esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the User Adaptation in a BCI Game Environment +
## 3 Materials and Methods
### Methods
#### 3.2.2 Classification  
To classify the data, two algorithms were tested, linear discriminant analysis (LDA) and multi-layer perceptron (MLP). These algorithms are already integrated by OpenViBE. The obtained features from the processing phase were used as input to the classifiers.  
LDA [25] is a common technique used for supervised classification problems. It can be used to reduce dimensions, visualize data and interpret the importance of the given features. LDA finds the projection hyperplane in order to minimize the interclass variance and maximize the distance of the projected means of the classes. Basically, the classifier finds a linear combination of features that can group data or separate them into two or more classes.  
MLP [25,26] is a neural network. It has three different layers: the input layer, the hidden layer and the output layer. The input signal is sent to the input layer to be processed. The computational power of this algorithm derives from the hidden layers. They are located between the input and output layers, and their number varies depending on the problem. The neurons in this classification algorithm are trained with the back-propagation learning algorithm. The output layer is responsible for predicting and classifying data. Multi-layer perceptrons are more commonly used in pattern classification.  
In Table 2 the classification accuracy and the confusion matrix of the offline classification process for the subjects that participated in the experiment are presented. The average classification accuracy for LDA is 98.75% while for Multi-layer Perceptron is 98.74%.  
Figure 4: Offline processing scenario to train the classifier.
# Ensemble and Deep Learning for Language-Independent Automatic Selection of Parallel Data
## 3 Materials and Methods
### Features  
As we mentioned in our previous work, we have a classification problem with three output values, which are the classes A, B and C. Every segment was represented as a tuple (Src, Trans 1-2-3, Ref). Each tuple was modeled as a feature-value vector, and all its dimensions are string-similarity, language independent features. Our preliminary approach (involving 82 features and Random Forest classification) [10] is initially applied to the English-Italian corpus. A part of our feature set was based on the work by [PERSON] et al. [27] and [PERSON] et al. [35]. The features were grouped into three categories:  
1. Basic-Simple Features (e.g., lengths in words/characters, some ratios, distances like Levenshtein [40], vocabulary containment [41], etc).
2. Noise-Based Features (nominal features (True-False) e.g., identifying long segments, in order to check the translation ability).
3. Similarity-Based Features (using a length factor [42]).  
In the present work, four novel features groups, that belong to the first category have been employed (increasing thereby the feature dimensions from 81 to 96).  
* the length in characters of Src-Trans 1-Trans 2-Trans 3-Ref.
* the Jaccard's Index (for Ref-Trans 1, Ref-Trans 2, Ref-Trans 3), which is an efficient metric for string comparison. It is calculated at a token level, and compares two segments. It is defined as the ratio of the absolute value of the intersection of the words between the two segments to the absolute value of the union of the two segments. [6].
* the Dice distance [6], which is related to Jaccard's Index. \[\text{Dice distance}=2\text{xaccard Index}/(1+\text{Jaccard Index}),\] (1)
* the Suffix feature. We observed that we have better translation when the translation output takes into account and does not alter the gender and grammatical number (singular or plural) of delcinable words. This feature is calculated as follows: \[\begin{array}{ll}\odot&\text{Step 1: The last character of every word were extracted from the Ref and the Trans 1, 2, 3 segments.}\\ \odot&\text{Step 2: The number of common last character for segments Ref-Trans 1, Ref-Trans 2 and Ref-Trans 3 was calculated.}\\ \odot&\text{Step 3: The ratio between the number and the number calculated in Step 2 and the total number of last characters.}\end{array}\] All feature values were calculated using MATLAB.
