esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dehydrogenase Activity  
Toxicity of the surfactants is an important criterion when striving towards sustainable and environmentally friendly remediation techniques. The dehydrogenase activity (DHA) test did not reveal differences between the surfactants when tested using the Hradcany soil. This may be due to bad conditions for microbial activity in these deeper soil layers, with a lack of nutrients, except for the petroleum hydrocarbons as the carbon source. Therefore, the test was repeated with a biologically active garden soil.  
Triton X-100 and biosurfactants A and C revealed an increase in DHA with an increase in surfactant concentrations (Figure 3). DHA activity for biosurfactant H, however, showed no trend. A direct interpretation of high DHA is that the surfactant is less toxic, hence safe for use. An alternative explanation might be that the activation of certain defence mechanisms caused microorganisms to convert TTC to TPF, which was detected as high DHA.
## 3 Results and Discussion
### Implementation Details
The metrics can be calculated by the following formulas: \[Precision=\frac{TP}{TP+FP}\] \[Recall=\frac{TP}{TP+FN}\] (8) \[F1=\frac{2\times Precision\times Recall}{Precision+Recall}\] \[mIoU=\frac{1}{N}\sum_{k=1}^{N}\frac{TP_{K}}{TP_{K}+FP_{K}+FN_{K}}\] (9) where \(TP_{K}\), \(FP_{K}\), and \(FN_{K}\) represent the true positive, false positive, and false negative, respectively; these are measured by the class throughout the test set, where \(N=2\).
## Materials and Methods
Figure 2: A schematic of the experimental setup. The optical coherence tomography (OCT) measurement is performed for a fully developed laminar flow of the MFC suspension in a straight glass tube with a diameter of \(D\) = 8.6 mm. The pressure measurement taps are located at \(L_{1}\) = 52\(D\) and \(L_{3}\) = 150\(D\) from the pipe inlet. The OCT is located at \(L_{2}\) = 110\(D\) from the pipe inlet. A computer-controlled scale is used for the mass flow rate measurements. Figure 3 shows an example of the measured velocity and amplitude signals. Velocity data could be obtained, in the best case, up to 200 \(\upmu\)m from the pipe wall, excluding the immediate vicinity of the wall. The closest point relative to the pipe wall having reliable velocity data was estimated to be 2 \(\upmu\)m, below which the profiles were affected by the signal originating from the wall.
# Singing-Voice Timbre Evaluations Based on Transfer Learning
## 2 Related Work
### Research on Automatic Evaluation System for Singing Voice  
[PERSON] [12] proposed a modeling and methodology for musical song performance in 2004. The model was built based on rules. Due to the technical conditions at that time, they did not use deep learning, so the experiment had great limitations from today's perspective. In 2008, [PERSON] et al. [1] published a paper on \"Research on Evaluation Criteria for Singing Performance of Untrained Singers\", using SVM regression method to find out the importance of the evaluation criteria and utilized [PERSON]'s rank correlation coefficient (SRCC) as an evaluation index. They concluded that it is meaningless to study the absolute scores of singing evaluations, and research should focus on vectorial consistency. The evaluation criteria derived from the experiments introduce possible research directions for future prospects, among which is the mention of timbre brightness as the third most important of the evaluation criteria.  
Most evaluation systems for singing voices are related to pitch and rhythm. For example, in 2017, [PERSON] [13] proposed various perceptually relevant features to evaluate singing quality based on the concept of the PESQ (perceptual evaluation of speech quality) rating index. They adopt the audio quality perception theory in singing quality assessments by giving high weightage to localised distortions, and they termed the novel score as the Perceptual Evaluation of Singing Quality (PESnQ) score. Moreover, the correlation between the predicted PESnQ scores of the final designed system and human ratings was 0.59. In the experiments, the distance between singing timbres (called the sub-tone range) was calculated by computing the DTW distance between MFCC vectors. There are some examples of application aspects of timbre analysis. [PERSON] et al. [14] proposed a multi-singer singing synthesis system, which can independently select singers' timbres and singing styles for modeling and synthesizing the target song.  
Current timbre analysis revolves around modeling and classification, although there have been discussions about scoring criteria, none of them have actually constructed a scoring model. There is still a gap in timbre scoring, which is an important aspect of timbre analysis. This research conducted a preliminary exploration in this regard.
