esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Nanotechnological solutions are increasingly developed to be used as antimicrobial agents in biomedicine. Physical and chemical techniques to produce nanoparticles (NPs) have largely been inserted with eco-friendly biological techniques [1, 2]. Nowadays, nanotechnology offers eco-friendly solutions not only for biomedicine but for agriculture and the remediation of polluted environments [3, 4, 5].  
The use of fungi in the synthesis of NPs has gained much attention recently. Fungi have been found to be efficient in reducing metal salts to NPs by producing proteins and catalysts that enable rapid synthesis [6, 7]. Fungi are especially suitable due to their metal resistance and ability to accumulate metals [8]. Moreover, fungi-mediated NPs are of controlled size and morphology [9, 10, 11, 12, 13]. As a whole, the fungi-mediated synthesis of NPs offers a technology that can be scaled up to industrial needs.  
Nanoparticles can be synthesized using one or more metals. Recent research has recognized the positive synergistic effects of the metals in bi-metallic NPs [14, 15]. The shapes of bi-metallic NPs are diverse and the ratio of surface to volume is high compared to mono-metallic NPs [16]. Moreover, bi-metallic NPs have higher stability and activity than mono-metallic NPs [17, 18]. Several combinations of metals such as Ag-Cu, Au-Pt, Co-Ni, Ag-Pd, and Fe-Pt have been presented [17, 18, 19, 20, 21]. Ag-Cu NPs have been reported for structural and thermal stability [19, 20, 21].  
Various techniques to synthesize NPs have been studied. Recently, the advantage of microwave reactors in the synthesizing process has been presented [22, 23]. The efficient penetration of microwaves enables the homogeneous, rapid heating of the materials. The use of microwaves produces a high yield of NPs and needs less time and chemicals. Thus, the microwave method fulfills the principles of green chemistry.  
In the synthesizing process, many parameter values such as the microwave power, the amount of biomass used, the concentration of metals, and the reaction time must be chosen. It is crucial, yet seldom carried out previously, to optimize the conditions by modeling. One method to optimize the conditions is response surface methodology (RSM) [3], which has also been taken into use in NP synthesis recently [24; 25]. The advantage of optimizing the conditions is studying several parameters and choosing the best combination of the parameter values to gain the maximum yield of NPs.  
The objectives of this study were to combine two novel aspects in nanoparticle research: to optimize the process parameters using statistical methods and to produce bi-metallic Ag-Cu NPs that have high biological activities. Treatments to optimize four parameters of the NP synthesizing process were carried out. Then, tests to study the antimicrobial, antioxidant, and cytotoxic activities of the synthesized Ag-Cu NPs were carried out.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 3 IEMACO Routing Algorithm
### Algorithm Convergence  
In the ant colony algorithm, after the ant completes a path search, the pheromone will be accumulated along the way thus the next ant will prefer the found route. In other words, the ant colony optimization algorithm has positive feedback characteristics, which will cause falling into local optimum. In Reference [26], the routing criteria of the ant colony optimization algorithm are improved as follows: the balance coefficient \(q_{0}\) is introduced to balance the importance of the ant to explore new paths and to utilize existing information. When \(q>q_{0}\), the ant uses the existing information to route, that is, uses the transfer probability formula to select the route; when \(q\leq q_{0}\), the ant explores a new path, and randomly selects one node from the set of reachable nodes as the next hop. The corresponding transition probability is formulated as the following:  
\(q\leq q_{0}\) is directly selecting the node with the highest transition probability as the next hop. Therefore, the selection of \(\theta\) is important, it must ensure the global optimization of the algorithm, and accelerate the convergence speed of the algorithm as much as possible. In this paper, the appropriate \(\theta\) is selected by comparing simulation results with varying parameters. The proposed IEMACO considers the case of \(q\leq q_{0}\), when each node in the candidate set corresponds to its own transition probability. When the transition probability of a certain path is significantly larger than other paths, it is directly selected as the next hop can speed up the convergence of the algorithm while ensuring the global convergence of the algorithm.
