esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Extraction Conditions
Our own findings show that the antioxidant activity (FRAP) of the herbal extracts tested was significantly and very strongly correlated with total polyphenols content (r = +0.825931 and +0.831350), and affected by the type of raw material used (Table 4). In both SEs and THEs, there was a very strong positive correlation between total phenolic acids content, total polyphenols content, and antioxidant activity (FRAP), with SEs demonstrating the highest correlation in the total phenolic acids content/antioxidant activity (FRAP system; r = +0.917172). In contrast, when DPPH was used, the SEs did not show significant correlations, but THEs showed a significant and very strong negative correlation between total flavonoids content and antioxidant activity (DPPH; r = -0.995743), as well as a very strong positive correlation between total phenolic acids content and antioxidant activity (DPPH; r = +0.940963). A correlation coefficient value higher than 0.61 and lower than 0.97 is indicative of a highly positive correlation [25]. [PERSON] et al. [54] correlated total polyphenols content with the antioxidant capacity of marjoram, oregano, lemon balm, and rosemary extracts. The r-value between the antioxidant capacity and total polyphenols content was 0.8866 according to FRAP and 0.9706 according to DPPH. The high r suggests that DPPH radical scavenging activity can be reliably predicted using the Folin-Ciocalteau test to determine total polyphenols content, and confirms that the antioxidant activity is attributable to the phenolic compounds. In [PERSON] et al. 's [8] study of extracts from several Algerian medicinal plants, the authors identified positive correlations between total polyphenols content and antioxidant activity (Trolox; R\({}^{2}\) = 0.7931), and between total flavonoids content and antioxidant activity (Trolox; R\({}^{2}\) = 0.7802), indicating that phenolics play a major role in antioxidant activity.
# A Simulation Study on Pressure Control in Oil Well Drilling Using Gain-Scheduled PID Controllers
## 4 Results
### GS Controller and MRAC: Simulation
#### 4.3.1 Pressure Tracking Control  
The system was simulated and responses were obtained for the GS and MRAC controllers, as already specified. We evaluated the performance and behavior of the response to the follow-up of the desired pressure. The pressure levels of the drilling could be elevated and varied, so in this study a pressure range from 190 to 200 bar was considered. For the different drilling depths in the study (500, 1000, 2000, 3000, 4000, and 5000 m), a desired positive-step function that varied from 19
## 4 Discussion  
The pad tilting and corresponding pivot rolling motion causes a shift of the pivot-housing contact area. If the pivot-housing degree of conformity is small, such an effect can be neglected as it is done in most bearing computational tools and in the literature. However, where stress and durability concerns lead to higher degrees of conformity, it should be considered because it can have consequences on the bearing performance. The contact shift means a new pivot angular position within the housing and a new offset of the pressure resultant on the pad. Moreover, each pad has a different configuration that depends on the load acting on it, and the bearing loses its symmetry with respect to the load line direction with the effects on bearing characteristics depicted in [PERSON] et al. [23].  
As described in Section 2 the effect of pivot shift can be obtained through a fictitious equivalent rotational stiffness obtaining the actual offset of the pad pressure resultant due to rolling. The difference with flexural pivot bearing is that, in the proposed RB bearing model, the rotational stiffness is load dependent and, consequently, each pad has its own. This last feature can be implemented in XLHydroDynTM.  
The value of \(K_{rot}\), for a given housing radius, depends on the choice of pad rocker back radius. Further, a lower clearance between the pivot and the housing increases the absolute value of the cross-coupled coefficients, which is detrimental to the bearing stability. This can be especially critical for the stability of flexible, high-speed rotors. On the other hand, a greater conformity helps to reduce contact stresses, fatigue, and wear. Therefore, an optimal trade-off exists between rotor dynamic stability and pivot durability.  
As shown in the previous section, including equivalent rotational stiffnesses produces non-negligible differences in the static and dynamic bearing performance for higher degrees of pivot-housing conformity. Compared to cross-coupled coefficients, direct coefficients are less influenced by the pivot rotational stiffness and their values are relatively close to those of the reference configuration in the investigated range. Cross-coupled coefficients show instead a much higher sensitivity to \(K_{rot}\). Their increase with it can be easily explained with the fact that the pad with finite rotational stiffness represents an intermediate situation between the pad perfectly free to rotate (\(K_{rot}=0\)) and the fixed pad (\(K_{rot}=\infty\)).