## 5 Results and Discussion
### Feature Importance Analysis  
To understand the environmental factors most critical for wildfire prediction, we employed a SHAP (Shapley Additive Explanations) analysis to interpret the contribution  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Dataset** & **Accuracy** & **F1-Score** & **AUC-ROC** \\ \hline \multirow{2}{*}{CNN} & Validation & 0.9991 & 0.9993 & 1.0000 \\  & Test & 0.9992 & 0.9994 & 1.0000 \\ \hline \multirow{2}{*}{Transformer} & Validation & 0.9994 & 0.9996 & 1.0000 \\  & Test & 0.9995 & 0.9997 & 1.0000 \\ \hline \multirow{2}{*}{Actor-Critic} & Validation & 0.9989 & 0.9992 & 1.0000 \\  & Test & 0.9990 & 0.9993 & 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean validation vs. test performance metrics across 100 experiments.  
of each input feature to the model predictions. This method provided a robust approach for identifying which variables had the most significant impact on the likelihood of wild-fire occurrence.  
Figure 9 presents the SHAP feature importance results for the three models (CNN, Transformer, and Actor-Critic). The most influential variables across all models were as follows:  
* Temperature (\"C): Higher temperatures strongly correlated with increased wild-fire risk.
* Relative Humidity (%): Inversely related to wildfire probability, with lower humidity contributing to drier conditions.
* Wind Speed (m/s): Strong winds facilitated fire spread, making this a crucial factor in predictions.
* Vegetation Type: Certain vegetation categories (e.g., dense forests, shrublands) exhibited higher susceptibility to ignition.
* Urban-Rural Interface (IUR): Areas closer to human settlements had higher fire occurrences, likely due to anthropogenic factors.  
The Transformer-based model exhibited the highest sensitivity to temporal variables, particularly wind speed variations over time. The CNN model, in contrast, showed a more significant reliance on spatial patterns, such as elevation and vegetation type. The Actor-Critic reinforcement learning approach dynamically adjusted its feature importance during training, indicating adaptability to varying wildfire conditions. These findings suggest that incorporating multi-source environmental data (e.g., meteorological patterns, land cover, and human activity) enhances the predictive performance of AI-driven wildfire models. Future improvements could explore ensemble methods combining CNN, Transformer, and reinforcement learning models to leverage their respective strengths. It is important to note that SHAP values may reflect biases from measurement instruments such as weather stations and satellite-derived indices. To mitigate this, the data preprocessing included normalization and smoothing filters.
## 4 Experiments
### Scalability Test  
The proposed system can train a language model of a size that could not be trained in the past or can train a very large batch size for a small language model. Figure 6 illustrates a comparison between our proposed method and the existing layer-based Model Parallelism, highlighting the maximum configurable batch sizes for different layer sizes. (Base = 2048, Large = 1024, XLarge = 378, and XXLarge = 128 for our proposed layer; Base = 168, Large = 32, XLarge = 12, and XXLarge = 4 for layer-based Model Parallelism). This approach allows for greater flexibility in configuring the Double-Buffered Context and provides a detailed measure of how the new method performs in comparison to existing techniques.  
Our experimental results demonstrate a model scalability exceeding tenfold than of the traditional layer-based Model Parallelism. Furthermore, our approach allows for the training of XLarge and XXLarge models, which were untrainable with the existing layer-based Model Parallelism. The batch sizes per layer proposed in our method reserve approximately 35 GB of GPU memory space, and the remaining space is sufficient to adequately store the next context for double buffering.