## 3 Deadlock Prevention Policy for Reconfigurable S\({}^{3}\)PR Net Based on Siphons
Therefore, a siphon \(S\) does not require control. In order to reduce the supervisor's complexity, these siphons are identified and no monitors are added. Thus, we have two sets of constraints: \(\mathcal{L}M\geq\mathcal{B}\) and \(\mathcal{L}_{o}.M\geq\mathcal{B}_{o}\) rather than a single set of constraints \(\mathcal{L}M\geq\mathcal{B}\). The deadlock prevention supervision of the original net needs enforcing \(\mathcal{L}M\geq\mathcal{B}\) and selecting an initial marking \(M_{o}\) such that \(\mathcal{L}_{o}.M_{o}\geq\mathcal{B}_{o}\) and \(\mathcal{L}.M_{o}\geq\mathcal{B}\). The constraints \(\mathcal{L}_{o}.M\geq\mathcal{B}_{o}\) are the constraints that all reachable markings satisfy when the initial markings satisfy them. Therefore, there are two cases to control a siphon:  
If \(V_{S}\bullet\subset\)\({}^{\bullet}S\), then \(S\) does not require monitor and \(V_{S}\) is not assigned to a net \(N\). Furthermore, \(V_{S}\bullet\subset\)\({}^{\bullet}S\) if and only if \(S\) is a trap. Thus, when \(S\) is also a siphon, it is (trap) controlled for all initial markings \(M_{o}\) that satisfy \(\sum_{p\in S}M_{o}(p)\geq 1\). Therefore, a siphon \(S\) is assigned to (\(\mathcal{L}_{o}\); \(\mathcal{B}_{o}\)). A. If \(V_{S}\bullet\subset\)\({}^{\bullet}S\), then \(S\) needs a monitor and \(V_{S}\) is assigned to \(N\). Therefore, the \(S\) is assigned to \((\mathcal{L};\mathcal{B})\). **Definition 37**. : _Let \(N_{R}\) be a reconfigurable \(S^{3}PR\) net with \(N_{R}\) = ((\(N\), \(M_{o}\)), \(\mathcal{R}\)). Let \(N_{k}\) be a state in \(N_{R}\) with \(N_{k}\) = (\(P_{\mathcal{C}k}\), \(T_{k}\), \(F_{k}\), \(W_{k}\), \(M_{\text{ko}}\), \(K_{k}\)). A siphon \(S\) in \(N_{k}\) is called controlled if for all \(M\in\)R(\(N_{k}\), \(M_{\text{ko}}\)), \(\sum_{p\in S}M(p)\geq 1\) and satisfy \(\mathcal{L}M\geq\mathcal{B}\) and \(\mathcal{L}_{o}.M\geq\mathcal{B}_{o}\)._  
**Definition 38**. : _Let \(N_{R}\) be a reconfigurable \(S^{3}PR\) net with \(N_{R}\) = ((\(N\), \(M_{o}\)), \(\mathcal{R}\)). Let \(N_{k}\) be a state in \(N_{R}\) with \(N_{k}\) = (\(P_{\mathcal{C}k}\), \(T_{k}\), \(F_{k}\), \(W_{k}\), \(M_{\text{ko}}\), \(K_{k}\)). The deadlock controller for (\(N_{k}\), \(M_{\text{ko}}\)) is expressed as (\(V\), \(M_{\text{Vo}}\)) = (\(P_{V}\), \(T_{V}\), \(F_{V}\), \(M_{\text{Vo}}\)), where (1) \(P_{V}\) = {\(V_{S}\backslash S\in\Pi\)} is set of monitors. (2) \(T_{V}\) = {\(t\backslash t\in\)\({}^{\bullet}V_{S}\cup V_{S}\)\({}^{\bullet}\)}. (3) \(F_{V}\) \(\subseteq\) (\(P_{V}\times T_{V}\)) \(\cup\) (\(T_{V}\times P_{V}\)) is called a flow relation of V. (4) for all \(V_{S}\in P_{V}\), \(M_{\text{Vo}}(V_{s})=\mathcal{B}-\mathcal{L}M_{\text{ko}}(V_{s})\), where \(M_{\text{Vo}}(V_{S})\) is called an initial marking of a monitor.