# Energy Efficiency Perspectives of PMR Networks +
## 2 Present and Future PMR Systems  
PMR systems have been introduced to provide a speech and data two-way radio communication service in non-public networks tailored to the specific professional operational needs. Typically, the main users of such kind of services are military, police and public safety forces, but also private institutions like transportation and logistic companies. The target PMR scenario is represented by the mission critical communications. It is characterized by four key requirements: resilience and highly availability of the infrastructure, reliability of the communications, security and possibility of point-to-multipoint communications to support group calls and messages. A resilient and highly available infrastructure can be obtained by redundant network architecture and fail-safe network elements, allowing a minimum service even if the connection to the infrastructure is not possible or limited. Moreover, the communication is considered reliable if the provided service is accessible and stable. Therefore, the target quality of service must be reached within the entire coverage area and the communication setup must be extremely fast. Security is needed to protect users from malevolent actions like jamming, interception and spoofing, providing features like mutual authentication of terminals, jamming compensations, end-to-end encryption and temporarily terminal disabling. Low power consumption is also needed to reduce the operational costs. Such requirements have been largely implemented in the mobile radio terminals by introducing, for instance, transmission power control in order to increase the battery lifetime [14]. Currently, TETRA [15; 16] is the most widely used PMR standard, providing a reliable infrastructure to support mission critical communications. As depicted in Figure 2, the PMR evolution, particularly referring to TETRA, is linked to the commercial radio system evolution since PMR manufacturers cannot afford in-depth research and development budgets to develop next generation PMR mobile radio technologies in parallel to or even ahead of commercial mobile radio manufacturers. Therefore, since its beginning, PMR systems have been exploiting commercial radio systems technologies and standards. The remainder of this section describes the TETRA evolution, starting by the original TETRA system and TETRA Enhanced Data Service (TEDS), representing the state-of-the-art, and a possible evolution related to the broadband services provided by the 4G commercial standard, identified as TETRA over LTE.
# From Genomics to AI: Revolutionizing Precision Medicine in Oncology
## 6 Epigenetics in Cancer: From Biomarkers to Therapeutic Targets
### Epigenetic Biomarkers for Diagnosis and Prognosis  
Aberrant DNA methylation in promoter regions of tumor suppressor genes is among the earliest molecular events in oncogenesis and offers strong potential as a diagnostic and prognostic biomarker. For example, hypermethylation of _BRCA1_, _RASSFIA_, _APC_, and _CDKN2A_ has been widely reported in breast, colorectal, and lung cancers, and it is associated with aggressive clinical pathological features, such as lymph node metastasis, higher tumor grade, and poor survival outcomes [64; 65].  
New technologies, particularly liquid biopsy platforms, now allow for the detection of circulating methylated DNA and tumor-derived non-coding RNAs, providing a non-invasive means for early diagnosis and MRD monitoring. For instance, promoter hypermethylation of _RASSFIA_ or _SEPT9_ in plasma cfdDNA has been shown to distinguish cancer patients from healthy individuals and predict recurrence after surgery [65].  
In addition to diagnosis, epigenetic profiles also enable risk stratification. Multi-gene methylation signatures or altered expression of histone-modifying enzymes (e.g., EZH2, HDAC1/2) have been linked to poor prognosis, immune evasion, and therapy resistance, especially in subtypes like triple-negative breast cancer (TNBC) [66; 67].
## 4 Simulation and Dataset Generation
### Functional Engineering Simulator  
All training datasets of this study are collected by running a high-fidelity functional engineering simulator: it is a validated environment, which replicates the Attitude Determination and Control System (ADCS) of a spacecraft, in MATLAB(R2024b) and Simulink(r). The choice for this Model-in-the-Loop (MIL) setup, the modularity, and the tunable parameters foresee a realistic implementation and validation phase. The simulator replicates continuous-time quantities and thus uses ode5 as a solver, with a fixed step size of 0.01. Furthermore, the amount of parameters randomized at the beginning of each simulation is very large: they concern noise and random errors on sensors and actuators, initial orientation of the satellite, target rotations for slew maneuvers, and physical characteristics as well, and they are explored in Section 4.2. By setting up the algorithm in this way, the process of building large datasets recalls the Monte-Carlo method to remove unwanted biases and to let the final learning agents be trained on widely different scenarios.  
