esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Models  
While it is anticipated that the selected models will achieve better classification accuracy at higher BACs, Section 5.3 shows that the classification accuracy is influenced by other factors as well. Various scenarios and datasets were examined during the training and testing of different models. While the literature typically focuses on identifying changes that occur, we went further by evaluating how reliably these changes can be used to detect a driver's state. Moreover, we advanced the analysis by integrating multiple sensor sources for state classification and comparing the outcomes. Various classification algorithms were employed to assess detection accuracy (see Section 5.3.4). The focus was on three simplified models--logistic regression, random forest, and XGBoost--which were then compared to a more complex LSTM network.  
As expected, combining multiple datasets improved model performance. Fusion at both the interpretation and data levels produced similar results. However, the driving scenario had a more significant impact on test accuracy than the choice of dataset. On average, the models performed best in urban and rural road scenarios, with significantly worse performance on highways. This observation aligns with the findings from the feature analysis, where the most significant differences were noted in city and rural conditions. The analysis across all scenarios resulted in test accuracies between those observed in city and highway settings.  
Similar trends were observed with the LSTM model, which achieved particularly high test accuracies on rural roads. A significant challenge for all models, especially the LSTM, was the relatively small sample size. The limited number of participants was insufficient for training the models effectively, leading to overfitting issues.
# Insights into Native Fermentation Process of Apples (_Malus domestica_) in Low Sodium Conditions
## 3 Results
### Physical and Chemical Analysis
#### 3.1.6 Concentration of Cations  
The replacement of NaCl used for preserving foods with other salts such as KCl and MgCl\({}_{2}\) is beneficial for human health and can also improve consumer perception [3]. The concentration of K, Na, and Mg in the peel of the fermented apples showed the same trend in intake, with the highest values on day 28 of the experiment (K: 5.75 \(\pm\) 0.04 mg/g; Na: 5.55 \(\pm\) 0.05 mg/g; Mg: 1.14 \(\pm\) 0.01 mg/g), while the lowest concentration in the peels was observed on the last day of the experiment (K: 2.86 \(\pm\) 0.03 mg/g; Na: 1.82 \(\pm\) 0.01 mg/g; Mg: 0.28 \(\pm\) 0.02 mg/g). In the case of KCl treatment, a steady increase was observed in K concentration in apple peels from day 7 of the experiment to day 28, after which a sharp decrease was attained on the last day of the experiment (Figure 5). Significant differences (\(p<0.01\)) were observed during the experimental trial regarding K, Na, and Mg intake in apple peels (Figure 5). However, while the intake of K and Na in the peel samples was similar in terms of concentrations, in the case of Mg, the concentration values were significantly lower. This can be attributed to the lower permeability of Mg across cell membranes as compared to Na and K [32]. In a study by [PERSON] et al. [33], the concentration of Na in the peel of figs exposed for 28 days to 100 mM NaCl was more than 0.16 mg/g dry weight.  
Regarding the concentration of cations in the brine, the concentration fluctuated during the experimental period, and no significant differences (\(p>0.05\)) were observed in terms of K and Na from the first analysis (day 7) and the last day of the experiment (day 35). Regarding the Mg concentration in the brine, a significant decreasing trend was observed from day 7 to day 28, after which a sharp increase was observed by the end of the experimental period (Table 2). Furthermore, the highly significant differences between the Mg concentration in the brine and K and Na, respectively, are due to differences in the molecular masses of salts and electrical charge [34]. In a soluble state, Mg can bind hydration water stronger than K and Na [35], and therefore, MgCl\({}_{2}\) is more soluble than NaCl and KCl [36].  
Figure 5: The dynamics of metallic ion concentrations in apple peel during the experimental trial. n.s. means not significant.
## 3 Results and Discussion
### Characterization of Nanoparticle of Garlic Extract by FTIR  
FTIR spectroscopy was used to discover the successful extraction of garlic nanoparticles, and spectra were obtained in the wavenumber range of 500-3500 cm\({}^{-1}\). The results of the FTIR spectrum of the ultra-sonicated garlic extract showed visible peaks at around 3345.8, 2934.1, 2360.5, 1635.6, 1417.5, 1130.7, 1025, 930.61, 815.4, and 591.5 cm\({}^{-1}\) wavenumbers (Figure 5).  