## 2 Materials and Methods
### GC-MS Analysis  
The dried fruit samples were analyzed with gas chromatography coupled with mass spectrometry in a Agilent/HP 6890 GC with 5973 MSD (Palo Alto, CA, USA) in selected ion monitoring (SIM) mode. The chromatographic separation parameters are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline  & **PCB** & **PAH** \\ \hline Sample injection & 2 \(\mu\)L & 2 \(\mu\)L \\ \hline Column type & ZB-5 MS (30 m \(\times\) 0.25 \(\mu\)m \(\times\) 250 \(\mu\)m) & ZB-5 MS (30 m \(\times\) 0.25 \(\mu\)m \(\times\) 250 \(\mu\)m) \\ \hline Carrier gas & He & He \\ \hline \multirow{4}{*}{Column oven temperature program} & 1.1 cm\({}^{3}\)/min & 1.2 cm\({}^{3}\)/min \\ \cline{2-3}  & 130 \({}^{\circ}\)C (0.5 min *) & 80 \({}^{\circ}\)C (0.5 min *) \\ \cline{2-3}  & increase rate 7 \({}^{\circ}\)C up to 200 \({}^{\circ}\)C & increase rate 10 \({}^{\circ}\)C up to 230 \({}^{\circ}\)C (10 min *) \\ \cline{2-3}  & temperature ramp 14 \({}^{\circ}\)C up to 280 \({}^{\circ}\)C (10 min *) & temperature ramp 5 \({}^{\circ}\)C up to 305 \({}^{\circ}\)C (10 min *) \\ \hline Electron multiplier voltage & 1920 V & 1920 V \\ \hline \end{tabular}
\end{table}
Table 2: Chromatographic separation parameters.  
Figure 1: Procedures for determinations of PAHs and PCBs, according to [PERSON] [32] ((**a**), method 3) and [PERSON] et al. [33] ((**b**), method 4).  
The parameters of SIM mode determined for indicator PCB congeners and non- and mono-ortho PCBs were as follows: PCB 28--256/258 (molecular ion/confirmation ions), 186; PCB 52--256/290, 220; PCB 77--292/290, 220; PCB 81--292/290, 220; PCB 101--326/254, 328; PCB 105--326/328, 254; PCB 114--326/328, 254; PCB 118--326/328, 254; PCB 118--326/328, 254; PCB 118--326/328, 254; PCB 126--326/254; PCB 138--360/362, 290; PCB 155--360/290; PCB 157--360/290; PCB 169--360/362, 290; and PCB 180--394/396, 324.  
The parameters of SIM mode during PAH analysis were as follows: NA--128/126, 102 (molecular ion/confirmation ions); ACL--152/151, 153; AC--154/153, 152; FA--166/165, 167; PHE--178/179, 176; AN--178/179, 176; FL--202/101, 203; PY--202/200, 203; Ba--228/229, 226; CHR--228/229, 226; BbF--252/253, 126; BkF--252/253, 126; BaP--252/253, 126; IP--276/138, 277; DBahA--278/139, 279; BghiP--276/138, 277; naphthalene D8--136/68, 137; acenaphthene D10--164/162, 165; phenanthrene D10--188/94, 189; chrysene D12--240/120, 241; benzo[a]pyrene D12--264/132, 265; and perylene D12--264/260, 265.