## 3 Results and Discussion
### Water-Soluble Anions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Anions**} & \multicolumn{2}{c}{**Athens, Greece**} & \multicolumn{2}{c}{**Athens, Greece**} & \multicolumn{2}{c}{**Aveiro, Portugal**} & \multirow{2}{*}{\begin{tabular}{c} **Chennai,** \\ **India** \\ **[**82**]** \\ \end{tabular} } \\  & \multicolumn{2}{c}{**Indoors**} & \multicolumn{2}{c}{**Outdoors**} & \multicolumn{2}{c}{**Indoors**} & \multirow{2}{*}{\begin{tabular}{c} **Outdoors** \\ **(\(\upmu\)g m\({}^{-3}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Indoors** \\ **(\(\upmu\)g m\({}^{-3}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Indoors** \\ **(\(\upmu\)g m\({}^{-3}\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Indoors** \\ **(\(\upmu\)g m\({}^{-3}\))** \\ \end{tabular} } \\ \hline Cold & & & & & & & \\ period & & & & & & \\ Cl\({}^{-}\) & 0.40 \(\pm\) 0.25 & 0.57 \(\pm\) 0.68 & - & - & - & - & 3.59 \(\pm\) 1.20 \\ NO\({}_{3}\)\({}^{-}\) & 0.41 \(\pm\) 0.20 & 1.41 \(\pm\) 0.95 & 2.3 \(\pm\) 1.80 & 3.8 \(\pm\) 2.20 & - & - & 5.92 \(\pm\) 1.67 \\ SO\({}_{4}\)\({}^{2-}\) & 1.45 \(\pm\) 0.82 & 1.78 \(\pm\) 0.79 & 5.2 \(\pm\) 1.40 & 6.7 \(\pm\) 2.60 & - & - & 10.57 \(\pm\) 2.92 \\ \hline Warm & & & & & & & \\ period & & & & & & & \\ Cl\({}^{-}\) & 0.28 \(\pm\) 0.11 & 0.08 \(\pm\) 0.10 & - & - & 0.67 \(\pm\) 0.60 & 0.93 \(\pm\) 1.05 & 3.65 \(\pm\) 2.65 \\ NO\({}_{3}\)\({}^{-}\) & 0.48 \(\pm\) 0.33 & 0.99 \(\pm\) 0.72 & - & - & 1.02 \(\pm\) 1.00 & 1.86 \(\pm\) 1.40 & 4.23 \(\pm\) 2.58 \\ SO\({}_{4}\)\({}^{2-}\) & 1.57 \(\pm\) 1.10 & 2.57 \(\pm\) 1.18 & - & - & 1.27 \(\pm\) 0.79 & 1.96 \(\pm\) 1.25 & 12.13 \(\pm\) 4.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average values of PM\({}_{10}\) anionic components in different sampling sites.
## 4 Discussion
The three studies used the same bacterial strain (_L. reuteri_); however, one study used intestinal strains [21], and two studies [22; 23] used strains from the oral cavity. Although the mechanism of action of probiotics in the oral cavity is analogous to that of the intestinal microbiota [35], the strains may have adhered differently to periodontal tissues. Another factor to highlight was the periodontal diagnosis before treatment. [PERSON] et al. 2018 [22] indicated that they included patients with a history of periodontitis, one study [23] did not report, and another study [21], indicated that periodontal healthy and/or periodontitis patients included in a maintenance program were included. There is evidence that diseased periodontal and peri-implant sites and their healthy counterparts have distinct microbiological profiles [36]. One study [37] showed that patients with implants and periodontal health have a significantly higher relative abundance of _Actinomyces_ and _Streptococcus genera_, while patients with a history of periodontitis with implants have a higher number of _Por-phyromonas gingivallis_ and _Prevotella_ spp. in subgingival plaque samples and, thus, a higher risk for PiM. This suggests the importance of periodontal diagnosis before PiM treatment, as patients with a history of periodontitis have an increase in periodontal pathogens. Therefore, this could be a reason why the studies obtained different results. In addition, the long-term use of probiotics as an adjuvant to conventional treatment for peri-implant mucositis can assist in reducing inflammation and controlling disease progression [18], because probiotics can modulate the inflammatory response. However, the effectiveness of the treatment will also depend on the level of oral hygiene, since good oral hygiene is essential for maintaining oral health [33]. Regarding adverse effects, none of the included studies reported any associated adverse events; therefore, probiotics can be considered a safe intervention. Currently, \"safety\", \"individualized treatment\", and \"internal interaction within the flora\" are requirements of potential next-generation probiotics (NGPs) [38]. Furthermore, in the complex ecosystem of humans and microbes, it is challenging to identify the relationship between specific strains, specific flora, and hosts to warrant a therapeutic intervention in the case of disease [38]. Despite some limitations of the study, the meta-analysis showed that probiotic therapy as an adjunct to scaling and root planing (SRP) has a greater effect in reducing clinical parameters such as BOP, PI, and PD more effectively than the control group without probiotics. However, with regard to microbiological parameters, probiotics did not show greater reductions in pathogenic bacteria associated with both periodontal and peri-implant diseases.