The simulator models all the main primary disturbances for a typical Earth-orbiting satellite: the first ones are gravity-gradient torques, applied on the spacecraft under the hypothesis of a constant free fall acceleration. Solar radiation pressure plays a small role in applying different radiation forces on the spacecraft surfaces; the result is once again in a very weak torque, but with cumulative effects over time. The electromagnetic field of the planet, modeled through the IGRF-13 standard, is much more location-specific with respect to the assumed gravity field, and by interacting with a parasitic magnetic dipole onboard, assumed to be constant and reported in Table 3, produces once again environmental torques on the spacecraft. One last source of small perturbations on the orientation of the satellite  
\begin{table}
\begin{tabular}{c c} \hline \hline FC = \(\frac{1}{N}\sum_{i=1}^{N}f_{i}\) & RMSF = \(\left(\frac{1}{N}\sum_{i=1}^{N}f_{i}^{2}\right)^{1/2}\) \\ RVF = \(\left(\frac{1}{N}\sum_{i=1}^{N}(f_{i}-\text{FC})^{2}\right)^{1/2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Frequency-domain statistical features.  
is air drag, which is retrieved by modeling Earth's atmosphere density (which is assumed to follow an exponential model, decreasing with altitude). Each run simulates at most a few minutes of satellite operations: in that small time frame, one of the main assumptions made is that perturbations act on the satellite orientation but have no effect on its orbit.
## 5 Performance Evaluation and Discussion
### Study on Mobile and Static Sink Cases  
LEACH is a classical clustering algorithm which utilizes randomized rotation of local cluster head nodes to evenly distribute the energy load across the network [3,4]. Compared with other ordinary routing algorithms or protocols, it can prolong the network lifetime up to eight times. Here we compare the performance of our proposed algorithm with LEACH.  
A comparison of the residual energy using multiple mobile sink nodes and LEACH is shown in Figure 6. We can find that energy consumption decreases with round (or time). The rate of decrease of LEACH is much faster than that of our proposed algorithm with multiple mobile sinks. The energy consumption rate is similar up to 17 rounds. After that, the energy consumption of our proposed algorithm is much less than that of LEACH. Consequently, the energy in LEACH network gets drained away much earlier, and our proposed routing algorithm outperforms LEACH.
## 3 Results and Discussion
### Gold Adsorption from Chloride Media  
The adsorption of Au(III) from HCl solutions by the multi-walled carbon nanotubes was also investigated. The adsorption equilibrium can be firstly described by the next general reaction:  
\[\rm AuCl_{4\;aq}^{-}\Leftrightarrow\rm AuCl_{4\;NTb}^{-} \tag{6}\]  
where aq and Ntb represent the aqueous and solid phases, respectively. However, there is evidence of reduction of gold(III) to metallic gold, as the SEM image shows (Figure 2). As it is observed from this figure, dark particles appeared on the gold-loaded carbon nanotubes. These particles are of metallic  
\begin{table}
\begin{tabular}{c c c} \hline
**L/S Relationship** & **\% Gold Adsorption 1 ** & **Gold Uptake**, **mg g\({}^{-1\;a}\)** \\ \hline
125 & 93.0 & 1.2 \\
250 & 90.0 & 2.3 \\
500 & 57.0 & 2.9 \\
1000 & 50.0 & 5.0 \\
2000 & 40.5 & 8.2 \\
4000 & 31.5 & 12.8 \\ \hline \end{tabular}
\end{table}
Table 4: Influence of the liquid/solid relationship on gold(I) adsorption.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Metal Pair** & **Complex** & **[M]**, **mg g\({}^{-1}\)** & **[M]**, **mg L\({}^{-1}\)** & **D**, **L g\({}^{-1}\)** & \(\beta_{\rm Au(I)/M}\) \\ \hline Au(I) & Au(CN)\({}_{2}\)\({}^{-}\) & 2.8 & 4.4 & 0.64 & \\ Cu(I) & Cu(CN)\({}_{4}\)\({}^{3-}\) & 0.03 & 3.2 & 0.01 & 64 \\ \hline Au(I) & Au(CN)\({}_{2}\)\({}^{-}\) & 3.1 & 3.9 & 0.79 & 7.2 \\ Fe(II) & Fe(CN)\({}_{6}\)\({}^{4-}\) & 0.25 & 2.3 & 0.11 & 7.2 \\ \hline Au(I) & Au(CN)\({}_{2}\)\({}^{-}\) & 3.3 & 3.5 & 0.94 & 7.2 \\ Fe(III) & Fe(CN)\({}_{6}\)\({}^{3-}\) & 0.29 & 2.2 & 0.13 & 7.2 \\ \hline \end{tabular}
\end{table}
Table 5: Adsorption of different metal-cyano complexes and selectivity of the system.  