The wide peak at 3345.8 cm\({}^{-1}\) can be assigned to the presence of the O-H stretching vibration in the hydroxyl group. These results also indicated that there is asymmetric stretching in the C-H bonds at 2934.1 cm\({}^{-1}\), at 1635 cm\({}^{-1}\) FTIR revealed the presence of carbonyl or carboxylic (C=O) stretching bands, the same results confirm the presence of the -O-H bend in carboxylic at 1417 cm\({}^{-1}\), at 1130.7 cm\({}^{-1}\) the results revealed the presence of an S=O bond, the presence of C-N stretching vibrations in primary amines was observed at 1025 cm\({}^{-1}\), at 930.6 cm\({}^{-1}\) FTIR showed the presence of a \(\gamma\)-C-H deformation in =CH2, at 815 cm\({}^{-1}\) we observed the presence of an S-C bond which might indicate the absorption of allicin, an organosulfur compound present in the garlic extract [49], and finally these results recorded the presence of a C-H bend in the alkynes at 530 cm\({}^{-1}\).  
Figure 4: Microplate spectrophotometer absorption of nanoparticles of the garlic extract prepared using various sonication time and power (Sample 1, 2, 3, 4: sonicated samples, control: unsoni-cated sample).  
The current ultrasonicated garlic extract FTIR spectrum matches previous research on the presence of functional groups in aqueous garlic extracts [50; 51]. Based on the spectrum mentioned above, we would say that phenolic, organosulfur compounds, amino acids, carboxylic groups, and proteins are the active groups that play a key part in the antibacterial activity of ultrasonicated garlic extract. This conclusion is supported by findings of several investigations that found the same major phytochemicals in garlic extract [52; 53].
## 4 Experiments and Analysis
### Results Analysis and Discussion
#### 4.4.1 Analysis of Results on the Sentiment140 Dataset  
Table 3 demonstrates our proposed model's comparisons with different models offered by various authors. An overall glance for the sentiment140 Twitter data shows that these given models acquired 56.95% to 87.12% accuracies. This table depicts our proposed model's highest accuracy with 87.12%, while TF-IDF-RNN [34] achieved the lowest accuracy.
## 3 Self-Referenced DPSSLs with Bulk Gain Material
### One-Micrometer Ytterbium-Doped Lasers
Several missions are under consideration by space agencies that will involve a frequency comb, e.g., in an optical master clock in space for fundamental physics experiments, geodesy, remote comparison of high accuracy ground-based optical clocks, _etc._ Such frequency combs have to survive challenging environmental conditions, in particular high-energy radiation. In this context, DPSSL frequency combs constitute a valuable alternative to fiber combs that are generally the preferred choice for space applications [71]. [PERSON] _et al._ have proved the capability of a modelocked Yb:KYW DPSSL to sustain a dose of gamma and protons radiation corresponding to more than five years in outer space on a highly elliptic orbit around Earth corresponding to a possible future candidate mission for a frequency comb in space [58]. The SESAM-modelocked laser tested under radiation had an emission spectrum centered at 1029 nm, with 148 mW average power emitted in 169 fs pulses at 40 MHz repetition rate. Different parameters of the laser were recorded during the irradiation, such as the output power, repetition rate, center wavelength, _etc._ Some of them showed only a minor change during the gamma irradiation (e.g., only 5% reduction of the laser output power). The initial output power was recovered a few days after the irradiation, while the recovery period was longer for the pulse width, but did not affect the overall performance of the laser. A second similar laser with a repetition rate of 100 MHz, an average output power of 215 mW emitted at 1031 nm and a pulse duration of 155 fs was fully-stabilized with an in-loop integrated phase noise of _f_\({}_{\mathrm{CEO}}\) of 1.1 rad (1 Hz-1 MHz) achieved with a loop bandwidth of about 100 kHz. Pulse compression to 100 fs realized using GTI mirrors after spectral broadening in a singlemode fiber was necessary for octave-spanning SC generation in a HNLF. This stabilized laser system was not subjected to irradiation.
