcripts_. Using combinations of the aforementioned search terms, we located several websites, books, logs, and forums with relevant content. The discovered sources belong to one of the following categories:  
* Social engineering dark websites (tutorials, guides, and others)
* Social engineering dark forums (text dialogues)
* Social engineering books
* Instant Messaging logs
* Telephone conversations  
The following table (Table 1) presents the top twenty web sites, forums and books used to acquire dialogues useful in producing the CSE corpus.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**No#** & **Web Sites/Forums** \\ \hline
1 & \textasciiti
## 4 Model Estimation and Results
### Structural Model  
The 'inner model' structural model was described in Figure 2. This indicates the connection between the exogenous variables in the model and the endogenous variable. The structural model [59] is described by the equation  
Figure 4: Measurement PLS-SEM 2 model. Source: Data analysis was performed by the authors using the SmartPLS software 3.2.8.  
\[\delta=\Gamma\delta+\Lambda\phi+\Sigma \tag{1}\]  
where:  
\(\delta\)-the vector for the endogenous latent variables;  
\(\phi\)-the vector for the exogenous latent variables;  
\(\Sigma\)-the residual variable vector;  
\(\Lambda\) and \(\Gamma\)-matrices of the path coefficients.
# Effect of Transducer Burn-In on Subjective and Objective Parameters of Loudspeakers
## 3 Research Methodology  
This study employed a combined methodological approach to evaluate the effects of the burn-in process on loudspeaker performance. Initially, a series of objective measurements was conducted to assess key electroacoustic parameters, including frequency response, total harmonic distortion, impedance characteristics, sensitivity, and resonant frequency. These measurements were carried out in a controlled acoustic environment using calibrated equipment and standardized test signals to ensure accuracy and repeatability.  
Following the objective analysis, subjective listening tests were performed to capture perceptual differences in sound quality. A panel of trained listeners participated in double-blind A/B comparison sessions conducted in an acoustically treated room. Participants evaluated various auditory attributes, such as tonal balance, clarity, spatial imaging, and overall listening experience. The subjective data were collected using standardized rating scales and subsequently analyzed to determine whether any perceptible changes could be attributed to the burn-in process.
## 1 Introduction
These previous works assumed a uniformly distributed wall thickness over the aneurysm dome; however, real aneurysms do not have a uniform wall thickness distribution, and this can be related to stress concentrations [23] or aneurysm expansion when present in the abdomen [24]. Even with current medical technology, wall thickness remains a difficult parameter to measure in patients without performing surgery or removing the aneurysm, especially for cerebral aneurysms. Furthermore, in terms of removing an aneurysm, simulations have shown that a non-uniform wall thickness distribution is associated with higher stress values at the rupture site compared with configurations with a constant wall thickness [25]. In the present work, the effect of geometrical parameters was studied, specifically, the effects of the aneurysm wall thickness and size on the rupture risk. Each parameter was varied separately to better show the individual effects on the rupture risk of cerebral aneurysms. Size changes alter the fluid dynamics and level of structural stress in aneurysms, but a reduction in the aneurysm wall thickness modifies the structural stress. We are interested in both phenomena. We additionally studied the WSS between the pre-aneurys and aneurysm state in lateral aneurysms. Wall thickness changes produce wall deformation and, therefore, changes in the fluid dynamics in the aneurysms. Finally, there is a change in the WSS, which can increase the rupture risk [23]. Six patient-specific geometries were chosen, and boundary conditions representing healthy conditions for an adult patient were used. Due to the importance of accounting for blood and wall interactions in simulations to accurately predict patient-specific hemodynamics [24; 25; 26; 27], a fluid-structure interaction (FSI) approach was used. Additionally, as differences in displacement of up to 30% have been observed when comparing two-way and one-way FSI simulations [28], the former method was used. Additionally, cases were included where the aneurysm was virtually removed from the lateral geometries with the aim of simulating the state prior to lesion initiation.
## 1 Introduction
### Proposed Work Contributions  
The SP-IoUAV model makes the following contributions:  
* **Innovative Privacy-Preserving Mechanism:** Our novel privacy-preserving mechanism combines federated learning, differential privacy, and secure multi-party computation in a powerful way to preserve privacy. This mechanism significantly enhances the detection and prevention of intrusions in UAV networks while ensuring the comprehensive protection of sensitive data.
* **Robust Intrusion Detection Engine:** An intrusion detection engine built on a CNN-LSTM deep neural network forms the basis of our model, which is robust in nature. Using this engine, UAVs can detect anomalies and classify them in real time, allowing them to identify security threats quickly.
* **Proactive Real-Time Decision Mechanism:** In addition to providing instant alerts and automatic blacklisting, our model also features a cutting-edge real-time decision mechanism and automatic blacklisting. In order to prevent potential breaches, this dynamic system responds quickly, notifies relevant personnel, and takes decisive action when intrusions are detected.
* **Multi-factor Authentication for Enhanced Security:** For enhanced security, we use multi-factor authentication (MFA) to control database access to the intrusion detection system (IDS). In addition to providing an additional layer of protection against unauthorized access, MFA demonstrates the robustness of our overall system and reassures users of its integrity.