gold as the EDS spectrum (Figure 3) showed two peaks at 9.7 keV (L\(\alpha\)) and 2.1 keV (M\(\alpha\)) characteristic of metallic gold. Very probably this reduction occurs on the carbon surface [17], being the reactions involved in the metal reduction:  
\[\mathrm{AuCl}_{4}^{-}+3\mathrm{e}^{-}\Leftrightarrow\mathrm{Au}^{0}+4\mathrm{ Cl}^{-},E^{0}=1.00\,\mathrm{V} \tag{7}\]  
if the gold(III) reduces directly to metallic gold, or:  
\[\mathrm{AuCl}_{4}^{-}+2\mathrm{e}^{-}\Leftrightarrow\mathrm{AuCl}_{2}^{-}+2 \mathrm{Cl}^{-},E^{0}=0.92\,\mathrm{V} \tag{8}\]  
\[\mathrm{AuCl}_{2}^{-}+\mathrm{e}^{-}\Leftrightarrow\mathrm{Au}^{0}+2\mathrm{ Cl}^{-},E^{0}=1.16\,\mathrm{V} \tag{9}\]  
if the reduction occurs via the formation of the \(\mathrm{AuCl}_{2}{}^{-}\) intermediate. In any case, the source for the electrons is the carbon nanotube:  
\[\mathrm{C}+2\mathrm{H}_{2}\mathrm{O}\Leftrightarrow 4\mathrm{H}^{+}+\mathrm{CO}_{ 2}+4\mathrm{e}^{-},E^{0}=0.21\,\mathrm{V} \tag{10}\]  
Figure 3: Energy Dispersive X-ray Spectroscopy spectrum of the dark particles (zero valent gold) of Figure 2.  
Figure 2: SEM image of gold-loaded multi-walled carbon nanotubes. Arrows pointing to zero valent gold particles.
## 4 Simulation And Analysis  
In our experiment, the simulator opportunistic network environment (ONE) is utilized to evaluate the experimental performance of the FPRDM routing algorithm, and the mathematical tool MATLAB could be used to assign different weight values to each social features of mobile users in OSNs. In addition, to comprehensively assess the performance of the FPRDM routing algorithm, it will be compared with other four traditional or latest routing protocols in OSNs: Spray and wait [13], Epidemic [2], ICMT (information cache management and data transmission algorithm exploiting comprehensive node similarity) [9], and FCNS (a fuzzy routing-forwarding algorithm exploiting comprehensive node similarity) [6]. Spray and wait, and Epidemic are traditional methods in opportunistic social networks. ICMT attempts to achieve an efficient routing connectivity using memory management and node collaboration, while the FCNS is a fuzzy routing-forwarding algorithm based on the Mamdani fuzzy system.
## 6 Practical Application  
In this section, a practical case study to verify all concepts discussed in the previous sections is conducted. Figure 5 shows an unbalanced three-phase linear three-wire system connected to an infinite power network with unbalanced voltages. An equivalent unbalanced load representing the entire network downstream of the bus is considered. The characteristics of the entire network from the bus are unknown. The following values of the line-to-line voltages and line currents have been measured on the bus (PCC):  
\[\frac{V_{ab}}{V_{bc}}=405.15\ e^{j38.50}\ \frac{V_{bc}}{V_{bc}}=353.49\ e^{- 95.68}\ \frac{V_{ca}}{V_{ca}}=299.10\ e^{j160.56}\]  
\[\frac{I_{a}}{V_{bc}}=14.88\ e^{-j138.77}\ \frac{I_{b}}{V_{bc}}=25.51\ e^{-j84.49} \frac{I_{c}}{V_{ca}}=36.27\ e^{j76.05}\]  
Figure 4: Joint compensation “SVC + NSCC” in delta connection.  
Considering the values of line-to-line voltages and line currents measured on the bus, Tables 1 and 2 show these values in symmetric components.  
Table 3 shows the values of the line-to-neutral voltages in symmetric components according to Equations (7) and (8). These values are calculated from Table 1.  
Next, we proceed to determine the reactance values in each phase of the SVC and NSCC compensators according to the scheme shown in Figure 2.
# Experimental Study of Explosion Mitigation by Deployed Metal Combined with Water Curtain
## 2 Materials and Methods  
### Samples  
Grids made of expanded metal were investigated (Figure 1). Expanded metal is a metal sheet that has been cut and stretched to form a regular pattern. Expanded metal is stronger than an equivalent weight of wire mesh because the material is deformed, allowing the metal to remain as a single piece. The expanded metal used in the present study had a hexagonal mesh. The characteristics were an open area of 24%, a hole size of 45 mm \(\times\) 13 mm, a thickness of 3 mm, an apparent thickness of 9 mm and a strand of 5 mm.