## 2 Materials and Methods
### Sentinel-2 Imagery: Acquisition and Processing  
The Sentinel-2A images (Level 2A) used in this study were downloaded using the Copernicus platform [38]. The downloaded images were cloud-free over the entire area of both fields for all the dates considered. Table 2 shows the spectral and spatial characteristics of Sentinel 2 images [39; 40]. Before reflectance extraction, the images with 20 m of resolution (b5, b6, b7, b8a, b11, b12) were resampled at a spatial resolution of 10 m through the nearest-neighbor (NN) method within the R environment (R v. 4.2.2--RStudio for Windows, v. 2024.04.4 + 764, PBC, Boston, MA, USA), using the \"Terra\" package (v. 1.7.71) [41]. This is because, in orchard contexts, a higher resolution is needed to align with tree spacing. Since preserving original reflectance values is particularly important for our study, NN was chosen. Also, given that the study was based on individual tree measurements rather than continuous canopy, it was important to maintain discrete pixel values and not alter original pixel values via interpolation methods [42]. Raster values were scaled by dividing by 10,000 to convert from stored integer values to surface reflectance, following Sentinel-2 data specifications [43]. Therefore, the reflectance value of each band was extracted considering the pixel overlapped with each sample point within the olive fields, using RStudio with  
\begin{table}
\begin{tabular}{c c} \hline
**Date** & **Water Applied (m\({}^{3}\) ha\({}^{-1}\))** \\ \hline June 15 th & 412 \\ \hline July 7 th & 412 \\ \hline July 27 th & 412 \\ \hline August 13 th & 412 \\ \hline August 31 st & 412 \\ \hline Total & 2062 \\ \hline \end{tabular}
\end{table}
Table 1: Irrigation schedule for Field A during the study period.  
Figure 1: Apulia region, southeastern Italy (blue boundaries) (OpenStreetMap Contributors, 2025); Sentinel-2 RGB images of the experimental olive orchards in Bitonto (field A) and Palombaio (field B) (black boundaries). Red points indicate sampled trees within the orchards (\(n\) = 18 per field).  
\"terra\" and \"sf\" packages [41; 44]. In our analysis, bands b01, b09, and b10 were excluded since they are not usually involved for agronomic purposes because they did not measure the surface reflectance of land objects [45]. In this study, we have calculated vegetation indices (VIs) by using Sentinel-2 bands reflectance; these VIs were selected because of their known relationship with water and the physiological status of olive trees (Table 3).
### Related Work  
[PERSON] et al. [10] conducted a study utilizing Public Key Infrastructure (PKI) to manage security services in VANETs, focusing on entities with wireless access and communication mechanisms. [PERSON] et al. [11] noted that PKI-based systems are advantageous for secure information exchange in VANETs, facilitating entity authentication and message integrity verification. [PERSON] et al. [12] proposed a signature-based protocol for rapid handover authentication in VANETs, acknowledging the incompatibility of long authentication latencies with the high mobility of vehicles in VANET environments. This protocol reduces latency in identity verification by removing the need for multiple-party interactions. Ref. [13] discussed a novel authentication protocol for fog-enabled VANETs based on the multiple-trusted-authority model, which reduces the chance of service unavailability and single-point-of-failure.  
[PERSON] et al. [14] introduced an LTE-A anonymous handover authentication scheme for vehicular networks, acknowledging the necessity of concealing vehicle identities from message recipients due to mobility across different Roadside Units (RSUs). Anonymous handover ensures the confidentiality of vehicle identities, thwarting potential location tracking by attackers. [PERSON] et al. [15] highlighted the inability of many handover authentication protocols to address serious threats such as unauthorized access and impersonation attacks. To counter these challenges, they suggested a decentralized and anonymous authentication scheme that supports seamless handover by incorporating blockchain technology.  
[PERSON] et al. [16] observed that vehicles entering new infrastructure coverage areas in VANETs face complex re-authentication processes. They recommended a scalable computation scheme aided by blockchain technology, facilitating vehicle handover authentication via secure ownership transfer mechanisms. [PERSON] et al. [17] merged bilinear pairing technology with blockchain to expedite handover authentication between RSUs, significantly minimizing computational overhead. [PERSON] et al. [18] proposed a hybrid blockchain-based privacy-preserving authentication scheme,while a pre-authentication mechanism was used to accelerate the cross-domain authentication of vehicles.
## 2 Measurements
### Results  
A total of 13 trains traveling on track #2 in the morning were recorded during the measurements. The running train consists of a six-unit B-type car, measuring approximately 120 m in length and traveling at an average speed of 13 km/h. The train speed ranges from 10.48 to 15.91 km/h, while the time taken for the train to pass through the measuring point falls between 27.15 and 41.22 s.  