## 2 Materials and Methods
### Data Link
The free space path loss is calculated using the following expression [51]:  
\[L_{FS}(dB)\ =\ 20\cdot log_{10}(d)+20\cdot log_{10}(f)+32.44(dB) \tag{6}\]  
where \(d\) is the distance between antennas in kilometers and \(f\) is the frequency in megahertz. The used data for power budget link calculation are given in Table 3. For example, if \(d\) = 5 km and \(f\) = 2400 MHz, the calculation using Equation (6) results in \(L_{FS}\) (dB) = 114.0236 dB and \(P_{RX}\) (dBm) = \(-\)68.5236 (dBm). Since the specified receiver sensitivity is 98 dBm, the link margin, \((-98)\) - \((-68.5236)\), is about \(-30\) dB, which is sufficient for use under adverse weather conditions.
## 2 Overview of the TMM
### The Classic TMM and the Analysis of Its Instability Mechanism  
The derivation of the classical TMM has been completed by previous researchers, and the details can be seen in Appendix A [19; 20]. This is not the main innovation of this paper, so it will not be elaborated here. It can be observed from the derivation presented in Appendix A that the field transfer matrix of the pipeline includes an exponential term, denoted as \(\exp(-sl/\lambda)\), whose value is significantly influenced by both the length and frequency of the pipeline. When the frequency remains constant, an increase in the pipeline length leads to exponential changes in the values of corresponding elements within the transfer matrix; some elements may experience a sharp increase while others approach zero. This phenomenon results in an escalation of the condition number of the transfer matrix towards singularity, which can cause instability during matrix inversion operations on a computer. Similarly, when maintaining a constant length, variations in frequency also induce exponential changes in element values within the transfer matrix. The same instability arises with increasing analysis frequencies. Thus, fundamentally speaking, stability issues associated with the transfer matrix represent a mathematical challenge. [PERSON] et al. [25] have mathematically demonstrated that for FSI involving pipelines, the condition number of the axial transfer matrix is consistently equal to 1; conversely, it has been established that there exists a positive correlation between both the length and frequency and the condition number of transverse transfer matrices. Consequently, for a simple straight pipeline, instabilities related to this method primarily manifest during solutions concerning transverse vibration.
## 3 Results and Discussion
### Effects of Reed Carbon Source on Denitrification Function Genes
#### 3.3.2 Changes in Abundances of \(nirK\), \(nirS\) and \(nrfA\) Genes  
The addition of the reed carbon source to the constructed wetland substrate created external conditions for microbial denitrification and dissimilatory nitrate reduction to ammonium (DNRA). The process of converting NO3-N to N2 by denitrification involves four steps: NO3-N \(\rightarrow\) NO2-N, NO2-N \(\rightarrow\) NO, NO \(\rightarrow\) N2O and N2O \(\rightarrow\) N2 [38]. The process of reducing NO2-N to NO is an important rate-limiting step in denitrification. Nitrite reductase is the rate-limiting enzyme that performs this step and the corresponding coding genes are \(nirK\) and \(nirS\)[15]. As shown in Figure 8a,b, the mean abundances of the \(nirK\) and \(nirS\) genes in RW, CW and N-RW were 1.26, 0.64 and 1.37 copies/\(\upmu\)g and 228.72, 147.60 and 243.12 copies/\(\upmu\)g, respectively. It was seen that the abundance of the \(nirS\) gene was much higher than that of the \(nirK\) gene, indicating that the nitrite reductase in the three wetland groups was mainly encoded by the \(nirS\) gene. Comparison of RW and CW showed that the addition of the reed carbon source had a significant effect on the abundances of \(nirK\) and \(nirS\) genes. Compared to CW, the abundances of \(nirK\) and  
Figure 7: (**a**) \(amoA\) gene abundance; (**b**) \(nxrB\) gene abundance; (**c**) \(lzo\) gene abundance in three groups of wetland systems. Note: \(p<0.05\) compared to RW.  