## 5 Experimental Results and Discussion
### Comparison of Previous Work
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Transmitter**} & \multirow{2}{*}{**Wavelength**} & **Bitrate** & **Distance** & **Additional** & **Algorithm** \\  & & & **(Gb/s)** & **Format** & **(km)** & **Device** & **Algorithm** \\ \hline
2016 [16] & DDMZM + DAC \(\times\) 2 & C-band & 105 & DMT & 80 & - & VNE + TCM \\
2016 [25] & DDMZM + DAC \(\times\) 2 & C-band & 128 & PAM-4 & 80 & - & Pre-CDC \\
2017 [15] & IQM+ DAC \(\times\) 2 & C-band & 4 \(\lambda\times 112\) & 16-QAM & 320 & - & Hilbert + KK \\
2018 [21] & MZM + DAC \(\times\) 1 & C-band & 56 & PAM-4 & 80 & - & THP \\
2018 [26] & IM + PM + DAC \(\times\) 2 & C-band & 108 & OFDM & 20 & - & Subcarrier allocation \\
2019 [11] & MZM + DAC \(\times\) 1 & C-band & 8 \(\lambda\times 50\) & PAM-4 & 100 & DCF & FFE \\
2019 [13] & DML + DAC \(\times\) 1 & C-band & 100 & PAM-4 & 45 & OBPF & VNE \\
2019 [27] & IQM + DAC \(\times\) 2 & C-band & 129.6 & OFDM & 20 & - & Subcarrier allocation \\
2019 [28] & PolM + DAC \(\times\) 1 & C-band & 100 & PAM-4 & 5 & - & VNE \\
2020 [10] & DML + DAC \(\times\) 1 & O-band & 100 & PAM-4 & 50 & - & VNE \\
2020 [22] & MZM + DAC \(\times\) 1 & C-band & 64 & OOK & 100 & - & VNE + DFE + MLD \\
2020 [23] & MZM + DAC \(\times\) 1 & C-band & 112 & PAM-4 & 20 & - & Pre-Equ. + FFE-DFE \\
2020 [29] & MZM + DAC \(\times\) 1 & C-band & 120 & PAM-8 & 10 & - & VNE \\
2020 [30] & MZM + DAC \(\times\) 1 & C-band & 96 & PAM-8 & 80 & - & VNE \\ This work & MZM + DAC \(\times\) 1 & C-band & 104 & PAM-8 & 20 & - & VNE \\ \hline \hline \end{tabular}  
* IQM: IQ modulator; PolM: polarization modulator; DCF: dispersion-compensating fiber; OBPF: optical band-pass filter; VNE: Volterra nonlinear equalizer; TCM: Trellis coded modulation; Pre-CDC: pre-chromatic dispersion compensation; KK: Kramers-Kronig receiver; THP: Tomlinson-Harashima precoding; FFE: feed forward equalizer; DFE: decision feedback equalizer; MLD: maximum likelihood sequence detection; Pre-Equ.: pre-equalization.  
\end{table} 
Table 1: Comparison of DD transmission experiments.
# New Insight on the Formation of 2-Aminoacetophenone in White Wines
## 2 Materials and Methods
### GC-MS/MS Measurement of 2-AAP  
All wines were analyzed for the content of 2-AAP by HS-SPME-GC-MS/MS. For this purpose, 5 mL of mQ water, 5 mL of sample, 50 \(\upmu\)L of 2-AAP-d5 solution prepared at 500 \(\upmu\)g/L in high purity grade ethanol and 3 g of NaCl were added to a 20 mL SPME vial. Analysis was performed using a gas chromatograph (type 7890 B A, Agilent Technologies, Santa Clara, CA, USA) with an injector, a controller, a CTC Analytics autosampler (Zwingen, Switzerland) and a triple-quad mass spectrometer (MS/MS) detector (type 7010B GC/MS Triple Quad) (Agilent Technologies, Santa Clara, CA, USA). Separation was performed using a ZB-5 MS column (30 m \(\times\) 0.25 mm I.D. \(\times\) 0.25 \(\upmu\)m df; Agilent Technologies, Santa Clara, CA, USA) at a column flow rate of 1.02 mL/min. Helium was used as the carrier gas. The blue fiber coated with polydimethylsiloxane-divinylbenzene 65 \(\upmu\)m (PDMS-DVB) (Supelco, Bellefonte, PA, USA) was used with the following settings: pre-incubation time: 60 s, incubation temp.: 70 \({}^{\circ}\)C, pre-incubation agitator speed: 500 rpm, agitator-on time: 5 s, agitator-off time: 2 s, fiber exposure: 12 \(\upmu\)L, vial penetration: 15 mm, extraction time: 2400 s, injection penetration: 32 mm, desorption time: 120 s, fib. cond. temp.: 250 \({}^{\circ}\)C, and post-fib. cond. time: 600 s. The inlet was set in splitless mode at 250 \({}^{\circ}\)C. The oven temperature was programmed at 50 \({}^{\circ}\)C for 2 min before a rise of 15 \({}^{\circ}\)C/min to reach 160 \({}^{\circ}\)C, which was held for 1 min. Then, the temperature was increased by 20 \({}^{\circ}\)C/min to reach 230 \({}^{\circ}\)C and held for 5 min. The postrun temperature was set at 60 \({}^{\circ}\)C. The total run time was 19 min. The transfer line temperature was set at 250 \({}^{\circ}\)C. The mass spectrometer was operated in electron ionization (EI) mode at 70 eV with multiple reaction monitoring (MRM). All of the monitored transitions are listed in Table 1. Data were acquired and analyzed through Agilent MassHunter Workstation software, version B.07.00. The calibration was performed in 8 calibration steps in the range from 0 to 9.6 \(\upmu\)g/L in the synthetic wine (R\({}^{2}\) = 0.9993). Limit of detection and limit of quantification were calculated at 0.1 and 0.3 \(\upmu\)g/L by considering signal to noise S/N = 3 and S/N = 10, respectively. Recovery was assessed at 99% by spiking one-year old white wine at 0.6 \(\upmu\)g/L.