The time histories and spectrogram of soil G1, ground column C1, and first floor C2 are shown in Figure 4. The peak vertical acceleration measured adjacent to column G1 is approximately 0.2 m/s\({}^{2}\), while the peak vertical acceleration recorded on column C1 amounts to around 0.02 m/s\({}^{2}\). Additionally, on the first floor of the over-track building, the peak vertical acceleration measured at the bottom of column C2 reaches approximately 0.011 m/s\({}^{2}\). The vertical vibration amplitude of G1 is significantly larger compared to C1 and C2, indicating a pronounced attenuation during the transfer from the soil to the column.  
Meanwhile, the spectrogram reveals that G1 exhibits predominant energy concentration within the frequency range of 20-80 Hz, whereas C1 and C2 predominantly exhibit an energy concentration within the frequency band of 20-40 Hz. The present observation implies a substantial attenuation of high-frequency components above 40 Hz during both the soil-to-column transition and over-track building processes.  
Figure 5 shows that the vertical vibration response of both the soil and columns during train operation exhibits a close resemblance within the frequency range of 12.5-31.5 Hz. The soil vibration resulting from train operation reaches its peak at 63 Hz, followed by a subsequent decrease in the vibration level within the high-frequency range. The transmission of vibration waves from G1 to C1 and C2 exhibits a significant amplification effect at 10 Hz, which is attributed to the resonance induced by the newly formed natural frequency between the building structure and the soil. The vertical vibrations attenuate in the medium- and high-frequency range above 40 Hz due to the dynamic interaction between the structure and soil, dependent on their contact area and respective character  
Figure 4: Time histories and spectrograms of soil, ground column, and 1 st floor.  
istics. The former exhibits a typical resonance between the building and soil, while the latter is attributed to the interaction between the soil and structure. The vibration response of the measurements will serve as a means for validating the finite element model. The measurement results can be compared with relevant national normative indicators [31] to assess their impact on the building and evaluate whether the vibrations exceed permissible limits, potentially affecting human occupants.
# Influence of Porosity on Drag Coefficient of Perforated Road Signs
## 2 Computational Fluid Dynamics Analysis of Perforated Road Sign
A wind tunnel test was conducted to analyze the drag coefficient reduction ratio of the perforated road sign, and the results were compared with the CFD analysis results. In the wind tunnel test, two types of wind speeds, 10 m/s and 20 m/s, were applied, and two types of road signs, a road sign with 0% porosity and a perforated road sign with 10% porosity, were tested. Figure 4(a) shows a road sign with 0% porosity and Figure 4(b) shows a perforated road sign with 10% porosity. Table 4 shows the result of comparing the wind tunnel test and CFD analysis; the difference in drag coefficient reduction ratio was analyzed to be 6.5% at a wind speed of 10 m/s, and 8.0% at 20 m/s. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Type** & **Wind Speed** & **Drag Coefficient Reduction Ratio** \\ \hline Wind tunnel test & 10 m/s & 0.974 \\
20 m/s & 0.978 \\ CFD analysis & 10 m/s & 0.911 \\
20 m/s & 0.900 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Drag coefficient reduction ratio of perforated road sign. Figure 4: Suitability verification of CFD analysis. Figure 3: Drag coefficient of perforated road sign with 0% and 30% porosity.
# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 5 Discussion  
### Results  
In this study, we compared bug report priority prediction performances between the proposed and baseline models (NB, Random Forest, Decision Tree, SVM, CNN, LSTM, and CNN-LSTM). Overall, the proposed model exhibits excellent accuracy.  
In addition, statistical verification showed that there was a significant difference between the baseline and the proposed models. The experimental results are analyzed below.  
In this study, we applied feature selection algorithms based on the priority of bug reports and predicted the priority using LSTM-Attention model learning for the extracted features.  
The experimental results in Figure 6 show approximately 50.42% P3 priority in the Eclipse project and approximately 67.16% P5 priority in the Mozilla project. An analysis of this result shows that an imbalance in the amount of data was found and the smallest data distribution was shown. In the future, the performance of the model can be improved through methods such as data normalization.  
The proposed model was compared with a model that did not apply a feature selection algorithm based on priority, and it was confirmed that the applied model performed better.  
