esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effects of TOC in CeO\({}_{2}\)NP Retention  
The TOC in soil solutions can influence the nanoparticle mobility in the soil, as reported by [42]. According to them, the dissolved organic carbon (DOC), which is a fraction of the TOC, showed a positive correlation with the concentration of CuO nanoparticles in the soil solution, being able to impede the aggregation by steric repulsion. In fact, the nanoparticles' aggregation could accelerate their sedimentation from the soil solution, avoiding their movement in the soil profile. According to [43], dispersed nanoparticles in an aquatic environment were partly deposited as sediment after aggregation as homoaggregates and heteroaggregates. The soil solution was extremely complex, and the fate of the nanoparticles was inevitably set by its properties, as well as the nanoparticles' properties. Many other authors have agreed with the conclusion that the DOC enhances the nanoparticles' stability, avoiding aggregation [40; 44]. Given this assumption, the CeO\({}_{2}\)NPs might have undergone different homo- and heteroaggregations (the latter with the soil natural colloids), depending on the presence (or absence) of the biochar and on the type of quenching procedure. Considering the effect of the treatments (the reduced TOC in the leachates of the treated soils compared to the controls), it might be concluded that a higher aggregation between the nanoparticles or between the nanoparticles and the soil colloids might have occurred, thereby justifying a smaller leaching of the nanoparticles as a direct consequence. Another possible explanation could be that the organic carbon retained in the biochar-amended soils could have otherwise coated the nanoparticles and bridged them to the soil/biochar surface, again reducing their mobility. This might, in part, explain the lower cumulative Ce in the leachate of the columns with DRY soil compared with the control (Figure 2). The same conclusion cannot be assumed when considering the WET soil. Therefore, the differences between the effects of the two biochars (BCdryQ and BCwetQ) cannot be ascribed to the sole TOC reduction; otherwise, the cumulative Ce trends of the two substrates WET and DRY would have been much more similar. Figure 2 shows that the DRY treatment was separate from the WET treatment and the control. This implies that other factors must have played a role in reducing the nanoparticles' leaching in the DRY substrate. This result must be explained with a different interaction between BCdryQ and the nanoparticle compared to BCwetQ. A possible explanation can be found in [27], which described the chemical surficial alteration of BCdryQ after the soil application, which determined a higher oxidation that was very important in determining the nanoparticles' retaining capacity of the biochar through electrostatic interactions.
## 2 Network Architecture and Training
### Dataset Construction  
In deep learning training, a sufficiently large and diverse dataset is crucial for enhancing network performance. To provide the network model with abundant learnable gravity data, this research constructs random subsurface object models: three prisms are randomly generated in the subsurface to simulate realistic, non-uniform medium distributions, with density perturbations set within \(\pm\)20% of typical rock densities to ensure greater data diversity. The noisy data are generated by adding Gaussian white noise to the clean gravity anomalies, where the noise amplitude is 5% of the standard deviation of the clean data. The final dataset was divided into a training set of 10,000 samples, validation set of 1000 samples, and a test set of 1000 samples, ensuring diversity and representativeness in the data distribution and providing the model with sufficient and physically consistent input samples for learning.  
The spatial position and shape of each prism are defined by specifying the center coordinates and parameters for length, width, and height. A 200 \(\times\) 200 grid (point spacing 0.1 km) is built, with the density generation range set to 1-5 g/cm\({}^{3}\). Based on the theoretical formula for the gravity anomaly of a prism and the above parameters, the corresponding gravity anomaly values are calculated to construct a gravity dataset suitable for model training. The gravity anomaly \(\Delta\)g at each grid point is computed using the theoretical formula for rectangular prisms (Equation (14)).  
\[g=G\cdot\rho\cdot\sum_{i}\text{Volume\,contribution\, of\, aprism} \tag{14}\]  
where G is the gravitational constant, \(\rho\) is the density, and the volumetric contribution of the prism is determined by its geometric parameters. The output is a two-dimensional array representing gravity anomaly values at each grid node. Gaussian noise is then added to the clean data to generate noisy data samples. This enables the network to focus better on learning the mapping from noisy to clean signals. Figure 5 shows the spatial distribution of such randomly generated subsurface objects, where color intensity represents the magnitude of object density. The physical properties of one set of randomly generated objects are presented in Table 1.  