## 6 Presentation and Analysis of Results
### Discussion
#### 6.5.2 Wind (At Different Speeds)  
Wind itself does not directly interfere with radar, but it mobilizes vegetation, introducing motion clutter. Under low wind (0-5 m/s) conditions, tree branches and grass sway slowly, adding minor clutter. Vehicle vibrations (with typically higher frequency content) can still be isolated. Under moderate wind (5-10 m/s), vegetation sway becomes more dynamic. Leaves and branches moving at ~0.5-2 Hz produce Doppler returns that overlap with low-frequency vehicle modes, like body bounce (~1-3 Hz), which can lead to false positives or signal blending. In high wind (>10 m/s), strong and irregular movement of vegetation introduces broadband Doppler clutter. Micro-Doppler features from vehicle components (e.g., chassis panels, engine vibrations) may become indistinct, since wind-driven vegetation can mimic the spectral shape of a vibrating object, confusing classification algorithms or causing tracking errors.
## 3 Proposed Hybrid JADE-DO Algorithm
### Algorithmic Overview  
The hybrid JADE-DO algorithm begins by randomly initializing a population of candidate solutions within predefined lower and upper bounds. Each individual in this population is then evaluated according to the specified objective function. Within each iteration, JADE's mutation, crossover, boundary handling, and selection procedures are applied. These steps include the adaptive tuning of crossover probabilities and scaling factors based on the performance of the most successful individuals in the population. Following the JADE procedure, the dandelion optimizer (DO) stages (rising, decline, and landing) refine the population further. This integration helps maintain exploration (global search) and exploitation (local search) in a synergistic manner. After updating the best solution in each iteration, the algorithm proceeds until the maximum number of iterations is reached or some other stopping criterion is met.
## 4 Discussion  
The purpose of this study was to assess and compare the ability of four different accelerometry derived metrics (IL, MAG, SUM, PL) and GNSS to predict a known distance completed using two movement constraints. A primary finding is that both GNSS and accelerometry derived measures are valid indicators of total distance when walking is performed around a small circle and a large circle. This may also suggest that both GNSS and accelerometry are similarly capable of quantifying the distance associated with sport-related training and competition under the current experimental conditions. The results of the study are demonstrated and summarized in Figure 7.  
While all accelerometer derived measures performed well, MAG (small circle, R = 0.992, R\({}^{2}\) = 0.983, RMSE = 0.112, \(p\) < 0.001; large circle, R = 0.997, R\({}^{2}\) = 0.995, RMSE = 0.064, \(p\) < 0.001) and SUM (small circle, R = 0.992, R\({}^{2}\) = 0.984, RMSE = 0.109, \(p\) < 0.001; large circle, R = 0.992, R\({}^{2}\) = 0.983, RMSE = 0.112, \(p\) < 0.001) performed best among all accelerometry models. MAG and SUM include locomotor and non-locomotor activities. This outcome is contrary to [PERSON] and [PERSON] [14], who proposed that using accelerometer-derived measures that exclude non-locomotor activities may be more useful [14]. However, this discrepancy could be attributed to the fact that little to no non-locomotor activity was included in this study, where sport includes a substantial quantity of both locomotive and non-locomotive activity. Our findings agree with previous suggestions that different accelerometry based metrics will not equally quantify training loads in sport-related events [17]. Further research is needed to determine which accelerometry derived metrics best quantify training load in sport. It is certainly possible that the simultaneous use of multiple accelerometry based load assessments is advantageous. Unlike GNSS, accelerometers can be influenced by between-subjects' variability in loading patterns (e.g., stride characteristics) [30]. However, in this study and others [9; 27; 28], strong relationships have been found between accelerometry derived loads and total distance, despite different participants completing various distances. Much of the criticism that PL