_nirS_ genes in RW increased by 95.51% and 54.96%, respectively. This suggested that the addition of a reed carbon source could increase the abundance of denitrification genes in substrates, thereby improving denitrification in wetland systems. Comparison of RW and N-RW showed that reed planting had no significant impact on the abundance of _nirK_ and _nirS_ genes. The abundances of _nirK_ and _nirS_ genes in RW were only 8.01% and 5.92% lower than that in N-RW, respectively.  
DNRA can reduce NO3\({}^{-}\)-N to NH4\({}^{+}\)-N, and then NH4\({}^{+}\)-N is further removed by microbial nitrification, denitrification and anammox. It can be seen that DNRA is an important linkage process in the nitrogen cycle and plays an important role in the nitrogen cycle of constructed wetlands. The reduction of nitrite to ammonium by nitrite reductase is a key step in DNRA, and its encoding gene is _nrfA_[39]. It can be seen from Figure 8c that the mean abundance of the _nrfA_ gene in RW, CW and N-RW were 12.89, 1.55 and 14.00 copies/ug, respectively. Comparison of RW and CW showed that the addition of the reed carbon source had a significant effect on the abundance of the _nrfA_ gene. The abundance of the _nrfA_ gene in RW increased by 731.95% compared to CW. This suggested that the addition of a reed carbon source could increase the abundance of the _nrfA_ gene in substrates and thereby improve DNRA in wetland systems. Comparing RW and N-RW showed that reed planting had no significant effect on the abundance of the _nrfA_ gene. The abundance of the _nrfA_ gene in RW was only 7.96% lower than in N-RW.
# Linear Vibration Analysis of Shells Using a Seven-Parameter Spectral/_hp_ Finite Element Model
## 5 Finite Element Model  
For natural vibration, the generalized displacements in Equation (9) are assumed of the form  
\[\underline{\mathbf{u}}\left(\varsigma^{\alpha},t\right) = \underline{\mathbf{u}}\left(\varsigma^{\alpha}\right)e^{-i\omega t}, \tag{28}\] \[\mathbf{\varphi}\left(\varsigma^{\alpha},t\right) = \mathbf{\varphi}\left(\varsigma^{\alpha}\right)e^{-i\omega t},\] (29) \[\Psi\left(\varsigma^{\alpha},t\right) = \Psi\left(\varsigma^{\alpha}\right)e^{-i\omega t}, \tag{30}\]  
where \(\omega\) represents the frequency of natural vibration, \(i=\sqrt{-1}\), and the finite element approximations of the displacements are given by  
\[\underline{\mathbf{u}}\left(\varsigma^{\alpha}\right)=\sum_{j=1}^{n}\psi_{j} \left(\varsigma^{\alpha}\right)\underline{\mathbf{u}},\qquad\mathbf{\varphi}\left( \varsigma^{\alpha}\right)=\sum_{j=1}^{n}\psi_{j}\left(\varsigma^{\alpha}\right) \mathbf{\varphi},\qquad\Psi\left(\varsigma^{\alpha}\right)=\sum_{j=1}^{n}\psi_{j} \left(\varsigma^{\alpha}\right)\mathbf{\Psi}, \tag{31}\]\(\psi_{j}\left(\xi^{\alpha}\right)\) being the Lagrange interpolation functions of order \(p\).  
Substituting the approximations in the virtual kinetic energy of Equation (25) and in the virtual strain energy of Equation (27), the mass and the stiffness matrices are obtained. It is important to mention that the terms involved in the stiffness matrix are extensive, and a computer program of algebraic manipulation is required; in this case, they were obtained using Maple.  
Finally, the finite element model to estimate the natural frequencies and the vibration modes of a shell is an eigenvalue problem of the form  
\[\left([\mathbf{K}^{e}]-\omega^{2}[\mathbf{M}^{e}]\right)\left\{\Delta^{e} \right\}=\left\{0\right\}, \tag{32}\]  
where \([\mathbf{K}^{e}]\) and \([\mathbf{M}^{e}]\) denote the stiffness and mass matrices of the \(e\) element, respectively, \(\omega\) denotes the natural frequency, and \(\left\{\Delta^{e}\right\}\) is the vibration mode vector associated with \(\omega\). In this work, the solution to the resulting eigenvalue problem is obtained by means of Eigen, a C++ template library for linear algebra [42].