Figure 4: Dynamic adjustment of physical constraint weight.
## 3 Data, Modelling Methods and Analysis Tools
### Estimating the Hurst Exponent and Entropy in Series of Malaria Incidence
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Empirical Series**} & \(\mathbf{(I_{36})}\) & \(\mathbf{(I_{36})}\) & \(\mathbf{(I_{36})}\) & \(\mathbf{(H_{36})}\) & \(\mathbf{(H_{36})}\) & \(\mathbf{(H_{36})}\) & \(\mathbf{(S_{36})}\) & \(\mathbf{(S_{36})}\) & \(\mathbf{(S_{36})}\) \\  & \(\mathbf{Median}\) & \(\mathbf{Q_{25}}\) & \(\mathbf{Q_{75}}\) & \(\mathbf{Median}\) & \(\mathbf{Q_{25}}\) & \(\mathbf{Q_{75}}\) & \(\mathbf{Median}\) & \(\mathbf{Q_{25}}\) & \(\mathbf{Q_{75}}\) \\ \hline \hline \multirow{2}{*}{[PERSON], 2014} & \multirow{2}{*}{0.22} & 0.16 & 0.26 & 0.80 & 0.66 & 0.90 & 5.14 & 5.13 & 5.14 \\ \cline{2-2} \cline{6-10} [PERSON], 2017 & & 7.43 & 4.36 & 9.68 & 0.94 & 0.90 & 1.15 & 4.77 & 4.68 & 4.96 \\ \hline \hline \multirow{2}{*}{[PERSON], 2016} & \multirow{2}{*}{11.2} & 10.4 & 12.4 & 1.10 & 1.00 & 1.15 & 5.16 & 5.16 & 5.16 \\ \cline{2-2} \cline{6-10} [PERSON], 2015 & & 12.5 & 12.4 & 12.5 & 0.93 & 0.87 & 0.96 & 5.12 & 5.12 & 5.12 \\ \hline \hline \multirow{2}{*}{[PERSON], 2012} & \multirow{2}{*}{24.0} & \multirow{2}{*}{21.0} & \multirow{2}{*}{28.4} & \multirow{2}{*}{0.76} & \multirow{2}{*}{0.70} & \multirow{2}{*}{0.80} & \multirow{2}{*}{5.06} & \multirow{2}{*}{5.05} & \multirow{2}{*}{5.08} \\ \cline{2-2} \cline{6-10} [PERSON], 2017 & & & & & & & & & \\ \hline \hline \multirow{2}{*}{[PERSON], 2007} & \multirow{2}{*}{115} & \multirow{2}{*}{83.5} & \multirow{2}{*}{122} & \multirow{2}{*}{0.95} & \multirow{2}{*}{0.89} & \multirow{2}{*}{1.00} & \multirow{2}{*}{4.67} & \multirow{2}{*}{4.59} & \multirow{2}{*}{4.72} \\ \cline{2-2} \cline{6-10} [PERSON], 2008 & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table with the empirical values of median, quartile 25 and 75 from the series of malaria incidence, entropy and the Hurst exponent, using moving windows of 36 months. the Hurst exponent or Shannon entropy in seven of the eight presented empirical malaria time series. The case of Bedane is an exception. The estimation of the Hurst exponent by \(R/S\)-analysis may be biased due to the shorter length of 36 months [31; 32; 33]. To ascertain how robust the results with 36-month windows are, we repeated our estimates for 24- and 48-month averages. The correlation is also reasonably evident in the 48-months time frame. Entropy is clearly more linearly correlated to malaria incidence than the Hurst exponent. In summary, the data shown in Table 3 suggests the presence of significant linear correlation between malaria incidence, and the Hurst exponent or Shannon entropy. In Appendix B we present a more detailed comparison between 36-month averages with 24- and 48-month averages.