## 3 Results
### Risk Perception Related to COVID19
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model 1**} & \multicolumn{2}{c}{**Model 2**} & \multicolumn{2}{c}{**Model 3**} & \multicolumn{2}{c}{**Model 4**} \\ \hline \multicolumn{2}{c}{**Good Knowledge about**} & \multicolumn{2}{c}{**Fear of Falling ill with**} & \multicolumn{2}{c}{**Practicing Respiratory**} & \multicolumn{2}{c}{**Adherence to Hand**} \\ \multicolumn{2}{c}{**COVID-19**} & \multicolumn{2}{c}{**COVID-19**} & \multicolumn{2}{c}{**Hypgene**} \\ \hline \hline \multicolumn{2}{c}{**Log Likelihood = - 809.41;**} & \multicolumn{2}{c}{**Log Likelihood = - 900.52;**} & \multicolumn{2}{c}{**Log Likelihood = -790.313;**} & \multicolumn{2}{c}{**Log Likelihood = -221.06;**} \\ \multicolumn{2}{c}{\(\chi^{2}\) = 27.02; \(p\) = 0.0001**} & \multicolumn{2}{c}{\(\chi^{2}\) = 24.2; \(p\) = 0.0002} & \multicolumn{2}{c}{\(\chi^{2}\) = 58.14; \(p\) = 0.0001} & \multicolumn{2}{c}{\(\chi^{2}\) = 40.2; \(p\) = 0.0001} \\ \hline \multicolumn{2}{c}{**OR**} & \multicolumn{2}{c}{**(95\% CI)**} & \multicolumn{2}{c}{**OR**} & \multicolumn{2}{c}{**(95\% CI)**} & \multicolumn{2}{c}{**OR**} & \multicolumn{2}{c}{**(95\% CI)**} \\ \hline Female gender (male as reference) & 1.07 & (0.83–1.39) & 1.58 & (1.24–2.01) & 1.53 & (1.15–2.05) & 2.52 & (1.48–4.31) \\ Age, years & 1.06 & (1.01–1.11) & 1.03 & (0.99–1.07) & 1.02 & (0.97–1.08) & 1.01 & (0.91–1.12) \\ Attending to medical or life sciences majors (no as reference) & 1.68 & (1.33–2.12) & 0.79 & (0.63–0.98) & 1.26 & (0.96–1.67) & 0.91 & (0.52–1.57) \\ Single status (married as reference) & 1.19 & (0.7–2.03) & 1.35 & (0.82–2.23) & 0.47 & (0.21–1.05) & 1.05 & (0.31–3.59) \\ Good knowledge about COVID-19 & & & 1.29 & (1.02–1.63) & 1.78 & (1.34–2.36) & 2.42 & (1.41–4.15) \\ (poor as reference) & & & & & & \\ Quite a bit/extreme fear of falling ill & & & & & & \\ with COVID-19 (not at & & & & 1.5 & (1.09–2.06) & 3.85 & (1.73–8.53) \\ all/little/moderate as reference) & & & & & & \\ Quite a bit/extreme vulnerability to & & & & & & \\ COVID-19 not at all/little/moderate as reference) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multiple logistic regression models for potential determinants of the different outcomes of interest. the outcome (OR = 1.29, 95% CI = 1.02-1.63). More than half (55.6%) of the respondents believed that the prevalence of COVID-19 in their community was \(\leq\)10%.
# A Study on DNN-Based Practical Model for Predicting Spot Color
## 2 Materials and Methods  
### Data Acquisition  
In this study, data collection was conducted to develop a color prediction model based on spot color ink mixing ratios. We obtained a total of 19,032 records of spot color ink production data from a small comprehensive packaging printing company, Deoksu Industries Co., Ltd., Cheongju-si, Republic of Korea, with KBA Rapida 105, a printing press from Koenig & Bauer AG, Wurzburg, Germany, from 20 May 2020 to 5 January 2022. Spot color inks are produced by mixing conventional inks to achieve specific colors. The data was collected during the actual printing process, specifically from the ink mixing procedures to produce spot color inks. It is important to note in the printing industry that the reproduction of ink colors on printed substrates is more important than the intrinsic color of the inks themselves. For this reason, a test print is made on a sample of the paper to be printed to assess the color reproducibility of the spot color inks prior to the start of the mass production run. This test printing serves as a measure of quality control for the color-matching process. So, we measured the color information printed on the test print paper for spot color inks created using 62 types of basic inks. Table 1 lists the data collected on the production information of the spot inks.  
The color of the printed ink was accurately measured using the eXact Standard spectrophotometer from X-Rite, Inc., Grand Rapids, MI, US. Each data point includes reflectance information for 31 segments within the visible light spectrum, as well as LAB values. Reflectance in the visible spectrum indicates the amount of visible light reflected by an object, which is critical to understanding color intensity and brightness. In the printing industry, it is used to quantify the color characteristics of substrates or inks. In our research, reflectance was measured by dividing the wavelength between 400 nm and 700 nm into 31 sections. On the other hand, LAB values refer to the three numerical components of a particular color in the LAB color space. The LAB color space is an absolute color representation method specified by the International Commission on Illumination(CIE) based on human vision. It encompasses all perceivable colors and consists of \(L^{*}\), \(a^{*}\), and \(b^{*}\) values, which indicate lightness, the range from green to red, and the range from blue to yellow, respectively. These values provide consistent color information under different lighting conditions and locations, making color comparisons easier. The mixing amount of each of the 62 basic inks was recorded as a variable necessary for ink manufacture, with the basic ink information represented by codes (e.g., 20120002) used by the supplier company. Since the test prints were not consistently made on identical paper during the color mixing process, the data also included the size and weight of each test printing paper, along with its reflectance for the 31 segments in the visible light range measured by the spectrophotometer.