## 5 Results
### Baseline Versus Automatic Hierarchy Method: Results
#### 5.1.2 Ease of Learning  
As shown in Figure 5b, questions 3 to 4 were asked to measure the ease of learning via each method. Q3 asked whether the participants could understand the operating method of each method. The participants showed a statistically significant difference (\(Z=-2.23\), \(p=0.03\)), with automatic hierarchy (\(M=5.75\), \(SD=0.97\)) being rated higher than the baseline (\(M=3.75\), \(SD=1.77\)). For Q4, showed no statistically significant difference (\(Z=-1.04\), \(p=0.29\)), with the baseline (\(M=5.42\), \(SD=1.98\)) and automatic hierarchy (\(M=4.92\), \(SD=1.24\)) being relatively similar.  
Figure 5: Questionnaire results from the comparison of perceived user experience between baseline and automatic hierarchy methods. The significance levels are as follows: * \(p<0.05\); ** \(p<0.01\).
## 2 Related Works and the Key Contribution of This Paper
### TWRC and Network Coding  
The study builds on the concept of TWRC, which has been explored in prior works, such as [10; 11; 12], for improving spectral efficiency and reducing latency in high-bandwidth applications. These studies provide a foundation for understanding how TWRC can facilitate bidirectional communication between nodes exchanging MVD content. Furthermore, the paper references the use of network coding strategies that enhance video transmission over relay channels, aiming to optimize the balance between transmission quality and bandwidth efficiency.
# Generative Modeling for Imbalanced Credit Card Fraud Transaction Detection
## 3 Generative Models
### Autoencoder (AE)  
Autoencoder is an unsupervised machine learning neural network model designed to learn efficient representations of input data, often for purposes such as dimensionality reduction or feature extraction [35]. The Autoencoder operates by encoding the input data into a lower-dimensional representation (encoding) using an encoder and then reconstructing the data back to their original forms (decoding) using a decoder, all while minimizing the reconstruction error [36]. As we can see from Figure 2, the Autoencoder architecture consists of two main components:  
1. **Encoder**: The encoder compresses the input data into a lower-dimensional space, creating a new representation known as the code or bottleneck [37]. The encoder is represented as a function \(f\) that maps the input \(X_{i}\) to the code layer \(h_{i}\), i.e., \(h_{i}=f(X_{i})\). This process is critical for dimensionality reduction and feature extraction. 2. **Decoder**: The decoder reconstructs the data from the lower-dimensional code layer back to the original input space. The function \(g\) takes the code layer \(h_{i}\) and produces the reconstructed output \(\widehat{X}_{i}\), i.e., \(\widehat{X}_{i}=g(h_{i})\). The reconstruction loss measures the difference between the input and the output, serving as an objective function to be minimized during training. This loss, often calculated using mean squared error or binary cross-entropy, ensures that the output \(\widehat{X}_{i}\) closely resembles the original input \(X_{i}\). The training objective can be formulated as  
\[\text{Argmin}_{f,g}\,\text{Loss}(X_{i},\widehat{X}_{i}) \tag{3}\]  
where the loss function measures the dissimilarity between the input \(X_{i}\) and the reconstructed output \(\widehat{X}_{i}\). In this paper, we used an Autoencoder architecture to balance our credit card dataset. The architecture and the algorithm employed are described in detail in Algorithm 1. In this study, the hyperparameters for this Autoencoder were carefully chosen to balance model complexity, prevent overfitting, and improve the model's ability to capture meaningful features of fraudulent samples. The model uses an input/output layer with 30 dimensions corresponding to the dataset's features. The encoder reduces dimensionality through three hidden layers (64, 32, and 8 units) with ReLU activation, addressing vanishing gradient issues and improving convergence. Batch normalization stabilizes training, while dropout rates of 0.2 and 0.3 prevent overfitting. The symmetric decoder structure aids in effective data regeneration, and the final sigmoid output layer is suitable for binary classification. The Adam optimizer with a learning rate of 0.001 ensures efficient training, and the model is trained for 100 epochs with a batch size of 32 to balance convergence speed and computational cost. Shuffling the data during training enhances generalization by reducing the impact of data ordering. Figure 2: Autoencoder architecture.