## 4 Discussion
### Limitations and Strengths  
This study has some limitations that should be considered when interpreting the results. Firstly, the cross-sectional design limits our ability to establish causal relationships between physical behaviors and musculoskeletal pain. Longitudinal studies are needed to further elucidate these associations. Secondly, the relatively small sample size (n = 75) may limit the statistical power and generalizability of the findings and may not be representative of the broader viculture worker population. Future studies with larger cohorts could provide more robust results. Our study did not assess psychosocial factors such as work stress or job satisfaction, which could influence musculoskeletal pain.  
Recent research has demonstrated strong associations between psychosocial work factors and musculoskeletal disorders among various occupational groups [34; 35]. For instance, job demands, job control, and social support have been identified as key psychosocial factors influencing the development and persistence of work-related musculoskeletal pain [36]. Future studies in this field should incorporate measures of psychosocial work factors to provide a more comprehensive understanding of the complex interplay between physical behaviors, psychosocial factors, and musculoskeletal health in viculture workers.  
Our study did not consider the potential impact of Long COVID, which could affect workers' physical behaviors and pain reports. Future studies should consider this factor, especially in research conducted during or after the COVID-19 pandemic. The binary categorization of pain intensity may have simplified the complex nature of pain experiences. Future analyses could consider using the full pain scale to provide more nuanced insights.  
Despite these limitations, our study has several strengths. Using accelerometry to objectively measure physical behaviors provides more accurate and reliable data than self-reported measures. Including work and non-work periods allows a more comprehensive understanding of workers' behavior patterns. Examining pain in three distinct body regions (neck, upper back, and lower back) provides a more complete picture of workers' musculoskeletal health. The inclusion of demographic variables such as age, sex, and BMI in the analysis allows for a more nuanced understanding of factors influencing musculoskeletal pain. Finally, our study provides novel insights into the relationship between sedentary behaviors and musculoskeletal pain in viculture workers, contributing to the existing literature on occupational health.  
Furthermore, our study primarily focused on pain intensity as an outcome measure. We recognize that pain is a multidimensional experience, and future studies could benefit from including other dimensions of pain and pain-related behavioral factors to provide a more comprehensive understanding of the relationship between physical behaviors and musculoskeletal pain.  
These strengths and limitations should be considered when interpreting our findings and planning future research in this area.
# Development and Testing of an Active Noise Control System for Urban Road Traffic Noise
## 3 Development of the Active Noise Control Prototype System
### Structure of the Active Noise Control System  
The design and implementation of active noise control systems can take many forms, including narrowband feedforward systems, broadband feedforward systems, feedback systems, hybrid systems, and multichannel systems. Considering the system's applicability and development cost, we adopted the structure of a broadband feedforward system for the research presented in this paper, as shown in Figure 4.  
The main components of the broadband feedforward active noise control system include a reference sensor, an error sensor, a secondary sound source, and a controller. The reference sensor, functioning as a non-acoustic sensor, has the primary role of predicting and capturing future noise, i.e., the reference signal. This predictive capability enables the feedforward system to respond before the noise arrives. The error sensor, typically placed in the area where noise reduction is needed, is responsible for capturing real-time noise that has not been eliminated, i.e., the so-called error signal. The controller analyzes the signals collected by the reference sensor and the error sensor, generating a signal with equal amplitude and the opposite phase to the noise to drive the secondary sound source. The secondary sound source then emits the inverse sound wave generated by the controller, interfering with the original noise to achieve noise reduction or elimination [49]. Broadband feedforward systems mainly target random broadband noises, such as traffic noise and wind noise. They rely on predicting the reference signal, which is crucial for handling non-periodic and random noise.  
In the designed adaptive active noise control system, the primary path and the secondary path are key components. The primary path describes the sound propagation characteristics from the original noise source to the error sensor. Under stable environmental conditions (e.g., enclosed indoor environments), the primary path remains relatively constant. However, in practical applications, minor changes in the environment (e.g., opening windows, moving locations, the appearance of new objects, etc.) can affect the primary path. The secondary path describes the sound propagation characteristics from the secondary sound source to the error sensor. This path involves components such as D/A converters, audio filters, power amplifiers, the acoustic path from the loudspeaker to the error sensor, the error sensor itself, and A/D converters. Due to its complexity, minor  
Figure 4: Schematic diagram of the broadband feedforward system [48].  
changes in components or different implementation methods may cause changes in the secondary path [50,51].