has attracted relates to calculating workloads by summing the rate of change in accelerations instead of the absolute value of accelerations [17]. However, in this study, although PL did not perform best among the accelerometry derived measures, its potential to detect training load was encouraging (small circle, R = 0.994, R\({}^{2}\) = 0.987, RMSE = 0.098, \(p\) < 0.001; large circle, R = 0.987, R\({}^{2}\) = 0.973, RMSE = 0.141, \(p\) < 0.001). Nonetheless, it may be important to address how PL would perform if repeated changes of direction were included, given that PL only increases with changes in acceleration. In accordance with the present results, a previous study demonstrated that the accelerometry derived metric average force (the product of the participant's body mass and MAG) was a better indicator
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 3 Multi-Scale Dictionary Derivation
### Dictionary Construction  
After the pre-processing described in the previous section, we have \(Q^{\prime}\leq Q\) waveforms from different patients stored in the QRS complexes database. These waveforms (\(z_{q}[n]\) for \(1\leq q\leq Q^{\prime}\) and \(0\leq n\leq L_{\max}-1\)) could be directly used to build the sub-dictionaries. However, they are highly correlated and thus the resulting dictionary would provide a poor performance and lead to a high computation time. Therefore, to obtain a reduced dictionary composed of distinct shapes, we performed the procedure described in the following sections.  
Figure 4: Several examples of the raised cosine window of Equation (11) for \(T_{s}=1\) ms, \(L_{\max}=201\), and different values of the parameter \(\alpha\).
## 3 Methodology  
### Dataset  
This study employed a dataset curated using the PISCES (Protein Sequence Culling Server) tool [26] and publicly released under the title Protein Secondary Structure 2022 on Kaggle. PISCES is a well-established platform in structural bioinformatics that filters protein sequences based on sequence identity and structural resolution criteria to produce non-redundant, high-quality datasets. For this study, a 25% sequence identity cutoff was applied to eliminate redundancy, and only crystallographic structures resolved at 2.5 A or better were retained. These constraints ensured the inclusion of unique and experimentally validated protein structures.  
The resulting dataset comprises 10,931 proteins, with chain lengths ranging from 40 to 1500 amino acid residues. Each sequence is annotated with its corresponding secondary structure under two classification schemes: Q3 (three-class) and Q8 (eight-class). The Q3 scheme categorizes residues into alpha helices (H), beta strands (E), and coils (C), while Q8 offers a more granular view, dividing residues into eight structural states, including beta bridges (B), 3\({}_{10}\) helices (G), \(\pi\) helices (I), turns (T), and bends (S). Table 1 summarizes the correspondence between the Q3 and Q8 classes, and Table 2 provides a statistical overview of the dataset.  
\begin{table}
\begin{tabular}{c c} \hline
**Q3 Classes** & **Q8 Classes** \\ \hline E (Beta) & E (\(\beta\) strand), B (\(\beta\) bridge) \\ H (Helical) & H (\(\alpha\) helix), G (3\({}_{10}\)-helix), I (\(\pi\) helix) \\ C (Coil) & C (coil), T (turn), S (bend) \\ \hline \end{tabular}
\end{table}
Table 1: Q3/Q8 secondary structure classifications.  
\begin{table}
\begin{tabular}{c c} \hline
**Feature** & **Value** \\ \hline Update Date & 17 December 2022 \\ Number of Proteins & 10,931 (non-redundant) \\ Resolution & \(\leq\)2.5 Å \\ Sequence Identity & \(\leq\)25\% \\ Minimum Residue Count & 40 \\ Maximum Residue Count & 1500 \\ \hline \end{tabular}
\end{table}
Table 2: Dataset characteristics.  
To support model training and evaluation, we first split the entire dataset into two disjoint parts: 80% for training and 20% for testing. This split was performed at the protein level to prevent information leakage due to overlapping subsequences. Subsequently, during training, we allocated 20% of the training data as a validation set. As a result, the final composition of the dataset was 64% training, 16% validation, and 20% testing. We used stratified sampling based on both Q3 and Q8 labels to maintain the consistency of class distribution across all subsets.