## 4 Results
### Post-Hoc Analysis  
In order to estimate the importance of each predictor, the permutation importance method assesses each predictor's significance by measuring the impact on model accuracy when the predictor's values are randomly permuted. Specifically, this method observes the model's performance change as each feature's relationship to the target variable is disrupted. If a feature is critical to the model's predictions, permuting it should result in a noticeable drop in performance, while less critical features will have minimal impact. A key metric in this analysis is the maximum feature importance, which represents the maximum drop in model performance when a feature is permuted. Higher values indicate a stronger predictive influence.  
To evaluate the importance of temperature, water content, and frost heave in our NARX model, the analysis was conducted at various training stages, specifically after 10 to 50 training epochs, with results normalized to reflect changes over time. The calculated Maximum Feature Importance values for each predictor at each stage are presented in Table 5.  
Results show that, at early training stages, all three factors contribute substantially to the model's predictions. However, as training progresses, temperature emerges as the dominant predictor, with its relative importance increasing over time. In contrast, the significance of water content and frost heave gradually diminishes with additional training. By the final stages, temperature consistently exhibits the highest importance, followed by frost heave, and then water content, as depicted in Figure 11.  
This analysis confirms that temperature is the primary factor influencing model predictions, while frost heave and water content play supporting roles with relatively smaller contributions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Epochs**} & \multirow{2}{*}{
\begin{tabular}{c} **Type of Neural** \\ **Network** \\ \end{tabular} } & \multicolumn{4}{c}{**Model Evaluation Metrics**} \\ \cline{3-5}  & & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline \multirow{2}{*}{0.5 m} & NARX & 0.18 & 0.15 & 0.99 \\  & BP & 0.75 & 0.58 & 0.46 \\ \hline \multirow{2}{*}{1.5 m} & NARX & 0.23 & 0.21 & 0.98 \\  & BP & 0.54 & 0.46 & 0.89 \\ \hline \multirow{2}{*}{2.9 m} & NARX & 0.30 & 0.24 & 0.99 \\  & BP & 0.71 & 0.67 & 0.96 \\ \hline \multirow{2}{*}{4.7 m} & NARX & 0.12 & 0.12 & 0.96 \\  & BP & 0.27 & 0.26 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model prediction accuracy at various depths of subgrade.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Epochs**} & \multicolumn{2}{c}{**Maximum Feature Importance**} & \multicolumn{4}{c}{**Normalized Importance Proportion**} \\ \cline{2-9}  & **Temperature** & **Water Content** & **Frost Heave** & **Temperature** & **Water Content** & **Frost Heave** & **Cumulative** \\ \hline
10 & 0.1119 & 0.0940 & 0.1032 & 36.20\% & 30.42\% & 33.38\% & 100\% \\
20 & 0.1240 & 0.0844 & 0.1154 & 38.30\% & 26.08\% & 35.62\% & 100\% \\
30 & 0.1080 & 0.0662 & 0.0986 & 39.61\% & 2
## 4 Experimental Results and Discussion
In contrast, some of the studies cited in the table utilized datasets (such as TIMIT and Kaggle) created in controlled environments, such as soundproof rooms. Considering this situation, the dataset used in this study is no simpler than other datasets, and therefore the comparison made here is fair. Nearly all the studies in the table can predict the gender classes of speakers with an accuracy rate of over 96%. Four of these studies have an accuracy rate of over 99%, including this study. Two of the studies achieving over 99% accuracy use the ensemble learning approach, while the other study uses a two-stage transfer learning approach. In this study, four different models were developed, and the accuracy of three of them, including KNN, is over 99%. The high accuracy achieved with a simple classifier like KNN demonstrates the effectiveness of the hybrid features used in the study. This suggests that the proposed gender classification model is highly practical and useful. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **CNN** & & \multicolumn{2}{c}{**KNN**} & \multicolumn{2}{c}{**MLP**} & \multicolumn{2}{c}{**LDA**} \\ \hline  & **Female** & **Male** & & **Female** & **Male** & & **Female** & **Male** & & **Female** & **Male** \\ \hline Female & 840 & 7 & Female & 843 & 4 & Female & 839 & 8 & Female & 833 & 14 \\ Male & 1 & 827 & Male & 9 & 819 & Male & 8 & 820 & Male & 25 & 803 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrices of different classifiers created with hybrid features.