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 5 Conclusions
### Limitations  
Despite its strengths, the proposed method is not without limitations. Chief among them is the combinatorial growth in the number of feature groups as the dimensionality of the dataset increases. While mitigated by initial redundant feature filtering and limiting the feature pool (e.g., max_n = 20), this complexity remains a core constraint of the approach.  
Another important limitation relates to dataset suitability. The method performs best when the target feature set size is of moderate size and when the dataset has sufficient structure for shallow transformations to capture informative patterns. It may be less effective in scenarios where:  
* Non-linear or hierarchical interactions dominate the data,
* Very large numbers of features (\(n\gg\) 10,000) are present without the possibility of effective pre-filtering;
* Real-time constraints require ultra-low latency beyond what even this method can accommodate;
* The application demands deeply contextual or sequential understanding of features (e.g., natural language or temporal series);
* The dataset is naturally very space, or there are a very low number of samples (in these cases, approaches such as meta-learning or few-shot learning would be better suited).  
Furthermore, the use of default hyper-parameters in the experimental setup, while beneficial for reproducibility, may limit the method's relative performance under different modeling configurations.
# Cross Talk between Synthetic Food Colors (Azo Dyes), Oral Flora, and Cardiovascular Disorders
## 2 Food Colors, Oral Flora and Cardiovascular Disorders
### Oral Flora and Periodontal Disease
#### 2.3.2 Periodontal Disease  
Periodontal disease is better known as a chronic inflammatory disease of periodontium, and its recent type is specified by periodontal ligament loss and damage of the surrounding alveolar bone [32]. It is considered to be one of the two major threats to oral health and is considered as a main cause of tooth loss. Almost 800 species of bacteria are recognized in the oral cavity [33], and it is hypothesized that the composite interaction of bacterial infections and the host response adapted by the behavioral factor can result in periodontal disease [34].  
Periodontal diseases are considered a usual oral condition of the human population [35]. About 14-47% of the adult population had calculus deposits collate, with 36-63% of the adults being in developing countries, and a larger percentage of individuals had 4-5 mm of periodontal pockets, whereas older individuals exhibited 6 mm of periodontal pockets or above in both developed and developing countries [36]. Overall, 20-50% of the population around the world is affected by periodontal disease [37].  
Digestive enzyme amylase is predominantly secreted by salivary glands and is also known as ptyalin. The principal function of amylase is to hydrolyze the glycosidic bonds in starch molecules and to help in breaking down the starch present in food into different simpler sugars (maltose and dextrin) [38]. Azo dyes contain higher staining properties, and because of the presence of the conjugate system, they are highly colored. The staining causes the formation of plaque, which promotes different microbes in oral flora such as _Porphyromonas gingivalis_, _Treponema deticola_, _Tamerella forythia_, _Streptococcus goronii_, and _Fusobacterium nucleatum_[39]. The chemistry of azo dyes contains different salts such as sodium sulphate, sodium chloride, potassium and calcium salts, and amines [40]. Different chemicals are released when food (sugar products, juices, and meat products) that contains azo dyes is grinded in the oral cavity. The most common bacterial species of oral flora that are involve in periodontal disorders are _P. gingivalis_ and _Strep. Gordonii_; these species accelerate the process.
## 1 Introduction
In addition, the issue of complexity is also addressed for the first time in this article, providing comparative information concerning the sizes of the instances of the ILP problems, as well as the computation times involved in their resolution. This work follows a traffic engineering (TE) approach that uses only topological information to be integrated in an integer linear programming (ILP) formulation from which optimized routes are obtained. These routes are primarily intended to be used alone as single-path static routes to provide load-balancing without the need for extra link-state signalization, but a combined use with other technologies is not precluded. The benefits of TE over SP are well-known. In fact load balancing is being successfully used in IP networks to map traffic from congested areas to lightly loaded ones, and several proposals were already deployed for the wavelength routed (WR) networks. However, these approaches cannot be directly applied to OBS networks. Different from IP networks where buffers are intensively used, and different from WR networks where wavelengths can be treated as virtual links between ingress/egress pairs with no data loss on the transmission path, buffering in OBS is minimal and wavelengths are statistically shared among many different ingress/egress pairs, leading to a certain finite burst loss probability. These and other intrinsic features of OBS (like the streamline effect, for example) manifestly justify further TE research on the context of OBS networks. It is also well known that static strategies may have limitations if the traffic pattern is changing over time. However, it is worth pointing out that, by its nature, OBS technology is mainly tailored to support transport backbones presenting aggregate traffic demands that are expected to change relatively slowly. In fact those traffic demands have been recently characterized as quasi-stationary [15]. Moreover, despite being widely believed that the dynamic operation of RWA helps to overcome the inefficiencies of static allocations (in terms of wavelength utilization), recent simulation studies show some static RWA algorithms which perform closely to dynamic ones, in particular in networks without wavelength conversion capability where the benefit of dynamic approaches is not significant [16, 17]. The paper is organized as follows. After this introductory section with the objective and the motivation for the proposed work, some OBS intrinsic problems are stated together with a brief state of the art that extends itself into Section 2. In Section 2 the OBS network functionality is presented together with the adopted QoS perspective. Section 3 presents the simulation model and describes the OBS simulator developed for testing. Section 4 presents the routing strategies for burst loss reduction. In Section 5 results are presented and discussed. Finally, Section 6 concludes the paper with final remarks.