# Modal Identification of Train Passenger Seats Based on Dynamic Tests and Output-Only Techniques
## 4 Case Study: Intercity Train
### Results  
#### 4.2.1 Transmissibility Curves  
Figure 17 shows the transmissibility curves of the three subjects within the two seats. It is possible to observe the resonance (red marks) and antiresonance peaks (blue marks).  
Table 3 presents the resonance and antiresonance peaks regarding both seats. Due to the seat specification, namely seat design and foam restrictions, Intercity's seat transmissibility is expected to differ from those of AP train seats.  
Comfort seat transmissibilities are characterised by resonance frequencies between 0.20-0.59 Hz and 4.30-4.69 Hz. As on AP train seats, the higher transmissibility peaks occur at low frequencies, namely 0.20 and 0.59 Hz, while the antiresonances are found between 1.37 and 1.95 Hz. Resonance and antiresonance frequencies are similar for all subjects, which agrees with the results of [PERSON] and [PERSON] [32]. The comfort seat is similar to those used in laboratory experiments. Therefore, a vertical resonance transmissibility between 4 and 6 Hz is anticipated, which agrees with the results presented by several authors and reinforce the influence of the movement restriction introduced by the cover leather on the AP train seats [23; 32; 33; 34].  
Standard seat transmissibility results are comparable for all subjects as the comfort seat has resonances of 0.20, 0.78-1.56, and 4.88-5.27 Hz and antiresonances between 0.59-1.17 and 2.34-2.93 Hz. Higher transmissibility peaks are found for this seat compared to the comfort class. Those results match the AP seat observation and comply with the studies conducted by [PERSON] and [PERSON] [23] and [PERSON] et al. [35].  
Figure 15: Seat metallic structural frame: (**a**) comfort seat structural frame; (**b**) standard seat structural frame.  
Due to the positioning of accelerometers on the seatback structure of the seats, it was possible to explore the seatback fore-and-aft/floor vertical transmissibility. This transmissibility, illustrated in Figure 18, may provide important information about the seatback's local bending movements.  
Prominent resonance equal to 0.59 Hz defines the seatback comfort seat, whereas those of the standard seat demonstrate main peaks of 0.98, 2.34, and 4.88 Hz. The modal identification is expected to prove the influence of seatback local bending movements regarding those frequencies.
# Resonant Laser Ionization and Fine-Structure Study of Silver in an Ablation Plume
## 2 Theory and Motivation
### Possible Resonant Schemes of Silver  
The \({}^{111}\)Ag isotope is of great interest to the ISOLPHARM project [16] due to its medical properties. It undergoes a \(\beta^{-}\) decay with a half-life of 7.45 days, with a convenient energy (\(\sim\)360 keV) that corresponds to a medium tissue penetration depth (1.8 mm).  
Silver has an ionization potential (IP) of 61,106.45 cm\({}^{-1}\) (7.6 eV) [17], which can be efficiently exceeded using RILIS with precise laser wavelengths that correspond to the atomic transitions between the electronic levels. Several known schemes for the photo-ionization of silver have been used in facilities such as IGISOL [18], TRIUMF-ISAC [19] and ISOLDE [20]. A number of electronic levels of silver [17] have been studied and the \"radiative\" levels which are of interest for our research have been clarified, as shown in Figure 1.  
Several combinations of these atomic transitions can be used to excite the ground level valence electron beyond the IP. Table 1 presents several selected choices that could provide a pathway for the resonant photo-ionization of Ag. As can be seen from Figure 1, after the two resonant transition steps, the electron has still not crossed the IP. However, from the highest excited level, the electron can be pushed beyond the IP by absorbing a photon from any of the previous excitation steps. Higher ionization efficiencies could be achieved with a third non-resonant laser with sufficient photon energy to cross the gap.  
Figure 1: Simplified level scheme of Ag and applied excitation path.  