# Attack Surface Score for Software Systems
## 4 Experimental Evaluation
### Experiment 3  
In this experiment, we compared two codebases of similar size: one seeded with CWEs drawn from the MITRE Top 20 list, referred to as PR5, and the other containing an equivalent number of CWEs not featured in that list, referred to as PR6. The objective is to determine whether CWEs ranked among the top 20 exhibit a larger attack surface under our methodology.  
Configuration and Results  
Table 12 lists the CWEs included in PR5 and PR6.  
Table 13 provides a comparative summary of PR5 and PR6, including lines of code (LOC), analysis durations (as a measure of technical debt), average execution times, and normalized attack surface scores.  
Contrary to expectations, the codebase containing MITRE top-20 CWEs (PR5) yielded a lower attack surface score than the non-top-20 codebase (PR6). This highlights a key distinction between our scoring method and MITRE's ranking, which is based on aggregated prevalence and average exploit severity across a broad range of real-world systems [4]. Our approach, by contrast, is context-sensitive, prioritizing the top five semantically relevant CVEs per CWE and incorporating exploit effort into the scoring.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & **PR5** & **PR6** \\ \hline Lines of code (LOC) & 159 & 133 \\ \hline Analysis duration & 2 h & 22 min \\ \hline Average execution Time & 3.69 s & 5.67 s \\ \hline Normalized attack surface score & 0.007 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of PR5 and PR6 across various metrics.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Metric** & **PR3** & **PR4** \\ \hline Lines of code (LOC) & 356,000 & 14,000 \\ \hline Technical debt (Analysis Duration) & 322 days & 13 days \\ \hline Average execution time & 21.1 s & 5.3 s \\ \hline Normalized attack surface score & 0.021 & 0.007 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of PR3 and PR4 across various metrics.  
Several factors explain this divergence. First, top-20 CWEs such as SQL Injection (CWE-89) or XSS (CWE-79) may be absent in backend or CLI projects, reducing their practical relevance. Second, our model down-weights high-impact CWEs when their associated CVEs require high effort to exploit, such as requiring authentication or physical access. Finally, limitations in CVE coverage for some CWEs may result in lower scores despite theoretical severity.
# Influence of Particulate Matter and Carbon Dioxide on Students' Emotions in a Smart Classroom
###### Abstract  
The effects of air quality on health and cognition are well documented, but few studies have focused on its impact on emotions, leaving this area underexplored. This study investigates the influence of environmental factors--specifically particulate matter (PM\({}_{1}\), PM\({}_{2.5}\), and PM\({}_{10}\)) and carbon dioxide (CO\({}_{2}\))--on students' basic emotions in secondary school classrooms. For the collection of environmental data, we used low-cost sensors, which were carefully calibrated to ensure acceptable accuracy for monitoring air quality variables, despite inherent precision limitations compared to traditional sensors. Emotions were recorded via camera and analyzed using a custom-developed code. Based on these data, we found significant but modest correlations, such as the negative correlation between PM levels and happiness, and positive correlations of CO\({}_{2}\) concentrations with fear and disgust. The regression models explained between 36% and 62% of the variance in emotions like neutrality, sadness, fear, and happiness, highlighting nonlinear relationships in some cases. These findings underscore the need for improved classroom environmental management, including the implementation of real-time air quality monitoring systems. Such systems would enable schools to mitigate the negative emotional effects of poor air quality, contributing to healthier and more conducive learning environments. Future research should explore the combined effects of multiple environmental factors to further understand their impact on student well-being.  