The Accuracy, Precision, Recall, and F-measure of the evaluation scale showed consistently high performances, with statistically significant differences confirmed by the consistent rejection of the null hypotheses across all comparisons (\(p\)-values ranging from 0.001953 to 1.015 \(\times\) 10\({}^{-11}\)), highlighting the reliability of the proposed Priority-Sensitive LSTM-Attention mechanism and its significant improvements over traditional machine learning and deep learning approaches.
## 2 Antiresonances in LTI Systems: Theoretical Background  
Let us model a linear time-invariant (LTI) vibrating system through \(N\) degrees of freedom (DOFs) and its mass matrix \(\mathbf{M}>0\in\mathbb{R}^{N\times N}\), its stiffness matrix \(\mathbf{K}\geq 0\in\mathbb{R}^{N\times N}\) and its damping matrix \(\mathbf{C}\in\mathbb{R}^{N\times N}\). The following system of linear equation of motion is formulated:  
\[\mathbf{M}\tilde{\mathbf{q}}(t)+\mathbf{C}\dot{\mathbf{q}}(t)+\mathbf{K} \mathbf{q}(t)=\mathbf{B}\mathbf{f}(t), \tag{1}\]  
The generalized displacements and the generalized forces are collected in vectors \(\mathbf{q}(t)\in\mathbb{R}^{N}\) and \(\mathbf{f}(t)\in\mathbb{R}^{N}\) respectively, while \(\mathbf{B}\in\mathbb{R}^{N\times N}\) is the force distribution matrix. Let us assume that harmonic forces with radian frequency \(\omega\) are applied (\(j=\sqrt{-1}\)). The system response in the frequency domain is therefore:  
\[\Big{(}-\omega^{2}\mathbf{M}+j\omega\mathbf{C}+\mathbf{K}\Big{)}\mathbf{q}(j \omega)=\mathbf{B}\mathbf{f}(j\omega). \tag{2}\]  
The response displacements are obtained through the receptance matrix \(\mathbf{H}(j\omega)\) as follows:  
\[\mathbf{q}(j\omega)=\mathbf{H}(j\omega)\mathbf{B}\mathbf{f}(j\omega) \tag{3}\]  
where:  
\[\mathbf{H}(j\omega)=\Big{(}-\omega^{2}\mathbf{M}+j\omega\mathbf{C}+\mathbf{K} \Big{)}^{-1}. \tag{4}\]  
The receptance \(h_{pq}(j\omega)\) from the force applied at the \(q\)-th DOF, \(f_{q}(j\omega)\), to the displacement of the \(p\)-th DOF, \(q_{p}(j\omega)\) is computed as follows [5,6]:  
\[h_{pq}(j\omega)=\frac{q_{p}(j\omega)}{f_{q}(j\omega)}=(-1)^{p+q}\frac{\det \!\left(-\omega^{2}\mathbf{M}_{--}+j\omega\mathbf{C}_{--}+\mathbf{K}_{--} \right)}{\det\!\left(-\omega^{2}\mathbf{M}+j\omega\mathbf{C}+\mathbf{K}\right)} \tag{5}\]  
The frequencies at which the numerator in Equation (5) becomes null are the antiresonances of \(h_{pq}(j\omega)\), that are computed as the roots of:  
\[\det\!\left(-\omega^{2}\mathbf{M}_{--}+j\omega\mathbf{C}_{\mathbf{q}\mathbf{ p}}+\mathbf{K}_{--}\right)=0 \tag{6}\]  
where the adjunct (or adjoint) system, defined through matrices \(\mathbf{M}_{--},\mathbf{C}_{--},\mathbf{K}_{--}\in\mathbb{R}^{(N-1)\times(N-1)}\), is obtained by removing the \(q\)-th row and \(p\)-th column from \(\mathbf{M},\mathbf{C},\mathbf{K}\).  
The steady-state response of the \(p\)-th coordinate is zero if the excitation force applied to the \(q\)-th coordinate matches an antiresonance frequency of \(h_{pq}(j\omega)\). Antiresonance frequencies will be henceforth denoted as \(\omega_{z,i}\), with \(0\leq i\leq N-1\). An antiresonance corresponds to a pair of complex and conjugate zeros in the complex plane, \(z_{i},z_{i}^{*}=-\xi_{z,i}\omega_{z,i}\) is \(j\omega_{z,i}\) by \(\sqrt{1-\xi_{z,i}}\), where \(\omega_{z,i}\) is the undamped antiresonance frequency and \(\xi_{z,i}\) is damping ratio.