The scheme chosen for this study is the scheme underlined in Table 1: 4d\({}^{10}\)5s \({}^{2}\)S\({}_{1/2}\)\(\rightarrow\) 4d\({}^{10}\)5p \({}^{2}\)P\({}^{o}_{3/2}\)\(\rightarrow\) 4d\({}^{10}\)6d \({}^{2}\)D\({}_{3/2}\).
# Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network
## 2 Proposed E-LFDA Method for WLAN Indoor Positioning
### Overview of the Proposed E-LFDA Method  
The proposed E-LFDA method deploys the most popular fingerprinting approach [4; 5; 6; 7]. It consists of two phases: offline phase and online phase. Figure 2 shows the overview of the proposed E-LFDA method.  
During the offline phase, more numbers of representative training samples are generated by signal perturbation and LFDA based transformation matrix is obtained. Firstly, RSS samples at different reference points are collected and stored to create the radiomap. Secondly, the number of training samples is increased by signal perturbation at each reference point. Thirdly, based on enlarged radiomap, transformation matrix \(\mathbf{A}\) (size \(m\times\ d\)) is obtained by LFDA. Finally, the new radiomap comprising extracted location features are constructed. For the \(m\)-dimensional RSS sample \(\mathbf{x}_{i}\) in radiomap, \(d\)-dimensional location feature vector is given: \(\mathbf{y}_{i}=\mathbf{A}^{\mathrm{T}}\mathbf{x}_{i}\).  
During the online phase, real-time RSS sample is collected by client device and the location is estimated based on pre-stored radiomap. Firstly, based on real-time RSS sample \(\mathbf{x}^{\prime}\), the real-time location feature \(\mathbf{y}^{\prime}=\mathbf{A}^{\mathrm{T}}\mathbf{x}^{\prime}\) is obtained. Secondly, compute the Euclidean distance between real-time location feature \(\mathbf{y}^{\prime}\) and pre-stored mean location feature of the \(i\)th reference point:  
\[d_{i}=\ ||\mathbf{y}^{\prime}-\overline{\mathbf{y}}^{i}\ ||_{2},1\leq i\leq n \tag{1}\]  
where \(n\) is the number of reference points, and \(\overline{\mathbf{y}}^{i}\) is the related mean location feature vector. Finally, the ultimate coordinate vector \(\mathbf{z}^{\prime}\) is estimated by weighting the \(K\) nearest reference points:  
\[\mathbf{z}^{\prime}=\sum_{i=1}^{K}\mathbf{z}_{i}/\widetilde{d}_{i} \tag{2}\]  
where \(\widetilde{d}_{i}\) is the normalized Euclidean distance, and \(\mathbf{z}_{i}\) is the coordinate vector of the related \(K\) nearest reference point.  
Figure 1: Multimodal distribution of RSS values at a fixed physical location.  
where the numerator and denominator measure the between-class separability and the local within-class variance, respectively. Therefore, under a certain local within-class variance, the discriminative information quantity contained in each feature dimension may be measured by the associated eigenvalue. The larger the eigenvalue, the stronger discrimination power feature dimension has. We determine the value of \(d\) by the cumulative percentage of obtained eigenvalues:  
\[\sum_{i=1}^{d}\lambda_{i}/\sum_{j=1}^{m}\lambda_{j}\geq\alpha^{*} \tag{11}\]  
where \(\alpha^{*}\) is a cutoff threshold, which represents the percentage of total discriminative information that selected dimensions exceed. During offline training phase, an appropriate value of \(\alpha^{*}\) can be found and used to find feature dimension, which is set as the smallest value that satisfying Equation (11).
## 3 Results and Discussion
### Characterization Studies
#### 3.1.2 Scm  
The surface morphologies of Cd-I-MA-_co_-AN before and after elution were characterized using SEM. Figure 2a,b show the Cd-I-MA-_co_-AN before elution, while Figure 2c,d show the Cd-I-MA-_co_-AN after elution. Phenomena worth noting are the fact that the surface of the Cd-I-MA-_co_-AN before elution was plat and smooth and showed a more compact surface. The surface of Cd-I-MA-_co_-AN was relatively rough after elution, resulting in an increase in specific surface area. This indicates that the template ions had been eluted. The synergy between holes left after eluting template ions and functional groups is beneficial for increasing the adsorption capacity and enhancing the selectivity performance.