emotions; particular matter; CO\({}_{2}\); secondary school; indoor air quality; classroom
## 1 Introduction
### Problem Statement  
The ultimate goal of any ML network, beyond academia purposes, is onboard deployment for measurement or estimation, integrating the data into other systems (in maritime, into navigational aid systems). In this order, two approaches can be sought. First is to train a versatile, though standalone network for any specific ship on a well-equipped local PC. The second is to feed the data from various ships into a cloud space where a mother network can be trained, updated, and further used for all connected ships to the network. Each approach has practical applications beyond the maritime field. For example, the first approach is used for smart phone fingerprint lock, and the second approach in training large-scale models like ChatGPT. Notably, the network behind ChatGPT was trained using over 175 billion text-based parameters [21], demonstrating the immense computational resources required for such tasks. This highlights the scalability of advanced ML techniques, which can be leveraged for maritime applications where large datasets and complex models are increasingly relevant. In the realm of interaction between water and structures, particularly floating structures, the challenge is amplified by the complex modeling of waves and the vast amount of data involved. Since waves exhibit stochastic characteristics, a broad spectrum of wave scenarios must be considered to accurately represent the diversity of real-world phenomena numerically. In practice, whether through simulation or field recordings, the spectral bandwidth required for inputting any ML network results in massive scenarios. Moreover, to fully capture the system dynamics, such as induced wave response, both the wave data and the vessel's output in some form (such as label) must be incorporated into the network for mapping as per Figure 1 and Equation (1). Moreover, the learnable features in recorded data from onboard sensors must be quantitatively considered. As highlighted in [22], the daily recorded data by a research vessel alone surpasses 1 TB. While not all data pertain directly to seakeeping, the portion attributed to seakeeping can be significant.  
As extensively discussed in [23], the features within the data can be represented in different forms, even though the data processing must be fine-featureed enough to replicate the nuance of data, otherwise, the features will be limited to very low-order features and general behavior of the system. All of these lead to a massive quantity of numeric data that are needed to be trained in the ML network. Yet, the conducted studies thus far have not only been trained on limited datasets but also have overlooked the computational efficiency of the trained network. So, as addressed, the pertinent computational costs (i.e., for a DL hamper utilizing sufficient data for more practical purposes) and the studies might only curb the theoretical appetite rather than practical implementation.
# Determinants of Memory Encoding of Altruistic Messages: M-Delphi and F-DEMATEL Approach
## 2 Literature Review
### Factors Associated with Memory Encoding of Altruistic Messages
#### 2.5.10 Homophily  
Homophily is an individual's perception of the similarity between the self and a message sender. Homophily enhances the acceptability of information communicated by the sender to the receiver by reducing the psychological distance between parties in communication [65]. If individuals perceive similarities between themselves and the communicating organization, they are likely to be more receptive to the information communicated to them.
## 3 The Proposed Rolling Bearing Fault Feature Extraction Method under Variable Speed Conditions
### The Optimization Algorithm Derivation
#### 3.2.1 The Solution of Sub-Problem (12)  
Combining similar terms and ignoring constant terms (the detailed process can be found in Appendix A), the sub-problem of Equation (12) is rewritten as  
\[\mathbf{X}^{(k+1)}=\arg\min_{\mathbf{X}^{(k)}}\frac{1}{2}\bigg{\|}\frac{1}{1+ \rho}\Big{(}\mathbf{Z}+\rho\Big{(}\mathbf{Y}^{(k)}+\mathbf{D}^{(k)}\Big{)} \Big{)}-\mathbf{X}^{(k)}\bigg{\|}_{F}^{2}+\frac{\lambda_{1}}{1+\rho}\Big{\|} \mathbf{X}^{(k)}\Big{\|}_{L1}. \tag{15}\]  
To minimize the \(L1\) norm, the soft-threshold function [38] is used, which can be defined as  
\[\text{soft}(a,T)=\left\{\begin{array}{ll}a+T&a\leq-T\\ 0&|a|\leq T\\ a-T&a\geq T\end{array}\right., \tag{16}\]  
where \(a\) can be regarded as an element in matrix \(\mathbf{A}\) and \(T\) is the threshold. Thus, \(\mathbf{X}^{(k+1)}\) can be calculated as  
\[\mathbf{X}^{(k+1)}=\text{soft}\bigg{(}\frac{1}{1+\rho}\Big{(}\mathbf{Z}+\rho \Big{(}\mathbf{Y}^{(k)}+\mathbf{D}^{(k)}\Big{)}\Big{)}-\mathbf{X}^{(k)},\frac {\lambda_{1}}{1+\rho}\bigg{)}. \tag{17}\]